diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..137cb04 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "graphrag/graphrag_kg_creation/backend/ai-toolkit"] + path = graphrag/graphrag_kg_creation/backend/ai-toolkit + url = https://github.com/memgraph/ai-toolkit diff --git a/README.md b/README.md index 8dd2ca9..d6d635d 100644 --- a/README.md +++ b/README.md @@ -38,6 +38,9 @@ Deploy Memgraph using methods that suit your environment, whether it's container ### Debugging - [Generating a core dump with Memgraph in Docker Compose](./debugging/docker_compose_with_core_dump_generation/) +### GraphRAG +- [Knowledge graph creation with Unstructured2Graph and MCP Server](./graphrag/graphrag_kg_creation/) + ### GraphQL - [Simple application with Memgraph and Neo4j GraphQL](./graphql/simple_app/) diff --git a/graphrag/graphrag_kg_creation/README.md b/graphrag/graphrag_kg_creation/README.md new file mode 100644 index 0000000..2e7535d --- /dev/null +++ b/graphrag/graphrag_kg_creation/README.md @@ -0,0 +1,49 @@ +# Memgraph GraphRAG Demo + +A demo application for creating knowledge graphs using Memgraph and GraphRAG. + +## Prerequisites + +- Docker and Docker Compose +- Python 3.x with `uv` package manager +- Node.js and npm + +## Setup + +### Memgraph + +Run Memgraph using Docker Compose: + +```bash +docker compose up +``` + +### Backend + +1. Navigate to the `backend` directory +2. Create a `.env` file with your OpenAI API key: + ``` + OPENAI_API_KEY=your_api_key_here + ``` +3. Set up the environment: + ```bash + uv venv + uv add -r requirements.txt + uv sync + ``` +4. Run the backend server: + ```bash + uv run uvicorn main:app --reload --host 0.0.0.0 --port 8000 + ``` + +### Frontend + +1. Navigate to the `frontend` directory +2. Install dependencies: + ```bash + npm install + ``` +3. Start the development server: + ```bash + npm run dev + ``` diff --git a/graphrag/graphrag_kg_creation/backend/.gitignore b/graphrag/graphrag_kg_creation/backend/.gitignore new file mode 100644 index 0000000..f92c58e --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/.gitignore @@ -0,0 +1,13 @@ +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +lightrag_storage.out/ +.env +*.log +.venv/ +venv/ +ENV/ +env/ + diff --git a/graphrag/graphrag_kg_creation/backend/.python-version b/graphrag/graphrag_kg_creation/backend/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/graphrag/graphrag_kg_creation/backend/README.md b/graphrag/graphrag_kg_creation/backend/README.md new file mode 100644 index 0000000..e69de29 diff --git a/graphrag/graphrag_kg_creation/backend/ai-toolkit b/graphrag/graphrag_kg_creation/backend/ai-toolkit new file mode 160000 index 0000000..a879b1a --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/ai-toolkit @@ -0,0 +1 @@ +Subproject commit a879b1a1eacd2da0029909f1b27eff687fdd8299 diff --git a/graphrag/graphrag_kg_creation/backend/main.py b/graphrag/graphrag_kg_creation/backend/main.py new file mode 100644 index 0000000..97209a7 --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/main.py @@ -0,0 +1,86 @@ +""" +Main FastAPI application entry point. +""" +import os +import logging +import sys + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from dotenv import load_dotenv + +# Add the ai-toolkit path to sys.path for imports +SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__)) +AI_TOOLKIT_DIR = os.path.join(SCRIPT_DIR, "ai-toolkit") +sys.path.insert(0, os.path.join(AI_TOOLKIT_DIR, "unstructured2graph", "src")) +sys.path.insert(0, os.path.join(AI_TOOLKIT_DIR, "integrations", "lightrag-memgraph", "src")) +sys.path.insert(0, os.path.join(AI_TOOLKIT_DIR, "memgraph-toolbox", "src")) + +load_dotenv() + +# Configure logging +# Default to DEBUG level to see detailed execution logs including custom tool calls +# Can be overridden with LOG_LEVEL environment variable +log_level = os.getenv("LOG_LEVEL", "DEBUG").upper() +logging.basicConfig( + level=getattr(logging, log_level, logging.DEBUG), + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger(__name__) + +# Create FastAPI app +app = FastAPI(title="GraphRAG KG Creation API", version="1.0.0") + +# CORS middleware +app.add_middleware( + CORSMiddleware, + allow_origins=["http://localhost:3000", "http://127.0.0.1:3000"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Import and register routes +from routes import ingest, query, stats, mcp + +app.include_router(ingest.router) +app.include_router(query.router) +app.include_router(stats.router) +app.include_router(mcp.router) + +# Import OpenAI Agents router if available (requires openai-agents package) +try: + from routes import openai_agents + app.include_router(openai_agents.router) +except ImportError: + logger.warning("OpenAI Agents router not available. Install openai-agents package to enable.") + +# Import OpenAI Agents With Planning router (multi-agent orchestration with planner-executor pattern) +try: + from routes import openai_agents_with_planning + app.include_router(openai_agents_with_planning.router) +except ImportError as e: + logger.warning(f"OpenAI Agents With Planning router not available: {e}") + +# Import OpenAI Agents With Reasoning router (multi-agent orchestration with planning, execution, and reasoning) +try: + from routes import openai_agents_with_reasoning + app.include_router(openai_agents_with_reasoning.router) +except ImportError as e: + logger.warning(f"OpenAI Agents With Reasoning router not available: {e}") + + +@app.get("/") +async def root(): + return {"message": "GraphRAG KG Creation API", "version": "1.0.0"} + + +@app.get("/health") +async def health(): + return {"status": "healthy"} + + +if __name__ == "__main__": + import uvicorn + uvicorn.run(app, host="0.0.0.0", port=8000) + diff --git a/graphrag/graphrag_kg_creation/backend/mcp_client.py b/graphrag/graphrag_kg_creation/backend/mcp_client.py new file mode 100644 index 0000000..04025f8 --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/mcp_client.py @@ -0,0 +1,209 @@ +""" +MCP (Model Context Protocol) client for communicating with mcp-memgraph service. +Handles SSE parsing, session management, and initialization. +""" +import os +import json +import re +import logging +import httpx +from fastapi import HTTPException + +logger = logging.getLogger(__name__) + +# Module-level session ID storage for MCP streamable-http +_mcp_session_id = None +_mcp_initialized = False + + +def parse_sse_response(text: str) -> dict: + """ + Parse Server-Sent Events (SSE) format response from MCP streamable-http. + Format: event: \ndata: \n\n + """ + if not text or not text.strip(): + raise ValueError("Empty SSE response") + + # Check if it's SSE format + if "event:" in text or "data:" in text: + lines = text.split('\n') + json_data = None + + for i, line in enumerate(lines): + if line.startswith('data:'): + # Extract JSON data after "data: " + data_str = line[5:].strip() # Remove "data: " prefix + if data_str: + try: + json_data = json.loads(data_str) + break + except json.JSONDecodeError: + # Try to find JSON in the line + continue + + if json_data is None: + # Try to find JSON anywhere in the response + json_match = re.search(r'\{.*\}', text, re.DOTALL) + if json_match: + try: + json_data = json.loads(json_match.group()) + except json.JSONDecodeError: + pass + + if json_data is None: + raise ValueError(f"Could not extract JSON from SSE response: {text[:200]}") + + return json_data + else: + # Not SSE format, try to parse as plain JSON + return json.loads(text) + + +async def initialize_mcp_session(mcp_url: str, timeout: float = 10.0) -> None: + """ + Initialize MCP session by sending an initialize request. + This must be called before making any tool calls. + """ + global _mcp_session_id, _mcp_initialized + + if _mcp_initialized and _mcp_session_id: + return # Already initialized + + async with httpx.AsyncClient(timeout=timeout, follow_redirects=True) as client: + headers = { + "Content-Type": "application/json", + "Accept": "application/json, text/event-stream" + } + + # Send initialize request + init_payload = { + "jsonrpc": "2.0", + "id": 0, + "method": "initialize", + "params": { + "protocolVersion": "2024-11-05", + "capabilities": {}, + "clientInfo": { + "name": "graphrag-backend", + "version": "1.0.0" + } + } + } + + response = await client.post(mcp_url, json=init_payload, headers=headers) + + # Extract session ID from response headers + if "mcp-session-id" in response.headers: + _mcp_session_id = response.headers["mcp-session-id"] + _mcp_initialized = True + logger.info(f"MCP session initialized with ID: {_mcp_session_id}") + else: + # Try to get session ID from error response (some servers send it even on errors) + if "mcp-session-id" in response.headers: + _mcp_session_id = response.headers["mcp-session-id"] + + # Check if initialization was successful + if response.status_code == 200: + _mcp_initialized = True + else: + # Try to parse error + try: + error_data = parse_sse_response(response.text) if response.text else {} + error_msg = error_data.get("error", {}).get("message", "Unknown error") + logger.warning(f"MCP initialization returned status {response.status_code}: {error_msg}") + except: + pass + + +async def call_mcp_service(mcp_url: str, payload: dict, timeout: float = 60.0) -> dict: + """ + Helper function to call MCP service with session management. + Handles session ID extraction and inclusion for streamable-http transport. + Parses SSE (Server-Sent Events) format responses. + Automatically initializes the session if needed. + """ + global _mcp_session_id, _mcp_initialized + + # Ensure session is initialized + if not _mcp_initialized or not _mcp_session_id: + await initialize_mcp_session(mcp_url, timeout=timeout) + + # Create a timeout object with longer read timeout for SSE streams + timeout_config = httpx.Timeout(timeout, connect=10.0, read=timeout) + + async with httpx.AsyncClient(timeout=timeout_config, follow_redirects=True) as client: + headers = { + "Content-Type": "application/json", + "Accept": "application/json, text/event-stream" + } + + # Always include session ID + if _mcp_session_id: + headers["mcp-session-id"] = _mcp_session_id + + # Use regular POST - httpx will read the full SSE stream automatically + response = await client.post(mcp_url, json=payload, headers=headers) + + # Extract session ID from response headers (in case it changed) + if "mcp-session-id" in response.headers: + _mcp_session_id = response.headers["mcp-session-id"] + + # If we got a "Missing session ID" error, try to re-initialize + if response.status_code == 400: + try: + error_data = parse_sse_response(response.text) if response.text else {} + error_message = error_data.get("error", {}).get("message", "") + if "Missing session ID" in error_message or "No valid session ID" in error_message or "before initialization" in error_message.lower(): + # Reset and re-initialize + _mcp_initialized = False + _mcp_session_id = None + await initialize_mcp_session(mcp_url, timeout=timeout) + + # Retry the request with new session ID + if _mcp_session_id: + headers["mcp-session-id"] = _mcp_session_id + response = await client.post(mcp_url, json=payload, headers=headers) + if "mcp-session-id" in response.headers: + _mcp_session_id = response.headers["mcp-session-id"] + except (json.JSONDecodeError, ValueError): + pass # Continue with original error handling + + # Check response status before parsing + try: + response.raise_for_status() + except httpx.HTTPStatusError as e: + # Try to parse error from SSE or JSON format + try: + error_data = parse_sse_response(e.response.text) if e.response.text else {} + error_message = error_data.get("error", {}).get("message", str(error_data)) + raise HTTPException( + status_code=e.response.status_code, + detail=f"Error from MCP service: {error_message}" + ) + except (json.JSONDecodeError, ValueError) as parse_err: + # If we can't parse, use the response text + error_text = e.response.text[:500] if e.response.text else "Unknown error" + raise HTTPException( + status_code=e.response.status_code, + detail=f"Error from MCP service (status {e.response.status_code}): {error_text}" + ) + + # Check if response has content + if not response.text or response.text.strip() == "": + logger.warning(f"Empty response from MCP service. Status: {response.status_code}, Headers: {dict(response.headers)}") + raise HTTPException( + status_code=500, + detail=f"Empty response from MCP service (status {response.status_code}). The service may be overloaded or the query timed out." + ) + + # Parse response (SSE or JSON format) + try: + return parse_sse_response(response.text) + except (json.JSONDecodeError, ValueError) as e: + # Log the actual response for debugging + logger.error(f"Failed to parse response from MCP service. Status: {response.status_code}, Content-Type: {response.headers.get('content-type')}, Body: {response.text[:500]}") + raise HTTPException( + status_code=500, + detail=f"Invalid response from MCP service: {str(e)}. Response: {response.text[:200]}" + ) + diff --git a/graphrag/graphrag_kg_creation/backend/pyproject.toml b/graphrag/graphrag_kg_creation/backend/pyproject.toml new file mode 100644 index 0000000..2d48910 --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/pyproject.toml @@ -0,0 +1,20 @@ +[project] +name = "backend" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "deepeval>=3.5.2", + "fastapi==0.109.0", + "httpx>=0.28.1", + "lightrag-hku[api]==1.4.8.2", + "litellm>=1.77.1", + "mcp>=1.14.1", + "neo4j>=5.28.1", + "numpy>=2.2.6", + "openai-agents==0.6.1", + "python-dotenv>=1.1.1", + "unstructured[all-docs]>=0.12.6", + "uvicorn[standard]>=0.31.1", +] diff --git a/graphrag/graphrag_kg_creation/backend/requirements.txt b/graphrag/graphrag_kg_creation/backend/requirements.txt new file mode 100644 index 0000000..4219d3e --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/requirements.txt @@ -0,0 +1,15 @@ +fastapi==0.109.0 +uvicorn[standard]>=0.31.1 +python-dotenv>=1.1.1 +httpx>=0.28.1 +openai-agents==0.6.1 + +# AI Toolkit dependencies +lightrag-hku[api]==1.4.8.2 +unstructured[all-docs]>=0.12.6 +numpy>=2.2.6 +neo4j>=5.28.1 +litellm>=1.77.1 +mcp>=1.14.1 +deepeval>=3.5.2 + diff --git a/graphrag/graphrag_kg_creation/backend/resources.py b/graphrag/graphrag_kg_creation/backend/resources.py new file mode 100644 index 0000000..413504e --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/resources.py @@ -0,0 +1,103 @@ +""" +Resource management for Memgraph and LightRAG wrapper. +Handles initialization and cleanup of database connections and wrappers. +""" +import os +import shutil +import logging +import sys + +# Add the ai-toolkit path to sys.path for imports +SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__)) +AI_TOOLKIT_DIR = os.path.join(SCRIPT_DIR, "ai-toolkit") +sys.path.insert(0, os.path.join(AI_TOOLKIT_DIR, "unstructured2graph", "src")) +sys.path.insert(0, os.path.join(AI_TOOLKIT_DIR, "integrations", "lightrag-memgraph", "src")) +sys.path.insert(0, os.path.join(AI_TOOLKIT_DIR, "memgraph-toolbox", "src")) + +from lightrag_memgraph import MemgraphLightRAGWrapper +from memgraph_toolbox.api.memgraph import Memgraph +from unstructured2graph import create_index, create_vector_search_index + +logger = logging.getLogger(__name__) + +SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__)) +LIGHTRAG_DIR = os.path.join(SCRIPT_DIR, "lightrag_storage.out") + +# Global variables for shared resources +_lightrag_wrapper = None +_memgraph = None + + +async def initialize_resources(cleanup: bool = False, create_vector_index: bool = False): + """Initialize Memgraph and LightRAG wrapper. Always creates fresh instances from scratch.""" + global _lightrag_wrapper, _memgraph + + # Always cleanup existing LightRAG wrapper if it exists + if _lightrag_wrapper is not None: + try: + await _lightrag_wrapper.afinalize() + except Exception as e: + logger.warning(f"Error finalizing existing LightRAG wrapper: {str(e)}") + _lightrag_wrapper = None + + # Always create a new Memgraph instance (don't reuse existing) + _memgraph = Memgraph() + + # Always cleanup Memgraph data if requested + if cleanup: + logger.info("Cleaning up existing data in Memgraph...") + try: + _memgraph.query("STORAGE MODE IN_MEMORY_ANALYTICAL") + _memgraph.query("DROP GRAPH") + _memgraph.query("STORAGE MODE IN_MEMORY_TRANSACTIONAL") + except Exception as e: + logger.warning(f"Error during Memgraph cleanup: {str(e)}") + + # Always create index for Chunk nodes (will fail gracefully if already exists) + try: + create_index(_memgraph, "Chunk", "hash") + except Exception as e: + logger.warning(f"Error creating index (may already exist): {str(e)}") + + if create_vector_index: + try: + create_vector_search_index(_memgraph, "Chunk", "embedding") + except Exception as e: + logger.warning(f"Error creating vector index (may already exist): {str(e)}") + + # Always setup LightRAG directory from scratch + lightrag_log_file = os.path.join(LIGHTRAG_DIR, "lightrag.log") + if cleanup: + # Cleanup existing LightRAG files + if os.path.exists(lightrag_log_file): + try: + os.remove(lightrag_log_file) + except Exception as e: + logger.warning(f"Error removing log file: {str(e)}") + if os.path.exists(LIGHTRAG_DIR): + try: + shutil.rmtree(LIGHTRAG_DIR) + except Exception as e: + logger.warning(f"Error removing LightRAG directory: {str(e)}") + + # Ensure LightRAG directory exists + if not os.path.exists(LIGHTRAG_DIR): + os.makedirs(LIGHTRAG_DIR, exist_ok=True) + + # Always create a new LightRAG wrapper instance + _lightrag_wrapper = MemgraphLightRAGWrapper( + log_level="WARNING", + disable_embeddings=True + ) + await _lightrag_wrapper.initialize(working_dir=LIGHTRAG_DIR) + + return _memgraph, _lightrag_wrapper + + +async def cleanup_resources(): + """Cleanup resources if needed.""" + global _lightrag_wrapper + if _lightrag_wrapper is not None: + await _lightrag_wrapper.afinalize() + _lightrag_wrapper = None + diff --git a/graphrag/graphrag_kg_creation/backend/routes/__init__.py b/graphrag/graphrag_kg_creation/backend/routes/__init__.py new file mode 100644 index 0000000..7ceec9b --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/routes/__init__.py @@ -0,0 +1,2 @@ +"""API route handlers.""" + diff --git a/graphrag/graphrag_kg_creation/backend/routes/common.py b/graphrag/graphrag_kg_creation/backend/routes/common.py new file mode 100644 index 0000000..1cd3d55 --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/routes/common.py @@ -0,0 +1,557 @@ +""" +Common utility functions for OpenAI Agents SDK integration. +Shared by openai_agents.py and openai_agents_with_planning.py. +""" +import json +from typing import Dict, List, Any, Optional +from agents.items import ToolCallItem, MessageOutputItem, ToolCallOutputItem, ReasoningItem +from agents.items import ItemHelpers + + +def extract_structured_content(result: Any) -> Any: + """ + Extract structuredContent from an MCP tool result. + + Handles various result formats: + - Objects with structuredContent attribute + - Dicts with structuredContent or content keys + - Objects with content attribute (including lists and text attributes) + - Fallback to the result itself + + Args: + result: The result object from an MCP tool call + + Returns: + The extracted structuredContent, or the result itself if extraction fails + """ + # First priority: structuredContent attribute + if hasattr(result, 'structuredContent'): + return result.structuredContent + + # Second priority: dict with structuredContent or content + if isinstance(result, dict): + return result.get('structuredContent') or result.get('content') + + # Third priority: content attribute + if hasattr(result, 'content'): + content = result.content + + # Handle list of content items + if hasattr(content, '__iter__') and not isinstance(content, str): + content_list = [] + for item in content: + if hasattr(item, 'text'): + content_list.append(item.text) + elif isinstance(item, str): + content_list.append(item) + else: + content_list.append(str(item)) + return '\n'.join(content_list) if content_list else None + + # Handle content with text attribute + if hasattr(content, 'text'): + return content.text + + # Return content as-is + return content + + # Fallback: return result as-is + return result + + +def extract_tools_used(result, include_nested: bool = True) -> list[dict]: + """ + Extract tool calls from the agent run result, including nested tool calls from agent-as-tool invocations. + + Args: + result: The RunResult object from Runner.run() + include_nested: If True, includes nested tool calls from agent-as-tool invocations (e.g., query_executor -> get_schema) + + Returns: + A list of dictionaries containing tool name, arguments, and nested tools for each tool call. + Format: { + "name": "tool_name", + "arguments": {...}, + "nested_tools": [...] # Only present if nested tools exist + } + """ + tools_used = [] + + # Agent-as-tool names that might have nested calls + agent_tool_names = ["query_executor", "query_planner"] + + # Track all items with their positions and types + items_with_positions = [] + for idx, item in enumerate(result.new_items): + item_type = None + tool_name = None + tool_arguments = None + + if isinstance(item, ToolCallItem): + item_type = "tool_call" + raw_item = item.raw_item + + # Handle different raw_item formats + if isinstance(raw_item, dict): + if "function" in raw_item: + tool_name = raw_item["function"].get("name") + tool_arguments = raw_item["function"].get("arguments") + elif "name" in raw_item: + tool_name = raw_item["name"] + tool_arguments = raw_item.get("arguments") + elif hasattr(raw_item, "function"): + tool_name = getattr(raw_item.function, "name", None) + tool_arguments = getattr(raw_item.function, "arguments", None) + elif hasattr(raw_item, "name"): + tool_name = getattr(raw_item, "name", None) + tool_arguments = getattr(raw_item, "arguments", None) + + elif isinstance(item, ToolCallOutputItem): + item_type = "tool_output" + + items_with_positions.append({ + "index": idx, + "type": item_type, + "tool_name": tool_name, + "tool_arguments": tool_arguments, + "item": item, + }) + + # If not including nested, return simple list + if not include_nested: + return [ + {"name": item["tool_name"], "arguments": item["tool_arguments"]} + for item in items_with_positions + if item["type"] == "tool_call" and item["tool_name"] + ] + + # Build tools_used with nested structure + # Match each tool call with its corresponding output to find nested calls + for i, item_info in enumerate(items_with_positions): + if item_info["type"] != "tool_call" or not item_info["tool_name"]: + continue + + tool_entry = { + "name": item_info["tool_name"], + "arguments": item_info["tool_arguments"], + } + + # Check if this is an agent-as-tool call that might have nested tools + if item_info["tool_name"] in agent_tool_names: + # Find the corresponding tool output for this tool call + # Look for the next tool_output item after this tool_call + tool_call_idx = item_info["index"] + corresponding_output_idx = None + + # Find the next tool output (which should correspond to this tool call) + for j in range(tool_call_idx + 1, len(items_with_positions)): + if items_with_positions[j]["type"] == "tool_output": + corresponding_output_idx = j + break + + # Collect nested tool calls between this tool call and its output + nested_tools = [] + if corresponding_output_idx is not None: + # Look for tool calls between this call and its output + for j in range(tool_call_idx + 1, corresponding_output_idx): + nested_item = items_with_positions[j] + if (nested_item["type"] == "tool_call" and + nested_item["tool_name"] and + nested_item["tool_name"] not in agent_tool_names): + nested_tools.append({ + "name": nested_item["tool_name"], + "arguments": nested_item["tool_arguments"], + }) + + if nested_tools: + tool_entry["nested_tools"] = nested_tools + + tools_used.append(tool_entry) + + return tools_used + + +def extract_conversation_history(result, user_question: str) -> list[dict]: + """ + Extract the conversation history between the agent and LLM. + + Args: + result: The RunResult object from Runner.run() + user_question: The original user question + + Returns: + A list of dictionaries representing the conversation history. + """ + conversation = [] + + # Add user question as first message + conversation.append({ + "role": "user", + "type": "message", + "content": user_question, + }) + + # Process all items in order + for item in result.new_items: + if isinstance(item, MessageOutputItem): + # Extract message content + message_text = ItemHelpers.text_message_output(item) + conversation.append({ + "role": "assistant", + "type": "message", + "content": message_text, + }) + + elif isinstance(item, ToolCallItem): + # Extract tool call information + raw_item = item.raw_item + tool_name = None + tool_arguments = None + + if isinstance(raw_item, dict): + if "function" in raw_item: + tool_name = raw_item["function"].get("name") + tool_arguments = raw_item["function"].get("arguments") + elif "name" in raw_item: + tool_name = raw_item["name"] + tool_arguments = raw_item.get("arguments") + elif hasattr(raw_item, "function"): + tool_name = getattr(raw_item.function, "name", None) + tool_arguments = getattr(raw_item.function, "arguments", None) + elif hasattr(raw_item, "name"): + tool_name = getattr(raw_item, "name", None) + tool_arguments = getattr(raw_item, "arguments", None) + + if tool_name: + conversation.append({ + "role": "assistant", + "type": "tool_call", + "tool_name": tool_name, + "arguments": tool_arguments, + }) + + elif isinstance(item, ToolCallOutputItem): + # Extract tool output + output = item.output + # Convert output to string if needed + if isinstance(output, str): + output_str = output + else: + try: + output_str = json.dumps(output, indent=2) + except: + output_str = str(output) + + conversation.append({ + "role": "tool", + "type": "tool_output", + "content": output_str, + }) + + elif isinstance(item, ReasoningItem): + # Extract reasoning content + raw_item = item.raw_item + reasoning_text = None + + if isinstance(raw_item, dict): + reasoning_text = raw_item.get("content") or raw_item.get("text") + elif hasattr(raw_item, "content"): + reasoning_text = getattr(raw_item, "content", None) + elif hasattr(raw_item, "text"): + reasoning_text = getattr(raw_item, "text", None) + + if reasoning_text: + conversation.append({ + "role": "assistant", + "type": "reasoning", + "content": reasoning_text, + }) + + return conversation + + +def build_tool_call_graph(tools_used: list[dict]) -> dict: + """ + Build a graph structure from tool calls showing relationships between tools. + + Args: + tools_used: List of tool call dictionaries with nested_tools + + Returns: + A dictionary with nodes and relationships: + { + "nodes": [ + {"id": "tool_name", "label": "tool_name", "type": "agent"|"mcp"|"custom"} + ], + "relationships": [ + {"source": "parent_tool", "target": "nested_tool", "type": "CALLS"} + ] + } + """ + nodes = {} + relationships = [] + + # Agent-as-tool names + agent_tool_names = ["query_executor", "query_planner", "query_manager"] + # MCP tool names + mcp_tool_names = ["get_schema", "run_query"] + # Custom tool names + custom_tool_names = ["vector_search_on_chunks"] + + def get_tool_type(tool_name: str) -> str: + """Determine tool type based on name.""" + if tool_name in agent_tool_names: + return "agent" + elif tool_name in mcp_tool_names: + return "mcp" + elif tool_name in custom_tool_names: + return "custom" + else: + return "unknown" + + def add_node(tool_name: str): + """Add a node if it doesn't exist.""" + if tool_name not in nodes: + nodes[tool_name] = { + "id": tool_name, + "label": tool_name, + "type": get_tool_type(tool_name), + } + + def add_relationship(source: str, target: str): + """Add a relationship if it doesn't exist.""" + # Check if relationship already exists + if not any(r["source"] == source and r["target"] == target for r in relationships): + relationships.append({ + "source": source, + "target": target, + "type": "CALLS", + }) + + # Process each tool call + for tool in tools_used: + tool_name = tool.get("name") + if not tool_name: + continue + + add_node(tool_name) + + # Add relationships to nested tools + nested_tools = tool.get("nested_tools", []) + for nested_tool in nested_tools: + nested_tool_name = nested_tool.get("name") + if nested_tool_name: + add_node(nested_tool_name) + add_relationship(tool_name, nested_tool_name) + + return { + "nodes": list(nodes.values()), + "relationships": relationships, + } + + +def build_trace_graph_from_items(result, user_question: str) -> Dict[str, Any]: + """ + Build a trace graph from result.new_items, similar to tracing processor output. + + Args: + result: The RunResult object from Runner.run() + user_question: The original user question + + Returns: + A dictionary containing trace data with nodes and relationships for graph visualization. + Format matches tracing processor output: + { + "nodes": [...], + "relationships": [...], + "trace_id": "...", + "trace_name": "..." + } + """ + nodes = [] + relationships = [] + node_set = set() + + # Agent-as-tool names that represent handoffs + agent_tool_names = ["query_executor", "query_planner", "query_manager"] + + # Create root trace node + trace_id = getattr(result, "run_id", "trace_unknown") + trace_node_id = f"trace_{trace_id}" + + # Extract token usage from result + token_usage = {} + if hasattr(result, "context_wrapper") and hasattr(result.context_wrapper, "usage"): + usage = result.context_wrapper.usage + token_usage = { + "input_tokens": getattr(usage, "input_tokens", 0), + "output_tokens": getattr(usage, "output_tokens", 0), + "total_tokens": getattr(usage, "total_tokens", 0), + } + + nodes.append({ + "id": trace_node_id, + "label": "Trace", + "type": "trace", + "trace_id": trace_id, + "details": token_usage, + }) + node_set.add(trace_node_id) + + # Track current parent and last tool call for linking outputs + parent_stack = [trace_node_id] # Stack to handle nested contexts + current_parent = trace_node_id + last_tool_call_id = None + + # Process items in order + for idx, item in enumerate(result.new_items): + node_id = None + node_label = None + node_type = None + details = {} + + if isinstance(item, ToolCallItem): + # Extract tool call information + raw_item = item.raw_item + tool_name = None + tool_arguments = None + + if isinstance(raw_item, dict): + if "function" in raw_item: + tool_name = raw_item["function"].get("name") + tool_arguments = raw_item["function"].get("arguments") + elif "name" in raw_item: + tool_name = raw_item["name"] + tool_arguments = raw_item.get("arguments") + elif hasattr(raw_item, "function"): + tool_name = getattr(raw_item.function, "name", None) + tool_arguments = getattr(raw_item.function, "arguments", None) + elif hasattr(raw_item, "name"): + tool_name = getattr(raw_item, "name", None) + tool_arguments = getattr(raw_item, "arguments", None) + + if tool_name: + node_id = f"tool_call_{idx}" + node_label = tool_name + last_tool_call_id = node_id + + # Determine if this is a handoff (agent calling another agent) + if tool_name in agent_tool_names: + node_type = "handoff" + details['target_agent'] = tool_name + if tool_arguments: + # Try to extract message from arguments + if isinstance(tool_arguments, dict): + details['message'] = tool_arguments.get('question') or tool_arguments.get('query') or tool_arguments + else: + details['message'] = tool_arguments + details['handoff_data'] = tool_arguments + else: + node_type = "function" + details['function_name'] = tool_name + if tool_arguments: + # Try to parse JSON arguments if they're strings + if isinstance(tool_arguments, str): + try: + details['arguments'] = json.loads(tool_arguments) + except: + details['arguments'] = tool_arguments + else: + details['arguments'] = tool_arguments + + elif isinstance(item, ToolCallOutputItem): + # Tool output - link to the last tool call + output = item.output + output_str = None + + if isinstance(output, str): + output_str = output + else: + try: + output_str = json.dumps(output, indent=2) + except: + output_str = str(output) + + # Add output as details to the last tool call node + if last_tool_call_id: + for node in nodes: + if node["id"] == last_tool_call_id: + node["details"]["result"] = output_str + break + + # Create a response node linked to the tool call + if last_tool_call_id: + node_id = f"tool_output_{idx}" + node_label = "Tool Output" + node_type = "response" + details['content'] = output_str + details['response'] = output_str + + elif isinstance(item, MessageOutputItem): + # LLM response/message + message_text = ItemHelpers.text_message_output(item) + node_id = f"message_{idx}" + node_label = "LLM Response" + node_type = "generation" + details['response'] = message_text + details['content'] = message_text + + elif isinstance(item, ReasoningItem): + # Reasoning item + raw_item = item.raw_item + reasoning_text = None + + if isinstance(raw_item, dict): + reasoning_text = raw_item.get("content") or raw_item.get("text") + elif hasattr(raw_item, "content"): + reasoning_text = getattr(raw_item, "content", None) + elif hasattr(raw_item, "text"): + reasoning_text = getattr(raw_item, "text", None) + + if reasoning_text: + node_id = f"reasoning_{idx}" + node_label = "Reasoning" + node_type = "generation" + details['response'] = reasoning_text + details['content'] = reasoning_text + + # Add node if we created one + if node_id and node_label and node_type: + nodes.append({ + "id": node_id, + "label": node_label, + "type": node_type, + "trace_id": trace_id, + "details": details, + }) + node_set.add(node_id) + + # Add relationship to current parent + relationships.append({ + "source": current_parent, + "target": node_id, + "type": "child", + }) + + # Update current parent for handoffs (they create a new context) + if node_type == "handoff": + parent_stack.append(node_id) + current_parent = node_id + # For tool outputs, link them to their tool call instead of parent + elif node_type == "response" and last_tool_call_id: + # Update relationship to link to tool call + relationships[-1]["source"] = last_tool_call_id + # If this is output for a handoff, pop the stack to return to previous parent + if last_tool_call_id in [n["id"] for n in nodes if n.get("type") == "handoff"]: + if len(parent_stack) > 1: + parent_stack.pop() + current_parent = parent_stack[-1] + + return { + "nodes": nodes, + "relationships": relationships, + "trace_id": trace_id, + "trace_name": "Agent Execution Trace", + "token_usage": token_usage, + } + diff --git a/graphrag/graphrag_kg_creation/backend/routes/custom_tools.py b/graphrag/graphrag_kg_creation/backend/routes/custom_tools.py new file mode 100644 index 0000000..07d58da --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/routes/custom_tools.py @@ -0,0 +1,490 @@ +""" +Custom function tools for OpenAI Agents SDK. +These tools extend the capabilities of agents beyond MCP tools. +""" + +import json +import logging +from agents.tool import function_tool +from agents.mcp.server import MCPServer +from .common import extract_structured_content + +logger = logging.getLogger(__name__) + + +def create_vector_search_tool(mcp_server: MCPServer): + """ + Create a vector_search_on_chunks tool that uses an MCP server (with interceptor). + All queries will go through the interceptor. + + Args: + mcp_server: MCP server instance to use for queries (should be InterceptingMCPServer) + + Returns: + A function_tool decorated function for vector search + """ + + @function_tool + async def vector_search_on_chunks(question: str, limit: int = 10) -> str: + """ + Perform vector search on chunks in Memgraph using embeddings. + + This tool generates embeddings from the user's question and searches for similar chunks + in the vector index 'vs_name'. It executes the query directly and returns results. + + Args: + question: The user's question to search for + limit: Maximum number of results to return (default: 10) - Recommendation is to use 10. + + Returns: + JSON string containing the search results with nodes and similarity scores. + Each result contains a node and its similarity score. + """ + # Sanitize the question to prevent Cypher injection + sanitized_query = question.replace("'", "\\'").replace("\\", "\\\\") + + # Construct the vector search query exactly as specified + cypher_query = f"""CALL embeddings.text(["{sanitized_query}"]) yield dimension, embeddings, success +WITH embeddings[0] as embedding +CALL vector_search.search("vs_name", {limit}, embedding) YIELD distance, node, similarity +RETURN node, similarity""" + + try: + logger.info( + f"🔍 vector_search_on_chunks called\n" + f" - Question: '{question[:100]}{'...' if len(question) > 100 else ''}'\n" + f" - Limit: {limit}" + ) + + # Use MCP server's call_tool method - this will go through the interceptor + tool_result = await mcp_server.call_tool( + "run_query", {"query": cypher_query} + ) + + # Extract structuredContent from MCP tool result + content = extract_structured_content(tool_result) + + if content is None: + logger.warning("vector_search_on_chunks: No structuredContent found in tool result") + return json.dumps( + {"error": "No structuredContent found in tool result"} + ) + + # Remove "embedding" fields to save tokens + def remove_embeddings(obj): + """Recursively remove 'embedding' keys from the object.""" + if isinstance(obj, dict): + return { + k: remove_embeddings(v) + for k, v in obj.items() + if k != "embedding" + } + elif isinstance(obj, list): + return [remove_embeddings(item) for item in obj] + else: + return obj + + cleaned_content = remove_embeddings(content) + + # Count results + result_count = len(cleaned_content) if isinstance(cleaned_content, list) else 1 + logger.info( + f"✅ vector_search_on_chunks completed successfully\n" + f" - Results found: {result_count}\n" + f" - Question: '{question[:50]}{'...' if len(question) > 50 else ''}'" + ) + + # Return as JSON string + return json.dumps(cleaned_content, indent=2) + + except Exception as e: + logger.error( + f"❌ Error executing vector_search_on_chunks\n" + f" - Question: '{question[:100]}{'...' if len(question) > 100 else ''}'\n" + f" - Error: {str(e)}", + exc_info=True + ) + return json.dumps({"error": f"Error executing vector search: {str(e)}"}) + + return vector_search_on_chunks + + +def create_inspect_node_properties_tool(mcp_server: MCPServer): + """ + Create an inspect_node_properties tool that inspects properties of nodes with a specific label. + + Args: + mcp_server: MCP server instance to use for queries (should be InterceptingMCPServer) + + Returns: + A function_tool decorated function for inspecting node properties + """ + + @function_tool + async def inspect_node_properties(label: str, sample_limit: int = 5) -> str: + """ + Inspect properties of nodes with a specific label in Memgraph. + + This tool queries the graph to discover what properties exist on nodes with the given label, + and also returns the sample property values to understand the data structure. + + Args: + label: The node label to inspect (e.g., "Chunk", "Person", "Document") + sample_limit: Maximum number of sample nodes to return (default: 5) + + Returns: + JSON string containing: + - properties: List of property keys found on nodes with this label + - sample_nodes: Sample nodes with their property values (limited by sample limit) + """ + # Sanitize the label to prevent Cypher injection + # Labels in Cypher are typically alphanumeric with underscores, but we'll be safe + sanitized_label = label.replace("'", "").replace("\\", "").replace("`", "") + + # Construct query to get properties and sample values for each property + cypher_query = f"""MATCH (n:`{sanitized_label}`) +WITH collect(DISTINCT keys(n)) as all_keys, collect(n) as nodes +WITH all_keys, nodes[0..{sample_limit}] as sample_nodes +WITH all_keys[0] as properties_list, sample_nodes +UNWIND properties_list as property_key +RETURN property_key, [node IN sample_nodes | node[property_key]] as sampled_values_for_property""" + + try: + logger.info( + f"🔍 inspect_node_properties called\n" + f" - Label: '{label}'\n" + f" - Sample limit: {sample_limit}" + ) + + tool_result = await mcp_server.call_tool( + "run_query", {"query": cypher_query} + ) + + # Extract structuredContent from MCP tool result + content = extract_structured_content(tool_result) + + if content is None: + logger.warning(f"inspect_node_properties: No structuredContent found for label '{label}'") + return json.dumps( + {"error": "No structuredContent found in tool result"} + ) + + # Format the response nicely + # The new query returns a list of rows, each with property_key and sampled_values_for_property + result = { + "label": label, + "properties": [] + } + + # Process the results - each row contains a property_key and its sampled values + if isinstance(content, list): + for row in content: + if isinstance(row, dict): + property_key = row.get("property_key") + sampled_values = row.get("sampled_values_for_property", []) + if property_key: + result["properties"].append({ + "key": property_key, + "sampled_values": sampled_values + }) + elif isinstance(content, dict): + # Single result format (shouldn't happen with new query, but handle it) + property_key = content.get("property_key") + sampled_values = content.get("sampled_values_for_property", []) + if property_key: + result["properties"].append({ + "key": property_key, + "sampled_values": sampled_values + }) + + property_count = len(result["properties"]) + logger.info( + f"✅ inspect_node_properties completed successfully\n" + f" - Label: '{label}'\n" + f" - Properties found: {property_count}" + ) + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error( + f"❌ Error executing inspect_node_properties\n" + f" - Label: '{label}'\n" + f" - Sample limit: {sample_limit}\n" + f" - Error: {str(e)}", + exc_info=True, + ) + return json.dumps({"error": f"Error inspecting node properties: {str(e)}"}) + + return inspect_node_properties + + +def create_keyword_search_tool(mcp_server: MCPServer): + """ + Create a keyword_search tool that performs text search on a specific property. + + Args: + mcp_server: MCP server instance to use for queries (should be InterceptingMCPServer) + + Returns: + A function_tool decorated function for keyword search + """ + @function_tool + async def keyword_search(property_name: str, search_term: str, limit: int = 10) -> str: + """ + Perform keyword search on nodes using text_search.search_all on a specific property. + + This tool searches for nodes where the specified property contains the search term. + It uses Memgraph's text_search module to find matching nodes and returns them ordered by relevance score. + + Args: + property_name: The property name to search on (e.g., "entity_id", "text", "name") + search_term: The keyword or phrase to search for + limit: Maximum number of results to return (default: 10) + + Returns: + JSON string containing: + - results: List of matching nodes with their relevance scores, ordered by score descending + - Each result contains: node (with all properties) and score (relevance score) + """ + # Sanitize inputs to prevent Cypher injection + sanitized_property = property_name.replace("'", "").replace("\\", "").replace("`", "") + sanitized_term = search_term.replace("'", "\\'").replace("\\", "\\\\") + + # Construct the keyword search query + cypher_query = f"""CALL text_search.search_all("{sanitized_property}", "{sanitized_term}") YIELD node, score +RETURN node, score +ORDER BY score DESC +LIMIT {limit}""" + + try: + logger.info( + f"🔍 keyword_search called\n" + f" - Property: '{property_name}'\n" + f" - Search term: '{search_term[:100]}{'...' if len(search_term) > 100 else ''}'\n" + f" - Limit: {limit}" + ) + + tool_result = await mcp_server.call_tool("run_query", {"query": cypher_query}) + + # Extract structuredContent from MCP tool result + content = extract_structured_content(tool_result) + + if content is None: + logger.warning( + f"keyword_search: No structuredContent found\n" + f" - Property: '{property_name}'\n" + f" - Search term: '{search_term[:50]}{'...' if len(search_term) > 50 else ''}'" + ) + return json.dumps({"error": "No structuredContent found in tool result"}) + + # Format the response + result = { + "property_searched": property_name, + "search_term": search_term, + "results": [] + } + + # Handle different response formats + if isinstance(content, list): + for row in content: + if isinstance(row, dict): + node_data = row.get("node", {}) + score = row.get("score", 0.0) + result["results"].append({ + "node": node_data, + "score": score + }) + elif isinstance(content, dict): + # Single result format + if "node" in content and "score" in content: + result["results"].append({ + "node": content.get("node", {}), + "score": content.get("score", 0.0) + }) + + result_count = len(result["results"]) + logger.info( + f"✅ keyword_search completed successfully\n" + f" - Property: '{property_name}'\n" + f" - Search term: '{search_term[:50]}{'...' if len(search_term) > 50 else ''}'\n" + f" - Results found: {result_count}" + ) + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error( + f"❌ Error executing keyword_search\n" + f" - Property: '{property_name}'\n" + f" - Search term: '{search_term[:100]}{'...' if len(search_term) > 100 else ''}'\n" + f" - Limit: {limit}\n" + f" - Error: {str(e)}", + exc_info=True + ) + return json.dumps({"error": f"Error executing keyword search: {str(e)}"}) + + return keyword_search + + +def create_relevance_expansion_tool(mcp_server: MCPServer): + """ + Create a relevance_expansion tool that expands from a node ID to its neighborhood. + + Args: + mcp_server: MCP server instance to use for queries (should be InterceptingMCPServer) + + Returns: + A function_tool decorated function for relevance expansion + """ + @function_tool + async def relevance_expansion(node_id: int) -> str: + """ + Expand from a node by ID to explore its neighborhood (connected nodes and relationships). + + This tool retrieves all nodes and relationships connected to a given node, allowing + exploration of the graph structure around an interesting node. Useful for discovering + related entities, context, or additional information connected to a node of interest. + + Args: + node_id: The internal Memgraph node ID to expand from + + Returns: + JSON string containing: + - center_node: The central node that was expanded from (with all properties) + - neighbors: List of neighboring nodes (with all properties) + - relationships: List of relationships connecting the center node to neighbors + - Each relationship entry contains: type, properties, and connected node info + """ + # Validate node_id is an integer + try: + node_id_int = int(node_id) + except (ValueError, TypeError): + logger.error( + f"❌ Invalid node_id for relevance_expansion\n" + f" - Provided node_id: {node_id}\n" + f" - Error: Must be an integer" + ) + return json.dumps({"error": f"Invalid node_id: {node_id}. Must be an integer."}) + + logger.info( + f"🔍 relevance_expansion called\n" + f" - Node ID: {node_id_int}" + ) + + # Construct the relevance expansion query + cypher_query = f"""MATCH (n)-[r]-(m) +WHERE id(n) = {node_id_int} +RETURN n as center_node, + collect(DISTINCT {{relationship: r, neighbor: m}}) as connections""" + + try: + tool_result = await mcp_server.call_tool("run_query", {"query": cypher_query}) + + # Extract structuredContent from MCP tool result + content = extract_structured_content(tool_result) + + if content is None: + logger.warning( + f"relevance_expansion: No structuredContent found\n" + f" - Node ID: {node_id_int}" + ) + return json.dumps({"error": "No structuredContent found in tool result"}) + + # Format the response + result = { + "center_node": None, + "neighbors": [], + "relationships": [] + } + + # Handle different response formats + if isinstance(content, list) and len(content) > 0: + row = content[0] + if isinstance(row, dict): + # Center node + center_node = row.get("center_node", {}) + if center_node: + result["center_node"] = center_node + + # Connections + connections = row.get("connections", []) + neighbors_set = set() # Use set to avoid duplicates + relationships_list = [] + + for conn in connections: + if isinstance(conn, dict): + rel_data = conn.get("relationship", {}) + neighbor = conn.get("neighbor", {}) + + if neighbor: + # Add neighbor (using node ID as key to avoid duplicates) + neighbor_id = neighbor.get("id") if isinstance(neighbor, dict) else None + if neighbor_id not in neighbors_set: + neighbors_set.add(neighbor_id) + result["neighbors"].append(neighbor) + + if rel_data: + # Extract relationship info + rel_info = { + "type": rel_data.get("type", ""), + "properties": rel_data.get("properties", {}), + "start_node_id": rel_data.get("start_node_id"), + "end_node_id": rel_data.get("end_node_id") + } + relationships_list.append(rel_info) + + result["relationships"] = relationships_list + elif isinstance(content, dict): + # Single result format + center_node = content.get("center_node", {}) + if center_node: + result["center_node"] = center_node + + connections = content.get("connections", []) + neighbors_set = set() + relationships_list = [] + + for conn in connections: + if isinstance(conn, dict): + rel_data = conn.get("relationship", {}) + neighbor = conn.get("neighbor", {}) + + if neighbor: + neighbor_id = neighbor.get("id") if isinstance(neighbor, dict) else None + if neighbor_id not in neighbors_set: + neighbors_set.add(neighbor_id) + result["neighbors"].append(neighbor) + + if rel_data: + rel_info = { + "type": rel_data.get("type", ""), + "properties": rel_data.get("properties", {}), + "start_node_id": rel_data.get("start_node_id"), + "end_node_id": rel_data.get("end_node_id") + } + relationships_list.append(rel_info) + + result["relationships"] = relationships_list + + neighbor_count = len(result["neighbors"]) + relationship_count = len(result["relationships"]) + logger.info( + f"✅ relevance_expansion completed successfully\n" + f" - Node ID: {node_id_int}\n" + f" - Neighbors found: {neighbor_count}\n" + f" - Relationships found: {relationship_count}" + ) + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error( + f"❌ Error executing relevance_expansion\n" + f" - Node ID: {node_id_int}\n" + f" - Error: {str(e)}", + exc_info=True + ) + return json.dumps({"error": f"Error executing relevance expansion: {str(e)}"}) + + return relevance_expansion diff --git a/graphrag/graphrag_kg_creation/backend/routes/ingest.py b/graphrag/graphrag_kg_creation/backend/routes/ingest.py new file mode 100644 index 0000000..1fd0897 --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/routes/ingest.py @@ -0,0 +1,246 @@ +""" +Ingestion endpoints for processing documents into the knowledge graph. +""" +import os +import json +import logging +import sys +from fastapi import APIRouter, HTTPException, Request + +# Add parent directory to path for imports +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from unstructured2graph import from_unstructured, make_chunks, compute_embeddings +from resources import initialize_resources + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/ingest", tags=["ingestion"]) + + +@router.post("") +async def ingest_documents(request: Request): + """ + Ingest unstructured documents from URLs into Memgraph using unstructured2graph. + + Expected JSON body: + { + "urls": ["url1", "url2", ...], + "only_chunks": false, + "link_chunks": true, + "create_vector_index": true, + "cleanup": true + } + """ + # Parse request body + try: + body = await request.json() + except json.JSONDecodeError: + raise HTTPException( + status_code=400, + detail="Invalid JSON in request body" + ) + + # Extract parameters with defaults + urls = body.get("urls", []) + only_chunks = body.get("only_chunks", False) + link_chunks = body.get("link_chunks", True) + create_vector_index = body.get("create_vector_index", True) + cleanup = body.get("cleanup", True) + + # Verify OPENAI_API_KEY is set + if not os.getenv("OPENAI_API_KEY"): + raise HTTPException( + status_code=500, + detail="OPENAI_API_KEY environment variable is not set. Please set your OpenAI API key." + ) + + if not urls or not isinstance(urls, list): + raise HTTPException( + status_code=400, + detail="No URLs provided. Please provide at least one URL in the 'urls' array." + ) + + try: + logger.info(f"Processing {len(urls)} documentation pages. Starting ingestion...") + + memgraph, lightrag_wrapper = await initialize_resources(cleanup=cleanup, create_vector_index=create_vector_index) + + # Ingest documents + await from_unstructured( + urls, + memgraph, + lightrag_wrapper, + only_chunks=only_chunks, + link_chunks=link_chunks, + ) + + # Create vector index and compute embeddings if requested + if create_vector_index: + logger.info("Creating vector search index and computing embeddings...") + try: + compute_embeddings(memgraph, "Chunk") + logger.info("Successfully created vector index and computed embeddings") + except Exception as e: + logger.warning(f"Error creating vector index or computing embeddings: {str(e)}") + + logger.info("Successfully ingested all documentation pages into Memgraph!") + + return { + "message": "Successfully ingested all documentation pages into Memgraph!", + "urls_processed": len(urls), + "status": "completed" + } + + except Exception as e: + logger.error(f"Error during ingestion: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error during ingestion: {str(e)}" + ) + + +@router.post("/single") +async def ingest_single_document(request: Request): + """ + Ingest a single document from a URL into Memgraph. + Useful for progress tracking - process one document at a time. + + Expected JSON body: + { + "url": "https://example.com/doc", + "only_chunks": false, + "link_chunks": true, + "create_vector_index": true, + "cleanup": false # Usually false for single document ingestion + } + """ + # Parse request body + try: + body = await request.json() + except json.JSONDecodeError: + raise HTTPException( + status_code=400, + detail="Invalid JSON in request body" + ) + + # Extract parameters with defaults + url = body.get("url") + only_chunks = body.get("only_chunks", False) + link_chunks = body.get("link_chunks", True) + create_vector_index = body.get("create_vector_index", True) + cleanup = body.get("cleanup", False) + + # Verify OPENAI_API_KEY is set + if not os.getenv("OPENAI_API_KEY"): + raise HTTPException( + status_code=500, + detail="OPENAI_API_KEY environment variable is not set. Please set your OpenAI API key." + ) + + if not url or not isinstance(url, str): + raise HTTPException( + status_code=400, + detail="No URL provided. Please provide a 'url' string." + ) + + try: + logger.info(f"Processing single document: {url}") + + memgraph, lightrag_wrapper = await initialize_resources(cleanup=cleanup, create_vector_index=create_vector_index) + + # Ingest single document + await from_unstructured( + [url], + memgraph, + lightrag_wrapper, + only_chunks=only_chunks, + link_chunks=link_chunks, + ) + + # Create vector index and compute embeddings if requested + if create_vector_index: + logger.info("Computing embeddings and creating vector search index...") + try: + # Compute embeddings for all chunks (including newly added ones) + compute_embeddings(memgraph, "Chunk") + logger.info("Successfully computed embeddings and created vector index") + except Exception as e: + logger.warning(f"Error computing embeddings or creating vector index: {str(e)}") + + logger.info(f"Successfully ingested document: {url}") + + return { + "message": f"Successfully ingested document: {url}", + "url": url, + "status": "completed" + } + + except Exception as e: + logger.error(f"Error during ingestion: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error during ingestion: {str(e)}" + ) + + +@router.post("/estimate") +async def estimate_ingestion(request: Request): + """ + Estimate the number of chunks and processing time for given URLs. + Returns chunk count per document and total estimated time (10 seconds per chunk). + + Expected JSON body: + { + "urls": ["url1", "url2", ...] + } + """ + try: + body = await request.json() + except json.JSONDecodeError: + raise HTTPException( + status_code=400, + detail="Invalid JSON in request body" + ) + + urls = body.get("urls", []) + + if not urls or not isinstance(urls, list): + raise HTTPException( + status_code=400, + detail="No URLs provided. Please provide at least one URL in the 'urls' array." + ) + + try: + # Get chunk estimates without full processing + chunked_documents = make_chunks(urls) + + estimates = [] + total_chunks = 0 + + for doc in chunked_documents: + chunk_count = len(doc.chunks) + total_chunks += chunk_count + estimated_time = chunk_count * 10 # 10 seconds per chunk + + estimates.append({ + "url": str(doc.source), + "chunk_count": chunk_count, + "estimated_time_seconds": estimated_time + }) + + total_estimated_time = total_chunks * 10 + + return { + "total_chunks": total_chunks, + "total_estimated_time_seconds": total_estimated_time, + "documents": estimates + } + + except Exception as e: + logger.error(f"Error estimating ingestion: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error estimating ingestion: {str(e)}" + ) + diff --git a/graphrag/graphrag_kg_creation/backend/routes/llm_utils.py b/graphrag/graphrag_kg_creation/backend/routes/llm_utils.py new file mode 100644 index 0000000..db28e4e --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/routes/llm_utils.py @@ -0,0 +1,80 @@ +""" +LLM utilities for generating answers from query results. +""" +import os +import json +import logging +from typing import Any, Optional +from openai import OpenAI +from .prompts import SYSTEM_MESSAGE, create_user_message + +logger = logging.getLogger(__name__) + + +def generate_answer( + query_result: Any, + question: str, + model: str = "gpt-4o", + temperature: float = 0.1, + api_key: Optional[str] = None, + fallback_to_raw: bool = True +) -> str: + """ + Generate an answer using LLM from query result. + + Args: + query_result: The query result (structuredContent) to use as context + question: The question to answer + model: OpenAI model to use (default: "gpt-4o") + temperature: Temperature for generation (default: 0.1) + api_key: OpenAI API key (if None, uses OPENAI_API_KEY env var) + fallback_to_raw: If True, return raw results if LLM fails (default: True) + + Returns: + Generated answer string or fallback message + + Raises: + ValueError: If no query result provided and fallback_to_raw is False + Exception: If LLM API call fails and fallback_to_raw is False + """ + if query_result is None: + if fallback_to_raw: + return f"No results found for your question: '{question}'." + else: + raise ValueError("No query result provided") + + # Get API key + openai_api_key = api_key or os.environ.get("OPENAI_API_KEY") + if not openai_api_key: + if fallback_to_raw: + logger.warning("OpenAI API key not set. Returning raw results.") + return f"Retrieved results for your question: '{question}'.\n\nResults:\n{json.dumps(query_result, indent=2)}\n\nNote: OpenAI API key not set. Set OPENAI_API_KEY environment variable to enable LLM answer generation." + else: + raise ValueError("OpenAI API key not set. Set OPENAI_API_KEY environment variable.") + + # Try to generate LLM answer + try: + # Convert query result to string context + context = json.dumps(query_result, indent=2) + user_message = create_user_message(context, question) + + # Call OpenAI API + client = OpenAI(api_key=openai_api_key) + completion = client.chat.completions.create( + model=model, + messages=[ + {"role": "system", "content": SYSTEM_MESSAGE}, + {"role": "user", "content": user_message}, + ], + temperature=temperature, + ) + + return completion.choices[0].message.content + except Exception as e: + # API call failed + logger.error(f"Error calling OpenAI API: {str(e)}", exc_info=True) + if fallback_to_raw: + return f"Retrieved results for your question: '{question}'.\n\nResults:\n{json.dumps(query_result, indent=2)}\n\nNote: LLM answer generation failed: {str(e)}" + else: + raise + diff --git a/graphrag/graphrag_kg_creation/backend/routes/mcp.py b/graphrag/graphrag_kg_creation/backend/routes/mcp.py new file mode 100644 index 0000000..9edcc66 --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/routes/mcp.py @@ -0,0 +1,187 @@ +""" +MCP (Model Context Protocol) test endpoints. +""" +import os +import logging +import sys +import httpx +from fastapi import APIRouter, HTTPException +from pydantic import BaseModel +from typing import Dict, Any, Optional + +# Add parent directory to path for imports +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from mcp_client import call_mcp_service + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/mcp", tags=["mcp"]) + +# MCP service URL - use service name when running in Docker, localhost when running locally +# Default to localhost:8001 for local development (host port), or set MCP_MEMGRAPH_URL env var +MCP_URL = os.getenv("MCP_MEMGRAPH_URL", "http://localhost:8001/mcp") + + +@router.get("/test") +async def test_mcp_memgraph(): + """ + Dummy API endpoint to test calling the mcp-memgraph service. + Calls the get_schema tool from mcp-memgraph. + """ + try: + # MCP uses JSON-RPC format for streamable-http transport + # Call the get_schema tool + payload = { + "jsonrpc": "2.0", + "id": 1, + "method": "tools/call", + "params": { + "name": "get_schema", + "arguments": {} + } + } + + result = await call_mcp_service(MCP_URL, payload, timeout=10.0) + + return { + "status": "success", + "mcp_url": MCP_URL, + "mcp_response": result + } + + except httpx.ConnectError as e: + logger.error(f"Failed to connect to mcp-memgraph at {MCP_URL}: {str(e)}") + raise HTTPException( + status_code=503, + detail=f"Could not connect to mcp-memgraph service at {MCP_URL}. Make sure the service is running." + ) + except httpx.HTTPStatusError as e: + logger.error(f"HTTP error from mcp-memgraph: {str(e)}") + raise HTTPException( + status_code=e.response.status_code, + detail=f"Error from mcp-memgraph service: {str(e)}" + ) + except Exception as e: + logger.error(f"Error calling mcp-memgraph: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error calling mcp-memgraph: {str(e)}" + ) + + +@router.get("/tools") +async def list_mcp_tools(): + """ + List all available tools from the mcp-memgraph service. + Uses the tools/list method from MCP JSON-RPC. + """ + try: + # MCP uses JSON-RPC format for streamable-http transport + # Call the tools/list method + payload = { + "jsonrpc": "2.0", + "id": 1, + "method": "tools/list", + "params": {} + } + + result = await call_mcp_service(MCP_URL, payload, timeout=10.0) + + # Extract tools from the response + tools = [] + if "result" in result and "tools" in result["result"]: + tools = result["result"]["tools"] + + return { + "status": "success", + "mcp_url": MCP_URL, + "tools": tools, + "mcp_response": result + } + + except httpx.ConnectError as e: + logger.error(f"Failed to connect to mcp-memgraph at {MCP_URL}: {str(e)}") + raise HTTPException( + status_code=503, + detail=f"Could not connect to mcp-memgraph service at {MCP_URL}. Make sure the service is running." + ) + except httpx.HTTPStatusError as e: + logger.error(f"HTTP error from mcp-memgraph: {str(e)}") + raise HTTPException( + status_code=e.response.status_code, + detail=f"Error from mcp-memgraph service: {str(e)}" + ) + except Exception as e: + logger.error(f"Error listing mcp-memgraph tools: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error listing mcp-memgraph tools: {str(e)}" + ) + + +class CallToolRequest(BaseModel): + tool_name: str + arguments: Dict[str, Any] = {} + + +@router.post("/call") +async def call_mcp_tool(request: CallToolRequest): + """ + Call a specific MCP tool with provided arguments. + """ + try: + # MCP uses JSON-RPC format for streamable-http transport + # Call the specified tool with provided arguments + payload = { + "jsonrpc": "2.0", + "id": 1, + "method": "tools/call", + "params": { + "name": request.tool_name, + "arguments": request.arguments + } + } + + result = await call_mcp_service(MCP_URL, payload, timeout=30.0) + + # Extract the result content + content = None + if "result" in result and "content" in result["result"]: + content = result["result"]["content"] + + return { + "status": "success", + "mcp_url": MCP_URL, + "tool_name": request.tool_name, + "arguments": request.arguments, + "result": content, + "mcp_response": result + } + + except httpx.ConnectError as e: + logger.error(f"Failed to connect to mcp-memgraph at {MCP_URL}: {str(e)}") + raise HTTPException( + status_code=503, + detail=f"Could not connect to mcp-memgraph service at {MCP_URL}. Make sure the service is running." + ) + except httpx.HTTPStatusError as e: + logger.error(f"HTTP error from mcp-memgraph: {str(e)}") + error_detail = f"Error from mcp-memgraph service: {str(e)}" + if e.response.text: + try: + error_data = e.response.json() + error_detail = error_data.get("detail", error_detail) + except: + error_detail = e.response.text[:200] + raise HTTPException( + status_code=e.response.status_code, + detail=error_detail + ) + except Exception as e: + logger.error(f"Error calling mcp-memgraph tool: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error calling mcp-memgraph tool: {str(e)}" + ) + diff --git a/graphrag/graphrag_kg_creation/backend/routes/mcp_interceptor.py b/graphrag/graphrag_kg_creation/backend/routes/mcp_interceptor.py new file mode 100644 index 0000000..5fdfa34 --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/routes/mcp_interceptor.py @@ -0,0 +1,196 @@ +""" +MCP Server interceptor for capturing tool calls. +This module provides a wrapper around MCP servers to intercept and log tool calls, +particularly useful for capturing run_query calls. +""" +import logging +import json +import traceback +import hashlib +from typing import List, Dict, Any, Optional, Callable, Awaitable +from agents.mcp.server import MCPServer +from .common import extract_structured_content + +logger = logging.getLogger(__name__) + + +class InterceptingMCPServer(MCPServer): + """ + Wrapper around an MCP server that intercepts tool calls to capture run_query calls. + This allows us to track all Cypher queries executed during a request. + Supports streaming callbacks for real-time updates. + Implements query result caching to prevent duplicate executions. + """ + + def __init__(self, wrapped_server: MCPServer, stream_callback: Optional[Callable[[Dict[str, Any]], Awaitable[None]]] = None): + """ + Args: + wrapped_server: The actual MCP server to wrap + stream_callback: Optional async callback function to stream messages when tools are called. + Should accept a dict with 'type', 'tool_name', 'query' (optional), and 'message' keys. + """ + super().__init__(use_structured_content=wrapped_server.use_structured_content) + self._wrapped = wrapped_server + self._run_query_calls: List[Dict[str, Any]] = [] + self._stream_callback = stream_callback + # Query result cache: maps query hash to cached result + self._query_cache: Dict[str, Any] = {} + + @property + def name(self) -> str: + return self._wrapped.name + + async def connect(self): + await self._wrapped.connect() + + async def cleanup(self): + await self._wrapped.cleanup() + + async def list_tools(self, run_context=None, agent=None): + return await self._wrapped.list_tools(run_context, agent) + + def _get_query_hash(self, query: str) -> str: + """Generate a hash for a query string to use as cache key.""" + return hashlib.sha256(query.encode('utf-8')).hexdigest() + + async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> Any: + """ + Intercept tool calls and capture run_query calls along with their results. + Streams temporary messages when tools are called. + Implements caching for run_query calls to prevent duplicate executions. + """ + # Handle run_query caching + if tool_name == "run_query" and arguments: + query = arguments.get("query", "") + query_hash = self._get_query_hash(query) + + # Check cache first + if query_hash in self._query_cache: + cached_result = self._query_cache[query_hash] + logger.info( + f"✅ CACHE HIT for run_query - returning cached result\n" + f" - Query: {query[:150]}...\n" + f" - Cache key: {query_hash[:16]}..." + ) + + # Extract structured content from cached result for tracking + cached_content = extract_structured_content(cached_result) + + # Still track this call (but mark it as cached) + call_index = len(self._run_query_calls) + self._run_query_calls.append({ + "query": query, + "index": call_index, + "result": cached_content, + "cached": True, # Mark as cached + }) + + # Stream cached result message + if self._stream_callback: + await self._stream_callback({ + "type": "tool_call_start", + "tool_name": tool_name, + "query": query, + "message": f"Calling {tool_name} (cached):\n\n{query}", + "cached": True + }) + await self._stream_callback({ + "type": "tool_call_complete", + "tool_name": tool_name, + "message": f"Completed {tool_name} (from cache)", + "cached": True + }) + + # Return cached result (original format preserved) + return cached_result + + # Not in cache - proceed with execution + logger.debug(f"Cache miss for query: {query[:100]}...") + + # Stream tool call start message + if self._stream_callback: + full_query = "" + if tool_name == "run_query" and arguments: + full_query = arguments.get("query", "") + + # Build full message with complete query + if tool_name == "run_query" and full_query: + message = f"Calling {tool_name}:\n\n{full_query}" + else: + message = f"Calling {tool_name}..." + + await self._stream_callback({ + "type": "tool_call_start", + "tool_name": tool_name, + "query": full_query if tool_name == "run_query" else None, + "message": message + }) + + # Capture run_query calls (before execution) + if tool_name == "run_query" and arguments: + query = arguments.get("query", "") + call_index = len(self._run_query_calls) + self._run_query_calls.append({ + "query": query, + "index": call_index, + "result": None, # Will be populated after the call + "cached": False, # Mark as not cached + }) + logger.info(f"Intercepted run_query call #{call_index + 1}: {query[:100]}...") + + # Call the actual tool + result = await self._wrapped.call_tool(tool_name, arguments) + + # Capture the result if this was a run_query call + if tool_name == "run_query" and arguments: + query = arguments.get("query", "") + query_hash = self._get_query_hash(query) + + # Cache the original result object (preserves format) + self._query_cache[query_hash] = result + logger.debug(f"Cached result for query hash: {query_hash[:16]}...") + + # Extract structuredContent from result for tracking + result_data = extract_structured_content(result) + + # Update the last call with the extracted result data + if self._run_query_calls: + self._run_query_calls[-1]["result"] = result_data + logger.info(f"Captured result for run_query call #{len(self._run_query_calls)}") + + # Stream tool call completion message + if self._stream_callback: + await self._stream_callback({ + "type": "tool_call_complete", + "tool_name": tool_name, + "message": f"Completed {tool_name}" + }) + + return result + + async def list_prompts(self): + return await self._wrapped.list_prompts() + + async def get_prompt(self, name: str, arguments: dict[str, Any] | None = None): + return await self._wrapped.get_prompt(name, arguments) + + def get_run_query_calls(self) -> List[Dict[str, Any]]: + """Get all intercepted run_query calls.""" + return self._run_query_calls.copy() + + def clear_run_query_calls(self): + """Clear the intercepted run_query calls list.""" + self._run_query_calls.clear() + + def clear_cache(self): + """Clear the query result cache.""" + self._query_cache.clear() + logger.debug("Query cache cleared") + + def get_cache_stats(self) -> Dict[str, Any]: + """Get statistics about the cache.""" + return { + "cache_size": len(self._query_cache), + "total_calls": len(self._run_query_calls), + "cached_calls": sum(1 for call in self._run_query_calls if call.get("cached", False)), + } diff --git a/graphrag/graphrag_kg_creation/backend/routes/openai_agents.py b/graphrag/graphrag_kg_creation/backend/routes/openai_agents.py new file mode 100644 index 0000000..c1f6190 --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/routes/openai_agents.py @@ -0,0 +1,116 @@ +""" +OpenAI Agents SDK integration with MCP servers. +Uses Streamable HTTP transport for MCP communication. +""" +import os +import logging +import sys +import json +from fastapi import APIRouter, HTTPException, Request +from pydantic import BaseModel + +# Add parent directory to path for imports +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/openai-agents", tags=["openai-agents"]) + +# MCP service URL - use service name when running in Docker, localhost when running locally +# Default to localhost:8001 for local development (host port), or set MCP_MEMGRAPH_URL env var +MCP_URL = os.getenv("MCP_MEMGRAPH_URL", "http://localhost:8001/mcp") + +# Model configuration - defaults to gpt-4o (supports temperature) +# Set OPENAI_AGENTS_MODEL env var to override (e.g., "gpt-4o", "gpt-4o-mini", "o1", etc.) +# Note: GPT-5 and o1 models don't support temperature parameter +AGENT_MODEL = os.getenv("OPENAI_AGENTS_MODEL", "gpt-4o") + +from agents import Agent, Runner +from agents.model_settings import ModelSettings +from agents.mcp import MCPServerStreamableHttp +from agents.mcp.util import create_static_tool_filter +from .prompts import DEFAULT_AGENT_INSTRUCTIONS +from .common import extract_tools_used, extract_conversation_history + + +class QueryRequest(BaseModel): + """Request model for agent queries.""" + question: str + + +@router.post("/query") +async def query_with_agent(request: QueryRequest): + """ + Query the knowledge graph using OpenAI Agents SDK with MCP server. + + The agent uses the MCP server tools to answer questions about the knowledge graph. + This leverages the OpenAI Agents SDK which handles tool selection and execution automatically. + + Expected JSON body: + { + "question": "What is Memgraph?" + } + """ + if not request.question or not isinstance(request.question, str): + raise HTTPException( + status_code=400, + detail="No question provided. Please provide a 'question' string." + ) + + try: + # Configure MCP server connection using Streamable HTTP transport + # The MCP server uses streamable-http transport on the /mcp endpoint + + # Create MCP server instance with Streamable HTTP transport and use as async context manager + # Filter tools to only allow run_query and get_schema + async with MCPServerStreamableHttp( + name="Memgraph MCP Server", + params={ + "url": MCP_URL, + "headers": {}, # Add any required headers here + "timeout": 60.0, # Increase timeout to 60 seconds for get_schema and run_query calls + }, + cache_tools_list=True, # Cache tools list to reduce latency + tool_filter=create_static_tool_filter(allowed_tool_names=["run_query", "get_schema"]), + ) as server: + # Configure agent - defaults to gpt-4o (supports temperature) + # Model can be set via OPENAI_AGENTS_MODEL env var + # Note: GPT-5 and o1 models don't support temperature parameter + agent_config = { + "name": "Knowledge Graph Assistant", + "instructions": DEFAULT_AGENT_INSTRUCTIONS, + "model": AGENT_MODEL, # Defaults to gpt-4o + "mcp_servers": [server], + "model_settings": ModelSettings( + tool_choice="required", # Always require tools + temperature=0.1, # Low temperature for deterministic responses (supported by gpt-4o) + ), + } + + # Create agent + agent = Agent(**agent_config) + + # Run the agent with the question + result = await Runner.run(agent, request.question) + + # Extract tool calls from the result + tools_used = extract_tools_used(result) + + # Extract conversation history + conversation_history = extract_conversation_history(result, request.question) + + return { + "question": request.question, + "answer": result.final_output, + "status": "success", + "agent_run_id": getattr(result, "run_id", None), + "tools_used": tools_used, + "conversation_history": conversation_history, + } + + except Exception as e: + logger.error(f"Error running agent: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error running agent: {str(e)}" + ) diff --git a/graphrag/graphrag_kg_creation/backend/routes/openai_agents_with_planning.py b/graphrag/graphrag_kg_creation/backend/routes/openai_agents_with_planning.py new file mode 100644 index 0000000..e845d91 --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/routes/openai_agents_with_planning.py @@ -0,0 +1,321 @@ +""" +OpenAI Agents SDK integration with MCP servers - Multi-Agent Orchestration with Planning. +This variant implements a Manager (agents as tools) pattern: +- Manager agent: Orchestrates the planning and execution process +- Planner agent: Exposed as a tool, generates 5-10 high-level query strategies +- Execution agent: Exposed as a tool, executes strategies using MCP tools + +Reference: https://openai.github.io/openai-agents-python/agents/#multi-agent-system-design-patterns +""" + +import os +import logging +import sys +import json +import re +import uuid +from pathlib import Path +from typing import List, Dict, Any +from fastapi import APIRouter, HTTPException, Request +from pydantic import BaseModel + +# Add parent directory to path for imports +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +logger = logging.getLogger(__name__) + +router = APIRouter( + prefix="/openai-agents-with-planning", tags=["openai-agents-with-planning"] +) + +# MCP service URL - use service name when running in Docker, localhost when running locally +# Default to localhost:8001 for local development (host port), or set MCP_MEMGRAPH_URL env var +MCP_URL = os.getenv("MCP_MEMGRAPH_URL", "http://localhost:8001/mcp") + +# Model configuration - defaults to gpt-4o (supports temperature) +# Set OPENAI_AGENTS_WITH_PLANNING_MODEL env var to override +PLANNING_MODEL = os.getenv("OPENAI_AGENTS_WITH_PLANNING_MODEL", "gpt-4o") + +from agents import Agent, Runner +from agents.model_settings import ModelSettings +from agents.mcp import MCPServerStreamableHttp +from agents.mcp.util import create_static_tool_filter +from agents.memory import SQLiteSession, Session +from .prompts import ( + PLANNER_AGENT_INSTRUCTIONS, + EXECUTION_AGENT_INSTRUCTIONS, + MANAGER_AGENT_INSTRUCTIONS, +) +from .common import extract_tools_used, build_trace_graph_from_items +from .custom_tools import ( + create_vector_search_tool, + create_keyword_search_tool, + create_relevance_expansion_tool, +) +from .mcp_interceptor import InterceptingMCPServer + +# Session storage - dictionary mapping session_id to Session objects +_sessions: dict[str, Session] = {} + +# Directory for session database files +SESSIONS_DIR = Path(os.getenv("SESSIONS_DIR", "sessions")) +SESSIONS_DIR.mkdir(exist_ok=True) # Create directory if it doesn't exist + + +def get_or_create_session(session_id: str | None) -> Session: + """ + Get existing session or create a new one with persistent storage. + + According to the OpenAI Agents SDK documentation: + - Use file-based SQLite for persistent conversations + - Use in-memory SQLite for temporary conversations + + Args: + session_id: Optional session ID. If None, creates a new session. + + Returns: + Session object with the given or newly generated session_id + """ + if session_id is None: + session_id = str(uuid.uuid4()) + + if session_id not in _sessions: + # Use a single shared database file for all sessions + # The SQLiteSession implementation handles session isolation internally + db_path = SESSIONS_DIR / "conversations.db" + + _sessions[session_id] = SQLiteSession( + session_id=session_id, + db_path=str(db_path) + ) + logger.info(f"Created new persistent session: {session_id} (db: {db_path})") + else: + logger.debug(f"Reusing existing session: {session_id}") + + return _sessions[session_id] + + +class QueryRequest(BaseModel): + """Request model for planning agent queries.""" + + question: str + session_id: str | None = None # Optional session ID for conversation continuity + + +def parse_strategies_from_planner_response(response_text: str) -> List[Dict[str, Any]]: + """ + Parse strategies from planner agent response. + Attempts to extract JSON array from the response. + + Args: + response_text: The planner agent's response text + + Returns: + List of strategy dictionaries + """ + strategies = [] + + try: + # Try to find JSON array in the response + json_match = re.search(r"\[[\s\S]*\]", response_text) + if json_match: + strategies_data = json.loads(json_match.group()) + if isinstance(strategies_data, list): + strategies = strategies_data + # Try to find JSON object with "strategies" key + elif re.search(r"\{", response_text): + json_obj_match = re.search(r"\{[\s\S]*\}", response_text, re.DOTALL) + if json_obj_match: + strategies_data = json.loads(json_obj_match.group()) + if ( + isinstance(strategies_data, dict) + and "strategies" in strategies_data + ): + strategies = strategies_data["strategies"] + elif isinstance(strategies_data, list): + strategies = strategies_data + except json.JSONDecodeError as e: + logger.warning(f"Could not parse JSON from planner response: {str(e)}") + logger.debug(f"Response text: {response_text[:500]}") + except Exception as e: + logger.warning(f"Error parsing planner strategies: {str(e)}") + + return strategies + + +def create_planner_agent(model: str) -> Agent: + """ + Create the planner agent that generates query strategies. + + Args: + model: The model to use for the planner + + Returns: + Configured planner agent + """ + return Agent( + name="Query Planner", + instructions=PLANNER_AGENT_INSTRUCTIONS, + model=model, + model_settings=ModelSettings( + tool_choice="none", # Planner doesn't need tools, just generates strategies + temperature=0.7, # Higher temperature for more creative/diverse strategies + ), + ) + + +def create_execution_agent(model: str, server) -> Agent: + """ + Create the execution agent that executes query strategies. + + Args: + model: The model to use for execution + server: The MCP server instance (should be InterceptingMCPServer) + + Returns: + Configured execution agent + """ + # Create custom tools that use the intercepting MCP server + # This way all queries go through the same interceptor + vector_search_tool = create_vector_search_tool(mcp_server=server) + keyword_search_tool = create_keyword_search_tool(mcp_server=server) + relevance_expansion_tool = create_relevance_expansion_tool(mcp_server=server) + + return Agent( + name="Query Executor", + instructions=EXECUTION_AGENT_INSTRUCTIONS, + model=model, + mcp_servers=[server], # Provides MCP tools: get_schema, run_query + tools=[vector_search_tool, keyword_search_tool, relevance_expansion_tool], # Custom tools (use MCP server, go through interceptor) + model_settings=ModelSettings( + tool_choice="required", + temperature=0.1, + parallel_tool_calls=False, + ), + ) + + +def create_manager_agent( + planner_agent: Agent, execution_agent: Agent, model: str +) -> Agent: + """ + Create the manager agent that orchestrates planning and execution. + Uses the Manager (agents as tools) pattern from OpenAI Agents SDK. + + Args: + planner_agent: The planner agent to expose as a tool + execution_agent: The execution agent to expose as a tool + model: The model to use for the manager + + Returns: + Configured manager agent + """ + return Agent( + name="Query Manager", + instructions=MANAGER_AGENT_INSTRUCTIONS, + model=model, + tools=[ + planner_agent.as_tool( + tool_name="query_planner", + tool_description="Generates 5-10 high-level strategies for querying the Memgraph knowledge graph to answer a question. Call this first to get query strategies.", + ), + execution_agent.as_tool( + tool_name="query_executor", + tool_description="Executes a specific query strategy using available tools: MCP tools (get_schema, run_query) and custom tools (vector_search_on_chunks).", + ), + ], + model_settings=ModelSettings( + tool_choice="auto", # Manager decides when to use tools + temperature=0.3, # Moderate temperature for balanced decision-making + ), + ) + + +@router.post("/query") +async def query_with_planning(request: QueryRequest): + """ + Query the knowledge graph using multi-agent orchestration with Manager pattern. + + This variant implements the Manager (agents as tools) pattern: + - Manager agent orchestrates the process and retains control of the conversation + - Planner agent is exposed as a tool that generates query strategies + - Execution agent is exposed as a tool that executes strategies using MCP tools + + Expected JSON body: + { + "question": "What is Memgraph?", + "session_id": "optional-session-id-for-conversation-continuity" + } + """ + if not request.question or not isinstance(request.question, str): + raise HTTPException( + status_code=400, + detail="No question provided. Please provide a 'question' string.", + ) + + try: + # Get or create session for conversation continuity + session = get_or_create_session(request.session_id) + + # Set up MCP server connection + async with MCPServerStreamableHttp( + name="Memgraph MCP Server", + params={ + "url": MCP_URL, + "headers": {}, + "timeout": 60.0, # Increase timeout to 60 seconds for get_schema and run_query calls + }, + cache_tools_list=True, + tool_filter=create_static_tool_filter( + allowed_tool_names=["run_query", "get_schema"] + ), + client_session_timeout_seconds=60.0, + ) as base_server: + # Wrap the server to intercept run_query calls + # The base_server is already connected via the async context manager + intercepting_server = InterceptingMCPServer(base_server) + + # Create specialized sub-agents with the intercepting server + planner_agent = create_planner_agent(PLANNING_MODEL) + execution_agent = create_execution_agent(PLANNING_MODEL, intercepting_server) + + # Create manager agent that orchestrates the sub-agents as tools + manager_agent = create_manager_agent( + planner_agent, execution_agent, PLANNING_MODEL + ) + + logger.info(f"Running manager agent with session {session.session_id}...") + + # Run the manager agent with session - this maintains conversation history + result = await Runner.run( + manager_agent, + request.question, + session=session # Pass session here for conversation continuity + ) + + # Build trace graph from result.new_items + trace_data = build_trace_graph_from_items(result, request.question) + + # Extract information from the manager's run + # Include nested tools from agent-as-tool invocations (e.g., query_executor -> get_schema, run_query) + tools_used = extract_tools_used(result, include_nested=True) + + # Get intercepted run_query calls (includes both MCP run_query and vector_search queries) + run_query_calls = intercepting_server.get_run_query_calls() + + return { + "question": request.question, + "answer": result.final_output, + "status": "success", + "session_id": session.session_id, # Return session_id so frontend can reuse it + "agent_run_id": getattr(result, "run_id", None), + "tools_used": tools_used, + "run_query_calls": run_query_calls, # All intercepted run_query calls + "tool_call_graph": trace_data, # Use trace data instead of tool call graph + } + + except Exception as e: + logger.error(f"Error running planning agent: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, detail=f"Error running planning agent: {str(e)}" + ) diff --git a/graphrag/graphrag_kg_creation/backend/routes/openai_agents_with_reasoning.py b/graphrag/graphrag_kg_creation/backend/routes/openai_agents_with_reasoning.py new file mode 100644 index 0000000..3dbe78c --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/routes/openai_agents_with_reasoning.py @@ -0,0 +1,539 @@ +""" +OpenAI Agents SDK integration with MCP servers - Multi-Agent Orchestration with Planning and Reasoning. +This variant implements a Manager (agents as tools) pattern with explicit reasoning: +- Manager agent: Orchestrates the planning, execution, and reasoning process +- Planner agent: Exposed as a tool, generates 5-10 high-level query strategies +- Execution agent: Exposed as a tool, executes strategies using MCP tools with chain-of-thought reasoning +- Reasoning agent: Exposed as a tool, analyzes execution results and guides next steps + +Reference: https://openai.github.io/openai-agents-python/agents/#multi-agent-system-design-patterns +""" + +import os +import logging +import sys +import json +import re +import uuid +from pathlib import Path +from typing import List, Dict, Any +from fastapi import APIRouter, HTTPException, Request +from fastapi.responses import StreamingResponse +import asyncio +from pydantic import BaseModel + +# Add parent directory to path for imports +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +logger = logging.getLogger(__name__) + +router = APIRouter( + prefix="/openai-agents-with-reasoning", tags=["openai-agents-with-reasoning"] +) + +# MCP service URL - use service name when running in Docker, localhost when running locally +# Default to localhost:8001 for local development (host port), or set MCP_MEMGRAPH_URL env var +MCP_URL = os.getenv("MCP_MEMGRAPH_URL", "http://localhost:8001/mcp") + +# Model configuration - defaults to gpt-4o (supports temperature) +# Set OPENAI_AGENTS_WITH_REASONING_MODEL env var to override +# For reasoning agent, you can use o1-preview or o1-mini for enhanced reasoning capabilities +REASONING_MODEL = os.getenv("OPENAI_AGENTS_WITH_REASONING_MODEL", "gpt-4o") +REASONING_AGENT_MODEL = os.getenv("REASONING_AGENT_MODEL", "gpt-4o") # Can be set to "o1-preview" or "o1-mini" + +from agents import Agent, Runner +from agents.model_settings import ModelSettings +from agents.mcp import MCPServerStreamableHttp +from agents.mcp.util import create_static_tool_filter +from agents.memory import SQLiteSession, Session +from .prompts import ( + PLANNER_AGENT_INSTRUCTIONS, + EXECUTION_AGENT_WITH_REASONING_INSTRUCTIONS, + MANAGER_AGENT_WITH_REASONING_INSTRUCTIONS, + REASONING_AGENT_INSTRUCTIONS, + GRAPH_SCHEMA_AGENT_INSTRUCTIONS, +) +from .common import extract_tools_used, build_trace_graph_from_items +from .custom_tools import ( + create_vector_search_tool, + create_inspect_node_properties_tool, + create_keyword_search_tool, + create_relevance_expansion_tool, +) +from .mcp_interceptor import InterceptingMCPServer + +# Session storage - dictionary mapping session_id to Session objects +_sessions: dict[str, Session] = {} + +# Directory for session database files +SESSIONS_DIR = Path(os.getenv("SESSIONS_DIR", "sessions")) +SESSIONS_DIR.mkdir(exist_ok=True) # Create directory if it doesn't exist + + +def get_or_create_session(session_id: str | None) -> Session: + """ + Get existing session or create a new one with persistent storage. + + According to the OpenAI Agents SDK documentation: + - Use file-based SQLite for persistent conversations + - Use in-memory SQLite for temporary conversations + + Args: + session_id: Optional session ID. If None, creates a new session. + + Returns: + Session object with the given or newly generated session_id + """ + if session_id is None: + session_id = str(uuid.uuid4()) + + if session_id not in _sessions: + # Use a single shared database file for all sessions + # The SQLiteSession implementation handles session isolation internally + db_path = SESSIONS_DIR / "conversations.db" + + _sessions[session_id] = SQLiteSession( + session_id=session_id, + db_path=str(db_path) + ) + logger.info(f"Created new persistent session: {session_id} (db: {db_path})") + else: + logger.debug(f"Reusing existing session: {session_id}") + + return _sessions[session_id] + + +class QueryRequest(BaseModel): + """Request model for reasoning agent queries.""" + + question: str + session_id: str | None = None # Optional session ID for conversation continuity + + +def parse_strategies_from_planner_response(response_text: str) -> List[Dict[str, Any]]: + """ + Parse strategies from planner agent response. + Attempts to extract JSON array from the response. + + Args: + response_text: The planner agent's response text + + Returns: + List of strategy dictionaries + """ + strategies = [] + + try: + # Try to find JSON array in the response + json_match = re.search(r"\[[\s\S]*\]", response_text) + if json_match: + strategies_data = json.loads(json_match.group()) + if isinstance(strategies_data, list): + strategies = strategies_data + # Try to find JSON object with "strategies" key + elif re.search(r"\{", response_text): + json_obj_match = re.search(r"\{[\s\S]*\}", response_text, re.DOTALL) + if json_obj_match: + strategies_data = json.loads(json_obj_match.group()) + if ( + isinstance(strategies_data, dict) + and "strategies" in strategies_data + ): + strategies = strategies_data["strategies"] + elif isinstance(strategies_data, list): + strategies = strategies_data + except json.JSONDecodeError as e: + logger.warning(f"Could not parse JSON from planner response: {str(e)}") + logger.debug(f"Response text: {response_text[:500]}") + except Exception as e: + logger.warning(f"Error parsing planner strategies: {str(e)}") + + return strategies + + +def create_planner_agent(model: str) -> Agent: + """ + Create the planner agent that generates query strategies. + + Args: + model: The model to use for the planner + + Returns: + Configured planner agent + """ + return Agent( + name="Query Planner", + instructions=PLANNER_AGENT_INSTRUCTIONS, + model=model, + model_settings=ModelSettings( + tool_choice="none", # Planner doesn't need tools, just generates strategies + temperature=0.7, # Higher temperature for more creative/diverse strategies + ), + ) + + +def create_execution_agent(model: str, server) -> Agent: + """ + Create the execution agent that executes query strategies with chain-of-thought reasoning. + + Args: + model: The model to use for execution + server: The MCP server instance (should be InterceptingMCPServer) + + Returns: + Configured execution agent + """ + # Create custom tools that use the intercepting MCP server + # This way all queries go through the same interceptor + vector_search_tool = create_vector_search_tool(mcp_server=server) + keyword_search_tool = create_keyword_search_tool(mcp_server=server) + relevance_expansion_tool = create_relevance_expansion_tool(mcp_server=server) + + return Agent( + name="Query Executor", + instructions=EXECUTION_AGENT_WITH_REASONING_INSTRUCTIONS, + model=model, + mcp_servers=[server], # Provides MCP tools: get_schema, run_query + tools=[vector_search_tool, keyword_search_tool, relevance_expansion_tool], # Custom tools (use MCP server, go through interceptor) + model_settings=ModelSettings( + tool_choice="required", + temperature=0.1, + parallel_tool_calls=False, + ), + ) + + +def create_graph_schema_agent(model: str, server) -> Agent: + """ + Create the graph schema agent that explores graph structure and node properties. + + Args: + model: The model to use for the schema agent + server: The MCP server instance (should be InterceptingMCPServer) + + Returns: + Configured graph schema agent + """ + # Create inspect_node_properties tool that uses the intercepting MCP server + inspect_properties_tool = create_inspect_node_properties_tool(mcp_server=server) + + return Agent( + name="Graph Schema Explorer", + instructions=GRAPH_SCHEMA_AGENT_INSTRUCTIONS, + model=model, + mcp_servers=[server], # Provides MCP tools: get_schema, run_query + tools=[inspect_properties_tool], # Custom tool for inspecting node properties + model_settings=ModelSettings( + tool_choice="required", + temperature=0.1, + parallel_tool_calls=False, + ), + ) + + +def create_reasoning_agent(model: str) -> Agent: + """ + Create the reasoning agent that analyzes execution results. + + Args: + model: The model to use for reasoning (can be o1-preview or o1-mini for enhanced reasoning) + + Returns: + Configured reasoning agent + """ + # Check if using o1 model (doesn't support temperature) + model_settings = {} + if model.startswith("o1"): + # o1 models don't support temperature parameter + model_settings = ModelSettings( + tool_choice="none", # Pure reasoning, no tools needed + ) + else: + model_settings = ModelSettings( + tool_choice="none", # Pure reasoning, no tools needed + temperature=0.3, # Balanced for analytical thinking + ) + + return Agent( + name="Query Reasoner", + instructions=REASONING_AGENT_INSTRUCTIONS, + model=model, + model_settings=model_settings, + ) + + +def create_manager_agent( + planner_agent: Agent, + execution_agent: Agent, + reasoning_agent: Agent, + graph_schema_agent: Agent, + model: str +) -> Agent: + """ + Create the manager agent that orchestrates planning, execution, and reasoning. + Uses the Manager (agents as tools) pattern from OpenAI Agents SDK. + + Args: + planner_agent: The planner agent to expose as a tool + execution_agent: The execution agent to expose as a tool + reasoning_agent: The reasoning agent to expose as a tool + graph_schema_agent: The graph schema agent to expose as a tool + model: The model to use for the manager + + Returns: + Configured manager agent + """ + return Agent( + name="Query Manager", + instructions=MANAGER_AGENT_WITH_REASONING_INSTRUCTIONS, + model=model, + tools=[ + planner_agent.as_tool( + tool_name="query_planner", + tool_description="Generates 5-10 high-level strategies for querying the Memgraph knowledge graph to answer a question. Call this first to get query strategies.", + ), + graph_schema_agent.as_tool( + tool_name="graph_schema_explorer", + tool_description="Explores the graph schema and inspects node properties. Use this to understand the structure of the graph database, discover available node labels and their properties, and get sample data. Call this when you need to understand what data exists in the graph or when you need to explore specific node types.", + ), + execution_agent.as_tool( + tool_name="query_executor", + tool_description="Executes a specific query strategy using available tools: MCP tools (get_schema, run_query) and custom tools (vector_search_on_chunks). Provides explicit reasoning about the execution process. When calling this tool, include the original user question in your input (formatted as 'Original user question: [QUESTION]. Strategy to execute: [strategy description]') so the executor has full context to make informed decisions about query phrasing.", + ), + reasoning_agent.as_tool( + tool_name="query_reasoner", + tool_description="Analyzes results from executed strategies, identifies gaps, synthesizes information, and recommends next steps. Call this after executing strategies to get reasoning insights and determine if more execution is needed.", + ), + ], + model_settings=ModelSettings( + tool_choice="auto", # Manager decides when to use tools + temperature=0.3, # Moderate temperature for balanced decision-making + ), + ) + + +@router.post("/query-stream") +async def query_with_reasoning_stream(request: QueryRequest): + """ + Query the knowledge graph with streaming updates via Server-Sent Events (SSE). + Streams temporary messages when tools are called. + """ + if not request.question or not isinstance(request.question, str): + raise HTTPException( + status_code=400, + detail="No question provided. Please provide a 'question' string.", + ) + + async def event_generator(): + """Generator function for SSE events.""" + message_queue = asyncio.Queue() + final_result = {} + error_occurred = False + + async def stream_callback(message: Dict[str, Any]): + """Callback to stream messages from the interceptor.""" + await message_queue.put(message) + + try: + # Get or create session for conversation continuity + session = get_or_create_session(request.session_id) + + # Set up MCP server connection + async with MCPServerStreamableHttp( + name="Memgraph MCP Server", + params={ + "url": MCP_URL, + "headers": {}, + "timeout": 60.0, + }, + cache_tools_list=True, + tool_filter=create_static_tool_filter( + allowed_tool_names=["run_query", "get_schema"] + ), + client_session_timeout_seconds=60.0, + ) as base_server: + # Wrap the server to intercept run_query calls with streaming callback + intercepting_server = InterceptingMCPServer(base_server, stream_callback=stream_callback) + + # Create specialized sub-agents with the intercepting server + planner_agent = create_planner_agent(REASONING_MODEL) + execution_agent = create_execution_agent(REASONING_MODEL, intercepting_server) + reasoning_agent = create_reasoning_agent(REASONING_AGENT_MODEL) + graph_schema_agent = create_graph_schema_agent(REASONING_MODEL, intercepting_server) + + # Create manager agent that orchestrates the sub-agents as tools + manager_agent = create_manager_agent( + planner_agent, execution_agent, reasoning_agent, graph_schema_agent, REASONING_MODEL + ) + + logger.info(f"Running manager agent with reasoning with session {session.session_id}...") + + # Run agent in background task + async def run_agent(): + try: + result = await Runner.run( + manager_agent, + request.question, + session=session + ) + + # Build trace graph from result.new_items + trace_data = build_trace_graph_from_items(result, request.question) + tools_used = extract_tools_used(result, include_nested=True) + run_query_calls = intercepting_server.get_run_query_calls() + + final_result.update({ + "question": request.question, + "answer": result.final_output, + "status": "success", + "session_id": session.session_id, + "agent_run_id": getattr(result, "run_id", None), + "tools_used": tools_used, + "run_query_calls": run_query_calls, + "tool_call_graph": trace_data, + }) + await message_queue.put({"type": "done"}) + except Exception as e: + logger.error(f"Error running reasoning agent: {str(e)}", exc_info=True) + await message_queue.put({"type": "error", "error": str(e)}) + + # Start agent task + agent_task = asyncio.create_task(run_agent()) + + # Stream messages while agent is running + while True: + try: + # Wait for message with timeout + message = await asyncio.wait_for(message_queue.get(), timeout=0.1) + + if message.get("type") == "done": + # Send final result + yield f"data: {json.dumps({'type': 'result', 'data': final_result})}\n\n" + break + elif message.get("type") == "error": + yield f"data: {json.dumps({'type': 'error', 'error': message.get('error')})}\n\n" + error_occurred = True + break + else: + # Stream temporary message + yield f"data: {json.dumps({'type': 'tool_call', 'data': message})}\n\n" + + except asyncio.TimeoutError: + # Check if agent task is done + if agent_task.done(): + if not error_occurred: + # Send final result + yield f"data: {json.dumps({'type': 'result', 'data': final_result})}\n\n" + break + continue + + # Wait for agent task to complete + await agent_task + + except Exception as e: + logger.error(f"Error in streaming: {str(e)}", exc_info=True) + yield f"data: {json.dumps({'type': 'error', 'error': str(e)})}\n\n" + + return StreamingResponse( + event_generator(), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "X-Accel-Buffering": "no", + } + ) + + +@router.post("/query") +async def query_with_reasoning(request: QueryRequest): + """ + Query the knowledge graph using multi-agent orchestration with planning and reasoning. + + This variant implements the Manager (agents as tools) pattern with explicit reasoning: + - Manager agent orchestrates the process and retains control of the conversation + - Planner agent is exposed as a tool that generates query strategies + - Execution agent is exposed as a tool that executes strategies with chain-of-thought reasoning + - Reasoning agent is exposed as a tool that analyzes results and guides next steps + + Expected JSON body: + { + "question": "What is Memgraph?", + "session_id": "optional-session-id-for-conversation-continuity" + } + """ + if not request.question or not isinstance(request.question, str): + raise HTTPException( + status_code=400, + detail="No question provided. Please provide a 'question' string.", + ) + + try: + # Get or create session for conversation continuity + session = get_or_create_session(request.session_id) + + # Set up MCP server connection + async with MCPServerStreamableHttp( + name="Memgraph MCP Server", + params={ + "url": MCP_URL, + "headers": {}, + "timeout": 60.0, # Increase timeout to 60 seconds for get_schema and run_query calls + }, + cache_tools_list=True, + tool_filter=create_static_tool_filter( + allowed_tool_names=["run_query", "get_schema"] + ), + client_session_timeout_seconds=60.0, + ) as base_server: + # Wrap the server to intercept run_query calls + # The base_server is already connected via the async context manager + intercepting_server = InterceptingMCPServer(base_server, stream_callback=None) + + # Create specialized sub-agents with the intercepting server + planner_agent = create_planner_agent(REASONING_MODEL) + execution_agent = create_execution_agent(REASONING_MODEL, intercepting_server) + reasoning_agent = create_reasoning_agent(REASONING_AGENT_MODEL) + graph_schema_agent = create_graph_schema_agent(REASONING_MODEL, intercepting_server) + + # Create manager agent that orchestrates the sub-agents as tools + manager_agent = create_manager_agent( + planner_agent, execution_agent, reasoning_agent, graph_schema_agent, REASONING_MODEL + ) + + logger.info(f"Running manager agent with reasoning with session {session.session_id}...") + + # Run the manager agent with session - this maintains conversation history + result = await Runner.run( + manager_agent, + request.question, + session=session # Pass session here for conversation continuity + ) + + # Build trace graph from result.new_items + trace_data = build_trace_graph_from_items(result, request.question) + + # Extract information from the manager's run + # Include nested tools from agent-as-tool invocations (e.g., query_executor -> get_schema, run_query) + tools_used = extract_tools_used(result, include_nested=True) + + # Get intercepted run_query calls (includes both MCP run_query and vector_search queries) + run_query_calls = intercepting_server.get_run_query_calls() + + return { + "question": request.question, + "answer": result.final_output, + "status": "success", + "session_id": session.session_id, # Return session_id so frontend can reuse it + "agent_run_id": getattr(result, "run_id", None), + "tools_used": tools_used, + "run_query_calls": run_query_calls, # All intercepted run_query calls + "tool_call_graph": trace_data, # Use trace data instead of tool call graph + } + + except Exception as e: + logger.error(f"Error running reasoning agent: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, detail=f"Error running reasoning agent: {str(e)}" + ) + diff --git a/graphrag/graphrag_kg_creation/backend/routes/prompts.py b/graphrag/graphrag_kg_creation/backend/routes/prompts.py new file mode 100644 index 0000000..8e21983 --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/routes/prompts.py @@ -0,0 +1,329 @@ +""" +Prompt templates for LLM interactions. +""" + +# System message for LLM-based answer generation +SYSTEM_MESSAGE = """You are a helpful assistant that answers questions based on the provided context. +Use only the information from the context to answer the question. +If the context doesn't contain enough information to answer the question, say so.""" + +# Default instructions for OpenAI Agents +DEFAULT_AGENT_INSTRUCTIONS = """You are a Memgraph expert that knows how to write Cypher queries to address knowledge +graph questions. + +The graph presented inside the database are excerpts from Memgraph documentation formed in unstructured way (:Chunk) +and other labels which was extracted in a meaningful label-property graph. + +As a Cypher expert, when writing queries: +* You must always ensure you have the data model schema (get_schema) to figure out how to form valid Cypher queries +* You should construct valid Cypher queries for Memgraph (using the run_query tool) in order to inspect what's there in + the dataset, and to figure out how to get to the required information. +* If an error is returned from the database, you may refactor your query or ask the user to provide additional information +* If an empty result is returned, you need to figure out if that's expected, or you need to explore the graph more with other different + techniques of graph exploration using Cypher queries. +* You should construct valid Cypher queries for Memgraph using the run_query tool in order to inspect what's there in + the dataset, and to figure out how to get to the required information in the graph + +If using a tool that does NOT require writing a Cypher query, you do not need the database schema. + +As a well respected graph expert: +* Ensure that you provide detailed responses with citations to the underlying data""" + + +# Planner agent instructions - generates high-level query strategies +PLANNER_AGENT_INSTRUCTIONS = """You are a query planning expert for Memgraph knowledge graphs. + +Your task is to analyze a user's question and generate 5-10 different high-level strategies for querying the Memgraph knowledge graph to answer that question. + +The graph contains: +- Excerpts from Memgraph documentation stored as unstructured :Chunk nodes +- Other meaningful labels extracted into a label-property graph structure + +For each strategy, provide: +1. A high-level description of the approach +2. What types of nodes/relationships you would explore +3. What patterns or paths you would look for +4. What information you would extract + +Think about different ways to approach the question: +- Running vector similarity search on document chunks with the users question as the query (you might want to do vector search on different variants of user's query which are similar) +- Inspecting values of properties of nodes and relationships to familiarize yourself with the graph +- Running Cypher queries to explore the graph + +Return your response as a JSON array with the following structure: +[ + { + "strategy_id": 1, + "description": "Description of the strategy", + "approach": "How to achieve it (call a tool or a set of tools, form this Cypher query, etc.)", + "information": "What information to extract from the graph", + "additional_notes": "Include the original user question here, and any previous information from previous retrievals, etc." + }, + ... +] + +Generate 5-10 diverse strategies that explore different angles of the question. + +**Key Principles for Strategy Generation**: +- Include strategies that try different phrasings of the question (synonyms, related terms, technical vs. plain language) +- Include strategies that explore the graph structure directly (using graph_schema_explorer, inspecting node properties) +- Include strategies that search for related concepts or adjacent topics +- Be creative - if one approach doesn't work, suggest alternative ways to find the same information +- The answer exists in the knowledge graph - generate strategies that systematically explore it + +IMPORTANT: In the additional_notes field of each strategy, include the original user question so the execution agent has access to it when deciding how to phrase queries.""" + + +# Execution agent instructions - for the multi-agent planning system +EXECUTION_AGENT_INSTRUCTIONS = """You are a Memgraph expert that knows how to write Cypher queries to address knowledge +graph questions. + +The graph presented inside the database are excerpts from Memgraph documentation formed in unstructured way (:Chunk) +and other labels which was extracted in a meaningful label-property graph. + +As a Cypher expert, when writing queries: +* You must always ensure you have the data model schema (get_schema) to figure out how to form valid Cypher queries +* You should construct valid Cypher queries for Memgraph (using the run_query tool) in order to inspect what's there in + the dataset, and to figure out how to get to the required information. +* If an error is returned from the database, you may refactor your query or ask the user to provide additional information +* If an empty result is returned, you need to figure out if that's expected, or you need to explore the graph more with other different + techniques of graph exploration using Cypher queries. +* You should construct valid Cypher queries for Memgraph using the run_query tool in order to inspect what's there in + the dataset, and to figure out how to get to the required information in the graph + +If using a tool that does NOT require writing a Cypher query, you do not need the database schema. + +You have access to the following tools: +* MCP tools: get_schema (to get the graph schema), run_query (to execute Cypher queries) +* Custom tool: vector_search_on_chunks (performs vector similarity search on chunks - use this when you need to find similar chunks based on text similarity. Pass the user's question as the 'question' parameter.) +* Custom tool: keyword_search (performs keyword search on nodes using text_search.search_all on a specific property. Takes property_name (e.g., "entity_id", "text", "name"), search_term (the keyword to search for), and optional limit (default: 10). Returns matching nodes ordered by relevance score. Use this when you need to find nodes by exact keyword matches in specific properties.) +* Custom tool: relevance_expansion (expands from a node by ID to explore its neighborhood - all connected nodes and relationships. Takes node_id (the internal Memgraph node ID). Returns the center node, all neighboring nodes, and relationships. Use this when you find an interesting node and want to explore its connections and context in the graph.) + +As a well respected graph expert: +* Ensure that you provide detailed responses with citations to the underlying data""" + + +# Manager agent instructions - orchestrates planning and execution +MANAGER_AGENT_INSTRUCTIONS = """You are a Knowledge Graph Query Manager that orchestrates query planning and execution. + +Your role is to: +0. If there is already some context that was retrieved from before and you know you have all the information to answer the question, then please proceed + to answer the question on your own. Otherwise, proceed to the next step. +1. First, call the query_planner tool to generate 5-10 different strategies for answering the user's question +2. Then, for each strategy, call the query_executor tool to execute it +3. See after each strategy if the answers from query executor answered the question. If not, proceed to execute more of the strategies. +4. Finally, synthesize all the results into a comprehensive answer + +The planner will provide you with multiple query strategies. Execute each one and combine the results to provide the best possible answer to the user's question. +If you don't know the answer based on the information you have, ask for users help about what are you struggling with to receive some hand guidance on discovering the answer.""" + + +# Reasoning agent instructions - analyzes execution results +REASONING_AGENT_INSTRUCTIONS = """You are a Reasoning and Reflection expert for knowledge graph queries. + +Your task is to analyze the results from executed query strategies and provide deep reasoning about: + +1. **Result Quality Assessment**: + - Are the results relevant to the original question? + - Are there gaps or missing information? + - Are there contradictions between different results? + +2. **Information Synthesis**: + - How do different results relate to each other? + - What patterns or connections emerge? + - What are the key insights? + +3. **Next Steps Reasoning**: + - Do we need more information? If so, what specifically? + - Are there alternative approaches to explore? + - What are the confidence levels for different pieces of information? + +4. **Answer Readiness**: + - Do we have enough information to answer the question? + - What are the limitations or uncertainties? + - What additional context would strengthen the answer? + - If the answer wasn't found, what alternative strategies should be tried? + +Provide your reasoning as structured analysis that helps the manager make informed decisions about whether to: +- Execute more strategies +- Refine existing queries +- Try different approaches (different phrasings, graph exploration, schema inspection) +- Synthesize the final answer + +Be explicit and detailed in your reasoning. Highlight what's working well and what needs improvement. + +**CRITICAL**: If the answer hasn't been found, recommend specific alternative approaches rather than suggesting the user look elsewhere. The answer exists in the knowledge graph - guide the manager to find it through systematic exploration.""" + + +# Enhanced execution agent instructions with chain-of-thought reasoning +EXECUTION_AGENT_WITH_REASONING_INSTRUCTIONS = """You are a Memgraph expert that executes query strategies with explicit reasoning. + +**Before executing a strategy, reason about**: +1. What information are you trying to find? +2. What tools/queries will best retrieve this information? +3. What do you expect to find? +4. What is the original user question? (Look for it in the input - it may be explicitly stated or in the strategy's additional_notes) + +**When executing**: +- Always get schema first if writing Cypher queries +- Use vector_search_on_chunks for semantic similarity searches + - **For vector_search_on_chunks**: You have flexibility in how you phrase the question. Consider: + * Use the exact original user question when the question is already well-formed and specific + * Use a simplified or rephrased version if the original question is verbose, contains typos, or includes irrelevant context + * Use a more specific version if the original question is too vague or general + * The goal is to find the most semantically similar chunks, so choose the phrasing that best captures the user's intent +- Use keyword_search for exact keyword matches in specific properties (e.g., searching for "entity_id" property with value "memory") +- Use relevance_expansion to explore the neighborhood of interesting nodes (takes a node ID you have seen before and returns all connected nodes and relationships) +- Use run_query for structured graph exploration + +**After getting results, reason about**: +1. Did the results match expectations? +2. Are the results relevant to the strategy? +3. What insights can be extracted? +4. Are there follow-up queries needed? +5. If the results don't contain the answer, what alternative approaches could work? + +**Provide your reasoning explicitly** in your responses so the manager can understand your thought process. + +**CRITICAL: Never give up easily**. If a search doesn't yield the answer: +- Try different question phrasings or synonyms +- Explore related concepts or adjacent topics +- Use graph_schema_explorer to understand what data exists +- Query the graph directly to find relevant information +- Be persistent - the answer is in the graph, you just need to find the right approach + +The graph presented inside the database are excerpts from Memgraph documentation formed in unstructured way (:Chunk) +and other labels which was extracted in a meaningful label-property graph. + +As a Cypher expert, when writing queries: +* You must always ensure you have the data model schema (get_schema) to figure out how to form valid Cypher queries +* You should construct valid Cypher queries for Memgraph (using the run_query tool) in order to inspect what's there in + the dataset, and to figure out how to get to the required information. +* If an error is returned from the database, you may refactor your query or ask the user to provide additional information +* If an empty result is returned, you need to figure out if that's expected, or you need to explore the graph more with other different + techniques of graph exploration using Cypher queries. + +If using a tool that does NOT require writing a Cypher query, you do not need the database schema. + +You have access to the following tools: +* MCP tools: get_schema (to get the graph schema), run_query (to execute Cypher queries) +* Custom tool: vector_search_on_chunks (performs vector similarity search on chunks - use this when you need to find similar chunks based on text similarity. Pass a question that best captures the user's intent - you can use the exact original question or a refined version that better matches the semantic search goal.) +* Custom tool: keyword_search (performs keyword search on nodes using text_search.search_all on a specific property. Takes property_name (e.g., "entity_id", "text", "name"), search_term (the keyword to search for), and optional limit (default: 10). Returns matching nodes ordered by relevance score. Use this when you need to find nodes by exact keyword matches in specific properties.) +* Custom tool: relevance_expansion (expands from a node by ID to explore its neighborhood - all connected nodes and relationships. Takes node_id (the internal Memgraph node ID). Returns the center node, all neighboring nodes, and relationships. Use this when you find an interesting node and want to explore its connections and context in the graph.) + +As a well respected graph expert: +* Ensure that you provide detailed responses with citations to the underlying data +* Always explain your reasoning process explicitly +* When using vector_search_on_chunks, choose the question phrasing that will yield the most relevant semantic matches +* **Never give up with generic answers** - if you don't find the answer immediately, try different approaches: + - Different question phrasings or synonyms + - Exploring related concepts + - Using graph_schema_explorer to understand available data + - Direct Cypher queries to explore the graph +* **Be persistent** - the information exists in the knowledge graph, explore systematically until you find it""" + + +# Graph schema agent instructions - explores graph schema and node properties +GRAPH_SCHEMA_AGENT_INSTRUCTIONS = """You are a Graph Schema Explorer expert for Memgraph knowledge graphs. + +Your task is to explore and understand the structure of the graph database by: +1. Getting the overall schema using get_schema tool +2. Inspecting properties of specific node labels using inspect_node_properties tool +3. Understanding what data exists in the graph and how it's structured + +**When to use each tool**: +- **get_schema**: Use this first to get an overview of all node labels, relationship types, and their properties in the graph +- **inspect_node_properties**: Use this to dive deeper into a specific node label to see: + * What properties exist on nodes with that label + * Sample property values to understand the data structure + * How many nodes exist with that label + +**Workflow**: +1. Start with get_schema to understand the overall structure +2. Based on the schema or the question at hand, identify relevant node labels to inspect +3. Use inspect_node_properties for each relevant label to understand the data +4. Provide a clear summary of what you discovered about the graph structure + +**Key Principles**: +- Always start with get_schema to get the big picture +- Use inspect_node_properties to understand specific node types in detail +- Explain what you find in a clear, structured way +- Help identify which labels and properties are relevant to answering the user's question""" + + +# Enhanced manager agent instructions with iterative reasoning +MANAGER_AGENT_WITH_REASONING_INSTRUCTIONS = """You are a Knowledge Graph Query Manager that orchestrates query planning, execution, and reasoning. + +Your workflow should be: + +1. **Initial Assessment**: + - If you already have sufficient context from previous interactions, proceed to answer + - If you need more context about the actual question, feel free to ask user for additional information before proceeding to the planning phase. + - Otherwise, proceed to planning + +2. **Planning Phase**: + - Call graph_schema_explorer if you need to understand the graph structure first, it's relevant for sampling data from the graph + - Call query_planner to generate 5-10 diverse strategies + - Review the strategies and prioritize them + +3. **Execution Phase** (Iterative): + - When calling query_executor, include the original user question in your input so the executor has access to it + - Format your call to query_executor as: "Original user question: [ORIGINAL QUESTION]. Strategy to execute: [strategy description]" + - The execution agent can then decide whether to use the exact question or a refined version based on what will yield the best semantic search results + - Execute 2-3 high-priority strategies using query_executor + - After each batch, call query_reasoner to analyze: + * What information was found? + * What's still missing? + * Are there contradictions or inconsistencies? + - Based on reasoning, decide: + * If enough info → proceed to synthesis + * If gaps exist → execute more strategies or refine queries + * If contradictions → investigate further + +4. **Reasoning Loop** (Repeat until sufficient): + - Execute strategies → Reason about results → Decide next steps + - Track what information has been gathered + - Identify what's still needed + - Don't execute all strategies blindly - use reasoning to guide execution + +5. **Synthesis Phase**: + - Once reasoning indicates sufficient information: + * Synthesize all gathered information + * Address any uncertainties or limitations + * Provide comprehensive answer to the initial user question with citations + +**Key Principles**: +- Quality over quantity - better to execute fewer, well-reasoned queries +- Use reasoning to guide execution, not just execute everything +- Be explicit about uncertainties and limitations +- Cite specific data sources in your final answer +- Always pass the original user question to query_executor so it has full context to make informed decisions about query phrasing +- **CRITICAL: Never give up with generic answers like "you might want to consult documentation" or "I don't have that information"** +- **Be persistent**: If initial searches don't find the answer, try different approaches: + * Try different phrasings of the question for vector search + * Use graph_schema_explorer to understand what data is available + * Query the graph directly with Cypher to explore relevant nodes and relationships + * Look for related concepts or synonyms + * Check if the information might be stored in a different format or under a different label +- **Explore thoroughly**: The answer exists in the knowledge graph - your job is to find it through systematic exploration +- Only ask for user's help if you've exhausted multiple strategies and can clearly explain what you've tried and why it didn't work""" + + +def create_user_message(context: str, question: str) -> str: + """ + Create a user message prompt with context and question. + + Args: + context: The context text to use for answering + question: The question to answer + + Returns: + Formatted user message string + """ + return f"""Based on the following context, please answer the question. + +Context: {context} + +Question: {question} + +Answer:""" diff --git a/graphrag/graphrag_kg_creation/backend/routes/query.py b/graphrag/graphrag_kg_creation/backend/routes/query.py new file mode 100644 index 0000000..7d6aa1d --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/routes/query.py @@ -0,0 +1,140 @@ +""" +Query endpoints for querying the knowledge graph. +""" +import os +import json +import logging +import sys +import httpx +from fastapi import APIRouter, HTTPException, Request + +# Add parent directory to path for imports +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from mcp_client import call_mcp_service +from routes.llm_utils import generate_answer + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/query", tags=["query"]) + +# MCP service URL - use service name when running in Docker, localhost when running locally +# Default to localhost:8001 for local development (host port), or set MCP_MEMGRAPH_URL env var +MCP_URL = os.getenv("MCP_MEMGRAPH_URL", "http://localhost:8001/mcp") + + +def remove_embeddings_from_response(data): + """ + Recursively remove 'embedding' keys from dictionaries in the response. + """ + if isinstance(data, dict): + return {k: remove_embeddings_from_response(v) for k, v in data.items() if k != "embedding"} + elif isinstance(data, list): + return [remove_embeddings_from_response(item) for item in data] + else: + return data + + +@router.post("") +async def query_graph(request: Request): + """ + Query the knowledge graph with a natural language question. + Uses mcp-memgraph service to execute queries. + + Expected JSON body: + { + "question": "What is Memgraph?" + } + """ + try: + body = await request.json() + except json.JSONDecodeError: + raise HTTPException( + status_code=400, + detail="Invalid JSON in request body" + ) + + question = body.get("question", "") + + if not question or not isinstance(question, str): + raise HTTPException( + status_code=400, + detail="No question provided. Please provide a 'question' string." + ) + + try: + # Use vector search to find relevant chunks based on question embedding + # Sanitize the question to prevent Cypher injection - escape single quotes + sanitized_question = question.replace("'", "\\'").replace("\\", "\\\\") + + # Build the GraphRAG query that embeds the prompt and searches for similar chunks + cypher_query = f""" + CALL embeddings.text(['{sanitized_question}']) YIELD embeddings, success + CALL vector_search.search('vs_name', 5, embeddings[0]) YIELD distance, node, similarity + MATCH (node)-[r*bfs]-(dst:Chunk) + WITH DISTINCT dst, degree(dst) AS degree ORDER BY degree DESC + RETURN dst LIMIT 5 + """ + + # Call mcp-memgraph service to execute the query + payload = { + "jsonrpc": "2.0", + "id": 1, + "method": "tools/call", + "params": { + "name": "run_query", + "arguments": { + "query": cypher_query.strip() + } + } + } + + mcp_result = await call_mcp_service(MCP_URL, payload, timeout=30.0) + + # Remove embedding from mcp_response before processing + cleaned_mcp_response = remove_embeddings_from_response(mcp_result) + + # Extract structuredContent from MCP response + structured_content = None + if "result" in cleaned_mcp_response and "structuredContent" in cleaned_mcp_response["result"]: + structured_content = cleaned_mcp_response["result"]["structuredContent"] + + if structured_content: + # Generate answer using LLM + answer = generate_answer(structured_content, question) + else: + answer = f"No results found for your question: '{question}'. The graph might not contain relevant information yet." + + return { + "question": question, + "answer": answer, + "status": "success", + "mcp_response": cleaned_mcp_response + } + + except httpx.ConnectError as e: + logger.error(f"Failed to connect to mcp-memgraph at {MCP_URL}: {str(e)}") + raise HTTPException( + status_code=503, + detail=f"Could not connect to mcp-memgraph service. Make sure the service is running. Error: {str(e)}" + ) + except httpx.HTTPStatusError as e: + logger.error(f"HTTP error from mcp-memgraph: {str(e)}") + error_detail = f"Error from mcp-memgraph service: {str(e)}" + if e.response.text: + try: + error_data = e.response.json() + error_detail = error_data.get("detail", error_detail) + except: + error_detail = e.response.text[:200] + raise HTTPException( + status_code=e.response.status_code, + detail=error_detail + ) + except Exception as e: + logger.error(f"Error querying graph: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error querying graph: {str(e)}" + ) + diff --git a/graphrag/graphrag_kg_creation/backend/routes/stats.py b/graphrag/graphrag_kg_creation/backend/routes/stats.py new file mode 100644 index 0000000..a0974ec --- /dev/null +++ b/graphrag/graphrag_kg_creation/backend/routes/stats.py @@ -0,0 +1,56 @@ +""" +Statistics endpoints for querying knowledge graph statistics. +""" +import logging +import sys +import os +from fastapi import APIRouter, HTTPException + +# Add parent directory to path for imports +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from memgraph_toolbox.api.memgraph import Memgraph + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/stats", tags=["statistics"]) + + +@router.get("") +async def get_stats(): + """ + Get statistics about the ingested data in Memgraph. + """ + try: + memgraph = Memgraph() + + # Get chunk count + chunk_result = memgraph.query("MATCH (n:Chunk) RETURN count(n) as count") + chunk_count = chunk_result[0]["count"] if chunk_result else 0 + + # Get entity count (base nodes from LightRAG) + entity_result = memgraph.query("MATCH (n:base) RETURN count(n) as count") + entity_count = entity_result[0]["count"] if entity_result else 0 + + # Get relationship count + rel_result = memgraph.query("MATCH ()-[r]->() RETURN count(r) as count") + rel_count = rel_result[0]["count"] if rel_result else 0 + + # Get total node count + node_result = memgraph.query("MATCH (n) RETURN count(n) as count") + node_count = node_result[0]["count"] if node_result else 0 + + return { + "chunks": chunk_count, + "entities": entity_count, + "relationships": rel_count, + "nodes": node_count + } + + except Exception as e: + logger.error(f"Error getting stats: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error getting stats: {str(e)}" + ) + diff --git a/graphrag/graphrag_kg_creation/backend/sessions/conversations.db b/graphrag/graphrag_kg_creation/backend/sessions/conversations.db new file mode 100644 index 0000000..e0dfbb8 Binary files /dev/null and b/graphrag/graphrag_kg_creation/backend/sessions/conversations.db differ diff --git a/graphrag/graphrag_kg_creation/backend/sessions/conversations.db-shm b/graphrag/graphrag_kg_creation/backend/sessions/conversations.db-shm new file mode 100644 index 0000000..99db65a Binary files /dev/null and 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"@vue/shared": "3.5.25" + }, + "peerDependencies": { + "typescript": "*" + }, + "peerDependenciesMeta": { + "typescript": { + "optional": true + } + } + } + } +} diff --git a/graphrag/graphrag_kg_creation/frontend/package.json b/graphrag/graphrag_kg_creation/frontend/package.json new file mode 100644 index 0000000..6ae8a2e --- /dev/null +++ b/graphrag/graphrag_kg_creation/frontend/package.json @@ -0,0 +1,19 @@ +{ + "name": "graphrag-kg-creation-frontend", + "version": "1.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "vite build", + "preview": "vite preview" + }, + "dependencies": { + "vue": "^3.4.0", + "axios": "^1.6.0" + }, + "devDependencies": { + "@vitejs/plugin-vue": "^5.0.0", + "vite": "^5.0.0" + } +} + diff --git a/graphrag/graphrag_kg_creation/frontend/src/App.vue b/graphrag/graphrag_kg_creation/frontend/src/App.vue new file mode 100644 index 0000000..fb1971b --- /dev/null +++ b/graphrag/graphrag_kg_creation/frontend/src/App.vue @@ -0,0 +1,4984 @@ + + + + + + diff --git a/graphrag/graphrag_kg_creation/frontend/src/main.js b/graphrag/graphrag_kg_creation/frontend/src/main.js new file mode 100644 index 0000000..216546d --- /dev/null +++ b/graphrag/graphrag_kg_creation/frontend/src/main.js @@ -0,0 +1,6 @@ +import { createApp } from 'vue' +import './style.css' +import App from './App.vue' + +createApp(App).mount('#app') + diff --git a/graphrag/graphrag_kg_creation/frontend/src/style.css b/graphrag/graphrag_kg_creation/frontend/src/style.css new file mode 100644 index 0000000..c050b3f --- /dev/null +++ b/graphrag/graphrag_kg_creation/frontend/src/style.css @@ -0,0 +1,20 @@ +* { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +body { + font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; + font-weight: 400; + background: #FFFFFF; + min-height: 100vh; + padding: 20px; + color: #231F20; +} + +#app { + max-width: 1200px; + margin: 0 auto; +} + diff --git a/graphrag/graphrag_kg_creation/frontend/vite.config.js b/graphrag/graphrag_kg_creation/frontend/vite.config.js new file mode 100644 index 0000000..b015880 --- /dev/null +++ b/graphrag/graphrag_kg_creation/frontend/vite.config.js @@ -0,0 +1,17 @@ +import { defineConfig } from 'vite' +import vue from '@vitejs/plugin-vue' + +export default defineConfig({ + plugins: [vue()], + server: { + port: 3000, + proxy: { + '/api': { + target: 'http://localhost:8000', + changeOrigin: true, + rewrite: (path) => path.replace(/^\/api/, '') + } + } + } +}) + diff --git a/graphrag/memgraph_assistant/.gitignore b/graphrag/memgraph_assistant/.gitignore new file mode 100644 index 0000000..557d073 --- /dev/null +++ b/graphrag/memgraph_assistant/.gitignore @@ -0,0 +1,47 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +env/ +venv/ +ENV/ +.venv +*.egg-info/ +dist/ +build/ +.pytest_cache/ +.coverage +htmlcov/ + +# Environment +.env +.env.local + +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# Logs +*.log +lightrag_storage.out/ +sessions/ + +# Cache +cache/ + +# Node +node_modules/ +dist/ +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# OS +.DS_Store +Thumbs.db + diff --git a/graphrag/memgraph_assistant/README.md b/graphrag/memgraph_assistant/README.md new file mode 100644 index 0000000..fb11cd0 --- /dev/null +++ b/graphrag/memgraph_assistant/README.md @@ -0,0 +1,145 @@ +# Memgraph Assistant + +A documentation-aware chat assistant for Memgraph that can understand the documentation and perform operations on the graph database. + +## Features + +- **Documentation Ingestion**: Automatically scrapes and ingests all documentation from memgraph.com/docs +- **Chat Assistant**: AI-powered wizard that understands the documentation and can help with graph operations +- **Graph Operations**: Perform queries, create nodes, relationships, and more based on documentation knowledge +- **Streaming Responses**: Real-time updates during chat interactions +- **Session Management**: Maintains conversation context across multiple interactions + +## Prerequisites + +- Docker and Docker Compose +- Python 3.13+ with `uv` package manager +- Node.js and npm +- OpenAI API key + +## Setup + +### 1. Start Services + +Run Memgraph and MCP using Docker Compose: + +```bash +docker compose up -d +``` + +This will start: +- Memgraph database on port 7687 +- MCP-Memgraph server on port 8001 + +### 2. Backend Setup + +1. Navigate to the `backend` directory +2. Create a `.env` file with your OpenAI API key: + ``` + OPENAI_API_KEY=your_api_key_here + MCP_MEMGRAPH_URL=http://localhost:8001/mcp + ``` +3. Set up the environment: + ```bash + cd backend + uv venv + uv sync + ``` +4. Run the backend server: + ```bash + uv run uvicorn main:app --reload --host 0.0.0.0 --port 8000 + ``` + +### 3. Frontend Setup + +1. Navigate to the `frontend` directory +2. Install dependencies: + ```bash + cd frontend + npm install + ``` +3. Start the development server: + ```bash + npm run dev + ``` + +The frontend will be available at http://localhost:3000 + +## Usage + +### Ingest Documentation + +1. Open the frontend application (http://localhost:3000) +2. Navigate to the "Ingest Documentation" tab +3. Click "Scrape Memgraph Documentation" to automatically discover and ingest all documentation pages from memgraph.com/docs +4. Wait for ingestion to complete (this may take several minutes depending on the number of pages) + +The ingestion process will: +- Discover all documentation URLs from memgraph.com/docs +- Scrape and process each page +- Create chunks and relationships in the graph +- Compute embeddings for vector search + +### Use the Chat Assistant + +1. Navigate to the "Chat Assistant" tab +2. Ask questions about Memgraph, such as: + - "How do I create a node in Memgraph?" + - "What is the syntax for MATCH queries?" + - "Show me how to use relationships" +3. Request graph operations: + - "Create a Person node with name 'Alice'" + - "Find all nodes connected to node 123" + - "Show me the graph schema" +4. The assistant will use its knowledge of the documentation to help you + +## Architecture + +- **Backend**: FastAPI application with: + - `/api/ingest/scrape-all` - Scrapes and ingests all documentation + - `/api/chat/query` - Non-streaming chat endpoint + - `/api/chat/query-stream` - Streaming chat endpoint (SSE) + - `/api/mcp/*` - MCP testing endpoints + +- **Frontend**: Vue.js application with: + - Ingestion interface + - Chat interface with streaming support + - Tool call visualization + +- **Database**: Memgraph graph database storing: + - Documentation chunks as :Chunk nodes + - Relationships between chunks + - Vector embeddings for semantic search + +- **MCP**: Model Context Protocol server for graph operations + +## API Endpoints + +### Ingestion +- `POST /api/ingest/scrape-all` - Scrape and ingest all documentation +- `POST /api/ingest/discover` - Discover documentation URLs without ingesting +- `POST /api/ingest` - Ingest specific URLs + +### Chat +- `POST /api/chat/query` - Chat with the assistant (non-streaming) +- `POST /api/chat/query-stream` - Chat with the assistant (streaming SSE) + +### MCP +- `GET /api/mcp/test` - Test MCP connection +- `GET /api/mcp/tools` - List available MCP tools +- `POST /api/mcp/call` - Call an MCP tool + +## Environment Variables + +- `OPENAI_API_KEY` - Your OpenAI API key (required) +- `MCP_MEMGRAPH_URL` - MCP server URL (default: http://localhost:8001/mcp) +- `LOG_LEVEL` - Logging level (default: DEBUG) +- `CHAT_MODEL` - Model to use for chat (default: gpt-4o) + +## Notes + +- The first ingestion may take 10-30 minutes depending on the number of documentation pages +- The chat assistant maintains conversation context using sessions +- All queries are cached to prevent duplicate executions +- The assistant can perform both read and write operations on the graph + diff --git a/graphrag/memgraph_assistant/backend/ai-toolkit b/graphrag/memgraph_assistant/backend/ai-toolkit new file mode 120000 index 0000000..8750c57 --- /dev/null +++ b/graphrag/memgraph_assistant/backend/ai-toolkit @@ -0,0 +1 @@ +../../graphrag_kg_creation/backend/ai-toolkit \ No newline at end of file diff --git a/graphrag/memgraph_assistant/backend/main.py b/graphrag/memgraph_assistant/backend/main.py new file mode 100644 index 0000000..89e8e4f --- /dev/null +++ b/graphrag/memgraph_assistant/backend/main.py @@ -0,0 +1,65 @@ +""" +Main FastAPI application entry point for Memgraph Assistant. +""" +import os +import logging +import sys + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from dotenv import load_dotenv + +# Add the ai-toolkit path to sys.path for imports +SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__)) +AI_TOOLKIT_DIR = os.path.join(SCRIPT_DIR, "ai-toolkit") +sys.path.insert(0, os.path.join(AI_TOOLKIT_DIR, "unstructured2graph", "src")) +sys.path.insert(0, os.path.join(AI_TOOLKIT_DIR, "integrations", "lightrag-memgraph", "src")) +sys.path.insert(0, os.path.join(AI_TOOLKIT_DIR, "memgraph-toolbox", "src")) + +load_dotenv() + +# Configure logging +log_level = os.getenv("LOG_LEVEL", "INFO").upper() +logging.basicConfig( + level=getattr(logging, log_level, logging.INFO), + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger(__name__) + +# Create FastAPI app +app = FastAPI(title="Memgraph Assistant API", version="1.0.0") + +# CORS middleware +app.add_middleware( + CORSMiddleware, + allow_origins=["http://localhost:3000", "http://127.0.0.1:3000"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Import and register routes +from routes import ingest, chat, mcp, stats, graph + +# Include routers with /api prefix to match frontend proxy +app.include_router(ingest.router, prefix="/api") +app.include_router(chat.router, prefix="/api") +app.include_router(mcp.router, prefix="/api") +app.include_router(stats.router, prefix="/api") +app.include_router(graph.router, prefix="/api") + + +@app.get("/") +async def root(): + return {"message": "Memgraph Assistant API", "version": "1.0.0"} + + +@app.get("/health") +async def health(): + return {"status": "healthy"} + + +if __name__ == "__main__": + import uvicorn + uvicorn.run(app, host="0.0.0.0", port=8000) + diff --git a/graphrag/memgraph_assistant/backend/mcp_client.py b/graphrag/memgraph_assistant/backend/mcp_client.py new file mode 100644 index 0000000..cb0ef3b --- /dev/null +++ b/graphrag/memgraph_assistant/backend/mcp_client.py @@ -0,0 +1,142 @@ +""" +MCP (Model Context Protocol) client for communicating with mcp-memgraph service. +""" +import os +import json +import re +import logging +import httpx +from fastapi import HTTPException + +logger = logging.getLogger(__name__) + +# Module-level session ID storage for MCP streamable-http +_mcp_session_id = None +_mcp_initialized = False + + +def parse_sse_response(text: str) -> dict: + """Parse Server-Sent Events (SSE) format response from MCP streamable-http.""" + if not text or not text.strip(): + raise ValueError("Empty SSE response") + + if "event:" in text or "data:" in text: + lines = text.split('\n') + json_data = None + + for i, line in enumerate(lines): + if line.startswith('data:'): + data_str = line[5:].strip() + if data_str: + try: + json_data = json.loads(data_str) + break + except json.JSONDecodeError: + continue + + if json_data is None: + json_match = re.search(r'\{.*\}', text, re.DOTALL) + if json_match: + try: + json_data = json.loads(json_match.group()) + except json.JSONDecodeError: + pass + + if json_data is None: + raise ValueError(f"Could not extract JSON from SSE response: {text[:200]}") + + return json_data + else: + return json.loads(text) + + +async def initialize_mcp_session(mcp_url: str, timeout: float = 10.0) -> None: + """Initialize MCP session by sending an initialize request.""" + global _mcp_session_id, _mcp_initialized + + if _mcp_initialized and _mcp_session_id: + return + + async with httpx.AsyncClient(timeout=timeout, follow_redirects=True) as client: + headers = { + "Content-Type": "application/json", + "Accept": "application/json, text/event-stream" + } + + init_payload = { + "jsonrpc": "2.0", + "id": 0, + "method": "initialize", + "params": { + "protocolVersion": "2024-11-05", + "capabilities": {}, + "clientInfo": { + "name": "memgraph-assistant-backend", + "version": "1.0.0" + } + } + } + + response = await client.post(mcp_url, json=init_payload, headers=headers) + + if "mcp-session-id" in response.headers: + _mcp_session_id = response.headers["mcp-session-id"] + _mcp_initialized = True + logger.info(f"MCP session initialized with ID: {_mcp_session_id}") + + +async def call_mcp_service(mcp_url: str, payload: dict, timeout: float = 60.0) -> dict: + """Helper function to call MCP service with session management.""" + global _mcp_session_id, _mcp_initialized + + if not _mcp_initialized or not _mcp_session_id: + await initialize_mcp_session(mcp_url, timeout=timeout) + + timeout_config = httpx.Timeout(timeout, connect=10.0, read=timeout) + + async with httpx.AsyncClient(timeout=timeout_config, follow_redirects=True) as client: + headers = { + "Content-Type": "application/json", + "Accept": "application/json, text/event-stream" + } + + if _mcp_session_id: + headers["mcp-session-id"] = _mcp_session_id + + response = await client.post(mcp_url, json=payload, headers=headers) + + if "mcp-session-id" in response.headers: + _mcp_session_id = response.headers["mcp-session-id"] + + try: + response.raise_for_status() + except httpx.HTTPStatusError as e: + try: + error_data = parse_sse_response(e.response.text) if e.response.text else {} + error_message = error_data.get("error", {}).get("message", str(error_data)) + raise HTTPException( + status_code=e.response.status_code, + detail=f"Error from MCP service: {error_message}" + ) + except (json.JSONDecodeError, ValueError): + error_text = e.response.text[:500] if e.response.text else "Unknown error" + raise HTTPException( + status_code=e.response.status_code, + detail=f"Error from MCP service (status {e.response.status_code}): {error_text}" + ) + + if not response.text or response.text.strip() == "": + raise HTTPException( + status_code=500, + detail=f"Empty response from MCP service (status {response.status_code})" + ) + + try: + return parse_sse_response(response.text) + except (json.JSONDecodeError, ValueError) as e: + logger.error(f"Failed to parse response from MCP service: {str(e)}") + raise HTTPException( + status_code=500, + detail=f"Invalid response from MCP service: {str(e)}" + ) + diff --git a/graphrag/memgraph_assistant/backend/pyproject.toml b/graphrag/memgraph_assistant/backend/pyproject.toml new file mode 100644 index 0000000..fafb602 --- /dev/null +++ b/graphrag/memgraph_assistant/backend/pyproject.toml @@ -0,0 +1,22 @@ +[project] +name = "memgraph-assistant-backend" +version = "0.1.0" +description = "Memgraph Assistant Backend - Documentation-aware chat assistant" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "fastapi==0.109.0", + "httpx>=0.28.1", + "lightrag-hku[api]==1.4.8.2", + "litellm>=1.77.1", + "mcp>=1.14.1", + "neo4j>=5.28.1", + "numpy>=2.2.6", + "openai-agents==0.6.1", + "python-dotenv>=1.1.1", + "beautifulsoup4>=4.12.0", + "requests>=2.31.0", + "unstructured[all-docs]>=0.12.6", + "uvicorn[standard]>=0.31.1", +] + diff --git a/graphrag/memgraph_assistant/backend/resources.py b/graphrag/memgraph_assistant/backend/resources.py new file mode 100644 index 0000000..5cce4c4 --- /dev/null +++ b/graphrag/memgraph_assistant/backend/resources.py @@ -0,0 +1,187 @@ +""" +Resource management for Memgraph and LightRAG wrapper. +Handles initialization and cleanup of database connections and wrappers. +""" +import os +import shutil +import logging +import sys + +# Add the ai-toolkit path to sys.path for imports +SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__)) +AI_TOOLKIT_DIR = os.path.join(SCRIPT_DIR, "ai-toolkit") +sys.path.insert(0, os.path.join(AI_TOOLKIT_DIR, "unstructured2graph", "src")) +sys.path.insert(0, os.path.join(AI_TOOLKIT_DIR, "integrations", "lightrag-memgraph", "src")) +sys.path.insert(0, os.path.join(AI_TOOLKIT_DIR, "memgraph-toolbox", "src")) + +from lightrag_memgraph import MemgraphLightRAGWrapper +from unstructured2graph import create_index, create_vector_search_index + +# Import Memgraph after setting up path - we'll import it dynamically for main graph +# to allow setting environment variables before initialization +try: + from memgraph_toolbox.api.memgraph import Memgraph +except ImportError: + Memgraph = None + +logger = logging.getLogger(__name__) + +SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__)) +LIGHTRAG_DIR = os.path.join(SCRIPT_DIR, "lightrag_storage.out") + +# Global variables for shared resources +_lightrag_wrapper = None +_memgraph = None +_main_memgraph = None + + +async def initialize_resources(cleanup: bool = False, create_vector_index: bool = False): + """Initialize Memgraph and LightRAG wrapper.""" + global _lightrag_wrapper, _memgraph + + # Always cleanup existing LightRAG wrapper if it exists + if _lightrag_wrapper is not None: + try: + await _lightrag_wrapper.afinalize() + except Exception as e: + logger.warning(f"Error finalizing existing LightRAG wrapper: {str(e)}") + _lightrag_wrapper = None + + # Create Memgraph instance + _memgraph = Memgraph() + + # Cleanup Memgraph data if requested + if cleanup: + logger.info("Cleaning up existing data in Memgraph...") + try: + _memgraph.query("STORAGE MODE IN_MEMORY_ANALYTICAL") + _memgraph.query("DROP GRAPH") + _memgraph.query("STORAGE MODE IN_MEMORY_TRANSACTIONAL") + except Exception as e: + logger.warning(f"Error during Memgraph cleanup: {str(e)}") + + # Create index for Chunk nodes + try: + create_index(_memgraph, "Chunk", "hash") + except Exception as e: + logger.warning(f"Error creating index (may already exist): {str(e)}") + + if create_vector_index: + try: + create_vector_search_index(_memgraph, "Chunk", "embedding") + except Exception as e: + logger.warning(f"Error creating vector index (may already exist): {str(e)}") + + # Setup LightRAG directory + lightrag_log_file = os.path.join(LIGHTRAG_DIR, "lightrag.log") + if cleanup: + if os.path.exists(lightrag_log_file): + try: + os.remove(lightrag_log_file) + except Exception as e: + logger.warning(f"Error removing log file: {str(e)}") + if os.path.exists(LIGHTRAG_DIR): + try: + shutil.rmtree(LIGHTRAG_DIR) + except Exception as e: + logger.warning(f"Error removing LightRAG directory: {str(e)}") + + # Ensure LightRAG directory exists + if not os.path.exists(LIGHTRAG_DIR): + os.makedirs(LIGHTRAG_DIR, exist_ok=True) + + # Create LightRAG wrapper instance + _lightrag_wrapper = MemgraphLightRAGWrapper( + log_level="WARNING", + disable_embeddings=True + ) + await _lightrag_wrapper.initialize(working_dir=LIGHTRAG_DIR) + + return _memgraph, _lightrag_wrapper + + +async def initialize_main_graph(): + """Initialize separate Memgraph instance for user data (main graph).""" + global _main_memgraph + + if _main_memgraph is None: + # Import Memgraph here to ensure environment variables are set + from memgraph_toolbox.api.memgraph import Memgraph + + # Store original environment variables + original_host = os.environ.get('MEMGRAPH_HOST') + original_port = os.environ.get('MEMGRAPH_PORT') + + # Set environment variables for main graph connection (port 7688) + os.environ['MEMGRAPH_HOST'] = 'localhost' + os.environ['MEMGRAPH_PORT'] = '7688' + + try: + _main_memgraph = Memgraph() + logger.info("Initialized main graph connection (port 7688)") + except Exception as e: + logger.error(f"Error initializing main graph: {e}") + # Try without environment variables - Memgraph might use defaults + _main_memgraph = Memgraph() + logger.warning("Initialized main graph with default connection (may not be port 7688)") + finally: + # Restore original environment variables + if original_host is not None: + os.environ['MEMGRAPH_HOST'] = original_host + else: + os.environ.pop('MEMGRAPH_HOST', None) + + if original_port is not None: + os.environ['MEMGRAPH_PORT'] = original_port + else: + os.environ.pop('MEMGRAPH_PORT', None) + + return _main_memgraph + + +def get_main_memgraph(): + """Get the main graph instance for user data.""" + global _main_memgraph + if _main_memgraph is None: + # Import Memgraph here to ensure environment variables are set + from memgraph_toolbox.api.memgraph import Memgraph + + # Store original environment variables + original_host = os.environ.get('MEMGRAPH_HOST') + original_port = os.environ.get('MEMGRAPH_PORT') + + # Set environment variables for main graph connection (port 7688) + os.environ['MEMGRAPH_HOST'] = 'localhost' + os.environ['MEMGRAPH_PORT'] = '7688' + + try: + _main_memgraph = Memgraph() + logger.info("Initialized main graph connection (port 7688)") + except Exception as e: + logger.error(f"Error initializing main graph: {e}") + # Try without environment variables - Memgraph might use defaults + _main_memgraph = Memgraph() + logger.warning("Initialized main graph with default connection (may not be port 7688)") + finally: + # Restore original environment variables + if original_host is not None: + os.environ['MEMGRAPH_HOST'] = original_host + else: + os.environ.pop('MEMGRAPH_HOST', None) + + if original_port is not None: + os.environ['MEMGRAPH_PORT'] = original_port + else: + os.environ.pop('MEMGRAPH_PORT', None) + return _main_memgraph + + +async def cleanup_resources(): + """Cleanup resources if needed.""" + global _lightrag_wrapper, _main_memgraph + if _lightrag_wrapper is not None: + await _lightrag_wrapper.afinalize() + _lightrag_wrapper = None + # Note: We don't close _main_memgraph connection as it's managed by the client + _main_memgraph = None + diff --git a/graphrag/memgraph_assistant/backend/routes/__init__.py b/graphrag/memgraph_assistant/backend/routes/__init__.py new file mode 100644 index 0000000..fb42c31 --- /dev/null +++ b/graphrag/memgraph_assistant/backend/routes/__init__.py @@ -0,0 +1,2 @@ +# Routes package + diff --git a/graphrag/memgraph_assistant/backend/routes/chat.py b/graphrag/memgraph_assistant/backend/routes/chat.py new file mode 100644 index 0000000..17253b6 --- /dev/null +++ b/graphrag/memgraph_assistant/backend/routes/chat.py @@ -0,0 +1,397 @@ +""" +Chat assistant endpoints for Memgraph Assistant. +Uses OpenAI Agents SDK with reasoning capabilities to understand documentation and perform graph operations. +""" +import os +import logging +import sys +import json +import uuid +from pathlib import Path +from typing import Dict, Any, Optional +from fastapi import APIRouter, HTTPException +from fastapi.responses import StreamingResponse +import asyncio +from .models import ChatRequest + +# Add parent directory to path for imports +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/chat", tags=["chat"]) + +# MCP service URLs +MCP_URL = os.getenv("MCP_MEMGRAPH_URL", "http://localhost:8001/mcp") # Documentation graph +MCP_MAIN_URL = os.getenv("MCP_MEMGRAPH_MAIN_URL", "http://localhost:8002/mcp") # Main graph + +# Model configuration +CHAT_MODEL = os.getenv("CHAT_MODEL", "gpt-4o") + +from agents import Agent, Runner +from agents.model_settings import ModelSettings +from agents.mcp import MCPServerStreamableHttp +from agents.mcp.util import create_static_tool_filter +from agents.memory import SQLiteSession, Session +from .prompts import ( + get_main_graph_agent_instructions, + get_documentation_agent_instructions, +) +from .common import extract_structured_content +from .custom_tools import ( + create_vector_search_tool, + create_keyword_search_tool, + create_relevance_expansion_tool, +) +from .mcp_interceptor import create_interceptor + +# Session storage +_sessions: dict[str, Session] = {} + +# Store intercepting servers per session for approval workflow +# Each session has two interceptors: one for documentation, one for main graph +_session_interceptors: dict[str, dict[str, Any]] = {} # {session_id: {"docs": interceptor, "main": interceptor}} + +# Directory for session database files +SESSIONS_DIR = Path(os.getenv("SESSIONS_DIR", "sessions")) +SESSIONS_DIR.mkdir(exist_ok=True) + + +def get_or_create_session(session_id: str | None) -> Session: + """Get existing session or create a new one with persistent storage.""" + if session_id is None: + session_id = str(uuid.uuid4()) + + if session_id not in _sessions: + db_path = SESSIONS_DIR / "conversations.db" + _sessions[session_id] = SQLiteSession( + session_id=session_id, + db_path=str(db_path) + ) + logger.info(f"Created new persistent session: {session_id}") + else: + logger.debug(f"Reusing existing session: {session_id}") + + return _sessions[session_id] + + +def create_documentation_agent(model: str, server) -> Agent: + """Create the documentation agent that searches and understands documentation.""" + vector_search_tool = create_vector_search_tool(mcp_server=server) + keyword_search_tool = create_keyword_search_tool(mcp_server=server) + relevance_expansion_tool = create_relevance_expansion_tool(mcp_server=server) + + return Agent( + name="Documentation Agent", + instructions=get_documentation_agent_instructions(), + model=model, + mcp_servers=[server], + tools=[vector_search_tool, keyword_search_tool, relevance_expansion_tool], + model_settings=ModelSettings( + tool_choice="required", + temperature=0.1, + parallel_tool_calls=False, + ), + ) + + +def create_main_graph_agent(model: str, main_server, docs_server=None, mode: str = "agent") -> Agent: + """ + Create the main graph agent that executes queries and operations on the main graph. + + Args: + model: The model to use + main_server: MCP server for the main graph + docs_server: Optional MCP server for documentation (allows agent to query docs for help) + mode: "agent" or "ask" - determines behavior for write operations + """ + tools = [] + mcp_servers = [main_server] + + # If documentation server is provided, add documentation tools + # Note: We don't add docs_server to mcp_servers to avoid duplicate tool names (run_query, get_schema) + # Instead, we use custom tools that wrap the docs_server functionality + if docs_server: + tools.extend([ + create_vector_search_tool(mcp_server=docs_server), + create_keyword_search_tool(mcp_server=docs_server), + create_relevance_expansion_tool(mcp_server=docs_server), + ]) + # Don't add docs_server to mcp_servers - it would cause duplicate tool names + + return Agent( + name="Main Graph Agent", + instructions=get_main_graph_agent_instructions(), + model=model, + mcp_servers=mcp_servers, + tools=tools, + model_settings=ModelSettings( + tool_choice="required", + temperature=0.1, + parallel_tool_calls=False, + ), + ) + + +def create_manager_agent(docs_agent: Agent, main_graph_agent: Agent, model: str, mode: str = "agent") -> Agent: + """Create the manager agent that orchestrates the chat and routes between agents.""" + from .prompts import get_manager_agent_instructions + return Agent( + name="Memgraph Assistant", + instructions=get_manager_agent_instructions(mode), + model=model, + tools=[ + docs_agent.as_tool( + tool_name="documentation_agent", + tool_description="Searches and understands Memgraph documentation. Use this when the user asks questions about Memgraph features, syntax, how to use something, or needs documentation information.", + ), + main_graph_agent.as_tool( + tool_name="main_graph_agent", + tool_description="Executes queries and operations on the main graph database (port 7688). Use this when the user wants to query or modify their actual data graph. Write operations will require approval.", + ), + ], + model_settings=ModelSettings( + tool_choice="auto", + temperature=0.3, + ), + ) + + + + +def create_mcp_server_context( + mcp_url: str, + timeout: float = 60.0, + allowed_tool_names: list[str] | None = None, + server_name: str = "Memgraph MCP Server" +) -> MCPServerStreamableHttp: + """ + Create an MCPServerStreamableHttp context manager with the specified configuration. + + Args: + mcp_url: URL of the MCP server (e.g., "http://localhost:8001/mcp") + timeout: Timeout for MCP server connections + allowed_tool_names: List of allowed tool names (default: ["run_query", "get_schema"]) + server_name: Name for the MCP server + + Returns: + MCPServerStreamableHttp instance to be used as async context manager + """ + if allowed_tool_names is None: + allowed_tool_names = ["run_query", "get_schema"] + + return MCPServerStreamableHttp( + name=server_name, + params={ + "url": mcp_url, + "headers": {}, + "timeout": timeout, + }, + cache_tools_list=True, + tool_filter=create_static_tool_filter( + allowed_tool_names=allowed_tool_names + ), + client_session_timeout_seconds=timeout, + ) + + +@router.post("/query-stream") +async def chat_stream(request: ChatRequest): + """ + Chat with the Memgraph Assistant with streaming updates via Server-Sent Events (SSE). + """ + if not request.question or not isinstance(request.question, str): + raise HTTPException( + status_code=400, + detail="No question provided. Please provide a 'question' string.", + ) + + async def event_generator(): + """Generator function for SSE events.""" + try: + session = get_or_create_session(request.session_id) + + # Create documentation MCP server and interceptor + async with create_mcp_server_context( + mcp_url=MCP_URL, + timeout=60.0, + server_name="Memgraph Documentation MCP Server" + ) as docs_base_server: + docs_interceptor = create_interceptor( + base_server=docs_base_server, + name="Documentation Graph" + ) + + logger.info( + f"🔧 Created Documentation InterceptingMCPServer for session {session.session_id}\n" + f" - MCP URL: {MCP_URL} (documentation graph)\n" + f" - Mode: {request.mode}" + ) + + # Create main graph MCP server and interceptor + async with create_mcp_server_context( + mcp_url=MCP_MAIN_URL, + timeout=60.0, + server_name="Memgraph Main Graph MCP Server" + ) as main_base_server: + main_interceptor = create_interceptor( + base_server=main_base_server, + name="Main Graph" + ) + + logger.info( + f"🔧 Created Main Graph InterceptingMCPServer for session {session.session_id}\n" + f" - Mode: {request.mode}\n" + f" - MCP URL: {MCP_MAIN_URL} (main graph)" + ) + + # Store both interceptors for this session + _session_interceptors[session.session_id] = { + "docs": docs_interceptor, + "main": main_interceptor + } + + # Create both agents with mode context + # Main graph agent gets access to documentation tools for help + docs_agent = create_documentation_agent(CHAT_MODEL, docs_interceptor) + main_graph_agent = create_main_graph_agent(CHAT_MODEL, main_interceptor, docs_interceptor, request.mode) + manager_agent = create_manager_agent(docs_agent, main_graph_agent, CHAT_MODEL, request.mode) + + logger.info(f"Running chat assistant with session {session.session_id}...") + + result = await Runner.run( + manager_agent, + request.question, + session=session + ) + + logger.info(f"Agent run completed. Final output: {result.final_output[:100] if result.final_output else 'None'}...") + + # Get queries from both interceptors + docs_run_query_calls = docs_interceptor.get_run_query_calls() + main_run_query_calls = main_interceptor.get_run_query_calls() + all_run_query_calls = [ + {**call, "source": "documentation"} for call in docs_run_query_calls + ] + [ + {**call, "source": "main_graph"} for call in main_run_query_calls + ] + + logger.info( + f"📊 INTERCEPTOR: Final statistics\n" + f" - Documentation run_query calls: {len(docs_run_query_calls)}\n" + f" - Main graph run_query calls: {len(main_run_query_calls)}\n" + f" - Total run_query calls: {len(all_run_query_calls)}" + ) + + answer = result.final_output or "I apologize, but I couldn't generate a response. Please try rephrasing your question." + + final_result = { + "question": request.question, + "answer": answer, + "status": "success", + "session_id": session.session_id, + "agent_run_id": getattr(result, "run_id", None), + "run_query_calls": all_run_query_calls, + } + + logger.info(f"Final result prepared. Answer length: {len(answer)}") + yield f"data: {json.dumps({'type': 'result', 'data': final_result})}\n\n" + + except Exception as e: + logger.error(f"Error in streaming: {str(e)}", exc_info=True) + yield f"data: {json.dumps({'type': 'error', 'error': str(e)})}\n\n" + + return StreamingResponse( + event_generator(), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "X-Accel-Buffering": "no", + } + ) + + +@router.post("/query") +async def chat(request: ChatRequest): + """ + Chat with the Memgraph Assistant. + Understands documentation and can perform graph operations. + """ + if not request.question or not isinstance(request.question, str): + raise HTTPException( + status_code=400, + detail="No question provided. Please provide a 'question' string.", + ) + + try: + session = get_or_create_session(request.session_id) + + # Create documentation MCP server and interceptor + async with create_mcp_server_context( + mcp_url=MCP_URL, + timeout=60.0, + server_name="Memgraph Documentation MCP Server" + ) as docs_base_server: + docs_interceptor = create_interceptor( + base_server=docs_base_server, + name="Documentation Graph" + ) + + # Create main graph MCP server and interceptor + async with create_mcp_server_context( + mcp_url=MCP_MAIN_URL, + timeout=60.0, + server_name="Memgraph Main Graph MCP Server" + ) as main_base_server: + main_interceptor = create_interceptor( + base_server=main_base_server, + name="Main Graph" + ) + + # Store both interceptors for this session + _session_interceptors[session.session_id] = { + "docs": docs_interceptor, + "main": main_interceptor + } + + # Create both agents with mode context + # Main graph agent gets access to documentation tools for help + docs_agent = create_documentation_agent(CHAT_MODEL, docs_interceptor) + main_graph_agent = create_main_graph_agent(CHAT_MODEL, main_interceptor, docs_interceptor, request.mode) + manager_agent = create_manager_agent(docs_agent, main_graph_agent, CHAT_MODEL, request.mode) + + logger.info(f"Running chat assistant with session {session.session_id}...") + + result = await Runner.run( + manager_agent, + request.question, + session=session + ) + + # Get queries from both interceptors + docs_run_query_calls = docs_interceptor.get_run_query_calls() + main_run_query_calls = main_interceptor.get_run_query_calls() + all_run_query_calls = [ + {**call, "source": "documentation"} for call in docs_run_query_calls + ] + [ + {**call, "source": "main_graph"} for call in main_run_query_calls + ] + + return { + "question": request.question, + "answer": result.final_output, + "status": "success", + "session_id": session.session_id, + "agent_run_id": getattr(result, "run_id", None), + "run_query_calls": all_run_query_calls, + } + + except Exception as e: + logger.error(f"Error running chat assistant: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, detail=f"Error running chat assistant: {str(e)}" + ) + + + diff --git a/graphrag/memgraph_assistant/backend/routes/common.py b/graphrag/memgraph_assistant/backend/routes/common.py new file mode 100644 index 0000000..28db77e --- /dev/null +++ b/graphrag/memgraph_assistant/backend/routes/common.py @@ -0,0 +1,557 @@ +""" +Common utility functions for OpenAI Agents SDK integration. +Shared by openai_agents.py and openai_agents_with_planning.py. +""" +import json +from typing import Dict, List, Any, Optional +from agents.items import ToolCallItem, MessageOutputItem, ToolCallOutputItem, ReasoningItem +from agents.items import ItemHelpers + + +def extract_structured_content(result: Any) -> Any: + """ + Extract structuredContent from an MCP tool result. + + Handles various result formats: + - Objects with structuredContent attribute + - Dicts with structuredContent or content keys + - Objects with content attribute (including lists and text attributes) + - Fallback to the result itself + + Args: + result: The result object from an MCP tool call + + Returns: + The extracted structuredContent, or the result itself if extraction fails + """ + # First priority: structuredContent attribute (only for non-dict objects) + if not isinstance(result, dict) and hasattr(result, 'structuredContent'): + return result.structuredContent + + # Second priority: dict with structuredContent or content + if isinstance(result, dict): + return result.get('structuredContent') or result.get('content') or result + + # Third priority: content attribute (only for non-dict objects) + if not isinstance(result, dict) and hasattr(result, 'content'): + content = result.content + + # Handle list of content items + if hasattr(content, '__iter__') and not isinstance(content, str): + content_list = [] + for item in content: + if hasattr(item, 'text'): + content_list.append(item.text) + elif isinstance(item, str): + content_list.append(item) + else: + content_list.append(str(item)) + return '\n'.join(content_list) if content_list else None + + # Handle content with text attribute + if hasattr(content, 'text'): + return content.text + + # Return content as-is + return content + + # Fallback: return result as-is + return result + + +def extract_tools_used(result, include_nested: bool = True) -> list[dict]: + """ + Extract tool calls from the agent run result, including nested tool calls from agent-as-tool invocations. + + Args: + result: The RunResult object from Runner.run() + include_nested: If True, includes nested tool calls from agent-as-tool invocations (e.g., query_executor -> get_schema) + + Returns: + A list of dictionaries containing tool name, arguments, and nested tools for each tool call. + Format: { + "name": "tool_name", + "arguments": {...}, + "nested_tools": [...] # Only present if nested tools exist + } + """ + tools_used = [] + + # Agent-as-tool names that might have nested calls + agent_tool_names = ["query_executor", "query_planner"] + + # Track all items with their positions and types + items_with_positions = [] + for idx, item in enumerate(result.new_items): + item_type = None + tool_name = None + tool_arguments = None + + if isinstance(item, ToolCallItem): + item_type = "tool_call" + raw_item = item.raw_item + + # Handle different raw_item formats + if isinstance(raw_item, dict): + if "function" in raw_item: + tool_name = raw_item["function"].get("name") + tool_arguments = raw_item["function"].get("arguments") + elif "name" in raw_item: + tool_name = raw_item["name"] + tool_arguments = raw_item.get("arguments") + elif hasattr(raw_item, "function"): + tool_name = getattr(raw_item.function, "name", None) + tool_arguments = getattr(raw_item.function, "arguments", None) + elif hasattr(raw_item, "name"): + tool_name = getattr(raw_item, "name", None) + tool_arguments = getattr(raw_item, "arguments", None) + + elif isinstance(item, ToolCallOutputItem): + item_type = "tool_output" + + items_with_positions.append({ + "index": idx, + "type": item_type, + "tool_name": tool_name, + "tool_arguments": tool_arguments, + "item": item, + }) + + # If not including nested, return simple list + if not include_nested: + return [ + {"name": item["tool_name"], "arguments": item["tool_arguments"]} + for item in items_with_positions + if item["type"] == "tool_call" and item["tool_name"] + ] + + # Build tools_used with nested structure + # Match each tool call with its corresponding output to find nested calls + for i, item_info in enumerate(items_with_positions): + if item_info["type"] != "tool_call" or not item_info["tool_name"]: + continue + + tool_entry = { + "name": item_info["tool_name"], + "arguments": item_info["tool_arguments"], + } + + # Check if this is an agent-as-tool call that might have nested tools + if item_info["tool_name"] in agent_tool_names: + # Find the corresponding tool output for this tool call + # Look for the next tool_output item after this tool_call + tool_call_idx = item_info["index"] + corresponding_output_idx = None + + # Find the next tool output (which should correspond to this tool call) + for j in range(tool_call_idx + 1, len(items_with_positions)): + if items_with_positions[j]["type"] == "tool_output": + corresponding_output_idx = j + break + + # Collect nested tool calls between this tool call and its output + nested_tools = [] + if corresponding_output_idx is not None: + # Look for tool calls between this call and its output + for j in range(tool_call_idx + 1, corresponding_output_idx): + nested_item = items_with_positions[j] + if (nested_item["type"] == "tool_call" and + nested_item["tool_name"] and + nested_item["tool_name"] not in agent_tool_names): + nested_tools.append({ + "name": nested_item["tool_name"], + "arguments": nested_item["tool_arguments"], + }) + + if nested_tools: + tool_entry["nested_tools"] = nested_tools + + tools_used.append(tool_entry) + + return tools_used + + +def extract_conversation_history(result, user_question: str) -> list[dict]: + """ + Extract the conversation history between the agent and LLM. + + Args: + result: The RunResult object from Runner.run() + user_question: The original user question + + Returns: + A list of dictionaries representing the conversation history. + """ + conversation = [] + + # Add user question as first message + conversation.append({ + "role": "user", + "type": "message", + "content": user_question, + }) + + # Process all items in order + for item in result.new_items: + if isinstance(item, MessageOutputItem): + # Extract message content + message_text = ItemHelpers.text_message_output(item) + conversation.append({ + "role": "assistant", + "type": "message", + "content": message_text, + }) + + elif isinstance(item, ToolCallItem): + # Extract tool call information + raw_item = item.raw_item + tool_name = None + tool_arguments = None + + if isinstance(raw_item, dict): + if "function" in raw_item: + tool_name = raw_item["function"].get("name") + tool_arguments = raw_item["function"].get("arguments") + elif "name" in raw_item: + tool_name = raw_item["name"] + tool_arguments = raw_item.get("arguments") + elif hasattr(raw_item, "function"): + tool_name = getattr(raw_item.function, "name", None) + tool_arguments = getattr(raw_item.function, "arguments", None) + elif hasattr(raw_item, "name"): + tool_name = getattr(raw_item, "name", None) + tool_arguments = getattr(raw_item, "arguments", None) + + if tool_name: + conversation.append({ + "role": "assistant", + "type": "tool_call", + "tool_name": tool_name, + "arguments": tool_arguments, + }) + + elif isinstance(item, ToolCallOutputItem): + # Extract tool output + output = item.output + # Convert output to string if needed + if isinstance(output, str): + output_str = output + else: + try: + output_str = json.dumps(output, indent=2) + except: + output_str = str(output) + + conversation.append({ + "role": "tool", + "type": "tool_output", + "content": output_str, + }) + + elif isinstance(item, ReasoningItem): + # Extract reasoning content + raw_item = item.raw_item + reasoning_text = None + + if isinstance(raw_item, dict): + reasoning_text = raw_item.get("content") or raw_item.get("text") + elif hasattr(raw_item, "content"): + reasoning_text = getattr(raw_item, "content", None) + elif hasattr(raw_item, "text"): + reasoning_text = getattr(raw_item, "text", None) + + if reasoning_text: + conversation.append({ + "role": "assistant", + "type": "reasoning", + "content": reasoning_text, + }) + + return conversation + + +def build_tool_call_graph(tools_used: list[dict]) -> dict: + """ + Build a graph structure from tool calls showing relationships between tools. + + Args: + tools_used: List of tool call dictionaries with nested_tools + + Returns: + A dictionary with nodes and relationships: + { + "nodes": [ + {"id": "tool_name", "label": "tool_name", "type": "agent"|"mcp"|"custom"} + ], + "relationships": [ + {"source": "parent_tool", "target": "nested_tool", "type": "CALLS"} + ] + } + """ + nodes = {} + relationships = [] + + # Agent-as-tool names + agent_tool_names = ["query_executor", "query_planner", "query_manager"] + # MCP tool names + mcp_tool_names = ["get_schema", "run_query"] + # Custom tool names + custom_tool_names = ["vector_search_on_chunks"] + + def get_tool_type(tool_name: str) -> str: + """Determine tool type based on name.""" + if tool_name in agent_tool_names: + return "agent" + elif tool_name in mcp_tool_names: + return "mcp" + elif tool_name in custom_tool_names: + return "custom" + else: + return "unknown" + + def add_node(tool_name: str): + """Add a node if it doesn't exist.""" + if tool_name not in nodes: + nodes[tool_name] = { + "id": tool_name, + "label": tool_name, + "type": get_tool_type(tool_name), + } + + def add_relationship(source: str, target: str): + """Add a relationship if it doesn't exist.""" + # Check if relationship already exists + if not any(r["source"] == source and r["target"] == target for r in relationships): + relationships.append({ + "source": source, + "target": target, + "type": "CALLS", + }) + + # Process each tool call + for tool in tools_used: + tool_name = tool.get("name") + if not tool_name: + continue + + add_node(tool_name) + + # Add relationships to nested tools + nested_tools = tool.get("nested_tools", []) + for nested_tool in nested_tools: + nested_tool_name = nested_tool.get("name") + if nested_tool_name: + add_node(nested_tool_name) + add_relationship(tool_name, nested_tool_name) + + return { + "nodes": list(nodes.values()), + "relationships": relationships, + } + + +def build_trace_graph_from_items(result, user_question: str) -> Dict[str, Any]: + """ + Build a trace graph from result.new_items, similar to tracing processor output. + + Args: + result: The RunResult object from Runner.run() + user_question: The original user question + + Returns: + A dictionary containing trace data with nodes and relationships for graph visualization. + Format matches tracing processor output: + { + "nodes": [...], + "relationships": [...], + "trace_id": "...", + "trace_name": "..." + } + """ + nodes = [] + relationships = [] + node_set = set() + + # Agent-as-tool names that represent handoffs + agent_tool_names = ["query_executor", "query_planner", "query_manager"] + + # Create root trace node + trace_id = getattr(result, "run_id", "trace_unknown") + trace_node_id = f"trace_{trace_id}" + + # Extract token usage from result + token_usage = {} + if hasattr(result, "context_wrapper") and hasattr(result.context_wrapper, "usage"): + usage = result.context_wrapper.usage + token_usage = { + "input_tokens": getattr(usage, "input_tokens", 0), + "output_tokens": getattr(usage, "output_tokens", 0), + "total_tokens": getattr(usage, "total_tokens", 0), + } + + nodes.append({ + "id": trace_node_id, + "label": "Trace", + "type": "trace", + "trace_id": trace_id, + "details": token_usage, + }) + node_set.add(trace_node_id) + + # Track current parent and last tool call for linking outputs + parent_stack = [trace_node_id] # Stack to handle nested contexts + current_parent = trace_node_id + last_tool_call_id = None + + # Process items in order + for idx, item in enumerate(result.new_items): + node_id = None + node_label = None + node_type = None + details = {} + + if isinstance(item, ToolCallItem): + # Extract tool call information + raw_item = item.raw_item + tool_name = None + tool_arguments = None + + if isinstance(raw_item, dict): + if "function" in raw_item: + tool_name = raw_item["function"].get("name") + tool_arguments = raw_item["function"].get("arguments") + elif "name" in raw_item: + tool_name = raw_item["name"] + tool_arguments = raw_item.get("arguments") + elif hasattr(raw_item, "function"): + tool_name = getattr(raw_item.function, "name", None) + tool_arguments = getattr(raw_item.function, "arguments", None) + elif hasattr(raw_item, "name"): + tool_name = getattr(raw_item, "name", None) + tool_arguments = getattr(raw_item, "arguments", None) + + if tool_name: + node_id = f"tool_call_{idx}" + node_label = tool_name + last_tool_call_id = node_id + + # Determine if this is a handoff (agent calling another agent) + if tool_name in agent_tool_names: + node_type = "handoff" + details['target_agent'] = tool_name + if tool_arguments: + # Try to extract message from arguments + if isinstance(tool_arguments, dict): + details['message'] = tool_arguments.get('question') or tool_arguments.get('query') or tool_arguments + else: + details['message'] = tool_arguments + details['handoff_data'] = tool_arguments + else: + node_type = "function" + details['function_name'] = tool_name + if tool_arguments: + # Try to parse JSON arguments if they're strings + if isinstance(tool_arguments, str): + try: + details['arguments'] = json.loads(tool_arguments) + except: + details['arguments'] = tool_arguments + else: + details['arguments'] = tool_arguments + + elif isinstance(item, ToolCallOutputItem): + # Tool output - link to the last tool call + output = item.output + output_str = None + + if isinstance(output, str): + output_str = output + else: + try: + output_str = json.dumps(output, indent=2) + except: + output_str = str(output) + + # Add output as details to the last tool call node + if last_tool_call_id: + for node in nodes: + if node["id"] == last_tool_call_id: + node["details"]["result"] = output_str + break + + # Create a response node linked to the tool call + if last_tool_call_id: + node_id = f"tool_output_{idx}" + node_label = "Tool Output" + node_type = "response" + details['content'] = output_str + details['response'] = output_str + + elif isinstance(item, MessageOutputItem): + # LLM response/message + message_text = ItemHelpers.text_message_output(item) + node_id = f"message_{idx}" + node_label = "LLM Response" + node_type = "generation" + details['response'] = message_text + details['content'] = message_text + + elif isinstance(item, ReasoningItem): + # Reasoning item + raw_item = item.raw_item + reasoning_text = None + + if isinstance(raw_item, dict): + reasoning_text = raw_item.get("content") or raw_item.get("text") + elif hasattr(raw_item, "content"): + reasoning_text = getattr(raw_item, "content", None) + elif hasattr(raw_item, "text"): + reasoning_text = getattr(raw_item, "text", None) + + if reasoning_text: + node_id = f"reasoning_{idx}" + node_label = "Reasoning" + node_type = "generation" + details['response'] = reasoning_text + details['content'] = reasoning_text + + # Add node if we created one + if node_id and node_label and node_type: + nodes.append({ + "id": node_id, + "label": node_label, + "type": node_type, + "trace_id": trace_id, + "details": details, + }) + node_set.add(node_id) + + # Add relationship to current parent + relationships.append({ + "source": current_parent, + "target": node_id, + "type": "child", + }) + + # Update current parent for handoffs (they create a new context) + if node_type == "handoff": + parent_stack.append(node_id) + current_parent = node_id + # For tool outputs, link them to their tool call instead of parent + elif node_type == "response" and last_tool_call_id: + # Update relationship to link to tool call + relationships[-1]["source"] = last_tool_call_id + # If this is output for a handoff, pop the stack to return to previous parent + if last_tool_call_id in [n["id"] for n in nodes if n.get("type") == "handoff"]: + if len(parent_stack) > 1: + parent_stack.pop() + current_parent = parent_stack[-1] + + return { + "nodes": nodes, + "relationships": relationships, + "trace_id": trace_id, + "trace_name": "Agent Execution Trace", + "token_usage": token_usage, + } + diff --git a/graphrag/memgraph_assistant/backend/routes/custom_tools.py b/graphrag/memgraph_assistant/backend/routes/custom_tools.py new file mode 100644 index 0000000..07d58da --- /dev/null +++ b/graphrag/memgraph_assistant/backend/routes/custom_tools.py @@ -0,0 +1,490 @@ +""" +Custom function tools for OpenAI Agents SDK. +These tools extend the capabilities of agents beyond MCP tools. +""" + +import json +import logging +from agents.tool import function_tool +from agents.mcp.server import MCPServer +from .common import extract_structured_content + +logger = logging.getLogger(__name__) + + +def create_vector_search_tool(mcp_server: MCPServer): + """ + Create a vector_search_on_chunks tool that uses an MCP server (with interceptor). + All queries will go through the interceptor. + + Args: + mcp_server: MCP server instance to use for queries (should be InterceptingMCPServer) + + Returns: + A function_tool decorated function for vector search + """ + + @function_tool + async def vector_search_on_chunks(question: str, limit: int = 10) -> str: + """ + Perform vector search on chunks in Memgraph using embeddings. + + This tool generates embeddings from the user's question and searches for similar chunks + in the vector index 'vs_name'. It executes the query directly and returns results. + + Args: + question: The user's question to search for + limit: Maximum number of results to return (default: 10) - Recommendation is to use 10. + + Returns: + JSON string containing the search results with nodes and similarity scores. + Each result contains a node and its similarity score. + """ + # Sanitize the question to prevent Cypher injection + sanitized_query = question.replace("'", "\\'").replace("\\", "\\\\") + + # Construct the vector search query exactly as specified + cypher_query = f"""CALL embeddings.text(["{sanitized_query}"]) yield dimension, embeddings, success +WITH embeddings[0] as embedding +CALL vector_search.search("vs_name", {limit}, embedding) YIELD distance, node, similarity +RETURN node, similarity""" + + try: + logger.info( + f"🔍 vector_search_on_chunks called\n" + f" - Question: '{question[:100]}{'...' if len(question) > 100 else ''}'\n" + f" - Limit: {limit}" + ) + + # Use MCP server's call_tool method - this will go through the interceptor + tool_result = await mcp_server.call_tool( + "run_query", {"query": cypher_query} + ) + + # Extract structuredContent from MCP tool result + content = extract_structured_content(tool_result) + + if content is None: + logger.warning("vector_search_on_chunks: No structuredContent found in tool result") + return json.dumps( + {"error": "No structuredContent found in tool result"} + ) + + # Remove "embedding" fields to save tokens + def remove_embeddings(obj): + """Recursively remove 'embedding' keys from the object.""" + if isinstance(obj, dict): + return { + k: remove_embeddings(v) + for k, v in obj.items() + if k != "embedding" + } + elif isinstance(obj, list): + return [remove_embeddings(item) for item in obj] + else: + return obj + + cleaned_content = remove_embeddings(content) + + # Count results + result_count = len(cleaned_content) if isinstance(cleaned_content, list) else 1 + logger.info( + f"✅ vector_search_on_chunks completed successfully\n" + f" - Results found: {result_count}\n" + f" - Question: '{question[:50]}{'...' if len(question) > 50 else ''}'" + ) + + # Return as JSON string + return json.dumps(cleaned_content, indent=2) + + except Exception as e: + logger.error( + f"❌ Error executing vector_search_on_chunks\n" + f" - Question: '{question[:100]}{'...' if len(question) > 100 else ''}'\n" + f" - Error: {str(e)}", + exc_info=True + ) + return json.dumps({"error": f"Error executing vector search: {str(e)}"}) + + return vector_search_on_chunks + + +def create_inspect_node_properties_tool(mcp_server: MCPServer): + """ + Create an inspect_node_properties tool that inspects properties of nodes with a specific label. + + Args: + mcp_server: MCP server instance to use for queries (should be InterceptingMCPServer) + + Returns: + A function_tool decorated function for inspecting node properties + """ + + @function_tool + async def inspect_node_properties(label: str, sample_limit: int = 5) -> str: + """ + Inspect properties of nodes with a specific label in Memgraph. + + This tool queries the graph to discover what properties exist on nodes with the given label, + and also returns the sample property values to understand the data structure. + + Args: + label: The node label to inspect (e.g., "Chunk", "Person", "Document") + sample_limit: Maximum number of sample nodes to return (default: 5) + + Returns: + JSON string containing: + - properties: List of property keys found on nodes with this label + - sample_nodes: Sample nodes with their property values (limited by sample limit) + """ + # Sanitize the label to prevent Cypher injection + # Labels in Cypher are typically alphanumeric with underscores, but we'll be safe + sanitized_label = label.replace("'", "").replace("\\", "").replace("`", "") + + # Construct query to get properties and sample values for each property + cypher_query = f"""MATCH (n:`{sanitized_label}`) +WITH collect(DISTINCT keys(n)) as all_keys, collect(n) as nodes +WITH all_keys, nodes[0..{sample_limit}] as sample_nodes +WITH all_keys[0] as properties_list, sample_nodes +UNWIND properties_list as property_key +RETURN property_key, [node IN sample_nodes | node[property_key]] as sampled_values_for_property""" + + try: + logger.info( + f"🔍 inspect_node_properties called\n" + f" - Label: '{label}'\n" + f" - Sample limit: {sample_limit}" + ) + + tool_result = await mcp_server.call_tool( + "run_query", {"query": cypher_query} + ) + + # Extract structuredContent from MCP tool result + content = extract_structured_content(tool_result) + + if content is None: + logger.warning(f"inspect_node_properties: No structuredContent found for label '{label}'") + return json.dumps( + {"error": "No structuredContent found in tool result"} + ) + + # Format the response nicely + # The new query returns a list of rows, each with property_key and sampled_values_for_property + result = { + "label": label, + "properties": [] + } + + # Process the results - each row contains a property_key and its sampled values + if isinstance(content, list): + for row in content: + if isinstance(row, dict): + property_key = row.get("property_key") + sampled_values = row.get("sampled_values_for_property", []) + if property_key: + result["properties"].append({ + "key": property_key, + "sampled_values": sampled_values + }) + elif isinstance(content, dict): + # Single result format (shouldn't happen with new query, but handle it) + property_key = content.get("property_key") + sampled_values = content.get("sampled_values_for_property", []) + if property_key: + result["properties"].append({ + "key": property_key, + "sampled_values": sampled_values + }) + + property_count = len(result["properties"]) + logger.info( + f"✅ inspect_node_properties completed successfully\n" + f" - Label: '{label}'\n" + f" - Properties found: {property_count}" + ) + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error( + f"❌ Error executing inspect_node_properties\n" + f" - Label: '{label}'\n" + f" - Sample limit: {sample_limit}\n" + f" - Error: {str(e)}", + exc_info=True, + ) + return json.dumps({"error": f"Error inspecting node properties: {str(e)}"}) + + return inspect_node_properties + + +def create_keyword_search_tool(mcp_server: MCPServer): + """ + Create a keyword_search tool that performs text search on a specific property. + + Args: + mcp_server: MCP server instance to use for queries (should be InterceptingMCPServer) + + Returns: + A function_tool decorated function for keyword search + """ + @function_tool + async def keyword_search(property_name: str, search_term: str, limit: int = 10) -> str: + """ + Perform keyword search on nodes using text_search.search_all on a specific property. + + This tool searches for nodes where the specified property contains the search term. + It uses Memgraph's text_search module to find matching nodes and returns them ordered by relevance score. + + Args: + property_name: The property name to search on (e.g., "entity_id", "text", "name") + search_term: The keyword or phrase to search for + limit: Maximum number of results to return (default: 10) + + Returns: + JSON string containing: + - results: List of matching nodes with their relevance scores, ordered by score descending + - Each result contains: node (with all properties) and score (relevance score) + """ + # Sanitize inputs to prevent Cypher injection + sanitized_property = property_name.replace("'", "").replace("\\", "").replace("`", "") + sanitized_term = search_term.replace("'", "\\'").replace("\\", "\\\\") + + # Construct the keyword search query + cypher_query = f"""CALL text_search.search_all("{sanitized_property}", "{sanitized_term}") YIELD node, score +RETURN node, score +ORDER BY score DESC +LIMIT {limit}""" + + try: + logger.info( + f"🔍 keyword_search called\n" + f" - Property: '{property_name}'\n" + f" - Search term: '{search_term[:100]}{'...' if len(search_term) > 100 else ''}'\n" + f" - Limit: {limit}" + ) + + tool_result = await mcp_server.call_tool("run_query", {"query": cypher_query}) + + # Extract structuredContent from MCP tool result + content = extract_structured_content(tool_result) + + if content is None: + logger.warning( + f"keyword_search: No structuredContent found\n" + f" - Property: '{property_name}'\n" + f" - Search term: '{search_term[:50]}{'...' if len(search_term) > 50 else ''}'" + ) + return json.dumps({"error": "No structuredContent found in tool result"}) + + # Format the response + result = { + "property_searched": property_name, + "search_term": search_term, + "results": [] + } + + # Handle different response formats + if isinstance(content, list): + for row in content: + if isinstance(row, dict): + node_data = row.get("node", {}) + score = row.get("score", 0.0) + result["results"].append({ + "node": node_data, + "score": score + }) + elif isinstance(content, dict): + # Single result format + if "node" in content and "score" in content: + result["results"].append({ + "node": content.get("node", {}), + "score": content.get("score", 0.0) + }) + + result_count = len(result["results"]) + logger.info( + f"✅ keyword_search completed successfully\n" + f" - Property: '{property_name}'\n" + f" - Search term: '{search_term[:50]}{'...' if len(search_term) > 50 else ''}'\n" + f" - Results found: {result_count}" + ) + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error( + f"❌ Error executing keyword_search\n" + f" - Property: '{property_name}'\n" + f" - Search term: '{search_term[:100]}{'...' if len(search_term) > 100 else ''}'\n" + f" - Limit: {limit}\n" + f" - Error: {str(e)}", + exc_info=True + ) + return json.dumps({"error": f"Error executing keyword search: {str(e)}"}) + + return keyword_search + + +def create_relevance_expansion_tool(mcp_server: MCPServer): + """ + Create a relevance_expansion tool that expands from a node ID to its neighborhood. + + Args: + mcp_server: MCP server instance to use for queries (should be InterceptingMCPServer) + + Returns: + A function_tool decorated function for relevance expansion + """ + @function_tool + async def relevance_expansion(node_id: int) -> str: + """ + Expand from a node by ID to explore its neighborhood (connected nodes and relationships). + + This tool retrieves all nodes and relationships connected to a given node, allowing + exploration of the graph structure around an interesting node. Useful for discovering + related entities, context, or additional information connected to a node of interest. + + Args: + node_id: The internal Memgraph node ID to expand from + + Returns: + JSON string containing: + - center_node: The central node that was expanded from (with all properties) + - neighbors: List of neighboring nodes (with all properties) + - relationships: List of relationships connecting the center node to neighbors + - Each relationship entry contains: type, properties, and connected node info + """ + # Validate node_id is an integer + try: + node_id_int = int(node_id) + except (ValueError, TypeError): + logger.error( + f"❌ Invalid node_id for relevance_expansion\n" + f" - Provided node_id: {node_id}\n" + f" - Error: Must be an integer" + ) + return json.dumps({"error": f"Invalid node_id: {node_id}. Must be an integer."}) + + logger.info( + f"🔍 relevance_expansion called\n" + f" - Node ID: {node_id_int}" + ) + + # Construct the relevance expansion query + cypher_query = f"""MATCH (n)-[r]-(m) +WHERE id(n) = {node_id_int} +RETURN n as center_node, + collect(DISTINCT {{relationship: r, neighbor: m}}) as connections""" + + try: + tool_result = await mcp_server.call_tool("run_query", {"query": cypher_query}) + + # Extract structuredContent from MCP tool result + content = extract_structured_content(tool_result) + + if content is None: + logger.warning( + f"relevance_expansion: No structuredContent found\n" + f" - Node ID: {node_id_int}" + ) + return json.dumps({"error": "No structuredContent found in tool result"}) + + # Format the response + result = { + "center_node": None, + "neighbors": [], + "relationships": [] + } + + # Handle different response formats + if isinstance(content, list) and len(content) > 0: + row = content[0] + if isinstance(row, dict): + # Center node + center_node = row.get("center_node", {}) + if center_node: + result["center_node"] = center_node + + # Connections + connections = row.get("connections", []) + neighbors_set = set() # Use set to avoid duplicates + relationships_list = [] + + for conn in connections: + if isinstance(conn, dict): + rel_data = conn.get("relationship", {}) + neighbor = conn.get("neighbor", {}) + + if neighbor: + # Add neighbor (using node ID as key to avoid duplicates) + neighbor_id = neighbor.get("id") if isinstance(neighbor, dict) else None + if neighbor_id not in neighbors_set: + neighbors_set.add(neighbor_id) + result["neighbors"].append(neighbor) + + if rel_data: + # Extract relationship info + rel_info = { + "type": rel_data.get("type", ""), + "properties": rel_data.get("properties", {}), + "start_node_id": rel_data.get("start_node_id"), + "end_node_id": rel_data.get("end_node_id") + } + relationships_list.append(rel_info) + + result["relationships"] = relationships_list + elif isinstance(content, dict): + # Single result format + center_node = content.get("center_node", {}) + if center_node: + result["center_node"] = center_node + + connections = content.get("connections", []) + neighbors_set = set() + relationships_list = [] + + for conn in connections: + if isinstance(conn, dict): + rel_data = conn.get("relationship", {}) + neighbor = conn.get("neighbor", {}) + + if neighbor: + neighbor_id = neighbor.get("id") if isinstance(neighbor, dict) else None + if neighbor_id not in neighbors_set: + neighbors_set.add(neighbor_id) + result["neighbors"].append(neighbor) + + if rel_data: + rel_info = { + "type": rel_data.get("type", ""), + "properties": rel_data.get("properties", {}), + "start_node_id": rel_data.get("start_node_id"), + "end_node_id": rel_data.get("end_node_id") + } + relationships_list.append(rel_info) + + result["relationships"] = relationships_list + + neighbor_count = len(result["neighbors"]) + relationship_count = len(result["relationships"]) + logger.info( + f"✅ relevance_expansion completed successfully\n" + f" - Node ID: {node_id_int}\n" + f" - Neighbors found: {neighbor_count}\n" + f" - Relationships found: {relationship_count}" + ) + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error( + f"❌ Error executing relevance_expansion\n" + f" - Node ID: {node_id_int}\n" + f" - Error: {str(e)}", + exc_info=True + ) + return json.dumps({"error": f"Error executing relevance expansion: {str(e)}"}) + + return relevance_expansion diff --git a/graphrag/memgraph_assistant/backend/routes/graph.py b/graphrag/memgraph_assistant/backend/routes/graph.py new file mode 100644 index 0000000..2cf483e --- /dev/null +++ b/graphrag/memgraph_assistant/backend/routes/graph.py @@ -0,0 +1,171 @@ +""" +Graph visualization endpoints for Memgraph Assistant. +""" +import os +import logging +import sys +from typing import Optional +from fastapi import APIRouter, HTTPException +from pydantic import BaseModel +from neo4j import GraphDatabase + +# Add parent directory to path for imports +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/graph", tags=["graph"]) + +# Main graph connection settings (port 7688) +MEMGRAPH_URI = os.getenv("MEMGRAPH_MAIN_URI", "bolt://localhost:7688") +MEMGRAPH_USER = os.getenv("MEMGRAPH_USER", "") +MEMGRAPH_PASSWORD = os.getenv("MEMGRAPH_PASSWORD", "") + +# Global driver instance +_driver = None + + +def get_driver(): + """Get Neo4j driver instance for main graph (reused across requests).""" + global _driver + if _driver is None: + auth = None + if MEMGRAPH_USER and MEMGRAPH_PASSWORD: + auth = (MEMGRAPH_USER, MEMGRAPH_PASSWORD) + _driver = GraphDatabase.driver(MEMGRAPH_URI, auth=auth) + return _driver + + +class GraphQueryRequest(BaseModel): + """Request model for graph queries.""" + query: Optional[str] = None + + +@router.post("/query") +async def query_graph(request: GraphQueryRequest): + """ + Execute a Cypher query on the main graph and return results in a format suitable for visualization. + + Default query: MATCH (n) RETURN n + """ + try: + # Default query if none provided + query = request.query or "MATCH (n) RETURN n" + + # Get driver and execute query + driver = get_driver() + + with driver.session() as session: + result = session.run(query) + + # Consume the result into a list first (Neo4j requires consuming before session closes) + records = list(result) + + # Extract nodes and edges from result + nodes = {} + edges = [] + edge_id_counter = 0 + + def process_node(node, nodes_dict): + """Helper function to process a node and add it to nodes_dict.""" + node_id = str(node.id) + if node_id not in nodes_dict: + labels = list(node.labels) if hasattr(node, 'labels') else [] + + # Extract properties - Neo4j nodes use _properties attribute + properties = {} + if hasattr(node, '_properties'): + try: + props = node._properties + if props: + if isinstance(props, dict): + properties = props + else: + properties = dict(props) + except Exception as e: + logger.warning(f"Error extracting properties from node {node_id}: {e}") + properties = {} + elif hasattr(node, 'properties'): + # Fallback to properties if _properties doesn't exist + try: + props = node.properties + if props: + if isinstance(props, dict): + properties = props + else: + properties = dict(props) + except Exception as e: + logger.warning(f"Error extracting properties from node {node_id}: {e}") + properties = {} + + nodes_dict[node_id] = { + "id": node_id, + "label": properties.get('name') or properties.get('title') or (labels[0] if labels else node_id), + "labels": labels, + "properties": properties + } + return node_id + + for record in records: + # Process all values in the record + for value in record.values(): + if value is None: + continue + + # Check if it's a Node + if hasattr(value, 'id') and hasattr(value, 'labels'): + process_node(value, nodes) + + # Check if it's a Relationship + elif hasattr(value, 'start_node') and hasattr(value, 'end_node'): + rel = value + source_id = process_node(rel.start_node, nodes) + target_id = process_node(rel.end_node, nodes) + rel_type = rel.type if hasattr(rel, 'type') else 'RELATED_TO' + + # Extract relationship properties - Neo4j relationships use _properties + rel_props = {} + if hasattr(rel, '_properties'): + try: + props = rel._properties + if props: + if isinstance(props, dict): + rel_props = props + else: + rel_props = dict(props) + except Exception as e: + logger.warning(f"Error extracting properties from relationship: {e}") + rel_props = {} + elif hasattr(rel, 'properties'): + # Fallback to properties if _properties doesn't exist + try: + props = rel.properties + if props: + if isinstance(props, dict): + rel_props = props + else: + rel_props = dict(props) + except Exception as e: + logger.warning(f"Error extracting properties from relationship: {e}") + rel_props = {} + + edges.append({ + "id": f"e{edge_id_counter}", + "source": source_id, + "target": target_id, + "label": rel_type, + "properties": rel_props + }) + edge_id_counter += 1 + + result = { + "nodes": list(nodes.values()), + "edges": edges, + "query": query + } + + return result + + except Exception as e: + logger.error(f"Error executing graph query: {str(e)}", exc_info=True) + raise HTTPException(status_code=500, detail=f"Error executing graph query: {str(e)}") diff --git a/graphrag/memgraph_assistant/backend/routes/ingest.py b/graphrag/memgraph_assistant/backend/routes/ingest.py new file mode 100644 index 0000000..eb83e32 --- /dev/null +++ b/graphrag/memgraph_assistant/backend/routes/ingest.py @@ -0,0 +1,750 @@ +""" +Ingestion endpoints for scraping and processing Memgraph documentation. +""" +import os +import json +import logging +import sys +import re +from typing import List +from fastapi import APIRouter, HTTPException, Request +from fastapi.responses import StreamingResponse +import httpx +from bs4 import BeautifulSoup +import json +import asyncio + +# Add parent directory to path for imports +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from unstructured2graph import from_unstructured, make_chunks, compute_embeddings +from resources import initialize_resources + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/ingest", tags=["ingestion"]) + +# Base URL for Memgraph documentation +MEMGRAPH_DOCS_BASE_URL = "https://memgraph.com/docs" + +# Cache file for discovered URLs +CACHE_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "cache") +CACHE_FILE = os.path.join(CACHE_DIR, "discovered_urls.json") + + +def ensure_cache_dir(): + """Ensure the cache directory exists.""" + os.makedirs(CACHE_DIR, exist_ok=True) + + +def load_cached_urls() -> List[str]: + """Load cached URLs from file if it exists.""" + ensure_cache_dir() + if os.path.exists(CACHE_FILE): + try: + with open(CACHE_FILE, 'r') as f: + data = json.load(f) + urls = data.get('urls', []) + logger.info(f"Loaded {len(urls)} URLs from cache") + return urls + except Exception as e: + logger.warning(f"Error loading cache: {str(e)}") + return [] + return [] + + +def save_urls_to_cache(urls: List[str]): + """Save discovered URLs to cache file.""" + ensure_cache_dir() + try: + with open(CACHE_FILE, 'w') as f: + json.dump({'urls': urls}, f, indent=2) + logger.info(f"Saved {len(urls)} URLs to cache") + except Exception as e: + logger.warning(f"Error saving cache: {str(e)}") + + +async def discover_documentation_urls_stream(use_cache: bool = True, force_refresh: bool = False): + """ + Generator that yields URLs as they are discovered. + + Args: + use_cache: If True, check cache first and use cached URLs if available + force_refresh: If True, ignore cache and scrape fresh URLs + """ + from collections import deque + + # Check cache first if enabled and not forcing refresh + if use_cache and not force_refresh: + cached_urls = load_cached_urls() + if cached_urls: + logger.info(f"Using {len(cached_urls)} cached URLs") + for url in cached_urls: + yield url + return + + urls = [] + visited = set() + seen_urls = set() # Track normalized URLs to avoid duplicates + to_visit = deque([(MEMGRAPH_DOCS_BASE_URL, 0)]) # (url, depth) + max_depth = 15 + + # Patterns to identify documentation navigation elements + nav_selectors = [ + 'nav a', # Navigation links + '.sidebar a', # Sidebar links + '.navigation a', # Navigation class + '[role="navigation"] a', # ARIA navigation + '.docs-nav a', # Docs-specific navigation + '.menu a', # Menu links + 'aside a', # Aside/sidebar + ] + + # Patterns to identify content links + content_selectors = [ + 'article a', + '.content a', + 'main a', + '.docs-content a', + ] + + async def is_valid_docs_url(url: str) -> bool: + """Check if URL is a valid documentation page.""" + if not url or 'memgraph.com/docs' not in url: + return False + + # Skip common non-content URLs + skip_patterns = [ + '/search', + '/api/', + '.pdf', + '.zip', + '.tar.gz', + 'mailto:', + 'javascript:', + '#', + ] + + for pattern in skip_patterns: + if pattern in url.lower(): + return False + + # Must be a docs page + return 'memgraph.com/docs' in url + + async def normalize_url(url: str) -> str: + """Normalize URL by removing fragments and query params.""" + # Remove fragment + url = url.split('#')[0] + # Remove query params (but keep path) + if '?' in url: + base, _ = url.split('?', 1) + # Only remove query if it's not essential + if 'page=' not in url.lower() and 'offset=' not in url.lower(): + url = base + return url.rstrip('/') + + async def extract_links_from_page(soup: BeautifulSoup, base_url: str) -> List[str]: + """Extract documentation links from a page.""" + found_links = set() + + # First, try to find navigation/sidebar links (higher priority) + for selector in nav_selectors: + try: + for link in soup.select(selector): + href = link.get('href', '') + if href: + # Convert to absolute URL + if href.startswith('/docs'): + full_url = f"https://memgraph.com{href}" + elif href.startswith('/'): + # Skip non-docs root links + continue + elif href.startswith('http') and 'memgraph.com/docs' in href: + full_url = href + else: + continue + + full_url = await normalize_url(full_url) + if await is_valid_docs_url(full_url): + found_links.add(full_url) + except Exception as e: + logger.debug(f"Error with selector {selector}: {str(e)}") + + # Then, find content links (lower priority, but still useful) + for selector in content_selectors: + try: + for link in soup.select(selector): + href = link.get('href', '') + if href: + if href.startswith('/docs'): + full_url = f"https://memgraph.com{href}" + elif href.startswith('http') and 'memgraph.com/docs' in href: + full_url = href + else: + continue + + full_url = await normalize_url(full_url) + if await is_valid_docs_url(full_url): + found_links.add(full_url) + except Exception as e: + logger.debug(f"Error with selector {selector}: {str(e)}") + + # Fallback: find all links if selectors didn't work + if not found_links: + for link in soup.find_all('a', href=True): + href = link['href'] + if href.startswith('/docs'): + full_url = f"https://memgraph.com{href}" + elif href.startswith('http') and 'memgraph.com/docs' in href: + full_url = href + else: + continue + + full_url = await normalize_url(full_url) + if await is_valid_docs_url(full_url): + found_links.add(full_url) + + return list(found_links) + + # Use queue-based BFS instead of recursion + async with httpx.AsyncClient(timeout=30.0, follow_redirects=True) as client: + while to_visit: + url, depth = to_visit.popleft() + + # Skip if already visited or too deep + if url in visited or depth > max_depth: + continue + + visited.add(url) + + try: + logger.info(f"Scraping page (depth {depth}): {url}") + response = await client.get(url) + response.raise_for_status() + + # Only process HTML pages + content_type = response.headers.get('content-type', '').lower() + if 'text/html' not in content_type: + logger.debug(f"Skipping non-HTML content: {url}") + continue + + soup = BeautifulSoup(response.text, 'html.parser') + + # Extract links from this page + found_links = await extract_links_from_page(soup, url) + + # Add found links to queue and results + for link_url in found_links: + if link_url not in visited: + # Normalize and check for duplicates + normalized = await normalize_url(link_url) + if normalized not in seen_urls: + seen_urls.add(normalized) + urls.append(link_url) + logger.info(f"Found documentation page: {link_url}") + # Yield the URL as it's discovered + yield link_url + + # Add to queue for further exploration (use original URL for queue) + if depth < max_depth: + to_visit.append((link_url, depth + 1)) + + except httpx.HTTPStatusError as e: + logger.warning(f"HTTP error scraping {url}: {e.response.status_code}") + except httpx.TimeoutException: + logger.warning(f"Timeout scraping {url}") + except Exception as e: + logger.warning(f"Error scraping {url}: {str(e)}") + + logger.info(f"Discovered {len(urls)} unique documentation pages") + + # Save discovered URLs to cache + if urls: + save_urls_to_cache(urls) + + # Note: Async generators cannot return values, they just end naturally + + +async def discover_documentation_urls() -> List[str]: + """ + Discover all documentation URLs from memgraph.com/docs. + Returns a list of all discovered URLs. + Uses the streaming version internally and collects all URLs. + """ + urls = [] + async for url in discover_documentation_urls_stream(): + urls.append(url) + return urls + + +@router.post("/discover") +async def discover_docs(request: Request): + """ + Discover all documentation URLs from memgraph.com/docs. + Returns a list of URLs that can be ingested. + + Optional JSON body: + { + "force_refresh": false # If true, ignore cache and scrape fresh URLs + } + """ + try: + try: + body = await request.json() + force_refresh = body.get("force_refresh", False) + except: + force_refresh = False + + urls = [] + async for url in discover_documentation_urls_stream(use_cache=True, force_refresh=force_refresh): + urls.append(url) + + return { + "urls": urls, + "count": len(urls), + "status": "success" + } + except Exception as e: + logger.error(f"Error discovering documentation: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error discovering documentation: {str(e)}" + ) + + +@router.post("/discover-stream") +async def discover_docs_stream(request: Request): + """ + Discover all documentation URLs from memgraph.com/docs with streaming. + Streams URLs as they are discovered via Server-Sent Events (SSE). + + Optional JSON body: + { + "force_refresh": false # If true, ignore cache and scrape fresh URLs + } + """ + try: + body = await request.json() + force_refresh = body.get("force_refresh", False) + except: + force_refresh = False + + async def event_generator(): + """Generator function for SSE events.""" + urls = [] + + try: + async for url in discover_documentation_urls_stream(use_cache=True, force_refresh=force_refresh): + urls.append(url) + yield f"data: {json.dumps({'type': 'url', 'url': url, 'count': len(urls)})}\n\n" + + # Send completion message + yield f"data: {json.dumps({'type': 'complete', 'total_count': len(urls)})}\n\n" + + except Exception as e: + logger.error(f"Error in streaming discovery: {str(e)}", exc_info=True) + yield f"data: {json.dumps({'type': 'error', 'error': str(e)})}\n\n" + + return StreamingResponse( + event_generator(), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "X-Accel-Buffering": "no", + } + ) + + +@router.post("/scrape-all") +async def scrape_all_docs(): + """ + Automatically discover and ingest all documentation from memgraph.com/docs. + """ + # Verify OPENAI_API_KEY is set + if not os.getenv("OPENAI_API_KEY"): + raise HTTPException( + status_code=500, + detail="OPENAI_API_KEY environment variable is not set. Please set your OpenAI API key." + ) + + try: + # Discover all documentation URLs + logger.info("Discovering documentation URLs...") + urls = await discover_documentation_urls() + + if not urls: + raise HTTPException( + status_code=500, + detail="No documentation URLs discovered. Please check the discovery endpoint." + ) + + logger.info(f"Processing {len(urls)} documentation pages. Starting ingestion...") + + # Initialize resources once at the beginning (with cleanup) + memgraph, lightrag_wrapper = await initialize_resources( + cleanup=True, + create_vector_index=True + ) + + # Process documents one by one - from_unstructured called once per URL + for idx, url in enumerate(urls, 1): + logger.info(f"Processing document {idx}/{len(urls)}: {url}") + + # Ingest single document + await from_unstructured( + [url], + memgraph, + lightrag_wrapper, + only_chunks=False, + link_chunks=True, + ) + + # Create vector index and compute embeddings after all documents are ingested + logger.info("Creating vector search index and computing embeddings...") + try: + compute_embeddings(memgraph, "Chunk") + logger.info("Successfully created vector index and computed embeddings") + except Exception as e: + logger.warning(f"Error creating vector index or computing embeddings: {str(e)}") + + logger.info("Successfully ingested all documentation pages into Memgraph!") + + return { + "message": "Successfully ingested all documentation pages into Memgraph!", + "urls_processed": len(urls), + "urls": urls[:10], # Return first 10 as sample + "status": "completed" + } + + except HTTPException: + raise + except Exception as e: + logger.error(f"Error during ingestion: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error during ingestion: {str(e)}" + ) + + +@router.post("") +async def ingest_documents(request: Request): + """ + Ingest unstructured documents from URLs into Memgraph. + + Expected JSON body: + { + "urls": ["url1", "url2", ...], + "only_chunks": false, + "link_chunks": true, + "create_vector_index": true, + "cleanup": true + } + """ + # Parse request body + try: + body = await request.json() + except json.JSONDecodeError: + raise HTTPException( + status_code=400, + detail="Invalid JSON in request body" + ) + + # Extract parameters with defaults + urls = body.get("urls", []) + only_chunks = body.get("only_chunks", False) + link_chunks = body.get("link_chunks", True) + create_vector_index = body.get("create_vector_index", True) + cleanup = body.get("cleanup", True) + + # Verify OPENAI_API_KEY is set + if not os.getenv("OPENAI_API_KEY"): + raise HTTPException( + status_code=500, + detail="OPENAI_API_KEY environment variable is not set. Please set your OpenAI API key." + ) + + if not urls or not isinstance(urls, list): + raise HTTPException( + status_code=400, + detail="No URLs provided. Please provide at least one URL in the 'urls' array." + ) + + try: + logger.info(f"Processing {len(urls)} documentation pages. Starting ingestion...") + + memgraph, lightrag_wrapper = await initialize_resources( + cleanup=cleanup, + create_vector_index=create_vector_index + ) + + # Ingest documents + await from_unstructured( + urls, + memgraph, + lightrag_wrapper, + only_chunks=only_chunks, + link_chunks=link_chunks, + ) + + # Create vector index and compute embeddings if requested + if create_vector_index: + logger.info("Creating vector search index and computing embeddings...") + try: + compute_embeddings(memgraph, "Chunk") + logger.info("Successfully created vector index and computed embeddings") + except Exception as e: + logger.warning(f"Error creating vector index or computing embeddings: {str(e)}") + + logger.info("Successfully ingested all documentation pages into Memgraph!") + + return { + "message": "Successfully ingested all documentation pages into Memgraph!", + "urls_processed": len(urls), + "status": "completed" + } + + except Exception as e: + logger.error(f"Error during ingestion: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error during ingestion: {str(e)}" + ) + + +@router.post("/single") +async def ingest_single_document(request: Request): + """ + Ingest a single document from a URL into Memgraph. + Useful for progress tracking - process one document at a time. + + Expected JSON body: + { + "url": "https://example.com/doc", + "only_chunks": false, + "link_chunks": true, + "create_vector_index": true, + "cleanup": false # Usually false for single document ingestion + } + """ + # Parse request body + try: + body = await request.json() + except json.JSONDecodeError: + raise HTTPException( + status_code=400, + detail="Invalid JSON in request body" + ) + + # Extract parameters with defaults + url = body.get("url") + only_chunks = body.get("only_chunks", False) + link_chunks = body.get("link_chunks", True) + create_vector_index = body.get("create_vector_index", True) + cleanup = body.get("cleanup", False) + + # Verify OPENAI_API_KEY is set + if not os.getenv("OPENAI_API_KEY"): + raise HTTPException( + status_code=500, + detail="OPENAI_API_KEY environment variable is not set. Please set your OpenAI API key." + ) + + if not url or not isinstance(url, str): + raise HTTPException( + status_code=400, + detail="No URL provided. Please provide a 'url' string." + ) + + try: + logger.info(f"Processing single document: {url}") + + memgraph, lightrag_wrapper = await initialize_resources(cleanup=cleanup, create_vector_index=create_vector_index) + + # Ingest single document + await from_unstructured( + [url], + memgraph, + lightrag_wrapper, + only_chunks=only_chunks, + link_chunks=link_chunks, + ) + + # Create vector index and compute embeddings if requested + if create_vector_index: + logger.info("Computing embeddings and creating vector search index...") + try: + # Compute embeddings for all chunks (including newly added ones) + compute_embeddings(memgraph, "Chunk") + logger.info("Successfully computed embeddings and created vector index") + except Exception as e: + logger.warning(f"Error computing embeddings or creating vector index: {str(e)}") + + logger.info(f"Successfully ingested document: {url}") + + return { + "message": f"Successfully ingested document: {url}", + "url": url, + "status": "completed" + } + + except Exception as e: + logger.error(f"Error during ingestion: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error during ingestion: {str(e)}" + ) + + +@router.post("/batch") +async def ingest_batch_documents(request: Request): + """ + Ingest multiple documents from URLs into Memgraph. + Processes documents one by one and returns success when all are done. + + Expected JSON body: + { + "urls": ["url1", "url2", ...], + "only_chunks": false, + "link_chunks": true, + "create_vector_index": true, + "cleanup": false + } + """ + # Parse request body + try: + body = await request.json() + except json.JSONDecodeError: + raise HTTPException( + status_code=400, + detail="Invalid JSON in request body" + ) + + # Extract parameters with defaults + urls = body.get("urls", []) + only_chunks = body.get("only_chunks", False) + link_chunks = body.get("link_chunks", True) + create_vector_index = body.get("create_vector_index", True) + cleanup = body.get("cleanup", False) + + # Verify OPENAI_API_KEY is set + if not os.getenv("OPENAI_API_KEY"): + raise HTTPException( + status_code=500, + detail="OPENAI_API_KEY environment variable is not set. Please set your OpenAI API key." + ) + + if not urls or not isinstance(urls, list): + raise HTTPException( + status_code=400, + detail="No URLs provided. Please provide at least one URL in the 'urls' array." + ) + + try: + logger.info(f"Processing batch of {len(urls)} documents. Starting ingestion...") + + # Initialize resources once at the beginning (with cleanup and vector index if requested) + memgraph, lightrag_wrapper = await initialize_resources( + cleanup=cleanup, + create_vector_index=create_vector_index + ) + + # Process documents one by one - from_unstructured called once per URL + for idx, url in enumerate(urls, 1): + logger.info(f"Processing document {idx}/{len(urls)}: {url}") + + # Ingest single document + await from_unstructured( + [url], + memgraph, + lightrag_wrapper, + only_chunks=only_chunks, + link_chunks=link_chunks, + ) + + # Compute embeddings after all documents are ingested (index is already created by initialize_resources) + if create_vector_index: + logger.info("Computing embeddings for all chunks...") + try: + compute_embeddings(memgraph, "Chunk") + logger.info("Successfully computed embeddings") + except Exception as e: + logger.warning(f"Error computing embeddings: {str(e)}") + + logger.info(f"Successfully ingested all {len(urls)} documents into Memgraph!") + + return { + "message": f"Successfully ingested {len(urls)} document(s) into Memgraph!", + "urls_processed": len(urls), + "status": "completed" + } + + except Exception as e: + logger.error(f"Error during batch ingestion: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error during batch ingestion: {str(e)}" + ) + + +@router.post("/estimate") +async def estimate_ingestion(request: Request): + """ + Estimate the number of chunks and processing time for given URLs. + Returns chunk count per document and total estimated time (10 seconds per chunk). + + Expected JSON body: + { + "urls": ["url1", "url2", ...] + } + """ + try: + body = await request.json() + except json.JSONDecodeError: + raise HTTPException( + status_code=400, + detail="Invalid JSON in request body" + ) + + urls = body.get("urls", []) + + if not urls or not isinstance(urls, list): + raise HTTPException( + status_code=400, + detail="No URLs provided. Please provide at least one URL in the 'urls' array." + ) + + try: + # Get chunk estimates without full processing + chunked_documents = make_chunks(urls) + + estimates = [] + total_chunks = 0 + + for doc in chunked_documents: + chunk_count = len(doc.chunks) + total_chunks += chunk_count + estimated_time = chunk_count * 10 # 10 seconds per chunk + + estimates.append({ + "url": str(doc.source), + "chunk_count": chunk_count, + "estimated_time_seconds": estimated_time + }) + + total_estimated_time = total_chunks * 10 + + return { + "total_chunks": total_chunks, + "total_estimated_time_seconds": total_estimated_time, + "documents": estimates + } + + except Exception as e: + logger.error(f"Error estimating ingestion: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error estimating ingestion: {str(e)}" + ) + diff --git a/graphrag/memgraph_assistant/backend/routes/mcp.py b/graphrag/memgraph_assistant/backend/routes/mcp.py new file mode 100644 index 0000000..56999a6 --- /dev/null +++ b/graphrag/memgraph_assistant/backend/routes/mcp.py @@ -0,0 +1,124 @@ +""" +MCP (Model Context Protocol) test endpoints. +""" +import os +import logging +import sys +import httpx +from fastapi import APIRouter, HTTPException +from pydantic import BaseModel +from typing import Dict, Any + +# Add parent directory to path for imports +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from mcp_client import call_mcp_service + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/mcp", tags=["mcp"]) + +# MCP service URL +MCP_URL = os.getenv("MCP_MEMGRAPH_URL", "http://localhost:8001/mcp") + + +@router.get("/test") +async def test_mcp_memgraph(): + """Test calling the mcp-memgraph service.""" + try: + payload = { + "jsonrpc": "2.0", + "id": 1, + "method": "tools/call", + "params": { + "name": "get_schema", + "arguments": {} + } + } + + result = await call_mcp_service(MCP_URL, payload, timeout=10.0) + + return { + "status": "success", + "mcp_url": MCP_URL, + "mcp_response": result + } + except Exception as e: + logger.error(f"Error calling mcp-memgraph: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error calling mcp-memgraph: {str(e)}" + ) + + +@router.get("/tools") +async def list_mcp_tools(): + """List all available tools from the mcp-memgraph service.""" + try: + payload = { + "jsonrpc": "2.0", + "id": 1, + "method": "tools/list", + "params": {} + } + + result = await call_mcp_service(MCP_URL, payload, timeout=10.0) + + tools = [] + if "result" in result and "tools" in result["result"]: + tools = result["result"]["tools"] + + return { + "status": "success", + "mcp_url": MCP_URL, + "tools": tools, + "mcp_response": result + } + except Exception as e: + logger.error(f"Error listing mcp-memgraph tools: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error listing mcp-memgraph tools: {str(e)}" + ) + + +class CallToolRequest(BaseModel): + tool_name: str + arguments: Dict[str, Any] = {} + + +@router.post("/call") +async def call_mcp_tool(request: CallToolRequest): + """Call a specific MCP tool with provided arguments.""" + try: + payload = { + "jsonrpc": "2.0", + "id": 1, + "method": "tools/call", + "params": { + "name": request.tool_name, + "arguments": request.arguments + } + } + + result = await call_mcp_service(MCP_URL, payload, timeout=30.0) + + content = None + if "result" in result and "content" in result["result"]: + content = result["result"]["content"] + + return { + "status": "success", + "mcp_url": MCP_URL, + "tool_name": request.tool_name, + "arguments": request.arguments, + "result": content, + "mcp_response": result + } + except Exception as e: + logger.error(f"Error calling mcp-memgraph tool: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error calling mcp-memgraph tool: {str(e)}" + ) + diff --git a/graphrag/memgraph_assistant/backend/routes/mcp_interceptor.py b/graphrag/memgraph_assistant/backend/routes/mcp_interceptor.py new file mode 100644 index 0000000..dbb62d2 --- /dev/null +++ b/graphrag/memgraph_assistant/backend/routes/mcp_interceptor.py @@ -0,0 +1,106 @@ +""" +MCP Server interceptor for capturing tool calls. +This module provides a wrapper around MCP servers to intercept and log tool calls, +particularly useful for capturing run_query calls. +""" +import logging +from typing import List, Dict, Any, Optional +from agents.mcp.server import MCPServer +from .common import extract_structured_content + +logger = logging.getLogger(__name__) + + +def create_interceptor( + base_server: MCPServer, + name: Optional[str] = None +) -> "InterceptingMCPServer": + """ + Create an InterceptingMCPServer wrapping the base MCP server. + + Args: + base_server: The base MCPServer instance (must be used within async context) + name: Optional name for the interceptor (for identification in logs/messages) + + Returns: + InterceptingMCPServer instance + """ + return InterceptingMCPServer( + base_server, + name=name + ) + + +class InterceptingMCPServer(MCPServer): + """ + Wrapper around an MCP server that intercepts tool calls to capture run_query calls. + This allows us to track all Cypher queries executed during a request. + """ + + def __init__(self, wrapped_server: MCPServer, name: Optional[str] = None): + """ + Args: + wrapped_server: The actual MCP server to wrap + name: Optional name for the interceptor (for identification in logs/messages) + """ + super().__init__(use_structured_content=wrapped_server.use_structured_content) + self._wrapped = wrapped_server + self._name = name or wrapped_server.name + self._run_query_calls: List[Dict[str, Any]] = [] + + @property + def name(self) -> str: + return self._name + + async def connect(self): + await self._wrapped.connect() + + async def cleanup(self): + await self._wrapped.cleanup() + + async def list_tools(self, run_context=None, agent=None): + return await self._wrapped.list_tools(run_context, agent) + + async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> Any: + """ + Intercept tool calls and capture run_query calls along with their results. + """ + # Track run_query calls before execution + if tool_name == "run_query" and arguments: + query = arguments.get("query", "") + call_index = len(self._run_query_calls) + self._run_query_calls.append({ + "query": query, + "index": call_index, + "result": None, # Will be populated after the call + }) + logger.info(f"🔍 INTERCEPTOR {self._name}: Executing run_query call #{call_index + 1}\n - Query: {query[:300]}...") + + # Call the actual tool + result = await self._wrapped.call_tool(tool_name, arguments) + + # Capture the result if this was a run_query call + if tool_name == "run_query" and arguments: + query = arguments.get("query", "") + result_data = extract_structured_content(result) + + # Update the last call with the extracted result data + if self._run_query_calls: + self._run_query_calls[-1]["result"] = result_data + logger.info(f"✅ INTERCEPTOR: Captured result for run_query call #{len(self._run_query_calls)}") + + return result + + async def list_prompts(self): + return await self._wrapped.list_prompts() + + async def get_prompt(self, name: str, arguments: dict[str, Any] | None = None): + return await self._wrapped.get_prompt(name, arguments) + + def get_run_query_calls(self) -> List[Dict[str, Any]]: + """Get all intercepted run_query calls.""" + return self._run_query_calls.copy() + + def clear_run_query_calls(self): + """Clear the intercepted run_query calls list.""" + self._run_query_calls.clear() \ No newline at end of file diff --git a/graphrag/memgraph_assistant/backend/routes/models.py b/graphrag/memgraph_assistant/backend/routes/models.py new file mode 100644 index 0000000..9537396 --- /dev/null +++ b/graphrag/memgraph_assistant/backend/routes/models.py @@ -0,0 +1,25 @@ +""" +Pydantic models for chat routes. +""" +from pydantic import BaseModel +from enum import Enum + + +class ChatMode(str, Enum): + """Chat mode enumeration.""" + AGENT = "agent" # Can execute queries, write operations require approval + ASK = "ask" # Only proposes plans, doesn't execute write operations + + +class ChatRequest(BaseModel): + """Request model for chat queries.""" + question: str + session_id: str | None = None + mode: ChatMode = ChatMode.AGENT # Default to agent mode + + +class ApproveRequest(BaseModel): + """Request model for approving queries.""" + query: str # The query to approve and execute + session_id: str | None = None + diff --git a/graphrag/memgraph_assistant/backend/routes/prompts.py b/graphrag/memgraph_assistant/backend/routes/prompts.py new file mode 100644 index 0000000..1845287 --- /dev/null +++ b/graphrag/memgraph_assistant/backend/routes/prompts.py @@ -0,0 +1,102 @@ +""" +Prompt templates for Memgraph Assistant chat agent. +""" + +def get_manager_agent_instructions(mode: str = "agent") -> str: + """Get manager agent instructions with mode context.""" + if mode == "ask": + mode_context = """ +**CURRENT MODE: ASK MODE** +- EXPLORE/READ operations: Execute immediately via main_graph_agent (no confirmation needed) +- CHANGE operations: PROPOSE first, wait for confirmation, then INFORM user they need to switch to AGENT mode +""" + else: + mode_context = """ +**CURRENT MODE: AGENT MODE** +- EXPLORE/READ operations: Execute immediately via main_graph_agent (no confirmation needed) +- CHANGE operations: PROPOSE first, wait for confirmation, then EXECUTE via main_graph_agent +""" + + return f"""You are a Memgraph Assistant - an AI wizard that understands Memgraph documentation and can help users with graph database operations. + +{mode_context} + +**System Architecture**: +- **Documentation Graph** (port 7687): Memgraph documentation - READ ONLY for searching +- **Main Graph** (port 7688): User's actual data - can be modified with approval + +**Available Agents**: +1. **documentation_agent**: Searches Memgraph documentation. Use for questions about features, syntax, or how-to guides. +2. **main_graph_agent**: Executes queries on the main graph. Use immediately for EXPLORE/READ operations, or after user confirmation for CHANGE operations. + +**Workflow for Graph Operations**: +1. **JUDGE INTENT**: Determine if the user wants to: + - **EXPLORE/READ**: Query the graph to see what's there (read operations) + - **CHANGE**: Modify the graph (write operations) + +2. **For EXPLORE/READ operations**: + - Plan the Cypher query + - Execute immediately via `main_graph_agent` (no confirmation needed) + - Show results to the user + +3. **For CHANGE operations**: + - **PROPOSE**: Show the Cypher query in a code block and explain what it would do + - **WAIT**: Ask for user confirmation (e.g., "Would you like me to execute this query?" or "Would you like me to run this query?") + - **EXECUTE or INFORM**: + - If AGENT mode and user confirms: Call `main_graph_agent` to EXECUTE/CHANGE the graph + - If ASK mode and user confirms: INFORM them they need to switch to AGENT mode to execute + +**Important Distinction**: +- **EXPLORE/READ**: Execute immediately - user wants to see what's in the graph +- **CHANGE**: Requires confirmation - user wants to modify the graph +- **INFORM**: Just tell the user what would happen (ASK mode after confirmation) +- **EXECUTE/CHANGE**: Actually run the query and modify the graph (AGENT mode after confirmation) + +**Routing Logic**: +- Documentation questions → `documentation_agent` (execute immediately) +- Explore/Read operations on main graph → Execute immediately via `main_graph_agent` (no confirmation needed) +- Change operations on main graph → Always propose first, wait for confirmation, then execute (agent mode) or inform (ask mode) + +**Key Principles**: +- Judge user intent first: EXPLORE/READ vs CHANGE +- EXPLORE/READ operations: Execute immediately without confirmation +- CHANGE operations: Always propose first, wait for confirmation, then execute (agent mode) or inform (ask mode) +- Respect the current mode - inform users about mode switching when needed +- Always explain what you're doing and why +- Cite documentation when answering documentation questions""" + +def get_documentation_agent_instructions() -> str: + """Get documentation agent instructions.""" + return """You are a specialized agent that searches and understands Memgraph documentation. + +**Your Role**: +- Search the graph for information about Memgraph features, syntax, and usage +- Answer questions about how to use Memgraph based on the documentation +- Use vector_search_on_chunks to find semantically similar documentation +- Use keyword_search for exact keyword matches +- Use relevance_expansion to explore related documentation nodes +- Use run_query to execute read queries on the documentation graph and find out information + +**Important**: +- Always cite documentation when answering questions +- Provide clear, helpful answers based on the documentation you find""" + + +def get_main_graph_agent_instructions() -> str: + """Get main graph agent instructions - always executes what it's told.""" + return """You are a specialized agent that executes queries and operations on the main graph database (port 7688). + +**Your Role**: +- Execute Cypher queries using `run_query` tool when instructed +- Get schema information using `get_schema` when needed +- Query documentation tools (vector_search_on_chunks, keyword_search, relevance_expansion) for help with Memgraph syntax and features + +**Important Rules**: +- Always do what you are told - execute queries when asked +- If `run_query` returns `{{"requires_approval": True, ...}}`, return that response to the caller - they will handle approval +- Always explain what queries do and report results clearly +- Report errors clearly and helpfully +- Use documentation tools to help construct correct Cypher queries""" + + + diff --git a/graphrag/memgraph_assistant/backend/routes/stats.py b/graphrag/memgraph_assistant/backend/routes/stats.py new file mode 100644 index 0000000..0922948 --- /dev/null +++ b/graphrag/memgraph_assistant/backend/routes/stats.py @@ -0,0 +1,61 @@ +""" +Statistics endpoints for querying knowledge graph statistics. +""" +import logging +import sys +import os +from fastapi import APIRouter, HTTPException + +# Add parent directory to path for imports +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +# Add the ai-toolkit path to sys.path for imports +SCRIPT_DIR = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) +AI_TOOLKIT_DIR = os.path.join(SCRIPT_DIR, "ai-toolkit") +sys.path.insert(0, os.path.join(AI_TOOLKIT_DIR, "memgraph-toolbox", "src")) + +from memgraph_toolbox.api.memgraph import Memgraph + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/stats", tags=["statistics"]) + + +@router.get("") +async def get_stats(): + """ + Get statistics about the ingested data in Memgraph. + """ + try: + memgraph = Memgraph() + + # Get chunk count + chunk_result = memgraph.query("MATCH (n:Chunk) RETURN count(n) as count") + chunk_count = chunk_result[0]["count"] if chunk_result else 0 + + # Get entity count (base nodes from LightRAG) + entity_result = memgraph.query("MATCH (n:base) RETURN count(n) as count") + entity_count = entity_result[0]["count"] if entity_result else 0 + + # Get relationship count + rel_result = memgraph.query("MATCH ()-[r]->() RETURN count(r) as count") + rel_count = rel_result[0]["count"] if rel_result else 0 + + # Get total node count + node_result = memgraph.query("MATCH (n) RETURN count(n) as count") + node_count = node_result[0]["count"] if node_result else 0 + + return { + "chunks": chunk_count, + "entities": entity_count, + "relationships": rel_count, + "nodes": node_count + } + + except Exception as e: + logger.error(f"Error getting stats: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"Error getting stats: {str(e)}" + ) + diff --git a/graphrag/memgraph_assistant/backend/uv.lock b/graphrag/memgraph_assistant/backend/uv.lock new file mode 100644 index 0000000..a2bc0f0 --- /dev/null +++ b/graphrag/memgraph_assistant/backend/uv.lock @@ -0,0 +1,3953 @@ +version = 1 +revision = 3 +requires-python = ">=3.13" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'darwin'", + "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version >= '3.14' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version < '3.14' and sys_platform == 'darwin'", + "python_full_version < '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version < '3.14' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.14' and sys_platform != 'darwin' and sys_platform != 'linux')", +] + +[[package]] +name = "accelerate" +version = "1.12.0" +source = { registry = 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@@ -0,0 +1,1218 @@ + + + + + diff --git a/graphrag/memgraph_assistant/frontend/src/components/GraphVisualization.vue b/graphrag/memgraph_assistant/frontend/src/components/GraphVisualization.vue new file mode 100644 index 0000000..ac5ae59 --- /dev/null +++ b/graphrag/memgraph_assistant/frontend/src/components/GraphVisualization.vue @@ -0,0 +1,579 @@ + + + + + diff --git a/graphrag/memgraph_assistant/frontend/src/main.js b/graphrag/memgraph_assistant/frontend/src/main.js new file mode 100644 index 0000000..cdc0ba5 --- /dev/null +++ b/graphrag/memgraph_assistant/frontend/src/main.js @@ -0,0 +1,6 @@ +import { createApp } from 'vue' +import App from './App.vue' +import './style.css' + +createApp(App).mount('#app') + diff --git a/graphrag/memgraph_assistant/frontend/src/style.css b/graphrag/memgraph_assistant/frontend/src/style.css new file mode 100644 index 0000000..5f1c161 --- /dev/null +++ b/graphrag/memgraph_assistant/frontend/src/style.css @@ -0,0 +1,1717 @@ +/* Memgraph Design System - 60:30:10 Rule */ +/* 60% White, 30% Light Gray (#F9F9F9), 10% Brand Accent */ + +* { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +body { + font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; + font-weight: 400; /* Regular - never medium or semibold for body */ + background: #FFFFFF; /* 60% White - main background */ + color: #231F20; /* Brand Black - primary text */ + line-height: 1.6; + min-height: 100vh; +} + +#app { + min-height: 100vh; +} + +.app { + min-height: 100vh; + display: flex; + flex-direction: column; +} + +/* Header - Use gradient for primary element */ +.header { + background: linear-gradient(30deg, #FFC500 0%, #DC2223 41%, #720096 100%); + color: white; + padding: 40px 20px; + margin-bottom: 40px; +} + +.logo-container { + display: flex; + align-items: center; + gap: 16px; + justify-content: center; + margin-bottom: 12px; +} + +.logo { + width: 48px; + height: 48px; + border-radius: 6px; /* Smaller radius for smaller element */ +} + +.header h1 { + font-size: 32px; + font-weight: 600; /* Semibold for headings */ + margin: 0; + color: white; +} + +.subtitle { + text-align: center; + font-size: 16px; + font-weight: 400; /* Regular */ + opacity: 0.95; + margin-top: 8px; + color: white; +} + +.container { + max-width: 1400px; + margin: 0 auto; + padding: 0 20px 60px; + flex: 1; + overflow: visible; +} + +/* Tabs */ +.tabs { + display: flex; + gap: 0; + margin-bottom: 40px; + border-bottom: 1px solid #F9F9F9; /* 30% Light Gray for subtle contrast */ + padding-bottom: 0; + justify-content: center; +} + +.tab-button { + padding: 16px 32px; + background: none; + border: none; + cursor: pointer; + font-size: 16px; + font-weight: 400; /* Regular - consistent weight to prevent shifting */ + color: #646265; /* Base Gray - secondary text */ + transition: color 0.2s ease; + position: relative; + box-sizing: border-box; + margin-bottom: -1px; /* Align with border-bottom of tabs container */ + min-width: fit-content; + white-space: nowrap; +} + +.tab-button::after { + content: ''; + position: absolute; + bottom: -1px; + left: 0; + right: 0; + height: 2px; + background: transparent; + transition: background 0.2s ease; +} + +.tab-button:hover { + color: #231F20; /* Brand Black on hover */ +} + +.tab-button.active { + color: #231F20; /* Brand Black */ + font-weight: 400; /* Keep same weight to prevent shifting - use color and underline for emphasis */ +} + +.tab-button.active::after { + background: #FB6E00; /* Brand accent */ +} + +.main-content { + animation: fadeIn 0.3s ease-in; +} + +@keyframes fadeIn { + from { + opacity: 0; + transform: translateY(10px); + } + to { + opacity: 1; + transform: translateY(0); + } +} + +/* Cards - Always white */ +.card { + background: #FFFFFF; /* 60% White */ + border-radius: 8px; /* Larger radius for larger elements */ + padding: 32px; + margin-bottom: 32px; +} + +.card h2 { + font-size: 24px; + font-weight: 600; /* Semibold for headings */ + margin-bottom: 12px; + color: #231F20; /* Brand Black */ +} + +.card h3 { + font-size: 20px; + font-weight: 600; /* Semibold for headings */ + margin-bottom: 20px; + color: #231F20; /* Brand Black */ +} + +.description { + color: #646265; /* Base Gray - secondary text */ + margin-bottom: 32px; + font-size: 15px; + font-weight: 400; /* Regular */ +} + +/* Buttons */ +.btn { + padding: 12px 24px; + border: none; + border-radius: 6px; /* Medium radius */ + font-size: 15px; + font-weight: 400; /* Regular */ + cursor: pointer; + transition: opacity 0.2s ease; + display: inline-flex; + align-items: center; + justify-content: center; + gap: 8px; +} + +/* Primary button - Use gradient */ +.btn-primary { + background: linear-gradient(30deg, #FFC500 0%, #DC2223 41%, #720096 100%); + color: white; +} + +.btn-primary:hover:not(:disabled) { + opacity: 0.9; +} + +.btn-secondary { + background: #FFFFFF; /* White */ + color: #231F20; /* Brand Black */ + border: 1px solid #F9F9F9; /* Light Gray border */ +} + +.btn-secondary:hover:not(:disabled) { + background: #F9F9F9; /* 30% Light Gray on hover */ +} + +.btn:disabled { + background: #F9F9F9; /* Light Gray */ + color: #646265; /* Base Gray */ + cursor: not-allowed; + opacity: 0.6; +} + +.ingestion-controls { + display: flex; + flex-direction: column; + gap: 16px; + margin-bottom: 32px; +} + +.ingestion-options { + display: flex; + flex-direction: column; + gap: 12px; +} + +.batch-options { + display: flex; + flex-direction: column; + gap: 12px; + margin-bottom: 16px; +} + +.ingestion-buttons { + display: flex; + gap: 12px; + flex-wrap: wrap; +} + +.checkbox-label { + display: flex; + align-items: center; + gap: 8px; + font-size: 14px; + font-weight: 400; /* Regular */ + color: #231F20; /* Brand Black */ + cursor: pointer; +} + +.checkbox-label input[type="checkbox"] { + width: 18px; + height: 18px; + cursor: pointer; + accent-color: #FB6E00; /* Brand accent */ +} + +/* Messages */ +.message { + padding: 16px 20px; + border-radius: 6px; + margin-bottom: 24px; + font-size: 15px; + font-weight: 400; /* Regular */ +} + +.message.success { + background: #FFFFFF; + border: 1px solid #F9F9F9; + color: #231F20; +} + +.message.error { + background: #FFFFFF; + border: 1px solid #F9F9F9; + color: #231F20; +} + +.success-card { + background: #FFFFFF; + border: 1px solid #F9F9F9; +} + +.success-card h3 { + color: #231F20; +} + +.custom-urls-card { + margin-top: 32px; +} + +.custom-urls-card h3 { + margin-bottom: 8px; +} + +.custom-urls-card .description { + margin-bottom: 16px; + font-size: 14px; + color: #646265; /* Base Gray */ +} + +.form-group { + margin-bottom: 16px; +} + +.urls-textarea { + width: 100%; + padding: 12px 14px; + border: 1px solid #F9F9F9; + border-radius: 6px; + font-size: 14px; + font-weight: 400; /* Regular */ + font-family: 'Inter', monospace; + background: #FFFFFF; + color: #231F20; /* Brand Black */ + transition: border-color 0.2s ease; + resize: vertical; + line-height: 1.5; +} + +.urls-textarea:focus { + outline: none; + border-color: #FB6E00; /* Brand accent */ +} + +.urls-textarea:disabled { + background: #F9F9F9; + cursor: not-allowed; +} + +.urls-textarea::placeholder { + color: #646265; /* Base Gray */ +} + +.discovered-urls-card { + margin-top: 32px; + max-height: 600px; + display: flex; + flex-direction: column; +} + +.urls-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 16px; +} + +.urls-header h3 { + margin-bottom: 0; +} + +.urls-stats { + font-size: 14px; + font-weight: 400; /* Regular */ + color: #646265; /* Base Gray */ +} + +.urls-search-container { + margin-bottom: 16px; +} + +.urls-search-input { + width: 100%; + padding: 10px 14px; + border: 1px solid #F9F9F9; + border-radius: 6px; + font-size: 14px; + font-weight: 400; /* Regular */ + font-family: 'Inter', sans-serif; + background: #FFFFFF; + color: #231F20; /* Brand Black */ + transition: border-color 0.2s ease; +} + +.urls-search-input:focus { + outline: none; + border-color: #FB6E00; /* Brand accent */ +} + +.urls-search-input::placeholder { + color: #646265; /* Base Gray */ +} + +.urls-list { + display: flex; + flex-direction: column; + gap: 8px; + overflow-y: auto; + flex: 1; + min-height: 0; +} + +.url-item { + padding: 10px 14px; + background: #F9F9F9; /* 30% Light Gray - secondary background */ + border-radius: 4px; /* Smaller radius */ + font-size: 13px; + font-weight: 400; /* Regular */ + font-family: 'Inter', monospace; + word-break: break-all; + color: #231F20; /* Brand Black */ + transition: background-color 0.2s ease; +} + +.url-item:hover { + background: #FFFFFF; + border: 1px solid #F9F9F9; +} + +.url-item-empty { + padding: 24px; + text-align: center; + font-size: 14px; + font-weight: 400; /* Regular */ + color: #646265; /* Base Gray */ + font-style: italic; +} + +.url-list { + margin-top: 16px; + padding-left: 24px; +} + +.url-list li { + margin-bottom: 12px; + font-size: 14px; + font-weight: 400; /* Regular */ + word-break: break-all; + color: #231F20; /* Brand Black */ +} + +.progress-card { + margin-top: 32px; +} + +.progress-overall-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 20px; +} + +.progress-overall-header h3 { + margin-bottom: 0; +} + +.progress-overall-stats { + font-size: 14px; + font-weight: 400; /* Regular */ + color: #646265; /* Base Gray */ +} + +.progress-overall-bar-container { + display: flex; + align-items: center; + gap: 16px; + margin-bottom: 24px; +} + +.progress-overall-bar { + flex: 1; + height: 24px; + background: #F9F9F9; /* Light Gray */ + border-radius: 4px; + overflow: hidden; +} + +.progress-overall-bar-fill { + height: 100%; + background: linear-gradient(30deg, #FFC500 0%, #DC2223 41%, #720096 100%); + transition: width 0.3s ease; + border-radius: 4px; +} + +.progress-overall-percentage { + font-size: 14px; + font-weight: 600; /* Semibold */ + color: #231F20; /* Brand Black */ + min-width: 50px; + text-align: right; +} + +.countdown-timer { + background: #FFFFFF; + border: 1px solid #F9F9F9; + border-radius: 6px; + padding: 20px; + margin-bottom: 24px; +} + +.countdown-header { + display: flex; + justify-content: space-between; + align-items: center; +} + +.countdown-label { + font-weight: 400; /* Regular */ + color: #646265; /* Base Gray */ +} + +.countdown-value { + font-size: 20px; + font-weight: 600; /* Semibold */ + color: #231F20; /* Brand Black */ +} + +.countdown-value.countdown-warning { + color: #FB6E00; /* Brand accent */ +} + +.countdown-finished { + background: #FFFFFF; + border-color: #F9F9F9; +} + +.countdown-finished .countdown-label, +.countdown-finished .countdown-value { + color: #646265; /* Base Gray */ +} + +.progress-item { + margin-bottom: 20px; + padding: 16px; + background: #FFFFFF; + border: 1px solid #F9F9F9; + border-radius: 6px; +} + +.progress-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 12px; +} + +.progress-url { + font-size: 14px; + font-weight: 400; /* Regular */ + color: #231F20; /* Brand Black */ + flex: 1; + word-break: break-all; + margin-right: 16px; +} + +.progress-status { + padding: 6px 12px; + border-radius: 4px; /* Smaller radius */ + font-size: 12px; + font-weight: 400; /* Regular */ + text-transform: uppercase; +} + +.progress-status.processing { + background: #F9F9F9; /* Light Gray */ + color: #231F20; /* Brand Black */ +} + +.progress-status.completed { + background: #F9F9F9; /* Light Gray */ + color: #231F20; /* Brand Black */ +} + +.progress-status.error { + background: #F9F9F9; /* Light Gray */ + color: #231F20; /* Brand Black */ +} + +.progress-bar { + width: 100%; + height: 4px; + background: #F9F9F9; /* Light Gray */ + border-radius: 2px; /* Smallest radius */ + overflow: hidden; +} + +.progress-bar-fill { + height: 100%; + background: linear-gradient(30deg, #FFC500 0%, #DC2223 41%, #720096 100%); + transition: width 0.3s ease; + border-radius: 2px; +} + +/* Chat Layout */ +.chat-layout { + display: flex; + gap: 0; + height: calc(100vh - 200px); + min-height: 700px; + max-height: 900px; +} + +.chat-panel { + flex: 1; + min-width: 0; + display: flex; + flex-direction: column; +} + +.graph-panel { + flex: 1; + min-width: 0; + display: flex; + flex-direction: column; +} + +/* Chat */ +.chat-card { + padding: 0; + overflow: visible; + display: flex; + flex-direction: column; + height: 100%; + flex: 1; +} + +.chat-header { + padding: 24px 32px; + border-bottom: 1px solid #E5E5E5; + background: #FFFFFF; + overflow: visible; + position: relative; + z-index: 10; +} + +.chat-header-content { + display: flex; + justify-content: flex-start; + align-items: center; + gap: 32px; + overflow: visible; + position: relative; +} + +.chat-title-section h2 { + margin-bottom: 4px; + font-size: 24px; + font-weight: 600; + color: #231F20; +} + +.chat-title-section .description { + margin-bottom: 0; + font-size: 14px; + color: #646265; +} + +.mode-selector { + display: flex; + align-items: center; + position: relative; + overflow: visible; + z-index: 100; +} + +.mode-toggle { + display: flex; + gap: 0; + background: #F3F4F6; + padding: 3px; + border-radius: 12px; + border: 1px solid #E5E7EB; + box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.mode-btn { + position: relative; + display: flex; + align-items: center; + gap: 8px; + padding: 10px 18px; + border: none; + background: transparent; + border-radius: 10px; + cursor: pointer; + transition: all 0.2s ease; + font-size: 14px; + font-weight: 500; + color: #6B7280; + white-space: nowrap; +} + +.mode-btn::after { + content: attr(data-tooltip); + position: absolute; + bottom: calc(100% + 10px); + left: 50%; + transform: translateX(-50%); + padding: 8px 12px; + background: #1F2937; + color: white; + font-size: 12px; + font-weight: 400; + border-radius: 6px; + white-space: normal; + word-wrap: break-word; + opacity: 0; + pointer-events: none; + transition: opacity 0.2s ease; + z-index: 10000; + box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15); + max-width: 350px; + min-width: 200px; + text-align: left; + line-height: 1.4; +} + +.mode-btn-agent::after { + left: 0; + transform: translateX(0); +} + +.mode-btn-ask::after { + left: auto; + right: 0; + transform: translateX(0); +} + +.mode-btn::before { + content: ''; + position: absolute; + bottom: calc(100% + 4px); + left: 50%; + transform: translateX(-50%); + border: 5px solid transparent; + border-top-color: #1F2937; + opacity: 0; + pointer-events: none; + transition: opacity 0.2s ease; + z-index: 10001; +} + +.mode-btn-agent::before { + left: 20px; + transform: translateX(0); +} + +.mode-btn-ask::before { + left: auto; + right: 20px; + transform: translateX(0); +} + +.mode-btn:hover:not(:disabled)::after, +.mode-btn:hover:not(:disabled)::before { + opacity: 1; +} + +.mode-btn:hover:not(:disabled) { + background: rgba(255, 255, 255, 0.6); + color: #231F20; +} + +.mode-btn.active { + background: #FFFFFF; + color: #231F20; + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.08); + font-weight: 600; +} + +.mode-btn.active .mode-btn-icon { + transform: scale(1.1); +} + +.mode-btn:disabled { + opacity: 0.5; + cursor: not-allowed; +} + +.mode-btn-icon { + font-size: 18px; + transition: transform 0.2s ease; + display: flex; + align-items: center; + justify-content: center; +} + +.mode-btn-text { + font-size: 14px; + letter-spacing: 0.01em; +} + +.chat-container { + display: flex; + flex-direction: column; + flex: 1; + overflow: hidden; + background: #FFFFFF; +} + +.chat-messages { + flex: 1; + overflow-y: auto; + padding: 16px 24px; + background: linear-gradient(to bottom, #FAFAFA 0%, #F5F5F5 100%); + display: flex; + flex-direction: column; + gap: 10px; +} + +.empty-state { + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + height: 100%; + color: #646265; + text-align: center; + padding: 48px; +} + +.empty-icon-wrapper { + width: 80px; + height: 80px; + margin-bottom: 24px; + display: flex; + align-items: center; + justify-content: center; + background: linear-gradient(135deg, #FFC500 0%, #DC2223 41%, #720096 100%); + border-radius: 50%; + opacity: 0.1; +} + +.empty-icon { + width: 48px; + height: 48px; + color: #231F20; + opacity: 0.3; +} + +.empty-state h3 { + font-size: 20px; + font-weight: 600; + color: #231F20; + margin-bottom: 8px; +} + +.empty-state p { + font-size: 15px; + font-weight: 400; + color: #646265; + max-width: 400px; +} + +.chat-message { + display: flex; + gap: 8px; + animation: slideIn 0.3s ease-out; + align-items: flex-start; +} + +.chat-message.user { + flex-direction: row-reverse; +} + +.chat-message.bot { + flex-direction: row; +} + +@keyframes slideIn { + from { + opacity: 0; + transform: translateY(8px); + } + to { + opacity: 1; + transform: translateY(0); + } +} + +.message-avatar { + width: 36px; + height: 36px; + border-radius: 50%; + flex-shrink: 0; + overflow: hidden; + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1); +} + +.bot-avatar { + background: #FFFFFF; + border: 2px solid #F0F0F0; +} + +.bot-avatar img { + width: 100%; + height: 100%; + object-fit: cover; +} + +.user-avatar { + background: linear-gradient(135deg, #FFC500 0%, #DC2223 41%, #720096 100%); + display: flex; + align-items: center; + justify-content: center; + color: white; + padding: 8px; +} + +.user-avatar svg { + width: 100%; + height: 100%; +} + +.message-content { + display: flex; + flex-direction: column; + max-width: 75%; + min-width: 0; + gap: 2px; +} + +.chat-message.user .message-content { + align-items: flex-end; +} + +.chat-message.bot .message-content { + align-items: flex-start; +} + +.message-header { + display: flex; + align-items: center; + gap: 8px; + font-size: 12px; + margin-bottom: 2px; +} + +.message-header strong { + color: #231F20; + font-weight: 600; + font-size: 13px; +} + +.message-header .message-time { + color: #9CA3AF; + font-size: 11px; + font-weight: 400; +} + +.message-footer { + display: flex; + gap: 8px; + align-items: center; + margin-top: 2px; + font-size: 11px; +} + +.chat-message.user .message-footer { + justify-content: flex-end; +} + +.chat-message.bot .message-footer { + justify-content: flex-start; +} + +.message-text { + background: #FFFFFF; + padding: 10px 14px; + border-radius: 12px; + line-height: 1.5; + word-wrap: break-word; + word-break: break-word; + font-weight: 400; + font-size: 15px; + display: inline-block; + max-width: 100%; + width: fit-content; + white-space: pre-wrap; + box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); + border: 1px solid #F0F0F0; +} + +.chat-message.user .message-text { + background: linear-gradient(135deg, #FFC500 0%, #DC2223 41%, #720096 100%); + color: white; + border: none; + box-shadow: 0 2px 8px rgba(220, 34, 35, 0.2); +} + +.temp-message { + background: #F8F9FA; + border: 1px solid #E5E7EB; + padding: 12px 16px; + border-radius: 10px; + font-size: 14px; + font-weight: 400; + display: flex; + gap: 12px; + align-items: flex-start; + width: fit-content; + max-width: 100%; + box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.temp-message-icon { + font-size: 18px; + flex-shrink: 0; +} + +.temp-message-content { + flex: 1; +} + +.temp-message-content strong { + color: #231F20; + font-weight: 600; + display: block; + margin-bottom: 8px; +} + +.response-time { + font-size: 11px; + font-weight: 400; + color: #9CA3AF; + background: #F3F4F6; + padding: 2px 8px; + border-radius: 4px; +} + +.typing-indicator { + opacity: 0.8; +} + +.typing-content { + display: flex; + align-items: center; +} + +.typing-dots { + display: flex; + gap: 6px; + padding: 14px 20px; + background: #FFFFFF; + border-radius: 12px; + box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); + border: 1px solid #F0F0F0; +} + +.typing-dots span { + width: 8px; + height: 8px; + border-radius: 50%; + background: #FB6E00; + animation: typing 1.4s infinite; +} + +.typing-dots span:nth-child(2) { + animation-delay: 0.2s; +} + +.typing-dots span:nth-child(3) { + animation-delay: 0.4s; +} + +@keyframes typing { + 0%, 60%, 100% { + transform: translateY(0); + opacity: 0.4; + } + 30% { + transform: translateY(-8px); + opacity: 1; + } +} + +.chat-input-container { + padding: 20px 24px; + background: #FFFFFF; + border-top: 1px solid #E5E7EB; + box-shadow: 0 -2px 8px rgba(0, 0, 0, 0.02); +} + +.chat-input-wrapper { + display: flex; + gap: 12px; + align-items: flex-end; + max-width: 100%; +} + +.chat-input { + flex: 1; + padding: 14px 18px; + border: 2px solid #E5E7EB; + border-radius: 12px; + font-size: 15px; + font-weight: 400; + font-family: 'Inter', sans-serif; + resize: none; + transition: all 0.2s ease; + background: #FFFFFF; + color: #231F20; + line-height: 1.5; + min-height: 24px; + max-height: 120px; +} + +.chat-input:focus { + outline: none; + border-color: #FB6E00; + box-shadow: 0 0 0 3px rgba(251, 110, 0, 0.1); +} + +.chat-input:disabled { + background: #F9F9F9; + cursor: not-allowed; + opacity: 0.6; +} + +.chat-send-btn { + min-width: 100px; + height: 48px; + display: flex; + align-items: center; + justify-content: center; + gap: 8px; + padding: 0 20px; + border-radius: 12px; + font-weight: 600; + transition: all 0.2s ease; + box-shadow: 0 2px 4px rgba(251, 110, 0, 0.2); +} + +.chat-send-btn:hover:not(:disabled) { + transform: translateY(-1px); + box-shadow: 0 4px 8px rgba(251, 110, 0, 0.3); +} + +.chat-send-btn:disabled { + opacity: 0.5; + cursor: not-allowed; + transform: none; +} + +.send-icon { + width: 18px; + height: 18px; + display: flex; + align-items: center; + justify-content: center; +} + +.send-icon svg { + width: 100%; + height: 100%; +} + +.sending-spinner { + width: 16px; + height: 16px; + border: 2px solid rgba(255, 255, 255, 0.3); + border-top-color: white; + border-radius: 50%; + animation: spin 0.8s linear infinite; +} + +@keyframes spin { + to { transform: rotate(360deg); } +} + +.send-text { + font-size: 14px; +} + +.query-preview { + background: #F8F9FA; + padding: 12px; + border-radius: 8px; + font-size: 13px; + margin-top: 12px; + overflow-x: auto; + font-family: 'SF Mono', 'Monaco', 'Inconsolata', 'Roboto Mono', 'Courier New', monospace; + font-weight: 400; + color: #231F20; + border: 1px solid #E5E7EB; + line-height: 1.6; +} + +.query-preview::-webkit-scrollbar { + height: 6px; +} + +.query-preview::-webkit-scrollbar-track { + background: #F3F4F6; + border-radius: 3px; +} + +.query-preview::-webkit-scrollbar-thumb { + background: #D1D5DB; + border-radius: 3px; +} + +.query-preview::-webkit-scrollbar-thumb:hover { + background: #9CA3AF; +} + +/* Scrollbar styling */ +.chat-messages::-webkit-scrollbar, +.urls-list::-webkit-scrollbar { + width: 8px; +} + +.chat-messages::-webkit-scrollbar-track, +.urls-list::-webkit-scrollbar-track { + background: transparent; +} + +.chat-messages::-webkit-scrollbar-thumb, +.urls-list::-webkit-scrollbar-thumb { + background: #D1D5DB; + border-radius: 4px; +} + +.chat-messages::-webkit-scrollbar-thumb:hover, +.urls-list::-webkit-scrollbar-thumb:hover { + background: #9CA3AF; +} + +/* Stats Tab Styles */ +.stats-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 24px; +} + +.stats-header h2 { + margin-bottom: 0; +} + +.header-buttons { + display: flex; + gap: 12px; +} + +.refresh-btn { + min-width: 100px; +} + +.stats-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); + gap: 20px; + margin-top: 24px; +} + +.stat-card { + background: #FFFFFF; + border: 1px solid #F9F9F9; + border-radius: 6px; + padding: 24px; + display: flex; + align-items: center; + gap: 16px; + transition: box-shadow 0.2s ease; +} + +.stat-card:hover { + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.08); +} + +.stat-icon { + font-size: 32px; + line-height: 1; +} + +.stat-content { + flex: 1; +} + +.stat-label { + font-size: 14px; + font-weight: 400; /* Regular */ + color: #646265; /* Base Gray */ + margin-bottom: 4px; +} + +.stat-value { + font-size: 24px; + font-weight: 600; /* Semibold */ + color: #231F20; /* Brand Black */ + line-height: 1.2; +} + +.stats-empty { + text-align: center; + padding: 40px; + color: #646265; /* Base Gray */ +} + +/* MCP Tab Styles */ +.mcp-test-result { + padding: 20px; + border-radius: 6px; + margin-bottom: 24px; + border: 1px solid #F9F9F9; +} + +.mcp-test-result.success { + background: #FFFFFF; +} + +.mcp-test-result.error { + background: #FFFFFF; + border-color: #F9F9F9; +} + +.mcp-test-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 12px; +} + +.mcp-test-header strong { + font-weight: 600; /* Semibold */ + color: #231F20; /* Brand Black */ +} + +.mcp-test-status { + font-size: 14px; + font-weight: 400; /* Regular */ + padding: 4px 12px; + border-radius: 4px; +} + +.mcp-test-result.success .mcp-test-status { + color: #231F20; +} + +.mcp-test-result.error .mcp-test-status { + color: #231F20; +} + +.mcp-test-details { + font-size: 14px; + font-weight: 400; /* Regular */ + color: #231F20; /* Brand Black */ +} + +.mcp-test-error { + font-size: 14px; + font-weight: 400; /* Regular */ + color: #231F20; /* Brand Black */ +} + +.tools-section { + margin-top: 32px; +} + +.tools-section h3 { + font-size: 20px; + font-weight: 600; /* Semibold */ + margin-bottom: 20px; + color: #231F20; /* Brand Black */ +} + +.tools-grid { + display: grid; + grid-template-columns: repeat(auto-fill, minmax(300px, 1fr)); + gap: 20px; +} + +.tool-card { + background: #FFFFFF; + border: 1px solid #F9F9F9; + border-radius: 6px; + padding: 24px; + transition: box-shadow 0.2s ease, border-color 0.2s ease; +} + +.tool-card:hover { + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.08); + border-color: #E0E0E0; +} + +.tool-header { + margin-bottom: 16px; + padding-bottom: 12px; + border-bottom: 1px solid #F9F9F9; +} + +.tool-name { + font-size: 18px; + font-weight: 600; /* Semibold */ + color: #231F20; /* Brand Black */ + margin: 0; +} + +.tool-description { + font-size: 14px; + font-weight: 400; /* Regular */ + color: #646265; /* Base Gray for description */ + margin-bottom: 16px; + line-height: 1.6; +} + +.tool-parameters { + font-size: 13px; + font-weight: 400; /* Regular */ + color: #231F20; /* Brand Black */ + margin-top: 16px; + padding-top: 16px; + border-top: 1px solid #F9F9F9; +} + +.tool-parameters strong { + font-weight: 600; /* Semibold */ + display: block; + margin-bottom: 12px; + color: #231F20; /* Brand Black */ + font-size: 14px; +} + +.tool-params-list { + list-style: none; + padding: 0; + margin: 0; +} + +.tool-params-list li { + padding: 8px 0; + font-size: 13px; + color: #231F20; /* Brand Black */ + line-height: 1.5; + border-bottom: 1px solid #F9F9F9; +} + +.tool-params-list li:last-child { + border-bottom: none; +} + +.tool-params-list code { + background: #F9F9F9; + padding: 3px 8px; + border-radius: 4px; + font-family: 'Inter', monospace; + font-size: 12px; + font-weight: 500; + color: #231F20; /* Brand Black */ + margin-right: 8px; +} + +.tools-empty { + text-align: center; + padding: 40px; + color: #646265; /* Base Gray */ +} + +/* Responsive design */ +@media (max-width: 768px) { + .header { + padding: 32px 20px; + } + + .header h1 { + font-size: 24px; + } + + .logo { + width: 40px; + height: 40px; + } + + .tab-button { + padding: 12px 20px; + font-size: 14px; + } + + .card { + padding: 24px; + } + + .chat-container { + height: 500px; + } + + .ingestion-controls { + flex-direction: column; + } + + .btn { + width: 100%; + } + + .stats-grid { + grid-template-columns: 1fr; + } + + .tools-grid { + grid-template-columns: 1fr; + } + + .header-buttons { + flex-direction: column; + width: 100%; + } + + .header-buttons .btn { + width: 100%; + } + + .stats-inline { + flex-direction: column; + align-items: flex-start; + gap: 16px; + } + + .ingestion-buttons { + flex-direction: column; + } + + .ingestion-buttons .btn { + width: 100%; + } +} + +/* Approval Message Styles */ +.approval-message { + background: #FFF9E6; + border: 2px solid #FFC500; + border-radius: 12px; + padding: 20px; + margin-top: 8px; + box-shadow: 0 2px 8px rgba(255, 197, 0, 0.15); +} + +.approval-header { + display: flex; + align-items: center; + gap: 10px; + margin-bottom: 12px; +} + +.approval-icon { + font-size: 20px; + flex-shrink: 0; +} + +.approval-title { + font-weight: 600; + color: #FB6E00; + font-size: 15px; +} + +.approval-text { + font-size: 14px; + color: #231F20; + margin-bottom: 16px; + white-space: pre-wrap; + line-height: 1.6; +} + +.approval-query { + background: #FFFFFF; + border: 1px solid #E5E7EB; + padding: 14px; + border-radius: 8px; + font-size: 13px; + font-family: 'SF Mono', 'Monaco', 'Inconsolata', 'Roboto Mono', 'Courier New', monospace; + margin-top: 12px; + margin-bottom: 16px; + max-height: 300px; + overflow-y: auto; + line-height: 1.5; + color: #231F20; +} + +.approval-actions { + display: flex; + gap: 10px; + margin-top: 16px; +} + +.btn-approve { + background: #10B981; + color: white; + border: none; + padding: 10px 20px; + border-radius: 8px; + font-weight: 600; + cursor: pointer; + transition: all 0.2s ease; + font-size: 14px; + display: flex; + align-items: center; + gap: 6px; + box-shadow: 0 2px 4px rgba(16, 185, 129, 0.2); +} + +.btn-approve:hover:not(:disabled) { + background: #059669; + transform: translateY(-1px); + box-shadow: 0 4px 8px rgba(16, 185, 129, 0.3); +} + +.btn-approve:disabled { + opacity: 0.6; + cursor: not-allowed; + transform: none; +} + +.btn-reject { + background: #DC2223; + color: white; + border: none; + padding: 10px 20px; + border-radius: 8px; + font-weight: 600; + cursor: pointer; + transition: all 0.2s ease; + font-size: 14px; + display: flex; + align-items: center; + gap: 6px; + box-shadow: 0 2px 4px rgba(220, 34, 35, 0.2); +} + +.btn-reject:hover:not(:disabled) { + background: #B01D1E; + transform: translateY(-1px); + box-shadow: 0 4px 8px rgba(220, 34, 35, 0.3); +} + +.btn-reject:disabled { + opacity: 0.6; + cursor: not-allowed; + transform: none; +} + +.approval-status { + margin-top: 12px; + padding: 10px 14px; + border-radius: 8px; + font-weight: 600; + font-size: 14px; + display: flex; + align-items: center; + gap: 8px; +} + +.status-icon { + font-size: 16px; +} + +.approval-status.approved { + background: #D1FAE5; + color: #065F46; +} + +.approval-status.rejected { + background: #FEE2E2; + color: #991B1B; +} + +.execution-result { + margin-top: 16px; + padding: 16px; + background: #FFFFFF; + border: 1px solid #E5E7EB; + border-radius: 8px; +} + +.execution-result-header { + font-weight: 600; + color: #231F20; + margin-bottom: 12px; + font-size: 14px; +} + +.execution-result pre { + margin: 0; + font-size: 12px; + line-height: 1.5; + color: #231F20; + overflow-x: auto; +} +} + +.execution-result strong { + display: block; + margin-bottom: 8px; + font-weight: 600; + color: #231F20; +} + +.execution-result pre { + font-size: 12px; + font-family: 'Courier New', monospace; + margin: 0; + white-space: pre-wrap; + word-wrap: break-word; + max-height: 400px; + overflow-y: auto; +} + +.typing-cursor { + display: inline-block; + animation: blink 1s infinite; + color: #231F20; + font-weight: 300; + margin-left: 2px; +} + +@keyframes blink { + 0%, 50% { + opacity: 1; + } + 51%, 100% { + opacity: 0; + } +} + +.interceptor-name { + font-size: 12px; + font-weight: 500; + color: #646265; + font-style: italic; + margin-left: 4px; +} diff --git a/graphrag/memgraph_assistant/frontend/vite.config.js b/graphrag/memgraph_assistant/frontend/vite.config.js new file mode 100644 index 0000000..1720454 --- /dev/null +++ b/graphrag/memgraph_assistant/frontend/vite.config.js @@ -0,0 +1,16 @@ +import { defineConfig } from 'vite' +import vue from '@vitejs/plugin-vue' + +export default defineConfig({ + plugins: [vue()], + server: { + port: 3000, + proxy: { + '/api': { + target: 'http://localhost:8000', + changeOrigin: true, + }, + }, + }, +}) +