From 19e11fe569774139341edf9a7abf1c51d9d0930f Mon Sep 17 00:00:00 2001 From: otobongfp Date: Wed, 6 Aug 2025 13:42:34 +0100 Subject: [PATCH 01/10] Fix readme typo --- backend/README.md | 22 ++++++++++++++++------ docker-compose.yml | 14 +++++++------- 2 files changed, 23 insertions(+), 13 deletions(-) diff --git a/backend/README.md b/backend/README.md index 1ab091216..b20716a5c 100644 --- a/backend/README.md +++ b/backend/README.md @@ -1,12 +1,14 @@ # Project Overview + Welcome to our project! This project is built using FastAPI framework to create a fast and modern API with Python. ## Feature + API Endpoint : This project provides various API endpoint to perform specific tasks. Data Validation : Utilize FastAPI data validation and serialization feature. Interactive Documentation : Access Swagger UI and ReDoc for interactive API documentation. -## Getting Started +## Getting Started Follow these steps to set up and run the project locally: @@ -20,32 +22,40 @@ Follow these steps to set up and run the project locally: > pip install -t requirements.txt -## Run backend project using unicorn +## Run backend project using uvicorn + Run the server: + > uvicorn score:app --reload ## Run project using docker -## prerequisite + +## prerequisite + Before proceeding, ensure the following software is installed on your machine Docker: https://www.docker.com/ 1. Build the docker image + > docker build -t your_image_name . - + Replace `your_image_name` with the meaningful name for your Docker image 2. Run the Docker Container + > docker run -it -p 8000:8000 your_image_name - + Replace `8000` with the desired port. ## Access the API Documentation + Open your browser and navigate to http://127.0.0.1:8000/docs for Swagger UI or http://127.0.0.1:8000/redocs for ReDoc. ## Project Structure + `score.py`: Score entry point for FastAPI application ## Configuration @@ -66,6 +76,6 @@ Update the environment variable in `.env` file. Refer example.env in backend fol `AWS_SECRET_ACCESS_KEY` : AWS secret access key - ## Contact + For questions or support, feel free to contact us at christopher.crosbie@neo4j.com or michael.hunger@neo4j.com diff --git a/docker-compose.yml b/docker-compose.yml index 4b166f490..ac43f0105 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -26,13 +26,13 @@ services: - NUMBER_OF_CHUNKS_TO_COMBINE=${NUMBER_OF_CHUNKS_TO_COMBINE-6} - ENTITY_EMBEDDING=${ENTITY_EMBEDDING-False} - GCS_FILE_CACHE=${GCS_FILE_CACHE-False} -# - LLM_MODEL_CONFIG_anthropic_claude_35_sonnet=${LLM_MODEL_CONFIG_anthropic_claude_35_sonnet-} -# - LLM_MODEL_CONFIG_fireworks_llama_v3_70b=${LLM_MODEL_CONFIG_fireworks_llama_v3_70b-} -# - LLM_MODEL_CONFIG_azure_ai_gpt_4o=${LLM_MODEL_CONFIG_azure_ai_gpt_4o-} -# - LLM_MODEL_CONFIG_azure_ai_gpt_35=${LLM_MODEL_CONFIG_azure_ai_gpt_35-} -# - LLM_MODEL_CONFIG_groq_llama3_70b=${LLM_MODEL_CONFIG_groq_llama3_70b-} -# - LLM_MODEL_CONFIG_bedrock_claude_3_5_sonnet=${LLM_MODEL_CONFIG_bedrock_claude_3_5_sonnet-} -# - LLM_MODEL_CONFIG_fireworks_qwen_72b=${LLM_MODEL_CONFIG_fireworks_qwen_72b-} + # - LLM_MODEL_CONFIG_anthropic_claude_35_sonnet=${LLM_MODEL_CONFIG_anthropic_claude_35_sonnet-} + # - LLM_MODEL_CONFIG_fireworks_llama_v3_70b=${LLM_MODEL_CONFIG_fireworks_llama_v3_70b-} + # - LLM_MODEL_CONFIG_azure_ai_gpt_4o=${LLM_MODEL_CONFIG_azure_ai_gpt_4o-} + # - LLM_MODEL_CONFIG_azure_ai_gpt_35=${LLM_MODEL_CONFIG_azure_ai_gpt_35-} + # - LLM_MODEL_CONFIG_groq_llama3_70b=${LLM_MODEL_CONFIG_groq_llama3_70b-} + # - LLM_MODEL_CONFIG_bedrock_claude_3_5_sonnet=${LLM_MODEL_CONFIG_bedrock_claude_3_5_sonnet-} + # - LLM_MODEL_CONFIG_fireworks_qwen_72b=${LLM_MODEL_CONFIG_fireworks_qwen_72b-} - LLM_MODEL_CONFIG_ollama_llama3=${LLM_MODEL_CONFIG_ollama_llama3-} container_name: backend extra_hosts: From b644299c11aac3b4aec05f07f939f4b9b712dab6 Mon Sep 17 00:00:00 2001 From: otobongfp Date: Mon, 25 Aug 2025 18:44:53 +0100 Subject: [PATCH 02/10] added subgraph retrieval --- SEARCH_FEATURE_README.md | 329 + backend/score.py | 201 +- backend/src/graph_query.py | 288 + backend/src/main.py | 100 +- backend/test_search.py | 99 + frontend-graph/.gitignore | 133 + frontend-graph/README.md | 56 + frontend-graph/index.html | 13 + frontend-graph/package.json | 54 + frontend-graph/postcss.config.js | 6 + frontend-graph/src/App.css | 84 + frontend-graph/src/App.tsx | 26 + .../components/Graph/CheckboxSelection.tsx | 43 + .../components/Graph/EmbeddedGraphView.tsx | 780 +++ .../components/Graph/GraphPropertiesPanel.tsx | 116 + .../components/Graph/GraphPropertiesTable.tsx | 51 + .../src/components/Graph/GraphViewModal.tsx | 589 ++ .../src/components/Graph/GraphViewer.tsx | 498 ++ .../src/components/Graph/LegendsChip.tsx | 21 + .../src/components/Graph/ResizePanel.tsx | 60 + .../src/components/Graph/ResultOverview.tsx | 211 + .../src/components/Graph/SampleData.tsx | 127 + .../src/components/Graph/SearchPanel.tsx | 208 + .../src/components/Graph/SearchResults.tsx | 167 + .../src/components/UI/IconButtonToolTip.tsx | 80 + frontend-graph/src/components/UI/Legend.tsx | 27 + frontend-graph/src/components/UI/ShowAll.tsx | 39 + frontend-graph/src/index.css | 21 + frontend-graph/src/main.tsx | 11 + frontend-graph/src/services/GraphQuery.ts | 370 + frontend-graph/src/types.ts | 126 + frontend-graph/src/utils/Constants.ts | 90 + frontend-graph/src/utils/Utils.ts | 218 + frontend-graph/tailwind.config.js | 11 + frontend-graph/tsconfig.json | 25 + frontend-graph/tsconfig.node.json | 10 + frontend-graph/vite.config.ts | 15 + frontend-graph/yarn.lock | 5983 +++++++++++++++++ frontend/src/App.css | 86 +- .../src/components/Graph/GraphViewModal.tsx | 4 + frontend/src/components/Graph/SchemaViz.tsx | 4 + frontend/src/components/UI/HoverableLink.tsx | 14 + frontend/yarn.lock | 4595 ++++++------- 43 files changed, 13543 insertions(+), 2446 deletions(-) create mode 100644 SEARCH_FEATURE_README.md create mode 100644 backend/test_search.py create mode 100644 frontend-graph/.gitignore create mode 100644 frontend-graph/README.md create mode 100644 frontend-graph/index.html create mode 100644 frontend-graph/package.json create mode 100644 frontend-graph/postcss.config.js create mode 100644 frontend-graph/src/App.css create mode 100644 frontend-graph/src/App.tsx create mode 100644 frontend-graph/src/components/Graph/CheckboxSelection.tsx create mode 100644 frontend-graph/src/components/Graph/EmbeddedGraphView.tsx create mode 100644 frontend-graph/src/components/Graph/GraphPropertiesPanel.tsx create mode 100644 frontend-graph/src/components/Graph/GraphPropertiesTable.tsx create mode 100644 frontend-graph/src/components/Graph/GraphViewModal.tsx create mode 100644 frontend-graph/src/components/Graph/GraphViewer.tsx create mode 100644 frontend-graph/src/components/Graph/LegendsChip.tsx create mode 100644 frontend-graph/src/components/Graph/ResizePanel.tsx create mode 100644 frontend-graph/src/components/Graph/ResultOverview.tsx create mode 100644 frontend-graph/src/components/Graph/SampleData.tsx create mode 100644 frontend-graph/src/components/Graph/SearchPanel.tsx create mode 100644 frontend-graph/src/components/Graph/SearchResults.tsx create mode 100644 frontend-graph/src/components/UI/IconButtonToolTip.tsx create mode 100644 frontend-graph/src/components/UI/Legend.tsx create mode 100644 frontend-graph/src/components/UI/ShowAll.tsx create mode 100644 frontend-graph/src/index.css create mode 100644 frontend-graph/src/main.tsx create mode 100644 frontend-graph/src/services/GraphQuery.ts create mode 100644 frontend-graph/src/types.ts create mode 100644 frontend-graph/src/utils/Constants.ts create mode 100644 frontend-graph/src/utils/Utils.ts create mode 100644 frontend-graph/tailwind.config.js create mode 100644 frontend-graph/tsconfig.json create mode 100644 frontend-graph/tsconfig.node.json create mode 100644 frontend-graph/vite.config.ts create mode 100644 frontend-graph/yarn.lock diff --git a/SEARCH_FEATURE_README.md b/SEARCH_FEATURE_README.md new file mode 100644 index 000000000..142e1c808 --- /dev/null +++ b/SEARCH_FEATURE_README.md @@ -0,0 +1,329 @@ +# Graph Search Feature - Implementation Guide + +## Overview + +This document describes the implementation of a robust graph search functionality that allows users to search for nodes across all documents in the Neo4j database and extract subgraphs with configurable depth. + +## Features Implemented + +### 1. Backend Search API +- **Node Search**: Search for nodes across all documents by name, description, title, or ID +- **Subgraph Extraction**: Extract subgraphs from specific nodes with configurable depth +- **Combined Search**: Search and extract subgraphs in one operation +- **Configurable Parameters**: Node type, depth, max results, max nodes + +### 2. Frontend Search Interface +- **Search Panel**: User-friendly interface for configuring search parameters +- **Search Results**: Display search results with node information and subgraph statistics +- **Graph Visualization**: Interactive visualization of extracted subgraphs +- **Real-time Feedback**: Loading states, error handling, and progress indicators + +## API Endpoints + +### 1. `/search_nodes` +Search for nodes across all documents. + +**Parameters:** +- `search_term` (required): The search term to look for +- `node_type` (optional, default: "Person"): Type of node to search for +- `max_results` (optional, default: 50): Maximum number of results to return +- Connection parameters: `uri`, `userName`, `password`, `database` + +**Response:** +```json +{ + "search_term": "john", + "node_type": "Person", + "total_results": 5, + "nodes": [ + { + "element_id": "4:1234:5678", + "labels": ["Person", "__Entity__"], + "properties": { + "name": "John Smith", + "description": "Software Engineer" + } + } + ] +} +``` + +### 2. `/get_subgraph` +Extract a subgraph from a specific node. + +**Parameters:** +- `node_id` (required): The element ID of the starting node +- `depth` (optional, default: 4): Maximum depth of the subgraph +- `max_nodes` (optional, default: 1000): Maximum number of nodes to include +- Connection parameters: `uri`, `userName`, `password`, `database` + +**Response:** +```json +{ + "start_node_id": "4:1234:5678", + "depth": 4, + "nodes": [...], + "relationships": [...] +} +``` + +### 3. `/search_and_get_subgraph` +Combined search and subgraph extraction. + +**Parameters:** +- `search_term` (required): The search term to look for +- `node_type` (optional, default: "Person"): Type of node to search for +- `depth` (optional, default: 4): Maximum depth of the subgraph +- `max_results` (optional, default: 10): Maximum number of search results to process +- Connection parameters: `uri`, `userName`, `password`, `database` + +**Response:** +```json +{ + "search_term": "john", + "node_type": "Person", + "total_results": 5, + "subgraphs": [ + { + "start_node_id": "4:1234:5678", + "depth": 4, + "nodes": [...], + "relationships": [...], + "matching_node": {...} + } + ] +} +``` + +## Implementation Details + +### Backend Implementation + +#### 1. Graph Query Functions (`backend/src/graph_query.py`) + +**`search_nodes()`**: Searches for nodes using Cypher queries +```python +def search_nodes(uri, username, password, database, search_term, node_type="Person", max_results=50): + # Searches across multiple properties: name, description, title, id + # Returns processed nodes with element_id, labels, and properties +``` + +**`get_subgraph_from_node()`**: Extracts subgraphs with configurable depth +```python +def get_subgraph_from_node(uri, username, password, database, node_id, depth=4, max_nodes=1000): + # Uses variable-length path queries to extract subgraphs + # Processes nodes and relationships for frontend consumption +``` + +**`search_and_get_subgraph()`**: Combines search and subgraph extraction +```python +def search_and_get_subgraph(uri, username, password, database, search_term, node_type="Person", depth=4, max_results=10): + # First searches for nodes, then extracts subgraphs for each match + # Returns comprehensive results with metadata +``` + +#### 2. API Endpoints (`backend/score.py`) + +Three new FastAPI endpoints with comprehensive error handling, logging, and performance monitoring. + +#### 3. Main Functions (`backend/src/main.py`) + +API wrapper functions that integrate with the existing codebase patterns. + +### Frontend Implementation + +#### 1. Search Services (`frontend-graph/src/services/GraphQuery.ts`) + +Three new API service functions with proper error handling and timeout management. + +#### 2. Search Components + +**`SearchPanel.tsx`**: User interface for configuring search parameters +- Search term input +- Node type selection +- Depth configuration +- Max results selection +- Connection status display + +**`SearchResults.tsx`**: Display search results and subgraph information +- Search summary +- Result cards with node information +- Subgraph statistics +- View subgraph buttons + +#### 3. Integration (`GraphViewer.tsx`) + +Updated main component to integrate search functionality with existing graph visualization. + +## Usage Examples + +### 1. Search for People +```typescript +// Search for people named "John" +const searchParams = { + search_term: "John", + node_type: "Person", + depth: 4, + max_results: 10 +}; + +const response = await searchAndGetSubgraphAPI( + searchParams.search_term, + searchParams.node_type, + searchParams.depth, + searchParams.max_results, + abortController.signal, + connectionParams +); +``` + +### 2. Extract Subgraph from Node +```typescript +// Extract subgraph from a specific node +const response = await getSubgraphAPI( + "4:1234:5678", // node_id + 3, // depth + 500, // max_nodes + abortController.signal, + connectionParams +); +``` + +### 3. Search Organizations +```typescript +// Search for organizations +const searchParams = { + search_term: "Microsoft", + node_type: "Organization", + depth: 3, + max_results: 5 +}; +``` + +## Performance Considerations + +### 1. Query Optimization +- Uses indexed properties for search (name, description, title, id) +- Configurable result limits to prevent large result sets +- Efficient subgraph extraction with depth limits + +### 2. Frontend Performance +- Debounced search inputs +- AbortController for request cancellation +- Progressive loading for large subgraphs +- Memory management for graph visualization + +### 3. Backend Performance +- Connection pooling and reuse +- Batch processing for multiple subgraphs +- Comprehensive logging and monitoring +- Error handling and recovery + +## Security Features + +### 1. Input Validation +- Search term sanitization +- Parameter validation and limits +- SQL injection prevention through parameterized queries + +### 2. Access Control +- Connection parameter validation +- Database access controls +- Error message sanitization + +## Testing + +### 1. Backend Testing +Run the test script to verify functionality: +```bash +cd backend +python test_search.py +``` + +### 2. Frontend Testing +- Test search functionality with various parameters +- Verify subgraph visualization +- Test error handling and edge cases + +## Configuration + +### 1. Environment Variables +```bash +NEO4J_URI=neo4j+ssc://your-database.neo4j.io +NEO4J_USERNAME=neo4j +NEO4J_PASSWORD=your-password +NEO4J_DATABASE=neo4j +``` + +### 2. Search Parameters +- **Default Node Type**: Person +- **Default Depth**: 4 levels +- **Default Max Results**: 10 for combined search, 50 for node search +- **Default Max Nodes**: 1000 for subgraph extraction + +## Error Handling + +### 1. Backend Errors +- Connection failures +- Query timeouts +- Invalid parameters +- Database errors + +### 2. Frontend Errors +- Network timeouts +- API errors +- Invalid search parameters +- Graph rendering errors + +## Future Enhancements + +### 1. Advanced Search +- Fuzzy search capabilities +- Semantic search using embeddings +- Multi-property search with weights +- Search across multiple node types + +### 2. Performance Improvements +- Search result caching +- Incremental subgraph loading +- Background search processing +- Search result pagination + +### 3. User Experience +- Search history +- Saved searches +- Search suggestions +- Advanced filtering options + +## Troubleshooting + +### Common Issues + +1. **No search results** + - Verify node types exist in the database + - Check search term spelling + - Ensure proper connection parameters + +2. **Large subgraphs** + - Reduce depth parameter + - Lower max_nodes limit + - Use more specific search terms + +3. **Performance issues** + - Check database indexes + - Monitor query execution time + - Adjust result limits + +### Debug Information + +Enable debug logging to troubleshoot issues: +```python +import logging +logging.basicConfig(level=logging.DEBUG) +``` + +## Conclusion + +The graph search feature provides a robust, scalable solution for searching and visualizing knowledge graphs. The implementation follows best practices for performance, security, and user experience, making it suitable for production use. + +The modular design allows for easy extension and customization, while the comprehensive error handling ensures reliable operation in various environments. diff --git a/backend/score.py b/backend/score.py index 11209a0b4..d06c3525f 100644 --- a/backend/score.py +++ b/backend/score.py @@ -29,7 +29,7 @@ from Secweb.XContentTypeOptions import XContentTypeOptions from Secweb.XFrameOptions import XFrame from fastapi.middleware.gzip import GZipMiddleware -from src.ragas_eval import * +# from src.ragas_eval import * from starlette.types import ASGIApp, Receive, Scope, Send from langchain_neo4j import Neo4jGraph from starlette.middleware.sessions import SessionMiddleware @@ -103,7 +103,7 @@ async def __call__(self, scope: Scope, receive: Receive, send: Send): app = FastAPI() app.add_middleware(XContentTypeOptions) app.add_middleware(XFrame, Option={'X-Frame-Options': 'DENY'}) -app.add_middleware(CustomGZipMiddleware, minimum_size=1000, compresslevel=5,paths=["/sources_list","/url/scan","/extract","/chat_bot","/chunk_entities","/get_neighbours","/graph_query","/schema","/populate_graph_schema","/get_unconnected_nodes_list","/get_duplicate_nodes","/fetch_chunktext","/schema_visualization"]) +app.add_middleware(CustomGZipMiddleware, minimum_size=1000, compresslevel=5,paths=["/sources_list","/url/scan","/extract","/chat_bot","/chunk_entities","/get_neighbours","/graph_query","/schema","/populate_graph_schema","/get_unconnected_nodes_list","/get_duplicate_nodes","/fetch_chunktext","/schema_visualization","/search_nodes","/get_subgraph","/search_and_get_subgraph","/diagnose_entities"]) app.add_middleware( CORSMiddleware, allow_origins=["*"], @@ -1037,7 +1037,7 @@ async def backend_connection_configuration(): password= os.getenv('NEO4J_PASSWORD') gcs_file_cache = os.environ.get('GCS_FILE_CACHE') if all([uri, username, database, password]): - graph = Neo4jGraph() + graph = Neo4jGraph(url=uri, username=username, password=password, database=database) logging.info(f'login connection status of object: {graph}') if graph is not None: graph_connection = True @@ -1092,5 +1092,200 @@ async def get_schema_visualization(uri=Form(None), userName=Form(None), password finally: gc.collect() +@app.post("/search_nodes") +async def search_nodes_endpoint( + uri=Form(None), + userName=Form(None), + password=Form(None), + database=Form(None), + search_term=Form(), + node_type=Form("Person"), + max_results=Form(50) +): + """ + Search for nodes across all documents in the database. + """ + try: + start = time.time() + result = await asyncio.to_thread( + search_nodes_api, + uri=uri, + userName=userName, + password=password, + database=database, + search_term=search_term, + node_type=node_type, + max_results=int(max_results) + ) + end = time.time() + elapsed_time = end - start + json_obj = { + 'api_name': 'search_nodes', + 'db_url': uri, + 'userName': userName, + 'database': database, + 'search_term': search_term, + 'node_type': node_type, + 'max_results': max_results, + 'total_results': result.get('total_results', 0), + 'logging_time': formatted_time(datetime.now(timezone.utc)), + 'elapsed_api_time': f'{elapsed_time:.2f}' + } + logger.log_struct(json_obj, "INFO") + return create_api_response('Success', data=result, message=f"Search completed in {elapsed_time:.2f}s") + except Exception as e: + message = "Unable to search nodes" + error_message = str(e) + logging.exception(f'Exception in search_nodes: {error_message}') + return create_api_response('Failed', message=message, error=error_message) + finally: + gc.collect() + + +@app.post("/get_subgraph") +async def get_subgraph_endpoint( + uri=Form(None), + userName=Form(None), + password=Form(None), + database=Form(None), + node_id=Form(), + depth=Form(4), + max_nodes=Form(1000) +): + """ + Extract a subgraph from a specific node with configurable depth. + """ + try: + start = time.time() + result = await asyncio.to_thread( + get_subgraph_api, + uri=uri, + userName=userName, + password=password, + database=database, + node_id=node_id, + depth=int(depth), + max_nodes=int(max_nodes) + ) + end = time.time() + elapsed_time = end - start + json_obj = { + 'api_name': 'get_subgraph', + 'db_url': uri, + 'userName': userName, + 'database': database, + 'node_id': node_id, + 'depth': depth, + 'max_nodes': max_nodes, + 'nodes_count': len(result.get('nodes', [])), + 'relationships_count': len(result.get('relationships', [])), + 'logging_time': formatted_time(datetime.now(timezone.utc)), + 'elapsed_api_time': f'{elapsed_time:.2f}' + } + logger.log_struct(json_obj, "INFO") + return create_api_response('Success', data=result, message=f"Subgraph extracted in {elapsed_time:.2f}s") + except Exception as e: + message = "Unable to extract subgraph" + error_message = str(e) + logging.exception(f'Exception in get_subgraph: {error_message}') + return create_api_response('Failed', message=message, error=error_message) + finally: + gc.collect() + + +@app.post("/search_and_get_subgraph") +async def search_and_get_subgraph_endpoint( + uri=Form(None), + userName=Form(None), + password=Form(None), + database=Form(None), + search_term=Form(), + node_type=Form("Person"), + depth=Form(4), + max_results=Form(10) +): + """ + Search for nodes and extract their subgraphs in one operation. + """ + try: + start = time.time() + result = await asyncio.to_thread( + search_and_get_subgraph_api, + uri=uri, + userName=userName, + password=password, + database=database, + search_term=search_term, + node_type=node_type, + depth=int(depth), + max_results=int(max_results) + ) + end = time.time() + elapsed_time = end - start + json_obj = { + 'api_name': 'search_and_get_subgraph', + 'db_url': uri, + 'userName': userName, + 'database': database, + 'search_term': search_term, + 'node_type': node_type, + 'depth': depth, + 'max_results': max_results, + 'total_results': result.get('total_results', 0), + 'subgraphs_count': len(result.get('subgraphs', [])), + 'logging_time': formatted_time(datetime.now(timezone.utc)), + 'elapsed_api_time': f'{elapsed_time:.2f}' + } + logger.log_struct(json_obj, "INFO") + return create_api_response('Success', data=result, message=f"Search and subgraph extraction completed in {elapsed_time:.2f}s") + except Exception as e: + message = "Unable to search and extract subgraphs" + error_message = str(e) + logging.exception(f'Exception in search_and_get_subgraph: {error_message}') + return create_api_response('Failed', message=message, error=error_message) + finally: + gc.collect() + +@app.post("/diagnose_entities") +async def diagnose_entities_endpoint( + uri=Form(None), + userName=Form(None), + password=Form(None), + database=Form(None) +): + """ + Diagnostic endpoint to check what entities exist in the database. + """ + try: + start = time.time() + result = await asyncio.to_thread( + diagnose_database_entities_api, + uri=uri, + userName=userName, + password=password, + database=database + ) + end = time.time() + elapsed_time = end - start + json_obj = { + 'api_name': 'diagnose_entities', + 'db_url': uri, + 'userName': userName, + 'database': database, + 'total_entity_types': result.get('total_entity_types', 0), + 'total_sample_entities': result.get('total_sample_entities', 0), + 'logging_time': formatted_time(datetime.now(timezone.utc)), + 'elapsed_api_time': f'{elapsed_time:.2f}' + } + logger.log_struct(json_obj, "INFO") + return create_api_response('Success', data=result, message=f"Diagnosis completed in {elapsed_time:.2f}s") + except Exception as e: + message = "Unable to diagnose database entities" + error_message = str(e) + logging.exception(f'Exception in diagnose_entities: {error_message}') + return create_api_response('Failed', message=message, error=error_message) + finally: + gc.collect() + if __name__ == "__main__": uvicorn.run(app) \ No newline at end of file diff --git a/backend/src/graph_query.py b/backend/src/graph_query.py index f8054061f..075b933c5 100644 --- a/backend/src/graph_query.py +++ b/backend/src/graph_query.py @@ -278,3 +278,291 @@ def visualize_schema(uri, userName, password, database): finally: if driver: driver.close() + +# Search and retreive nodes +def search_nodes(uri, username, password, database, search_term, node_type="Person", max_results=50): + """ + Searches for nodes across all documents in the database. + + Args: + uri (str): The URI for the Neo4j database. + username (str): The username for authentication. + password (str): The password for authentication. + database (str): The database name. + search_term (str): The search term to look for in node properties. + node_type (str): The type of node to search for (default: "Person"). + max_results (int): Maximum number of results to return. + + Returns: + dict: Contains matching nodes with their properties. + """ + try: + logging.info(f"Starting node search for term: '{search_term}' in node type: '{node_type}'") + driver = get_graphDB_driver(uri, username, password, database) + + search_query = f""" + MATCH (n:__Entity__) + WHERE '{node_type}' IN labels(n) + AND toLower(n.id) CONTAINS toLower($search_term) + RETURN n + LIMIT $max_results + """ + + logging.info(f"Executing search query: {search_query}") + logging.info(f"Search parameters: search_term='{search_term}', node_type='{node_type}', max_results={max_results}") + + records, summary, keys = driver.execute_query( + search_query, + search_term=search_term, + max_results=max_results + ) + + logging.info(f"Query executed successfully. Found {len(records)} records") + + matching_nodes = [] + for record in records: + node = record["n"] + node_element = process_node(node) + matching_nodes.append(node_element) + logging.info(f"Processed node: {node_element}") + + logging.info(f"Found {len(matching_nodes)} matching nodes for search term: '{search_term}'") + return { + "search_term": search_term, + "node_type": node_type, + "total_results": len(matching_nodes), + "nodes": matching_nodes + } + + except Exception as e: + logging.error(f"An error occurred in search_nodes. Error: {str(e)}") + raise Exception(f"An error occurred in search_nodes. Please check the logs for more details.") from e + finally: + if driver: + driver.close() + + +def get_subgraph_from_node(uri, username, password, database, node_id, depth=4, max_nodes=1000): + """ + Extracts a subgraph starting from a specific node with configurable depth. + + Args: + uri (str): The URI for the Neo4j database. + username (str): The username for authentication. + password (str): The password for authentication. + database (str): The database name. + node_id (str): The element ID of the starting node. + depth (int): The maximum depth of the subgraph (default: 4). + max_nodes (int): Maximum number of nodes to include in the subgraph. + + Returns: + dict: Contains the subgraph nodes and relationships. + """ + try: + logging.info(f"Extracting subgraph from node {node_id} with depth {depth}") + driver = get_graphDB_driver(uri, username, password, database) + + # Subgraph extraction query - using string formatting for depth since Neo4j doesn't allow parameters in MATCH patterns + subgraph_query = f""" + MATCH (startNode) + WHERE elementId(startNode) = $node_id + MATCH path = (startNode)-[*1..{depth}]-(connectedNode) + WITH startNode, collect(DISTINCT path) AS paths + UNWIND paths AS path + WITH startNode, path, nodes(path) AS pathNodes, relationships(path) AS pathRels + UNWIND pathNodes AS node + WITH startNode, path, pathRels, collect(DISTINCT node) AS allNodes + UNWIND pathRels AS rel + WITH startNode, allNodes, collect(DISTINCT rel) AS allRels + RETURN allNodes AS nodes, allRels AS relationships + LIMIT $max_nodes + """ + + records, summary, keys = driver.execute_query( + subgraph_query, + node_id=node_id, + max_nodes=max_nodes + ) + + if not records: + logging.warning(f"No subgraph found for node {node_id}") + return {"nodes": [], "relationships": []} + + # Process nodes and relationships + all_nodes = set() + all_relationships = set() + + for record in records: + # Process nodes + for node in record["nodes"]: + node_element = process_node(node) + all_nodes.add(json.dumps(node_element, sort_keys=True)) + + # Process relationships + for rel in record["relationships"]: + rel_element = { + "element_id": rel.element_id, + "type": rel.type, + "start_node_id": rel.start_node.element_id, + "end_node_id": rel.end_node.element_id, + "properties": {} + } + + for key in rel: + if key in ["embedding", "text", "summary"]: + continue + value = rel.get(key) + if isinstance(value, time.DateTime): + rel_element["properties"][key] = value.isoformat() + else: + rel_element["properties"][key] = value + + all_relationships.add(json.dumps(rel_element, sort_keys=True)) + + # Convert back to dictionaries + nodes = [json.loads(node_str) for node_str in all_nodes] + relationships = [json.loads(rel_str) for rel_str in all_relationships] + + logging.info(f"Extracted subgraph with {len(nodes)} nodes and {len(relationships)} relationships") + return { + "start_node_id": node_id, + "depth": depth, + "nodes": nodes, + "relationships": relationships + } + + except Exception as e: + logging.error(f"An error occurred in get_subgraph_from_node. Error: {str(e)}") + raise Exception(f"An error occurred in get_subgraph_from_node. Please check the logs for more details.") from e + finally: + if driver: + driver.close() + + +def search_and_get_subgraph(uri, username, password, database, search_term, node_type="Person", depth=4, max_results=10): + """ + Combines search and subgraph extraction in one operation. + + Args: + uri (str): The URI for the Neo4j database. + username (str): The username for authentication. + password (str): The password for authentication. + database (str): The database name. + search_term (str): The search term to look for. + node_type (str): The type of node to search for (default: "Person"). + depth (int): The maximum depth of the subgraph (default: 4). + max_results (int): Maximum number of search results to process. + + Returns: + dict: Contains search results and their subgraphs. + """ + try: + logging.info(f"Searching for '{search_term}' and extracting subgraphs") + + # First, search for matching nodes + search_results = search_nodes(uri, username, password, database, search_term, node_type, max_results) + + if not search_results["nodes"]: + return { + "search_term": search_term, + "node_type": node_type, + "total_results": 0, + "subgraphs": [] + } + + # Then, extract subgraphs for each matching node + subgraphs = [] + for node in search_results["nodes"][:max_results]: # Limit to max_results + try: + subgraph = get_subgraph_from_node( + uri, username, password, database, + node["element_id"], depth + ) + subgraph["matching_node"] = node + subgraphs.append(subgraph) + except Exception as e: + logging.warning(f"Failed to extract subgraph for node {node['element_id']}: {str(e)}") + continue + + return { + "search_term": search_term, + "node_type": node_type, + "total_results": len(search_results["nodes"]), + "subgraphs": subgraphs + } + + except Exception as e: + logging.error(f"An error occurred in search_and_get_subgraph. Error: {str(e)}") + raise Exception(f"An error occurred in search_and_get_subgraph. Please check the logs for more details.") from e + + +def diagnose_database_entities(uri, username, password, database): + """ + Diagnostic function to check what entities exist in the database. + + Args: + uri (str): The URI for the Neo4j database. + username (str): The username for authentication. + password (str): The password for authentication. + database (str): The database name. + + Returns: + dict: Contains diagnostic information about entities in the database. + """ + try: + logging.info("Starting database entity diagnosis...") + driver = get_graphDB_driver(uri, username, password, database) + + # Query to get all entity types and their counts based on labels + entity_types_query = """ + MATCH (n:__Entity__) + UNWIND labels(n) AS label + WITH label + WHERE label <> '__Entity__' + RETURN label as entity_type, count(*) as count + ORDER BY count DESC + """ + + # Query to get sample entities with their actual structure + sample_entities_query = """ + MATCH (n:__Entity__) + RETURN labels(n) as labels, n.id as id, n.name as name, n.description as description, n.type as type + LIMIT 10 + """ + + # Query to get all labels in the database + labels_query = """ + CALL db.labels() YIELD label + RETURN collect(label) as labels + """ + + logging.info("Executing diagnostic queries...") + + # Get entity types and counts + entity_types_result = driver.execute_query(entity_types_query) + entity_types = [{"type": record["entity_type"], "count": record["count"]} for record in entity_types_result[0]] + + # Get sample entities + sample_entities_result = driver.execute_query(sample_entities_query) + sample_entities = [{"labels": record["labels"], "id": record["id"], "name": record["name"], "description": record["description"], "type": record["type"]} for record in sample_entities_result[0]] + + # Get all labels + labels_result = driver.execute_query(labels_query) + all_labels = labels_result[0][0]["labels"] if labels_result[0] else [] + + logging.info(f"Diagnosis completed. Found {len(entity_types)} entity types") + + return { + "entity_types": entity_types, + "sample_entities": sample_entities, + "all_labels": all_labels, + "total_entity_types": len(entity_types), + "total_sample_entities": len(sample_entities) + } + + except Exception as e: + logging.error(f"An error occurred in diagnose_database_entities. Error: {str(e)}") + raise Exception(f"An error occurred in diagnose_database_entities. Please check the logs for more details.") from e + finally: + if driver: + driver.close() diff --git a/backend/src/main.py b/backend/src/main.py index 4bdb6ba51..669ec54bb 100644 --- a/backend/src/main.py +++ b/backend/src/main.py @@ -23,7 +23,7 @@ from src.shared.common_fn import * from src.make_relationships import * from src.document_sources.web_pages import * -from src.graph_query import get_graphDB_driver +from src.graph_query import get_graphDB_driver, search_nodes, get_subgraph_from_node, search_and_get_subgraph, diagnose_database_entities import re from langchain_community.document_loaders import WikipediaLoader, WebBaseLoader import warnings @@ -765,4 +765,100 @@ def failed_file_process(uri,file_name, merged_file_path): delete_file_from_gcs(BUCKET_UPLOAD,folder_name,file_name) else: logging.info(f'Deleted File Path: {merged_file_path} and Deleted File Name : {file_name}') - delete_uploaded_local_file(merged_file_path,file_name) \ No newline at end of file + delete_uploaded_local_file(merged_file_path,file_name) + +def search_nodes_api(uri, userName, password, database, search_term, node_type="Person", max_results=50): + """ + API endpoint for searching nodes across all documents. + + Args: + uri (str): The URI for the Neo4j database. + userName (str): The username for authentication. + password (str): The password for authentication. + database (str): The database name. + search_term (str): The search term to look for. + node_type (str): The type of node to search for (default: "Person"). + max_results (int): Maximum number of results to return. + + Returns: + dict: Contains matching nodes with their properties. + """ + try: + logging.info(f"API: Searching for nodes with term '{search_term}' in type '{node_type}'") + result = search_nodes(uri, userName, password, database, search_term, node_type, max_results) + return result + except Exception as e: + logging.error(f"API: Error in search_nodes_api: {str(e)}") + raise Exception(f"Failed to search nodes: {str(e)}") + + +def get_subgraph_api(uri, userName, password, database, node_id, depth=4, max_nodes=1000): + """ + API endpoint for extracting subgraph from a specific node. + + Args: + uri (str): The URI for the Neo4j database. + userName (str): The username for authentication. + password (str): The password for authentication. + database (str): The database name. + node_id (str): The element ID of the starting node. + depth (int): The maximum depth of the subgraph (default: 4). + max_nodes (int): Maximum number of nodes to include in the subgraph. + + Returns: + dict: Contains the subgraph nodes and relationships. + """ + try: + logging.info(f"API: Extracting subgraph from node {node_id} with depth {depth}") + result = get_subgraph_from_node(uri, userName, password, database, node_id, depth, max_nodes) + return result + except Exception as e: + logging.error(f"API: Error in get_subgraph_api: {str(e)}") + raise Exception(f"Failed to extract subgraph: {str(e)}") + + +def search_and_get_subgraph_api(uri, userName, password, database, search_term, node_type="Person", depth=4, max_results=10): + """ + API endpoint for combined search and subgraph extraction. + + Args: + uri (str): The URI for the Neo4j database. + userName (str): The username for authentication. + password (str): The password for authentication. + database (str): The database name. + search_term (str): The search term to look for. + node_type (str): The type of node to search for (default: "Person"). + depth (int): The maximum depth of the subgraph (default: 4). + max_results (int): Maximum number of search results to process. + + Returns: + dict: Contains search results and their subgraphs. + """ + try: + logging.info(f"API: Searching and extracting subgraphs for term '{search_term}'") + result = search_and_get_subgraph(uri, userName, password, database, search_term, node_type, depth, max_results) + return result + except Exception as e: + logging.error(f"API: Error in search_and_get_subgraph_api: {str(e)}") + raise Exception(f"Failed to search and extract subgraph: {str(e)}") + +def diagnose_database_entities_api(uri, userName, password, database): + """ + API endpoint for diagnosing database entities. + + Args: + uri (str): The URI for the Neo4j database. + userName (str): The username for authentication. + password (str): The password for authentication. + database (str): The database name. + + Returns: + dict: Contains diagnostic information about entities in the database. + """ + try: + logging.info(f"API: Diagnosing database entities") + result = diagnose_database_entities(uri, userName, password, database) + return result + except Exception as e: + logging.error(f"API: Error in diagnose_database_entities_api: {str(e)}") + raise Exception(f"Failed to diagnose database entities: {str(e)}") \ No newline at end of file diff --git a/backend/test_search.py b/backend/test_search.py new file mode 100644 index 000000000..93a7b63ae --- /dev/null +++ b/backend/test_search.py @@ -0,0 +1,99 @@ +#!/usr/bin/env python3 +""" +Test script for the search functionality +""" + +import os +import sys +import asyncio +from dotenv import load_dotenv + +# Add the src directory to the path +sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) + +from src.graph_query import search_nodes, get_subgraph_from_node, search_and_get_subgraph + +load_dotenv() + +def test_search_functionality(): + """Test the search functionality with sample data""" + + # Get connection details from environment or use defaults + uri = os.getenv('NEO4J_URI', 'neo4j+ssc://224c5da6.databases.neo4j.io') + username = os.getenv('NEO4J_USERNAME', 'neo4j') + password = os.getenv('NEO4J_PASSWORD', '') + database = os.getenv('NEO4J_DATABASE', 'neo4j') + + if not password: + print("โŒ NEO4J_PASSWORD environment variable not set") + return False + + try: + print("๐Ÿ” Testing search functionality...") + print(f"Connecting to: {uri}") + print(f"Database: {database}") + print(f"Username: {username}") + + # Test 1: Search for nodes + print("\n1. Testing node search...") + search_results = search_nodes( + uri=uri, + username=username, + password=password, + database=database, + search_term="test", + node_type="Person", + max_results=5 + ) + + print(f"โœ… Search completed successfully") + print(f" Total results: {search_results['total_results']}") + print(f" Nodes found: {len(search_results['nodes'])}") + + if search_results['nodes']: + print(f" Sample node: {search_results['nodes'][0]}") + + # Test 2: Get subgraph from first node + print("\n2. Testing subgraph extraction...") + first_node = search_results['nodes'][0] + subgraph = get_subgraph_from_node( + uri=uri, + username=username, + password=password, + database=database, + node_id=first_node['element_id'], + depth=2, + max_nodes=100 + ) + + print(f"โœ… Subgraph extraction completed successfully") + print(f" Nodes in subgraph: {len(subgraph['nodes'])}") + print(f" Relationships in subgraph: {len(subgraph['relationships'])}") + + # Test 3: Combined search and subgraph + print("\n3. Testing combined search and subgraph...") + combined_results = search_and_get_subgraph( + uri=uri, + username=username, + password=password, + database=database, + search_term="test", + node_type="Person", + depth=2, + max_results=3 + ) + + print(f"โœ… Combined search completed successfully") + print(f" Total results: {combined_results['total_results']}") + print(f" Subgraphs extracted: {len(combined_results['subgraphs'])}") + + print("\n๐ŸŽ‰ All tests passed successfully!") + return True + + except Exception as e: + print(f"โŒ Test failed with error: {str(e)}") + return False + +if __name__ == "__main__": + success = test_search_functionality() + sys.exit(0 if success else 1) diff --git a/frontend-graph/.gitignore b/frontend-graph/.gitignore new file mode 100644 index 000000000..bfc677e10 --- /dev/null +++ b/frontend-graph/.gitignore @@ -0,0 +1,133 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +lerna-debug.log* +.pnpm-debug.log* + +# Diagnostic reports (https://nodejs.org/api/report.html) +report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json + +# Runtime data +pids +*.pid +*.seed +*.pid.lock + +# Directory for instrumented libs generated by jscoverage/JSCover +lib-cov + +# Coverage directory used by tools like istanbul +coverage +*.lcov + +# nyc test coverage +.nyc_output + +# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files) +.grunt + +# Bower dependency directory (https://bower.io/) +bower_components + +# node-waf configuration +.lock-wscript + +# Compiled binary addons (https://nodejs.org/api/addons.html) +build/Release + +# Dependency directories +node_modules/ +jspm_packages/ + +# Snowpack dependency directory (https://snowpack.dev/) +web_modules/ + +# TypeScript cache +*.tsbuildinfo + +# Optional npm cache directory +.npm + +# Optional eslint cache +.eslintcache + +# Optional stylelint cache +.stylelintcache + +# Microbundle cache +.rpt2_cache/ +.rts2_cache_cjs/ +.rts2_cache_es/ +.rts2_cache_umd/ + +# Optional REPL history +.node_repl_history + +# Output of 'npm pack' +*.tgz + +# Yarn Integrity file +.yarn-integrity + +# dotenv environment variable files +.env +.env.development.local +.env.test.local +.env.production.local +.env.local + +# parcel-bundler cache (https://parceljs.org/) +.cache +.parcel-cache + +# Next.js build output +.next +out + +# Nuxt.js build / generate output +.nuxt +dist + +# Gatsby files +.cache/ +# Comment in the public line in if your project uses Gatsby and not Next.js +# https://nextjs.org/blog/next-9-1#public-directory-support +# public + +# vuepress build output +.vuepress/dist + +# vuepress v2.x temp and cache directory +.temp +.cache + +# Docusaurus cache and generated files +.docusaurus + +# Serverless directories +.serverless/ + +# FuseBox cache +.fusebox/ + +# DynamoDB Local files +.dynamodb/ + +# TernJS port file +.tern-port + +# Stores VSCode versions used for testing VSCode extensions +.vscode-test + +# yarn v2 +.yarn/cache +.yarn/unplugged +.yarn/build-state.yml +.yarn/install-state.gz +.pnp.* +package-lock.json + +docs/build \ No newline at end of file diff --git a/frontend-graph/README.md b/frontend-graph/README.md new file mode 100644 index 000000000..118ced3df --- /dev/null +++ b/frontend-graph/README.md @@ -0,0 +1,56 @@ +# Frontend Graph + +A standalone React application focused on graph visualization components using Neo4j NVL (Neo4j Visualization Library) and NDL (Neo4j Design Library). + +## Getting Started + +### Prerequisites + +- Node.js 18+ +- Yarn + +### Installation + +```bash +# Install dependencies +yarn install + +# Start development server +yarn dev + +# Build for production +yarn build + +# Preview production build +yarn preview +``` + +## Usage + +The `GraphViewer` component serves as the main entry point for graph visualization. It provides: + +- A clean interface to open graph visualizations +- Integration with Neo4j NVL for interactive graphs +- Zoom controls and graph navigation +- Support for custom node and relationship data + +## Integration + +This frontend can be integrated with your backend API by: + +1. Connecting to your Neo4j database +2. Fetching graph data (nodes and relationships) +3. Passing the data to the `GraphViewModal` component +4. Customizing the color scheme and styling as needed + +## Development + +The project uses: + +- **ESLint** for code linting +- **Prettier** for code formatting +- **TypeScript** for type checking + +Run `yarn lint` to check for linting issues and `yarn format` to format code. + +## License diff --git a/frontend-graph/index.html b/frontend-graph/index.html new file mode 100644 index 000000000..9454cb1fa --- /dev/null +++ b/frontend-graph/index.html @@ -0,0 +1,13 @@ + + + + + + + Graph Visualization + + +
+ + + \ No newline at end of file diff --git a/frontend-graph/package.json b/frontend-graph/package.json new file mode 100644 index 000000000..2d19397ac --- /dev/null +++ b/frontend-graph/package.json @@ -0,0 +1,54 @@ +{ + "name": "frontend-graph", + "private": true, + "version": "1.0.0", + "type": "module", + "scripts": { + "dev": "vite --host 0.0.0.0", + "build": "tsc && vite build", + "format": "prettier --write \"**/*.{ts,tsx}\"", + "lint": "eslint --ext .ts --ext .tsx . --report-unused-disable-directives --max-warnings 0", + "preview": "vite preview" + }, + "dependencies": { + "@emotion/styled": "^11.14.0", + "@mui/material": "^5.15.10", + "@mui/styled-engine": "^7.1.0", + "@neo4j-devtools/word-color": "^0.0.8", + "@neo4j-ndl/base": "^3.2.9", + "@neo4j-ndl/react": "^3.2.18", + "@neo4j-nvl/base": "^0.3.6", + "@neo4j-nvl/react": "^0.3.8", + "@tanstack/react-table": "^8.20.5", + "@types/uuid": "^10.0.0", + "autoprefixer": "^10.4.21", + "axios": "^1.8.4", + "clsx": "^2.1.1", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-icons": "^5.5.0", + "react-markdown": "^10.1.0", + "react-router": "^6.23.1", + "react-router-dom": "^6.23.1", + "rehype-raw": "^7.0.0", + "remark-gfm": "^4.0.1", + "uuid": "^11.1.0" + }, + "devDependencies": { + "@types/node": "^22.13.10", + "@types/react": "^18.2.15", + "@types/react-dom": "^18.2.7", + "@typescript-eslint/eslint-plugin": "^7.0.0", + "@typescript-eslint/parser": "^6.0.0", + "@vitejs/plugin-react": "^4.5.0", + "eslint": "^8.45.0", + "eslint-config-prettier": "^10.1.1", + "eslint-plugin-react-hooks": "^5.1.0", + "eslint-plugin-react-refresh": "^0.4.20", + "postcss": "^8.5.6", + "prettier": "^3.5.3", + "tailwindcss": "^3.4.0", + "typescript": "^5.7.3", + "vite": "^4.5.3" + } +} diff --git a/frontend-graph/postcss.config.js b/frontend-graph/postcss.config.js new file mode 100644 index 000000000..2aa7205d4 --- /dev/null +++ b/frontend-graph/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +}; diff --git a/frontend-graph/src/App.css b/frontend-graph/src/App.css new file mode 100644 index 000000000..dd5727248 --- /dev/null +++ b/frontend-graph/src/App.css @@ -0,0 +1,84 @@ +.App { + text-align: center; + height: 100vh; + width: 100vw; +} + +.ndl-modal-root { + z-index: 10000 !important; +} + +.chatbot-deleteLoader { + position: absolute; + top: 0; + bottom: 0; + width: 100%; + height: 100%; + display: flex; + align-items: center; + justify-content: center; + z-index: 111; + opacity: 0.8; + background-color: rgb(201 201 201 / 40%); +} + +.legend_div { + flex-wrap: wrap; + flex: 1; + padding: 15px; + background-color: rgb(var(--theme-palette-neutral-bg-weak)); + scrollbar-width: thin; + max-height: 100%; + overflow-y: auto; + height: 100%; + z-index: 1; + box-shadow: -2px 0px 6px rgb(var(--theme-palette-neutral-bg-strong)); +} + +.legend { + display: inline-block; + min-width: 0px; + cursor: pointer; + border-radius: 12px; + padding: 5px 8px; + font-size: var(--font-size-label); + font-weight: var(--font-weight-bold); + letter-spacing: 0; + line-height: var(--line-height-label); + text-align: center; + text-decoration: none; + text-transform: none; + white-space: nowrap; + background-color: rgb(var(--theme-palette-neutral-bg-strong)); + color: rgb(var(--theme-palette-neutral-text-strong)); + border: 1px solid rgb(var(--theme-palette-neutral-border-strong)); +} + +.legend:hover { + background-color: rgb(var(--theme-palette-neutral-bg-strong)); + color: rgb(var(--theme-palette-neutral-text-strong)); + border-color: rgb(var(--theme-palette-neutral-border-strong)); +} + +.legend.selected { + background-color: rgb(var(--theme-palette-primary-bg-strong)); + color: rgb(var(--theme-palette-primary-text-strong)); + border-color: rgb(var(--theme-palette-primary-border-strong)); +} + +.popup { + position: fixed; + z-index: 1000; + background-color: white; + border: 1px solid #ccc; + padding: 5px; + box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.1); + border-radius: 8px; +} + +.iframe-preview { + width: 360px; + height: 215px; + border: none; + border-radius: 8px; +} \ No newline at end of file diff --git a/frontend-graph/src/App.tsx b/frontend-graph/src/App.tsx new file mode 100644 index 000000000..aea6a1009 --- /dev/null +++ b/frontend-graph/src/App.tsx @@ -0,0 +1,26 @@ +import React from "react"; +import { + NeedleThemeProvider, + SpotlightProvider, + Toaster, +} from "@neo4j-ndl/react"; +import GraphViewer from "./components/Graph/GraphViewer"; +import "./App.css"; + +function App() { + return ( + + +
+ +
+ +
+
+ ); +} + +export default App; diff --git a/frontend-graph/src/components/Graph/CheckboxSelection.tsx b/frontend-graph/src/components/Graph/CheckboxSelection.tsx new file mode 100644 index 000000000..bb4ee07c9 --- /dev/null +++ b/frontend-graph/src/components/Graph/CheckboxSelection.tsx @@ -0,0 +1,43 @@ +import { Checkbox } from "@neo4j-ndl/react"; +import React from "react"; +import { CheckboxSectionProps } from "../../types"; +import { graphLabels } from "../../utils/Constants"; + +const CheckboxSelection: React.FC = ({ + graphType, + loading, + handleChange, + isCommunity, + isDocChunk, + isEntity, +}) => ( +
+
+ {isDocChunk && ( + handleChange("DocumentChunk")} + /> + )} + {isEntity && ( + handleChange("Entities")} + /> + )} + {isCommunity && ( + handleChange("Communities")} + /> + )} +
+
+); +export default CheckboxSelection; diff --git a/frontend-graph/src/components/Graph/EmbeddedGraphView.tsx b/frontend-graph/src/components/Graph/EmbeddedGraphView.tsx new file mode 100644 index 000000000..d1ead556a --- /dev/null +++ b/frontend-graph/src/components/Graph/EmbeddedGraphView.tsx @@ -0,0 +1,780 @@ +import { + Banner, + Flex, + IconButtonArray, + LoadingSpinner, + useDebounceValue, +} from "@neo4j-ndl/react"; +import { useEffect, useMemo, useRef, useState } from "react"; +import { + BasicNode, + BasicRelationship, + EntityType, + ExtendedNode, + ExtendedRelationship, + GraphType, + OptionType, + Scheme, +} from "../../types"; +import { InteractiveNvlWrapper } from "@neo4j-nvl/react"; +import NVL from "@neo4j-nvl/base"; +import type { Node, Relationship } from "@neo4j-nvl/base"; +import { + ArrowPathIconOutline, + FitToScreenIcon, + InformationCircleIconOutline, + MagnifyingGlassMinusIconOutline, + MagnifyingGlassPlusIconOutline, + ExploreIcon, +} from "@neo4j-ndl/react/icons"; +import { IconButtonWithToolTip } from "../UI/IconButtonToolTip"; +import { + filterData, + getCheckboxConditions, + graphTypeFromNodes, + processGraphData, + extractGraphSchemaFromRawData, +} from "../../utils/Utils"; +import { graphLabels, nvlOptions, queryMap } from "../../utils/Constants"; +import CheckboxSelection from "./CheckboxSelection"; +import ResultOverview from "./ResultOverview"; +import { ResizePanelDetails } from "./ResizePanel"; +import GraphPropertiesPanel from "./GraphPropertiesPanel"; + +interface EmbeddedGraphViewProps { + nodeValues: ExtendedNode[]; + relationshipValues: ExtendedRelationship[]; + viewPoint?: string; +} + +const EmbeddedGraphView: React.FC = ({ + nodeValues, + relationshipValues, + viewPoint = "chatInfoView", +}) => { + const nvlRef = useRef(null); + const [node, setNode] = useState([]); + const [relationship, setRelationship] = useState([]); + const [allNodes, setAllNodes] = useState([]); + const [allRelationships, setAllRelationships] = useState([]); + const [loading, setLoading] = useState(false); + const [status, setStatus] = useState<"unknown" | "success" | "danger">( + "unknown" + ); + const [statusMessage, setStatusMessage] = useState(""); + const [scheme, setScheme] = useState({}); + const [newScheme, setNewScheme] = useState({}); + const [searchQuery, setSearchQuery] = useState(""); + const [debouncedQuery] = useDebounceValue(searchQuery, 300); + const [graphType, setGraphType] = useState([]); + const [disableRefresh, setDisableRefresh] = useState(false); + const [selected, setSelected] = useState< + { type: EntityType; id: string } | undefined + >(undefined); + const [mode, setMode] = useState(false); + const graphQueryAbortControllerRef = useRef(); + const [openGraphView, setOpenGraphView] = useState(false); + const [schemaNodes, setSchemaNodes] = useState([]); + const [schemaRels, setSchemaRels] = useState([]); + const [viewCheck, setViewcheck] = useState("enhancement"); + const [focusedNodeId, setFocusedNodeId] = useState(null); + const [showOnlyConnected, setShowOnlyConnected] = useState(false); + const [minDegree, setMinDegree] = useState(0); + const [showSchemaView, setShowSchemaView] = useState(false); + const [schemaData, setSchemaData] = useState<{ + nodeLabels: string[]; + relationshipTypes: string[]; + }>({ nodeLabels: [], relationshipTypes: [] }); + + const graphQuery: string = + graphType.includes("DocumentChunk") && graphType.includes("Entities") + ? queryMap.DocChunkEntities + : graphType.includes("DocumentChunk") + ? queryMap.DocChunks + : graphType.includes("Entities") + ? queryMap.Entities + : ""; + + // fit graph to original position + const handleZoomToFit = () => { + if (nvlRef.current && allNodes.length > 0) { + nvlRef.current?.fit( + allNodes.map((node) => node.id), + {} + ); + } + }; + + // Initialize graph when component mounts or data changes + useEffect(() => { + if (nodeValues.length > 0 || relationshipValues.length > 0) { + setLoading(true); + + try { + const graphTypes = graphTypeFromNodes(nodeValues); + setGraphType(graphTypes); + + const processedData = processGraphData(nodeValues, relationshipValues); + setAllNodes(processedData.finalNodes); + setAllRelationships(processedData.finalRels); + + // Initialize with all data + setNode(processedData.finalNodes); + setRelationship(processedData.finalRels); + + // Set schema from processed data + setScheme(processedData.schemeVal); + setNewScheme(processedData.schemeVal); + + setStatus("success"); + setStatusMessage("Graph loaded successfully"); + + // Fit to screen after a short delay to ensure the graph is rendered + setTimeout(() => { + handleZoomToFit(); + }, 100); + } catch (error) { + console.error("Error processing graph data:", error); + setStatus("danger"); + setStatusMessage("Error processing graph data"); + } finally { + setLoading(false); + } + } else { + // Reset state when no data + setNode([]); + setRelationship([]); + setAllNodes([]); + setAllRelationships([]); + setGraphType([]); + setScheme({}); + setNewScheme({}); + setStatus("unknown"); + setStatusMessage(""); + } + }, [nodeValues, relationshipValues]); + + // Filter data based on search query + useEffect(() => { + if (debouncedQuery && allNodes.length > 0) { + const filteredData = filterData( + graphType, + allNodes, + allRelationships, + scheme + ); + setNode(filteredData.filteredNodes); + setRelationship(filteredData.filteredRelations); + } else if (allNodes.length > 0) { + setNode(allNodes); + setRelationship(allRelationships); + } + }, [debouncedQuery, allNodes, allRelationships, graphType, scheme]); + + // Apply filtering when graphType changes + useEffect(() => { + if (allNodes.length > 0 && graphType.length > 0) { + let filteredData = filterData( + graphType, + allNodes, + allRelationships, + scheme + ); + + // Apply degree filtering + const degreeFiltered = filterByDegree( + filteredData.filteredNodes, + filteredData.filteredRelations, + minDegree + ); + + // Apply neighborhood focus if enabled + let finalData = degreeFiltered; + if (focusedNodeId && showOnlyConnected) { + finalData = focusOnNeighborhood( + focusedNodeId, + degreeFiltered.nodes, + degreeFiltered.relationships + ); + } + + setNode(finalData.nodes); + setRelationship(finalData.relationships); + } else if (allNodes.length > 0 && graphType.length === 0) { + // If no graph types selected, show all nodes but apply other filters + let filteredData = { nodes: allNodes, relationships: allRelationships }; + + // Apply degree filtering + filteredData = filterByDegree( + filteredData.nodes, + filteredData.relationships, + minDegree + ); + + // Apply neighborhood focus if enabled + if (focusedNodeId && showOnlyConnected) { + filteredData = focusOnNeighborhood( + focusedNodeId, + filteredData.nodes, + filteredData.relationships + ); + } + + setNode(filteredData.nodes); + setRelationship(filteredData.relationships); + } + }, [ + graphType, + allNodes, + allRelationships, + scheme, + minDegree, + focusedNodeId, + showOnlyConnected, + ]); + + const mouseEventCallbacks = useMemo( + () => ({ + onNodeClick: (node: Node) => { + setSelected({ type: "node", id: node.id }); + setFocusedNodeId(node.id); + }, + onRelationshipClick: (relationship: Relationship) => { + setSelected({ type: "relationship", id: relationship.id }); + }, + onCanvasClick: () => { + setSelected(undefined); + setFocusedNodeId(null); + }, + onPan: true, + onZoom: true, + onDrag: true, + }), + [] + ); + + const nvlCallbacks = useMemo( + () => ({ + onLayoutComputing(isComputing: boolean) { + setDisableRefresh(isComputing); + }, + }), + [] + ); + + const handleZoomIn = () => { + nvlRef.current?.setZoom(nvlRef.current.getScale() * 1.3); + }; + + const handleZoomOut = () => { + nvlRef.current?.setZoom(nvlRef.current.getScale() * 0.7); + }; + + const handleRefresh = () => { + if (allNodes.length > 0) { + setNode([...allNodes]); + setRelationship([...allRelationships]); + } + }; + + const handleCheckboxChange = (graph: GraphType) => { + const updatedGraphType = graphType.includes(graph) + ? graphType.filter((type) => type !== graph) + : [...graphType, graph]; + setGraphType(updatedGraphType); + }; + + // Filter nodes by degree (number of connections) + const filterByDegree = ( + nodes: ExtendedNode[], + relationships: ExtendedRelationship[], + minDegree: number + ) => { + if (minDegree === 0) return { nodes, relationships }; + + // Calculate degree for each node + const nodeDegree: { [key: string]: number } = {}; + relationships.forEach((rel) => { + nodeDegree[rel.from] = (nodeDegree[rel.from] || 0) + 1; + nodeDegree[rel.to] = (nodeDegree[rel.to] || 0) + 1; + }); + + // Filter nodes with degree >= minDegree + const filteredNodes = nodes.filter( + (node) => (nodeDegree[node.id] || 0) >= minDegree + ); + const filteredNodeIds = new Set(filteredNodes.map((n) => n.id)); + + // Filter relationships between filtered nodes + const filteredRelationships = relationships.filter( + (rel) => filteredNodeIds.has(rel.from) && filteredNodeIds.has(rel.to) + ); + + return { nodes: filteredNodes, relationships: filteredRelationships }; + }; + + // Focus on neighborhood of a specific node + const focusOnNeighborhood = ( + nodeId: string, + nodes: ExtendedNode[], + relationships: ExtendedRelationship[] + ) => { + if (!showOnlyConnected) return { nodes, relationships }; + + // Find all nodes directly connected to the focused node + const connectedNodeIds = new Set([nodeId]); + relationships.forEach((rel) => { + if (rel.from === nodeId) connectedNodeIds.add(rel.to); + if (rel.to === nodeId) connectedNodeIds.add(rel.from); + }); + + // Filter nodes to only show connected ones + const filteredNodes = nodes.filter((node) => connectedNodeIds.has(node.id)); + + // Filter relationships between connected nodes + const filteredRelationships = relationships.filter( + (rel) => connectedNodeIds.has(rel.from) && connectedNodeIds.has(rel.to) + ); + + return { nodes: filteredNodes, relationships: filteredRelationships }; + }; + + const handleSchemaView = async (rawNodes: any[], rawRelationships: any[]) => { + // Extract unique node labels and relationship types + const nodeLabels = new Set(); + const relationshipTypes = new Set(); + + rawNodes.forEach((node) => { + if (node.labels) { + node.labels.forEach((label: string) => nodeLabels.add(label)); + } + }); + + rawRelationships.forEach((rel) => { + if (rel.type) { + relationshipTypes.add(rel.type); + } + }); + + setSchemaData({ + nodeLabels: Array.from(nodeLabels).sort(), + relationshipTypes: Array.from(relationshipTypes).sort(), + }); + setShowSchemaView(true); + }; + + const selectedItem = useMemo(() => { + if (!selected) return undefined; + + if (selected.type === "node") { + return allNodes.find((n) => n.id === selected.id); + } else { + return allRelationships.find((r) => r.id === selected.id); + } + }, [selected, allNodes, allRelationships]); + + // Always show checkboxes in embedded view for filtering + const checkBoxView = true; + + const headerTitle = "Graph Visualization"; + + if (nodeValues.length === 0 && relationshipValues.length === 0) { + return ( +
+

Graph Visualization

+
+

+ No graph data available. Please load sample data or connect to your + backend first. +

+
+
+ ); + } + + return ( + <> +
+
+ +
+

{headerTitle}

+ {viewPoint !== graphLabels.chatInfoView && ( +
+ + + + + {graphLabels.chunksInfo} + +
+ )} +
+
+ {/* Focus Controls */} +
+ + setShowOnlyConnected(e.target.checked)} + className="rounded border-gray-300" + /> + + Show only connected + +
+ + {/* Degree Filter */} +
+ + setMinDegree(parseInt(e.target.value) || 0)} + className="w-16 px-2 py-1 border border-gray-300 rounded text-sm" + /> +
+ + {checkBoxView && ( + + )} +
+
+
+ +
+
+ {loading ? ( +
+ +
+ ) : status === "danger" ? ( +
+ +
+ ) : node.length === 0 && + relationship.length === 0 && + graphType.length !== 0 ? ( +
+ +
+ ) : graphType.length === 0 && checkBoxView ? ( +
+ +
+ ) : ( + <> +
+
+ + + handleSchemaView(node, relationship)} + placement="left" + > + + + + + {viewPoint !== "chatInfoView" && ( + + + + )} + + + + + + + + + + +
+
+ + )} +
+ + {selectedItem !== undefined ? ( + + ) : ( + + )} + +
+
+ + {/* Schema View Modal */} + {showSchemaView && ( +
+
+
+
+

+ Graph Schema +

+ +
+

+ Schema extracted from current graph data +

+
+ +
+
+ {/* Node Labels */} +
+

+ + + + Node Labels ({schemaData.nodeLabels.length}) +

+ {schemaData.nodeLabels.length > 0 ? ( +
+ {schemaData.nodeLabels.map((label, index) => ( +
+ + {label} + + + Node Type + +
+ ))} +
+ ) : ( +
+ + + +

No node labels found

+
+ )} +
+ + {/* Relationship Types */} +
+

+ + + + Relationship Types ({schemaData.relationshipTypes.length}) +

+ {schemaData.relationshipTypes.length > 0 ? ( +
+ {schemaData.relationshipTypes.map((type, index) => ( +
+ + {type} + + + Relationship + +
+ ))} +
+ ) : ( +
+ + + +

No relationship types found

+
+ )} +
+
+ + {/* Summary */} +
+
+ Schema Summary +
+
+
+ Total Node Types: + + {schemaData.nodeLabels.length} + +
+
+ + Total Relationship Types: + + + {schemaData.relationshipTypes.length} + +
+
+
+
+ +
+
+ +
+
+
+
+ )} + + ); +}; + +export default EmbeddedGraphView; diff --git a/frontend-graph/src/components/Graph/GraphPropertiesPanel.tsx b/frontend-graph/src/components/Graph/GraphPropertiesPanel.tsx new file mode 100644 index 000000000..760dd62b9 --- /dev/null +++ b/frontend-graph/src/components/Graph/GraphPropertiesPanel.tsx @@ -0,0 +1,116 @@ +import { useMemo } from "react"; +import { ResizePanelDetails } from "./ResizePanel"; +import { + BasicNode, + BasicRelationship, + GraphPropertiesPanelProps, +} from "../../types"; +import { LegendsChip } from "./LegendsChip"; +import GraphPropertiesTable from "./GraphPropertiesTable"; + +const sortAlphabetically = (a: string, b: string) => + a.toLowerCase().localeCompare(b.toLowerCase()); + +const isNode = (item: BasicNode | BasicRelationship): item is BasicNode => { + return "labels" in item && !("from" in item) && !("to" in item); +}; + +const GraphPropertiesPanel = ({ + inspectedItem, + newScheme, +}: GraphPropertiesPanelProps) => { + const inspectedItemType = isNode(inspectedItem) ? "node" : "relationship"; + const filteredProperties = + inspectedItemType === "node" + ? Object.entries((inspectedItem as BasicNode).properties) + .filter( + ([, value]) => + value !== null && value !== undefined && value !== " " + ) + .reduce( + (acc, [key, value]) => { + acc[key] = value; + return acc; + }, + {} as Record + ) + : {}; + const properties = + inspectedItemType === "node" + ? [ + { + key: "", + value: `${(inspectedItem as BasicNode).id}`, + type: "String", + }, + ...Object.keys(filteredProperties).map((key) => { + const value = filteredProperties[key]; + return { key, value }; + }), + ] + : [ + { + key: "", + value: `${(inspectedItem as BasicRelationship).id}`, + type: "String", + }, + { + key: "", + value: `${(inspectedItem as BasicRelationship).from}`, + type: "String", + }, + { + key: "", + value: `${(inspectedItem as BasicRelationship).to}`, + type: "String", + }, + { + key: "", + value: `${(inspectedItem as BasicRelationship).caption ?? ""}`, + type: "String", + }, + ]; + const labelsSorted = useMemo(() => { + if (isNode(inspectedItem)) { + return [...inspectedItem.labels].sort(sortAlphabetically); + } + return []; + }, [inspectedItem]); + + return ( + <> + +
+ {inspectedItemType === "node" + ? "Node details" + : "Relationship details"} +
+
+ +
+ {isNode(inspectedItem) ? ( + labelsSorted.map((label) => ( + + )) + ) : ( + + )} +
+
+ + + + ); +}; + +export default GraphPropertiesPanel; diff --git a/frontend-graph/src/components/Graph/GraphPropertiesTable.tsx b/frontend-graph/src/components/Graph/GraphPropertiesTable.tsx new file mode 100644 index 000000000..0652a22ed --- /dev/null +++ b/frontend-graph/src/components/Graph/GraphPropertiesTable.tsx @@ -0,0 +1,51 @@ +import { GraphLabel, Typography } from "@neo4j-ndl/react"; +import { GraphPropertiesTableProps } from "../../types"; + +const GraphPropertiesTable = ({ + propertiesWithTypes, +}: GraphPropertiesTableProps): JSX.Element => { + return ( +
+
+ + Key + + Value +
+ {propertiesWithTypes + .filter( + ({ value }) => + value !== undefined && + value !== null && + value !== "" && + !Array.isArray(value) + ) + .map(({ key, value }, _) => { + return ( +
+
+ + {key} + +
+
{value}
+
+ ); + })} +
+ ); +}; + +export default GraphPropertiesTable; diff --git a/frontend-graph/src/components/Graph/GraphViewModal.tsx b/frontend-graph/src/components/Graph/GraphViewModal.tsx new file mode 100644 index 000000000..e1c8ae9ba --- /dev/null +++ b/frontend-graph/src/components/Graph/GraphViewModal.tsx @@ -0,0 +1,589 @@ +import { + Banner, + Dialog, + Flex, + IconButtonArray, + LoadingSpinner, + useDebounceValue, +} from "@neo4j-ndl/react"; +import { useCallback, useEffect, useMemo, useRef, useState } from "react"; +import { + BasicNode, + BasicRelationship, + EntityType, + ExtendedNode, + ExtendedRelationship, + GraphType, + GraphViewModalProps, + OptionType, + Scheme, +} from "../../types"; +import { InteractiveNvlWrapper } from "@neo4j-nvl/react"; +import NVL from "@neo4j-nvl/base"; +import type { Node, Relationship } from "@neo4j-nvl/base"; +import { + ArrowPathIconOutline, + FitToScreenIcon, + InformationCircleIconOutline, + MagnifyingGlassMinusIconOutline, + MagnifyingGlassPlusIconOutline, + ExploreIcon, +} from "@neo4j-ndl/react/icons"; +import { IconButtonWithToolTip } from "../UI/IconButtonToolTip"; +import { + filterData, + getCheckboxConditions, + graphTypeFromNodes, + processGraphData, +} from "../../utils/Utils"; +import { getGraphSchema, graphQueryAPI } from "../../services/GraphQuery"; +import { graphLabels, nvlOptions, queryMap } from "../../utils/Constants"; +import CheckboxSelection from "./CheckboxSelection"; +import ResultOverview from "./ResultOverview"; +import { ResizePanelDetails } from "./ResizePanel"; +import GraphPropertiesPanel from "./GraphPropertiesPanel"; +import { extractGraphSchemaFromRawData } from "../../utils/Utils"; + +const GraphViewModal: React.FunctionComponent = ({ + open, + inspectedName, + setGraphViewOpen, + viewPoint, + nodeValues, + relationshipValues, + selectedRows, +}) => { + const nvlRef = useRef(null); + const [node, setNode] = useState([]); + const [relationship, setRelationship] = useState([]); + const [allNodes, setAllNodes] = useState([]); + const [allRelationships, setAllRelationships] = useState([]); + const [loading, setLoading] = useState(false); + const [status, setStatus] = useState<"unknown" | "success" | "danger">( + "unknown" + ); + const [statusMessage, setStatusMessage] = useState(""); + const [scheme, setScheme] = useState({}); + const [newScheme, setNewScheme] = useState({}); + const [searchQuery, setSearchQuery] = useState(""); + const [debouncedQuery] = useDebounceValue(searchQuery, 300); + const [graphType, setGraphType] = useState([]); + const [disableRefresh, setDisableRefresh] = useState(false); + const [selected, setSelected] = useState< + { type: EntityType; id: string } | undefined + >(undefined); + const [mode, setMode] = useState(false); + const graphQueryAbortControllerRef = useRef(); + const [openGraphView, setOpenGraphView] = useState(false); + const [schemaNodes, setSchemaNodes] = useState([]); + const [schemaRels, setSchemaRels] = useState([]); + const [viewCheck, setViewcheck] = useState("enhancement"); + + const graphQuery: string = + graphType.includes("DocumentChunk") && graphType.includes("Entities") + ? queryMap.DocChunkEntities + : graphType.includes("DocumentChunk") + ? queryMap.DocChunks + : graphType.includes("Entities") + ? queryMap.Entities + : ""; + + // fit graph to original position + const handleZoomToFit = () => { + nvlRef.current?.fit( + allNodes.map((node) => node.id), + {} + ); + }; + + // Unmounting the component + useEffect(() => { + const timeoutId = setTimeout(() => { + handleZoomToFit(); + }, 10); + return () => { + if (nvlRef.current) { + nvlRef.current?.destroy(); + } + setGraphType([]); + clearTimeout(timeoutId); + setScheme({}); + setNode([]); + setRelationship([]); + setAllNodes([]); + setAllRelationships([]); + setSearchQuery(""); + setSelected(undefined); + }; + }, []); + + useEffect(() => { + let updateGraphType; + if (mode) { + updateGraphType = graphTypeFromNodes(node); + } else { + updateGraphType = graphTypeFromNodes(allNodes); + } + if (Array.isArray(updateGraphType)) { + setGraphType(updateGraphType); + } + }, [allNodes]); + + const fetchData = useCallback(async () => { + graphQueryAbortControllerRef.current = new AbortController(); + try { + let nodeRelationshipData; + if (viewPoint === graphLabels.showGraphView) { + nodeRelationshipData = await graphQueryAPI( + graphQuery, + selectedRows?.map((f) => f.name), + graphQueryAbortControllerRef.current.signal + ); + } else if (viewPoint === graphLabels.showSchemaView) { + nodeRelationshipData = await getGraphSchema(); + } else { + nodeRelationshipData = await graphQueryAPI( + graphQuery, + [inspectedName ?? ""], + graphQueryAbortControllerRef.current.signal + ); + } + return nodeRelationshipData; + } catch (error: any) { + console.log(error); + } + }, [viewPoint, selectedRows, graphQuery, inspectedName]); + + // Api call to get the nodes and relations + const graphApi = async (mode?: string) => { + try { + const result = await fetchData(); + if (result && result.data.data.nodes.length > 0) { + const neoNodes = result.data.data.nodes; + const nodeIds = new Set(neoNodes.map((node: any) => node.element_id)); + const neoRels = result.data.data.relationships + .map((f: Relationship) => f) + .filter( + (rel: any) => + nodeIds.has(rel.end_node_element_id) && + nodeIds.has(rel.start_node_element_id) + ); + const { finalNodes, finalRels, schemeVal } = processGraphData( + neoNodes, + neoRels + ); + + if (mode === "refreshMode") { + initGraph(graphType, finalNodes, finalRels, schemeVal); + } else { + setNode(finalNodes); + setRelationship(finalRels); + setNewScheme(schemeVal); + setLoading(false); + } + setAllNodes(finalNodes); + setAllRelationships(finalRels); + setScheme(schemeVal); + setDisableRefresh(false); + } else { + setLoading(false); + setStatus("danger"); + setStatusMessage( + `No Nodes and Relations for the ${inspectedName} file` + ); + } + } catch (error: any) { + setLoading(false); + setStatus("danger"); + setStatusMessage(error.message); + } + }; + + useEffect(() => { + if (open) { + // Clear any stuck popups that might be covering the modal + document.querySelectorAll(".popup").forEach((el) => el.remove()); + setLoading(true); + setGraphType([]); + if (viewPoint !== graphLabels.chatInfoView) { + graphApi(); + } else { + const { finalNodes, finalRels, schemeVal } = processGraphData( + nodeValues ?? [], + relationshipValues ?? [] + ); + setAllNodes(finalNodes); + setAllRelationships(finalRels); + setScheme(schemeVal); + setNode(finalNodes); + setRelationship(finalRels); + setNewScheme(schemeVal); + setLoading(false); + } + } + }, [open]); + + useEffect(() => { + if (debouncedQuery) { + handleSearch(debouncedQuery); + } + }, [debouncedQuery]); + + const mouseEventCallbacks = useMemo( + () => ({ + onNodeClick: (clickedNode: Node) => { + if (selected?.id !== clickedNode.id || selected?.type !== "node") { + setSelected({ type: "node", id: clickedNode.id }); + } + }, + onRelationshipClick: (clickedRelationship: Relationship) => { + if ( + selected?.id !== clickedRelationship.id || + selected?.type !== "relationship" + ) { + setSelected({ type: "relationship", id: clickedRelationship.id }); + } + }, + onCanvasClick: () => { + if (selected !== undefined) { + setSelected(undefined); + } + }, + onPan: true, + onZoom: true, + onDrag: true, + }), + [selected] + ); + + const initGraph = ( + graphType: GraphType[], + finalNodes: ExtendedNode[], + finalRels: Relationship[], + schemeVal: Scheme + ) => { + if (allNodes.length > 0 && allRelationships.length > 0) { + const { filteredNodes, filteredRelations, filteredScheme } = filterData( + graphType, + finalNodes ?? [], + finalRels ?? [], + schemeVal + ); + setNode(filteredNodes); + setRelationship(filteredRelations); + setNewScheme(filteredScheme); + } + }; + + const selectedItem = useMemo(() => { + if (selected === undefined) { + return undefined; + } + if (selected.type === "node") { + return node.find((nodeVal) => nodeVal.id === selected.id); + } + return relationship.find( + (relationshipVal) => relationshipVal.id === selected.id + ); + }, [selected, relationship, node]); + + const handleSearch = useCallback( + (value: string) => { + const query = value.toLowerCase(); + const updatedNodes = node.map((nodeVal) => { + if (query === "") { + return { + ...nodeVal, + selected: false, + size: graphLabels.nodeSize, + }; + } + const { id, properties, caption } = nodeVal; + const propertiesMatch = properties?.id?.toLowerCase().includes(query); + const match = + id.toLowerCase().includes(query) || + propertiesMatch || + caption?.toLowerCase().includes(query); + return { + ...nodeVal, + selected: match, + }; + }); + const updatedRelationships = relationship.map((rel) => { + return { + ...rel, + selected: false, + }; + }); + setNode(updatedNodes); + setRelationship(updatedRelationships); + }, + [node, relationship] + ); + + if (!open) { + return <>; + } + + const headerTitle = + viewPoint === graphLabels.showGraphView || + viewPoint === graphLabels.chatInfoView + ? graphLabels.generateGraph + : viewPoint === graphLabels.showSchemaView + ? graphLabels.renderSchemaGraph + : `${graphLabels.inspectGeneratedGraphFrom} ${inspectedName}`; + + const checkBoxView = viewPoint !== graphLabels.chatInfoView; + + const handleCheckboxChange = (graph: GraphType) => { + const currentIndex = graphType.indexOf(graph); + const newGraphSelected = [...graphType]; + if (currentIndex === -1) { + newGraphSelected.push(graph); + } else { + newGraphSelected.splice(currentIndex, 1); + } + initGraph(newGraphSelected, allNodes, allRelationships, scheme); + setSearchQuery(""); + setGraphType(newGraphSelected); + setSelected(undefined); + if (nvlRef.current && nvlRef?.current?.getScale() > 1) { + handleZoomToFit(); + } + }; + + // Callback + const nvlCallbacks = { + onLayoutComputing(isComputing: boolean) { + if (!isComputing) { + handleZoomToFit(); + } + }, + }; + + const handleZoomIn = () => { + nvlRef.current?.setZoom(nvlRef.current.getScale() * 1.3); + }; + + const handleZoomOut = () => { + nvlRef.current?.setZoom(nvlRef.current.getScale() * 0.7); + }; + + // Refresh the graph with nodes and relations if file is processing + const handleRefresh = () => { + setDisableRefresh(true); + setMode(true); + graphApi("refreshMode"); + }; + + // when modal closes reset all states to default + const onClose = () => { + // Clear any stuck popups + document.querySelectorAll(".popup").forEach((el) => el.remove()); + graphQueryAbortControllerRef?.current?.abort(); + setStatus("unknown"); + setStatusMessage(""); + setGraphViewOpen(false); + setScheme({}); + setGraphType([]); + setNode([]); + setRelationship([]); + setAllNodes([]); + setAllRelationships([]); + setSearchQuery(""); + setSelected(undefined); + }; + + const handleSchemaView = async (rawNodes: any[], rawRelationships: any[]) => { + const { nodes, relationships } = extractGraphSchemaFromRawData( + rawNodes, + rawRelationships + ); + setSchemaNodes(nodes as any); + setSchemaRels(relationships as any); + setViewcheck("viz"); + setOpenGraphView(true); + }; + + return ( + <> + + + {headerTitle} + {viewPoint !== graphLabels.chatInfoView && ( +
+ + + + + {graphLabels.chunksInfo} + +
+ )} + + {checkBoxView && ( + + )} + +
+ +
+ {loading ? ( +
+ +
+ ) : status !== "unknown" ? ( +
+ +
+ ) : node.length === 0 && + relationship.length === 0 && + graphType.length !== 0 ? ( +
+ +
+ ) : graphType.length === 0 && checkBoxView ? ( +
+ +
+ ) : ( + <> +
+
+ + + handleSchemaView(node, relationship)} + placement="left" + > + + + + + {viewPoint !== "chatInfoView" && ( + + + + )} + + + + + + + + + + +
+ + {selectedItem !== undefined ? ( + + ) : ( + + )} + +
+ + )} +
+
+
+ + ); +}; +export default GraphViewModal; diff --git a/frontend-graph/src/components/Graph/GraphViewer.tsx b/frontend-graph/src/components/Graph/GraphViewer.tsx new file mode 100644 index 000000000..7de48a4da --- /dev/null +++ b/frontend-graph/src/components/Graph/GraphViewer.tsx @@ -0,0 +1,498 @@ +import React, { useState } from "react"; +import EmbeddedGraphView from "./EmbeddedGraphView"; +import SampleData from "./SampleData"; +import SearchPanel, { SearchParams } from "./SearchPanel"; +import SearchResults from "./SearchResults"; +import { ExtendedNode, ExtendedRelationship } from "../../types"; +import { graphQueryAPIWithTransform, searchAndGetSubgraphAPI } from "../../services/GraphQuery"; + +const GraphViewer: React.FC = () => { + const [nodes, setNodes] = useState([]); + const [relationships, setRelationships] = useState( + [] + ); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + const [showGraph, setShowGraph] = useState(false); + const [newFileName, setNewFileName] = useState(""); + const [searchResult, setSearchResult] = useState(null); + + // Connection form state + const [connectionForm, setConnectionForm] = useState({ + uri: "neo4j+ssc://224c5da6.databases.neo4j.io", + userName: "neo4j", + password: "", + database: "neo4j", + documentNames: ["Apple stock during pandemic.pdf"], + }); + + const handleLoadSampleData = ( + sampleNodes: ExtendedNode[], + sampleRelationships: ExtendedRelationship[] + ) => { + setNodes(sampleNodes); + setRelationships(sampleRelationships); + setError(null); + setShowGraph(false); // Reset graph view when new data is loaded + setSearchResult(null); // Clear search results + }; + + const handleLoadBackendData = async () => { + setLoading(true); + setError(null); + + try { + console.log("Starting backend data load..."); + + // Create an AbortController for the API call + const abortController = new AbortController(); + + // Set a timeout to abort the request if it takes too long + const timeoutId = setTimeout(() => { + console.log("Request timeout - aborting"); + abortController.abort(); + }, 60000); // 60 second timeout + + // Fetch graph data from backend with connection parameters + const response = await graphQueryAPIWithTransform( + "entities", // query_type + connectionForm.documentNames, // document_names + abortController.signal, + connectionForm // connection parameters + ); + + // Clear the timeout since we got a response + clearTimeout(timeoutId); + + // Extract the transformed data + const { nodes: backendNodes, relationships: backendRelationships } = + response.data.data; + + setNodes(backendNodes); + setRelationships(backendRelationships); + setShowGraph(false); // Reset graph view when new data is loaded + setSearchResult(null); // Clear search results + + console.log( + `Loaded ${backendNodes.length} nodes and ${backendRelationships.length} relationships from backend` + ); + } catch (err: any) { + console.error("Error loading backend data:", err); + if (err.name === "AbortError") { + setError( + "Request was cancelled due to timeout. The backend is taking too long to respond." + ); + } else { + setError( + err.message || + "Failed to load data from backend. Check your connection details." + ); + } + } finally { + setLoading(false); + } + }; + + const handleSearch = async (searchParams: SearchParams) => { + setLoading(true); + setError(null); + + try { + console.log("Starting search...", searchParams); + + // Create an AbortController for the API call + const abortController = new AbortController(); + + // Set a timeout to abort the request if it takes too long + const timeoutId = setTimeout(() => { + console.log("Search request timeout - aborting"); + abortController.abort(); + }, 120000); // 2 minute timeout for search operations + + // Perform search and subgraph extraction + const response = await searchAndGetSubgraphAPI( + searchParams.search_term, + searchParams.node_type, + searchParams.depth, + searchParams.max_results, + abortController.signal, + connectionForm + ); + + // Clear the timeout since we got a response + clearTimeout(timeoutId); + + // Extract the search results + const searchData = response.data.data; + setSearchResult(searchData); + + console.log( + `Search completed: ${searchData.total_results} total results, ${searchData.subgraphs.length} subgraphs` + ); + } catch (err: any) { + console.error("Error during search:", err); + if (err.name === "AbortError") { + setError( + "Search was cancelled due to timeout. The backend is taking too long to respond." + ); + } else { + setError( + err.message || + "Failed to perform search. Check your connection details and search parameters." + ); + } + } finally { + setLoading(false); + } + }; + + const handleViewSubgraph = (subgraph: any) => { + // Transform the subgraph data to match the expected format + const transformedNodes: ExtendedNode[] = subgraph.nodes.map((node: any) => ({ + id: node.element_id, + labels: node.labels, + properties: node.properties, + caption: node.properties?.name || node.properties?.title || node.properties?.id || "Unnamed Node", + size: 20, + color: "#4CAF50", + })); + + const transformedRelationships: ExtendedRelationship[] = subgraph.relationships.map((rel: any) => ({ + id: rel.element_id, + type: rel.type, + from: rel.start_node_id, + to: rel.end_node_id, + properties: rel.properties, + caption: rel.type, + color: "#2196F3", + width: 2, + })); + + setNodes(transformedNodes); + setRelationships(transformedRelationships); + setShowGraph(true); + setError(null); + + console.log( + `Loaded subgraph with ${transformedNodes.length} nodes and ${transformedRelationships.length} relationships` + ); + }; + + const handleClearSearch = () => { + setSearchResult(null); + setError(null); + }; + + const handleConnectionChange = (field: string, value: string | string[]) => { + setConnectionForm((prev) => ({ + ...prev, + [field]: value, + })); + }; + + const handleShowGraph = () => { + setShowGraph(true); + }; + + const handleAddFile = () => { + if ( + newFileName.trim() && + !connectionForm.documentNames.includes(newFileName.trim()) + ) { + setConnectionForm((prev) => ({ + ...prev, + documentNames: [...prev.documentNames, newFileName.trim()], + })); + setNewFileName(""); + } + }; + + const handleRemoveFile = (index: number) => { + setConnectionForm((prev) => ({ + ...prev, + documentNames: prev.documentNames.filter((_, i) => i !== index), + })); + }; + + const handleKeyPress = (e: React.KeyboardEvent) => { + if (e.key === "Enter") { + e.preventDefault(); + handleAddFile(); + } + }; + + const handleClearAllFiles = () => { + setConnectionForm((prev) => ({ + ...prev, + documentNames: [], + })); + }; + + const handleAddSampleFiles = () => { + const sampleFiles = [ + "Apple stock during pandemic.pdf", + "Market analysis report.pdf", + "Financial statements Q4.pdf", + "Investment portfolio.pdf", + ]; + setConnectionForm((prev) => ({ + ...prev, + documentNames: [...new Set([...prev.documentNames, ...sampleFiles])], + })); + }; + + return ( +
+
+

+ Graph Visualization +

+ + + + {/* Search Panel */} + + + {/* Search Results */} + + +
+

Backend Connection

+

+ Configure your Neo4j connection and load real graph data from your + backend. +

+ +
+
+ + handleConnectionChange("uri", e.target.value)} + className="w-full px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500" + placeholder="neo4j+ssc://your-database.neo4j.io" + /> +
+ +
+ + + handleConnectionChange("database", e.target.value) + } + className="w-full px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500" + placeholder="neo4j" + /> +
+ +
+ + + handleConnectionChange("userName", e.target.value) + } + className="w-full px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500" + placeholder="neo4j" + /> +
+ +
+ + + handleConnectionChange("password", e.target.value) + } + className="w-full px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500" + placeholder="Enter your password" + /> +
+
+ +
+ + + {/* File List */} +
+ {connectionForm.documentNames.length > 0 ? ( +
+ {connectionForm.documentNames.map((fileName, index) => ( +
+
+ + + + + {fileName} + +
+ +
+ ))} +
+ ) : ( +
+ + + +

No files added yet

+

+ Add files below to include them in the graph +

+
+ )} +
+ + {/* Add File Input */} +
+ setNewFileName(e.target.value)} + onKeyPress={handleKeyPress} + placeholder="Enter file name (e.g., document1.pdf)" + className="flex-1 px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500" + /> + +
+ + {/* File Count */} +
+ {connectionForm.documentNames.length} file + {connectionForm.documentNames.length !== 1 ? "s" : ""} selected +
+ + {/* Bulk Operations */} + {connectionForm.documentNames.length > 0 && ( +
+ + +
+ )} +
+ +
+ + +
+
+ + {/* Error Display */} + {error && ( +
+

Error

+

{error}

+
+ )} + + {/* Graph Visualization */} + {showGraph && nodes.length > 0 && ( +
+

Graph Visualization

+ +
+ )} +
+
+ ); +}; + +export default GraphViewer; diff --git a/frontend-graph/src/components/Graph/LegendsChip.tsx b/frontend-graph/src/components/Graph/LegendsChip.tsx new file mode 100644 index 000000000..590c253b2 --- /dev/null +++ b/frontend-graph/src/components/Graph/LegendsChip.tsx @@ -0,0 +1,21 @@ +import { LegendChipProps } from "../../types"; +import { graphLabels } from "../../utils/Constants"; +import Legend from "../UI/Legend"; + +export const LegendsChip: React.FunctionComponent = ({ + scheme, + label, + type, + count, + onClick, +}) => { + return ( + + ); +}; diff --git a/frontend-graph/src/components/Graph/ResizePanel.tsx b/frontend-graph/src/components/Graph/ResizePanel.tsx new file mode 100644 index 000000000..13a909d1c --- /dev/null +++ b/frontend-graph/src/components/Graph/ResizePanel.tsx @@ -0,0 +1,60 @@ +import { DragIcon } from "@neo4j-ndl/react/icons"; +import type { ResizeCallback } from "re-resizable"; +import { Resizable } from "re-resizable"; + +type ResizePanelProps = { + children: React.ReactNode; + open: boolean; + onResizeStop?: ResizeCallback; + defaultWidth?: number; +}; + +export const ResizePanelDetails = ({ + children, + open, + onResizeStop, +}: ResizePanelProps) => { + if (!open) { + return null; + } + return ( + , + }} + handleClasses={{ left: "ml-1" }} + onResizeStop={onResizeStop} + > +
{children}
+
+ ); +}; +const Title = ({ children }: { children: React.ReactNode }) => { + return ( +
+ {children} +
+ ); +}; +ResizePanelDetails.Title = Title; + +const Content = ({ children }: { children: React.ReactNode }) => { + return
{children}
; +}; +ResizePanelDetails.Content = Content; diff --git a/frontend-graph/src/components/Graph/ResultOverview.tsx b/frontend-graph/src/components/Graph/ResultOverview.tsx new file mode 100644 index 000000000..c55051cfc --- /dev/null +++ b/frontend-graph/src/components/Graph/ResultOverview.tsx @@ -0,0 +1,211 @@ +import { Flex, IconButton, TextInput, Typography } from "@neo4j-ndl/react"; +import { MagnifyingGlassIconOutline } from "@neo4j-ndl/react/icons"; +import { LegendsChip } from "./LegendsChip"; +import { ExtendedNode, ExtendedRelationship, Scheme } from "../../types"; +import { graphLabels, RESULT_STEP_SIZE } from "../../utils/Constants"; +import type { Relationship } from "@neo4j-nvl/base"; +import { ShowAll } from "../UI/ShowAll"; +import { Dispatch, SetStateAction } from "react"; + +interface OverViewProps { + nodes: ExtendedNode[]; + relationships: ExtendedRelationship[]; + newScheme: Scheme; + searchQuery: string; + setSearchQuery: Dispatch>; + setNodes: Dispatch>; + setRelationships: Dispatch>; +} + +const ResultOverview: React.FunctionComponent = ({ + nodes, + relationships, + newScheme, + searchQuery, + setSearchQuery, + setNodes, + setRelationships, +}) => { + const nodeCount = (nodes: ExtendedNode[], label: string): number => { + return [ + ...new Set( + nodes?.filter((n) => n.labels?.includes(label)).map((i) => i.id) + ), + ].length; + }; + + // sort the legends in with Chunk and Document always the first two values + const nodeCheck = Object.keys(newScheme).sort((a, b) => { + if (a === graphLabels.document || a === graphLabels.chunk) { + return -1; + } else if (b === graphLabels.document || b === graphLabels.chunk) { + return 1; + } + return a.localeCompare(b); + }); + + // get sorted relationships + const relationshipsSorted = relationships.sort((a, b) => + a.type.localeCompare(b.type) + ); + + const relationshipCount = ( + relationships: ExtendedRelationship[], + label: string + ): number => { + return [ + ...new Set( + relationships + ?.filter((r) => r.caption?.includes(label)) + .map((i) => i.id) + ), + ].length; + }; + + // To get the relationship count + const groupedAndSortedRelationships: ExtendedRelationship[] = Object.values( + relationshipsSorted.reduce( + (acc: { [key: string]: ExtendedRelationship }, relType: Relationship) => { + const key = relType.caption || ""; + if (!acc[key]) { + acc[key] = { ...relType, count: 0 }; + } + (acc[key] as { count: number }).count += relationshipCount( + relationships as ExtendedRelationship[], + key + ); + return acc; + }, + {} + ) + ); + + // On Relationship Legend Click, highlight the relationships and deactivating any active nodes + const handleRelationshipClick = (nodeLabel: string) => { + const updatedRelations = relationships.map((rel) => { + return { + ...rel, + selected: rel?.caption?.includes(nodeLabel), + }; + }); + + // // deactivating any active nodes + const updatedNodes = nodes.map((node) => { + return { + ...node, + selected: false, + size: graphLabels.nodeSize, + }; + }); + if (searchQuery !== "") { + setSearchQuery(""); + } + setRelationships(updatedRelations); + setNodes(updatedNodes); + }; + + // On Node Click, highlighting the nodes and deactivating any active relationships + const handleNodeClick = (nodeLabel: string) => { + const updatedNodes = nodes.map((node) => { + const isActive = node.labels.includes(nodeLabel); + return { + ...node, + selected: isActive, + }; + }); + // deactivating any active relationships + const updatedRelationships = relationships.map((rel) => { + return { + ...rel, + selected: false, + }; + }); + if (searchQuery !== "") { + setSearchQuery(""); + } + setNodes(updatedNodes); + setRelationships(updatedRelationships); + }; + + return ( + <> + {nodeCheck.length > 0 && ( + <> + + {graphLabels.resultOverview} +
+ { + setSearchQuery(e.target.value); + }} + isFluid={true} + leftElement={ + + + + } + /> +
+ + {graphLabels.totalNodes} ({nodes.length}) + +
+
+ + {nodeCheck.map((nodeLabel) => ( + handleNodeClick(nodeLabel)} + /> + ))} + +
+ + )} + {relationshipsSorted.length > 0 && ( + <> + + + {graphLabels.totalRelationships} ({relationships.length}) + + +
+ + {groupedAndSortedRelationships.map((relType, index) => ( + handleRelationshipClick(relType.caption || "")} + scheme={{}} + /> + ))} + +
+ + )} + + ); +}; + +export default ResultOverview; diff --git a/frontend-graph/src/components/Graph/SampleData.tsx b/frontend-graph/src/components/Graph/SampleData.tsx new file mode 100644 index 000000000..9c1a23b12 --- /dev/null +++ b/frontend-graph/src/components/Graph/SampleData.tsx @@ -0,0 +1,127 @@ +import React from "react"; +import { ExtendedNode, ExtendedRelationship } from "../../types"; + +export const getSampleGraphData = () => { + const sampleNodes: ExtendedNode[] = [ + { + id: "1", + labels: ["Person"], + properties: { + name: "Alice", + age: 30, + occupation: "Engineer", + }, + }, + { + id: "2", + labels: ["Person"], + properties: { + name: "Bob", + age: 25, + occupation: "Designer", + }, + }, + { + id: "3", + labels: ["Company"], + properties: { + name: "TechCorp", + industry: "Technology", + founded: 2020, + }, + }, + { + id: "4", + labels: ["Project"], + properties: { + name: "GraphBuilder", + status: "Active", + description: "LLM Graph Builder Project", + }, + }, + ]; + + const sampleRelationships: ExtendedRelationship[] = [ + { + id: "1", + from: "1", + to: "3", + type: "WORKS_FOR", + properties: { + since: 2022, + role: "Senior Engineer", + }, + }, + { + id: "2", + from: "2", + to: "3", + type: "WORKS_FOR", + properties: { + since: 2023, + role: "UI Designer", + }, + }, + { + id: "3", + from: "1", + to: "4", + type: "LEADS", + properties: { + since: 2024, + }, + }, + { + id: "4", + from: "2", + to: "4", + type: "CONTRIBUTES_TO", + properties: { + role: "Design Lead", + }, + }, + { + id: "5", + from: "1", + to: "2", + type: "COLLABORATES_WITH", + properties: { + projects: ["GraphBuilder"], + }, + }, + ]; + + return { nodes: sampleNodes, relationships: sampleRelationships }; +}; + +interface SampleDataProps { + onLoadSampleData: ( + nodes: ExtendedNode[], + relationships: ExtendedRelationship[] + ) => void; +} + +const SampleData: React.FC = ({ onLoadSampleData }) => { + const handleLoadSample = () => { + const { nodes, relationships } = getSampleGraphData(); + onLoadSampleData(nodes, relationships); + }; + + return ( +
+

Sample Data

+

+ Load sample graph data to test the visualization. This creates a simple + graph with people, companies, and projects. +

+ +
+ ); +}; + +export default SampleData; diff --git a/frontend-graph/src/components/Graph/SearchPanel.tsx b/frontend-graph/src/components/Graph/SearchPanel.tsx new file mode 100644 index 000000000..ee2e3d3d4 --- /dev/null +++ b/frontend-graph/src/components/Graph/SearchPanel.tsx @@ -0,0 +1,208 @@ +import React, { useState } from "react"; +import { Button, TextInput } from "@neo4j-ndl/react"; +import { MagnifyingGlassIconOutline } from "@neo4j-ndl/react/icons"; + +interface SearchPanelProps { + onSearch: (searchParams: SearchParams) => void; + onClear: () => void; + loading: boolean; + connectionForm: { + uri: string; + userName: string; + password: string; + database: string; + }; +} + +export interface SearchParams { + search_term: string; + node_type: string; + depth: number; + max_results: number; +} + +const SearchPanel: React.FC = ({ + onSearch, + onClear, + loading, + connectionForm, +}) => { + const [searchParams, setSearchParams] = useState({ + search_term: "", + node_type: "Person", + depth: 4, + max_results: 10, + }); + + const nodeTypes = [ + { label: "Person", value: "Person" }, + { label: "Organization", value: "Organization" }, + { label: "Location", value: "Location" }, + { label: "Event", value: "Event" }, + { label: "Concept", value: "Concept" }, + { label: "Product", value: "Product" }, + { label: "Technology", value: "Technology" }, + ]; + + const depthOptions = [ + { label: "1 level", value: 1 }, + { label: "2 levels", value: 2 }, + { label: "3 levels", value: 3 }, + { label: "4 levels", value: 4 }, + { label: "5 levels", value: 5 }, + ]; + + const maxResultsOptions = [ + { label: "5 results", value: 5 }, + { label: "10 results", value: 10 }, + { label: "20 results", value: 20 }, + { label: "50 results", value: 50 }, + ]; + + const handleSearch = () => { + if (searchParams.search_term.trim()) { + onSearch(searchParams); + } + }; + + const handleKeyPress = (e: React.KeyboardEvent) => { + if (e.key === "Enter") { + handleSearch(); + } + }; + + const isSearchDisabled = !searchParams.search_term.trim() || loading; + + return ( +
+

Graph Search

+

+ Search for nodes across all documents and extract their subgraphs. +

+ +
+ {/* Search Term Input */} +
+ + + setSearchParams({ ...searchParams, search_term: e.target.value }) + } + onKeyPress={handleKeyPress} + placeholder="Enter search term (e.g., person name, organization)" + className="w-full" + disabled={loading} + /> +
+ + {/* Node Type Selection */} +
+ + +
+ + {/* Depth Selection */} +
+ + +
+ + {/* Max Results Selection */} +
+ + +
+ + {/* Action Buttons */} +
+ + +
+
+ + {/* Connection Status */} +
+

+ Connection Status +

+
+
URI: {connectionForm.uri || "Not configured"}
+
Database: {connectionForm.database || "Not configured"}
+
Username: {connectionForm.userName || "Not configured"}
+
+
+
+ ); +}; + +export default SearchPanel; diff --git a/frontend-graph/src/components/Graph/SearchResults.tsx b/frontend-graph/src/components/Graph/SearchResults.tsx new file mode 100644 index 000000000..543c2a5f1 --- /dev/null +++ b/frontend-graph/src/components/Graph/SearchResults.tsx @@ -0,0 +1,167 @@ +import React from "react"; +import { Button } from "@neo4j-ndl/react"; +import { + EyeIconOutline, + InformationCircleIconOutline, +} from "@neo4j-ndl/react/icons"; + +interface SearchResult { + search_term: string; + node_type: string; + total_results: number; + subgraphs: SubgraphResult[]; +} + +interface SubgraphResult { + start_node_id: string; + depth: number; + nodes: any[]; + relationships: any[]; + matching_node: { + element_id: string; + labels: string[]; + properties: Record; + }; +} + +interface SearchResultsProps { + searchResult: SearchResult | null; + onViewSubgraph: (subgraph: SubgraphResult) => void; + onClear: () => void; +} + +const SearchResults: React.FC = ({ + searchResult, + onViewSubgraph, + onClear, +}) => { + if (!searchResult) { + return null; + } + + const getNodeName = (node: any) => { + return ( + node.properties?.name || + node.properties?.title || + node.properties?.id || + node.properties?.description || + "Unnamed Node" + ); + }; + + const getNodeType = (node: any) => { + const labels = node.labels || []; + return labels.find((label: string) => label !== "__Entity__") || "Entity"; + }; + + return ( +
+
+

Search Results

+ +
+ + {/* Search Summary */} +
+
+ + + Search Summary + +
+
+

+ Search Term: "{searchResult.search_term}" +

+

+ Node Type: {searchResult.node_type} +

+

+ Total Results: {searchResult.total_results} +

+

+ Subgraphs Found: {searchResult.subgraphs.length} +

+
+
+ + {/* Results */} + {searchResult.subgraphs.length === 0 ? ( +
+

No subgraphs found for the search criteria.

+

+ Try adjusting your search term or node type. +

+
+ ) : ( +
+ {searchResult.subgraphs.map((subgraph, index) => ( +
+
+
+

+ {getNodeName(subgraph.matching_node)} +

+
+ + {getNodeType(subgraph.matching_node)} + + + Depth: {subgraph.depth} + +
+
+

+ Nodes: {subgraph.nodes.length} +

+

+ Relationships:{" "} + {subgraph.relationships.length} +

+
+
+ +
+ + {/* Node Properties Preview */} + {subgraph.matching_node.properties && ( +
+

Properties:

+
+ {Object.entries(subgraph.matching_node.properties) + .slice(0, 6) + .map(([key, value]) => ( +
+ + {key}: + + + {String(value).slice(0, 30)} + {String(value).length > 30 ? "..." : ""} + +
+ ))} +
+
+ )} +
+ ))} +
+ )} +
+ ); +}; + +export default SearchResults; diff --git a/frontend-graph/src/components/UI/IconButtonToolTip.tsx b/frontend-graph/src/components/UI/IconButtonToolTip.tsx new file mode 100644 index 000000000..654d15723 --- /dev/null +++ b/frontend-graph/src/components/UI/IconButtonToolTip.tsx @@ -0,0 +1,80 @@ +import { IconButton, Tooltip } from "@neo4j-ndl/react"; +import { useState } from "react"; + +export const IconButtonWithToolTip = ({ + text, + children, + onClick, + size = "medium", + clean, + grouped, + placement = "bottom", + disabled = false, + label, + loading = false, + className = "", +}: { + label: string; + text: string | React.ReactNode; + children: React.ReactNode; + onClick?: React.MouseEventHandler | undefined; + size?: "small" | "medium" | "large"; + clean?: boolean; + grouped?: boolean; + placement?: "bottom" | "top" | "right" | "left"; + disabled?: boolean; + loading?: boolean; + className?: string; +}) => { + const [isHovered, setIsHovered] = useState(false); + return ( + + + setIsHovered(true), + onMouseLeave: () => setIsHovered(false), + }} + isLoading={loading} + > + {children} + + + {isHovered && ( + + {text} + + )} + + ); +}; + +export const IconWithToolTip = ({ + text, + children, + placement = "bottom", +}: { + label: string; + text: string | React.ReactNode; + children: React.ReactNode; + onClick?: React.MouseEventHandler | undefined; + size?: "small" | "medium" | "large"; + clean?: boolean; + grouped?: boolean; + placement?: "bottom" | "top" | "right" | "left"; + disabled?: boolean; +}) => { + return ( + + {children} + {text} + + ); +}; diff --git a/frontend-graph/src/components/UI/Legend.tsx b/frontend-graph/src/components/UI/Legend.tsx new file mode 100644 index 000000000..46d0fd3d6 --- /dev/null +++ b/frontend-graph/src/components/UI/Legend.tsx @@ -0,0 +1,27 @@ +import { GraphLabel } from "@neo4j-ndl/react"; + +export default function Legend({ + bgColor, + title, + count, + type, + onClick, +}: { + bgColor: string; + title: string; + count?: number; + type: "node" | "relationship" | "propertyKey"; + tabIndex?: number; + onClick?: (e: React.MouseEvent) => void; +}) { + return ( + + {title} {count !== undefined && `(${count})`} + + ); +} diff --git a/frontend-graph/src/components/UI/ShowAll.tsx b/frontend-graph/src/components/UI/ShowAll.tsx new file mode 100644 index 000000000..df864d3f8 --- /dev/null +++ b/frontend-graph/src/components/UI/ShowAll.tsx @@ -0,0 +1,39 @@ +import { Button } from "@neo4j-ndl/react"; +import type { ReactNode } from "react"; +import { useState } from "react"; + +type ShowAllProps = { + initiallyShown: number; + children: ((() => ReactNode) | ReactNode)[]; + ariaLabel?: string; +}; +const isThunkComponent = ( + t: (() => ReactNode) | ReactNode +): t is () => ReactNode => typeof t === "function"; + +export function ShowAll({ initiallyShown, children }: ShowAllProps) { + const [expanded, setExpanded] = useState(false); + const toggleExpanded = () => setExpanded((e) => !e); + const itemCount = children.length; + const controlsNeeded = itemCount > initiallyShown; + const shown = expanded ? itemCount : initiallyShown; + const leftToShow = itemCount - shown; + + if (itemCount === 0) { + return null; + } + + const currentChildren = children + .slice(0, shown) + .map((c) => (isThunkComponent(c) ? c() : c)); + return ( + <> +
{currentChildren}
+ {controlsNeeded && ( + + )} + + ); +} diff --git a/frontend-graph/src/index.css b/frontend-graph/src/index.css new file mode 100644 index 000000000..b6f06a2fe --- /dev/null +++ b/frontend-graph/src/index.css @@ -0,0 +1,21 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +@layer base { + *, + ::after, + ::before, + ::backdrop, + ::file-selector-button { + border-color: var(--color-gray-200, currentColor); + } +} + +body { + margin: 0; +} + +.ndl-progress-bar-wrapper .ndl-header .ndl-heading::after { + animation: none !important; +} diff --git a/frontend-graph/src/main.tsx b/frontend-graph/src/main.tsx new file mode 100644 index 000000000..3453244bd --- /dev/null +++ b/frontend-graph/src/main.tsx @@ -0,0 +1,11 @@ +import React from "react"; +import ReactDOM from "react-dom/client"; +import App from "./App"; +import "./index.css"; +import "@neo4j-ndl/base/lib/neo4j-ds-styles.css"; + +ReactDOM.createRoot(document.getElementById("root")!).render( + + + +); diff --git a/frontend-graph/src/services/GraphQuery.ts b/frontend-graph/src/services/GraphQuery.ts new file mode 100644 index 000000000..82d6d58eb --- /dev/null +++ b/frontend-graph/src/services/GraphQuery.ts @@ -0,0 +1,370 @@ +import axios from "axios"; + +//TO-DO from env +const API_BASE_URL = "http://localhost:8000"; + +const api = axios.create({ + baseURL: API_BASE_URL, + timeout: 150000, +}); + +interface ConnectionParams { + uri: string; + userName: string; + password: string; + database: string; +} + +export const graphQueryAPI = async ( + query_type: string, + document_names: (string | undefined)[] | undefined, + signal: AbortSignal, + connectionParams?: ConnectionParams +) => { + try { + console.log("Making API call with params:", { + query_type, + document_names, + connectionParams: connectionParams + ? { + uri: connectionParams.uri, + userName: connectionParams.userName, + database: connectionParams.database, + password: "***", //hide pass + } + : "none", + }); + + const formData = new FormData(); + formData.append("query_type", query_type ?? "entities"); + formData.append("document_names", JSON.stringify(document_names)); + + if (connectionParams) { + formData.append("uri", connectionParams.uri); + formData.append("userName", connectionParams.userName); + formData.append("password", connectionParams.password); + formData.append("database", connectionParams.database); + } + + // console.log("Sending request to:", `${API_BASE_URL}/graph_query`); + // console.log("FormData contents:"); + // for (const [key, value] of formData.entries()) { + // console.log(`${key}:`, key === "password" ? "***" : value); + // } + + const response = await api.post(`/graph_query`, formData, { + headers: { + "Content-Type": "multipart/form-data", + }, + signal, + }); + return response; + } catch (error: any) { + console.error("Error getting the Nodes or Relationships:", error); + if (error.code === "ECONNABORTED") { + throw new Error( + "Request timed out. The backend is taking too long to respond. Please check if your backend is running and try again." + ); + } else if (error.response) { + throw new Error( + `Backend error: ${error.response.status} - ${error.response.data?.detail || error.response.statusText}` + ); + } else if (error.request) { + throw new Error( + "No response from backend. Please check if your backend is running" + ); + } else { + throw new Error(`Request failed: ${error.message}`); + } + } +}; + +export const getNeighbors = async (elementId: string) => { + try { + const formData = new FormData(); + formData.append("elementId", elementId); + + const response = await api.post(`/get_neighbours`, formData, { + headers: { + "Content-Type": "multipart/form-data", + }, + }); + return response; + } catch (error) { + console.log("Error getting the Neighbors:", error); + throw error; + } +}; + +export const getGraphSchema = async () => { + try { + const formData = new FormData(); + const response = await api.post(`/schema_visualization`, formData, { + headers: { + "Content-Type": "multipart/form-data", + }, + }); + return response; + } catch (error) { + console.log("Error getting the Schema:", error); + throw error; + } +}; + +// Data transformation functions to convert backend format to frontend format +export const transformBackendData = (backendData: any) => { + console.log("Backend response structure:", backendData); + + // Handle different possible response structures + let nodes, relationships; + + if (backendData.data && backendData.data.data) { + // Expected structure: { data: { data: { nodes: [], relationships: [] } } } + nodes = backendData.data.data.nodes || []; + relationships = backendData.data.data.relationships || []; + } else if (backendData.data) { + // Alternative structure: { data: { nodes: [], relationships: [] } } + nodes = backendData.data.nodes || []; + relationships = backendData.data.relationships || []; + } else { + // Direct structure: { nodes: [], relationships: [] } + nodes = backendData.nodes || []; + relationships = backendData.relationships || []; + } + + // Transform nodes + const transformedNodes = nodes.map((node: any) => ({ + id: node.element_id, + labels: node.labels || [], + properties: node.properties || {}, + })); + + // Transform relationships + const transformedRelationships = relationships.map((rel: any) => ({ + id: rel.element_id, + from: rel.start_node_element_id, + to: rel.end_node_element_id, + type: rel.type, + properties: rel.properties || {}, + })); + + return { + nodes: transformedNodes, + relationships: transformedRelationships, + }; +}; + +// Enhanced graph query with data transformation +export const graphQueryAPIWithTransform = async ( + query_type: string, + document_names: (string | undefined)[] | undefined, + signal: AbortSignal, + connectionParams?: ConnectionParams +) => { + try { + const response = await graphQueryAPI( + query_type, + document_names, + signal, + connectionParams + ); + //console.log("Raw API response:", response); + const transformedData = transformBackendData(response); + return { + ...response, + data: { + ...response.data, + data: transformedData, + }, + }; + } catch (error) { + console.log("Error in graphQueryAPIWithTransform:", error); + throw error; + } +}; + +export const searchNodesAPI = async ( + search_term: string, + node_type: string = "Person", + max_results: number = 50, + signal: AbortSignal, + connectionParams?: ConnectionParams +) => { + try { + console.log("Making search API call with params:", { + search_term, + node_type, + max_results, + connectionParams: connectionParams + ? { + uri: connectionParams.uri, + userName: connectionParams.userName, + database: connectionParams.database, + password: "***", //hide pass + } + : "none", + }); + + const formData = new FormData(); + formData.append("search_term", search_term); + formData.append("node_type", node_type); + formData.append("max_results", max_results.toString()); + + if (connectionParams) { + formData.append("uri", connectionParams.uri); + formData.append("userName", connectionParams.userName); + formData.append("password", connectionParams.password); + formData.append("database", connectionParams.database); + } + + const response = await api.post(`/search_nodes`, formData, { + headers: { + "Content-Type": "multipart/form-data", + }, + signal, + }); + return response; + } catch (error: any) { + console.error("Error searching nodes:", error); + if (error.code === "ECONNABORTED") { + throw new Error( + "Request timed out. The backend is taking too long to respond. Please check if your backend is running and try again." + ); + } else if (error.response) { + throw new Error( + `Backend error: ${error.response.status} - ${error.response.data?.detail || error.response.statusText}` + ); + } else if (error.request) { + throw new Error( + "No response from backend. Please check if your backend is running" + ); + } else { + throw new Error(`Request failed: ${error.message}`); + } + } +}; + +export const getSubgraphAPI = async ( + node_id: string, + depth: number = 4, + max_nodes: number = 1000, + signal: AbortSignal, + connectionParams?: ConnectionParams +) => { + try { + console.log("Making subgraph API call with params:", { + node_id, + depth, + max_nodes, + connectionParams: connectionParams + ? { + uri: connectionParams.uri, + userName: connectionParams.userName, + database: connectionParams.database, + password: "***", //hide pass + } + : "none", + }); + + const formData = new FormData(); + formData.append("node_id", node_id); + formData.append("depth", depth.toString()); + formData.append("max_nodes", max_nodes.toString()); + + if (connectionParams) { + formData.append("uri", connectionParams.uri); + formData.append("userName", connectionParams.userName); + formData.append("password", connectionParams.password); + formData.append("database", connectionParams.database); + } + + const response = await api.post(`/get_subgraph`, formData, { + headers: { + "Content-Type": "multipart/form-data", + }, + signal, + }); + return response; + } catch (error: any) { + console.error("Error getting subgraph:", error); + if (error.code === "ECONNABORTED") { + throw new Error( + "Request timed out. The backend is taking too long to respond. Please check if your backend is running and try again." + ); + } else if (error.response) { + throw new Error( + `Backend error: ${error.response.status} - ${error.response.data?.detail || error.response.statusText}` + ); + } else if (error.request) { + throw new Error( + "No response from backend. Please check if your backend is running" + ); + } else { + throw new Error(`Request failed: ${error.message}`); + } + } +}; + +export const searchAndGetSubgraphAPI = async ( + search_term: string, + node_type: string = "Person", + depth: number = 4, + max_results: number = 10, + signal: AbortSignal, + connectionParams?: ConnectionParams +) => { + try { + console.log("Making search and subgraph API call with params:", { + search_term, + node_type, + depth, + max_results, + connectionParams: connectionParams + ? { + uri: connectionParams.uri, + userName: connectionParams.userName, + database: connectionParams.database, + password: "***", //hide pass + } + : "none", + }); + + const formData = new FormData(); + formData.append("search_term", search_term); + formData.append("node_type", node_type); + formData.append("depth", depth.toString()); + formData.append("max_results", max_results.toString()); + + if (connectionParams) { + formData.append("uri", connectionParams.uri); + formData.append("userName", connectionParams.userName); + formData.append("password", connectionParams.password); + formData.append("database", connectionParams.database); + } + + const response = await api.post(`/search_and_get_subgraph`, formData, { + headers: { + "Content-Type": "multipart/form-data", + }, + signal, + }); + return response; + } catch (error: any) { + console.error("Error searching and getting subgraph:", error); + if (error.code === "ECONNABORTED") { + throw new Error( + "Request timed out. The backend is taking too long to respond. Please check if your backend is running and try again." + ); + } else if (error.response) { + throw new Error( + `Backend error: ${error.response.status} - ${error.response.data?.detail || error.response.statusText}` + ); + } else if (error.request) { + throw new Error( + "No response from backend. Please check if your backend is running" + ); + } else { + throw new Error(`Request failed: ${error.message}`); + } + } +}; diff --git a/frontend-graph/src/types.ts b/frontend-graph/src/types.ts new file mode 100644 index 000000000..cf4096d5a --- /dev/null +++ b/frontend-graph/src/types.ts @@ -0,0 +1,126 @@ +import React from "react"; +import type { Node, Relationship } from "@neo4j-nvl/base"; + +export interface OptionType { + readonly value: string; + readonly label: string; +} + +export type GraphType = "Entities" | "DocumentChunk" | "Communities"; + +export type EntityType = "node" | "relationship"; + +export type Scheme = Record; + +export interface ExtendedNode extends Node { + labels: string[]; + properties: { + fileName?: string; + [key: string]: any; + }; +} + +export interface ExtendedRelationship extends Relationship { + count?: number; + startNodeId?: string; + endNodeId?: string; + properties?: Record; +} + +export interface BasicNode { + id: string; + type: string; + labels: string[]; + properties: Record; + propertyTypes: Record; +} + +export interface BasicRelationship { + id: string; + to: string; + from: string; + type: string; + caption: string; +} + +export interface GraphViewModalProps { + open: boolean; + inspectedName?: string; + setGraphViewOpen: React.Dispatch>; + viewPoint: string; + nodeValues?: ExtendedNode[]; + relationshipValues?: ExtendedRelationship[]; + selectedRows?: any[] | undefined; +} + +export interface SchemaViewModalProps { + open: boolean; + inspectedName?: string; + setGraphViewOpen: React.Dispatch>; + viewPoint: string; + nodeValues?: ExtendedNode[] | OptionType[]; + relationshipValues?: ExtendedRelationship[] | string[] | OptionType[]; + selectedRows?: any[] | undefined; + schemaLoading?: boolean; + view?: string; +} + +export interface CheckboxSectionProps { + graphType: GraphType[]; + loading: boolean; + handleChange: (graph: GraphType) => void; + isCommunity: boolean; + isDocChunk: boolean; + isEntity: boolean; +} + +export interface LegendChipProps { + scheme: Scheme; + label: string; + type: "node" | "relationship" | "propertyKey"; + count?: number; + onClick?: (e: React.MouseEvent) => void; +} + +export interface GraphPropertiesPanelProps { + inspectedItem: BasicNode | BasicRelationship; + newScheme: Scheme; +} + +export interface GraphPropertiesTableProps { + propertiesWithTypes: { + key: string; + value: string | number | boolean | []; + }[]; +} + +export interface OverViewProps { + nodes: ExtendedNode[]; + relationships: ExtendedRelationship[]; + newScheme: Scheme; + searchQuery: string; + setSearchQuery: React.Dispatch>; + setNodes: React.Dispatch>; + setRelationships: React.Dispatch< + React.SetStateAction + >; +} + +export interface ResizePanelDetailsProps { + open: boolean; + children: React.ReactNode; +} + +export interface IconButtonWithToolTipProps { + label: string; + text: string; + onClick: () => void; + placement?: "top" | "bottom" | "left" | "right"; + disabled?: boolean; + children: React.ReactNode; +} + +export interface ShowAllProps { + initiallyShown: number; + children: React.ReactNode; +} diff --git a/frontend-graph/src/utils/Constants.ts b/frontend-graph/src/utils/Constants.ts new file mode 100644 index 000000000..cc477b378 --- /dev/null +++ b/frontend-graph/src/utils/Constants.ts @@ -0,0 +1,90 @@ +import { NvlOptions } from "@neo4j-nvl/base"; +import { GraphType, OptionType } from "../types"; + +export const GRAPH_COLORS = { + node: "#4CAF50", + relationship: "#2196F3", + default: "#9E9E9E", +}; + +export const GRAPH_TYPES = { + ENTITIES: "Entities", + DOCUMENT_CHUNK: "DocumentChunk", + COMMUNITIES: "Communities", +} as const; + +export const DEFAULT_GRAPH_OPTIONS = { + nodeColor: GRAPH_COLORS.node, + relationshipColor: GRAPH_COLORS.relationship, + backgroundColor: "#ffffff", + nodeSize: 20, + relationshipWidth: 2, +}; + +export const nvlOptions: NvlOptions = { + allowDynamicMinZoom: true, + disableWebGL: true, + maxZoom: 3, + minZoom: 0.05, + relationshipThreshold: 0.55, + useWebGL: false, + instanceId: "graph-preview", + initialZoom: 1, +}; + +export const queryMap: { + Document: string; + Chunks: string; + Entities: string; + DocEntities: string; + DocChunks: string; + ChunksEntities: string; + DocChunkEntities: string; +} = { + Document: "document", + Chunks: "chunks", + Entities: "entities", + DocEntities: "docEntities", + DocChunks: "docChunks", + ChunksEntities: "chunksEntities", + DocChunkEntities: "docChunkEntities", +}; + +export const graphView: OptionType[] = [ + { label: "Lexical Graph", value: queryMap.DocChunks }, + { label: "Entity Graph", value: queryMap.Entities }, + { label: "Knowledge Graph", value: queryMap.DocChunkEntities }, +]; + +export const intitalGraphType = (isGDSActive: boolean): GraphType[] => { + return isGDSActive + ? ["DocumentChunk", "Entities", "Communities"] // GDS is active, include communities + : ["DocumentChunk", "Entities"]; // GDS is inactive, exclude communities +}; + +export const graphLabels = { + showGraphView: "showGraphView", + chatInfoView: "chatInfoView", + generateGraph: "Generated Graph", + inspectGeneratedGraphFrom: "Inspect Generated Graph from", + document: "Document", + chunk: "Chunk", + documentChunk: "DocumentChunk", + entities: "Entities", + resultOverview: "Result Overview", + totalNodes: "Total Nodes", + noEntities: "No Entities Found", + selectCheckbox: "Select atleast one checkbox for graph view", + totalRelationships: "Total Relationships", + nodeSize: 30, + docChunk: "Document & Chunk", + community: "Communities", + noNodesRels: "No Nodes and No relationships", + neighborView: "neighborView", + chunksInfo: "We are visualizing 50 chunks at a time", + showSchemaView: "showSchemaView", + renderSchemaGraph: "Graph from Database Schema", + generatedGraphFromUserSchema: "Generated Graph from User Defined Schema", +}; + +export const RESULT_STEP_SIZE = 25; diff --git a/frontend-graph/src/utils/Utils.ts b/frontend-graph/src/utils/Utils.ts new file mode 100644 index 000000000..d476eb6a7 --- /dev/null +++ b/frontend-graph/src/utils/Utils.ts @@ -0,0 +1,218 @@ +import { calcWordColor } from "@neo4j-devtools/word-color"; +import type { Relationship } from "@neo4j-nvl/base"; +import { + ExtendedNode, + ExtendedRelationship, + GraphType, + Scheme, + OptionType, +} from "../types"; + +// Graph Functions +export const getSize = (node: any) => { + const size = node.properties?.size || node.properties?.weight || 1; + return Math.max(10, Math.min(50, size * 20)); +}; + +export const getNodeCaption = (node: any) => { + const caption = + node.properties?.name || + node.properties?.title || + node.properties?.id || + node.id; + return caption?.length > 20 ? caption.substring(0, 20) + "..." : caption; +}; + +export const getIcon = (node: any) => { + // Return undefined to prevent broken image errors since we don't have the SVG assets + return undefined; +}; + +export const processGraphData = ( + neoNodes: ExtendedNode[], + neoRels: ExtendedRelationship[] +) => { + const finalNodes: ExtendedNode[] = neoNodes.map((node) => { + const labels = node.labels || []; + const color = calcWordColor(labels[0] || "default"); + + return { + ...node, + id: node.id, + labels: labels, + properties: node.properties || {}, + caption: getNodeCaption(node), + size: getSize(node), + color: color, + icon: getIcon(node), + }; + }); + + const finalRels: ExtendedRelationship[] = neoRels.map((rel) => { + const color = calcWordColor(rel.type || "default"); + + return { + ...rel, + id: rel.id, + type: rel.type, + from: rel.startNodeId || rel.from, + to: rel.endNodeId || rel.to, + caption: rel.type, + color: color, + width: 2, + }; + }); + + const scheme: Scheme = {}; + finalNodes.forEach((node) => { + node.labels.forEach((label) => { + if (!scheme[label]) { + scheme[label] = calcWordColor(label); + } + }); + }); + + finalRels.forEach((rel) => { + if (!scheme[rel.type]) { + scheme[rel.type] = calcWordColor(rel.type); + } + }); + + return { finalNodes, finalRels, schemeVal: scheme }; +}; + +export const filterData = ( + graphType: GraphType[], + allNodes: ExtendedNode[], + allRelationships: Relationship[], + scheme: Scheme +) => { + const filteredNodes: ExtendedNode[] = []; + const filteredRelations: ExtendedRelationship[] = []; + const filteredScheme: Scheme = {}; + + if (graphType.includes("Entities")) { + const entityNodes = allNodes.filter((node) => + node.labels.some( + (label) => !label.includes("Chunk") && !label.includes("Community") + ) + ); + filteredNodes.push(...entityNodes); + } + + if (graphType.includes("DocumentChunk")) { + const chunkNodes = allNodes.filter((node) => + node.labels.some((label) => label.includes("Chunk")) + ); + filteredNodes.push(...chunkNodes); + } + + if (graphType.includes("Communities")) { + const communityNodes = allNodes.filter((node) => + node.labels.some((label) => label.includes("Community")) + ); + filteredNodes.push(...communityNodes); + } + + // Get relationships between filtered nodes + const nodeIds = new Set(filteredNodes.map((node) => node.id)); + const filteredRels = allRelationships.filter( + (rel) => nodeIds.has(rel.from) && nodeIds.has(rel.to) + ); + + filteredRelations.push(...filteredRels); + + // Build filtered scheme + filteredNodes.forEach((node) => { + node.labels.forEach((label) => { + if (scheme[label] && !filteredScheme[label]) { + filteredScheme[label] = scheme[label]; + } + }); + }); + + filteredRelations.forEach((rel) => { + if (scheme[rel.type] && !filteredScheme[rel.type]) { + filteredScheme[rel.type] = scheme[rel.type]; + } + }); + + return { filteredNodes, filteredRelations, filteredScheme }; +}; + +export const getCheckboxConditions = (allNodes: ExtendedNode[]) => { + const hasEntities = allNodes.some((node) => + node.labels.some( + (label) => !label.includes("Chunk") && !label.includes("Community") + ) + ); + const hasDocChunk = allNodes.some((node) => + node.labels.some((label) => label.includes("Chunk")) + ); + const hasCommunity = allNodes.some((node) => + node.labels.some((label) => label.includes("Community")) + ); + + return { + isEntity: hasEntities, + isDocChunk: hasDocChunk, + isCommunity: hasCommunity, + }; +}; + +export const graphTypeFromNodes = (allNodes: ExtendedNode[]): GraphType[] => { + const graphTypes: GraphType[] = []; + + const hasEntities = allNodes.some((node) => + node.labels.some( + (label) => !label.includes("Chunk") && !label.includes("Community") + ) + ); + const hasDocChunk = allNodes.some((node) => + node.labels.some((label) => label.includes("Chunk")) + ); + const hasCommunity = allNodes.some((node) => + node.labels.some((label) => label.includes("Community")) + ); + + if (hasEntities) graphTypes.push("Entities"); + if (hasDocChunk) graphTypes.push("DocumentChunk"); + if (hasCommunity) graphTypes.push("Communities"); + + return graphTypes; +}; + +export const extractGraphSchemaFromRawData = ( + nodes: any[], + relationships: any[] +): { + nodes: OptionType[]; + relationships: OptionType[]; +} => { + const nodeLabels = new Set(); + const relTypes = new Set(); + + nodes.forEach((node) => { + if (node.labels) { + node.labels.forEach((label: string) => nodeLabels.add(label)); + } + }); + + relationships.forEach((rel) => { + if (rel.type) { + relTypes.add(rel.type); + } + }); + + const nodeOptions: OptionType[] = Array.from(nodeLabels).map((label) => ({ + value: label, + label: label, + })); + + const relOptions: OptionType[] = Array.from(relTypes).map((type) => ({ + value: type, + label: type, + })); + + return { nodes: nodeOptions, relationships: relOptions }; +}; diff --git a/frontend-graph/tailwind.config.js b/frontend-graph/tailwind.config.js new file mode 100644 index 000000000..da7c9efa5 --- /dev/null +++ b/frontend-graph/tailwind.config.js @@ -0,0 +1,11 @@ +/** @type {import('tailwindcss').Config} */ +export default { + content: [ + "./index.html", + "./src/**/*.{js,ts,jsx,tsx}", + ], + theme: { + extend: {}, + }, + plugins: [], +} \ No newline at end of file diff --git a/frontend-graph/tsconfig.json b/frontend-graph/tsconfig.json new file mode 100644 index 000000000..ce6a6d404 --- /dev/null +++ b/frontend-graph/tsconfig.json @@ -0,0 +1,25 @@ +{ + "compilerOptions": { + "target": "ES2020", + "useDefineForClassFields": true, + "lib": ["ES2020", "DOM", "DOM.Iterable"], + "module": "ESNext", + "skipLibCheck": true, + + /* Bundler mode */ + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "resolveJsonModule": true, + "isolatedModules": true, + "noEmit": true, + "jsx": "react-jsx", + + /* Linting */ + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "noFallthroughCasesInSwitch": true + }, + "include": ["src"], + "references": [{ "path": "./tsconfig.node.json" }] +} \ No newline at end of file diff --git a/frontend-graph/tsconfig.node.json b/frontend-graph/tsconfig.node.json new file mode 100644 index 000000000..862dfb2b3 --- /dev/null +++ b/frontend-graph/tsconfig.node.json @@ -0,0 +1,10 @@ +{ + "compilerOptions": { + "composite": true, + "skipLibCheck": true, + "module": "ESNext", + "moduleResolution": "bundler", + "allowSyntheticDefaultImports": true + }, + "include": ["vite.config.ts"] +} \ No newline at end of file diff --git a/frontend-graph/vite.config.ts b/frontend-graph/vite.config.ts new file mode 100644 index 000000000..ab8b9b740 --- /dev/null +++ b/frontend-graph/vite.config.ts @@ -0,0 +1,15 @@ +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' + +// https://vitejs.dev/config/ +export default defineConfig({ + plugins: [react()], + server: { + port: 3000, + host: true + }, + build: { + outDir: 'dist', + sourcemap: true + } +}) \ No newline at end of file diff --git a/frontend-graph/yarn.lock b/frontend-graph/yarn.lock new file mode 100644 index 000000000..5b315c360 --- /dev/null +++ b/frontend-graph/yarn.lock @@ -0,0 +1,5983 @@ +# THIS IS AN AUTOGENERATED FILE. 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animation: shadowPulse 2s linear infinite; } @keyframes shadowPulse { 33% { - background: #FFF; - box-shadow: -12px 0 rgb(var(--theme-palette-primary-bg-strong)), 12px 0 #FFF; + background: #fff; + box-shadow: + -12px 0 rgb(var(--theme-palette-primary-bg-strong)), + 12px 0 #fff; } 66% { background: rgb(var(--theme-palette-primary-bg-strong)); - box-shadow: -12px 0 #FFF, 12px 0 #FFF; + box-shadow: + -12px 0 #fff, + 12px 0 #fff; } 100% { - background: #FFF; - box-shadow: -12px 0 #FFF, 12px 0 rgb(var(--theme-palette-primary-bg-strong)); + background: #fff; + box-shadow: + -12px 0 #fff, + 12px 0 rgb(var(--theme-palette-primary-bg-strong)); } } -.dropdownbtn{ +.dropdownbtn { background-color: #014063; color: white !important; border-radius: unset !important; width: 35px; padding: 0.5rem; } -.dropdownbtn:hover{ +.dropdownbtn:hover { background-color: #02293d !important; } -.graphbtn{ +.graphbtn { border-top-right-radius: 0px !important; border-bottom-right-radius: 0px !important; } -.dropdownbtn.darktheme{ - background-color: #51A6B1; +.dropdownbtn.darktheme { + background-color: #51a6b1; } -.dropdownbtn.small{ +.dropdownbtn.small { height: 24px; } -.darktheme:hover{ +.darktheme:hover { background-color: #44929c !important; } @@ -132,7 +140,7 @@ } .dropdownbtn.darktheme { - background-color: #51A6B1; + background-color: #51a6b1; } .dropdownbtn.small { @@ -143,7 +151,7 @@ background-color: #44929c !important; } -.ndl-dropzone>div { +.ndl-dropzone > div { padding-top: 10px !important; padding-bottom: 10px !important; } @@ -154,7 +162,6 @@ scrollbar-gutter: stable; } - .connectionmodal__input { width: 48.5%; margin-right: 1.5%; @@ -175,7 +182,7 @@ .connectionmodal__protocal__input { width: 25%; - display: inline-block + display: inline-block; } .connectionstatus__container { @@ -214,8 +221,8 @@ white-space: nowrap; } -.ndl-widget-content>div { - overflow-wrap: break-word +.ndl-widget-content > div { + overflow-wrap: break-word; } .word-break { @@ -230,13 +237,13 @@ } .ndl-type-checkbox, -.ndl-label-before>span { +.ndl-label-before > span { font-weight: 600; font-size: larger; } .ndl-upload-img-wrapper { - background: url("./assets/images/dropzone.svg"); + background: url('./assets/images/dropzone.svg'); background-repeat: no-repeat; background-position: center; } @@ -257,7 +264,7 @@ container-type: size; } -.source-container>* { +.source-container > * { width: 100%; } @@ -274,7 +281,6 @@ .outline-dashed .wikipedia-div { padding: 12px; } - } @container (min-height:400px) and (max-height:500px) { @@ -290,7 +296,6 @@ .outline-dashed .wikipedia-div { padding: 10px; } - } @container (max-height:300px) { @@ -311,7 +316,7 @@ margin-bottom: 5px !important; } - .imageBg>div { + .imageBg > div { padding: 5px; } @@ -365,7 +370,7 @@ } .ndl-modal-root { - z-index: 39 !important; + z-index: 10000 !important; } .tbody-dark .ndl-data-grid-tr:hover { @@ -393,14 +398,13 @@ .layout-wrapper { display: grid; grid-template-rows: auto; - grid-template-columns: 64px 1fr minmax(min-content,4fr) 1.1fr 64px; + grid-template-columns: 64px 1fr minmax(min-content, 4fr) 1.1fr 64px; max-height: calc(100vh - 58px); max-width: 100%; } .layout-wrapper.drawerdropzoneclosed { grid-template-columns: 64px 4fr 1.1fr 64px; - } .layout-wrapper.drawerchatbotclosed { @@ -419,27 +423,24 @@ height: 100%; } -@media screen and (min-width:1025px) and (max-width:1440px){ - .layout-wrapper{ - grid-template-columns: 64px 1fr minmax(min-content,4fr) minmax(min-content,2fr) 64px; - - } +@media screen and (min-width: 1025px) and (max-width: 1440px) { + .layout-wrapper { + grid-template-columns: 64px 1fr minmax(min-content, 4fr) minmax(min-content, 2fr) 64px; + } } -@media screen and (min-width:1536px) and (max-width:2560px) { +@media screen and (min-width: 1536px) and (max-width: 2560px) { .layout-wrapper { grid-template-columns: 64px 1fr minmax(min-content, 6.5fr) minmax(max-content, 1fr) 64px; - } } .sidenav-container { height: calc(100vh - 58px); min-height: 200px; - display: flex + display: flex; } - .resource-sections.blur-sm { filter: blur(2px); } @@ -468,18 +469,18 @@ max-height: fit-content; height: 150px; padding: 10px; - width:100%; + width: 100%; } @keyframes highlightAnimation { 0% { - background-color:rgb(var(--theme-palette-neutral-bg-weak)); + background-color: rgb(var(--theme-palette-neutral-bg-weak)); border-color: rgb(var(--theme-palette-primary-bg-weak)); transform: scale(1); } 50% { - background-color:rgb(var(--theme-palette-neutral-bg-weak)); + background-color: rgb(var(--theme-palette-neutral-bg-weak)); border-color: rgb(var(--theme-palette-primary-bg-strong)); transform: scale(1.05); } @@ -495,4 +496,3 @@ animation: highlightAnimation 1.5s ease-in-out; border: 2px solid rgb(var(--theme-palette-primary-bg-strong)); } - diff --git a/frontend/src/components/Graph/GraphViewModal.tsx b/frontend/src/components/Graph/GraphViewModal.tsx index 114b8471b..76ad6b61b 100644 --- a/frontend/src/components/Graph/GraphViewModal.tsx +++ b/frontend/src/components/Graph/GraphViewModal.tsx @@ -180,6 +180,8 @@ const GraphViewModal: React.FunctionComponent = ({ useEffect(() => { if (open) { + // Clear any stuck popups that might be covering the modal + document.querySelectorAll('.popup').forEach((el) => el.remove()); setLoading(true); setGraphType([]); if (viewPoint !== graphLabels.chatInfoView) { @@ -345,6 +347,8 @@ const GraphViewModal: React.FunctionComponent = ({ // when modal closes reset all states to default const onClose = () => { + // Clear any stuck popups + document.querySelectorAll('.popup').forEach((el) => el.remove()); graphQueryAbortControllerRef?.current?.abort(); setStatus('unknown'); setStatusMessage(''); diff --git a/frontend/src/components/Graph/SchemaViz.tsx b/frontend/src/components/Graph/SchemaViz.tsx index 0bb7d9762..e9e023ea9 100644 --- a/frontend/src/components/Graph/SchemaViz.tsx +++ b/frontend/src/components/Graph/SchemaViz.tsx @@ -70,6 +70,8 @@ const SchemaViz: React.FunctionComponent = ({ useEffect(() => { if (open) { + // Clear any stuck popups that might be covering the modal + document.querySelectorAll('.popup').forEach((el) => el.remove()); if (view !== 'viz') { setLoading(true); const { nodes, relationships, scheme } = userDefinedGraphSchema( @@ -171,6 +173,8 @@ const SchemaViz: React.FunctionComponent = ({ // when modal closes reset all states to default const onClose = () => { + // Clear any stuck popups + document.querySelectorAll('.popup').forEach((el) => el.remove()); graphQueryAbortControllerRef?.current?.abort(); setStatus('unknown'); setStatusMessage(''); diff --git a/frontend/src/components/UI/HoverableLink.tsx b/frontend/src/components/UI/HoverableLink.tsx index 4c320a14a..8143c9048 100644 --- a/frontend/src/components/UI/HoverableLink.tsx +++ b/frontend/src/components/UI/HoverableLink.tsx @@ -15,6 +15,20 @@ const HoverableLink: React.FC = ({ url, children }) => { } return () => clearTimeout(timer); }, [hovering, url]); + + // Add global click handler to close popup when clicking elsewhere + useEffect(() => { + const handleGlobalClick = () => { + setHovering(false); + }; + + if (hovering) { + document.addEventListener('click', handleGlobalClick); + return () => { + document.removeEventListener('click', handleGlobalClick); + }; + } + }, [hovering]); const handleMouseEnter = (event: React.MouseEvent) => { setHovering(true); setMousePosition({ x: event.clientX, y: event.clientY }); diff --git a/frontend/yarn.lock b/frontend/yarn.lock index 03f6418b3..2e517616b 100644 --- a/frontend/yarn.lock +++ b/frontend/yarn.lock @@ -7,7 +7,7 @@ resolved "https://registry.yarnpkg.com/@alloc/quick-lru/-/quick-lru-5.2.0.tgz#7bf68b20c0a350f936915fcae06f58e32007ce30" integrity sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw== -"@ampproject/remapping@^2.2.0": +"@ampproject/remapping@^2.2.0", "@ampproject/remapping@^2.3.0": version "2.3.0" resolved 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call-bind "^1.0.5" - is-array-buffer "^3.0.4" - -array-buffer-byte-length@^1.0.2: +array-buffer-byte-length@^1.0.1, array-buffer-byte-length@^1.0.2: version "1.0.2" resolved "https://registry.yarnpkg.com/array-buffer-byte-length/-/array-buffer-byte-length-1.0.2.tgz#384d12a37295aec3769ab022ad323a18a51ccf8b" integrity sha512-LHE+8BuR7RYGDKvnrmcuSq3tDcKv9OFEXQt/HpbZhY7V6h0zlUXutnAD82GiFx9rdieCMjkvtcsPqBwgUl1Iiw== @@ -2562,16 +2731,18 @@ array-buffer-byte-length@^1.0.2: is-array-buffer "^3.0.5" array-includes@^3.1.6, array-includes@^3.1.8: - version "3.1.8" - resolved "https://registry.yarnpkg.com/array-includes/-/array-includes-3.1.8.tgz#5e370cbe172fdd5dd6530c1d4aadda25281ba97d" - integrity sha512-itaWrbYbqpGXkGhZPGUulwnhVf5Hpy1xiCFsGqyIGglbBxmG5vSjxQen3/WGOjPpNEv1RtBLKxbmVXm8HpJStQ== + version "3.1.9" + resolved "https://registry.yarnpkg.com/array-includes/-/array-includes-3.1.9.tgz#1f0ccaa08e90cdbc3eb433210f903ad0f17c3f3a" + integrity sha512-FmeCCAenzH0KH381SPT5FZmiA/TmpndpcaShhfgEN9eCVjnFBqq3l1xrI42y8+PPLI6hypzou4GXw00WHmPBLQ== dependencies: - call-bind "^1.0.7" + call-bind "^1.0.8" + call-bound "^1.0.4" define-properties "^1.2.1" - es-abstract "^1.23.2" - es-object-atoms "^1.0.0" - get-intrinsic "^1.2.4" - is-string "^1.0.7" + es-abstract "^1.24.0" + es-object-atoms "^1.1.1" + get-intrinsic "^1.3.0" + is-string "^1.1.1" + math-intrinsics "^1.1.0" array-union@^2.1.0: version "2.1.0" @@ -2591,14 +2762,14 @@ array.prototype.findlast@^1.2.5: es-shim-unscopables "^1.0.2" array.prototype.flat@^1.3.1: - version "1.3.2" - resolved "https://registry.yarnpkg.com/array.prototype.flat/-/array.prototype.flat-1.3.2.tgz#1476217df8cff17d72ee8f3ba06738db5b387d18" - integrity sha512-djYB+Zx2vLewY8RWlNCUdHjDXs2XOgm602S9E7P/UpHgfeHL00cRiIF+IN/G/aUJ7kGPb6yO/ErDI5V2s8iycA== + version "1.3.3" + resolved "https://registry.yarnpkg.com/array.prototype.flat/-/array.prototype.flat-1.3.3.tgz#534aaf9e6e8dd79fb6b9a9917f839ef1ec63afe5" + integrity sha512-rwG/ja1neyLqCuGZ5YYrznA62D4mZXg0i1cIskIUKSiqF3Cje9/wXAls9B9s1Wa2fomMsIv8czB8jZcPmxCXFg== dependencies: - call-bind "^1.0.2" - define-properties "^1.2.0" - es-abstract "^1.22.1" - es-shim-unscopables "^1.0.0" + call-bind "^1.0.8" + define-properties "^1.2.1" + es-abstract "^1.23.5" + es-shim-unscopables "^1.0.2" array.prototype.flatmap@^1.3.3: version "1.3.3" @@ -2621,20 +2792,6 @@ array.prototype.tosorted@^1.1.4: es-errors "^1.3.0" es-shim-unscopables "^1.0.2" -arraybuffer.prototype.slice@^1.0.3: - version "1.0.3" - resolved "https://registry.yarnpkg.com/arraybuffer.prototype.slice/-/arraybuffer.prototype.slice-1.0.3.tgz#097972f4255e41bc3425e37dc3f6421cf9aefde6" - integrity sha512-bMxMKAjg13EBSVscxTaYA4mRc5t1UAXa2kXiGTNfZ079HIWXEkKmkgFrh/nJqamaLSrXO5H4WFFkPEaLJWbs3A== - dependencies: - array-buffer-byte-length "^1.0.1" - call-bind "^1.0.5" - define-properties "^1.2.1" - es-abstract "^1.22.3" - es-errors "^1.2.1" - get-intrinsic "^1.2.3" - is-array-buffer "^3.0.4" - is-shared-array-buffer "^1.0.2" - arraybuffer.prototype.slice@^1.0.4: version "1.0.4" resolved "https://registry.yarnpkg.com/arraybuffer.prototype.slice/-/arraybuffer.prototype.slice-1.0.4.tgz#9d760d84dbdd06d0cbf92c8849615a1a7ab3183c" @@ -2648,6 +2805,11 @@ arraybuffer.prototype.slice@^1.0.4: get-intrinsic "^1.2.6" is-array-buffer "^3.0.4" +async-function@^1.0.0: + version "1.0.0" + resolved "https://registry.yarnpkg.com/async-function/-/async-function-1.0.0.tgz#509c9fca60eaf85034c6829838188e4e4c8ffb2b" + integrity sha512-hsU18Ae8CDTR6Kgu9DYf0EbCr/a5iGL0rytQDobUcdpYOKokk8LEjVphnXkDkgpi0wYVsqrXuP0bZxJaTqdgoA== + asynckit@^0.4.0: version "0.4.0" resolved "https://registry.yarnpkg.com/asynckit/-/asynckit-0.4.0.tgz#c79ed97f7f34cb8f2ba1bc9790bcc366474b4b79" @@ -2666,12 +2828,12 @@ available-typed-arrays@^1.0.7: possible-typed-array-names "^1.0.0" axios@^1.8.4: - version "1.8.4" - resolved "https://registry.yarnpkg.com/axios/-/axios-1.8.4.tgz#78990bb4bc63d2cae072952d374835950a82f447" - integrity sha512-eBSYY4Y68NNlHbHBMdeDmKNtDgXWhQsJcGqzO3iLUM0GraQFSS9cVgPX5I9b3lbdFKyYoAEGAZF1DwhTaljNAw== + version "1.11.0" + resolved "https://registry.yarnpkg.com/axios/-/axios-1.11.0.tgz#c2ec219e35e414c025b2095e8b8280278478fdb6" + integrity sha512-1Lx3WLFQWm3ooKDYZD1eXmoGO9fxYQjrycfHFC8P0sCfQVXyROp0p9PFWBehewBOdCwHc+f/b8I0fMto5eSfwA== dependencies: follow-redirects "^1.15.6" - form-data "^4.0.0" + form-data "^4.0.4" proxy-from-env "^1.1.0" babel-plugin-macros@^3.1.0: @@ -2693,23 +2855,28 @@ balanced-match@^1.0.0: resolved "https://registry.yarnpkg.com/balanced-match/-/balanced-match-1.0.2.tgz#e83e3a7e3f300b34cb9d87f615fa0cbf357690ee" integrity sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw== +base64-arraybuffer@^1.0.2: + version "1.0.2" + resolved "https://registry.yarnpkg.com/base64-arraybuffer/-/base64-arraybuffer-1.0.2.tgz#1c37589a7c4b0746e34bd1feb951da2df01c1bdc" + integrity 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sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ== - dependencies: - ansi-styles "^3.2.1" - escape-string-regexp "^1.0.5" - supports-color "^5.3.0" - chalk@^4.0.0: version "4.1.2" resolved "https://registry.yarnpkg.com/chalk/-/chalk-4.1.2.tgz#aac4e2b7734a740867aeb16bf02aad556a1e7a01" @@ -2800,9 +2947,9 @@ chalk@^4.0.0: supports-color "^7.1.0" chalk@^5.4.1: - version "5.4.1" - resolved "https://registry.yarnpkg.com/chalk/-/chalk-5.4.1.tgz#1b48bf0963ec158dce2aacf69c093ae2dd2092d8" - integrity sha512-zgVZuo2WcZgfUEmsn6eO3kINexW8RAE4maiQ8QNs8CtpPCSyMiYsULR3HQYkm3w8FIA3SberyMJMSldGsW+U3w== + version "5.5.0" + resolved "https://registry.yarnpkg.com/chalk/-/chalk-5.5.0.tgz#67ada1df5ca809dc84c9b819d76418ddcf128428" + integrity sha512-1tm8DTaJhPBG3bIkVeZt1iZM9GfSX2lzOeDVZH9R9ffRHpmHvxZ/QhgQH/aDTkswQVt+YHdXAdS/In/30OjCbg== character-entities-html4@^2.0.0: version "2.1.0" @@ -2839,6 +2986,11 @@ character-reference-invalid@^2.0.0: resolved "https://registry.yarnpkg.com/character-reference-invalid/-/character-reference-invalid-2.0.1.tgz#85c66b041e43b47210faf401278abf808ac45cb9" integrity sha512-iBZ4F4wRbyORVsu0jPV7gXkOsGYjGHPmAyv+HiHG8gi5PtC9KI2j1+v8/tlibRvjoWX027ypmG/n0HtO5t7unw== +chownr@^3.0.0: + version "3.0.0" + resolved "https://registry.yarnpkg.com/chownr/-/chownr-3.0.0.tgz#9855e64ecd240a9cc4267ce8a4aa5d24a1da15e4" + integrity sha512-+IxzY9BZOQd/XuYPRmrvEVjF/nqj5kgT4kEq7VofrDoM1MxoRjEWkrCC3EtLi59TVawxTAn+orJwFQcrqEN1+g== + chroma-js@2.4.2: version "2.4.2" resolved "https://registry.yarnpkg.com/chroma-js/-/chroma-js-2.4.2.tgz#dffc214ed0c11fa8eefca2c36651d8e57cbfb2b0" @@ -2869,13 +3021,6 @@ clsx@^2.0.0, clsx@^2.1.0, clsx@^2.1.1: resolved "https://registry.yarnpkg.com/clsx/-/clsx-2.1.1.tgz#eed397c9fd8bd882bfb18deab7102049a2f32999" integrity sha512-eYm0QWBtUrBWZWG0d386OGAw16Z995PiOVo2B7bjWSbHedGl5e0ZWaq65kOGgUSNesEIDkB9ISbTg/JK9dhCZA== -color-convert@^1.9.0: - version "1.9.3" - resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-1.9.3.tgz#bb71850690e1f136567de629d2d5471deda4c1e8" - integrity sha512-QfAUtd+vFdAtFQcC8CCyYt1fYWxSqAiK2cSD6zDB8N3cpsEBAvRxp9zOGg6G/SHHJYAT88/az/IuDGALsNVbGg== - dependencies: - color-name "1.1.3" - color-convert@^2.0.1: version "2.0.1" resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-2.0.1.tgz#72d3a68d598c9bdb3af2ad1e84f21d896abd4de3" @@ -2883,11 +3028,6 @@ color-convert@^2.0.1: dependencies: color-name "~1.1.4" -color-name@1.1.3: - version "1.1.3" - resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.3.tgz#a7d0558bd89c42f795dd42328f740831ca53bc25" - integrity sha512-72fSenhMw2HZMTVHeCA9KCmpEIbzWiQsjN+BHcBbS9vr1mtt+vJjPdksIBNUmKAW8TFUDPJK5SUU3QhE9NEXDw== - color-name@^1.0.0, color-name@~1.1.4: version "1.1.4" resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2" @@ -2906,6 +3046,16 @@ colorette@^2.0.20: resolved 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-define-properties@^1.1.3, define-properties@^1.2.0, define-properties@^1.2.1: +define-properties@^1.1.3, define-properties@^1.2.1: version "1.2.1" resolved "https://registry.yarnpkg.com/define-properties/-/define-properties-1.2.1.tgz#10781cc616eb951a80a034bafcaa7377f6af2b6c" integrity sha512-8QmQKqEASLd5nx0U1B1okLElbUuuttJ/AnYmRXbbbGDWh6uS208EjD4Xqq/I9wK7u0v6O08XhTWnt5XtEbR6Dg== @@ -3173,10 +3299,10 @@ detect-browser@5.3.0: resolved "https://registry.yarnpkg.com/detect-browser/-/detect-browser-5.3.0.tgz#9705ef2bddf46072d0f7265a1fe300e36fe7ceca" integrity sha512-53rsFbGdwMwlF7qvCt0ypLM5V5/Mbl0szB7GPN8y9NCcbknYOeVVXdrXEq+90IwAfrrzt6Hd+u2E2ntakICU8w== -detect-libc@^1.0.3: - version "1.0.3" - resolved "https://registry.yarnpkg.com/detect-libc/-/detect-libc-1.0.3.tgz#fa137c4bd698edf55cd5cd02ac559f91a4c4ba9b" - integrity sha512-pGjwhsmsp4kL2RTz08wcOlGN83otlqHeD/Z5T8GXZB+/YcpQ/dgo+lbU8ZsGxV0HIvqqxo9l7mqYwyYMD9bKDg== +detect-libc@^2.0.3, detect-libc@^2.0.4: + version "2.0.4" + resolved 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-electron-to-chromium@^1.5.160: - version "1.5.161" - resolved "https://registry.yarnpkg.com/electron-to-chromium/-/electron-to-chromium-1.5.161.tgz#650376bd3be7ff8e581031409fc2d4f150620b12" - integrity sha512-hwtetwfKNZo/UlwHIVBlKZVdy7o8bIZxxKs0Mv/ROPiQQQmDgdm5a+KvKtBsxM8ZjFzTaCeLoodZ8jiBE3o9rA== +electron-to-chromium@^1.5.173: + version "1.5.197" + resolved "https://registry.yarnpkg.com/electron-to-chromium/-/electron-to-chromium-1.5.197.tgz#117f9d1afd82ae84bbfedd168ddcf52e4afb6216" + integrity sha512-m1xWB3g7vJ6asIFz+2pBUbq3uGmfmln1M9SSvBe4QIFWYrRHylP73zL/3nMjDmwz8V+1xAXQDfBd6+HPW0WvDQ== emoji-regex@^10.3.0: version "10.4.0" @@ -3244,17 +3370,17 @@ emoji-regex@^10.3.0: integrity sha512-EC+0oUMY1Rqm4O6LLrgjtYDvcVYTy7chDnM4Q7030tP4Kwj3u/pR6gP9ygnp2CJMK5Gq+9Q2oqmrFJAz01DXjw== enhanced-resolve@^5.18.1: - version "5.18.1" - resolved "https://registry.yarnpkg.com/enhanced-resolve/-/enhanced-resolve-5.18.1.tgz#728ab082f8b7b6836de51f1637aab5d3b9568faf" - integrity 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get-intrinsic "^1.3.0" + get-proto "^1.0.1" get-symbol-description "^1.1.0" globalthis "^1.0.4" gopd "^1.2.0" @@ -3359,21 +3433,24 @@ es-abstract@^1.23.5, es-abstract@^1.23.6, es-abstract@^1.23.9: is-array-buffer "^3.0.5" is-callable "^1.2.7" is-data-view "^1.0.2" + is-negative-zero "^2.0.3" is-regex "^1.2.1" + is-set "^2.0.3" is-shared-array-buffer "^1.0.4" is-string "^1.1.1" is-typed-array "^1.1.15" - is-weakref "^1.1.0" + is-weakref "^1.1.1" math-intrinsics "^1.1.0" - object-inspect "^1.13.3" + object-inspect "^1.13.4" object-keys "^1.1.1" object.assign "^4.1.7" own-keys "^1.0.1" - regexp.prototype.flags "^1.5.3" + regexp.prototype.flags "^1.5.4" safe-array-concat "^1.1.3" safe-push-apply "^1.0.0" safe-regex-test "^1.1.0" set-proto "^1.0.0" + stop-iteration-iterator "^1.1.0" string.prototype.trim "^1.2.10" string.prototype.trimend "^1.0.9" string.prototype.trimstart "^1.0.8" @@ -3382,21 +3459,14 @@ es-abstract@^1.23.5, es-abstract@^1.23.6, es-abstract@^1.23.9: 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@@ -3545,9 +3585,9 @@ eslint-plugin-react-refresh@^0.4.20: integrity sha512-XpbHQ2q5gUF8BGOX4dHe+71qoirYMhApEPZ7sfhF/dNnOF1UXnCMGZf79SFTBO7Bz5YEIT4TMieSlJBWhP9WBA== eslint-plugin-react@^7.37.4: - version "7.37.4" - resolved "https://registry.yarnpkg.com/eslint-plugin-react/-/eslint-plugin-react-7.37.4.tgz#1b6c80b6175b6ae4b26055ae4d55d04c414c7181" - integrity sha512-BGP0jRmfYyvOyvMoRX/uoUeW+GqNj9y16bPQzqAHf3AYII/tDs+jMN0dBVkl88/OZwNGwrVFxE7riHsXVfy/LQ== + version "7.37.5" + resolved "https://registry.yarnpkg.com/eslint-plugin-react/-/eslint-plugin-react-7.37.5.tgz#2975511472bdda1b272b34d779335c9b0e877065" + integrity sha512-Qteup0SqU15kdocexFNAJMvCJEfa2xUKNV4CC1xsVMrIIqEy3SQ/rqyxCWNzfrd3/ldy6HMlD2e0JDVpDg2qIA== dependencies: array-includes "^3.1.8" array.prototype.findlast "^1.2.5" @@ -3559,7 +3599,7 @@ eslint-plugin-react@^7.37.4: hasown "^2.0.2" jsx-ast-utils "^2.4.1 || ^3.0.0" minimatch "^3.1.2" - object.entries "^1.1.8" + object.entries "^1.1.9" object.fromentries "^2.0.8" object.values "^1.2.1" prop-types "^15.8.1" @@ -3576,21 +3616,21 @@ eslint-scope@^7.2.2: esrecurse "^4.3.0" estraverse "^5.2.0" -eslint-visitor-keys@^3.3.0, eslint-visitor-keys@^3.4.1, eslint-visitor-keys@^3.4.3: +eslint-visitor-keys@^3.4.1, eslint-visitor-keys@^3.4.3: version "3.4.3" resolved "https://registry.yarnpkg.com/eslint-visitor-keys/-/eslint-visitor-keys-3.4.3.tgz#0cd72fe8550e3c2eae156a96a4dddcd1c8ac5800" integrity sha512-wpc+LXeiyiisxPlEkUzU6svyS1frIO3Mgxj1fdy7Pm8Ygzguax2N3Fa/D/ag1WqbOprdI+uY6wMUl8/a2G+iag== eslint@^8.45.0: - version "8.57.0" - resolved "https://registry.yarnpkg.com/eslint/-/eslint-8.57.0.tgz#c786a6fd0e0b68941aaf624596fb987089195668" - integrity sha512-dZ6+mexnaTIbSBZWgou51U6OmzIhYM2VcNdtiTtI7qPNZm35Akpr0f6vtw3w1Kmn5PYo+tZVfh13WrhpS6oLqQ== + version "8.57.1" + resolved "https://registry.yarnpkg.com/eslint/-/eslint-8.57.1.tgz#7df109654aba7e3bbe5c8eae533c5e461d3c6ca9" + integrity 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-has-flag@^3.0.0: - version "3.0.0" - resolved "https://registry.yarnpkg.com/has-flag/-/has-flag-3.0.0.tgz#b5d454dc2199ae225699f3467e5a07f3b955bafd" - integrity sha512-sKJf1+ceQBr4SMkvQnBDNDtf4TXpVhVGateu0t918bl30FnbE2m4vNLX+VWe/dpjlb+HugGYzW7uQXH98HPEYw== +has-bigints@^1.0.2: + version "1.1.0" + resolved "https://registry.yarnpkg.com/has-bigints/-/has-bigints-1.1.0.tgz#28607e965ac967e03cd2a2c70a2636a1edad49fe" + integrity sha512-R3pbpkcIqv2Pm3dUwgjclDRVmWpTJW2DcMzcIhEXEx1oh/CEMObMm3KLmRJOdvhM7o4uQBnwr8pzRK2sJWIqfg== has-flag@^4.0.0: version "4.0.0" @@ -4076,11 +4078,6 @@ has-property-descriptors@^1.0.0, has-property-descriptors@^1.0.2: dependencies: es-define-property "^1.0.0" -has-proto@^1.0.1, has-proto@^1.0.3: - version "1.0.3" - resolved "https://registry.yarnpkg.com/has-proto/-/has-proto-1.0.3.tgz#b31ddfe9b0e6e9914536a6ab286426d0214f77fd" - integrity sha512-SJ1amZAJUiZS+PhsVLf5tGydlaVB8EdFpaSO4gmiUKUOxk8qzn5AIy4ZeJUmh22znIdk/uMAUT2pl3FxzVUH+Q== - has-proto@^1.2.0: version "1.2.0" resolved "https://registry.yarnpkg.com/has-proto/-/has-proto-1.2.0.tgz#5de5a6eabd95fdffd9818b43055e8065e39fe9d5" @@ -4088,24 +4085,19 @@ has-proto@^1.2.0: dependencies: dunder-proto "^1.0.0" -has-symbols@^1.0.2, has-symbols@^1.0.3: - version "1.0.3" - resolved "https://registry.yarnpkg.com/has-symbols/-/has-symbols-1.0.3.tgz#bb7b2c4349251dce87b125f7bdf874aa7c8b39f8" - integrity sha512-l3LCuF6MgDNwTDKkdYGEihYjt5pRPbEg46rtlmnSPlUbgmB8LOIrKJbYYFBSbnPaJexMKtiPO8hmeRjRz2Td+A== - -has-symbols@^1.1.0: +has-symbols@^1.0.3, has-symbols@^1.1.0: version "1.1.0" resolved "https://registry.yarnpkg.com/has-symbols/-/has-symbols-1.1.0.tgz#fc9c6a783a084951d0b971fe1018de813707a338" integrity sha512-1cDNdwJ2Jaohmb3sg4OmKaMBwuC48sYni5HUw2DvsC8LjGTLK9h+eb1X6RyuOHe4hT0ULCW68iomhjUoKUqlPQ== -has-tostringtag@^1.0.0, has-tostringtag@^1.0.2: +has-tostringtag@^1.0.2: version "1.0.2" resolved "https://registry.yarnpkg.com/has-tostringtag/-/has-tostringtag-1.0.2.tgz#2cdc42d40bef2e5b4eeab7c01a73c54ce7ab5abc" integrity sha512-NqADB8VjPFLM2V0VvHUewwwsw0ZWBaIdgo+ieHtK3hasLz4qeCRjYcqfB6AQrBggRKppKF8L52/VqdVsO47Dlw== dependencies: has-symbols "^1.0.3" -hasown@^2.0.0, hasown@^2.0.1, hasown@^2.0.2: +hasown@^2.0.2: version "2.0.2" resolved "https://registry.yarnpkg.com/hasown/-/hasown-2.0.2.tgz#003eaf91be7adc372e84ec59dc37252cedb80003" integrity sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ== @@ -4158,9 +4150,9 @@ hast-util-raw@^9.0.0: zwitch "^2.0.0" hast-util-to-jsx-runtime@^2.0.0: - version "2.3.0" - resolved "https://registry.yarnpkg.com/hast-util-to-jsx-runtime/-/hast-util-to-jsx-runtime-2.3.0.tgz#3ed27caf8dc175080117706bf7269404a0aa4f7c" - integrity sha512-H/y0+IWPdsLLS738P8tDnrQ8Z+dj12zQQ6WC11TIM21C8WFVoIxcqWXf2H3hiTVZjF1AWqoimGwrTWecWrnmRQ== + version "2.3.6" + resolved "https://registry.yarnpkg.com/hast-util-to-jsx-runtime/-/hast-util-to-jsx-runtime-2.3.6.tgz#ff31897aae59f62232e21594eac7ef6b63333e98" + integrity 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"https://registry.yarnpkg.com/human-signals/-/human-signals-5.0.0.tgz#42665a284f9ae0dade3ba41ebc37eb4b852f3a28" @@ -4257,15 +4257,15 @@ hyphenate-style-name@^1.0.3: resolved "https://registry.yarnpkg.com/hyphenate-style-name/-/hyphenate-style-name-1.1.0.tgz#1797bf50369588b47b72ca6d5e65374607cf4436" integrity sha512-WDC/ui2VVRrz3jOVi+XtjqkDjiVjTtFaAGiW37k6b+ohyQ5wYDOGkvCZa8+H0nx3gyvv0+BST9xuOgIyGQ00gw== -ignore@^5.2.0, ignore@^5.2.4: - version "5.3.1" - resolved "https://registry.yarnpkg.com/ignore/-/ignore-5.3.1.tgz#5073e554cd42c5b33b394375f538b8593e34d4ef" - integrity sha512-5Fytz/IraMjqpwfd34ke28PTVMjZjJG2MPn5t7OE4eUCUNf8BAa7b5WUS9/Qvr6mwOQS7Mk6vdsMno5he+T8Xw== +ignore@^5.2.0, ignore@^5.3.1: + version "5.3.2" + resolved "https://registry.yarnpkg.com/ignore/-/ignore-5.3.2.tgz#3cd40e729f3643fd87cb04e50bf0eb722bc596f5" + integrity sha512-hsBTNUqQTDwkWtcdYI2i06Y/nUBEsNEDJKjWdigLvegy8kDuJAS8uRlpkkcQpyEXL0Z/pjDy5HBmMjRCJ2gq+g== import-fresh@^3.2.1: - version "3.3.0" - resolved 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integrity sha512-qlD8YNDqyTKTyuITrDOffsl6Tdhv+UC4hcdAVuQsK4IMQ99nSgd1MIA/Q+jQYoh9r3hVUXhYh7urSRmXPkW04g== +inline-style-parser@0.2.4: + version "0.2.4" + resolved "https://registry.yarnpkg.com/inline-style-parser/-/inline-style-parser-0.2.4.tgz#f4af5fe72e612839fcd453d989a586566d695f22" + integrity sha512-0aO8FkhNZlj/ZIbNi7Lxxr12obT7cL1moPfE4tg1LkX7LlLfC6DeX4l2ZEud1ukP9jNQyNnfzQVqwbwmAATY4Q== inline-style-prefixer@^7.0.1: version "7.0.1" @@ -4300,15 +4300,6 @@ inline-style-prefixer@^7.0.1: dependencies: css-in-js-utils "^3.1.0" -internal-slot@^1.0.7: - version "1.0.7" - resolved "https://registry.yarnpkg.com/internal-slot/-/internal-slot-1.0.7.tgz#c06dcca3ed874249881007b0a5523b172a190802" - integrity sha512-NGnrKwXzSms2qUUih/ILZ5JBqNTSa1+ZmP6flaIp6KmSElgE9qdndzS3cqjrDovwFdmwsGsLdeFgB6suw+1e9g== - dependencies: - es-errors "^1.3.0" - hasown "^2.0.0" - side-channel "^1.0.4" - internal-slot@^1.1.0: version "1.1.0" resolved 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version "1.1.0" @@ -4402,14 +4382,6 @@ is-bigint@^1.1.0: dependencies: has-bigints "^1.0.2" -is-boolean-object@^1.1.0: - version "1.1.2" - resolved "https://registry.yarnpkg.com/is-boolean-object/-/is-boolean-object-1.1.2.tgz#5c6dc200246dd9321ae4b885a114bb1f75f63719" - integrity sha512-gDYaKHJmnj4aWxyj6YHyXVpdQawtVLHU5cb+eztPGczf6cjuTdwve5ZIEfgXqH4e57An1D1AKf8CZ3kYrQRqYA== - dependencies: - call-bind "^1.0.2" - has-tostringtag "^1.0.0" - is-boolean-object@^1.2.1: version "1.2.2" resolved "https://registry.yarnpkg.com/is-boolean-object/-/is-boolean-object-1.2.2.tgz#7067f47709809a393c71ff5bb3e135d8a9215d9e" @@ -4418,26 +4390,19 @@ is-boolean-object@^1.2.1: call-bound "^1.0.3" has-tostringtag "^1.0.2" -is-callable@^1.1.3, is-callable@^1.1.4, is-callable@^1.2.7: +is-callable@^1.2.7: version "1.2.7" resolved "https://registry.yarnpkg.com/is-callable/-/is-callable-1.2.7.tgz#3bc2a85ea742d9e36205dcacdd72ca1fdc51b055" integrity 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is-typed-array "^1.1.13" + hasown "^2.0.2" -is-data-view@^1.0.2: +is-data-view@^1.0.1, is-data-view@^1.0.2: version "1.0.2" resolved "https://registry.yarnpkg.com/is-data-view/-/is-data-view-1.0.2.tgz#bae0a41b9688986c2188dda6657e56b8f9e63b8e" integrity sha512-RKtWF8pGmS87i2D6gqQu/l7EYRlVdfzemCJN/P3UOs//x1QE7mfhvzHIApBTRf7axvT6DMGwSwBXYCT0nfB9xw== @@ -4446,14 +4411,7 @@ is-data-view@^1.0.2: get-intrinsic "^1.2.6" is-typed-array "^1.1.13" -is-date-object@^1.0.1, is-date-object@^1.0.5: - version "1.0.5" - resolved "https://registry.yarnpkg.com/is-date-object/-/is-date-object-1.0.5.tgz#0841d5536e724c25597bf6ea62e1bd38298df31f" - integrity sha512-9YQaSxsAiSwcvS33MBk3wTCVnWK+HhF8VZR2jRxehM16QcVOdHqPn4VPHmRK4lSr38n9JriurInLcP90xsYNfQ== - dependencies: - has-tostringtag "^1.0.0" - -is-date-object@^1.1.0: +is-date-object@^1.0.5, is-date-object@^1.1.0: version "1.1.0" resolved "https://registry.yarnpkg.com/is-date-object/-/is-date-object-1.1.0.tgz#ad85541996fc7aa8b2729701d27b7319f95d82f7" 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is-negative-zero@^2.0.3: resolved "https://registry.yarnpkg.com/is-negative-zero/-/is-negative-zero-2.0.3.tgz#ced903a027aca6381b777a5743069d7376a49747" integrity sha512-5KoIu2Ngpyek75jXodFvnafB6DJgr3u8uuK0LEZJjrU19DrMD3EVERaR8sjz8CCGgpZvxPl9SuE1GMVPFHx1mw== -is-number-object@^1.0.4: - version "1.0.7" - resolved "https://registry.yarnpkg.com/is-number-object/-/is-number-object-1.0.7.tgz#59d50ada4c45251784e9904f5246c742f07a42fc" - integrity sha512-k1U0IRzLMo7ZlYIfzRu23Oh6MiIFasgpb9X76eqfFZAqwH44UI4KTBvBYIZ1dSL9ZzChTB9ShHfLkR4pdW5krQ== - dependencies: - has-tostringtag "^1.0.0" - is-number-object@^1.1.1: version "1.1.1" resolved "https://registry.yarnpkg.com/is-number-object/-/is-number-object-1.1.1.tgz#144b21e95a1bc148205dcc2814a9134ec41b2541" @@ -4559,14 +4513,6 @@ is-plain-obj@^4.0.0: resolved "https://registry.yarnpkg.com/is-plain-obj/-/is-plain-obj-4.1.0.tgz#d65025edec3657ce032fd7db63c97883eaed71f0" integrity 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"https://registry.yarnpkg.com/is-shared-array-buffer/-/is-shared-array-buffer-1.0.3.tgz#1237f1cba059cdb62431d378dcc37d9680181688" - integrity sha512-nA2hv5XIhLR3uVzDDfCIknerhx8XUKnstuOERPNNIinXG7v9u+ohXF67vxm4TPTEPU6lm61ZkwP3c9PCB97rhg== - dependencies: - call-bind "^1.0.7" - is-shared-array-buffer@^1.0.4: version "1.0.4" resolved "https://registry.yarnpkg.com/is-shared-array-buffer/-/is-shared-array-buffer-1.0.4.tgz#9b67844bd9b7f246ba0708c3a93e34269c774f6f" @@ -4601,13 +4540,6 @@ is-stream@^3.0.0: resolved "https://registry.yarnpkg.com/is-stream/-/is-stream-3.0.0.tgz#e6bfd7aa6bef69f4f472ce9bb681e3e57b4319ac" integrity sha512-LnQR4bZ9IADDRSkvpqMGvt/tEJWclzklNgSw48V5EAaAeDd6qGvN8ei6k5p0tvxSR171VmGyHuTiAOfxAbr8kA== -is-string@^1.0.5, is-string@^1.0.7: - version "1.0.7" - resolved "https://registry.yarnpkg.com/is-string/-/is-string-1.0.7.tgz#0dd12bf2006f255bb58f695110eff7491eebc0fd" - integrity sha512-tE2UXzivje6ofPW7l23cjDOMa09gb7xlAqG6jG5ej6uPV32TlWP3NKPigtaGeHNu9fohccRYvIiZMfOOnOYUtg== - dependencies: - has-tostringtag "^1.0.0" - is-string@^1.1.1: version "1.1.1" resolved "https://registry.yarnpkg.com/is-string/-/is-string-1.1.1.tgz#92ea3f3d5c5b6e039ca8677e5ac8d07ea773cbb9" @@ -4616,13 +4548,6 @@ is-string@^1.1.1: call-bound "^1.0.3" has-tostringtag "^1.0.2" -is-symbol@^1.0.2, is-symbol@^1.0.3: - version "1.0.4" - resolved "https://registry.yarnpkg.com/is-symbol/-/is-symbol-1.0.4.tgz#a6dac93b635b063ca6872236de88910a57af139c" - integrity sha512-C/CPBqKWnvdcxqIARxyOh4v1UUEOCHpgDa0WYgpKDFMszcrPcffg5uhwSgPCLD2WWxmq6isisz87tzT01tuGhg== - dependencies: - has-symbols "^1.0.2" - is-symbol@^1.0.4, is-symbol@^1.1.1: version "1.1.1" resolved "https://registry.yarnpkg.com/is-symbol/-/is-symbol-1.1.1.tgz#f47761279f532e2b05a7024a7506dbbedacd0634" @@ -4632,14 +4557,7 @@ is-symbol@^1.0.4, is-symbol@^1.1.1: has-symbols "^1.1.0" safe-regex-test "^1.1.0" -is-typed-array@^1.1.13: - version "1.1.13" - resolved 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+ version "15.5.2" + resolved "https://registry.yarnpkg.com/lint-staged/-/lint-staged-15.5.2.tgz#beff028fd0681f7db26ffbb67050a21ed4d059a3" + integrity sha512-YUSOLq9VeRNAo/CTaVmhGDKG+LBtA8KF1X4K5+ykMSwWST1vDxJRB2kv2COgLb1fvpCo+A/y9A0G0znNVmdx4w== dependencies: chalk "^5.4.1" commander "^13.1.0" @@ -4887,9 +4793,9 @@ lint-staged@^15.5.0: yaml "^2.7.0" listr2@^8.2.5: - version "8.2.5" - resolved "https://registry.yarnpkg.com/listr2/-/listr2-8.2.5.tgz#5c9db996e1afeb05db0448196d3d5f64fec2593d" - integrity sha512-iyAZCeyD+c1gPyE9qpFu8af0Y+MRtmKOncdGoA2S5EY8iFq99dmmvkNnHiWo+pj0s7yH7l3KPIgee77tKpXPWQ== + version "8.3.3" + resolved "https://registry.yarnpkg.com/listr2/-/listr2-8.3.3.tgz#815fc8f738260ff220981bf9e866b3e11e8121bf" + integrity sha512-LWzX2KsqcB1wqQ4AHgYb4RsDXauQiqhjLk+6hjbaeHG4zpjjVAB6wC/gz6X0l+Du1cN3pUB5ZlrvTbhGSNnUQQ== dependencies: cli-truncate "^4.0.0" colorette "^2.0.20" @@ -4932,9 +4838,9 @@ log-update@^6.1.0: wrap-ansi "^9.0.0" loglevel@^1.8.0: - version "1.9.1" - resolved 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"https://registry.yarnpkg.com/markdown-table/-/markdown-table-3.0.4.tgz#fe44d6d410ff9d6f2ea1797a3f60aa4d2b631c2a" @@ -4984,9 +4897,9 @@ mdast-util-find-and-replace@^3.0.0: unist-util-visit-parents "^6.0.0" mdast-util-from-markdown@^2.0.0: - version "2.0.1" - resolved "https://registry.yarnpkg.com/mdast-util-from-markdown/-/mdast-util-from-markdown-2.0.1.tgz#32a6e8f512b416e1f51eb817fc64bd867ebcd9cc" - integrity sha512-aJEUyzZ6TzlsX2s5B4Of7lN7EQtAxvtradMMglCQDyaTFgse6CmtmdJ15ElnVRlCg1vpNyVtbem0PWzlNieZsA== + version "2.0.2" + resolved "https://registry.yarnpkg.com/mdast-util-from-markdown/-/mdast-util-from-markdown-2.0.2.tgz#4850390ca7cf17413a9b9a0fbefcd1bc0eb4160a" + integrity sha512-uZhTV/8NBuw0WHkPTrCqDOl0zVe1BIng5ZtHoDk49ME1qqcjYmmLmOf0gELgcRMxN4w2iuIeVso5/6QymSrgmA== dependencies: "@types/mdast" "^4.0.0" "@types/unist" "^3.0.0" @@ -5067,9 +4980,9 @@ mdast-util-gfm@^3.0.0: mdast-util-to-markdown "^2.0.0" mdast-util-mdx-expression@^2.0.0: - version "2.0.0" - resolved 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-set-function-name@^2.0.1, set-function-name@^2.0.2: +set-function-name@^2.0.2: version "2.0.2" resolved "https://registry.yarnpkg.com/set-function-name/-/set-function-name-2.0.2.tgz#16a705c5a0dc2f5e638ca96d8a8cd4e1c2b90985" integrity sha512-7PGFlmtwsEADb0WYyvCMa1t+yke6daIG4Wirafur5kcf+MhUnPms1UeR0CKQdTZD81yESwMHbtn+TR+dMviakQ== @@ -6475,16 +6363,6 @@ side-channel-weakmap@^1.0.2: object-inspect "^1.13.3" side-channel-map "^1.0.1" -side-channel@^1.0.4: - version "1.0.6" - resolved "https://registry.yarnpkg.com/side-channel/-/side-channel-1.0.6.tgz#abd25fb7cd24baf45466406b1096b7831c9215f2" - integrity sha512-fDW/EZ6Q9RiO8eFG8Hj+7u/oW+XrPTIChwCOM2+th2A6OblDtYYIpve9m+KvI9Z4C9qSEXlaGR6bTEYHReuglA== - dependencies: - call-bind "^1.0.7" - es-errors "^1.3.0" - get-intrinsic "^1.2.4" - object-inspect "^1.13.1" - side-channel@^1.1.0: version "1.1.0" resolved "https://registry.yarnpkg.com/side-channel/-/side-channel-1.1.0.tgz#c3fcff9c4da932784873335ec9765fa94ff66bc9" @@ -6593,6 +6471,14 @@ stacktrace-js@^2.0.2: stack-generator "^2.0.5" stacktrace-gps "^3.0.4" +stop-iteration-iterator@^1.1.0: + version "1.1.0" + resolved "https://registry.yarnpkg.com/stop-iteration-iterator/-/stop-iteration-iterator-1.1.0.tgz#f481ff70a548f6124d0312c3aa14cbfa7aa542ad" + integrity sha512-eLoXW/DHyl62zxY4SCaIgnRhuMr6ri4juEYARS8E6sCEqzKpOiE521Ucofdx+KnDZl5xmvGYaaKCk5FEOxJCoQ== + dependencies: + es-errors "^1.3.0" + internal-slot "^1.1.0" + string-argv@^0.3.2: version "0.3.2" resolved "https://registry.yarnpkg.com/string-argv/-/string-argv-0.3.2.tgz#2b6d0ef24b656274d957d54e0a4bbf6153dc02b6" @@ -6647,25 +6533,6 @@ string.prototype.trim@^1.2.10: es-object-atoms "^1.0.0" has-property-descriptors "^1.0.2" -string.prototype.trim@^1.2.9: - version "1.2.9" - resolved "https://registry.yarnpkg.com/string.prototype.trim/-/string.prototype.trim-1.2.9.tgz#b6fa326d72d2c78b6df02f7759c73f8f6274faa4" - integrity sha512-klHuCNxiMZ8MlsOihJhJEBJAiMVqU3Z2nEXWfWnIqjN0gEFS9J9+IxKozWWtQGcgoa1WUZzLjKPTr4ZHNFTFxw== - 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"https://registry.yarnpkg.com/yaml/-/yaml-2.8.1.tgz#1870aa02b631f7e8328b93f8bc574fac5d6c4d79" + integrity sha512-lcYcMxX2PO9XMGvAJkJ3OsNMw+/7FKes7/hgerGUYWIoWu5j/+YQqcZr5JnPZWzOsEBgMbSbiSTn/dv/69Mkpw== yocto-queue@^0.1.0: version "0.1.0" From 32b7924f4f7d05158cfcf7e32964ad4407e337b0 Mon Sep 17 00:00:00 2001 From: otobongfp Date: Thu, 28 Aug 2025 13:02:02 +0100 Subject: [PATCH 03/10] support sub-graph and risk extraction --- backend/URL_SOURCES_EXPLANATION.md | 189 +++++++++ backend/analyze_url_sources.py | 211 ++++++++++ backend/debug_url_extraction.py | 221 +++++++++++ backend/explain_nil_sources.py | 52 +++ backend/risk_assessment_Bill_Gates.json | 82 ++++ backend/risk_assessment_Microsoft.json | 81 ++++ backend/risk_assessment_OpenAI.json | 81 ++++ backend/score.py | 91 ++++- backend/src/graph_query.py | 407 +++++++++++++++++-- backend/src/main.py | 61 ++- backend/src/make_relationships.py | 25 +- backend/src/risk_assessment.py | 435 +++++++++++++++++++++ backend/test_adaptive_scoring.py | 166 ++++++++ backend/test_bill_gates_sources.py | 174 +++++++++ backend/test_chunk_diagnosis.py | 135 +++++++ backend/test_chunk_extraction.py | 83 ++++ backend/test_enhanced_source_extraction.py | 177 +++++++++ backend/test_enhanced_sources.py | 149 +++++++ backend/test_entity_types.py | 154 ++++++++ backend/test_name_variations.py | 167 ++++++++ backend/test_performance_improvement.py | 118 ++++++ backend/test_risk_assessment.py | 176 +++++++++ backend/test_search_method.py | 106 +++++ backend/test_source_extraction.py | 148 +++++++ backend/test_url_copying.py | 196 ++++++++++ backend/test_vector_search.py | 132 +++++++ 26 files changed, 3970 insertions(+), 47 deletions(-) create mode 100644 backend/URL_SOURCES_EXPLANATION.md create mode 100644 backend/analyze_url_sources.py create mode 100644 backend/debug_url_extraction.py create mode 100644 backend/explain_nil_sources.py create mode 100644 backend/risk_assessment_Bill_Gates.json create mode 100644 backend/risk_assessment_Microsoft.json create mode 100644 backend/risk_assessment_OpenAI.json create mode 100644 backend/src/risk_assessment.py create mode 100644 backend/test_adaptive_scoring.py create mode 100644 backend/test_bill_gates_sources.py create mode 100644 backend/test_chunk_diagnosis.py create mode 100644 backend/test_chunk_extraction.py create mode 100644 backend/test_enhanced_source_extraction.py create mode 100644 backend/test_enhanced_sources.py create mode 100644 backend/test_entity_types.py create mode 100644 backend/test_name_variations.py create mode 100644 backend/test_performance_improvement.py create mode 100644 backend/test_risk_assessment.py create mode 100644 backend/test_search_method.py create mode 100644 backend/test_source_extraction.py create mode 100644 backend/test_url_copying.py create mode 100644 backend/test_vector_search.py diff --git a/backend/URL_SOURCES_EXPLANATION.md b/backend/URL_SOURCES_EXPLANATION.md new file mode 100644 index 000000000..d753bf880 --- /dev/null +++ b/backend/URL_SOURCES_EXPLANATION.md @@ -0,0 +1,189 @@ +# ๐Ÿ” URL Sources Analysis: Why URLs Are Missing in Most Cases + +## ๐Ÿ“‹ **The Problem** + +You're experiencing "Sources: nil" in risk assessments because **URLs are not being properly preserved and linked to chunks** in the knowledge graph. Here's the complete analysis: + +## ๐Ÿ” **Root Cause Analysis** + +### **Issue 1: Document vs Chunk Node Creation** + +**โœ… Document Nodes DO store URLs:** + +- **S3**: `obj_source_node.url = str(source_url+file_name)` +- **GCS**: `obj_source_node.url = file_metadata['url']` +- **Web**: `obj_source_node.url = urllib.parse.unquote(source_url)` +- **Wikipedia**: `obj_source_node.url = pages[0].metadata['source']` +- **YouTube**: `obj_source_node.url = source_url` +- **Local**: โŒ **NO URL stored** (file_source = 'local file') + +**โŒ Chunk Nodes do NOT store URLs:** + +- Chunk nodes only store: `text`, `position`, `length`, `fileName`, `content_offset` +- **Missing**: `url`, `source_url`, `document_url` properties +- Only relationship: `(c:Chunk)-[:PART_OF]->(d:Document)` + +### **Issue 2: Source Extraction Logic** + +The risk assessment tries these fallbacks (in order): + +1. `node_props.get('url')` - โŒ **Chunks don't have URL property** +2. `node_props.get('source_url')` - โŒ **Chunks don't have source_url property** +3. `doc_info.get('url')` - โœ… **Document nodes have URL** +4. `doc_info.get('source')` - โŒ **Document nodes don't have 'source' property** +5. `node_props.get('source', '')` - โŒ **Chunks don't have source property** + +### **Issue 3: Subgraph Extraction** + +- Subgraph extraction uses **depth-based traversal** +- **Document nodes may not be included** in the subgraph +- Even if Document nodes are included, the linking logic is complex + +## ๐Ÿ’ก **Solutions Implemented** + +### **๐Ÿ”ง Solution 1: Copy URL to Chunk Nodes (IMPLEMENTED)** + +**Modified `create_relation_between_chunks()` in `make_relationships.py`:** + +```python +# Get document URL and source information first +doc_url = None +file_source = None +try: + doc_url_query = "MATCH (d:Document {fileName: $f_name}) RETURN d.url as url, d.fileSource as source" + doc_result = execute_graph_query(graph, doc_url_query, {"f_name": file_name}) + if doc_result: + doc_url = doc_result[0]['url'] + file_source = doc_result[0]['source'] + logging.info(f"Found document URL: {doc_url}, source: {file_source}") + else: + logging.warning(f"No document found for file: {file_name}") +except Exception as e: + logging.warning(f"Error getting document URL for {file_name}: {str(e)}") + +# Create local file reference if no URL exists +if not doc_url and file_source == 'local file': + doc_url = f"local://{file_name}" + logging.info(f"Created local file reference: {doc_url}") + +# Add URL to chunk data +chunk_data = { + "id": current_chunk_id, + "pg_content": chunk_document.page_content, + "position": position, + "length": chunk_document.metadata["length"], + "f_name": file_name, + "url": doc_url, # โ† ADDED THIS + "fileSource": file_source, # โ† ADDED THIS + "previous_id": previous_chunk_id, + "content_offset": offset +} + +# Updated chunk creation query +query_to_create_chunk_and_PART_OF_relation = """ + UNWIND $batch_data AS data + MERGE (c:Chunk {id: data.id}) + SET c.text = data.pg_content, c.position = data.position, + c.length = data.length, c.fileName=data.f_name, + c.url = data.url, c.fileSource=data.fileSource, c.content_offset=data.content_offset + ... +""" +``` + +### **๐Ÿ”ง Solution 2: Enhanced Risk Assessment (IMPLEMENTED)** + +**Modified `extract_chunks_from_subgraph()` in `risk_assessment.py`:** + +```python +# Build source information - prioritize chunk's own URL property +source_url = ( + node_props.get('url') or # Chunk's own URL (NEW!) + node_props.get('source_url') or + doc_info.get('url') or + doc_info.get('source') or + node_props.get('source', '') +) +``` + +## ๐Ÿ“Š **Source Types Analysis** + +| Source Type | URL Status | Example | +| -------------- | ---------- | ------------------------------------ | +| **local file** | โŒ No URL | `file_source = 'local file'` | +| **s3 bucket** | โœ… Has URL | `s3://bucket/path/file.pdf` | +| **gcs bucket** | โœ… Has URL | `https://storage.googleapis.com/...` | +| **web page** | โœ… Has URL | `https://example.com` | +| **Wikipedia** | โœ… Has URL | `https://en.wikipedia.org/wiki/...` | +| **youtube** | โœ… Has URL | `https://youtube.com/watch?v=...` | + +## ๐Ÿš€ **Impact of the Fix** + +### **Before the Fix:** + +- โŒ Chunk nodes had no URL property +- โŒ Risk assessment couldn't find URLs +- โŒ "Sources: nil" for most indicators +- โŒ Local files had no URL representation + +### **After the Fix:** + +- โœ… Chunk nodes now have `url` and `fileSource` properties +- โœ… Risk assessment can directly access chunk URLs +- โœ… Local files get `local://filename` references +- โœ… All source types properly represented + +## ๐Ÿงช **Testing the Fix** + +Run the test script to verify URL copying: + +```bash +python3 test_url_copying.py +``` + +This will show: + +- How many Document nodes have URLs +- How many Chunk nodes have URLs +- Whether the fix is working for new chunks +- Local file reference creation + +## ๐Ÿ“ˆ **Expected Results** + +### **For New Documents:** + +- All chunks will have URL properties +- Risk assessments will show actual URLs +- Local files will show `local://filename` references + +### **For Existing Documents:** + +- Existing chunks won't have URLs (created before fix) +- New processing will add URLs to chunks +- Mixed results until all documents are reprocessed + +## ๐ŸŽฏ **Next Steps** + +1. **Test the fix** with new document uploads +2. **Reprocess existing documents** to add URLs to chunks +3. **Monitor risk assessment results** for improved source citations +4. **Consider implementing** additional solutions if needed + +## ๐Ÿ” **Why This Happened** + +The system was designed with a **separation of concerns**: + +- **Document nodes** store metadata (including URLs) +- **Chunk nodes** store content (text, position, etc.) +- **Relationships** link chunks to documents + +However, this design made it **difficult to access URLs** during risk assessment because: + +1. Subgraph extraction might not include Document nodes +2. Complex linking logic was required +3. Local files had no URL representation + +The fix **preserves the design** while **adding URL accessibility** directly to chunk nodes. + +--- + +**Result**: URLs should now be properly available in risk assessments, reducing "Sources: nil" occurrences significantly! diff --git a/backend/analyze_url_sources.py b/backend/analyze_url_sources.py new file mode 100644 index 000000000..a05b77bf6 --- /dev/null +++ b/backend/analyze_url_sources.py @@ -0,0 +1,211 @@ +#!/usr/bin/env python3 +""" +URL Sources Analysis - Why URLs are Missing in Most Cases + +This script analyzes the document processing pipeline to understand why URL sources +are not being properly preserved and linked to chunks in the knowledge graph. +""" + +import logging +import json +from typing import Dict, List, Any + +# Configure logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +def analyze_url_source_issues(): + """ + Comprehensive analysis of why URL sources are missing in most cases. + """ + + print("๐Ÿ” URL SOURCES ANALYSIS: Why URLs Are Missing in Most Cases") + print("=" * 80) + + # Issue 1: Document Node Creation + print("\n๐Ÿ“‹ ISSUE 1: Document Node Creation") + print("-" * 50) + print("โœ… URLs ARE stored in Document nodes during creation:") + print(" - S3: obj_source_node.url = str(source_url+file_name)") + print(" - GCS: obj_source_node.url = file_metadata['url']") + print(" - Web: obj_source_node.url = urllib.parse.unquote(source_url)") + print(" - Wikipedia: obj_source_node.url = pages[0].metadata['source']") + print(" - YouTube: obj_source_node.url = source_url") + print(" - Local: โŒ NO URL stored (file_source = 'local file')") + + # Issue 2: Chunk Node Creation + print("\n๐Ÿ“‹ ISSUE 2: Chunk Node Creation") + print("-" * 50) + print("โŒ URLs are NOT copied to chunk nodes during creation:") + print(" - Chunk nodes only store: text, position, length, fileName, content_offset") + print(" - Missing: url, source_url, document_url properties") + print(" - Only relationship: (c:Chunk)-[:PART_OF]->(d:Document)") + + # Issue 3: Source Extraction Logic + print("\n๐Ÿ“‹ ISSUE 3: Source Extraction in Risk Assessment") + print("-" * 50) + print("๐Ÿ” Current extraction logic tries multiple fallbacks:") + print(" 1. node_props.get('url') - โŒ Chunks don't have URL property") + print(" 2. node_props.get('source_url') - โŒ Chunks don't have source_url property") + print(" 3. doc_info.get('url') - โœ… Document nodes have URL") + print(" 4. doc_info.get('source') - โŒ Document nodes don't have 'source' property") + print(" 5. node_props.get('source', '') - โŒ Chunks don't have source property") + + # Issue 4: Document-Chunk Relationship + print("\n๐Ÿ“‹ ISSUE 4: Document-Chunk Relationship") + print("-" * 50) + print("๐Ÿ”— Current relationship structure:") + print(" (c:Chunk)-[:PART_OF]->(d:Document)") + print(" - Chunk has: fileName property") + print(" - Document has: fileName and url properties") + print(" - โŒ No direct URL transfer during chunk creation") + + # Issue 5: Source Types Analysis + print("\n๐Ÿ“‹ ISSUE 5: Source Types Analysis") + print("-" * 50) + source_types = { + "local file": "โŒ No URL (local files)", + "s3 bucket": "โœ… Has URL (s3://bucket/path/file.pdf)", + "gcs bucket": "โœ… Has URL (https://storage.googleapis.com/...)", + "web page": "โœ… Has URL (https://example.com)", + "Wikipedia": "โœ… Has URL (https://en.wikipedia.org/wiki/...)", + "youtube": "โœ… Has URL (https://youtube.com/watch?v=...)" + } + + for source_type, url_status in source_types.items(): + print(f" {source_type:15} : {url_status}") + + # Issue 6: Risk Assessment Source Extraction + print("\n๐Ÿ“‹ ISSUE 6: Risk Assessment Source Extraction") + print("-" * 50) + print("๐Ÿ” Current extraction process:") + print(" 1. Extract subgraph with depth-based traversal") + print(" 2. Find Document nodes in subgraph") + print(" 3. Find Chunk nodes in subgraph") + print(" 4. Try to link chunks to document URLs") + print(" โŒ Problem: Not all Document nodes may be in subgraph") + print(" โŒ Problem: Chunk nodes don't have direct URL references") + + # Solutions + print("\n๐Ÿ’ก SOLUTIONS") + print("=" * 80) + + print("\n๐Ÿ”ง SOLUTION 1: Copy URL to Chunk Nodes") + print("-" * 40) + print("Modify create_relation_between_chunks() in make_relationships.py:") + print(""" + # Get document URL first + doc_url_query = "MATCH (d:Document {fileName: $f_name}) RETURN d.url as url" + doc_result = execute_graph_query(graph, doc_url_query, {"f_name": file_name}) + doc_url = doc_result[0]['url'] if doc_result else None + + # Add URL to chunk data + chunk_data = { + "id": current_chunk_id, + "pg_content": chunk_document.page_content, + "position": position, + "length": chunk_document.metadata["length"], + "f_name": file_name, + "url": doc_url, # โ† ADD THIS + "previous_id": previous_chunk_id, + "content_offset": offset + } + + # Update chunk creation query + query_to_create_chunk_and_PART_OF_relation = ''' + UNWIND $batch_data AS data + MERGE (c:Chunk {id: data.id}) + SET c.text = data.pg_content, c.position = data.position, + c.length = data.length, c.fileName=data.f_name, + c.url = data.url, c.content_offset=data.content_offset + ... + ''' + """) + + print("\n๐Ÿ”ง SOLUTION 2: Enhanced Source Extraction") + print("-" * 40) + print("Modify extract_chunks_from_subgraph() in risk_assessment.py:") + print(""" + # Add direct URL lookup for chunks + def get_document_url_for_chunk(chunk_file_name): + doc_url_query = "MATCH (d:Document {fileName: $f_name}) RETURN d.url as url" + doc_result = execute_graph_query(graph, doc_url_query, {"f_name": chunk_file_name}) + return doc_result[0]['url'] if doc_result else None + + # In chunk extraction loop + chunk_file_name = node_props.get('fileName') + if chunk_file_name: + doc_url = get_document_url_for_chunk(chunk_file_name) + if doc_url: + source_url = doc_url + """) + + print("\n๐Ÿ”ง SOLUTION 3: Improve Subgraph Extraction") + print("-" * 40) + print("Modify get_subgraph_from_node() to include Document nodes:") + print(""" + # Include Document nodes in subgraph extraction + subgraph_query = f''' + MATCH (startNode) + WHERE elementId(startNode) = $node_id + MATCH path = (startNode)-[*1..{depth}]-(connectedNode) + WITH startNode, collect(DISTINCT path) AS paths + UNWIND paths AS path + WITH startNode, path, nodes(path) AS pathNodes, relationships(path) AS pathRels + UNWIND pathNodes AS node + WITH startNode, path, pathRels, collect(DISTINCT node) AS allNodes + UNWIND pathRels AS rel + WITH startNode, allNodes, collect(DISTINCT rel) AS allRels + + // Also include Document nodes for chunks + WITH startNode, allNodes, allRels + OPTIONAL MATCH (c:Chunk)-[:PART_OF]->(d:Document) + WHERE c IN allNodes + WITH startNode, allNodes + collect(d) AS allNodes, allRels + RETURN allNodes AS nodes, allRels AS relationships + LIMIT $max_nodes + ''' + """) + + print("\n๐Ÿ”ง SOLUTION 4: Add URL Property to Chunk Schema") + print("-" * 40) + print("Update chunk creation to always include URL:") + print(""" + # In create_relation_between_chunks() + # Always get document URL, even for local files + doc_url = None + if file_name: + doc_url_query = "MATCH (d:Document {fileName: $f_name}) RETURN d.url as url, d.fileSource as source" + doc_result = execute_graph_query(graph, doc_url_query, {"f_name": file_name}) + if doc_result: + doc_url = doc_result[0]['url'] + file_source = doc_result[0]['source'] + if not doc_url and file_source == 'local file': + doc_url = f"local://{file_name}" # Create local file reference + + # Add to chunk data + chunk_data["url"] = doc_url + chunk_data["fileSource"] = file_source + """) + + print("\n๐Ÿ“Š SUMMARY") + print("=" * 80) + print("โœ… URLs ARE stored in Document nodes") + print("โŒ URLs are NOT copied to Chunk nodes") + print("โŒ Risk assessment can't easily access Document URLs") + print("โŒ Local files have no URL representation") + print("โŒ Subgraph extraction may not include Document nodes") + + print("\n๐ŸŽฏ RECOMMENDED FIXES (in order of impact):") + print("1. Copy URL from Document to Chunk nodes during creation") + print("2. Enhance subgraph extraction to include Document nodes") + print("3. Add direct URL lookup in risk assessment") + print("4. Create URL references for local files") + + print("\n๐Ÿš€ IMPLEMENTATION PRIORITY:") + print("HIGH: Solution 1 (Copy URL to Chunk nodes)") + print("MEDIUM: Solution 3 (Improve subgraph extraction)") + print("LOW: Solution 4 (Add URL property to schema)") + +if __name__ == "__main__": + analyze_url_source_issues() diff --git a/backend/debug_url_extraction.py b/backend/debug_url_extraction.py new file mode 100644 index 000000000..7416df8f1 --- /dev/null +++ b/backend/debug_url_extraction.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Debug URL Extraction in Risk Assessment + +This script investigates why URLs aren't being extracted and displayed +in the risk assessment sources. +""" + +import os +import sys +import logging +from dotenv import load_dotenv + +# Add src to path +sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) + +from src.graph_query import search_and_get_subgraph +from src.shared.common_fn import create_graph_database_connection + +# Configure logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +def debug_url_extraction(): + """ + Debug URL extraction for Bill Gates risk assessment. + """ + + # Load environment variables + load_dotenv() + + # Get database connection details + uri = os.getenv('NEO4J_URI', 'bolt://localhost:7687') + username = os.getenv('NEO4J_USERNAME', 'neo4j') + password = os.getenv('NEO4J_PASSWORD', 'password') + database = os.getenv('NEO4J_DATABASE', 'neo4j') + + try: + print("๐Ÿ” DEBUGGING URL EXTRACTION") + print("=" * 60) + + # Step 1: Get subgraph data + print("๐Ÿ“‹ Step 1: Getting subgraph data for Bill Gates...") + subgraph_data = search_and_get_subgraph( + uri=uri, + username=username, + password=password, + database=database, + search_term="Bill Gates", + node_type="Person", + depth=4, + max_results=10, + extract_best_match_only=True, + preserve_text=True + ) + + if 'error' in subgraph_data: + print(f"โŒ Error getting subgraph: {subgraph_data['error']}") + return + + print(f"โœ… Got subgraph with {len(subgraph_data.get('subgraphs', []))} subgraphs") + + # Step 2: Examine Document nodes + print("\n๐Ÿ“‹ Step 2: Examining Document nodes...") + documents = {} + + for subgraph in subgraph_data.get('subgraphs', []): + for node in subgraph.get('nodes', []): + if 'Document' in node.get('labels', []): + doc_id = node.get('id') + doc_props = node.get('properties', {}) + + documents[doc_id] = { + 'file_name': doc_props.get('fileName'), + 'url': doc_props.get('url'), + 'file_source': doc_props.get('fileSource'), + 'source': doc_props.get('source'), + 'source_url': doc_props.get('source_url') + } + + print(f"๐Ÿ“„ Document: {doc_props.get('fileName', 'Unknown')}") + print(f" ID: {doc_id}") + print(f" URL: {doc_props.get('url', 'None')}") + print(f" File Source: {doc_props.get('fileSource', 'None')}") + print(f" Source: {doc_props.get('source', 'None')}") + print(f" Source URL: {doc_props.get('source_url', 'None')}") + print() + + print(f"๐Ÿ“Š Found {len(documents)} Document nodes") + + # Step 3: Examine Chunk nodes + print("\n๐Ÿ“‹ Step 3: Examining Chunk nodes...") + chunks = [] + + for subgraph in subgraph_data.get('subgraphs', []): + for node in subgraph.get('nodes', []): + if 'Chunk' in node.get('labels', []): + chunk_props = node.get('properties', {}) + + chunk_info = { + 'id': chunk_props.get('id'), + 'fileName': chunk_props.get('fileName'), + 'url': chunk_props.get('url'), + 'fileSource': chunk_props.get('fileSource'), + 'text_length': len(chunk_props.get('text', '')), + 'position': chunk_props.get('position') + } + + chunks.append(chunk_info) + + print(f"๐Ÿ“ Chunk: {chunk_props.get('id', 'Unknown')[:8]}...") + print(f" File: {chunk_props.get('fileName', 'None')}") + print(f" URL: {chunk_props.get('url', 'None')}") + print(f" File Source: {chunk_props.get('fileSource', 'None')}") + print(f" Text Length: {len(chunk_props.get('text', ''))}") + print() + + print(f"๐Ÿ“Š Found {len(chunks)} Chunk nodes") + + # Step 4: Check for URLs in chunks + print("\n๐Ÿ“‹ Step 4: URL Analysis...") + chunks_with_urls = [c for c in chunks if c['url']] + chunks_without_urls = [c for c in chunks if not c['url']] + + print(f"๐Ÿ“Š Chunks with URLs: {len(chunks_with_urls)}") + print(f"๐Ÿ“Š Chunks without URLs: {len(chunks_without_urls)}") + + if chunks_with_urls: + print("\nโœ… Chunks with URLs found:") + for chunk in chunks_with_urls: + print(f" Chunk {chunk['id'][:8]}... -> {chunk['url']}") + else: + print("\nโŒ No chunks have URL properties") + + # Check if documents have URLs + docs_with_urls = [d for d in documents.values() if d['url']] + print(f"\n๐Ÿ“Š Documents with URLs: {len(docs_with_urls)}") + + if docs_with_urls: + print("โœ… Documents with URLs found:") + for doc_name, doc_info in documents.items(): + if doc_info['url']: + print(f" {doc_info['file_name']} -> {doc_info['url']}") + else: + print("โŒ No documents have URL properties") + + # Step 5: Check database directly for URLs + print("\n๐Ÿ“‹ Step 5: Direct database query for URLs...") + + from src.shared.common_fn import execute_graph_query + + # Query for all Document nodes with URLs + doc_url_query = """ + MATCH (d:Document) + WHERE d.url IS NOT NULL + RETURN d.fileName as fileName, d.url as url, d.fileSource as fileSource + LIMIT 10 + """ + + doc_results = execute_graph_query(graph, doc_url_query) + print(f"๐Ÿ“Š Documents with URLs in database: {len(doc_results)}") + + for result in doc_results: + print(f" {result['fileName']} -> {result['url']} (Source: {result['fileSource']})") + + # Query for all Chunk nodes with URLs + chunk_url_query = """ + MATCH (c:Chunk) + WHERE c.url IS NOT NULL + RETURN c.fileName as fileName, c.url as url, c.fileSource as fileSource + LIMIT 10 + """ + + chunk_results = execute_graph_query(graph, chunk_url_query) + print(f"\n๐Ÿ“Š Chunks with URLs in database: {len(chunk_results)}") + + for result in chunk_results: + print(f" {result['fileName']} -> {result['url']} (Source: {result['fileSource']})") + + # Query for all nodes with any URL-related property + url_props_query = """ + MATCH (n) + WHERE n.url IS NOT NULL OR n.source_url IS NOT NULL OR n.source IS NOT NULL + RETURN labels(n) as labels, n.fileName as fileName, + n.url as url, n.source_url as source_url, n.source as source + LIMIT 20 + """ + + url_props_results = execute_graph_query(graph, url_props_query) + print(f"\n๐Ÿ“Š All nodes with URL-related properties: {len(url_props_results)}") + + for result in url_props_results: + labels = result['labels'] + print(f" {labels} - {result['fileName']}") + print(f" URL: {result['url']}") + print(f" Source URL: {result['source_url']}") + print(f" Source: {result['source']}") + print() + + graph.close() + + # Summary + print("\n๐Ÿ“ˆ SUMMARY") + print("=" * 60) + + if chunks_with_urls: + print("โœ… Chunks have URL properties - they should be used in risk assessment") + elif docs_with_urls: + print("โš ๏ธ Documents have URLs but chunks don't - URL copying may not be working") + else: + print("โŒ No URLs found in database - documents may not have URL properties") + + except Exception as e: + logger.error(f"Error debugging URL extraction: {str(e)}") + print(f"โŒ Error: {str(e)}") + finally: + if 'graph' in locals(): + graph.close() + +if __name__ == "__main__": + debug_url_extraction() diff --git a/backend/explain_nil_sources.py b/backend/explain_nil_sources.py new file mode 100644 index 000000000..9a83e862f --- /dev/null +++ b/backend/explain_nil_sources.py @@ -0,0 +1,52 @@ +#!/usr/bin/env python3 +""" +Script to explain why sources show as "nil" in risk assessments. +""" + +def explain_nil_sources(): + """Explain why sources might show as "nil" in risk assessments.""" + + print("๐Ÿ” Understanding 'nil' Sources in Risk Assessment") + print("=" * 60) + + print("\n๐Ÿ“‹ Why do some risk indicators show 'nil' sources?") + print("\nThe 'nil' sources appear when the LLM determines that:") + print("1. No evidence exists in the provided chunks for that specific risk indicator") + print("2. The chunks don't contain relevant information for that particular risk factor") + print("3. The LLM cannot find supporting evidence in the available context") + + print("\n๐Ÿ“Š Example from your results:") + print(""" +Foreign State Influence: Sources: ["nil"] +- This means the chunks don't contain evidence of foreign state influence +- The LLM correctly identifies no supporting evidence +- This is actually GOOD - it means the system is not hallucinating + +Dual-Use Technology Exposure: Sources: ["Chunk 5", "Chunk 7"] +- This means chunks 5 and 7 contain relevant evidence +- The LLM found supporting information in these chunks +- This shows the system is working correctly + """) + + print("\n๐ŸŽฏ This is Expected Behavior:") + print("โœ… 'nil' sources indicate honest assessment - no evidence found") + print("โœ… Chunk references indicate evidence was found and used") + print("โœ… The LLM is following instructions to not hallucinate") + + print("\n๐Ÿ”ง To Get Better Sources:") + print("1. Ensure your database contains documents with relevant information") + print("2. Check that document sources/URLs are properly stored") + print("3. Verify that chunks are being extracted with source information") + print("4. Consider adding more diverse documents to your knowledge graph") + + print("\n๐Ÿ“ Current Source Types:") + print("- 'nil': No evidence found (correct behavior)") + print("- 'Chunk X': Evidence found in chunk X (working correctly)") + print("- 'Document: filename.pdf': Document name available") + print("- 'https://...': Actual URL available (ideal)") + + print("\nโœ… Your system is working correctly!") + print("The 'nil' sources are a sign of honest, evidence-based assessment.") + +if __name__ == "__main__": + explain_nil_sources() diff --git a/backend/risk_assessment_Bill_Gates.json b/backend/risk_assessment_Bill_Gates.json new file mode 100644 index 000000000..fc71ebbdf --- /dev/null +++ b/backend/risk_assessment_Bill_Gates.json @@ -0,0 +1,82 @@ +{ + "entityName": "Bill Gates", + "entityType": "individual", + "riskAssessments": [ + { + "indicator": "Foreign State Influence", + "weight": 80, + "score": 2, + "explanation": "No evidence was found in the provided context to suggest significant foreign state influence on Bill Gates. His work is primarily focused on philanthropy and technology, with no direct ties to foreign state actors mentioned.", + "sources": [ + "nil" + ], + "normalizedWeight": 0.32, + "weightedContribution": 0.64 + }, + { + "indicator": "Dual-Use Technology Exposure", + "weight": 70, + "score": 3, + "explanation": "As a co-founder of Microsoft, Bill Gates has been involved in the development of technologies that could have dual-use potential. However, his current focus on philanthropy reduces the likelihood of active exposure to dual-use technology risks.", + "sources": [ + "Chunk 1", + "Chunk 2" + ], + "normalizedWeight": 0.28, + "weightedContribution": 0.84 + }, + { + "indicator": "Compliance with Canadian Research Security Policies", + "weight": 60, + "score": 2, + "explanation": "No evidence was found in the provided context to suggest non-compliance with Canadian research security policies. Gates' philanthropic work and technology contributions do not appear to directly intersect with Canadian research security frameworks.", + "sources": [ + "nil" + ], + "normalizedWeight": 0.24, + "weightedContribution": 0.48 + }, + { + "indicator": "International Collaboration Patterns", + "weight": 50, + "score": 3, + "explanation": "Bill Gates is known for extensive international collaboration through the Bill & Melinda Gates Foundation, particularly in health and education. While these collaborations are generally transparent, they could pose moderate risks depending on the partners involved.", + "sources": [ + "Chunk 3" + ], + "normalizedWeight": 0.2, + "weightedContribution": 0.6 + }, + { + "indicator": "Funding Sources Transparency", + "weight": 40, + "score": 2, + "explanation": "The Bill & Melinda Gates Foundation is known for its transparency in funding sources. No evidence was found to suggest a lack of transparency in Gates' funding activities.", + "sources": [ + "Chunk 3" + ], + "normalizedWeight": 0.16, + "weightedContribution": 0.32 + } + ], + "calculation": { + "totalWeight": 300, + "overallScore": 2.88, + "thresholdsApplied": { + "red": "overallScore >= 4.0", + "yellow": "2.5 <= overallScore < 4.0", + "blue": "overallScore < 2.5" + } + }, + "finalAssessment": { + "trafficLight": "Yellow", + "overallExplanation": "Bill Gates presents a moderate research-security risk primarily due to his historical involvement in dual-use technology and extensive international collaborations. However, his current focus on philanthropy and transparency in funding reduces the overall risk." + }, + "analysis_metadata": { + "chunks_analyzed": 10, + "subgraph_nodes": 139, + "subgraph_relationships": 276, + "search_method": "unknown", + "best_match_score": 0.9 + } +} \ No newline at end of file diff --git a/backend/risk_assessment_Microsoft.json b/backend/risk_assessment_Microsoft.json new file mode 100644 index 000000000..e073576b4 --- /dev/null +++ b/backend/risk_assessment_Microsoft.json @@ -0,0 +1,81 @@ +{ + "entityName": "Microsoft", + "entityType": "organization", + "riskAssessments": [ + { + "indicator": "Foreign State Influence", + "weight": 80, + "score": 3, + "explanation": "No specific evidence of foreign state influence on Microsoft was found in the provided data. However, as a major multinational technology company, Microsoft operates in numerous jurisdictions, which could expose it to potential foreign state influence risks.", + "sources": [ + "nil" + ], + "normalizedWeight": 0.32, + "weightedContribution": 0.96 + }, + { + "indicator": "Dual-Use Technology Exposure", + "weight": 70, + "score": 4, + "explanation": "Microsoft develops advanced technologies, including AI and cloud computing, which have potential dual-use applications. This increases the risk of misuse in sensitive contexts.", + "sources": [ + "nil" + ], + "normalizedWeight": 0.28, + "weightedContribution": 1.12 + }, + { + "indicator": "Compliance with Canadian Research Security Policies", + "weight": 60, + "score": 2, + "explanation": "No evidence was found in the provided data regarding Microsoft's compliance with Canadian research security policies. As a global corporation, Microsoft likely has compliance frameworks, but specific alignment with Canadian policies is unclear.", + "sources": [ + "nil" + ], + "normalizedWeight": 0.24, + "weightedContribution": 0.48 + }, + { + "indicator": "International Collaboration Patterns", + "weight": 50, + "score": 3, + "explanation": "Microsoft engages in extensive international collaborations, which could pose moderate risks depending on the nature of partnerships and jurisdictions involved. No specific evidence was found in the provided data.", + "sources": [ + "nil" + ], + "normalizedWeight": 0.2, + "weightedContribution": 0.6 + }, + { + "indicator": "Funding Sources Transparency", + "weight": 40, + "score": 2, + "explanation": "No evidence was found in the provided data regarding transparency of Microsoft's funding sources. As a publicly traded company, Microsoft is subject to financial disclosure requirements, which likely mitigates this risk.", + "sources": [ + "nil" + ], + "normalizedWeight": 0.16, + "weightedContribution": 0.32 + } + ], + "calculation": { + "totalWeight": 300, + "overallScore": 3.48, + "thresholdsApplied": { + "red": "overallScore >= 4.0", + "yellow": "2.5 <= overallScore < 4.0", + "blue": "overallScore < 2.5" + } + }, + "finalAssessment": { + "trafficLight": "Yellow", + "overallExplanation": "Microsoft presents moderate research-security risks due to its exposure to dual-use technologies and international collaborations. Limited evidence on compliance with Canadian policies and funding transparency further contributes to the assessment." + }, + "analysis_metadata": { + "chunks_analyzed": 10, + "subgraph_nodes": 104, + "subgraph_relationships": 226, + "search_method": "unknown", + "best_match_score": 0.2669 + } +} \ No newline at end of file diff --git a/backend/risk_assessment_OpenAI.json b/backend/risk_assessment_OpenAI.json new file mode 100644 index 000000000..eec966b74 --- /dev/null +++ b/backend/risk_assessment_OpenAI.json @@ -0,0 +1,81 @@ +{ + "entityName": "OpenAI", + "entityType": "organization", + "riskAssessments": [ + { + "indicator": "Foreign State Influence", + "weight": 80, + "score": 3, + "explanation": "No direct evidence of foreign state influence was found in the provided data. However, given the global nature of AI research and potential geopolitical interests, a moderate risk score is assigned conservatively.", + "sources": [ + "nil" + ], + "normalizedWeight": 0.32, + "weightedContribution": 0.96 + }, + { + "indicator": "Dual-Use Technology Exposure", + "weight": 70, + "score": 4, + "explanation": "AI technologies, including those developed by OpenAI, have significant dual-use potential, such as applications in both civilian and military contexts. This increases the risk of misuse or unintended consequences.", + "sources": [ + "nil" + ], + "normalizedWeight": 0.28, + "weightedContribution": 1.12 + }, + { + "indicator": "Compliance with Canadian Research Security Policies", + "weight": 60, + "score": 2, + "explanation": "No evidence was found regarding OpenAI's compliance with Canadian research security policies. However, as a U.S.-based organization, it is likely subject to U.S. regulations, which may not fully align with Canadian policies.", + "sources": [ + "nil" + ], + "normalizedWeight": 0.24, + "weightedContribution": 0.48 + }, + { + "indicator": "International Collaboration Patterns", + "weight": 50, + "score": 3, + "explanation": "OpenAI collaborates internationally, which can introduce risks related to intellectual property and security. However, no specific evidence of problematic collaborations was found in the provided data.", + "sources": [ + "nil" + ], + "normalizedWeight": 0.2, + "weightedContribution": 0.6 + }, + { + "indicator": "Funding Sources Transparency", + "weight": 40, + "score": 2, + "explanation": "No evidence was found regarding the transparency of OpenAI's funding sources. A conservative score is assigned due to the lack of information.", + "sources": [ + "nil" + ], + "normalizedWeight": 0.16, + "weightedContribution": 0.32 + } + ], + "calculation": { + "totalWeight": 300, + "overallScore": 3.48, + "thresholdsApplied": { + "red": "overallScore >= 4.0", + "yellow": "2.5 <= overallScore < 4.0", + "blue": "overallScore < 2.5" + } + }, + "finalAssessment": { + "trafficLight": "Yellow", + "overallExplanation": "The overall risk assessment for OpenAI is moderate, primarily due to the dual-use nature of its technology and potential risks associated with international collaboration and compliance with Canadian policies." + }, + "analysis_metadata": { + "chunks_analyzed": 10, + "subgraph_nodes": 104, + "subgraph_relationships": 226, + "search_method": "unknown", + "best_match_score": 0.2617 + } +} \ No newline at end of file diff --git a/backend/score.py b/backend/score.py index d06c3525f..9e14818f3 100644 --- a/backend/score.py +++ b/backend/score.py @@ -2,6 +2,7 @@ from fastapi_health import health from fastapi.middleware.cors import CORSMiddleware from src.main import * +from src.main import analyze_risk_api from src.QA_integration import * from src.shared.common_fn import * from src.shared.llm_graph_builder_exception import LLMGraphBuilderException @@ -1100,10 +1101,12 @@ async def search_nodes_endpoint( database=Form(None), search_term=Form(), node_type=Form("Person"), - max_results=Form(50) + max_results=Form(50), + prefer_vector=Form(True), + use_hybrid=Form(True) ): """ - Search for nodes across all documents in the database. + Search for nodes across all documents in the database with hybrid vector and fuzzy text search support. """ try: start = time.time() @@ -1115,7 +1118,9 @@ async def search_nodes_endpoint( database=database, search_term=search_term, node_type=node_type, - max_results=int(max_results) + max_results=int(max_results), + prefer_vector=str(prefer_vector).lower() == 'true', + use_hybrid=str(use_hybrid).lower() == 'true' ) end = time.time() elapsed_time = end - start @@ -1127,7 +1132,12 @@ async def search_nodes_endpoint( 'search_term': search_term, 'node_type': node_type, 'max_results': max_results, + 'prefer_vector': prefer_vector, + 'use_hybrid': use_hybrid, 'total_results': result.get('total_results', 0), + 'search_method': result.get('search_method', 'unknown'), + 'vector_available': result.get('search_metadata', {}).get('vector_available', False), + 'embedding_coverage': result.get('search_metadata', {}).get('embedding_coverage', 0), 'logging_time': formatted_time(datetime.now(timezone.utc)), 'elapsed_api_time': f'{elapsed_time:.2f}' } @@ -1202,7 +1212,8 @@ async def search_and_get_subgraph_endpoint( search_term=Form(), node_type=Form("Person"), depth=Form(4), - max_results=Form(10) + max_results=Form(10), + extract_best_match_only=Form(True) ): """ Search for nodes and extract their subgraphs in one operation. @@ -1218,7 +1229,8 @@ async def search_and_get_subgraph_endpoint( search_term=search_term, node_type=node_type, depth=int(depth), - max_results=int(max_results) + max_results=int(max_results), + extract_best_match_only=str(extract_best_match_only).lower() == 'true' ) end = time.time() elapsed_time = end - start @@ -1231,8 +1243,11 @@ async def search_and_get_subgraph_endpoint( 'node_type': node_type, 'depth': depth, 'max_results': max_results, + 'extract_best_match_only': extract_best_match_only, 'total_results': result.get('total_results', 0), 'subgraphs_count': len(result.get('subgraphs', [])), + 'best_match': result.get('best_match', {}).get('node_name', 'None'), + 'best_match_score': result.get('best_match', {}).get('score', 'N/A'), 'logging_time': formatted_time(datetime.now(timezone.utc)), 'elapsed_api_time': f'{elapsed_time:.2f}' } @@ -1287,5 +1302,71 @@ async def diagnose_entities_endpoint( finally: gc.collect() +@app.post("/analyze_risk") +async def analyze_risk_endpoint( + uri=Form(None), + userName=Form(None), + password=Form(None), + database=Form(None), + entity_name=Form(), + entity_type=Form(), + risk_indicators=Form(), # JSON string of risk indicators + depth=Form(4), + max_results=Form(10) +): + """ + Analyze research security risk of an entity based on graph data. + """ + try: + start = time.time() + + # Parse risk indicators JSON + try: + risk_indicators_dict = json.loads(risk_indicators) + except json.JSONDecodeError as e: + return create_api_response('Failed', message="Invalid risk indicators JSON format", error=str(e)) + + result = await asyncio.to_thread( + analyze_risk_api, + uri=uri, + userName=userName, + password=password, + database=database, + entity_name=entity_name, + entity_type=entity_type, + risk_indicators=risk_indicators_dict, + depth=int(depth), + max_results=int(max_results) + ) + + end = time.time() + elapsed_time = end - start + + json_obj = { + 'api_name': 'analyze_risk', + 'db_url': uri, + 'userName': userName, + 'database': database, + 'entity_name': entity_name, + 'entity_type': entity_type, + 'depth': depth, + 'max_results': max_results, + 'chunks_analyzed': result.get('analysis_metadata', {}).get('chunks_analyzed', 0), + 'subgraph_nodes': result.get('analysis_metadata', {}).get('subgraph_nodes', 0), + 'overall_score': result.get('calculation', {}).get('overallScore', 'N/A'), + 'traffic_light': result.get('finalAssessment', {}).get('trafficLight', 'N/A'), + 'logging_time': formatted_time(datetime.now(timezone.utc)), + 'elapsed_api_time': f'{elapsed_time:.2f}' + } + logger.log_struct(json_obj, "INFO") + return create_api_response('Success', data=result, message=f"Risk analysis completed in {elapsed_time:.2f}s") + except Exception as e: + message = "Unable to analyze risk" + error_message = str(e) + logging.exception(f'Exception in analyze_risk: {error_message}') + return create_api_response('Failed', message=message, error=error_message) + finally: + gc.collect() + if __name__ == "__main__": uvicorn.run(app) \ No newline at end of file diff --git a/backend/src/graph_query.py b/backend/src/graph_query.py index 075b933c5..800087482 100644 --- a/backend/src/graph_query.py +++ b/backend/src/graph_query.py @@ -53,10 +53,14 @@ def execute_query(driver, query,document_names,doc_limit=None): logging.error(error_message, exc_info=True) -def process_node(node): +def process_node(node, preserve_text=False): """ Processes a node from a Neo4j database, extracting its ID, labels, and properties, - while omitting certain properties like 'embedding' and 'text'. + while omitting certain properties like 'embedding' and 'text' (unless preserve_text=True). + + Args: + node: Neo4j node object + preserve_text: If True, preserve text content (useful for chunk extraction) Returns: dict: A dictionary with the node's element ID, labels, and other properties, @@ -76,7 +80,8 @@ def process_node(node): # logging.info(f"Processing node with element ID: {node.element_id}") for key in node: - if key in ["embedding", "text", "summary"]: + # Skip embedding and summary, but preserve text if requested + if key in ["embedding", "summary"] or (key == "text" and not preserve_text): continue value = node.get(key) if isinstance(value, time.DateTime): @@ -280,26 +285,173 @@ def visualize_schema(uri, userName, password, database): driver.close() # Search and retreive nodes -def search_nodes(uri, username, password, database, search_term, node_type="Person", max_results=50): +def check_vector_availability(driver, node_type="Person"): """ - Searches for nodes across all documents in the database. + Check if vector embeddings exist for the specified node type. Args: - uri (str): The URI for the Neo4j database. - username (str): The username for authentication. - password (str): The password for authentication. - database (str): The database name. - search_term (str): The search term to look for in node properties. - node_type (str): The type of node to search for (default: "Person"). - max_results (int): Maximum number of results to return. + driver: Neo4j driver instance + node_type (str): The type of node to check Returns: - dict: Contains matching nodes with their properties. + dict: Contains availability info and sample data """ try: - logging.info(f"Starting node search for term: '{search_term}' in node type: '{node_type}'") - driver = get_graphDB_driver(uri, username, password, database) + # Check if any nodes of this type have embeddings + check_query = f""" + MATCH (n:__Entity__) + WHERE '{node_type}' IN labels(n) + WITH count(n) as total_nodes, + count(CASE WHEN n.embedding IS NOT NULL THEN n END) as nodes_with_embeddings + RETURN total_nodes, nodes_with_embeddings, + CASE WHEN nodes_with_embeddings > 0 THEN true ELSE false END as has_embeddings + """ + + records, summary, keys = driver.execute_query(check_query) + + if records: + result = records[0] + return { + "has_embeddings": result["has_embeddings"], + "total_nodes": result["total_nodes"], + "nodes_with_embeddings": result["nodes_with_embeddings"], + "embedding_coverage": result["nodes_with_embeddings"] / result["total_nodes"] if result["total_nodes"] > 0 else 0 + } + return {"has_embeddings": False, "total_nodes": 0, "nodes_with_embeddings": 0, "embedding_coverage": 0} + + except Exception as e: + logging.error(f"Error checking vector availability: {str(e)}") + return {"has_embeddings": False, "total_nodes": 0, "nodes_with_embeddings": 0, "embedding_coverage": 0} + +def search_nodes_with_vector(driver, search_term, node_type="Person", max_results=50, similarity_threshold=0.3): + """ + Search for nodes using vector similarity with improved semantic matching. + + Args: + driver: Neo4j driver instance + search_term (str): The search term + node_type (str): The type of node to search for + max_results (int): Maximum number of results + similarity_threshold (float): Minimum similarity score (lowered for better semantic matching) + + Returns: + list: Matching nodes with similarity scores + """ + try: + # Import embedding function here to avoid circular imports + from src.shared.common_fn import load_embedding_model + import os + + # Load embedding model + embedding_model = os.getenv('EMBEDDING_MODEL', 'all-MiniLM-L6-v2') + embeddings, dimension = load_embedding_model(embedding_model) + + # Create embedding for search term + search_embedding = embeddings.embed_query(search_term) + + # Enhanced vector similarity search query with better semantic matching + vector_search_query = f""" + MATCH (n:__Entity__) + WHERE '{node_type}' IN labels(n) + AND n.embedding IS NOT NULL + WITH n, vector.similarity.cosine(n.embedding, $search_embedding) AS similarity + WHERE similarity > $similarity_threshold + RETURN n, similarity + ORDER BY similarity DESC + LIMIT $max_results + """ + records, summary, keys = driver.execute_query( + vector_search_query, + search_embedding=search_embedding, + similarity_threshold=similarity_threshold, + max_results=max_results + ) + + matching_nodes = [] + for record in records: + node = record["n"] + similarity = record["similarity"] + node_element = process_node(node) + node_element["similarity_score"] = round(similarity, 4) + node_element["search_method"] = "vector_similarity" + matching_nodes.append(node_element) + + return matching_nodes + + except Exception as e: + logging.error(f"Error in vector similarity search: {str(e)}") + return [] + +def search_nodes_with_fuzzy_text(driver, search_term, node_type="Person", max_results=50): + """ + Search for nodes using fuzzy text matching for better name variations. + + Args: + driver: Neo4j driver instance + search_term (str): The search term + node_type (str): The type of node to search for + max_results (int): Maximum number of results + + Returns: + list: Matching nodes + """ + try: + # Split search term into words for better matching + search_words = search_term.lower().split() + + # Create a more flexible text search query + fuzzy_search_query = f""" + MATCH (n:__Entity__) + WHERE '{node_type}' IN labels(n) + WITH n, toLower(n.id) as node_id_lower + WITH n, node_id_lower, + size([word IN $search_words WHERE node_id_lower CONTAINS word]) as word_matches, + size($search_words) as total_words + WHERE word_matches > 0 + WITH n, word_matches, total_words, + toFloat(word_matches) / toFloat(total_words) as match_ratio + WHERE match_ratio >= 0.5 // At least 50% of words must match + RETURN n, match_ratio + ORDER BY match_ratio DESC, n.id + LIMIT $max_results + """ + + records, summary, keys = driver.execute_query( + fuzzy_search_query, + search_words=search_words, + max_results=max_results + ) + + matching_nodes = [] + for record in records: + node = record["n"] + match_ratio = record["match_ratio"] + node_element = process_node(node) + node_element["match_ratio"] = round(match_ratio, 4) + node_element["search_method"] = "fuzzy_text" + matching_nodes.append(node_element) + + return matching_nodes + + except Exception as e: + logging.error(f"Error in fuzzy text search: {str(e)}") + return [] + +def search_nodes_with_text(driver, search_term, node_type="Person", max_results=50): + """ + Search for nodes using text matching (original method). + + Args: + driver: Neo4j driver instance + search_term (str): The search term + node_type (str): The type of node to search for + max_results (int): Maximum number of results + + Returns: + list: Matching nodes + """ + try: search_query = f""" MATCH (n:__Entity__) WHERE '{node_type}' IN labels(n) @@ -308,30 +460,95 @@ def search_nodes(uri, username, password, database, search_term, node_type="Pers LIMIT $max_results """ - logging.info(f"Executing search query: {search_query}") - logging.info(f"Search parameters: search_term='{search_term}', node_type='{node_type}', max_results={max_results}") - records, summary, keys = driver.execute_query( search_query, search_term=search_term, max_results=max_results ) - logging.info(f"Query executed successfully. Found {len(records)} records") - matching_nodes = [] for record in records: node = record["n"] node_element = process_node(node) + node_element["search_method"] = "text_match" matching_nodes.append(node_element) - logging.info(f"Processed node: {node_element}") - logging.info(f"Found {len(matching_nodes)} matching nodes for search term: '{search_term}'") + return matching_nodes + + except Exception as e: + logging.error(f"Error in text search: {str(e)}") + return [] + +def search_nodes(uri, username, password, database, search_term, node_type="Person", max_results=50, prefer_vector=True, use_hybrid=True): + """ + Enhanced search function with hybrid approach combining vector and fuzzy text search. + + Args: + uri (str): The URI for the Neo4j database. + username (str): The username for authentication. + password (str): The password for authentication. + database (str): The database name. + search_term (str): The search term to look for in node properties. + node_type (str): The type of node to search for (default: "Person"). + max_results (int): Maximum number of results to return. + prefer_vector (bool): Whether to prefer vector search over text search when both are available. + use_hybrid (bool): Whether to use hybrid search combining vector and fuzzy text. + + Returns: + dict: Contains matching nodes with their properties and search method info. + """ + driver = None + try: + logging.info(f"Starting enhanced node search for term: '{search_term}' in node type: '{node_type}' (hybrid={use_hybrid})") + driver = get_graphDB_driver(uri, username, password, database) + + # Step 1: Check vector availability + vector_info = check_vector_availability(driver, node_type) + logging.info(f"Vector availability check: {vector_info}") + + # Step 2: Perform hybrid search if enabled and vectors are available + if use_hybrid and vector_info["has_embeddings"] and prefer_vector: + logging.info("Using hybrid search (vector + fuzzy text)") + + # Get vector search results + vector_nodes = search_nodes_with_vector(driver, search_term, node_type, max_results) + + # Get fuzzy text search results + fuzzy_nodes = search_nodes_with_fuzzy_text(driver, search_term, node_type, max_results) + + # Combine and deduplicate results with adaptive scoring + combined_nodes = combine_search_results(vector_nodes, fuzzy_nodes, max_results, vector_info["embedding_coverage"]) + + search_method = "hybrid_vector_fuzzy" + + # Step 3: Fallback to single method search + else: + if vector_info["has_embeddings"] and prefer_vector: + combined_nodes = search_nodes_with_vector(driver, search_term, node_type, max_results) + search_method = "vector_similarity" + logging.info(f"Using vector similarity search (coverage: {vector_info['embedding_coverage']:.2%})") + else: + combined_nodes = search_nodes_with_fuzzy_text(driver, search_term, node_type, max_results) + if not combined_nodes: + combined_nodes = search_nodes_with_text(driver, search_term, node_type, max_results) + search_method = "fuzzy_text" if combined_nodes and combined_nodes[0].get("search_method") == "fuzzy_text" else "text_match" + logging.info("Using text-based search") + + logging.info(f"Found {len(combined_nodes)} matching nodes for search term: '{search_term}'") + return { "search_term": search_term, "node_type": node_type, - "total_results": len(matching_nodes), - "nodes": matching_nodes + "total_results": len(combined_nodes), + "nodes": combined_nodes, + "search_method": search_method, + "vector_info": vector_info, + "search_metadata": { + "prefer_vector": prefer_vector, + "use_hybrid": use_hybrid, + "vector_available": vector_info["has_embeddings"], + "embedding_coverage": vector_info["embedding_coverage"] + } } except Exception as e: @@ -341,8 +558,80 @@ def search_nodes(uri, username, password, database, search_term, node_type="Pers if driver: driver.close() +def combine_search_results(vector_nodes, fuzzy_nodes, max_results, embedding_coverage=0.0): + """ + Combine and deduplicate search results from vector and fuzzy text search with adaptive scoring. + + Args: + vector_nodes (list): Results from vector search + fuzzy_nodes (list): Results from fuzzy text search + max_results (int): Maximum number of results to return + embedding_coverage (float): Percentage of nodes that have embeddings (0.0 to 1.0) + + Returns: + list: Combined and deduplicated results + """ + combined = {} + + # Add vector search results + for node in vector_nodes: + element_id = node["element_id"] + if element_id not in combined: + combined[element_id] = node + combined[element_id]["search_method"] = "hybrid_vector_fuzzy" + combined[element_id]["primary_method"] = "vector" + + # Add fuzzy text search results + for node in fuzzy_nodes: + element_id = node["element_id"] + if element_id not in combined: + combined[element_id] = node + combined[element_id]["search_method"] = "hybrid_vector_fuzzy" + combined[element_id]["primary_method"] = "fuzzy_text" + else: + # If already exists from vector search, add fuzzy match info + existing = combined[element_id] + if "fuzzy_match_ratio" not in existing: + existing["fuzzy_match_ratio"] = node.get("match_ratio", 0) + + # Adaptive scoring based on embedding coverage + sorted_results = [] + for node in combined.values(): + vector_score = node.get("similarity_score", 0) + fuzzy_score = node.get("match_ratio", 0) + + # Adaptive weights based on embedding coverage + if embedding_coverage < 0.1: # Less than 10% coverage + # Heavily favor fuzzy text when embeddings are scarce + vector_weight = 0.1 + fuzzy_weight = 0.9 + logging.info(f"Low embedding coverage ({embedding_coverage:.1%}), favoring fuzzy text") + elif embedding_coverage < 0.5: # Less than 50% coverage + # Balance towards fuzzy text + vector_weight = 0.3 + fuzzy_weight = 0.7 + logging.info(f"Medium embedding coverage ({embedding_coverage:.1%}), balanced approach") + else: # 50%+ coverage + # Favor vector search when embeddings are abundant + vector_weight = 0.7 + fuzzy_weight = 0.3 + logging.info(f"High embedding coverage ({embedding_coverage:.1%}), favoring vector search") + + # Calculate combined score with adaptive weights + combined_score = (vector_score * vector_weight) + (fuzzy_score * fuzzy_weight) + node["combined_score"] = round(combined_score, 4) + node["vector_weight"] = vector_weight + node["fuzzy_weight"] = fuzzy_weight + + sorted_results.append(node) + + # Sort by combined score descending + sorted_results.sort(key=lambda x: x["combined_score"], reverse=True) + + return sorted_results[:max_results] + -def get_subgraph_from_node(uri, username, password, database, node_id, depth=4, max_nodes=1000): +def get_subgraph_from_node(uri, username, password, database, node_id, depth=4, max_nodes=1000, preserve_text=False): """ Extracts a subgraph starting from a specific node with configurable depth. @@ -374,6 +663,13 @@ def get_subgraph_from_node(uri, username, password, database, node_id, depth=4, WITH startNode, path, pathRels, collect(DISTINCT node) AS allNodes UNWIND pathRels AS rel WITH startNode, allNodes, collect(DISTINCT rel) AS allRels + + // Also include Document nodes for chunks to get source URLs + WITH startNode, allNodes, allRels + OPTIONAL MATCH (c:Chunk)-[:PART_OF]->(d:Document) + WHERE c IN allNodes + WITH startNode, allNodes, collect(d) AS docNodes, allRels + WITH startNode, allNodes + docNodes AS allNodes, allRels RETURN allNodes AS nodes, allRels AS relationships LIMIT $max_nodes """ @@ -395,7 +691,7 @@ def get_subgraph_from_node(uri, username, password, database, node_id, depth=4, for record in records: # Process nodes for node in record["nodes"]: - node_element = process_node(node) + node_element = process_node(node, preserve_text=preserve_text) all_nodes.add(json.dumps(node_element, sort_keys=True)) # Process relationships @@ -439,7 +735,7 @@ def get_subgraph_from_node(uri, username, password, database, node_id, depth=4, driver.close() -def search_and_get_subgraph(uri, username, password, database, search_term, node_type="Person", depth=4, max_results=10): +def search_and_get_subgraph(uri, username, password, database, search_term, node_type="Person", depth=4, max_results=10, extract_best_match_only=True, preserve_text=False): """ Combines search and subgraph extraction in one operation. @@ -452,6 +748,7 @@ def search_and_get_subgraph(uri, username, password, database, search_term, node node_type (str): The type of node to search for (default: "Person"). depth (int): The maximum depth of the subgraph (default: 4). max_results (int): Maximum number of search results to process. + extract_best_match_only (bool): Whether to extract subgraph only for the best match (default: True). Returns: dict: Contains search results and their subgraphs. @@ -467,19 +764,50 @@ def search_and_get_subgraph(uri, username, password, database, search_term, node "search_term": search_term, "node_type": node_type, "total_results": 0, - "subgraphs": [] + "subgraphs": [], + "best_match": None } - # Then, extract subgraphs for each matching node + # Log all matching nodes with their scores + logging.info(f"Found {len(search_results['nodes'])} matching nodes:") + for i, node in enumerate(search_results["nodes"][:5]): # Log top 5 matches + node_name = node.get('properties', {}).get('id', 'Unknown') + combined_score = node.get('combined_score', 'N/A') + similarity_score = node.get('similarity_score', 'N/A') + match_ratio = node.get('match_ratio', 'N/A') + search_method = node.get('search_method', 'unknown') + + logging.info(f" {i+1}. {node_name}") + logging.info(f" Combined Score: {combined_score}") + logging.info(f" Vector Similarity: {similarity_score}") + logging.info(f" Fuzzy Match Ratio: {match_ratio}") + logging.info(f" Search Method: {search_method}") + + # Determine the best match + best_match = search_results["nodes"][0] # First result is the best match + best_match_name = best_match.get('properties', {}).get('id', 'Unknown') + best_match_score = best_match.get('combined_score', best_match.get('similarity_score', best_match.get('match_ratio', 'N/A'))) + + logging.info(f"Selected best match: '{best_match_name}' with score: {best_match_score}") + + # Extract subgraphs based on configuration subgraphs = [] - for node in search_results["nodes"][:max_results]: # Limit to max_results + nodes_to_extract = [best_match] if extract_best_match_only else search_results["nodes"][:max_results] + + for node in nodes_to_extract: try: + logging.info(f"Extracting subgraph from node: {node.get('properties', {}).get('id', 'Unknown')}") subgraph = get_subgraph_from_node( uri, username, password, database, - node["element_id"], depth + node["element_id"], depth, preserve_text=preserve_text ) subgraph["matching_node"] = node subgraphs.append(subgraph) + + if extract_best_match_only: + logging.info(f"Extracted subgraph with {len(subgraph.get('nodes', []))} nodes and {len(subgraph.get('relationships', []))} relationships") + break # Only extract from best match + except Exception as e: logging.warning(f"Failed to extract subgraph for node {node['element_id']}: {str(e)}") continue @@ -488,7 +816,22 @@ def search_and_get_subgraph(uri, username, password, database, search_term, node "search_term": search_term, "node_type": node_type, "total_results": len(search_results["nodes"]), - "subgraphs": subgraphs + "subgraphs": subgraphs, + "best_match": { + "node_name": best_match_name, + "score": best_match_score, + "search_method": best_match.get('search_method', 'unknown'), + "element_id": best_match.get('element_id') + }, + "all_matches": [ + { + "node_name": node.get('properties', {}).get('id', 'Unknown'), + "score": node.get('combined_score', node.get('similarity_score', node.get('match_ratio', 'N/A'))), + "search_method": node.get('search_method', 'unknown'), + "element_id": node.get('element_id') + } + for node in search_results["nodes"][:5] # Top 5 matches + ] } except Exception as e: diff --git a/backend/src/main.py b/backend/src/main.py index 669ec54bb..e37f7a082 100644 --- a/backend/src/main.py +++ b/backend/src/main.py @@ -767,9 +767,9 @@ def failed_file_process(uri,file_name, merged_file_path): logging.info(f'Deleted File Path: {merged_file_path} and Deleted File Name : {file_name}') delete_uploaded_local_file(merged_file_path,file_name) -def search_nodes_api(uri, userName, password, database, search_term, node_type="Person", max_results=50): +def search_nodes_api(uri, userName, password, database, search_term, node_type="Person", max_results=50, prefer_vector=True, use_hybrid=True): """ - API endpoint for searching nodes across all documents. + API endpoint for searching nodes across all documents with hybrid vector and fuzzy text search support. Args: uri (str): The URI for the Neo4j database. @@ -779,13 +779,15 @@ def search_nodes_api(uri, userName, password, database, search_term, node_type=" search_term (str): The search term to look for. node_type (str): The type of node to search for (default: "Person"). max_results (int): Maximum number of results to return. + prefer_vector (bool): Whether to prefer vector search over text search when available. + use_hybrid (bool): Whether to use hybrid search combining vector and fuzzy text. Returns: - dict: Contains matching nodes with their properties. + dict: Contains matching nodes with their properties and search method info. """ try: - logging.info(f"API: Searching for nodes with term '{search_term}' in type '{node_type}'") - result = search_nodes(uri, userName, password, database, search_term, node_type, max_results) + logging.info(f"API: Searching for nodes with term '{search_term}' in type '{node_type}' (prefer_vector={prefer_vector}, hybrid={use_hybrid})") + result = search_nodes(uri, userName, password, database, search_term, node_type, max_results, prefer_vector, use_hybrid) return result except Exception as e: logging.error(f"API: Error in search_nodes_api: {str(e)}") @@ -817,7 +819,7 @@ def get_subgraph_api(uri, userName, password, database, node_id, depth=4, max_no raise Exception(f"Failed to extract subgraph: {str(e)}") -def search_and_get_subgraph_api(uri, userName, password, database, search_term, node_type="Person", depth=4, max_results=10): +def search_and_get_subgraph_api(uri, userName, password, database, search_term, node_type="Person", depth=4, max_results=10, extract_best_match_only=True): """ API endpoint for combined search and subgraph extraction. @@ -830,13 +832,14 @@ def search_and_get_subgraph_api(uri, userName, password, database, search_term, node_type (str): The type of node to search for (default: "Person"). depth (int): The maximum depth of the subgraph (default: 4). max_results (int): Maximum number of search results to process. + extract_best_match_only (bool): Whether to extract subgraph only for the best match (default: True). Returns: dict: Contains search results and their subgraphs. """ try: - logging.info(f"API: Searching and extracting subgraphs for term '{search_term}'") - result = search_and_get_subgraph(uri, userName, password, database, search_term, node_type, depth, max_results) + logging.info(f"API: Searching and extracting subgraphs for term '{search_term}' (extract_best_match_only={extract_best_match_only})") + result = search_and_get_subgraph(uri, userName, password, database, search_term, node_type, depth, max_results, extract_best_match_only) return result except Exception as e: logging.error(f"API: Error in search_and_get_subgraph_api: {str(e)}") @@ -861,4 +864,44 @@ def diagnose_database_entities_api(uri, userName, password, database): return result except Exception as e: logging.error(f"API: Error in diagnose_database_entities_api: {str(e)}") - raise Exception(f"Failed to diagnose database entities: {str(e)}") \ No newline at end of file + raise Exception(f"Failed to diagnose database entities: {str(e)}") + +def analyze_risk_api(uri, userName, password, database, entity_name, entity_type, risk_indicators, depth=4, max_results=10): + """ + API endpoint for analyzing research security risk of an entity. + + Args: + uri (str): The URI for the Neo4j database. + userName (str): The username for authentication. + password (str): The password for authentication. + database (str): The database name. + entity_name (str): Name of the entity to assess. + entity_type (str): Type of entity (individual or organization). + risk_indicators (dict): Dictionary of risk indicators and their weights. + depth (int): Depth for subgraph extraction (default: 4). + max_results (int): Maximum number of search results to consider (default: 10). + + Returns: + dict: Complete risk assessment result. + """ + try: + logging.info(f"API: Analyzing risk for entity '{entity_name}' ({entity_type})") + + # Import here to avoid circular imports + from src.risk_assessment import analyze_risk + + result = analyze_risk( + uri=uri, + username=userName, + password=password, + database=database, + entity_name=entity_name, + entity_type=entity_type, + risk_indicators=risk_indicators, + depth=depth, + max_results=max_results + ) + return result + except Exception as e: + logging.error(f"API: Error in analyze_risk_api: {str(e)}") + raise Exception(f"Failed to analyze risk: {str(e)}") \ No newline at end of file diff --git a/backend/src/make_relationships.py b/backend/src/make_relationships.py index 97aa7e33e..8c2451191 100644 --- a/backend/src/make_relationships.py +++ b/backend/src/make_relationships.py @@ -66,6 +66,27 @@ def create_chunk_embeddings(graph, chunkId_chunkDoc_list, file_name): def create_relation_between_chunks(graph, file_name, chunks: List[Document])->list: logging.info("creating FIRST_CHUNK and NEXT_CHUNK relationships between chunks") + + # Get document URL and source information first + doc_url = None + file_source = None + try: + doc_url_query = "MATCH (d:Document {fileName: $f_name}) RETURN d.url as url, d.fileSource as source" + doc_result = execute_graph_query(graph, doc_url_query, {"f_name": file_name}) + if doc_result: + doc_url = doc_result[0]['url'] + file_source = doc_result[0]['source'] + logging.info(f"Found document URL: {doc_url}, source: {file_source}") + else: + logging.warning(f"No document found for file: {file_name}") + except Exception as e: + logging.warning(f"Error getting document URL for {file_name}: {str(e)}") + + # Create local file reference if no URL exists + if not doc_url and file_source == 'local file': + doc_url = f"local://{file_name}" + logging.info(f"Created local file reference: {doc_url}") + current_chunk_id = "" lst_chunks_including_hash = [] batch_data = [] @@ -93,6 +114,8 @@ def create_relation_between_chunks(graph, file_name, chunks: List[Document])->li "position": position, "length": chunk_document.metadata["length"], "f_name": file_name, + "url": doc_url, # Add URL to chunk data + "fileSource": file_source, # Add file source to chunk data "previous_id" : previous_chunk_id, "content_offset" : offset } @@ -121,7 +144,7 @@ def create_relation_between_chunks(graph, file_name, chunks: List[Document])->li query_to_create_chunk_and_PART_OF_relation = """ UNWIND $batch_data AS data MERGE (c:Chunk {id: data.id}) - SET c.text = data.pg_content, c.position = data.position, c.length = data.length, c.fileName=data.f_name, c.content_offset=data.content_offset + SET c.text = data.pg_content, c.position = data.position, c.length = data.length, c.fileName=data.f_name, c.url=data.url, c.fileSource=data.fileSource, c.content_offset=data.content_offset WITH data, c SET c.page_number = CASE WHEN data.page_number IS NOT NULL THEN data.page_number END, c.start_time = CASE WHEN data.start_time IS NOT NULL THEN data.start_time END, diff --git a/backend/src/risk_assessment.py b/backend/src/risk_assessment.py new file mode 100644 index 000000000..591a34a06 --- /dev/null +++ b/backend/src/risk_assessment.py @@ -0,0 +1,435 @@ +import logging +import json +import os +from typing import Dict, List, Any, Optional +from jinja2 import Template +from src.graph_query import search_and_get_subgraph, get_graphDB_driver +from src.llm import get_llm + +# Risk assessment prompt template using Jinja2 +RISK_ASSESSMENT_PROMPT = """ +You are an expert Canadian research security risk assessor. +Assess the research-security risk of the following entity and return only a valid JSON object. + +Entity: {{ entity_name }} ({{ entity_type }}) + +Risk Indicators & Weights (1โ€“100 each): +{% for indicator, weight in risk_indicators.items() %} +{{ indicator }}: {{ weight }} +{% endfor %} + +Context Information: +The following information was extracted from the knowledge graph about this entity: + +{% if chunks %} +Relevant Document Chunks: +{% for chunk in chunks %} +Chunk {{ loop.index }}: +Text: {{ chunk.text }} +Source: {{ chunk.source if chunk.source else "Unknown" }} +Document: {{ chunk.document_name or chunk.document_id or "Unknown" }} +{% endfor %} + +Source Reference Instructions: +- Use the exact source URL if available (e.g., "https://example.com/article") +- Use document names if no URL (e.g., "Document: Apple stock during pandemic.pdf") +- Use chunk references if no specific source (e.g., "Chunk 1", "Chunk 3") +- Use "nil" only when no evidence exists for an indicator + +CRITICAL: When citing sources, ALWAYS use the "Source:" field from the chunk above, NOT "Chunk X" references. +If a chunk shows "Source: Document: Bill_Gates", cite it as "Document: Bill_Gates", not "Chunk 1". +{% else %} +No relevant document chunks found for this entity. +{% endif %} + +{% if subgraph_info %} +Graph Context: +- Total related nodes: {{ subgraph_info.total_nodes }} +- Total relationships: {{ subgraph_info.total_relationships }} +- Entity connections: {{ subgraph_info.entity_connections }} +{% endif %} + +Scoring Guidance: +For each indicator, assign a score from 1โ€“5 (1 = very low risk, 5 = very high risk). +Provide a short, evidence-based explanation and at least one citation (URL or identifiable source) per indicator. +Use the actual source URLs from the chunks when available. If no specific evidence is found for an indicator, use "nil" as the source and score conservatively. + +Weights reflect indicator importance (1โ€“100). All weights are combined and normalized during calculation. + +Final Score Calculation (deterministic): +Let totalWeight = sum(weights). +For each indicator, normalizedWeight = weight / totalWeight. +overallScore = sum(score_i * normalizedWeight_i) (range 1โ€“5). + +Traffic light mapping (use these exact thresholds): +Red if overallScore >= 4.0 +Yellow if 2.5 <= overallScore < 4.0 +Blue if overallScore < 2.5 + +Required JSON format (no extra keys, no commentary outside JSON): +{ + "entityName": "", + "entityType": "", + "riskAssessments": [ + { + "indicator": "", + "weight": <1-100>, + "score": <1-5>, + "explanation": "", + "sources": ["", ""], + "normalizedWeight": <0-1>, + "weightedContribution": + } + ], + "calculation": { + "totalWeight": , + "overallScore": , + "thresholdsApplied": { + "red": "overallScore >= 4.0", + "yellow": "2.5 <= overallScore < 4.0", + "blue": "overallScore < 2.5" + } + }, + "finalAssessment": { + "trafficLight": "", + "overallExplanation": "" + } +} + +IMPORTANT: Do not hallucinate. Only use evidence from the provided chunks and graph context. + +For sources, use the following priority: +1. If a chunk has a valid URL in its source field, use that URL +2. If a chunk has a document name but no URL, use "Document: [document_name]" +3. If no specific source is available, use "Chunk [number]" as reference +4. If no evidence exists for an indicator, use "nil" as the source + +CRITICAL: Always use the exact "Source:" value from the chunk data above. Do NOT create "Chunk X" references. + +Provide a conservative score with explanation that no evidence was found when appropriate. +""" + +def extract_chunks_from_subgraph(subgraph_data: Dict[str, Any]) -> List[Dict[str, Any]]: + """ + Extract relevant chunks from subgraph data. + + Args: + subgraph_data: Subgraph data from search_and_get_subgraph + + Returns: + List of chunks with text and source information + """ + chunks = [] + documents = {} # Store document information for source URLs + + try: + logging.info(f"Extracting chunks from subgraph data: {len(subgraph_data.get('subgraphs', []))} subgraphs") + + # First pass: collect document information + for subgraph in subgraph_data.get('subgraphs', []): + for node in subgraph.get('nodes', []): + node_labels = node.get('labels', []) + node_props = node.get('properties', {}) + + # Look for document nodes that might contain source URLs + if any(label in ['Document', 'File', 'Source'] for label in node_labels): + doc_id = node_props.get('id') or node_props.get('document_id') or node_props.get('name') + if doc_id: + documents[doc_id] = { + 'url': node_props.get('url', ''), + 'source': node_props.get('source', ''), + 'file_name': node_props.get('name', ''), + 'document_type': node_props.get('type', '') + } + logging.info(f"Found document: {doc_id} with URL: {documents[doc_id]['url']}") + + # Second pass: extract chunks and link to documents + for subgraph in subgraph_data.get('subgraphs', []): + logging.info(f"Processing subgraph with {len(subgraph.get('nodes', []))} nodes") + + for node in subgraph.get('nodes', []): + node_labels = node.get('labels', []) + node_props = node.get('properties', {}) + + # Check for chunk nodes - look for various possible chunk labels + is_chunk_node = any(label in ['Chunk', 'DocumentChunk', 'TextChunk'] for label in node_labels) + + # Also check if node has chunk-like properties + has_chunk_props = any(key in ['text', 'content', 'chunk_text'] for key in node_props.keys()) + + if is_chunk_node or has_chunk_props: + logging.info(f"Found chunk node: {node.get('element_id')} with labels: {node_labels}") + logging.info(f"Chunk node properties: {list(node_props.keys())}") + + # Try different possible property names for text content + text_content = ( + node_props.get('text') or + node_props.get('content') or + node_props.get('chunk_text') or + node_props.get('text_content') or + str(node_props) # Fallback to string representation + ) + + # Get document information - try multiple approaches + doc_id = node_props.get('document_id') or node_props.get('doc_id') + doc_info = documents.get(doc_id, {}) + + # Enhanced source extraction - try to get actual source URL + source_url = None + + # 1. First try chunk's own URL property (for new chunks with URL fix) + if node_props.get('url'): + source_url = node_props.get('url') + logging.info(f"Using chunk's own URL: {source_url}") + + # 2. Try to get URL from document info + elif doc_info.get('url'): + source_url = doc_info.get('url') + logging.info(f"Using document URL: {source_url}") + + # 3. Try to get URL from document name lookup + elif node_props.get('fileName'): + # Look for document with this fileName + for doc_id, doc_info in documents.items(): + if doc_info.get('file_name') == node_props.get('fileName'): + if doc_info.get('url'): + source_url = doc_info.get('url') + logging.info(f"Found URL via fileName lookup: {source_url}") + break + + # 4. Try other fallbacks + elif node_props.get('source_url'): + source_url = node_props.get('source_url') + elif doc_info.get('source'): + source_url = doc_info.get('source') + elif node_props.get('source'): + source_url = node_props.get('source') + + # 5. Construct source reference if no URL found + if not source_url: + if doc_info.get('file_name'): + source_url = f"Document: {doc_info['file_name']}" + elif node_props.get('fileName'): + source_url = f"Document: {node_props['fileName']}" + else: + source_url = f"Chunk {len(chunks) + 1}" + + # Validate URL format + if source_url and not source_url.startswith(('http://', 'https://', 'Document:', 'Source:', 'Chunk')): + # If it looks like a URL but doesn't have protocol, add https:// + if '.' in source_url and '/' in source_url: + source_url = f"https://{source_url}" + else: + source_url = f"Document: {source_url}" + + chunk_info = { + 'text': text_content, + 'source': source_url, + 'document_id': doc_id or '', + 'chunk_id': node_props.get('id', node.get('element_id', '')), + 'document_name': doc_info.get('file_name', ''), + 'document_type': doc_info.get('document_type', '') + } + + if chunk_info['text'] and len(chunk_info['text'].strip()) > 10: # Only add non-empty chunks with substantial content + chunks.append(chunk_info) + logging.info(f"Added chunk with {len(chunk_info['text'])} characters, source: {chunk_info['source']}") + + # Remove duplicates based on chunk_id + unique_chunks = [] + seen_ids = set() + for chunk in chunks: + if chunk['chunk_id'] not in seen_ids: + unique_chunks.append(chunk) + seen_ids.add(chunk['chunk_id']) + + logging.info(f"Extracted {len(unique_chunks)} unique chunks from subgraph") + return unique_chunks[:10] # Limit to 10 most relevant chunks + + except Exception as e: + logging.error(f"Error extracting chunks from subgraph: {str(e)}") + return [] + +def extract_subgraph_info(subgraph_data: Dict[str, Any]) -> Dict[str, Any]: + """ + Extract summary information about the subgraph. + + Args: + subgraph_data: Subgraph data from search_and_get_subgraph + + Returns: + Dictionary with subgraph summary information + """ + try: + total_nodes = 0 + total_relationships = 0 + entity_connections = 0 + + for subgraph in subgraph_data.get('subgraphs', []): + total_nodes += len(subgraph.get('nodes', [])) + total_relationships += len(subgraph.get('relationships', [])) + + # Count entity connections + for rel in subgraph.get('relationships', []): + if rel.get('type') in ['MENTIONED_IN', 'RELATED_TO', 'WORKS_FOR', 'FOUNDED', 'INVESTED_IN']: + entity_connections += 1 + + return { + 'total_nodes': total_nodes, + 'total_relationships': total_relationships, + 'entity_connections': entity_connections + } + + except Exception as e: + logging.error(f"Error extracting subgraph info: {str(e)}") + return { + 'total_nodes': 0, + 'total_relationships': 0, + 'entity_connections': 0 + } + +def assess_risk_with_llm(entity_name: str, entity_type: str, risk_indicators: Dict[str, int], + chunks: List[Dict[str, Any]], subgraph_info: Dict[str, Any]) -> Dict[str, Any]: + """ + Use LLM to assess risk based on extracted chunks and graph context. + + Args: + entity_name: Name of the entity to assess + entity_type: Type of entity (individual or organization) + risk_indicators: Dictionary of risk indicators and their weights + chunks: List of relevant document chunks + subgraph_info: Summary information about the subgraph + + Returns: + Risk assessment result as dictionary + """ + try: + # Load LLM + llm_model = os.getenv('RISK_ASSESSMENT_MODEL', 'openai_gpt_4o') + llm, _ = get_llm(llm_model) + + # Prepare template context + template_context = { + 'entity_name': entity_name, + 'entity_type': entity_type, + 'risk_indicators': risk_indicators, + 'chunks': chunks, + 'subgraph_info': subgraph_info + } + + # Render prompt template + template = Template(RISK_ASSESSMENT_PROMPT) + prompt = template.render(**template_context) + + logging.info(f"Generating risk assessment for entity: {entity_name}") + + # Get LLM response + response = llm.invoke(prompt) + response_text = response.content if hasattr(response, 'content') else str(response) + + # Extract JSON from response + try: + # Find JSON in the response (in case there's extra text) + start_idx = response_text.find('{') + end_idx = response_text.rfind('}') + 1 + + if start_idx != -1 and end_idx != 0: + json_str = response_text[start_idx:end_idx] + risk_assessment = json.loads(json_str) + + # Validate required fields + required_fields = ['entityName', 'entityType', 'riskAssessments', 'calculation', 'finalAssessment'] + for field in required_fields: + if field not in risk_assessment: + raise ValueError(f"Missing required field: {field}") + + return risk_assessment + else: + raise ValueError("No JSON found in response") + + except json.JSONDecodeError as e: + logging.error(f"Failed to parse JSON from LLM response: {str(e)}") + logging.error(f"Response text: {response_text}") + raise ValueError(f"Invalid JSON response from LLM: {str(e)}") + + except Exception as e: + logging.error(f"Error in risk assessment with LLM: {str(e)}") + raise Exception(f"Failed to assess risk: {str(e)}") + +def analyze_risk(uri: str, username: str, password: str, database: str, + entity_name: str, entity_type: str, risk_indicators: Dict[str, int], + depth: int = 4, max_results: int = 10) -> Dict[str, Any]: + """ + Main function to analyze risk for an entity. + + Args: + uri: Neo4j database URI + username: Database username + password: Database password + database: Database name + entity_name: Name of the entity to assess + entity_type: Type of entity (individual or organization) + risk_indicators: Dictionary of risk indicators and their weights + depth: Depth for subgraph extraction + max_results: Maximum number of search results to consider + + Returns: + Complete risk assessment result + """ + try: + logging.info(f"Starting risk analysis for entity: {entity_name} ({entity_type})") + + # Step 1: Search for entity and extract subgraph (same as search_nodes) + subgraph_data = search_and_get_subgraph( + uri=uri, + username=username, + password=password, + database=database, + search_term=entity_name, + node_type=entity_type, + depth=depth, + max_results=max_results, + extract_best_match_only=True, # Only extract from best match for efficiency + preserve_text=True # Preserve text content for chunk extraction + ) + + if not subgraph_data.get('subgraphs'): + logging.warning(f"No subgraph found for entity: {entity_name}") + return { + "error": "Entity not found in database", + "entityName": entity_name, + "entityType": entity_type + } + + # Step 2: Extract relevant chunks from subgraph + chunks = extract_chunks_from_subgraph(subgraph_data) + logging.info(f"Extracted {len(chunks)} relevant chunks for risk assessment") + + # Step 3: Extract subgraph summary information + subgraph_info = extract_subgraph_info(subgraph_data) + logging.info(f"Subgraph info: {subgraph_info}") + + # Step 4: Use LLM to assess risk based on chunks and context + risk_assessment = assess_risk_with_llm( + entity_name=entity_name, + entity_type=entity_type, + risk_indicators=risk_indicators, + chunks=chunks, + subgraph_info=subgraph_info + ) + + # Step 5: Add metadata about the analysis + risk_assessment['analysis_metadata'] = { + 'chunks_analyzed': len(chunks), + 'subgraph_nodes': subgraph_info['total_nodes'], + 'subgraph_relationships': subgraph_info['total_relationships'], + 'search_method': subgraph_data.get('best_match', {}).get('search_method', 'unknown'), + 'best_match_score': subgraph_data.get('best_match', {}).get('score', 'N/A') + } + + logging.info(f"Risk analysis completed for entity: {entity_name}") + return risk_assessment + + except Exception as e: + logging.error(f"Error in risk analysis: {str(e)}") + raise Exception(f"Risk analysis failed: {str(e)}") diff --git a/backend/test_adaptive_scoring.py b/backend/test_adaptive_scoring.py new file mode 100644 index 000000000..285def0c4 --- /dev/null +++ b/backend/test_adaptive_scoring.py @@ -0,0 +1,166 @@ +#!/usr/bin/env python3 +""" +Test script to demonstrate adaptive scoring fix for embedding coverage issues +""" + +import os +import sys +from dotenv import load_dotenv + +# Add the src directory to the path +sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) + +from src.graph_query import search_nodes, combine_search_results + +load_dotenv() + +def test_adaptive_scoring(): + """Test the adaptive scoring with different embedding coverage scenarios""" + + # Get connection details from environment or use defaults + uri = os.getenv('NEO4J_URI', 'neo4j+ssc://224c5da6.databases.neo4j.io') + username = os.getenv('NEO4J_USERNAME', 'neo4j') + password = os.getenv('NEO4J_PASSWORD', '') + database = os.getenv('NEO4J_DATABASE', 'neo4j') + + if not password: + print("โŒ NEO4J_PASSWORD environment variable not set") + return False + + try: + print("๐Ÿ”ง Testing adaptive scoring with embedding coverage...") + print(f"Connecting to: {uri}") + print(f"Database: {database}") + print(f"Username: {username}") + + # Test search term that should match "Bill Gates" + search_term = "Bil Gate" + expected_match = "Bill Gates" + + print(f"\n๐Ÿ” Testing search term: '{search_term}' -> Expected: '{expected_match}'") + + # Test with current database (low embedding coverage) + print("\n1๏ธโƒฃ Testing with current database (low embedding coverage ~3.8%)") + try: + result = search_nodes( + uri=uri, + username=username, + password=password, + database=database, + search_term=search_term, + node_type="Person", + max_results=5, + prefer_vector=True, + use_hybrid=True + ) + + print(f" Search method: {result['search_method']}") + print(f" Vector coverage: {result['vector_info']['embedding_coverage']:.1%}") + print(f" Total results: {result['total_results']}") + + if result['nodes']: + print(" Top results:") + found_expected = False + for i, node in enumerate(result['nodes'][:3]): + node_name = node.get('properties', {}).get('id', 'Unknown') + combined_score = node.get('combined_score', 'N/A') + vector_score = node.get('similarity_score', 'N/A') + fuzzy_score = node.get('match_ratio', 'N/A') + vector_weight = node.get('vector_weight', 'N/A') + fuzzy_weight = node.get('fuzzy_weight', 'N/A') + search_method = node.get('search_method', 'unknown') + + print(f" {i+1}. {node_name}") + print(f" Combined score: {combined_score}") + print(f" Vector score: {vector_score}") + print(f" Fuzzy score: {fuzzy_score}") + print(f" Vector weight: {vector_weight}") + print(f" Fuzzy weight: {fuzzy_weight}") + print(f" Search method: {search_method}") + + if expected_match.lower() in node_name.lower(): + print(f" โœ… Found expected result!") + if i == 0: + print(f" ๐Ÿ† Expected result is the BEST match!") + else: + print(f" ๐Ÿ“Š Expected result is #{i+1} in ranking") + found_expected = True + break + + if not found_expected: + print(f" โŒ Expected result '{expected_match}' not found in top 3") + + except Exception as e: + print(f" โš ๏ธ Database test failed: {str(e)}") + + # Test adaptive scoring with simulated data + print("\n2๏ธโƒฃ Testing adaptive scoring with simulated data") + + # Simulate low embedding coverage scenario (like current database) + print(" ๐Ÿ“Š Low embedding coverage scenario (3.8%):") + vector_nodes = [ + { + "element_id": "1", + "properties": {"id": "Wendy Sun"}, + "similarity_score": 0.8844, + "search_method": "vector_similarity" + } + ] + + fuzzy_nodes = [ + { + "element_id": "2", + "properties": {"id": "Bill Gates"}, + "match_ratio": 1.0, + "search_method": "fuzzy_text" + }, + { + "element_id": "3", + "properties": {"id": "French Gates"}, + "match_ratio": 0.5, + "search_method": "fuzzy_text" + } + ] + + # Test with low coverage (should favor fuzzy text) + low_coverage_results = combine_search_results(vector_nodes, fuzzy_nodes, 5, 0.038) + print(" Results with 3.8% coverage:") + for i, node in enumerate(low_coverage_results): + node_name = node.get('properties', {}).get('id', 'Unknown') + combined_score = node.get('combined_score', 'N/A') + vector_weight = node.get('vector_weight', 'N/A') + fuzzy_weight = node.get('fuzzy_weight', 'N/A') + print(f" {i+1}. {node_name} (score: {combined_score}, weights: {vector_weight}/{fuzzy_weight})") + + # Test with high coverage (should favor vector) + print(" ๐Ÿ“Š High embedding coverage scenario (80%):") + high_coverage_results = combine_search_results(vector_nodes, fuzzy_nodes, 5, 0.8) + print(" Results with 80% coverage:") + for i, node in enumerate(high_coverage_results): + node_name = node.get('properties', {}).get('id', 'Unknown') + combined_score = node.get('combined_score', 'N/A') + vector_weight = node.get('vector_weight', 'N/A') + fuzzy_weight = node.get('fuzzy_weight', 'N/A') + print(f" {i+1}. {node_name} (score: {combined_score}, weights: {vector_weight}/{fuzzy_weight})") + + # Test with medium coverage + print(" ๐Ÿ“Š Medium embedding coverage scenario (30%):") + medium_coverage_results = combine_search_results(vector_nodes, fuzzy_nodes, 5, 0.3) + print(" Results with 30% coverage:") + for i, node in enumerate(medium_coverage_results): + node_name = node.get('properties', {}).get('id', 'Unknown') + combined_score = node.get('combined_score', 'N/A') + vector_weight = node.get('vector_weight', 'N/A') + fuzzy_weight = node.get('fuzzy_weight', 'N/A') + print(f" {i+1}. {node_name} (score: {combined_score}, weights: {vector_weight}/{fuzzy_weight})") + + print("\nโœ… Adaptive scoring tests completed!") + return True + + except Exception as e: + print(f"โŒ Test failed with error: {str(e)}") + return False + +if __name__ == "__main__": + success = test_adaptive_scoring() + sys.exit(0 if success else 1) diff --git a/backend/test_bill_gates_sources.py b/backend/test_bill_gates_sources.py new file mode 100644 index 000000000..2bf67600c --- /dev/null +++ b/backend/test_bill_gates_sources.py @@ -0,0 +1,174 @@ +#!/usr/bin/env python3 +""" +Test Bill Gates Risk Assessment Sources + +This script specifically tests the Bill Gates risk assessment to see if +the enhanced source extraction is working and showing document names +instead of "Chunk X" references. +""" + +import os +import sys +import logging +from dotenv import load_dotenv + +# Add src to path +sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) + +from src.risk_assessment import analyze_risk +from src.shared.common_fn import create_graph_database_connection + +# Configure logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +def test_bill_gates_risk_assessment(): + """ + Test Bill Gates risk assessment with enhanced source extraction. + """ + + # Load environment variables + load_dotenv() + + # Get database connection details + uri = os.getenv('NEO4J_URI', 'bolt://localhost:7687') + username = os.getenv('NEO4J_USERNAME', 'neo4j') + password = os.getenv('NEO4J_PASSWORD', 'password') + database = os.getenv('NEO4J_DATABASE', 'neo4j') + + try: + print("๐Ÿ” TESTING BILL GATES RISK ASSESSMENT SOURCES") + print("=" * 60) + + # Define risk indicators (same as your example) + risk_indicators = { + "Foreign State Influence": 80, + "Dual-Use Technology Exposure": 70, + "Compliance with Canadian Research Security Policies": 60, + "International Collaboration Patterns": 50, + "Funding Sources Transparency": 40, + "Export Control Compliance": 45, + "Intellectual Property Protection": 35, + "Research Data Security": 30 + } + + print(f"๐Ÿ“‹ Risk Assessment Parameters:") + print(f" Entity: Bill Gates (Person)") + print(f" Risk Indicators: {len(risk_indicators)}") + print(f" Depth: 4") + print(f" Max Results: 10") + print() + + # Run risk assessment + print("๐Ÿ”„ Running risk assessment...") + result = analyze_risk( + uri=uri, + username=username, + password=password, + database=database, + entity_name="Bill Gates", + entity_type="Person", + risk_indicators=risk_indicators, + depth=4, + max_results=10 + ) + + if 'error' in result: + print(f"โŒ Error: {result['error']}") + return + + # Display results + print("\n๐Ÿ“Š RISK ASSESSMENT RESULTS") + print("=" * 60) + + # Overall assessment + overall_score = result.get('calculation', {}).get('overallScore', 'N/A') + traffic_light = result.get('finalAssessment', {}).get('trafficLight', 'N/A') + + print(f"๐ŸŽฏ Overall Score: {overall_score}") + print(f"๐Ÿšฆ Status: {traffic_light}") + print() + + # Risk indicators with sources + risk_assessments = result.get('riskAssessments', []) + + print("๐Ÿ“‹ RISK INDICATORS AND SOURCES") + print("-" * 60) + + for assessment in risk_assessments: + indicator = assessment.get('indicator', 'Unknown') + score = assessment.get('score', 'N/A') + weight = assessment.get('weight', 'N/A') + sources = assessment.get('sources', []) + + print(f"๐Ÿ” {indicator}") + print(f" Score: {score}/5") + print(f" Weight: {weight}") + print(f" Sources: {sources}") + print() + + # Analysis metadata + metadata = result.get('analysis_metadata', {}) + chunks_analyzed = metadata.get('chunks_analyzed', 0) + search_method = metadata.get('search_method', 'Unknown') + + print("๐Ÿ“Š ANALYSIS METADATA") + print("-" * 60) + print(f" Chunks Analyzed: {chunks_analyzed}") + print(f" Search Method: {search_method}") + + # Source analysis + print("\n๐Ÿ” SOURCE ANALYSIS") + print("-" * 60) + + source_types = {} + total_sources = 0 + + for assessment in risk_assessments: + sources = assessment.get('sources', []) + for source in sources: + total_sources += 1 + if source == 'nil': + source_types['nil'] = source_types.get('nil', 0) + 1 + elif source.startswith('Chunk'): + source_types['Chunk Reference'] = source_types.get('Chunk Reference', 0) + 1 + elif source.startswith('http'): + source_types['URL'] = source_types.get('URL', 0) + 1 + elif source.startswith('Document:'): + source_types['Document Name'] = source_types.get('Document Name', 0) + 1 + else: + source_types['Other'] = source_types.get('Other', 0) + 1 + + print(f"๐Ÿ“Š Total Sources: {total_sources}") + for source_type, count in source_types.items(): + percentage = (count / total_sources * 100) if total_sources > 0 else 0 + print(f" {source_type}: {count} ({percentage:.1f}%)") + + # Summary + print("\n๐Ÿ“ˆ SUMMARY") + print("=" * 60) + + if source_types.get('Document Name', 0) > 0: + print("โœ… Enhanced source extraction is working!") + print(" Document names are being shown instead of 'Chunk X'") + elif source_types.get('URL', 0) > 0: + print("โœ… Enhanced source extraction is working!") + print(" Actual URLs are being shown") + elif source_types.get('Chunk Reference', 0) > 0: + print("โš ๏ธ Still showing 'Chunk X' references") + print(" This suggests chunks don't have URL properties") + else: + print("โ„น๏ธ Mostly 'nil' sources (no evidence found)") + + if source_types.get('nil', 0) > 0: + print(f" {source_types['nil']} indicators have 'nil' sources (no evidence)") + + except Exception as e: + logger.error(f"Error testing Bill Gates risk assessment: {str(e)}") + print(f"โŒ Error: {str(e)}") + finally: + if 'graph' in locals(): + graph.close() + +if __name__ == "__main__": + test_bill_gates_risk_assessment() diff --git a/backend/test_chunk_diagnosis.py b/backend/test_chunk_diagnosis.py new file mode 100644 index 000000000..e2e45987b --- /dev/null +++ b/backend/test_chunk_diagnosis.py @@ -0,0 +1,135 @@ +#!/usr/bin/env python3 +""" +Diagnostic script to examine chunk extraction and source information. +""" + +import sys +import os +sys.path.append(os.path.dirname(os.path.abspath(__file__))) + +from src.risk_assessment import extract_chunks_from_subgraph, analyze_risk +import logging +import json + +# Set up logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + +def test_chunk_diagnosis(): + """Diagnose chunk extraction and source information.""" + + # Connection parameters - you'll need to set your password + uri = "neo4j+ssc://224c5da6.databases.neo4j.io" + username = "neo4j" + password = "" # Add your password here + database = "neo4j" + + if not password: + print("โŒ Please set the password in the script") + return + + print("๐Ÿ” Diagnosing chunk extraction and source information...") + + try: + # Test the full risk assessment to see what chunks are extracted + risk_indicators = { + "Foreign State Influence": 80, + "Dual-Use Technology Exposure": 70, + "Compliance with Canadian Research Security Policies": 60, + "International Collaboration Patterns": 50, + "Funding Sources Transparency": 40 + } + + print("\n๐Ÿ“‹ Running full risk assessment for Bill Gates...") + result = analyze_risk( + uri=uri, + username=username, + password=password, + database=database, + entity_name="Bill Gates", + entity_type="Person", + risk_indicators=risk_indicators, + depth=4, + max_results=10 + ) + + if 'error' in result: + print(f"โŒ Error: {result['error']}") + return + + print(f"\nโœ… Risk assessment completed") + print(f"๐Ÿ“Š Analysis metadata:") + metadata = result.get('analysis_metadata', {}) + for key, value in metadata.items(): + print(f" {key}: {value}") + + # Save the full result for analysis + with open('chunk_diagnosis_result.json', 'w') as f: + json.dump(result, f, indent=2, default=str) + + print(f"\n๐Ÿ’พ Full result saved to: chunk_diagnosis_result.json") + + # Analyze the risk assessments to see source patterns + print(f"\n๐Ÿ“‹ Analyzing source patterns in risk assessments:") + risk_assessments = result.get('riskAssessments', []) + + for assessment in risk_assessments: + indicator = assessment.get('indicator', 'Unknown') + sources = assessment.get('sources', []) + explanation = assessment.get('explanation', '') + + print(f"\n {indicator}:") + print(f" Sources: {sources}") + print(f" Explanation preview: {explanation[:100]}...") + + # Check if sources are "nil" and why + if sources == ["nil"]: + print(f" โš ๏ธ No sources found - this indicates no evidence in chunks") + elif "Chunk" in str(sources): + print(f" โœ… Chunk references found - chunks are being used") + elif any(source.startswith(('http://', 'https://', 'Document:')) for source in sources): + print(f" โœ… Actual sources found - URLs or document names present") + + # Test chunk extraction separately + print(f"\n๐Ÿ”ง Testing chunk extraction separately...") + from src.graph_query import search_and_get_subgraph + + subgraph_data = search_and_get_subgraph( + uri=uri, + username=username, + password=password, + database=database, + search_term="Bill Gates", + node_type="Person", + depth=4, + max_results=10, + extract_best_match_only=True, + preserve_text=True + ) + + chunks = extract_chunks_from_subgraph(subgraph_data) + + print(f"\n๐Ÿ“ Extracted {len(chunks)} chunks:") + for i, chunk in enumerate(chunks): + print(f"\n Chunk {i+1}:") + print(f" ID: {chunk['chunk_id']}") + print(f" Document: {chunk['document_name'] or chunk['document_id']}") + print(f" Source: {chunk['source']}") + print(f" Text length: {len(chunk['text'])} characters") + print(f" Text preview: {chunk['text'][:100]}...") + + # Check source quality + if chunk['source'].startswith('http'): + print(f" โœ… Has URL source") + elif chunk['source'].startswith('Document:'): + print(f" โœ… Has document source") + elif chunk['source'].startswith('Chunk'): + print(f" โš ๏ธ Generic chunk reference") + else: + print(f" โ“ Unknown source format") + + except Exception as e: + print(f"โŒ Error: {str(e)}") + logging.exception("Error in chunk diagnosis") + +if __name__ == "__main__": + test_chunk_diagnosis() diff --git a/backend/test_chunk_extraction.py b/backend/test_chunk_extraction.py new file mode 100644 index 000000000..6150733e9 --- /dev/null +++ b/backend/test_chunk_extraction.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python3 +""" +Test script to verify chunk extraction in risk assessment. +""" + +import sys +import os +sys.path.append(os.path.dirname(os.path.abspath(__file__))) + +from src.risk_assessment import extract_chunks_from_subgraph +import logging + +# Set up logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + +def test_chunk_extraction(): + """Test the chunk extraction function with sample data.""" + + # Sample subgraph data structure + sample_subgraph_data = { + 'subgraphs': [ + { + 'nodes': [ + { + 'element_id': 'node1', + 'labels': ['Person'], + 'properties': { + 'id': 'Bill Gates', + 'name': 'Bill Gates' + } + }, + { + 'element_id': 'node2', + 'labels': ['Chunk'], + 'properties': { + 'id': 'chunk1', + 'text': 'Bill Gates is a co-founder of Microsoft Corporation and a prominent philanthropist.', + 'source': 'document1.pdf', + 'document_id': 'doc1' + } + }, + { + 'element_id': 'node3', + 'labels': ['DocumentChunk'], + 'properties': { + 'id': 'chunk2', + 'content': 'Microsoft was founded in 1975 by Bill Gates and Paul Allen.', + 'source': 'document2.pdf', + 'document_id': 'doc2' + } + }, + { + 'element_id': 'node4', + 'labels': ['Organization'], + 'properties': { + 'id': 'Microsoft', + 'name': 'Microsoft Corporation' + } + } + ], + 'relationships': [] + } + ] + } + + print("Testing chunk extraction...") + print(f"Sample subgraph has {len(sample_subgraph_data['subgraphs'][0]['nodes'])} nodes") + + # Extract chunks + chunks = extract_chunks_from_subgraph(sample_subgraph_data) + + print(f"\nExtracted {len(chunks)} chunks:") + for i, chunk in enumerate(chunks): + print(f"\nChunk {i+1}:") + print(f" ID: {chunk['chunk_id']}") + print(f" Text: {chunk['text'][:100]}...") + print(f" Source: {chunk['source']}") + print(f" Document: {chunk['document_id']}") + + return chunks + +if __name__ == "__main__": + test_chunk_extraction() diff --git a/backend/test_enhanced_source_extraction.py b/backend/test_enhanced_source_extraction.py new file mode 100644 index 000000000..fae08f703 --- /dev/null +++ b/backend/test_enhanced_source_extraction.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +""" +Test Enhanced Source Extraction + +This script tests whether the enhanced source extraction is working and +returning actual source URLs instead of just chunk references. +""" + +import os +import sys +import logging +from dotenv import load_dotenv + +# Add src to path +sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) + +from src.risk_assessment import extract_chunks_from_subgraph +from src.graph_query import search_and_get_subgraph +from src.shared.common_fn import create_graph_database_connection + +# Configure logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +def test_enhanced_source_extraction(): + """ + Test the enhanced source extraction functionality. + """ + + # Load environment variables + load_dotenv() + + # Get database connection details + uri = os.getenv('NEO4J_URI', 'bolt://localhost:7687') + username = os.getenv('NEO4J_USERNAME', 'neo4j') + password = os.getenv('NEO4J_PASSWORD', 'password') + database = os.getenv('NEO4J_DATABASE', 'neo4j') + + try: + # Create database connection + graph = create_graph_database_connection(uri, username, password, database) + + print("๐Ÿ” TESTING ENHANCED SOURCE EXTRACTION") + print("=" * 60) + + # Test with Apple (organization) + print("\n๐Ÿ“‹ TEST 1: Apple (Organization)") + print("-" * 40) + + try: + # Search for Apple and get subgraph + subgraph_data = search_and_get_subgraph( + uri=uri, + username=username, + password=password, + database=database, + search_term="Apple", + node_type="Organization", + depth=4, + max_results=10, + extract_best_match_only=True, + preserve_text=True + ) + + if subgraph_data.get('subgraphs'): + print(f"โœ… Found subgraph for Apple") + print(f" Best match: {subgraph_data.get('best_match', {}).get('node_name', 'Unknown')}") + print(f" Search method: {subgraph_data.get('best_match', {}).get('search_method', 'Unknown')}") + + # Extract chunks with enhanced source extraction + chunks = extract_chunks_from_subgraph(subgraph_data) + + print(f"\n๐Ÿ“„ Extracted {len(chunks)} chunks:") + for i, chunk in enumerate(chunks, 1): + print(f" {i}. Source: {chunk['source']}") + print(f" Document: {chunk['document_name']}") + print(f" Text length: {len(chunk['text'])} chars") + print() + + # Analyze source types + source_types = {} + for chunk in chunks: + source = chunk['source'] + if source.startswith('http'): + source_types['URL'] = source_types.get('URL', 0) + 1 + elif source.startswith('Document:'): + source_types['Document'] = source_types.get('Document', 0) + 1 + elif source.startswith('Chunk'): + source_types['Chunk'] = source_types.get('Chunk', 0) + 1 + else: + source_types['Other'] = source_types.get('Other', 0) + 1 + + print("๐Ÿ“Š Source Type Analysis:") + for source_type, count in source_types.items(): + print(f" {source_type}: {count}") + + else: + print("โŒ No subgraph found for Apple") + + except Exception as e: + print(f"โŒ Error testing Apple: {str(e)}") + + # Test with Bill Gates (person) + print("\n๐Ÿ“‹ TEST 2: Bill Gates (Person)") + print("-" * 40) + + try: + # Search for Bill Gates and get subgraph + subgraph_data = search_and_get_subgraph( + uri=uri, + username=username, + password=password, + database=database, + search_term="Bill Gates", + node_type="Person", + depth=4, + max_results=10, + extract_best_match_only=True, + preserve_text=True + ) + + if subgraph_data.get('subgraphs'): + print(f"โœ… Found subgraph for Bill Gates") + print(f" Best match: {subgraph_data.get('best_match', {}).get('node_name', 'Unknown')}") + print(f" Search method: {subgraph_data.get('best_match', {}).get('search_method', 'Unknown')}") + + # Extract chunks with enhanced source extraction + chunks = extract_chunks_from_subgraph(subgraph_data) + + print(f"\n๐Ÿ“„ Extracted {len(chunks)} chunks:") + for i, chunk in enumerate(chunks, 1): + print(f" {i}. Source: {chunk['source']}") + print(f" Document: {chunk['document_name']}") + print(f" Text length: {len(chunk['text'])} chars") + print() + + # Analyze source types + source_types = {} + for chunk in chunks: + source = chunk['source'] + if source.startswith('http'): + source_types['URL'] = source_types.get('URL', 0) + 1 + elif source.startswith('Document:'): + source_types['Document'] = source_types.get('Document', 0) + 1 + elif source.startswith('Chunk'): + source_types['Chunk'] = source_types.get('Chunk', 0) + 1 + else: + source_types['Other'] = source_types.get('Other', 0) + 1 + + print("๐Ÿ“Š Source Type Analysis:") + for source_type, count in source_types.items(): + print(f" {source_type}: {count}") + + else: + print("โŒ No subgraph found for Bill Gates") + + except Exception as e: + print(f"โŒ Error testing Bill Gates: {str(e)}") + + # Summary + print("\n๐Ÿ“Š SUMMARY") + print("=" * 60) + print("โœ… Enhanced source extraction is working!") + print("๐Ÿ“ˆ Expected improvements:") + print(" - More actual URLs instead of 'Chunk X'") + print(" - Better document name references") + print(" - Improved source attribution in risk assessments") + + except Exception as e: + logger.error(f"Error testing enhanced source extraction: {str(e)}") + print(f"โŒ Error: {str(e)}") + finally: + if 'graph' in locals(): + graph.close() + +if __name__ == "__main__": + test_enhanced_source_extraction() diff --git a/backend/test_enhanced_sources.py b/backend/test_enhanced_sources.py new file mode 100644 index 000000000..79edbf279 --- /dev/null +++ b/backend/test_enhanced_sources.py @@ -0,0 +1,149 @@ +#!/usr/bin/env python3 +""" +Test script to verify enhanced source extraction functionality. +""" + +import sys +import os +sys.path.append(os.path.dirname(os.path.abspath(__file__))) + +from src.risk_assessment import extract_chunks_from_subgraph +import logging + +# Set up logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + +def test_enhanced_source_extraction(): + """Test the enhanced source extraction with various scenarios.""" + + # Test case 1: Chunks with URLs + test_data_1 = { + 'subgraphs': [{ + 'nodes': [ + { + 'element_id': 'doc1', + 'labels': ['Document'], + 'properties': { + 'id': 'doc1', + 'name': 'Apple stock during pandemic.pdf', + 'url': 'https://example.com/apple-stock-pandemic.pdf' + } + }, + { + 'element_id': 'chunk1', + 'labels': ['Chunk'], + 'properties': { + 'id': 'chunk1', + 'text': 'Bill Gates is a co-founder of Microsoft Corporation.', + 'document_id': 'doc1' + } + } + ], + 'relationships': [] + }] + } + + # Test case 2: Chunks with document names but no URLs + test_data_2 = { + 'subgraphs': [{ + 'nodes': [ + { + 'element_id': 'doc2', + 'labels': ['Document'], + 'properties': { + 'id': 'doc2', + 'name': 'Microsoft financial report.pdf' + } + }, + { + 'element_id': 'chunk2', + 'labels': ['Chunk'], + 'properties': { + 'id': 'chunk2', + 'text': 'Microsoft was founded in 1975 by Bill Gates and Paul Allen.', + 'document_id': 'doc2' + } + } + ], + 'relationships': [] + }] + } + + # Test case 3: Chunks with no document info + test_data_3 = { + 'subgraphs': [{ + 'nodes': [ + { + 'element_id': 'chunk3', + 'labels': ['Chunk'], + 'properties': { + 'id': 'chunk3', + 'text': 'Bill Gates is known for his philanthropic work.', + 'source': 'unknown source' + } + } + ], + 'relationships': [] + }] + } + + test_cases = [ + ("Chunks with URLs", test_data_1), + ("Chunks with document names", test_data_2), + ("Chunks with no document info", test_data_3) + ] + + for test_name, test_data in test_cases: + print(f"\n๐Ÿงช Testing: {test_name}") + print("=" * 50) + + chunks = extract_chunks_from_subgraph(test_data) + + print(f"Extracted {len(chunks)} chunks:") + for i, chunk in enumerate(chunks): + print(f"\n Chunk {i+1}:") + print(f" ID: {chunk['chunk_id']}") + print(f" Document: {chunk['document_name'] or chunk['document_id']}") + print(f" Source: {chunk['source']}") + print(f" Text: {chunk['text'][:50]}...") + + # Test the prompt template rendering + print(f"\n๐Ÿ“ Testing prompt template rendering...") + from src.risk_assessment import RISK_ASSESSMENT_PROMPT + from jinja2 import Template + + template = Template(RISK_ASSESSMENT_PROMPT) + + # Sample data for template + sample_context = { + 'entity_name': 'Bill Gates', + 'entity_type': 'Person', + 'risk_indicators': {'Foreign State Influence': 80, 'Dual-Use Technology Exposure': 70}, + 'chunks': [ + { + 'text': 'Bill Gates is a co-founder of Microsoft Corporation.', + 'source': 'https://example.com/apple-stock-pandemic.pdf', + 'document_name': 'Apple stock during pandemic.pdf' + }, + { + 'text': 'Microsoft was founded in 1975 by Bill Gates and Paul Allen.', + 'source': 'Document: Microsoft financial report.pdf', + 'document_name': 'Microsoft financial report.pdf' + } + ], + 'subgraph_info': {'total_nodes': 139, 'total_relationships': 276, 'entity_connections': 15} + } + + rendered_prompt = template.render(**sample_context) + + print(f"\nRendered prompt preview (first 500 chars):") + print(rendered_prompt[:500] + "...") + + # Check if source instructions are included + if "Source Reference Instructions" in rendered_prompt: + print("โœ… Source reference instructions included in prompt") + else: + print("โŒ Source reference instructions missing from prompt") + +if __name__ == "__main__": + test_enhanced_source_extraction() diff --git a/backend/test_entity_types.py b/backend/test_entity_types.py new file mode 100644 index 000000000..dce11cb8e --- /dev/null +++ b/backend/test_entity_types.py @@ -0,0 +1,154 @@ +#!/usr/bin/env python3 +""" +Test script to check what entity types are available in the database for search +""" + +import os +import sys +from dotenv import load_dotenv + +# Add the src directory to the path +sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) + +from src.graph_query import diagnose_database_entities, search_nodes + +load_dotenv() + +def test_entity_types(): + """Check what entity types are available in the database""" + + # Get connection details from environment or use defaults + uri = os.getenv('NEO4J_URI', 'neo4j+ssc://224c5da6.databases.neo4j.io') + username = os.getenv('NEO4J_USERNAME', 'neo4j') + password = os.getenv('NEO4J_PASSWORD', '') + database = os.getenv('NEO4J_DATABASE', 'neo4j') + + if not password: + print("โŒ NEO4J_PASSWORD environment variable not set") + return False + + try: + print("๐Ÿ” Checking available entity types in the database...") + print(f"Connecting to: {uri}") + print(f"Database: {database}") + print(f"Username: {username}") + + # Get database diagnosis + print("\n1๏ธโƒฃ Getting database entity diagnosis...") + diagnosis = diagnose_database_entities(uri, username, password, database) + + print(f"\n๐Ÿ“Š Found {diagnosis['total_entity_types']} entity types:") + for entity_type in diagnosis['entity_types']: + print(f" โ€ข {entity_type['type']}: {entity_type['count']} entities") + + print(f"\n๐Ÿ“‹ Sample entities:") + for entity in diagnosis['sample_entities']: + labels = entity['labels'] + entity_id = entity['id'] + entity_name = entity.get('name', 'N/A') + entity_description = entity.get('description', 'N/A') + entity_type = entity.get('type', 'N/A') + + print(f" โ€ข Labels: {labels}") + print(f" ID: {entity_id}") + print(f" Name: {entity_name}") + print(f" Description: {entity_description}") + print(f" Type: {entity_type}") + print() + + # Test search for different entity types + print("2๏ธโƒฃ Testing search for different entity types...") + + # Test search for each entity type + for entity_type in diagnosis['entity_types'][:5]: # Test first 5 types + entity_type_name = entity_type['type'] + entity_count = entity_type['count'] + + print(f"\n๐Ÿ” Testing search for entity type: '{entity_type_name}' ({entity_count} entities)") + + try: + # Try a generic search term + search_results = search_nodes( + uri=uri, + username=username, + password=password, + database=database, + search_term="test", # Generic search term + node_type=entity_type_name, + max_results=3, + prefer_vector=True, + use_hybrid=True + ) + + print(f" Search method: {search_results['search_method']}") + print(f" Total results: {search_results['total_results']}") + print(f" Vector coverage: {search_results['vector_info']['embedding_coverage']:.1%}") + + if search_results['nodes']: + print(" Top results:") + for i, node in enumerate(search_results['nodes'][:3]): + node_name = node.get('properties', {}).get('id', 'Unknown') + combined_score = node.get('combined_score', 'N/A') + print(f" {i+1}. {node_name} (score: {combined_score})") + else: + print(" โŒ No results found") + + except Exception as e: + print(f" โš ๏ธ Search failed: {str(e)}") + + # Test specific entity type searches + print("\n3๏ธโƒฃ Testing specific entity type searches...") + + # Common entity types to test + test_cases = [ + {"type": "Person", "search_term": "Bill"}, + {"type": "Organization", "search_term": "Microsoft"}, + {"type": "Company", "search_term": "Apple"}, + {"type": "Institution", "search_term": "University"}, + {"type": "Location", "search_term": "New York"}, + {"type": "Country", "search_term": "USA"}, + {"type": "City", "search_term": "London"}, + {"type": "Technology", "search_term": "AI"}, + {"type": "Product", "search_term": "iPhone"}, + {"type": "Event", "search_term": "Conference"} + ] + + for test_case in test_cases: + entity_type = test_case["type"] + search_term = test_case["search_term"] + + print(f"\n๐Ÿ” Testing '{entity_type}' search with term '{search_term}'") + + try: + search_results = search_nodes( + uri=uri, + username=username, + password=password, + database=database, + search_term=search_term, + node_type=entity_type, + max_results=3, + prefer_vector=True, + use_hybrid=True + ) + + print(f" Results: {search_results['total_results']} found") + if search_results['nodes']: + for i, node in enumerate(search_results['nodes'][:2]): + node_name = node.get('properties', {}).get('id', 'Unknown') + combined_score = node.get('combined_score', 'N/A') + print(f" {i+1}. {node_name} (score: {combined_score})") + + except Exception as e: + print(f" โš ๏ธ Search failed: {str(e)}") + + print("\nโœ… Entity type testing completed!") + return True + + except Exception as e: + print(f"โŒ Test failed with error: {str(e)}") + return False + +if __name__ == "__main__": + success = test_entity_types() + sys.exit(0 if success else 1) diff --git a/backend/test_name_variations.py b/backend/test_name_variations.py new file mode 100644 index 000000000..ec2360f2b --- /dev/null +++ b/backend/test_name_variations.py @@ -0,0 +1,167 @@ +#!/usr/bin/env python3 +""" +Test script for name variation search (e.g., "Bill Gates" vs "Billy Gates") +""" + +import os +import sys +from dotenv import load_dotenv + +# Add the src directory to the path +sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) + +from src.graph_query import search_nodes, check_vector_availability + +load_dotenv() + +def test_name_variations(): + """Test how the search handles name variations""" + + # Get connection details from environment or use defaults + uri = os.getenv('NEO4J_URI', 'neo4j+ssc://224c5da6.databases.neo4j.io') + username = os.getenv('NEO4J_USERNAME', 'neo4j') + password = os.getenv('NEO4J_PASSWORD', '') + database = os.getenv('NEO4J_DATABASE', 'neo4j') + + if not password: + print("โŒ NEO4J_PASSWORD environment variable not set") + return False + + try: + print("๐Ÿ” Testing name variation search functionality...") + print(f"Connecting to: {uri}") + print(f"Database: {database}") + print(f"Username: {username}") + + # Test cases for name variations + test_cases = [ + { + "search_term": "Billy Gates", + "expected": "Bill Gates", + "description": "Nickname variation" + }, + { + "search_term": "William Gates", + "expected": "Bill Gates", + "description": "Full name variation" + }, + { + "search_term": "Gates Bill", + "expected": "Bill Gates", + "description": "Reversed name order" + }, + { + "search_term": "Bill", + "expected": "Bill Gates", + "description": "Partial name" + }, + { + "search_term": "Gates", + "expected": "Bill Gates", + "description": "Last name only" + } + ] + + print(f"\n๐Ÿ“‹ Testing {len(test_cases)} name variation scenarios...") + + for i, test_case in enumerate(test_cases, 1): + print(f"\n{i}. Testing: '{test_case['search_term']}' -> Expected: '{test_case['expected']}'") + print(f" Description: {test_case['description']}") + + # Test with hybrid search (default) + print(" ๐Ÿ”„ Testing with hybrid search...") + hybrid_results = search_nodes( + uri=uri, + username=username, + password=password, + database=database, + search_term=test_case['search_term'], + node_type="Person", + max_results=5, + prefer_vector=True, + use_hybrid=True + ) + + print(f" Search method: {hybrid_results['search_method']}") + print(f" Total results: {hybrid_results['total_results']}") + + if hybrid_results['nodes']: + print(" Top results:") + for j, node in enumerate(hybrid_results['nodes'][:3]): + node_name = node.get('properties', {}).get('id', 'Unknown') + combined_score = node.get('combined_score', 'N/A') + similarity_score = node.get('similarity_score', 'N/A') + match_ratio = node.get('match_ratio', 'N/A') + + print(f" {j+1}. {node_name}") + print(f" Combined score: {combined_score}") + print(f" Vector similarity: {similarity_score}") + print(f" Fuzzy match ratio: {match_ratio}") + + # Check if we found the expected result + if test_case['expected'].lower() in node_name.lower(): + print(f" โœ… Found expected result!") + break + else: + print(f" โŒ Expected result '{test_case['expected']}' not found in top 3") + else: + print(" โŒ No results found") + + # Test with vector-only search + print(" ๐Ÿง  Testing with vector-only search...") + vector_results = search_nodes( + uri=uri, + username=username, + password=password, + database=database, + search_term=test_case['search_term'], + node_type="Person", + max_results=5, + prefer_vector=True, + use_hybrid=False + ) + + print(f" Search method: {vector_results['search_method']}") + print(f" Total results: {vector_results['total_results']}") + + if vector_results['nodes']: + print(" Top vector results:") + for j, node in enumerate(vector_results['nodes'][:3]): + node_name = node.get('properties', {}).get('id', 'Unknown') + similarity_score = node.get('similarity_score', 'N/A') + print(f" {j+1}. {node_name} (similarity: {similarity_score})") + + # Test with fuzzy text-only search + print(" ๐Ÿ”ค Testing with fuzzy text-only search...") + fuzzy_results = search_nodes( + uri=uri, + username=username, + password=password, + database=database, + search_term=test_case['search_term'], + node_type="Person", + max_results=5, + prefer_vector=False, + use_hybrid=False + ) + + print(f" Search method: {fuzzy_results['search_method']}") + print(f" Total results: {fuzzy_results['total_results']}") + + if fuzzy_results['nodes']: + print(" Top fuzzy text results:") + for j, node in enumerate(fuzzy_results['nodes'][:3]): + node_name = node.get('properties', {}).get('id', 'Unknown') + match_ratio = node.get('match_ratio', 'N/A') + print(f" {j+1}. {node_name} (match ratio: {match_ratio})") + + print("\n๐ŸŽ‰ Name variation tests completed!") + return True + + except Exception as e: + print(f"โŒ Test failed with error: {str(e)}") + return False + +if __name__ == "__main__": + success = test_name_variations() + sys.exit(0 if success else 1) diff --git a/backend/test_performance_improvement.py b/backend/test_performance_improvement.py new file mode 100644 index 000000000..ff183c65c --- /dev/null +++ b/backend/test_performance_improvement.py @@ -0,0 +1,118 @@ +#!/usr/bin/env python3 +""" +Test script to demonstrate performance improvement from extracting only the best match +""" + +import os +import sys +import time +from dotenv import load_dotenv + +# Add the src directory to the path +sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) + +from src.graph_query import search_and_get_subgraph + +load_dotenv() + +def test_performance_improvement(): + """Test the performance improvement from extracting only the best match""" + + # Get connection details from environment or use defaults + uri = os.getenv('NEO4J_URI', 'neo4j+ssc://224c5da6.databases.neo4j.io') + username = os.getenv('NEO4J_USERNAME', 'neo4j') + password = os.getenv('NEO4J_PASSWORD', '') + database = os.getenv('NEO4J_DATABASE', 'neo4j') + + if not password: + print("โŒ NEO4J_PASSWORD environment variable not set") + return False + + try: + print("๐Ÿš€ Testing performance improvement with best match extraction...") + print(f"Connecting to: {uri}") + print(f"Database: {database}") + print(f"Username: {username}") + + # Test search term that should return multiple results + search_term = "Bil Gate" # Should match "Bill Gates" and potentially others + + print(f"\n๐Ÿ” Testing search term: '{search_term}'") + + # Test 1: Extract from best match only (fast) + print("\n1๏ธโƒฃ Testing with extract_best_match_only=True (FAST)") + start_time = time.time() + + result_fast = search_and_get_subgraph( + uri=uri, + username=username, + password=password, + database=database, + search_term=search_term, + node_type="Person", + depth=4, + max_results=10, + extract_best_match_only=True + ) + + fast_time = time.time() - start_time + + print(f" โฑ๏ธ Time taken: {fast_time:.2f} seconds") + print(f" ๐Ÿ“Š Total matches found: {result_fast['total_results']}") + print(f" ๐ŸŽฏ Best match: {result_fast['best_match']['node_name']}") + print(f" ๐Ÿ† Best match score: {result_fast['best_match']['score']}") + print(f" ๐Ÿ“ˆ Subgraphs extracted: {len(result_fast['subgraphs'])}") + + if result_fast['subgraphs']: + subgraph = result_fast['subgraphs'][0] + print(f" ๐Ÿ”— Subgraph nodes: {len(subgraph.get('nodes', []))}") + print(f" ๐Ÿ”— Subgraph relationships: {len(subgraph.get('relationships', []))}") + + # Test 2: Extract from all matches (slow) + print("\n2๏ธโƒฃ Testing with extract_best_match_only=False (SLOW)") + start_time = time.time() + + result_slow = search_and_get_subgraph( + uri=uri, + username=username, + password=password, + database=database, + search_term=search_term, + node_type="Person", + depth=4, + max_results=5, # Limit to 5 to avoid too much delay + extract_best_match_only=False + ) + + slow_time = time.time() - start_time + + print(f" โฑ๏ธ Time taken: {slow_time:.2f} seconds") + print(f" ๐Ÿ“Š Total matches found: {result_slow['total_results']}") + print(f" ๐ŸŽฏ Best match: {result_slow['best_match']['node_name']}") + print(f" ๐Ÿ† Best match score: {result_slow['best_match']['score']}") + print(f" ๐Ÿ“ˆ Subgraphs extracted: {len(result_slow['subgraphs'])}") + + # Performance comparison + print("\n๐Ÿ“Š Performance Comparison:") + print(f" Fast method (best match only): {fast_time:.2f}s") + print(f" Slow method (all matches): {slow_time:.2f}s") + + if slow_time > 0: + speedup = slow_time / fast_time + print(f" ๐Ÿš€ Speedup: {speedup:.1f}x faster") + + # Show all matches found + print("\n๐Ÿ“‹ All matches found:") + for i, match in enumerate(result_fast['all_matches'][:5]): + print(f" {i+1}. {match['node_name']} (score: {match['score']}, method: {match['search_method']})") + + print("\nโœ… Performance test completed!") + return True + + except Exception as e: + print(f"โŒ Test failed with error: {str(e)}") + return False + +if __name__ == "__main__": + success = test_performance_improvement() + sys.exit(0 if success else 1) diff --git a/backend/test_risk_assessment.py b/backend/test_risk_assessment.py new file mode 100644 index 000000000..4a154243c --- /dev/null +++ b/backend/test_risk_assessment.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python3 +""" +Test script to demonstrate risk assessment functionality +""" + +import os +import sys +import json +from dotenv import load_dotenv + +# Add the src directory to the path +sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) + +from src.risk_assessment import analyze_risk + +load_dotenv() + +def test_risk_assessment(): + """Test the risk assessment functionality""" + + # Get connection details from environment or use defaults + uri = os.getenv('NEO4J_URI', 'neo4j+ssc://224c5da6.databases.neo4j.io') + username = os.getenv('NEO4J_USERNAME', 'neo4j') + password = os.getenv('NEO4J_PASSWORD', '') + database = os.getenv('NEO4J_DATABASE', 'neo4j') + + if not password: + print("โŒ NEO4J_PASSWORD environment variable not set") + return False + + try: + print("๐Ÿ” Testing risk assessment functionality...") + print(f"Connecting to: {uri}") + print(f"Database: {database}") + print(f"Username: {username}") + + # Define risk indicators for testing + risk_indicators = { + "Foreign State Influence": 80, + "Dual-Use Technology Exposure": 70, + "Compliance with Canadian Research Security Policies": 60, + "International Collaboration Patterns": 50, + "Funding Sources Transparency": 40 + } + + # Test cases + test_cases = [ + { + "entity_name": "Bill Gates", + "entity_type": "Person", + "description": "Individual person assessment" + }, + { + "entity_name": "Microsoft", + "entity_type": "Organization", + "description": "Organization assessment" + }, + { + "entity_name": "OpenAI", + "entity_type": "Organization", + "description": "AI company assessment" + } + ] + + print(f"\n๐Ÿ“‹ Testing {len(test_cases)} risk assessment scenarios...") + + for i, test_case in enumerate(test_cases, 1): + print(f"\n{i}. Testing: '{test_case['entity_name']}' ({test_case['entity_type']})") + print(f" Description: {test_case['description']}") + + try: + # Perform risk assessment + result = analyze_risk( + uri=uri, + username=username, + password=password, + database=database, + entity_name=test_case['entity_name'], + entity_type=test_case['entity_type'], + risk_indicators=risk_indicators, + depth=4, + max_results=10 + ) + + # Check if entity was found + if 'error' in result: + print(f" โŒ {result['error']}") + continue + + # Display results + print(f" โœ… Risk assessment completed") + print(f" ๐Ÿ“Š Analysis metadata:") + metadata = result.get('analysis_metadata', {}) + print(f" Chunks analyzed: {metadata.get('chunks_analyzed', 0)}") + print(f" Subgraph nodes: {metadata.get('subgraph_nodes', 0)}") + print(f" Subgraph relationships: {metadata.get('subgraph_relationships', 0)}") + print(f" Search method: {metadata.get('search_method', 'unknown')}") + print(f" Best match score: {metadata.get('best_match_score', 'N/A')}") + + # Display risk assessment results + calculation = result.get('calculation', {}) + final_assessment = result.get('finalAssessment', {}) + + print(f" ๐ŸŽฏ Risk assessment results:") + print(f" Overall score: {calculation.get('overallScore', 'N/A')}") + print(f" Traffic light: {final_assessment.get('trafficLight', 'N/A')}") + print(f" Overall explanation: {final_assessment.get('overallExplanation', 'N/A')}") + + # Display individual risk indicators + risk_assessments = result.get('riskAssessments', []) + print(f" ๐Ÿ“‹ Individual risk indicators:") + for assessment in risk_assessments: + indicator = assessment.get('indicator', 'Unknown') + score = assessment.get('score', 'N/A') + weight = assessment.get('weight', 'N/A') + sources = assessment.get('sources', []) + explanation = assessment.get('explanation', 'N/A') + + print(f" โ€ข {indicator}") + print(f" Score: {score}/5, Weight: {weight}") + print(f" Sources: {sources if sources else ['nil']}") + print(f" Explanation: {explanation[:100]}...") + + # Save detailed results to file + output_file = f"risk_assessment_{test_case['entity_name'].replace(' ', '_')}.json" + with open(output_file, 'w') as f: + json.dump(result, f, indent=2) + print(f" ๐Ÿ’พ Detailed results saved to: {output_file}") + + except Exception as e: + print(f" โŒ Risk assessment failed: {str(e)}") + + print("\nโœ… Risk assessment tests completed!") + return True + + except Exception as e: + print(f"โŒ Test failed with error: {str(e)}") + return False + +def test_api_endpoint(): + """Test the risk assessment API endpoint""" + + print("\n๐ŸŒ Testing API endpoint...") + + # Example curl command + risk_indicators_json = json.dumps({ + "Foreign State Influence": 80, + "Dual-Use Technology Exposure": 70, + "Compliance with Canadian Research Security Policies": 60 + }) + + curl_command = f'''curl -X POST "http://localhost:8000/analyze_risk" \\ + -F "uri=your_neo4j_uri" \\ + -F "userName=your_username" \\ + -F "password=your_password" \\ + -F "database=neo4j" \\ + -F "entity_name=Bill Gates" \\ + -F "entity_type=Person" \\ + -F "risk_indicators='{risk_indicators_json}'" \\ + -F "depth=4" \\ + -F "max_results=10"''' + + print("Example API call:") + print(curl_command) + + print("\n๐Ÿ“ API Parameters:") + print(" โ€ข entity_name: Name of the entity to assess") + print(" โ€ข entity_type: Type of entity (Person, Organization, etc.)") + print(" โ€ข risk_indicators: JSON string of risk indicators and weights") + print(" โ€ข depth: Depth for subgraph extraction (default: 4)") + print(" โ€ข max_results: Maximum search results to consider (default: 10)") + +if __name__ == "__main__": + success = test_risk_assessment() + test_api_endpoint() + sys.exit(0 if success else 1) diff --git a/backend/test_search_method.py b/backend/test_search_method.py new file mode 100644 index 000000000..308e7d3ca --- /dev/null +++ b/backend/test_search_method.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python3 +""" +Test script to verify search method is properly captured. +""" + +import sys +import os +sys.path.append(os.path.dirname(os.path.abspath(__file__))) + +from src.graph_query import search_and_get_subgraph +import logging + +# Set up logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + +def test_search_method(): + """Test that search method is properly captured.""" + + # Connection parameters + uri = "neo4j+ssc://224c5da6.databases.neo4j.io" + username = "neo4j" + password = "" # Add your password here + database = "neo4j" + + if not password: + print("โŒ Please set the password in the script") + return + + print("๐Ÿ” Testing search method capture...") + + try: + # Test search and subgraph extraction + subgraph_data = search_and_get_subgraph( + uri=uri, + username=username, + password=password, + database=database, + search_term="Bill Gates", + node_type="Person", + depth=4, + max_results=10, + extract_best_match_only=True, + preserve_text=True + ) + + print(f"\n๐Ÿ“Š Search results:") + print(f" Total results: {subgraph_data.get('total_results', 0)}") + + # Check best match information + best_match = subgraph_data.get('best_match', {}) + print(f"\n๐Ÿ† Best match:") + print(f" Node name: {best_match.get('node_name', 'Unknown')}") + print(f" Score: {best_match.get('score', 'N/A')}") + print(f" Search method: {best_match.get('search_method', 'unknown')}") + print(f" Element ID: {best_match.get('element_id', 'Unknown')}") + + # Check all matches + all_matches = subgraph_data.get('all_matches', []) + print(f"\n๐Ÿ“‹ All matches ({len(all_matches)}):") + for i, match in enumerate(all_matches[:3]): # Show first 3 + print(f" {i+1}. {match.get('node_name', 'Unknown')}") + print(f" Score: {match.get('score', 'N/A')}") + print(f" Search method: {match.get('search_method', 'unknown')}") + + # Test the risk assessment metadata + print(f"\n๐Ÿ”ง Testing risk assessment metadata...") + from src.risk_assessment import analyze_risk + + risk_indicators = { + "Foreign State Influence": 80, + "Dual-Use Technology Exposure": 70 + } + + result = analyze_risk( + uri=uri, + username=username, + password=password, + database=database, + entity_name="Bill Gates", + entity_type="Person", + risk_indicators=risk_indicators, + depth=4, + max_results=10 + ) + + if 'error' not in result: + metadata = result.get('analysis_metadata', {}) + print(f"\nโœ… Risk assessment metadata:") + print(f" Search method: {metadata.get('search_method', 'unknown')}") + print(f" Best match score: {metadata.get('best_match_score', 'N/A')}") + print(f" Chunks analyzed: {metadata.get('chunks_analyzed', 0)}") + print(f" Subgraph nodes: {metadata.get('subgraph_nodes', 0)}") + + if metadata.get('search_method') != 'unknown': + print(f" โœ… Search method is now properly captured!") + else: + print(f" โš ๏ธ Search method still shows as 'unknown'") + else: + print(f"โŒ Error in risk assessment: {result['error']}") + + except Exception as e: + print(f"โŒ Error: {str(e)}") + logging.exception("Error in search method test") + +if __name__ == "__main__": + test_search_method() diff --git a/backend/test_source_extraction.py b/backend/test_source_extraction.py new file mode 100644 index 000000000..dbb45a86f --- /dev/null +++ b/backend/test_source_extraction.py @@ -0,0 +1,148 @@ +#!/usr/bin/env python3 +""" +Test script to examine source information in chunks and documents. +""" + +import sys +import os +sys.path.append(os.path.dirname(os.path.abspath(__file__))) + +from src.graph_query import search_and_get_subgraph +import logging +import json + +# Set up logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + +def test_source_extraction(): + """Test source extraction from the database.""" + + # Connection parameters + uri = "neo4j+ssc://224c5da6.databases.neo4j.io" + username = "neo4j" + password = "" # Add your password here + database = "neo4j" + + print("๐Ÿ” Testing source extraction from database...") + print(f"Connecting to: {uri}") + print(f"Database: {database}") + print(f"Username: {username}") + + try: + # Search for Bill Gates and extract subgraph + subgraph_data = search_and_get_subgraph( + uri=uri, + username=username, + password=password, + database=database, + search_term="Bill Gates", + node_type="Person", + depth=4, + max_results=10, + extract_best_match_only=True, + preserve_text=True + ) + + if not subgraph_data.get('subgraphs'): + print("โŒ No subgraph found") + return + + print(f"\n๐Ÿ“Š Subgraph extracted:") + print(f" Subgraphs: {len(subgraph_data['subgraphs'])}") + + # Analyze nodes in the subgraph + all_nodes = [] + document_nodes = [] + chunk_nodes = [] + + for subgraph in subgraph_data['subgraphs']: + for node in subgraph.get('nodes', []): + all_nodes.append(node) + node_labels = node.get('labels', []) + node_props = node.get('properties', {}) + + # Categorize nodes + if any(label in ['Document', 'File', 'Source'] for label in node_labels): + document_nodes.append({ + 'element_id': node.get('element_id'), + 'labels': node_labels, + 'properties': node_props + }) + elif any(label in ['Chunk', 'DocumentChunk', 'TextChunk'] for label in node_labels): + chunk_nodes.append({ + 'element_id': node.get('element_id'), + 'labels': node_labels, + 'properties': node_props + }) + + print(f" Total nodes: {len(all_nodes)}") + print(f" Document nodes: {len(document_nodes)}") + print(f" Chunk nodes: {len(chunk_nodes)}") + + # Analyze document nodes + print(f"\n๐Ÿ“„ Document nodes found:") + for i, doc in enumerate(document_nodes[:5]): # Show first 5 + print(f"\n Document {i+1}:") + print(f" Element ID: {doc['element_id']}") + print(f" Labels: {doc['labels']}") + print(f" Properties: {list(doc['properties'].keys())}") + + # Show key properties + for key in ['id', 'name', 'url', 'source', 'document_id', 'type']: + if key in doc['properties']: + print(f" {key}: {doc['properties'][key]}") + + # Analyze chunk nodes + print(f"\n๐Ÿ“ Chunk nodes found:") + for i, chunk in enumerate(chunk_nodes[:5]): # Show first 5 + print(f"\n Chunk {i+1}:") + print(f" Element ID: {chunk['element_id']}") + print(f" Labels: {chunk['labels']}") + print(f" Properties: {list(chunk['properties'].keys())}") + + # Show key properties + for key in ['id', 'text', 'content', 'source', 'document_id', 'doc_id', 'url']: + if key in chunk['properties']: + value = chunk['properties'][key] + if key in ['text', 'content'] and len(str(value)) > 100: + value = str(value)[:100] + "..." + print(f" {key}: {value}") + + # Test the enhanced chunk extraction + print(f"\n๐Ÿ”ง Testing enhanced chunk extraction...") + from src.risk_assessment import extract_chunks_from_subgraph + + chunks = extract_chunks_from_subgraph(subgraph_data) + + print(f"\n๐Ÿ“‹ Extracted {len(chunks)} chunks:") + for i, chunk in enumerate(chunks): + print(f"\n Chunk {i+1}:") + print(f" ID: {chunk['chunk_id']}") + print(f" Document: {chunk['document_name'] or chunk['document_id']}") + print(f" Source: {chunk['source']}") + print(f" Text length: {len(chunk['text'])} characters") + print(f" Text preview: {chunk['text'][:100]}...") + + # Save detailed results + results = { + 'subgraph_summary': { + 'total_nodes': len(all_nodes), + 'document_nodes': len(document_nodes), + 'chunk_nodes': len(chunk_nodes) + }, + 'document_nodes': document_nodes, + 'chunk_nodes': chunk_nodes, + 'extracted_chunks': chunks + } + + with open('source_extraction_results.json', 'w') as f: + json.dump(results, f, indent=2, default=str) + + print(f"\n๐Ÿ’พ Detailed results saved to: source_extraction_results.json") + + except Exception as e: + print(f"โŒ Error: {str(e)}") + logging.exception("Error in source extraction test") + +if __name__ == "__main__": + test_source_extraction() diff --git a/backend/test_url_copying.py b/backend/test_url_copying.py new file mode 100644 index 000000000..870404037 --- /dev/null +++ b/backend/test_url_copying.py @@ -0,0 +1,196 @@ +#!/usr/bin/env python3 +""" +Test URL Copying to Chunk Nodes + +This script tests whether URLs are now being properly copied from Document nodes +to Chunk nodes during the chunk creation process. +""" + +import os +import sys +import logging +from dotenv import load_dotenv + +# Add src to path +sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) + +from src.graphDB_dataAccess import graphDBdataAccess +from src.shared.common_fn import create_graph_database_connection + +# Configure logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +def test_url_copying(): + """ + Test whether URLs are being copied to chunk nodes. + """ + + # Load environment variables + load_dotenv() + + # Get database connection details + uri = os.getenv('NEO4J_URI', 'bolt://localhost:7687') + username = os.getenv('NEO4J_USERNAME', 'neo4j') + password = os.getenv('NEO4J_PASSWORD', 'password') + database = os.getenv('NEO4J_DATABASE', 'neo4j') + + try: + # Create database connection + graph = create_graph_database_connection(uri, username, password, database) + graph_db_access = graphDBdataAccess(graph) + + print("๐Ÿ” TESTING URL COPYING TO CHUNK NODES") + print("=" * 60) + + # Test 1: Check Document nodes with URLs + print("\n๐Ÿ“‹ TEST 1: Document Nodes with URLs") + print("-" * 40) + + doc_query = """ + MATCH (d:Document) + WHERE d.url IS NOT NULL + RETURN d.fileName as fileName, d.url as url, d.fileSource as source + LIMIT 5 + """ + + doc_results = graph.query(doc_query) + if doc_results: + print("โœ… Found Document nodes with URLs:") + for record in doc_results: + print(f" ๐Ÿ“„ {record['fileName']}") + print(f" URL: {record['url']}") + print(f" Source: {record['source']}") + print() + else: + print("โŒ No Document nodes with URLs found") + + # Test 2: Check Chunk nodes with URLs + print("\n๐Ÿ“‹ TEST 2: Chunk Nodes with URLs") + print("-" * 40) + + chunk_query = """ + MATCH (c:Chunk) + WHERE c.url IS NOT NULL + RETURN c.fileName as fileName, c.url as url, c.fileSource as source, c.position as position + LIMIT 10 + """ + + chunk_results = graph.query(chunk_query) + if chunk_results: + print("โœ… Found Chunk nodes with URLs:") + for record in chunk_results: + print(f" ๐Ÿ“„ {record['fileName']} (Position: {record['position']})") + print(f" URL: {record['url']}") + print(f" Source: {record['source']}") + print() + else: + print("โŒ No Chunk nodes with URLs found") + print(" This means the URL copying fix hasn't been applied to existing data") + + # Test 3: Check Document-Chunk relationships + print("\n๐Ÿ“‹ TEST 3: Document-Chunk Relationships") + print("-" * 40) + + rel_query = """ + MATCH (c:Chunk)-[:PART_OF]->(d:Document) + WHERE d.url IS NOT NULL + RETURN d.fileName as docName, d.url as docUrl, + c.fileName as chunkName, c.url as chunkUrl, + c.fileSource as chunkSource + LIMIT 5 + """ + + rel_results = graph.query(rel_query) + if rel_results: + print("โœ… Document-Chunk relationships:") + for record in rel_results: + print(f" ๐Ÿ“„ Document: {record['docName']}") + print(f" Doc URL: {record['docUrl']}") + print(f" Chunk: {record['chunkName']}") + print(f" Chunk URL: {record['chunkUrl']}") + print(f" Chunk Source: {record['chunkSource']}") + print() + else: + print("โŒ No Document-Chunk relationships found") + + # Test 4: Check for chunks without URLs + print("\n๐Ÿ“‹ TEST 4: Chunks Without URLs") + print("-" * 40) + + no_url_query = """ + MATCH (c:Chunk) + WHERE c.url IS NULL + RETURN c.fileName as fileName, c.fileSource as source, c.position as position + LIMIT 5 + """ + + no_url_results = graph.query(no_url_query) + if no_url_results: + print("โš ๏ธ Found Chunk nodes WITHOUT URLs:") + for record in no_url_results: + print(f" ๐Ÿ“„ {record['fileName']} (Position: {record['position']})") + print(f" Source: {record['source']}") + print() + print(" These are likely from before the URL copying fix") + else: + print("โœ… All Chunk nodes have URLs!") + + # Test 5: Check local file references + print("\n๐Ÿ“‹ TEST 5: Local File References") + print("-" * 40) + + local_query = """ + MATCH (c:Chunk) + WHERE c.url STARTS WITH 'local://' + RETURN c.fileName as fileName, c.url as url, c.fileSource as source + LIMIT 5 + """ + + local_results = graph.query(local_query) + if local_results: + print("โœ… Found local file references:") + for record in local_results: + print(f" ๐Ÿ“„ {record['fileName']}") + print(f" URL: {record['url']}") + print(f" Source: {record['source']}") + print() + else: + print("โ„น๏ธ No local file references found") + + # Summary + print("\n๐Ÿ“Š SUMMARY") + print("=" * 60) + + # Count documents with URLs + doc_count_query = "MATCH (d:Document) WHERE d.url IS NOT NULL RETURN count(d) as count" + doc_count = graph.query(doc_count_query)[0]['count'] + + # Count chunks with URLs + chunk_count_query = "MATCH (c:Chunk) WHERE c.url IS NOT NULL RETURN count(c) as count" + chunk_count = graph.query(chunk_count_query)[0]['count'] + + # Count total chunks + total_chunks_query = "MATCH (c:Chunk) RETURN count(c) as count" + total_chunks = graph.query(total_chunks_query)[0]['count'] + + print(f"๐Ÿ“„ Documents with URLs: {doc_count}") + print(f"๐Ÿ“„ Chunks with URLs: {chunk_count}/{total_chunks} ({chunk_count/total_chunks*100:.1f}%)") + + if chunk_count > 0: + print("โœ… URL copying is working for new chunks!") + else: + print("โŒ URL copying not working - check the implementation") + + if chunk_count < total_chunks: + print("โ„น๏ธ Some chunks don't have URLs (likely from before the fix)") + + except Exception as e: + logger.error(f"Error testing URL copying: {str(e)}") + print(f"โŒ Error: {str(e)}") + finally: + if 'graph' in locals(): + graph.close() + +if __name__ == "__main__": + test_url_copying() diff --git a/backend/test_vector_search.py b/backend/test_vector_search.py new file mode 100644 index 000000000..cde66c635 --- /dev/null +++ b/backend/test_vector_search.py @@ -0,0 +1,132 @@ +#!/usr/bin/env python3 +""" +Test script for the enhanced vector search functionality +""" + +import os +import sys +import asyncio +from dotenv import load_dotenv + +# Add the src directory to the path +sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) + +from src.graph_query import search_nodes, check_vector_availability + +load_dotenv() + +def test_vector_search_functionality(): + """Test the enhanced vector search functionality""" + + # Get connection details from environment or use defaults + uri = os.getenv('NEO4J_URI', 'neo4j+ssc://224c5da6.databases.neo4j.io') + username = os.getenv('NEO4J_USERNAME', 'neo4j') + password = os.getenv('NEO4J_PASSWORD', '') + database = os.getenv('NEO4J_DATABASE', 'neo4j') + + if not password: + print("โŒ NEO4J_PASSWORD environment variable not set") + return False + + try: + print("๐Ÿ” Testing enhanced vector search functionality...") + print(f"Connecting to: {uri}") + print(f"Database: {database}") + print(f"Username: {username}") + + # Test 1: Check vector availability for different node types + print("\n1. Checking vector availability...") + from src.graph_query import get_graphDB_driver + + driver = get_graphDB_driver(uri, username, password, database) + + node_types_to_check = ["Person", "Organization", "Location", "Technology"] + + for node_type in node_types_to_check: + vector_info = check_vector_availability(driver, node_type) + print(f" {node_type}: {vector_info['has_embeddings']} " + f"({vector_info['nodes_with_embeddings']}/{vector_info['total_nodes']} nodes, " + f"{vector_info['embedding_coverage']:.1%} coverage)") + + driver.close() + + # Test 2: Search with vector preference (default) + print("\n2. Testing search with vector preference...") + search_results = search_nodes( + uri=uri, + username=username, + password=password, + database=database, + search_term="software engineer", + node_type="Person", + max_results=5, + prefer_vector=True + ) + + print(f"โœ… Search completed successfully") + print(f" Search method used: {search_results['search_method']}") + print(f" Vector available: {search_results['search_metadata']['vector_available']}") + print(f" Embedding coverage: {search_results['search_metadata']['embedding_coverage']:.1%}") + print(f" Total results: {search_results['total_results']}") + print(f" Nodes found: {len(search_results['nodes'])}") + + if search_results['nodes']: + print(f" Sample node: {search_results['nodes'][0]}") + + # Show similarity scores if using vector search + if search_results['search_method'] == 'vector_similarity': + print(" Similarity scores:") + for i, node in enumerate(search_results['nodes'][:3]): + print(f" {i+1}. {node.get('properties', {}).get('id', 'Unknown')}: {node.get('similarity_score', 'N/A')}") + + # Test 3: Search with text preference + print("\n3. Testing search with text preference...") + text_search_results = search_nodes( + uri=uri, + username=username, + password=password, + database=database, + search_term="John", + node_type="Person", + max_results=5, + prefer_vector=False + ) + + print(f"โœ… Text search completed successfully") + print(f" Search method used: {text_search_results['search_method']}") + print(f" Total results: {text_search_results['total_results']}") + + # Test 4: Search for organizations + print("\n4. Testing organization search...") + org_search_results = search_nodes( + uri=uri, + username=username, + password=password, + database=database, + search_term="technology company", + node_type="Organization", + max_results=3, + prefer_vector=True + ) + + print(f"โœ… Organization search completed successfully") + print(f" Search method used: {org_search_results['search_method']}") + print(f" Total results: {org_search_results['total_results']}") + + if org_search_results['nodes']: + print(" Organizations found:") + for i, node in enumerate(org_search_results['nodes']): + org_name = node.get('properties', {}).get('id', 'Unknown') + similarity = node.get('similarity_score', 'N/A') + print(f" {i+1}. {org_name} (similarity: {similarity})") + + print("\n๐ŸŽ‰ All tests passed successfully!") + return True + + except Exception as e: + print(f"โŒ Test failed with error: {str(e)}") + return False + +if __name__ == "__main__": + success = test_vector_search_functionality() + sys.exit(0 if success else 1) From 699e579b14ab0539a83879cc00d3c5b13fad46d9 Mon Sep 17 00:00:00 2001 From: otobongfp Date: Thu, 28 Aug 2025 17:45:35 +0100 Subject: [PATCH 04/10] cleanups --- backend/fix_chunk_urls.py | 195 +++++++++++++++++++++++++++++++++ backend/src/risk_assessment.py | 101 ++++++++++++++++- 2 files changed, 294 insertions(+), 2 deletions(-) create mode 100644 backend/fix_chunk_urls.py diff --git a/backend/fix_chunk_urls.py b/backend/fix_chunk_urls.py new file mode 100644 index 000000000..12268a824 --- /dev/null +++ b/backend/fix_chunk_urls.py @@ -0,0 +1,195 @@ +#!/usr/bin/env python3 +""" +Fix Chunk URLs - Copy URL Properties from Documents to Chunks + +This script updates all Chunk nodes to copy URL and fileSource properties +from their parent Document nodes, enabling proper URL citations in risk assessment. +""" + +import os +import sys +import logging +from dotenv import load_dotenv + +# Add src to path +sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) + +from src.shared.common_fn import create_graph_database_connection, execute_graph_query + +# Configure logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +def fix_chunk_urls(): + """ + Copy URL properties from Document nodes to their associated Chunk nodes. + """ + + # Load environment variables + load_dotenv() + + # Get database connection details + uri = os.getenv('NEO4J_URI', 'bolt://localhost:7687') + username = os.getenv('NEO4J_USERNAME', 'neo4j') + password = os.getenv('NEO4J_PASSWORD', 'password') + database = os.getenv('NEO4J_DATABASE', 'neo4j') + + try: + print("๐Ÿ”ง FIXING CHUNK URLS - COPYING FROM DOCUMENTS") + print("=" * 60) + + # Connect to database + graph = create_graph_database_connection(uri, username, password, database) + + # Step 1: Check current state + print("\n๐Ÿ“‹ Step 1: Checking current state") + + # Count documents with URLs + doc_url_query = """ + MATCH (d:Document) + WHERE d.url IS NOT NULL + RETURN count(d) as docs_with_urls + """ + doc_result = execute_graph_query(graph, doc_url_query) + docs_with_urls = doc_result[0]['docs_with_urls'] if doc_result else 0 + + # Count chunks with URLs + chunk_url_query = """ + MATCH (c:Chunk) + WHERE c.url IS NOT NULL + RETURN count(c) as chunks_with_urls + """ + chunk_result = execute_graph_query(graph, chunk_url_query) + chunks_with_urls = chunk_result[0]['chunks_with_urls'] if chunk_result else 0 + + # Count total chunks + total_chunks_query = """ + MATCH (c:Chunk) + RETURN count(c) as total_chunks + """ + total_result = execute_graph_query(graph, total_chunks_query) + total_chunks = total_result[0]['total_chunks'] if total_result else 0 + + print(f"๐Ÿ“Š Documents with URLs: {docs_with_urls}") + print(f"๐Ÿ“Š Chunks with URLs: {chunks_with_urls}") + print(f"๐Ÿ“Š Total chunks: {total_chunks}") + + # Step 2: Show documents with URLs + print("\n๐Ÿ“‹ Step 2: Documents with URLs") + doc_details_query = """ + MATCH (d:Document) + WHERE d.url IS NOT NULL + RETURN d.fileName as fileName, d.url as url, d.fileSource as fileSource + LIMIT 10 + """ + doc_details = execute_graph_query(graph, doc_details_query) + + for doc in doc_details: + print(f" ๐Ÿ“„ {doc['fileName']}") + print(f" URL: {doc['url']}") + print(f" Source: {doc['fileSource']}") + + # Step 3: Find chunks that need URL properties + print("\n๐Ÿ“‹ Step 3: Finding chunks that need URL properties") + chunks_needing_urls_query = """ + MATCH (c:Chunk)-[:PART_OF]->(d:Document) + WHERE c.url IS NULL AND d.url IS NOT NULL + RETURN count(c) as chunks_needing_urls + """ + chunks_needing_result = execute_graph_query(graph, chunks_needing_urls_query) + chunks_needing_urls = chunks_needing_result[0]['chunks_needing_urls'] if chunks_needing_result else 0 + + print(f"๐Ÿ“Š Chunks needing URL properties: {chunks_needing_urls}") + + if chunks_needing_urls == 0: + print("โœ… All chunks already have URL properties!") + return + + # Step 4: Update chunks with URL properties + print("\n๐Ÿ“‹ Step 4: Updating chunks with URL properties") + update_query = """ + MATCH (c:Chunk)-[:PART_OF]->(d:Document) + WHERE c.url IS NULL AND d.url IS NOT NULL + SET c.url = d.url, c.fileSource = d.fileSource + RETURN count(c) as updated_count + """ + + update_result = execute_graph_query(graph, update_query) + updated_count = update_result[0]['updated_count'] if update_result else 0 + print(f"๐Ÿ“Š Successfully updated {updated_count} chunks with URL properties") + + # Step 5: Verify the fix + print("\n๐Ÿ“‹ Step 5: Verifying the fix") + + # Check updated chunks + verify_query = """ + MATCH (c:Chunk) + WHERE c.url IS NOT NULL + RETURN c.fileName as fileName, c.url as url, c.fileSource as fileSource + LIMIT 5 + """ + verify_results = execute_graph_query(graph, verify_query) + + print(f"๐Ÿ“Š Chunks with URLs after fix: {len(verify_results)}") + for result in verify_results: + print(f" ๐Ÿ“ {result['fileName']} -> {result['url']} (Source: {result['fileSource']})") + + # Step 6: Check Bill Gates chunks specifically + print("\n๐Ÿ“‹ Step 6: Bill Gates chunks after fix") + bill_gates_query = """ + MATCH (c:Chunk) + WHERE c.fileName = 'Bill_Gates' AND c.url IS NOT NULL + RETURN c.fileName as fileName, c.url as url, c.fileSource as fileSource + LIMIT 3 + """ + bill_gates_results = execute_graph_query(graph, bill_gates_query) + + print(f"๐Ÿ“Š Bill Gates chunks with URLs: {len(bill_gates_results)}") + for result in bill_gates_results: + print(f" ๐Ÿ“ {result['fileName']} -> {result['url']} (Source: {result['fileSource']})") + + # Step 7: Final statistics + print("\n๐Ÿ“‹ Step 7: Final statistics") + final_stats_query = """ + MATCH (c:Chunk) + RETURN + count(c) as total_chunks, + count(CASE WHEN c.url IS NOT NULL THEN c END) as chunks_with_urls, + count(CASE WHEN c.url IS NULL THEN c END) as chunks_without_urls + """ + final_stats = execute_graph_query(graph, final_stats_query) + + if final_stats: + stats = final_stats[0] + print(f"๐Ÿ“Š Total chunks: {stats['total_chunks']}") + print(f"๐Ÿ“Š Chunks with URLs: {stats['chunks_with_urls']}") + print(f"๐Ÿ“Š Chunks without URLs: {stats['chunks_without_urls']}") + + if stats['total_chunks'] > 0: + coverage = (stats['chunks_with_urls'] / stats['total_chunks']) * 100 + print(f"๐Ÿ“Š URL coverage: {coverage:.1f}%") + + graph.close() + + # Summary + print("\n๐Ÿ“ˆ SUMMARY") + print("=" * 60) + + if updated_count > 0: + print(f"โœ… Successfully updated {updated_count} chunks with URL properties") + print("โœ… Risk assessment should now show actual URLs instead of 'Chunk X'") + print("โœ… Example: 'https://en.wikipedia.org/wiki/Bill_Gates' instead of 'Chunk 7'") + else: + print("โ„น๏ธ No chunks needed updating - they already have URL properties") + + print("\n๐Ÿš€ Next Steps:") + print(" 1. Run a risk assessment to see the improved URL citations") + print(" 2. Check that sources now show actual URLs") + print(" 3. Verify that 'Chunk X' references are replaced with document URLs") + + except Exception as e: + logger.error(f"Error fixing chunk URLs: {str(e)}") + print(f"โŒ Error: {str(e)}") + +if __name__ == "__main__": + fix_chunk_urls() diff --git a/backend/src/risk_assessment.py b/backend/src/risk_assessment.py index 591a34a06..552229986 100644 --- a/backend/src/risk_assessment.py +++ b/backend/src/risk_assessment.py @@ -111,7 +111,7 @@ def extract_chunks_from_subgraph(subgraph_data: Dict[str, Any]) -> List[Dict[str, Any]]: """ - Extract relevant chunks from subgraph data. + Extract relevant chunks from subgraph data with enhanced URL extraction. Args: subgraph_data: Subgraph data from search_and_get_subgraph @@ -125,7 +125,7 @@ def extract_chunks_from_subgraph(subgraph_data: Dict[str, Any]) -> List[Dict[str try: logging.info(f"Extracting chunks from subgraph data: {len(subgraph_data.get('subgraphs', []))} subgraphs") - # First pass: collect document information + # First pass: collect document information from subgraph for subgraph in subgraph_data.get('subgraphs', []): for node in subgraph.get('nodes', []): node_labels = node.get('labels', []) @@ -250,6 +250,99 @@ def extract_chunks_from_subgraph(subgraph_data: Dict[str, Any]) -> List[Dict[str logging.error(f"Error extracting chunks from subgraph: {str(e)}") return [] +def enhance_chunks_with_urls(uri: str, username: str, password: str, database: str, + chunks: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """ + Enhance chunks with URL information by querying the database for Document URLs. + + Args: + uri: Neo4j database URI + username: Database username + password: Database password + database: Database name + chunks: List of chunks to enhance + + Returns: + Enhanced chunks with URL information + """ + try: + from src.shared.common_fn import create_graph_database_connection, execute_graph_query + + # Connect to database + graph = create_graph_database_connection(uri, username, password, database) + + # Get unique file names from chunks + file_names = set() + for chunk in chunks: + if chunk.get('document_name'): + file_names.add(chunk['document_name']) + elif 'fileName' in chunk.get('source', ''): + # Extract fileName from source like "Document: Bill_Gates" + source = chunk['source'] + if source.startswith('Document: '): + file_names.add(source.replace('Document: ', '')) + + # Query database for Document URLs + if file_names: + placeholders = ', '.join([f"'{name}'" for name in file_names]) + url_query = f""" + MATCH (d:Document) + WHERE d.fileName IN [{placeholders}] + RETURN d.fileName as fileName, d.url as url, d.fileSource as fileSource + """ + + url_results = execute_graph_query(graph, url_query) + + # Create mapping of fileName to URL + url_mapping = {} + for result in url_results: + if result.get('url'): + url_mapping[result['fileName']] = { + 'url': result['url'], + 'fileSource': result.get('fileSource', '') + } + + # Enhance chunks with URLs + enhanced_chunks = [] + for chunk in chunks: + enhanced_chunk = chunk.copy() + + # Try to find URL for this chunk + chunk_url = None + + # 1. Check if chunk already has a URL + if chunk.get('source', '').startswith('http'): + chunk_url = chunk['source'] + + # 2. Try to get URL from document name + elif chunk.get('document_name') and chunk['document_name'] in url_mapping: + chunk_url = url_mapping[chunk['document_name']]['url'] + + # 3. Try to extract fileName from source and look up URL + elif chunk.get('source', '').startswith('Document: '): + doc_name = chunk['source'].replace('Document: ', '') + if doc_name in url_mapping: + chunk_url = url_mapping[doc_name]['url'] + + # Update chunk source with URL if found + if chunk_url: + enhanced_chunk['source'] = chunk_url + logging.info(f"Enhanced chunk with URL: {chunk_url}") + else: + logging.info(f"Could not find URL for chunk: {chunk.get('source', 'Unknown')}") + + enhanced_chunks.append(enhanced_chunk) + + graph.close() + return enhanced_chunks + + graph.close() + return chunks + + except Exception as e: + logging.error(f"Error enhancing chunks with URLs: {str(e)}") + return chunks + def extract_subgraph_info(subgraph_data: Dict[str, Any]) -> Dict[str, Any]: """ Extract summary information about the subgraph. @@ -405,6 +498,10 @@ def analyze_risk(uri: str, username: str, password: str, database: str, chunks = extract_chunks_from_subgraph(subgraph_data) logging.info(f"Extracted {len(chunks)} relevant chunks for risk assessment") + # Step 2.5: Enhance chunks with URLs from database + chunks = enhance_chunks_with_urls(uri, username, password, database, chunks) + logging.info(f"Enhanced {len(chunks)} chunks with URL information") + # Step 3: Extract subgraph summary information subgraph_info = extract_subgraph_info(subgraph_data) logging.info(f"Subgraph info: {subgraph_info}") From ae85e95c5e376066b56d0d28efdc447d9ae45718 Mon Sep 17 00:00:00 2001 From: otobongfp Date: Thu, 28 Aug 2025 18:51:19 +0100 Subject: [PATCH 05/10] updates --- .../src/components/Graph/GraphViewer.tsx | 802 +++++++++++++----- .../components/Graph/RiskAssessmentModal.tsx | 292 +++++++ .../src/components/Graph/SearchPanel.tsx | 19 +- frontend-graph/src/services/GraphQuery.ts | 67 ++ 4 files changed, 990 insertions(+), 190 deletions(-) create mode 100644 frontend-graph/src/components/Graph/RiskAssessmentModal.tsx diff --git a/frontend-graph/src/components/Graph/GraphViewer.tsx b/frontend-graph/src/components/Graph/GraphViewer.tsx index 7de48a4da..3560b9465 100644 --- a/frontend-graph/src/components/Graph/GraphViewer.tsx +++ b/frontend-graph/src/components/Graph/GraphViewer.tsx @@ -2,9 +2,15 @@ import React, { useState } from "react"; import EmbeddedGraphView from "./EmbeddedGraphView"; import SampleData from "./SampleData"; import SearchPanel, { SearchParams } from "./SearchPanel"; -import SearchResults from "./SearchResults"; import { ExtendedNode, ExtendedRelationship } from "../../types"; -import { graphQueryAPIWithTransform, searchAndGetSubgraphAPI } from "../../services/GraphQuery"; +import { + graphQueryAPIWithTransform, + searchAndGetSubgraphAPI, + analyzeRiskAPI, +} from "../../services/GraphQuery"; + +// Tab types +type TabType = "graph" | "search" | "risk"; const GraphViewer: React.FC = () => { const [nodes, setNodes] = useState([]); @@ -17,13 +23,29 @@ const GraphViewer: React.FC = () => { const [newFileName, setNewFileName] = useState(""); const [searchResult, setSearchResult] = useState(null); + // Risk assessment state + const [riskAssessment, setRiskAssessment] = useState(null); + const [riskLoading, setRiskLoading] = useState(false); + + // Search loading state + const [searchLoading, setSearchLoading] = useState(false); + + // Tab management state + const [activeTab, setActiveTab] = useState("graph"); + // Connection form state - const [connectionForm, setConnectionForm] = useState({ + const [connectionForm, setConnectionForm] = useState<{ + uri: string; + userName: string; + password: string; + database: string; + documentNames: string[]; + }>({ uri: "neo4j+ssc://224c5da6.databases.neo4j.io", userName: "neo4j", password: "", database: "neo4j", - documentNames: ["Apple stock during pandemic.pdf"], + documentNames: [], // Disabled default document names }); const handleLoadSampleData = ( @@ -94,7 +116,7 @@ const GraphViewer: React.FC = () => { }; const handleSearch = async (searchParams: SearchParams) => { - setLoading(true); + setSearchLoading(true); setError(null); try { @@ -142,35 +164,43 @@ const GraphViewer: React.FC = () => { ); } } finally { - setLoading(false); + setSearchLoading(false); } }; const handleViewSubgraph = (subgraph: any) => { // Transform the subgraph data to match the expected format - const transformedNodes: ExtendedNode[] = subgraph.nodes.map((node: any) => ({ - id: node.element_id, - labels: node.labels, - properties: node.properties, - caption: node.properties?.name || node.properties?.title || node.properties?.id || "Unnamed Node", - size: 20, - color: "#4CAF50", - })); + const transformedNodes: ExtendedNode[] = subgraph.nodes.map( + (node: any) => ({ + id: node.element_id, + labels: node.labels, + properties: node.properties, + caption: + node.properties?.name || + node.properties?.title || + node.properties?.id || + "Unnamed Node", + size: 20, + color: "#4CAF50", + }) + ); - const transformedRelationships: ExtendedRelationship[] = subgraph.relationships.map((rel: any) => ({ - id: rel.element_id, - type: rel.type, - from: rel.start_node_id, - to: rel.end_node_id, - properties: rel.properties, - caption: rel.type, - color: "#2196F3", - width: 2, - })); + const transformedRelationships: ExtendedRelationship[] = + subgraph.relationships.map((rel: any) => ({ + id: rel.element_id, + type: rel.type, + from: rel.start_node_id, + to: rel.end_node_id, + properties: rel.properties, + caption: rel.type, + color: "#2196F3", + width: 2, + })); setNodes(transformedNodes); setRelationships(transformedRelationships); setShowGraph(true); + setActiveTab("graph"); // Switch to graph tab setError(null); console.log( @@ -183,6 +213,71 @@ const GraphViewer: React.FC = () => { setError(null); }; + const handleAnalyzeRisk = async (entityName: string, entityType: string) => { + setRiskLoading(true); + setError(null); + + try { + console.log("Starting risk analysis...", { entityName, entityType }); + + // Create an AbortController for the API call + const abortController = new AbortController(); + + // Set a timeout to abort the request if it takes too long + const timeoutId = setTimeout(() => { + console.log("Risk analysis request timeout - aborting"); + abortController.abort(); + }, 120000); // 2 minute timeout for risk analysis + + // Default risk indicators for Canadian research security + const riskIndicators = { + "Foreign State Influence": 80, + "Dual-Use Technology Exposure": 70, + "Compliance with Canadian Research Security Policies": 60, + "International Collaboration Patterns": 50, + "Funding Sources Transparency": 40, + "Export Control Compliance": 45, + "Intellectual Property Protection": 35, + "Research Data Security": 30, + }; + + // Perform risk analysis + const response = await analyzeRiskAPI( + entityName, + entityType, + riskIndicators, + 4, // depth + 10, // max_results + abortController.signal, + connectionForm + ); + + // Clear the timeout since we got a response + clearTimeout(timeoutId); + + // Extract the risk assessment results + const riskData = response.data.data; + setRiskAssessment(riskData); + setActiveTab("risk"); // Switch to risk assessment tab + + console.log("Risk analysis completed:", riskData); + } catch (err: any) { + console.error("Error during risk analysis:", err); + if (err.name === "AbortError") { + setError( + "Risk analysis was cancelled due to timeout. The backend is taking too long to respond." + ); + } else { + setError( + err.message || + "Failed to perform risk analysis. Check your connection details and entity information." + ); + } + } finally { + setRiskLoading(false); + } + }; + const handleConnectionChange = (field: string, value: string | string[]) => { setConnectionForm((prev) => ({ ...prev, @@ -190,6 +285,52 @@ const GraphViewer: React.FC = () => { })); }; + const handleSaveCredentials = () => { + // Save connection credentials to session storage + sessionStorage.setItem("neo4j_uri", connectionForm.uri); + sessionStorage.setItem("neo4j_username", connectionForm.userName); + sessionStorage.setItem("neo4j_password", connectionForm.password); + sessionStorage.setItem("neo4j_database", connectionForm.database); + + // Show success feedback + alert("Credentials saved to session storage successfully!"); + }; + + // Helper function to render clickable links + const renderClickableSource = (source: string) => { + if (source.startsWith("http://") || source.startsWith("https://")) { + return ( + + {source} + + ); + } + return {source}; + }; + + // Load credentials from session storage on component mount + React.useEffect(() => { + const savedUri = sessionStorage.getItem("neo4j_uri"); + const savedUsername = sessionStorage.getItem("neo4j_username"); + const savedPassword = sessionStorage.getItem("neo4j_password"); + const savedDatabase = sessionStorage.getItem("neo4j_database"); + + if (savedUri || savedUsername || savedPassword || savedDatabase) { + setConnectionForm((prev) => ({ + ...prev, + uri: savedUri || prev.uri, + userName: savedUsername || prev.userName, + password: savedPassword || prev.password, + database: savedDatabase || prev.database, + })); + } + }, []); + const handleShowGraph = () => { setShowGraph(true); }; @@ -248,31 +389,19 @@ const GraphViewer: React.FC = () => { Graph Visualization - - - {/* Search Panel */} - - - {/* Search Results */} - - + {/* Backend Connection Configuration */}
-

Backend Connection

-

- Configure your Neo4j connection and load real graph data from your - backend. -

+
+

+ Backend Connection +

+

+ Configure your Neo4j connection and load real graph data from your + backend. +

+
-
+
@@ -296,7 +425,7 @@ const GraphViewer: React.FC = () => { onChange={(e) => handleConnectionChange("database", e.target.value) } - className="w-full px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500" + className="w-full px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-transparent" placeholder="neo4j" />
@@ -311,7 +440,7 @@ const GraphViewer: React.FC = () => { onChange={(e) => handleConnectionChange("userName", e.target.value) } - className="w-full px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500" + className="w-full px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-transparent" placeholder="neo4j" />
@@ -326,150 +455,456 @@ const GraphViewer: React.FC = () => { onChange={(e) => handleConnectionChange("password", e.target.value) } - className="w-full px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500" - placeholder="Enter your password" + className="w-full px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-transparent" + placeholder="โ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ข" />
-
- - - {/* File List */} -
- {connectionForm.documentNames.length > 0 ? ( -
- {connectionForm.documentNames.map((fileName, index) => ( -
+ +
+
+ + + + {/* Search Panel */} + + + {/* Tabbed Content Area */} +
+ {/* Tab Navigation */} +
+ +
+ + {/* Tab Content */} +
+ {/* Graph Tab */} + {activeTab === "graph" && ( +
+ {showGraph && nodes.length > 0 ? ( + + ) : ( +
+ -
- + +

No Graph Data

+

+ Load sample data or search for entities to visualize the + graph. +

+
+ +
+
+ )} +
+ )} + + {/* Search Results Tab */} + {activeTab === "search" && ( +
+ {searchLoading ? ( +
+
+ + + - - - - {fileName} - -
+ d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z" + > + + Extracting Subgraph... +
+
+ ) : searchResult ? ( +
+
+

Search Results

+
+ {searchResult.subgraphs.map( + (subgraph: any, index: number) => ( +
+
+

+ Subgraph {index + 1} +

+ +
+
+

Nodes: {subgraph.nodes.length}

+

+ Relationships: {subgraph.relationships.length} +

+
+
+ ) + )} +
+
+ ) : ( +
+ + + +

+ No Search Results +

+

+ Use the search panel above to find entities and extract + subgraphs. +

+
+ )} +
+ )} + + {/* Risk Assessment Tab */} + {activeTab === "risk" && ( +
+ {riskLoading ? ( +
+
+ + - - + strokeWidth="4" + > + + + Analyzing Risk... +
+
+ ) : riskAssessment ? ( +
+
+

+ Risk Assessment Results +

+
- ))} -
- ) : ( -
- - - -

No files added yet

-

- Add files below to include them in the graph -

-
- )} -
- {/* Add File Input */} -
- setNewFileName(e.target.value)} - onKeyPress={handleKeyPress} - placeholder="Enter file name (e.g., document1.pdf)" - className="flex-1 px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500" - /> - -
+ {/* Entity Information */} +
+

+ Entity Details +

+
+
+ Name: + + {riskAssessment.entityName} + +
+
+ Type: + + {riskAssessment.entityType} + +
+
+
- {/* File Count */} -
- {connectionForm.documentNames.length} file - {connectionForm.documentNames.length !== 1 ? "s" : ""} selected -
+ {/* Overall Assessment */} +
+

+ Overall Assessment +

+
+
+ + Score: + + + {riskAssessment.calculation?.overallScore || "N/A"} + +
+
+ + Status: + + + {riskAssessment.finalAssessment?.trafficLight || + "N/A"} + +
+
+

+ {riskAssessment.finalAssessment?.overallExplanation || + "No explanation available."} +

+
+ + {/* Risk Indicators */} +
+

+ Risk Indicators +

+ {riskAssessment.riskAssessments?.map( + (assessment: any, index: number) => ( +
+
+
+ {assessment.indicator} +
+
+ + Score: + + + {assessment.score}/5 + + + Weight: + + + {assessment.weight} + +
+
+

+ {assessment.explanation} +

+ {assessment.sources && + assessment.sources.length > 0 && ( +
+ + Sources: + +
+ {assessment.sources.map( + (source: string, sourceIndex: number) => ( +
+ {renderClickableSource(source)} +
+ ) + )} +
+
+ )} +
+ ) + )} +
- {/* Bulk Operations */} - {connectionForm.documentNames.length > 0 && ( -
- - + {/* Analysis Metadata */} + {riskAssessment.analysis_metadata && ( +
+

+ Analysis Details +

+
+
+ + Chunks Analyzed: + + + {riskAssessment.analysis_metadata.chunks_analyzed} + +
+
+ + Subgraph Nodes: + + + {riskAssessment.analysis_metadata.subgraph_nodes} + +
+
+ + Search Method: + + + {riskAssessment.analysis_metadata.search_method} + +
+
+ + Best Match Score: + + + { + riskAssessment.analysis_metadata + .best_match_score + } + +
+
+
+ )} +
+ ) : ( +
+ + + +

+ No Risk Assessment +

+

+ Use the "Analyze Risk" button in the search panel to + assess entity risk. +

+
+ )}
)}
- -
- - -
{/* Error Display */} @@ -479,17 +914,6 @@ const GraphViewer: React.FC = () => {

{error}

)} - - {/* Graph Visualization */} - {showGraph && nodes.length > 0 && ( -
-

Graph Visualization

- -
- )} ); diff --git a/frontend-graph/src/components/Graph/RiskAssessmentModal.tsx b/frontend-graph/src/components/Graph/RiskAssessmentModal.tsx new file mode 100644 index 000000000..6eb78418e --- /dev/null +++ b/frontend-graph/src/components/Graph/RiskAssessmentModal.tsx @@ -0,0 +1,292 @@ +import React from "react"; +import { Button, Modal, Typography } from "@neo4j-ndl/react"; +import { XMarkIconOutline } from "@neo4j-ndl/react/icons"; + +interface RiskAssessment { + indicator: string; + weight: number; + score: number; + explanation: string; + sources: string[]; + normalizedWeight: number; + weightedContribution: number; +} + +interface RiskAssessmentResult { + entityName: string; + entityType: string; + riskAssessments: RiskAssessment[]; + calculation: { + totalWeight: number; + overallScore: number; + thresholdsApplied: { + red: string; + yellow: string; + blue: string; + }; + }; + finalAssessment: { + trafficLight: "Red" | "Yellow" | "Blue"; + overallExplanation: string; + }; + analysis_metadata?: { + chunks_analyzed: number; + subgraph_nodes: number; + subgraph_relationships: number; + search_method: string; + best_match_score: number | string; + }; +} + +interface RiskAssessmentModalProps { + isOpen: boolean; + onClose: () => void; + riskAssessment: RiskAssessmentResult | null; + loading: boolean; +} + +const RiskAssessmentModal: React.FC = ({ + isOpen, + onClose, + riskAssessment, + loading, +}) => { + const getTrafficLightColor = (trafficLight: string) => { + switch (trafficLight) { + case "Red": + return "bg-red-500"; + case "Yellow": + return "bg-yellow-500"; + case "Blue": + return "bg-blue-500"; + default: + return "bg-gray-500"; + } + }; + + const getScoreColor = (score: number) => { + if (score >= 4) return "text-red-600"; + if (score >= 2.5) return "text-yellow-600"; + return "text-blue-600"; + }; + + if (!isOpen) return null; + + return ( + +
+
+ Risk Assessment Results + +
+ + {loading && ( +
+
+ + Analyzing risk... + +
+ )} + + {!loading && riskAssessment && ( +
+ {/* Entity Information */} +
+
+
+ + {riskAssessment.entityName} + + + Entity Type: {riskAssessment.entityType} + +
+
+
+ + {riskAssessment.finalAssessment.trafficLight} + +
+
+
+ + {/* Overall Score */} +
+ + Overall Risk Score + +
+
+ + {riskAssessment.calculation.overallScore.toFixed(2)} + + + out of 5.0 + +
+
+ + {riskAssessment.finalAssessment.overallExplanation} + +
+
Red: โ‰ฅ 4.0 (High Risk)
+
Yellow: 2.5 - 3.99 (Medium Risk)
+
Blue: < 2.5 (Low Risk)
+
+
+
+
+ + {/* Individual Risk Assessments */} +
+ Risk Indicators + {riskAssessment.riskAssessments.map((assessment, index) => ( +
+
+
+ + {assessment.indicator} + +
+ Weight: {assessment.weight} + Score: {assessment.score}/5 + + Contribution:{" "} + {assessment.weightedContribution.toFixed(3)} + +
+
+ = 4 + ? "bg-red-100 text-red-800" + : assessment.score >= 2.5 + ? "bg-yellow-100 text-yellow-800" + : "bg-blue-100 text-blue-800" + }`} + > + {assessment.score}/5 + +
+ + + {assessment.explanation} + + +
+ + Sources: + +
+ {assessment.sources.length > 0 ? ( + assessment.sources.map((source, sourceIndex) => ( + + {source === "nil" ? "No source available" : source} + + )) + ) : ( + + No sources available + + )} +
+
+
+ ))} +
+ + {/* Analysis Metadata */} + {riskAssessment.analysis_metadata && ( +
+ + Analysis Information + +
+
+ + Chunks Analyzed + + + {riskAssessment.analysis_metadata.chunks_analyzed} + +
+
+ + Subgraph Nodes + + + {riskAssessment.analysis_metadata.subgraph_nodes} + +
+
+ + Search Method + + + {riskAssessment.analysis_metadata.search_method} + +
+
+ + Match Score + + + {riskAssessment.analysis_metadata.best_match_score} + +
+
+
+ )} +
+ )} + + {!loading && !riskAssessment && ( +
+ + No risk assessment data available + +
+ )} + +
+ +
+
+
+ ); +}; + +export default RiskAssessmentModal; diff --git a/frontend-graph/src/components/Graph/SearchPanel.tsx b/frontend-graph/src/components/Graph/SearchPanel.tsx index ee2e3d3d4..ecec5c68a 100644 --- a/frontend-graph/src/components/Graph/SearchPanel.tsx +++ b/frontend-graph/src/components/Graph/SearchPanel.tsx @@ -5,6 +5,7 @@ import { MagnifyingGlassIconOutline } from "@neo4j-ndl/react/icons"; interface SearchPanelProps { onSearch: (searchParams: SearchParams) => void; onClear: () => void; + onAnalyzeRisk: (entityName: string, entityType: string) => void; loading: boolean; connectionForm: { uri: string; @@ -24,6 +25,7 @@ export interface SearchParams { const SearchPanel: React.FC = ({ onSearch, onClear, + onAnalyzeRisk, loading, connectionForm, }) => { @@ -71,6 +73,12 @@ const SearchPanel: React.FC = ({ } }; + const handleAnalyzeRisk = () => { + if (searchParams.search_term.trim()) { + onAnalyzeRisk(searchParams.search_term, searchParams.node_type); + } + }; + const isSearchDisabled = !searchParams.search_term.trim() || loading; return ( @@ -173,12 +181,21 @@ const SearchPanel: React.FC = ({ +