From bf716b49c8402e2c668808279c18c51ad5e887d5 Mon Sep 17 00:00:00 2001 From: Edwin Jose Date: Wed, 24 Dec 2025 10:08:11 -0500 Subject: [PATCH 1/5] mcp --- sdks/mcp/README.md | 166 +++++ sdks/mcp/pyproject.toml | 33 + sdks/mcp/src/openrag_mcp/__init__.py | 7 + sdks/mcp/src/openrag_mcp/__main__.py | 7 + sdks/mcp/src/openrag_mcp/config.py | 49 ++ sdks/mcp/src/openrag_mcp/server.py | 71 ++ sdks/mcp/src/openrag_mcp/tools/__init__.py | 8 + sdks/mcp/src/openrag_mcp/tools/chat.py | 100 +++ sdks/mcp/src/openrag_mcp/tools/documents.py | 236 +++++++ sdks/mcp/src/openrag_mcp/tools/search.py | 96 +++ sdks/mcp/uv.lock | 745 ++++++++++++++++++++ 11 files changed, 1518 insertions(+) create mode 100644 sdks/mcp/README.md create mode 100644 sdks/mcp/pyproject.toml create mode 100644 sdks/mcp/src/openrag_mcp/__init__.py create mode 100644 sdks/mcp/src/openrag_mcp/__main__.py create mode 100644 sdks/mcp/src/openrag_mcp/config.py create mode 100644 sdks/mcp/src/openrag_mcp/server.py create mode 100644 sdks/mcp/src/openrag_mcp/tools/__init__.py create mode 100644 sdks/mcp/src/openrag_mcp/tools/chat.py create mode 100644 sdks/mcp/src/openrag_mcp/tools/documents.py create mode 100644 sdks/mcp/src/openrag_mcp/tools/search.py create mode 100644 sdks/mcp/uv.lock diff --git a/sdks/mcp/README.md b/sdks/mcp/README.md new file mode 100644 index 00000000..f185e1b4 --- /dev/null +++ b/sdks/mcp/README.md @@ -0,0 +1,166 @@ +# OpenRAG MCP Server + +An MCP (Model Context Protocol) server that exposes OpenRAG capabilities to AI assistants like Claude Desktop and Cursor. + +## Features + +- **Chat** - RAG-enhanced conversations with your knowledge base +- **Search** - Semantic search over your documents +- **Document Management** - Ingest files/URLs and manage your knowledge base + +## Available Tools + +| Tool | Description | +|:-----|:------------| +| `openrag_chat` | Send a message and get a RAG-enhanced response | +| `openrag_search` | Semantic search over the knowledge base | +| `openrag_ingest_file` | Ingest a local file into the knowledge base | +| `openrag_ingest_url` | Ingest content from a URL | +| `openrag_list_documents` | List documents in the knowledge base | +| `openrag_delete_document` | Delete a document from the knowledge base | + +## Prerequisites + +1. A running OpenRAG instance +2. An OpenRAG API key (create one in Settings → API Keys) +3. Python 3.10+ with `uv` installed + +## Installation + +### Option 1: Run from Source (Recommended for Development) + +```bash +# Clone the OpenRAG repository (if you haven't already) +git clone https://github.com/your-org/openrag.git +cd openrag/sdks/mcp + +# Install dependencies +uv sync +``` + +### Option 2: Install from GitHub + +```bash +pip install "openrag-mcp @ git+https://github.com/your-org/openrag.git#subdirectory=sdks/mcp" +``` + +## Configuration + +The MCP server requires two environment variables: + +| Variable | Description | Default | +|:---------|:------------|:--------| +| `OPENRAG_URL` | URL of your OpenRAG instance | `http://localhost:3000` | +| `OPENRAG_API_KEY` | Your OpenRAG API key | (required) | + +## Usage with Claude Desktop + +Add this to your Claude Desktop configuration file: + +**macOS:** `~/Library/Application Support/Claude/claude_desktop_config.json` +**Windows:** `%APPDATA%\Claude\claude_desktop_config.json` + +```json +{ + "mcpServers": { + "openrag": { + "command": "uv", + "args": [ + "run", + "--directory", + "/path/to/openrag/sdks/mcp", + "openrag-mcp" + ], + "env": { + "OPENRAG_URL": "https://your-openrag-instance.com", + "OPENRAG_API_KEY": "orag_your_api_key_here" + } + } + } +} +``` + +**Important:** Replace `/path/to/openrag` with the actual path to your OpenRAG repository. + +## Usage with Cursor + +Add this to your Cursor MCP configuration: + +**Location:** `~/.cursor/mcp.json` + +```json +{ + "mcpServers": { + "openrag": { + "command": "uv", + "args": [ + "run", + "--directory", + "/path/to/openrag/sdks/mcp", + "openrag-mcp" + ], + "env": { + "OPENRAG_URL": "https://your-openrag-instance.com", + "OPENRAG_API_KEY": "orag_your_api_key_here" + } + } + } +} +``` + +## Testing + +You can test the MCP server locally: + +```bash +cd /path/to/openrag/sdks/mcp + +# Set environment variables +export OPENRAG_URL="http://localhost:3000" +export OPENRAG_API_KEY="orag_your_test_key" + +# Run the server (it will wait for MCP protocol input) +uv run openrag-mcp +``` + +## Example Prompts + +Once configured, you can ask Claude or Cursor things like: + +- *"Search my knowledge base for information about machine learning"* +- *"What documents do I have in OpenRAG?"* +- *"Chat with OpenRAG about the quarterly report"* +- *"Ingest this PDF: /path/to/document.pdf"* +- *"Delete the file 'old_report.pdf' from my knowledge base"* + +## Troubleshooting + +### "OPENRAG_API_KEY environment variable is required" + +Make sure you've set the `OPENRAG_API_KEY` in the `env` section of your MCP configuration. + +### "Connection refused" errors + +1. Check that your OpenRAG instance is running +2. Verify the `OPENRAG_URL` is correct +3. Ensure there are no firewall rules blocking the connection + +### Tools not appearing in Claude Desktop + +1. Restart Claude Desktop after updating the configuration +2. Check the Claude Desktop logs for error messages +3. Verify the path to the `openrag/sdks/mcp` directory is correct + +## Architecture + +``` +┌─────────────────┐ stdio ┌─────────────────┐ HTTPS ┌─────────────────┐ +│ Claude Desktop │◄─────────────────►│ openrag-mcp │◄──────────────────►│ OpenRAG │ +│ or Cursor │ JSON-RPC (MCP) │ (subprocess) │ X-API-Key auth │ deployment │ +└─────────────────┘ └─────────────────┘ └─────────────────┘ +``` + +## License + +Apache 2.0 - See [LICENSE](../../LICENSE) for details. + diff --git a/sdks/mcp/pyproject.toml b/sdks/mcp/pyproject.toml new file mode 100644 index 00000000..46c93848 --- /dev/null +++ b/sdks/mcp/pyproject.toml @@ -0,0 +1,33 @@ +[project] +name = "openrag-mcp" +version = "0.1.0" +description = "MCP server for OpenRAG - Chat, Search, and Ingest documents" +readme = "README.md" +requires-python = ">=3.10" +license = { text = "Apache-2.0" } +authors = [{ name = "OpenRAG Team" }] +keywords = ["mcp", "openrag", "rag", "ai", "llm"] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", +] +dependencies = [ + "mcp>=1.0.0", + "httpx>=0.27.0", +] + +[project.scripts] +openrag-mcp = "openrag_mcp:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/openrag_mcp"] + diff --git a/sdks/mcp/src/openrag_mcp/__init__.py b/sdks/mcp/src/openrag_mcp/__init__.py new file mode 100644 index 00000000..1297f007 --- /dev/null +++ b/sdks/mcp/src/openrag_mcp/__init__.py @@ -0,0 +1,7 @@ +"""OpenRAG MCP Server - Expose OpenRAG capabilities via Model Context Protocol.""" + +from openrag_mcp.server import main + +__version__ = "0.1.0" +__all__ = ["main"] + diff --git a/sdks/mcp/src/openrag_mcp/__main__.py b/sdks/mcp/src/openrag_mcp/__main__.py new file mode 100644 index 00000000..39abbfc1 --- /dev/null +++ b/sdks/mcp/src/openrag_mcp/__main__.py @@ -0,0 +1,7 @@ +"""Entry point for running as a module: python -m openrag_mcp""" + +from openrag_mcp.server import main + +if __name__ == "__main__": + main() + diff --git a/sdks/mcp/src/openrag_mcp/config.py b/sdks/mcp/src/openrag_mcp/config.py new file mode 100644 index 00000000..3bd40b9a --- /dev/null +++ b/sdks/mcp/src/openrag_mcp/config.py @@ -0,0 +1,49 @@ +"""Configuration for OpenRAG MCP server.""" + +import os + + +class Config: + """Configuration loaded from environment variables.""" + + def __init__(self): + self.openrag_url = os.environ.get("OPENRAG_URL", "http://localhost:3000") + self.api_key = os.environ.get("OPENRAG_API_KEY") + + if not self.api_key: + raise ValueError( + "OPENRAG_API_KEY environment variable is required. " + "Create an API key in OpenRAG Settings > API Keys." + ) + + @property + def headers(self) -> dict[str, str]: + """Get HTTP headers for API requests.""" + return { + "X-API-Key": self.api_key, + "Content-Type": "application/json", + } + + +_config: Config | None = None + + +def get_config() -> Config: + """Get singleton config instance.""" + global _config + if _config is None: + _config = Config() + return _config + + +def get_client(): + """Get an httpx async client configured for OpenRAG.""" + import httpx + + config = get_config() + return httpx.AsyncClient( + base_url=config.openrag_url, + headers=config.headers, + timeout=60.0, + ) + diff --git a/sdks/mcp/src/openrag_mcp/server.py b/sdks/mcp/src/openrag_mcp/server.py new file mode 100644 index 00000000..5c04d27f --- /dev/null +++ b/sdks/mcp/src/openrag_mcp/server.py @@ -0,0 +1,71 @@ +"""OpenRAG MCP Server - Main server setup and entry point.""" + +import asyncio +import logging + +from mcp.server import Server +from mcp.server.stdio import stdio_server + +from openrag_mcp.config import get_config +from openrag_mcp.tools.chat import register_chat_tools +from openrag_mcp.tools.search import register_search_tools +from openrag_mcp.tools.documents import register_document_tools + +# Configure logging to stderr (stdout is used for MCP protocol) +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + handlers=[logging.StreamHandler()], +) +logger = logging.getLogger("openrag-mcp") + + +def create_server() -> Server: + """Create and configure the MCP server with all tools registered.""" + # Validate configuration early + config = get_config() + logger.info(f"Connecting to OpenRAG at {config.openrag_url}") + + # Create server instance + server = Server("openrag-mcp") + + # Register all tools + register_chat_tools(server) + register_search_tools(server) + register_document_tools(server) + + logger.info("OpenRAG MCP server initialized with all tools") + return server + + +async def run_server(): + """Run the MCP server with stdio transport.""" + server = create_server() + + async with stdio_server() as (read_stream, write_stream): + logger.info("Starting OpenRAG MCP server with stdio transport") + await server.run( + read_stream, + write_stream, + server.create_initialization_options(), + ) + + +def main(): + """Entry point for the MCP server.""" + try: + asyncio.run(run_server()) + except KeyboardInterrupt: + logger.info("Server stopped by user") + except ValueError as e: + # Configuration errors + logger.error(f"Configuration error: {e}") + raise SystemExit(1) + except Exception as e: + logger.error(f"Server error: {e}") + raise SystemExit(1) + + +if __name__ == "__main__": + main() + diff --git a/sdks/mcp/src/openrag_mcp/tools/__init__.py b/sdks/mcp/src/openrag_mcp/tools/__init__.py new file mode 100644 index 00000000..a175fa8c --- /dev/null +++ b/sdks/mcp/src/openrag_mcp/tools/__init__.py @@ -0,0 +1,8 @@ +"""OpenRAG MCP tools.""" + +from openrag_mcp.tools.chat import register_chat_tools +from openrag_mcp.tools.search import register_search_tools +from openrag_mcp.tools.documents import register_document_tools + +__all__ = ["register_chat_tools", "register_search_tools", "register_document_tools"] + diff --git a/sdks/mcp/src/openrag_mcp/tools/chat.py b/sdks/mcp/src/openrag_mcp/tools/chat.py new file mode 100644 index 00000000..64be71be --- /dev/null +++ b/sdks/mcp/src/openrag_mcp/tools/chat.py @@ -0,0 +1,100 @@ +"""Chat tool for OpenRAG MCP server.""" + +import json +import logging + +from mcp.server import Server +from mcp.types import TextContent, Tool + +from openrag_mcp.config import get_client + +logger = logging.getLogger("openrag-mcp.chat") + + +def register_chat_tools(server: Server) -> None: + """Register chat-related tools with the MCP server.""" + + @server.list_tools() + async def list_chat_tools() -> list[Tool]: + """List chat tools.""" + return [ + Tool( + name="openrag_chat", + description=( + "Send a message to OpenRAG and get a RAG-enhanced response. " + "The response is informed by documents in your knowledge base. " + "Use chat_id to continue a previous conversation." + ), + inputSchema={ + "type": "object", + "properties": { + "message": { + "type": "string", + "description": "Your question or message to send to OpenRAG", + }, + "chat_id": { + "type": "string", + "description": "Optional conversation ID to continue a previous chat", + }, + "limit": { + "type": "integer", + "description": "Maximum number of sources to retrieve (default: 10)", + "default": 10, + }, + }, + "required": ["message"], + }, + ), + ] + + @server.call_tool() + async def call_chat_tool(name: str, arguments: dict) -> list[TextContent]: + """Handle chat tool calls.""" + if name != "openrag_chat": + return [] + + message = arguments.get("message", "") + chat_id = arguments.get("chat_id") + limit = arguments.get("limit", 10) + + if not message: + return [TextContent(type="text", text="Error: message is required")] + + try: + async with get_client() as client: + payload = { + "message": message, + "stream": False, + "limit": limit, + } + if chat_id: + payload["chat_id"] = chat_id + + response = await client.post("/api/v1/chat", json=payload) + response.raise_for_status() + data = response.json() + + # Format the response + result_text = data.get("response", "") + sources = data.get("sources", []) + new_chat_id = data.get("chat_id") + + # Build formatted response + output_parts = [result_text] + + if sources: + output_parts.append("\n\n---\n**Sources:**") + for i, source in enumerate(sources, 1): + filename = source.get("filename", "Unknown") + score = source.get("score", 0) + output_parts.append(f"\n{i}. {filename} (relevance: {score:.2f})") + + if new_chat_id: + output_parts.append(f"\n\n_Chat ID: {new_chat_id}_") + + return [TextContent(type="text", text="".join(output_parts))] + + except Exception as e: + logger.error(f"Chat error: {e}") + return [TextContent(type="text", text=f"Error: {str(e)}")] + diff --git a/sdks/mcp/src/openrag_mcp/tools/documents.py b/sdks/mcp/src/openrag_mcp/tools/documents.py new file mode 100644 index 00000000..529386b5 --- /dev/null +++ b/sdks/mcp/src/openrag_mcp/tools/documents.py @@ -0,0 +1,236 @@ +"""Document tools for OpenRAG MCP server.""" + +import logging +import os +from pathlib import Path + +from mcp.server import Server +from mcp.types import TextContent, Tool + +from openrag_mcp.config import get_client + +logger = logging.getLogger("openrag-mcp.documents") + + +def register_document_tools(server: Server) -> None: + """Register document-related tools with the MCP server.""" + + @server.list_tools() + async def list_document_tools() -> list[Tool]: + """List document tools.""" + return [ + Tool( + name="openrag_ingest_file", + description=( + "Ingest a local file into the OpenRAG knowledge base. " + "Supported formats: PDF, DOCX, TXT, MD, HTML, and more." + ), + inputSchema={ + "type": "object", + "properties": { + "file_path": { + "type": "string", + "description": "Absolute path to the file to ingest", + }, + }, + "required": ["file_path"], + }, + ), + Tool( + name="openrag_ingest_url", + description=( + "Ingest content from a URL into the OpenRAG knowledge base. " + "The URL content will be fetched, processed, and stored." + ), + inputSchema={ + "type": "object", + "properties": { + "url": { + "type": "string", + "description": "The URL to fetch and ingest", + }, + }, + "required": ["url"], + }, + ), + Tool( + name="openrag_list_documents", + description="List documents in the OpenRAG knowledge base.", + inputSchema={ + "type": "object", + "properties": { + "limit": { + "type": "integer", + "description": "Maximum number of documents to return (default: 50)", + "default": 50, + }, + }, + "required": [], + }, + ), + Tool( + name="openrag_delete_document", + description="Delete a document from the OpenRAG knowledge base.", + inputSchema={ + "type": "object", + "properties": { + "filename": { + "type": "string", + "description": "Name of the file to delete", + }, + }, + "required": ["filename"], + }, + ), + ] + + @server.call_tool() + async def call_document_tool(name: str, arguments: dict) -> list[TextContent]: + """Handle document tool calls.""" + if name == "openrag_ingest_file": + return await _ingest_file(arguments) + elif name == "openrag_ingest_url": + return await _ingest_url(arguments) + elif name == "openrag_list_documents": + return await _list_documents(arguments) + elif name == "openrag_delete_document": + return await _delete_document(arguments) + return [] + + +async def _ingest_file(arguments: dict) -> list[TextContent]: + """Ingest a local file into OpenRAG.""" + file_path = arguments.get("file_path", "") + + if not file_path: + return [TextContent(type="text", text="Error: file_path is required")] + + path = Path(file_path) + + if not path.exists(): + return [TextContent(type="text", text=f"Error: File not found: {file_path}")] + + if not path.is_file(): + return [TextContent(type="text", text=f"Error: Path is not a file: {file_path}")] + + try: + async with get_client() as client: + # Read file and upload + with open(path, "rb") as f: + files = {"file": (path.name, f)} + # Remove Content-Type header for multipart upload + headers = dict(client.headers) + headers.pop("Content-Type", None) + + response = await client.post( + "/api/v1/documents/ingest", + files=files, + headers=headers, + ) + response.raise_for_status() + data = response.json() + + task_id = data.get("task_id") + filename = data.get("filename", path.name) + + result = f"Successfully queued '{filename}' for ingestion." + if task_id: + result += f"\nTask ID: {task_id}" + + return [TextContent(type="text", text=result)] + + except Exception as e: + logger.error(f"Ingest file error: {e}") + return [TextContent(type="text", text=f"Error ingesting file: {str(e)}")] + + +async def _ingest_url(arguments: dict) -> list[TextContent]: + """Ingest content from a URL into OpenRAG.""" + url = arguments.get("url", "") + + if not url: + return [TextContent(type="text", text="Error: url is required")] + + if not url.startswith(("http://", "https://")): + return [TextContent(type="text", text="Error: url must start with http:// or https://")] + + try: + # Use chat with a special prompt to trigger URL ingestion via the agent + async with get_client() as client: + payload = { + "message": f"Please ingest the content from this URL into the knowledge base: {url}", + "stream": False, + } + + response = await client.post("/api/v1/chat", json=payload) + response.raise_for_status() + data = response.json() + + result_text = data.get("response", "") + return [TextContent(type="text", text=f"URL ingestion requested.\n\n{result_text}")] + + except Exception as e: + logger.error(f"Ingest URL error: {e}") + return [TextContent(type="text", text=f"Error ingesting URL: {str(e)}")] + + +async def _list_documents(arguments: dict) -> list[TextContent]: + """List documents in the knowledge base.""" + limit = arguments.get("limit", 50) + + try: + async with get_client() as client: + response = await client.get("/api/v1/documents", params={"limit": limit}) + response.raise_for_status() + data = response.json() + + documents = data.get("documents", []) + + if not documents: + return [TextContent(type="text", text="No documents found in the knowledge base.")] + + output_parts = [f"Found {len(documents)} document(s):\n"] + + for doc in documents: + filename = doc.get("filename", "Unknown") + chunks = doc.get("chunk_count", 0) + created = doc.get("created_at", "") + + output_parts.append(f"\n- **{filename}** ({chunks} chunks)") + if created: + output_parts.append(f" - Added: {created[:10]}") + + return [TextContent(type="text", text="".join(output_parts))] + + except Exception as e: + logger.error(f"List documents error: {e}") + return [TextContent(type="text", text=f"Error listing documents: {str(e)}")] + + +async def _delete_document(arguments: dict) -> list[TextContent]: + """Delete a document from the knowledge base.""" + filename = arguments.get("filename", "") + + if not filename: + return [TextContent(type="text", text="Error: filename is required")] + + try: + async with get_client() as client: + response = await client.request( + "DELETE", + "/api/v1/documents", + json={"filename": filename}, + ) + response.raise_for_status() + data = response.json() + + deleted_count = data.get("deleted_count", 0) + return [TextContent( + type="text", + text=f"Successfully deleted '{filename}' ({deleted_count} chunks removed).", + )] + + except Exception as e: + logger.error(f"Delete document error: {e}") + return [TextContent(type="text", text=f"Error deleting document: {str(e)}")] + diff --git a/sdks/mcp/src/openrag_mcp/tools/search.py b/sdks/mcp/src/openrag_mcp/tools/search.py new file mode 100644 index 00000000..0a2a05ae --- /dev/null +++ b/sdks/mcp/src/openrag_mcp/tools/search.py @@ -0,0 +1,96 @@ +"""Search tool for OpenRAG MCP server.""" + +import logging + +from mcp.server import Server +from mcp.types import TextContent, Tool + +from openrag_mcp.config import get_client + +logger = logging.getLogger("openrag-mcp.search") + + +def register_search_tools(server: Server) -> None: + """Register search-related tools with the MCP server.""" + + @server.list_tools() + async def list_search_tools() -> list[Tool]: + """List search tools.""" + return [ + Tool( + name="openrag_search", + description=( + "Search the OpenRAG knowledge base using semantic search. " + "Returns matching document chunks with relevance scores." + ), + inputSchema={ + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "The search query", + }, + "limit": { + "type": "integer", + "description": "Maximum number of results (default: 10)", + "default": 10, + }, + }, + "required": ["query"], + }, + ), + ] + + @server.call_tool() + async def call_search_tool(name: str, arguments: dict) -> list[TextContent]: + """Handle search tool calls.""" + if name != "openrag_search": + return [] + + query = arguments.get("query", "") + limit = arguments.get("limit", 10) + + if not query: + return [TextContent(type="text", text="Error: query is required")] + + try: + async with get_client() as client: + payload = { + "query": query, + "limit": limit, + } + + response = await client.post("/api/v1/search", json=payload) + response.raise_for_status() + data = response.json() + + results = data.get("results", []) + + if not results: + return [TextContent(type="text", text="No results found.")] + + # Format results + output_parts = [f"Found {len(results)} result(s):\n"] + + for i, result in enumerate(results, 1): + filename = result.get("filename", "Unknown") + score = result.get("score", 0) + content = result.get("content", "") + page = result.get("page_number") + + output_parts.append(f"\n---\n**{i}. {filename}**") + if page: + output_parts.append(f" (page {page})") + output_parts.append(f"\nRelevance: {score:.2f}\n") + + # Truncate long content + if len(content) > 500: + content = content[:500] + "..." + output_parts.append(f"\n{content}\n") + + return [TextContent(type="text", text="".join(output_parts))] + + except Exception as e: + logger.error(f"Search error: {e}") + return [TextContent(type="text", text=f"Error: {str(e)}")] + diff --git a/sdks/mcp/uv.lock b/sdks/mcp/uv.lock new file mode 100644 index 00000000..1926ba1a --- /dev/null +++ b/sdks/mcp/uv.lock @@ -0,0 +1,745 @@ +version = 1 +revision = 1 +requires-python 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++++++++++--------- 9 files changed, 1650 insertions(+), 1560 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 5e16de69..1338ba61 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,7 +35,8 @@ dependencies = [ "docling-serve==1.5.0", "docling-core==2.48.1", "easyocr>=1.7.1; sys_platform != 'darwin'", - "zxcvbn>=4.5.0" + "zxcvbn>=4.5.0", + "openrag-sdk>=0.1.0", ] [dependency-groups] diff --git a/sdks/mcp/pyproject.toml b/sdks/mcp/pyproject.toml index 46c93848..3f74a8ad 100644 --- a/sdks/mcp/pyproject.toml +++ b/sdks/mcp/pyproject.toml @@ -19,6 +19,7 @@ classifiers = [ dependencies = [ "mcp>=1.0.0", "httpx>=0.27.0", + "openrag-sdk>=0.1.0", ] [project.scripts] diff --git a/sdks/mcp/src/openrag_mcp/config.py b/sdks/mcp/src/openrag_mcp/config.py index 3bd40b9a..4fbeeedd 100644 --- a/sdks/mcp/src/openrag_mcp/config.py +++ b/sdks/mcp/src/openrag_mcp/config.py @@ -2,6 +2,8 @@ import os +from openrag_sdk import OpenRAGClient + class Config: """Configuration loaded from environment variables.""" @@ -26,6 +28,7 @@ class Config: _config: Config | None = None +_openrag_client: OpenRAGClient | None = None def get_config() -> Config: @@ -36,8 +39,21 @@ def get_config() -> Config: return _config +def get_openrag_client() -> OpenRAGClient: + """Get singleton OpenRAGClient instance.""" + global _openrag_client + if _openrag_client is None: + # OpenRAGClient reads OPENRAG_API_KEY and OPENRAG_URL from env + _openrag_client = OpenRAGClient() + return _openrag_client + + def get_client(): - """Get an httpx async client configured for OpenRAG.""" + """Get an httpx async client configured for OpenRAG. + + This is kept for backward compatibility with operations + not yet supported by the SDK (list_documents, ingest_url). + """ import httpx config = get_config() @@ -46,4 +62,3 @@ def get_client(): headers=config.headers, timeout=60.0, ) - diff --git a/sdks/mcp/src/openrag_mcp/server.py b/sdks/mcp/src/openrag_mcp/server.py index 5c04d27f..ca16ed6a 100644 --- a/sdks/mcp/src/openrag_mcp/server.py +++ b/sdks/mcp/src/openrag_mcp/server.py @@ -8,8 +8,8 @@ from mcp.server.stdio import stdio_server from openrag_mcp.config import get_config from openrag_mcp.tools.chat import register_chat_tools -from openrag_mcp.tools.search import register_search_tools -from openrag_mcp.tools.documents import register_document_tools +# from openrag_mcp.tools.search import register_search_tools +# from openrag_mcp.tools.documents import register_document_tools # Configure logging to stderr (stdout is used for MCP protocol) logging.basicConfig( @@ -31,8 +31,8 @@ def create_server() -> Server: # Register all tools register_chat_tools(server) - register_search_tools(server) - register_document_tools(server) + # register_search_tools(server) + # register_document_tools(server) logger.info("OpenRAG MCP server initialized with all tools") return server diff --git a/sdks/mcp/src/openrag_mcp/tools/__init__.py b/sdks/mcp/src/openrag_mcp/tools/__init__.py index a175fa8c..3a2fcc43 100644 --- a/sdks/mcp/src/openrag_mcp/tools/__init__.py +++ b/sdks/mcp/src/openrag_mcp/tools/__init__.py @@ -1,8 +1,8 @@ """OpenRAG MCP tools.""" from openrag_mcp.tools.chat import register_chat_tools -from openrag_mcp.tools.search import register_search_tools -from openrag_mcp.tools.documents import register_document_tools +# from openrag_mcp.tools.search import register_search_tools +# from openrag_mcp.tools.documents import register_document_tools -__all__ = ["register_chat_tools", "register_search_tools", "register_document_tools"] +__all__ = ["register_chat_tools"] diff --git a/sdks/mcp/src/openrag_mcp/tools/chat.py b/sdks/mcp/src/openrag_mcp/tools/chat.py index 64be71be..0d751036 100644 --- a/sdks/mcp/src/openrag_mcp/tools/chat.py +++ b/sdks/mcp/src/openrag_mcp/tools/chat.py @@ -1,12 +1,19 @@ """Chat tool for OpenRAG MCP server.""" -import json import logging from mcp.server import Server from mcp.types import TextContent, Tool -from openrag_mcp.config import get_client +from openrag_sdk import ( + AuthenticationError, + OpenRAGError, + RateLimitError, + ServerError, + ValidationError, +) + +from openrag_mcp.config import get_openrag_client logger = logging.getLogger("openrag-mcp.chat") @@ -23,7 +30,8 @@ def register_chat_tools(server: Server) -> None: description=( "Send a message to OpenRAG and get a RAG-enhanced response. " "The response is informed by documents in your knowledge base. " - "Use chat_id to continue a previous conversation." + "Use chat_id to continue a previous conversation, or filter_id " + "to apply a knowledge filter." ), inputSchema={ "type": "object", @@ -36,11 +44,20 @@ def register_chat_tools(server: Server) -> None: "type": "string", "description": "Optional conversation ID to continue a previous chat", }, + "filter_id": { + "type": "string", + "description": "Optional knowledge filter ID to apply", + }, "limit": { "type": "integer", "description": "Maximum number of sources to retrieve (default: 10)", "default": 10, }, + "score_threshold": { + "type": "number", + "description": "Minimum relevance score threshold (default: 0)", + "default": 0, + }, }, "required": ["message"], }, @@ -55,46 +72,51 @@ def register_chat_tools(server: Server) -> None: message = arguments.get("message", "") chat_id = arguments.get("chat_id") + filter_id = arguments.get("filter_id") limit = arguments.get("limit", 10) + score_threshold = arguments.get("score_threshold", 0) if not message: return [TextContent(type="text", text="Error: message is required")] try: - async with get_client() as client: - payload = { - "message": message, - "stream": False, - "limit": limit, - } - if chat_id: - payload["chat_id"] = chat_id + client = get_openrag_client() + response = await client.chat.create( + message=message, + chat_id=chat_id, + filter_id=filter_id, + limit=limit, + score_threshold=score_threshold, + ) - response = await client.post("/api/v1/chat", json=payload) - response.raise_for_status() - data = response.json() + # Build formatted response + output_parts = [response.response] - # Format the response - result_text = data.get("response", "") - sources = data.get("sources", []) - new_chat_id = data.get("chat_id") + if response.sources: + output_parts.append("\n\n---\n**Sources:**") + for i, source in enumerate(response.sources, 1): + output_parts.append(f"\n{i}. {source.filename} (relevance: {source.score:.2f})") - # Build formatted response - output_parts = [result_text] + if response.chat_id: + output_parts.append(f"\n\n_Chat ID: {response.chat_id}_") - if sources: - output_parts.append("\n\n---\n**Sources:**") - for i, source in enumerate(sources, 1): - filename = source.get("filename", "Unknown") - score = source.get("score", 0) - output_parts.append(f"\n{i}. {filename} (relevance: {score:.2f})") - - if new_chat_id: - output_parts.append(f"\n\n_Chat ID: {new_chat_id}_") - - return [TextContent(type="text", text="".join(output_parts))] + return [TextContent(type="text", text="".join(output_parts))] + except AuthenticationError as e: + logger.error(f"Authentication error: {e.message}") + return [TextContent(type="text", text=f"Authentication error: {e.message}")] + except ValidationError as e: + logger.error(f"Validation error: {e.message}") + return [TextContent(type="text", text=f"Invalid request: {e.message}")] + except RateLimitError as e: + logger.error(f"Rate limit error: {e.message}") + return [TextContent(type="text", text=f"Rate limited: {e.message}")] + except ServerError as e: + logger.error(f"Server error: {e.message}") + return [TextContent(type="text", text=f"Server error: {e.message}")] + except OpenRAGError as e: + logger.error(f"OpenRAG error: {e.message}") + return [TextContent(type="text", text=f"Error: {e.message}")] except Exception as e: logger.error(f"Chat error: {e}") return [TextContent(type="text", text=f"Error: {str(e)}")] - diff --git a/sdks/mcp/src/openrag_mcp/tools/documents.py b/sdks/mcp/src/openrag_mcp/tools/documents.py index 529386b5..ec3bf101 100644 --- a/sdks/mcp/src/openrag_mcp/tools/documents.py +++ b/sdks/mcp/src/openrag_mcp/tools/documents.py @@ -1,236 +1,257 @@ -"""Document tools for OpenRAG MCP server.""" +# """Document tools for OpenRAG MCP server.""" -import logging -import os -from pathlib import Path +# import logging +# from pathlib import Path -from mcp.server import Server -from mcp.types import TextContent, Tool +# from mcp.server import Server +# from mcp.types import TextContent, Tool -from openrag_mcp.config import get_client +# from openrag_sdk import ( +# AuthenticationError, +# NotFoundError, +# OpenRAGError, +# RateLimitError, +# ServerError, +# ValidationError, +# ) -logger = logging.getLogger("openrag-mcp.documents") +# from openrag_mcp.config import get_client, get_openrag_client + +# logger = logging.getLogger("openrag-mcp.documents") -def register_document_tools(server: Server) -> None: - """Register document-related tools with the MCP server.""" +# def register_document_tools(server: Server) -> None: +# """Register document-related tools with the MCP server.""" - @server.list_tools() - async def list_document_tools() -> list[Tool]: - """List document tools.""" - return [ - Tool( - name="openrag_ingest_file", - description=( - "Ingest a local file into the OpenRAG knowledge base. " - "Supported formats: PDF, DOCX, TXT, MD, HTML, and more." - ), - inputSchema={ - "type": "object", - "properties": { - "file_path": { - "type": "string", - "description": "Absolute path to the file to ingest", - }, - }, - "required": ["file_path"], - }, - ), - Tool( - name="openrag_ingest_url", - description=( - "Ingest content from a URL into the OpenRAG knowledge base. " - "The URL content will be fetched, processed, and stored." - ), - inputSchema={ - "type": "object", - "properties": { - "url": { - "type": "string", - "description": "The URL to fetch and ingest", - }, - }, - "required": ["url"], - }, - ), - Tool( - name="openrag_list_documents", - description="List documents in the OpenRAG knowledge base.", - inputSchema={ - "type": "object", - "properties": { - "limit": { - "type": "integer", - "description": "Maximum number of documents to return (default: 50)", - "default": 50, - }, - }, - "required": [], - }, - ), - Tool( - name="openrag_delete_document", - description="Delete a document from the OpenRAG knowledge base.", - inputSchema={ - "type": "object", - "properties": { - "filename": { - "type": "string", - "description": "Name of the file to delete", - }, - }, - "required": ["filename"], - }, - ), - ] +# @server.list_tools() +# async def list_document_tools() -> list[Tool]: +# """List document tools.""" +# return [ +# Tool( +# name="openrag_ingest_file", +# description=( +# "Ingest a local file into the OpenRAG knowledge base. " +# "Supported formats: PDF, DOCX, TXT, MD, HTML, and more." +# ), +# inputSchema={ +# "type": "object", +# "properties": { +# "file_path": { +# "type": "string", +# "description": "Absolute path to the file to ingest", +# }, +# }, +# "required": ["file_path"], +# }, +# ), +# Tool( +# name="openrag_ingest_url", +# description=( +# "Ingest content from a URL into the OpenRAG knowledge base. " +# "The URL content will be fetched, processed, and stored." +# ), +# inputSchema={ +# "type": "object", +# "properties": { +# "url": { +# "type": "string", +# "description": "The URL to fetch and ingest", +# }, +# }, +# "required": ["url"], +# }, +# ), +# Tool( +# name="openrag_list_documents", +# description="List documents in the OpenRAG knowledge base.", +# inputSchema={ +# "type": "object", +# "properties": { +# "limit": { +# "type": "integer", +# "description": "Maximum number of documents to return (default: 50)", +# "default": 50, +# }, +# }, +# "required": [], +# }, +# ), +# Tool( +# name="openrag_delete_document", +# description="Delete a document from the OpenRAG knowledge base.", +# inputSchema={ +# "type": "object", +# "properties": { +# "filename": { +# "type": "string", +# "description": "Name of the file to delete", +# }, +# }, +# "required": ["filename"], +# }, +# ), +# ] - @server.call_tool() - async def call_document_tool(name: str, arguments: dict) -> list[TextContent]: - """Handle document tool calls.""" - if name == "openrag_ingest_file": - return await _ingest_file(arguments) - elif name == "openrag_ingest_url": - return await _ingest_url(arguments) - elif name == "openrag_list_documents": - return await _list_documents(arguments) - elif name == "openrag_delete_document": - return await _delete_document(arguments) - return [] +# @server.call_tool() +# async def call_document_tool(name: str, arguments: dict) -> list[TextContent]: +# """Handle document tool calls.""" +# if name == "openrag_ingest_file": +# return await _ingest_file(arguments) +# elif name == "openrag_ingest_url": +# return await _ingest_url(arguments) +# elif name == "openrag_list_documents": +# return await _list_documents(arguments) +# elif name == "openrag_delete_document": +# return await _delete_document(arguments) +# return [] -async def _ingest_file(arguments: dict) -> list[TextContent]: - """Ingest a local file into OpenRAG.""" - file_path = arguments.get("file_path", "") +# async def _ingest_file(arguments: dict) -> list[TextContent]: +# """Ingest a local file into OpenRAG using the SDK.""" +# file_path = arguments.get("file_path", "") - if not file_path: - return [TextContent(type="text", text="Error: file_path is required")] +# if not file_path: +# return [TextContent(type="text", text="Error: file_path is required")] - path = Path(file_path) +# path = Path(file_path) - if not path.exists(): - return [TextContent(type="text", text=f"Error: File not found: {file_path}")] +# if not path.exists(): +# return [TextContent(type="text", text=f"Error: File not found: {file_path}")] - if not path.is_file(): - return [TextContent(type="text", text=f"Error: Path is not a file: {file_path}")] +# if not path.is_file(): +# return [TextContent(type="text", text=f"Error: Path is not a file: {file_path}")] - try: - async with get_client() as client: - # Read file and upload - with open(path, "rb") as f: - files = {"file": (path.name, f)} - # Remove Content-Type header for multipart upload - headers = dict(client.headers) - headers.pop("Content-Type", None) +# try: +# client = get_openrag_client() +# # Use wait=False to return immediately with task_id +# response = await client.documents.ingest(file_path=path, wait=False) - response = await client.post( - "/api/v1/documents/ingest", - files=files, - headers=headers, - ) - response.raise_for_status() - data = response.json() +# result = f"Successfully queued '{response.filename or path.name}' for ingestion." +# if response.task_id: +# result += f"\nTask ID: {response.task_id}" - task_id = data.get("task_id") - filename = data.get("filename", path.name) +# return [TextContent(type="text", text=result)] - result = f"Successfully queued '{filename}' for ingestion." - if task_id: - result += f"\nTask ID: {task_id}" - - return [TextContent(type="text", text=result)] - - except Exception as e: - logger.error(f"Ingest file error: {e}") - return [TextContent(type="text", text=f"Error ingesting file: {str(e)}")] +# except AuthenticationError as e: +# logger.error(f"Authentication error: {e.message}") +# return [TextContent(type="text", text=f"Authentication error: {e.message}")] +# except ValidationError as e: +# logger.error(f"Validation error: {e.message}") +# return [TextContent(type="text", text=f"Invalid request: {e.message}")] +# except RateLimitError as e: +# logger.error(f"Rate limit error: {e.message}") +# return [TextContent(type="text", text=f"Rate limited: {e.message}")] +# except ServerError as e: +# logger.error(f"Server error: {e.message}") +# return [TextContent(type="text", text=f"Server error: {e.message}")] +# except OpenRAGError as e: +# logger.error(f"OpenRAG error: {e.message}") +# return [TextContent(type="text", text=f"Error: {e.message}")] +# except Exception as e: +# logger.error(f"Ingest file error: {e}") +# return [TextContent(type="text", text=f"Error ingesting file: {str(e)}")] -async def _ingest_url(arguments: dict) -> list[TextContent]: - """Ingest content from a URL into OpenRAG.""" - url = arguments.get("url", "") +# async def _ingest_url(arguments: dict) -> list[TextContent]: +# """Ingest content from a URL into OpenRAG. - if not url: - return [TextContent(type="text", text="Error: url is required")] +# Note: This uses the SDK's chat to trigger URL ingestion via the agent. +# """ +# url = arguments.get("url", "") - if not url.startswith(("http://", "https://")): - return [TextContent(type="text", text="Error: url must start with http:// or https://")] +# if not url: +# return [TextContent(type="text", text="Error: url is required")] - try: - # Use chat with a special prompt to trigger URL ingestion via the agent - async with get_client() as client: - payload = { - "message": f"Please ingest the content from this URL into the knowledge base: {url}", - "stream": False, - } +# if not url.startswith(("http://", "https://")): +# return [TextContent(type="text", text="Error: url must start with http:// or https://")] - response = await client.post("/api/v1/chat", json=payload) - response.raise_for_status() - data = response.json() +# try: +# # Use chat with a special prompt to trigger URL ingestion via the agent +# client = get_openrag_client() +# response = await client.chat.create( +# message=f"Please ingest the content from this URL into the knowledge base: {url}", +# ) - result_text = data.get("response", "") - return [TextContent(type="text", text=f"URL ingestion requested.\n\n{result_text}")] +# return [TextContent(type="text", text=f"URL ingestion requested.\n\n{response.response}")] - except Exception as e: - logger.error(f"Ingest URL error: {e}") - return [TextContent(type="text", text=f"Error ingesting URL: {str(e)}")] +# except AuthenticationError as e: +# logger.error(f"Authentication error: {e.message}") +# return [TextContent(type="text", text=f"Authentication error: {e.message}")] +# except ServerError as e: +# logger.error(f"Server error: {e.message}") +# return [TextContent(type="text", text=f"Server error: {e.message}")] +# except OpenRAGError as e: +# logger.error(f"OpenRAG error: {e.message}") +# return [TextContent(type="text", text=f"Error: {e.message}")] +# except Exception as e: +# logger.error(f"Ingest URL error: {e}") +# return [TextContent(type="text", text=f"Error ingesting URL: {str(e)}")] -async def _list_documents(arguments: dict) -> list[TextContent]: - """List documents in the knowledge base.""" - limit = arguments.get("limit", 50) +# async def _list_documents(arguments: dict) -> list[TextContent]: +# """List documents in the knowledge base. - try: - async with get_client() as client: - response = await client.get("/api/v1/documents", params={"limit": limit}) - response.raise_for_status() - data = response.json() +# Note: This uses direct HTTP calls as the SDK doesn't yet support listing documents. +# """ +# limit = arguments.get("limit", 50) - documents = data.get("documents", []) +# try: +# async with get_client() as client: +# response = await client.get("/api/v1/documents", params={"limit": limit}) +# response.raise_for_status() +# data = response.json() - if not documents: - return [TextContent(type="text", text="No documents found in the knowledge base.")] +# documents = data.get("documents", []) - output_parts = [f"Found {len(documents)} document(s):\n"] +# if not documents: +# return [TextContent(type="text", text="No documents found in the knowledge base.")] - for doc in documents: - filename = doc.get("filename", "Unknown") - chunks = doc.get("chunk_count", 0) - created = doc.get("created_at", "") +# output_parts = [f"Found {len(documents)} document(s):\n"] - output_parts.append(f"\n- **{filename}** ({chunks} chunks)") - if created: - output_parts.append(f" - Added: {created[:10]}") +# for doc in documents: +# filename = doc.get("filename", "Unknown") +# chunks = doc.get("chunk_count", 0) +# created = doc.get("created_at", "") - return [TextContent(type="text", text="".join(output_parts))] +# output_parts.append(f"\n- **{filename}** ({chunks} chunks)") +# if created: +# output_parts.append(f" - Added: {created[:10]}") - except Exception as e: - logger.error(f"List documents error: {e}") - return [TextContent(type="text", text=f"Error listing documents: {str(e)}")] +# return [TextContent(type="text", text="".join(output_parts))] + +# except Exception as e: +# logger.error(f"List documents error: {e}") +# return [TextContent(type="text", text=f"Error listing documents: {str(e)}")] -async def _delete_document(arguments: dict) -> list[TextContent]: - """Delete a document from the knowledge base.""" - filename = arguments.get("filename", "") +# async def _delete_document(arguments: dict) -> list[TextContent]: +# """Delete a document from the knowledge base using the SDK.""" +# filename = arguments.get("filename", "") - if not filename: - return [TextContent(type="text", text="Error: filename is required")] +# if not filename: +# return [TextContent(type="text", text="Error: filename is required")] - try: - async with get_client() as client: - response = await client.request( - "DELETE", - "/api/v1/documents", - json={"filename": filename}, - ) - response.raise_for_status() - data = response.json() +# try: +# client = get_openrag_client() +# response = await client.documents.delete(filename) - deleted_count = data.get("deleted_count", 0) - return [TextContent( - type="text", - text=f"Successfully deleted '{filename}' ({deleted_count} chunks removed).", - )] - - except Exception as e: - logger.error(f"Delete document error: {e}") - return [TextContent(type="text", text=f"Error deleting document: {str(e)}")] +# return [TextContent( +# type="text", +# text=f"Successfully deleted '{filename}' ({response.deleted_chunks} chunks removed).", +# )] +# except NotFoundError as e: +# logger.error(f"Document not found: {e.message}") +# return [TextContent(type="text", text=f"Document not found: {e.message}")] +# except AuthenticationError as e: +# logger.error(f"Authentication error: {e.message}") +# return [TextContent(type="text", text=f"Authentication error: {e.message}")] +# except ServerError as e: +# logger.error(f"Server error: {e.message}") +# return [TextContent(type="text", text=f"Server error: {e.message}")] +# except OpenRAGError as e: +# logger.error(f"OpenRAG error: {e.message}") +# return [TextContent(type="text", text=f"Error: {e.message}")] +# except Exception as e: +# logger.error(f"Delete document error: {e}") +# return [TextContent(type="text", text=f"Error deleting document: {str(e)}")] diff --git a/sdks/mcp/uv.lock b/sdks/mcp/uv.lock index 1926ba1a..3ce3375f 100644 --- a/sdks/mcp/uv.lock +++ b/sdks/mcp/uv.lock @@ -337,12 +337,27 @@ source = { editable = "." } dependencies = [ { name = "httpx" }, { name = "mcp" }, + { name = "openrag-sdk" }, ] [package.metadata] requires-dist = [ { name = "httpx", specifier = ">=0.27.0" }, { name = "mcp", specifier = ">=1.0.0" }, + { name = "openrag-sdk", specifier = 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openrag_mcp.tools.chat import register_chat_tools -# from openrag_mcp.tools.search import register_search_tools -# from openrag_mcp.tools.documents import register_document_tools +from openrag_mcp.tools.chat import get_chat_tools, handle_chat_tool +from openrag_mcp.tools.search import get_search_tools, handle_search_tool +from openrag_mcp.tools.documents import get_document_tools, handle_document_tool # Configure logging to stderr (stdout is used for MCP protocol) logging.basicConfig( @@ -29,10 +30,35 @@ def create_server() -> Server: # Create server instance server = Server("openrag-mcp") - # Register all tools - register_chat_tools(server) - # register_search_tools(server) - # register_document_tools(server) + # Register a single list_tools handler that combines all tools + @server.list_tools() + async def list_all_tools() -> list[Tool]: + """List all available tools.""" + tools = [] + tools.extend(get_chat_tools()) + tools.extend(get_search_tools()) + tools.extend(get_document_tools()) + return tools + + # Register a single call_tool handler that dispatches to appropriate handler + @server.call_tool() + async def call_tool(name: str, arguments: dict) -> list[TextContent]: + """Handle all tool calls by dispatching to the appropriate handler.""" + # Try each handler in order + result = await handle_chat_tool(name, arguments) + if result is not None: + return result + + result = await handle_search_tool(name, arguments) + if result is not None: + return result + + result = await handle_document_tool(name, arguments) + if result is not None: + return result + + # Unknown tool + return [TextContent(type="text", text=f"Unknown tool: {name}")] logger.info("OpenRAG MCP server initialized with all tools") return server @@ -68,4 +94,3 @@ def main(): if __name__ == "__main__": main() - diff --git a/sdks/mcp/src/openrag_mcp/tools/__init__.py b/sdks/mcp/src/openrag_mcp/tools/__init__.py index 3a2fcc43..da78b042 100644 --- a/sdks/mcp/src/openrag_mcp/tools/__init__.py +++ b/sdks/mcp/src/openrag_mcp/tools/__init__.py @@ -1,8 +1,14 @@ """OpenRAG MCP tools.""" -from openrag_mcp.tools.chat import register_chat_tools -# from openrag_mcp.tools.search import register_search_tools -# from openrag_mcp.tools.documents import register_document_tools - -__all__ = ["register_chat_tools"] +from openrag_mcp.tools.chat import get_chat_tools, handle_chat_tool +from openrag_mcp.tools.search import get_search_tools, handle_search_tool +from openrag_mcp.tools.documents import get_document_tools, handle_document_tool +__all__ = [ + "get_chat_tools", + "handle_chat_tool", + "get_search_tools", + "handle_search_tool", + "get_document_tools", + "handle_document_tool", +] diff --git a/sdks/mcp/src/openrag_mcp/tools/chat.py b/sdks/mcp/src/openrag_mcp/tools/chat.py index 0d751036..fd422e48 100644 --- a/sdks/mcp/src/openrag_mcp/tools/chat.py +++ b/sdks/mcp/src/openrag_mcp/tools/chat.py @@ -2,7 +2,6 @@ import logging -from mcp.server import Server from mcp.types import TextContent, Tool from openrag_sdk import ( @@ -18,105 +17,101 @@ from openrag_mcp.config import get_openrag_client logger = logging.getLogger("openrag-mcp.chat") -def register_chat_tools(server: Server) -> None: - """Register chat-related tools with the MCP server.""" - - @server.list_tools() - async def list_chat_tools() -> list[Tool]: - """List chat tools.""" - return [ - Tool( - name="openrag_chat", - description=( - "Send a message to OpenRAG and get a RAG-enhanced response. " - "The response is informed by documents in your knowledge base. " - "Use chat_id to continue a previous conversation, or filter_id " - "to apply a knowledge filter." - ), - inputSchema={ - "type": "object", - "properties": { - "message": { - "type": "string", - "description": "Your question or message to send to OpenRAG", - }, - "chat_id": { - "type": "string", - "description": "Optional conversation ID to continue a previous chat", - }, - "filter_id": { - "type": "string", - "description": "Optional knowledge filter ID to apply", - }, - "limit": { - "type": "integer", - "description": "Maximum number of sources to retrieve (default: 10)", - "default": 10, - }, - "score_threshold": { - "type": "number", - "description": "Minimum relevance score threshold (default: 0)", - "default": 0, - }, - }, - "required": ["message"], - }, +def get_chat_tools() -> list[Tool]: + """Return chat-related tools.""" + return [ + Tool( + name="openrag_chat", + description=( + "Send a message to OpenRAG and get a RAG-enhanced response. " + "The response is informed by documents in your knowledge base. " + "Use chat_id to continue a previous conversation, or filter_id " + "to apply a knowledge filter." ), - ] + inputSchema={ + "type": "object", + "properties": { + "message": { + "type": "string", + "description": "Your question or message to send to OpenRAG", + }, + "chat_id": { + "type": "string", + "description": "Optional conversation ID to continue a previous chat", + }, + "filter_id": { + "type": "string", + "description": "Optional knowledge filter ID to apply", + }, + "limit": { + "type": "integer", + "description": "Maximum number of sources to retrieve (default: 10)", + "default": 10, + }, + "score_threshold": { + "type": "number", + "description": "Minimum relevance score threshold (default: 0)", + "default": 0, + }, + }, + "required": ["message"], + }, + ), + ] - @server.call_tool() - async def call_chat_tool(name: str, arguments: dict) -> list[TextContent]: - """Handle chat tool calls.""" - if name != "openrag_chat": - return [] - message = arguments.get("message", "") - chat_id = arguments.get("chat_id") - filter_id = arguments.get("filter_id") - limit = arguments.get("limit", 10) - score_threshold = arguments.get("score_threshold", 0) +async def handle_chat_tool(name: str, arguments: dict) -> list[TextContent] | None: + """Handle chat tool calls. Returns None if tool not handled.""" + if name != "openrag_chat": + return None - if not message: - return [TextContent(type="text", text="Error: message is required")] + message = arguments.get("message", "") + chat_id = arguments.get("chat_id") + filter_id = arguments.get("filter_id") + limit = arguments.get("limit", 10) + score_threshold = arguments.get("score_threshold", 0) - try: - client = get_openrag_client() - response = await client.chat.create( - message=message, - chat_id=chat_id, - filter_id=filter_id, - limit=limit, - score_threshold=score_threshold, - ) + if not message: + return [TextContent(type="text", text="Error: message is required")] - # Build formatted response - output_parts = [response.response] + try: + client = get_openrag_client() + response = await client.chat.create( + message=message, + chat_id=chat_id, + filter_id=filter_id, + limit=limit, + score_threshold=score_threshold, + ) - if response.sources: - output_parts.append("\n\n---\n**Sources:**") - for i, source in enumerate(response.sources, 1): - output_parts.append(f"\n{i}. {source.filename} (relevance: {source.score:.2f})") + # Build formatted response + output_parts = [response.response] - if response.chat_id: - output_parts.append(f"\n\n_Chat ID: {response.chat_id}_") + if response.sources: + output_parts.append("\n\n---\n**Sources:**") + for i, source in enumerate(response.sources, 1): + output_parts.append(f"\n{i}. {source.filename} (relevance: {source.score:.2f})") - return [TextContent(type="text", text="".join(output_parts))] + if response.chat_id: + output_parts.append(f"\n\n_Chat ID: {response.chat_id}_") - except AuthenticationError as e: - logger.error(f"Authentication error: {e.message}") - return [TextContent(type="text", text=f"Authentication error: {e.message}")] - except ValidationError as e: - logger.error(f"Validation error: {e.message}") - return [TextContent(type="text", text=f"Invalid request: {e.message}")] - except RateLimitError as e: - logger.error(f"Rate limit error: {e.message}") - return [TextContent(type="text", text=f"Rate limited: {e.message}")] - except ServerError as e: - logger.error(f"Server error: {e.message}") - return [TextContent(type="text", text=f"Server error: {e.message}")] - except OpenRAGError as e: - logger.error(f"OpenRAG error: {e.message}") - return [TextContent(type="text", text=f"Error: {e.message}")] - except Exception as e: - logger.error(f"Chat error: {e}") - return [TextContent(type="text", text=f"Error: {str(e)}")] + return [TextContent(type="text", text="".join(output_parts))] + + except AuthenticationError as e: + logger.error(f"Authentication error: {e.message}") + return [TextContent(type="text", text=f"Authentication error: {e.message}")] + except ValidationError as e: + logger.error(f"Validation error: {e.message}") + return [TextContent(type="text", text=f"Invalid request: {e.message}")] + except RateLimitError as e: + logger.error(f"Rate limit error: {e.message}") + return [TextContent(type="text", text=f"Rate limited: {e.message}")] + except ServerError as e: + logger.error(f"Server error: {e.message}") + return [TextContent(type="text", text=f"Server error: {e.message}")] + except OpenRAGError as e: + logger.error(f"OpenRAG error: {e.message}") + return [TextContent(type="text", text=f"Error: {e.message}")] + except Exception as e: + logger.error(f"Chat error: {e}") + return [TextContent(type="text", text=f"Error: {str(e)}")] diff --git a/sdks/mcp/src/openrag_mcp/tools/documents.py b/sdks/mcp/src/openrag_mcp/tools/documents.py index ec3bf101..ca679cd4 100644 --- a/sdks/mcp/src/openrag_mcp/tools/documents.py +++ b/sdks/mcp/src/openrag_mcp/tools/documents.py @@ -1,257 +1,357 @@ -# """Document tools for OpenRAG MCP server.""" +"""Document tools for OpenRAG MCP server.""" -# import logging -# from pathlib import Path +import logging +from pathlib import Path -# from mcp.server import Server -# from mcp.types import TextContent, Tool +from mcp.types import TextContent, Tool -# from openrag_sdk import ( -# AuthenticationError, -# NotFoundError, -# OpenRAGError, -# RateLimitError, -# ServerError, -# ValidationError, -# ) +from openrag_sdk import ( + AuthenticationError, + NotFoundError, + OpenRAGError, + RateLimitError, + ServerError, + ValidationError, +) -# from openrag_mcp.config import get_client, get_openrag_client +from openrag_mcp.config import get_openrag_client -# logger = logging.getLogger("openrag-mcp.documents") +logger = logging.getLogger("openrag-mcp.documents") -# def register_document_tools(server: Server) -> None: -# """Register document-related tools with the MCP server.""" - -# @server.list_tools() -# async def list_document_tools() -> list[Tool]: -# """List document tools.""" -# return [ -# Tool( -# name="openrag_ingest_file", -# description=( -# "Ingest a local file into the OpenRAG knowledge base. " -# "Supported formats: PDF, DOCX, TXT, MD, HTML, and more." -# ), -# inputSchema={ -# "type": "object", -# "properties": { -# "file_path": { -# "type": "string", -# "description": "Absolute path to the file to ingest", -# }, -# }, -# "required": ["file_path"], -# }, -# ), -# Tool( -# name="openrag_ingest_url", -# description=( -# "Ingest content from a URL into the OpenRAG knowledge base. " -# "The URL content will be fetched, processed, and stored." -# ), -# inputSchema={ -# "type": "object", -# "properties": { -# "url": { -# "type": "string", -# "description": "The URL to fetch and ingest", -# }, -# }, -# "required": ["url"], -# }, -# ), -# Tool( -# name="openrag_list_documents", -# description="List documents in the OpenRAG knowledge base.", -# inputSchema={ -# "type": "object", -# "properties": { -# "limit": { -# "type": "integer", -# "description": "Maximum number of documents to return (default: 50)", -# "default": 50, -# }, -# }, -# "required": [], -# }, -# ), -# Tool( -# name="openrag_delete_document", -# description="Delete a document from the OpenRAG knowledge base.", -# inputSchema={ -# "type": "object", -# "properties": { -# "filename": { -# "type": "string", -# "description": "Name of the file to delete", -# }, -# }, -# "required": ["filename"], -# }, -# ), -# ] - -# @server.call_tool() -# async def call_document_tool(name: str, arguments: dict) -> list[TextContent]: -# """Handle document tool calls.""" -# if name == "openrag_ingest_file": -# return await _ingest_file(arguments) -# elif name == "openrag_ingest_url": -# return await _ingest_url(arguments) -# elif name == "openrag_list_documents": -# return await _list_documents(arguments) -# elif name == "openrag_delete_document": -# return await _delete_document(arguments) -# return [] +def get_document_tools() -> list[Tool]: + """Return document-related tools.""" + return [ + Tool( + name="openrag_ingest_file", + description=( + "Ingest a local file into the OpenRAG knowledge base. " + "Supported formats: PDF, DOCX, TXT, MD, HTML, and more. " + "By default waits for ingestion to complete. Set wait=false to return immediately." + ), + inputSchema={ + "type": "object", + "properties": { + "file_path": { + "type": "string", + "description": "Absolute path to the file to ingest", + }, + "wait": { + "type": "boolean", + "description": "Wait for ingestion to complete (default: true). Set to false to return immediately with task_id.", + "default": True, + }, + }, + "required": ["file_path"], + }, + ), + Tool( + name="openrag_ingest_url", + description=( + "Ingest content from a URL into the OpenRAG knowledge base. " + "The URL content will be fetched, processed, and stored." + ), + inputSchema={ + "type": "object", + "properties": { + "url": { + "type": "string", + "description": "The URL to fetch and ingest", + }, + }, + "required": ["url"], + }, + ), + Tool( + name="openrag_get_task_status", + description=( + "Check the status of an ingestion task. " + "Use the task_id returned from openrag_ingest_file when wait=false." + ), + inputSchema={ + "type": "object", + "properties": { + "task_id": { + "type": "string", + "description": "The task ID to check status for", + }, + }, + "required": ["task_id"], + }, + ), + Tool( + name="openrag_wait_for_task", + description=( + "Wait for an ingestion task to complete. " + "Polls the task status until it completes or fails." + ), + inputSchema={ + "type": "object", + "properties": { + "task_id": { + "type": "string", + "description": "The task ID to wait for", + }, + "timeout": { + "type": "number", + "description": "Maximum seconds to wait (default: 300)", + "default": 300, + }, + }, + "required": ["task_id"], + }, + ), + Tool( + name="openrag_delete_document", + description="Delete a document from the OpenRAG knowledge base.", + inputSchema={ + "type": "object", + "properties": { + "filename": { + "type": "string", + "description": "Name of the file to delete", + }, + }, + "required": ["filename"], + }, + ), + ] -# async def _ingest_file(arguments: dict) -> list[TextContent]: -# """Ingest a local file into OpenRAG using the SDK.""" -# file_path = arguments.get("file_path", "") - -# if not file_path: -# return [TextContent(type="text", text="Error: file_path is required")] - -# path = Path(file_path) - -# if not path.exists(): -# return [TextContent(type="text", text=f"Error: File not found: {file_path}")] - -# if not path.is_file(): -# return [TextContent(type="text", text=f"Error: Path is not a file: {file_path}")] - -# try: -# client = get_openrag_client() -# # Use wait=False to return immediately with task_id -# response = await client.documents.ingest(file_path=path, wait=False) - -# result = f"Successfully queued '{response.filename or path.name}' for ingestion." -# if response.task_id: -# result += f"\nTask ID: {response.task_id}" - -# return [TextContent(type="text", text=result)] - -# except AuthenticationError as e: -# logger.error(f"Authentication error: {e.message}") -# return [TextContent(type="text", text=f"Authentication error: {e.message}")] -# except ValidationError as e: -# logger.error(f"Validation error: {e.message}") -# return [TextContent(type="text", text=f"Invalid request: {e.message}")] -# except RateLimitError as e: -# logger.error(f"Rate limit error: {e.message}") -# return [TextContent(type="text", text=f"Rate limited: {e.message}")] -# except ServerError as e: -# logger.error(f"Server error: {e.message}") -# return [TextContent(type="text", text=f"Server error: {e.message}")] -# except OpenRAGError as e: -# logger.error(f"OpenRAG error: {e.message}") -# return [TextContent(type="text", text=f"Error: {e.message}")] -# except Exception as e: -# logger.error(f"Ingest file error: {e}") -# return [TextContent(type="text", text=f"Error ingesting file: {str(e)}")] +async def handle_document_tool(name: str, arguments: dict) -> list[TextContent] | None: + """Handle document tool calls. Returns None if tool not handled.""" + if name == "openrag_ingest_file": + return await _ingest_file(arguments) + elif name == "openrag_ingest_url": + return await _ingest_url(arguments) + elif name == "openrag_get_task_status": + return await _get_task_status(arguments) + elif name == "openrag_wait_for_task": + return await _wait_for_task(arguments) + elif name == "openrag_delete_document": + return await _delete_document(arguments) + return None -# async def _ingest_url(arguments: dict) -> list[TextContent]: -# """Ingest content from a URL into OpenRAG. +async def _ingest_file(arguments: dict) -> list[TextContent]: + """Ingest a local file into OpenRAG using the SDK.""" + file_path = arguments.get("file_path", "") + wait = arguments.get("wait", True) -# Note: This uses the SDK's chat to trigger URL ingestion via the agent. -# """ -# url = arguments.get("url", "") + if not file_path: + return [TextContent(type="text", text="Error: file_path is required")] -# if not url: -# return [TextContent(type="text", text="Error: url is required")] + path = Path(file_path) -# if not url.startswith(("http://", "https://")): -# return [TextContent(type="text", text="Error: url must start with http:// or https://")] + if not path.exists(): + return [TextContent(type="text", text=f"Error: File not found: {file_path}")] -# try: -# # Use chat with a special prompt to trigger URL ingestion via the agent -# client = get_openrag_client() -# response = await client.chat.create( -# message=f"Please ingest the content from this URL into the knowledge base: {url}", -# ) + if not path.is_file(): + return [TextContent(type="text", text=f"Error: Path is not a file: {file_path}")] -# return [TextContent(type="text", text=f"URL ingestion requested.\n\n{response.response}")] + try: + client = get_openrag_client() + response = await client.documents.ingest(file_path=path, wait=wait) -# except AuthenticationError as e: -# logger.error(f"Authentication error: {e.message}") -# return [TextContent(type="text", text=f"Authentication error: {e.message}")] -# except ServerError as e: -# logger.error(f"Server error: {e.message}") -# return [TextContent(type="text", text=f"Server error: {e.message}")] -# except OpenRAGError as e: -# logger.error(f"OpenRAG error: {e.message}") -# return [TextContent(type="text", text=f"Error: {e.message}")] -# except Exception as e: -# logger.error(f"Ingest URL error: {e}") -# return [TextContent(type="text", text=f"Error ingesting URL: {str(e)}")] + if wait: + # Response is IngestTaskStatus when wait=True + status = response.status + successful = response.successful_files + failed = response.failed_files + + if status == "completed": + result = f"Successfully ingested '{path.name}'." + result += f"\nStatus: {status}" + result += f"\nSuccessful files: {successful}" + if failed > 0: + result += f"\nFailed files: {failed}" + else: + result = f"Ingestion finished with status: {status}" + result += f"\nSuccessful files: {successful}" + result += f"\nFailed files: {failed}" + else: + # Response is IngestResponse when wait=False + result = f"Successfully queued '{response.filename or path.name}' for ingestion." + if response.task_id: + result += f"\nTask ID: {response.task_id}" + result += "\n\nUse openrag_get_task_status or openrag_wait_for_task to check progress." + + return [TextContent(type="text", text=result)] + + except AuthenticationError as e: + logger.error(f"Authentication error: {e.message}") + return [TextContent(type="text", text=f"Authentication error: {e.message}")] + except ValidationError as e: + logger.error(f"Validation error: {e.message}") + return [TextContent(type="text", text=f"Invalid request: {e.message}")] + except RateLimitError as e: + logger.error(f"Rate limit error: {e.message}") + return [TextContent(type="text", text=f"Rate limited: {e.message}")] + except ServerError as e: + logger.error(f"Server error: {e.message}") + return [TextContent(type="text", text=f"Server error: {e.message}")] + except OpenRAGError as e: + logger.error(f"OpenRAG error: {e.message}") + return [TextContent(type="text", text=f"Error: {e.message}")] + except TimeoutError as e: + logger.error(f"Timeout error: {e}") + return [TextContent(type="text", text=f"Ingestion timed out: {str(e)}")] + except Exception as e: + logger.error(f"Ingest file error: {e}") + return [TextContent(type="text", text=f"Error ingesting file: {str(e)}")] -# async def _list_documents(arguments: dict) -> list[TextContent]: -# """List documents in the knowledge base. +async def _ingest_url(arguments: dict) -> list[TextContent]: + """Ingest content from a URL into OpenRAG. -# Note: This uses direct HTTP calls as the SDK doesn't yet support listing documents. -# """ -# limit = arguments.get("limit", 50) + Note: This uses the SDK's chat to trigger URL ingestion via the agent. + """ + url = arguments.get("url", "") -# try: -# async with get_client() as client: -# response = await client.get("/api/v1/documents", params={"limit": limit}) -# response.raise_for_status() -# data = response.json() + if not url: + return [TextContent(type="text", text="Error: url is required")] -# documents = data.get("documents", []) + if not url.startswith(("http://", "https://")): + return [TextContent(type="text", text="Error: url must start with http:// or https://")] -# if not documents: -# return [TextContent(type="text", text="No documents found in the knowledge base.")] + try: + # Use chat with a special prompt to trigger URL ingestion via the agent + client = get_openrag_client() + response = await client.chat.create( + message=f"Please ingest the content from this URL into the knowledge base: {url}", + ) -# output_parts = [f"Found {len(documents)} document(s):\n"] + return [TextContent(type="text", text=f"URL ingestion requested.\n\n{response.response}")] -# for doc in documents: -# filename = doc.get("filename", "Unknown") -# chunks = doc.get("chunk_count", 0) -# created = doc.get("created_at", "") - -# output_parts.append(f"\n- **{filename}** ({chunks} chunks)") -# if created: -# output_parts.append(f" - Added: {created[:10]}") - -# return [TextContent(type="text", text="".join(output_parts))] - -# except Exception as e: -# logger.error(f"List documents error: {e}") -# return [TextContent(type="text", text=f"Error listing documents: {str(e)}")] + except AuthenticationError as e: + logger.error(f"Authentication error: {e.message}") + return [TextContent(type="text", text=f"Authentication error: {e.message}")] + except ServerError as e: + logger.error(f"Server error: {e.message}") + return [TextContent(type="text", text=f"Server error: {e.message}")] + except OpenRAGError as e: + logger.error(f"OpenRAG error: {e.message}") + return [TextContent(type="text", text=f"Error: {e.message}")] + except Exception as e: + logger.error(f"Ingest URL error: {e}") + return [TextContent(type="text", text=f"Error ingesting URL: {str(e)}")] -# async def _delete_document(arguments: dict) -> list[TextContent]: -# """Delete a document from the knowledge base using the SDK.""" -# filename = arguments.get("filename", "") +async def _get_task_status(arguments: dict) -> list[TextContent]: + """Get the status of an ingestion task.""" + task_id = arguments.get("task_id", "") -# if not filename: -# return [TextContent(type="text", text="Error: filename is required")] + if not task_id: + return [TextContent(type="text", text="Error: task_id is required")] -# try: -# client = get_openrag_client() -# response = await client.documents.delete(filename) + try: + client = get_openrag_client() + status = await client.documents.get_task_status(task_id) -# return [TextContent( -# type="text", -# text=f"Successfully deleted '{filename}' ({response.deleted_chunks} chunks removed).", -# )] + output_parts = [f"**Task Status: {status.status}**"] + output_parts.append(f"\nTask ID: {status.task_id}") + output_parts.append(f"\nTotal files: {status.total_files}") + output_parts.append(f"\nProcessed: {status.processed_files}") + output_parts.append(f"\nSuccessful: {status.successful_files}") + output_parts.append(f"\nFailed: {status.failed_files}") -# except NotFoundError as e: -# logger.error(f"Document not found: {e.message}") -# return [TextContent(type="text", text=f"Document not found: {e.message}")] -# except AuthenticationError as e: -# logger.error(f"Authentication error: {e.message}") -# return [TextContent(type="text", text=f"Authentication error: {e.message}")] -# except ServerError as e: -# logger.error(f"Server error: {e.message}") -# return [TextContent(type="text", text=f"Server error: {e.message}")] -# except OpenRAGError as e: -# logger.error(f"OpenRAG error: {e.message}") -# return [TextContent(type="text", text=f"Error: {e.message}")] -# except Exception as e: -# logger.error(f"Delete document error: {e}") -# return [TextContent(type="text", text=f"Error deleting document: {str(e)}")] + if status.files: + output_parts.append("\n\n**File Details:**") + for filename, file_status in status.files.items(): + output_parts.append(f"\n- {filename}: {file_status}") + + return [TextContent(type="text", text="".join(output_parts))] + + except NotFoundError as e: + logger.error(f"Task not found: {e.message}") + return [TextContent(type="text", text=f"Task not found: {e.message}")] + except AuthenticationError as e: + logger.error(f"Authentication error: {e.message}") + return [TextContent(type="text", text=f"Authentication error: {e.message}")] + except OpenRAGError as e: + logger.error(f"OpenRAG error: {e.message}") + return [TextContent(type="text", text=f"Error: {e.message}")] + except Exception as e: + logger.error(f"Get task status error: {e}") + return [TextContent(type="text", text=f"Error getting task status: {str(e)}")] + + +async def _wait_for_task(arguments: dict) -> list[TextContent]: + """Wait for an ingestion task to complete.""" + task_id = arguments.get("task_id", "") + timeout = arguments.get("timeout", 300) + + if not task_id: + return [TextContent(type="text", text="Error: task_id is required")] + + try: + client = get_openrag_client() + status = await client.documents.wait_for_task(task_id, timeout=timeout) + + output_parts = [f"**Task Completed: {status.status}**"] + output_parts.append(f"\nTask ID: {status.task_id}") + output_parts.append(f"\nTotal files: {status.total_files}") + output_parts.append(f"\nSuccessful: {status.successful_files}") + output_parts.append(f"\nFailed: {status.failed_files}") + + if status.files: + output_parts.append("\n\n**File Details:**") + for filename, file_status in status.files.items(): + output_parts.append(f"\n- {filename}: {file_status}") + + return [TextContent(type="text", text="".join(output_parts))] + + except TimeoutError as e: + logger.error(f"Wait for task timeout: {e}") + return [TextContent(type="text", text=f"Task did not complete within {timeout} seconds.")] + except NotFoundError as e: + logger.error(f"Task not found: {e.message}") + return [TextContent(type="text", text=f"Task not found: {e.message}")] + except AuthenticationError as e: + logger.error(f"Authentication error: {e.message}") + return [TextContent(type="text", text=f"Authentication error: {e.message}")] + except OpenRAGError as e: + logger.error(f"OpenRAG error: {e.message}") + return [TextContent(type="text", text=f"Error: {e.message}")] + except Exception as e: + logger.error(f"Wait for task error: {e}") + return [TextContent(type="text", text=f"Error waiting for task: {str(e)}")] + + +async def _delete_document(arguments: dict) -> list[TextContent]: + """Delete a document from the knowledge base using the SDK.""" + filename = arguments.get("filename", "") + + if not filename: + return [TextContent(type="text", text="Error: filename is required")] + + try: + client = get_openrag_client() + response = await client.documents.delete(filename) + + if response.success: + return [TextContent( + type="text", + text=f"Successfully deleted '{filename}' ({response.deleted_chunks} chunks removed).", + )] + else: + return [TextContent( + type="text", + text=f"Failed to delete '{filename}'.", + )] + + except NotFoundError as e: + logger.error(f"Document not found: {e.message}") + return [TextContent(type="text", text=f"Document not found: {e.message}")] + except AuthenticationError as e: + logger.error(f"Authentication error: {e.message}") + return [TextContent(type="text", text=f"Authentication error: {e.message}")] + except ServerError as e: + logger.error(f"Server error: {e.message}") + return [TextContent(type="text", text=f"Server error: {e.message}")] + except OpenRAGError as e: + logger.error(f"OpenRAG error: {e.message}") + return [TextContent(type="text", text=f"Error: {e.message}")] + except Exception as e: + logger.error(f"Delete document error: {e}") + return [TextContent(type="text", text=f"Error deleting document: {str(e)}")] diff --git a/sdks/mcp/src/openrag_mcp/tools/search.py b/sdks/mcp/src/openrag_mcp/tools/search.py index 0a2a05ae..3185be97 100644 --- a/sdks/mcp/src/openrag_mcp/tools/search.py +++ b/sdks/mcp/src/openrag_mcp/tools/search.py @@ -2,95 +2,139 @@ import logging -from mcp.server import Server from mcp.types import TextContent, Tool -from openrag_mcp.config import get_client +from openrag_sdk import ( + AuthenticationError, + OpenRAGError, + RateLimitError, + SearchFilters, + ServerError, + ValidationError, +) + +from openrag_mcp.config import get_openrag_client logger = logging.getLogger("openrag-mcp.search") -def register_search_tools(server: Server) -> None: - """Register search-related tools with the MCP server.""" - - @server.list_tools() - async def list_search_tools() -> list[Tool]: - """List search tools.""" - return [ - Tool( - name="openrag_search", - description=( - "Search the OpenRAG knowledge base using semantic search. " - "Returns matching document chunks with relevance scores." - ), - inputSchema={ - "type": "object", - "properties": { - "query": { - "type": "string", - "description": "The search query", - }, - "limit": { - "type": "integer", - "description": "Maximum number of results (default: 10)", - "default": 10, - }, - }, - "required": ["query"], - }, +def get_search_tools() -> list[Tool]: + """Return search-related tools.""" + return [ + Tool( + name="openrag_search", + description=( + "Search the OpenRAG knowledge base using semantic search. " + "Returns matching document chunks with relevance scores. " + "Optionally filter by data sources or document types." ), - ] + inputSchema={ + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "The search query", + }, + "limit": { + "type": "integer", + "description": "Maximum number of results (default: 10)", + "default": 10, + }, + "score_threshold": { + "type": "number", + "description": "Minimum relevance score threshold (default: 0)", + "default": 0, + }, + "filter_id": { + "type": "string", + "description": "Optional knowledge filter ID to apply", + }, + "data_sources": { + "type": "array", + "items": {"type": "string"}, + "description": "Optional list of filenames to filter by", + }, + "document_types": { + "type": "array", + "items": {"type": "string"}, + "description": "Optional list of MIME types to filter by (e.g., 'application/pdf')", + }, + }, + "required": ["query"], + }, + ), + ] - @server.call_tool() - async def call_search_tool(name: str, arguments: dict) -> list[TextContent]: - """Handle search tool calls.""" - if name != "openrag_search": - return [] - query = arguments.get("query", "") - limit = arguments.get("limit", 10) +async def handle_search_tool(name: str, arguments: dict) -> list[TextContent] | None: + """Handle search tool calls. Returns None if tool not handled.""" + if name != "openrag_search": + return None - if not query: - return [TextContent(type="text", text="Error: query is required")] + query = arguments.get("query", "") + limit = arguments.get("limit", 10) + score_threshold = arguments.get("score_threshold", 0) + filter_id = arguments.get("filter_id") + data_sources = arguments.get("data_sources") + document_types = arguments.get("document_types") - try: - async with get_client() as client: - payload = { - "query": query, - "limit": limit, - } + if not query: + return [TextContent(type="text", text="Error: query is required")] - response = await client.post("/api/v1/search", json=payload) - response.raise_for_status() - data = response.json() + try: + client = get_openrag_client() - results = data.get("results", []) + # Build filters if provided + filters = None + if data_sources or document_types: + filters = SearchFilters( + data_sources=data_sources, + document_types=document_types, + ) - if not results: - return [TextContent(type="text", text="No results found.")] + response = await client.search.query( + query=query, + limit=limit, + score_threshold=score_threshold, + filter_id=filter_id, + filters=filters, + ) - # Format results - output_parts = [f"Found {len(results)} result(s):\n"] + if not response.results: + return [TextContent(type="text", text="No results found.")] - for i, result in enumerate(results, 1): - filename = result.get("filename", "Unknown") - score = result.get("score", 0) - content = result.get("content", "") - page = result.get("page_number") + # Format results + output_parts = [f"Found {len(response.results)} result(s):\n"] - output_parts.append(f"\n---\n**{i}. {filename}**") - if page: - output_parts.append(f" (page {page})") - output_parts.append(f"\nRelevance: {score:.2f}\n") + for i, result in enumerate(response.results, 1): + output_parts.append(f"\n---\n**{i}. {result.filename}**") + if result.page: + output_parts.append(f" (page {result.page})") + output_parts.append(f"\nRelevance: {result.score:.2f}\n") - # Truncate long content - if len(content) > 500: - content = content[:500] + "..." - output_parts.append(f"\n{content}\n") + # Truncate long content + content = result.text + if len(content) > 500: + content = content[:500] + "..." + output_parts.append(f"\n{content}\n") - return [TextContent(type="text", text="".join(output_parts))] - - except Exception as e: - logger.error(f"Search error: {e}") - return [TextContent(type="text", text=f"Error: {str(e)}")] + return [TextContent(type="text", text="".join(output_parts))] + except AuthenticationError as e: + logger.error(f"Authentication error: {e.message}") + return [TextContent(type="text", text=f"Authentication error: {e.message}")] + except ValidationError as e: + logger.error(f"Validation error: {e.message}") + return [TextContent(type="text", text=f"Invalid request: {e.message}")] + except RateLimitError as e: + logger.error(f"Rate limit error: {e.message}") + return [TextContent(type="text", text=f"Rate limited: {e.message}")] + except ServerError as e: + logger.error(f"Server error: {e.message}") + return [TextContent(type="text", text=f"Server error: {e.message}")] + except OpenRAGError as e: + logger.error(f"OpenRAG error: {e.message}") + return [TextContent(type="text", text=f"Error: {e.message}")] + except Exception as e: + logger.error(f"Search error: {e}") + return [TextContent(type="text", text=f"Error: {str(e)}")] From 91f90d9d9d03499ce67c39da82c5f4c9e3158f38 Mon Sep 17 00:00:00 2001 From: Edwin Jose Date: Fri, 26 Dec 2025 14:21:03 -0500 Subject: [PATCH 4/5] update to add tool registry --- sdks/mcp/pyproject.toml | 2 +- sdks/mcp/src/openrag_mcp/server.py | 32 +-- sdks/mcp/src/openrag_mcp/tools/__init__.py | 24 +- sdks/mcp/src/openrag_mcp/tools/chat.py | 89 ++++--- sdks/mcp/src/openrag_mcp/tools/documents.py | 267 +++++++++++--------- sdks/mcp/src/openrag_mcp/tools/registry.py | 37 +++ sdks/mcp/src/openrag_mcp/tools/search.py | 99 ++++---- 7 files changed, 292 insertions(+), 258 deletions(-) create mode 100644 sdks/mcp/src/openrag_mcp/tools/registry.py diff --git a/sdks/mcp/pyproject.toml b/sdks/mcp/pyproject.toml index 3f74a8ad..9f288f2c 100644 --- a/sdks/mcp/pyproject.toml +++ b/sdks/mcp/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "openrag-mcp" -version = "0.1.0" +version = "0.1.1" description = "MCP server for OpenRAG - Chat, Search, and Ingest documents" readme = "README.md" requires-python = ">=3.10" diff --git a/sdks/mcp/src/openrag_mcp/server.py b/sdks/mcp/src/openrag_mcp/server.py index 6bfa1182..bfc21776 100644 --- a/sdks/mcp/src/openrag_mcp/server.py +++ b/sdks/mcp/src/openrag_mcp/server.py @@ -1,4 +1,5 @@ """OpenRAG MCP Server - Main server setup and entry point.""" +#TODO: utilize the SDK directly so that any changes in parameters in SDK directky reflects the MCP import asyncio import logging @@ -8,9 +9,9 @@ from mcp.server.stdio import stdio_server from mcp.types import TextContent, Tool from openrag_mcp.config import get_config -from openrag_mcp.tools.chat import get_chat_tools, handle_chat_tool -from openrag_mcp.tools.search import get_search_tools, handle_search_tool -from openrag_mcp.tools.documents import get_document_tools, handle_document_tool + +# Import tools module to trigger registration, then get registry functions +from openrag_mcp.tools import get_all_tools, get_handler # Configure logging to stderr (stdout is used for MCP protocol) logging.basicConfig( @@ -30,34 +31,17 @@ def create_server() -> Server: # Create server instance server = Server("openrag-mcp") - # Register a single list_tools handler that combines all tools @server.list_tools() async def list_all_tools() -> list[Tool]: """List all available tools.""" - tools = [] - tools.extend(get_chat_tools()) - tools.extend(get_search_tools()) - tools.extend(get_document_tools()) - return tools + return get_all_tools() - # Register a single call_tool handler that dispatches to appropriate handler @server.call_tool() async def call_tool(name: str, arguments: dict) -> list[TextContent]: """Handle all tool calls by dispatching to the appropriate handler.""" - # Try each handler in order - result = await handle_chat_tool(name, arguments) - if result is not None: - return result - - result = await handle_search_tool(name, arguments) - if result is not None: - return result - - result = await handle_document_tool(name, arguments) - if result is not None: - return result - - # Unknown tool + handler = get_handler(name) + if handler: + return await handler(arguments) return [TextContent(type="text", text=f"Unknown tool: {name}")] logger.info("OpenRAG MCP server initialized with all tools") diff --git a/sdks/mcp/src/openrag_mcp/tools/__init__.py b/sdks/mcp/src/openrag_mcp/tools/__init__.py index da78b042..0225eac5 100644 --- a/sdks/mcp/src/openrag_mcp/tools/__init__.py +++ b/sdks/mcp/src/openrag_mcp/tools/__init__.py @@ -1,14 +1,14 @@ -"""OpenRAG MCP tools.""" +"""OpenRAG MCP tools. -from openrag_mcp.tools.chat import get_chat_tools, handle_chat_tool -from openrag_mcp.tools.search import get_search_tools, handle_search_tool -from openrag_mcp.tools.documents import get_document_tools, handle_document_tool +Import this module to register all tools with the registry. +""" -__all__ = [ - "get_chat_tools", - "handle_chat_tool", - "get_search_tools", - "handle_search_tool", - "get_document_tools", - "handle_document_tool", -] +# Import tools to trigger registration +from openrag_mcp.tools import chat # noqa: F401 +from openrag_mcp.tools import search # noqa: F401 +from openrag_mcp.tools import documents # noqa: F401 + +# Re-export registry functions for convenience +from openrag_mcp.tools.registry import get_all_tools, get_handler + +__all__ = ["get_all_tools", "get_handler"] diff --git a/sdks/mcp/src/openrag_mcp/tools/chat.py b/sdks/mcp/src/openrag_mcp/tools/chat.py index fd422e48..13199460 100644 --- a/sdks/mcp/src/openrag_mcp/tools/chat.py +++ b/sdks/mcp/src/openrag_mcp/tools/chat.py @@ -13,58 +13,53 @@ from openrag_sdk import ( ) from openrag_mcp.config import get_openrag_client +from openrag_mcp.tools.registry import register_tool logger = logging.getLogger("openrag-mcp.chat") -def get_chat_tools() -> list[Tool]: - """Return chat-related tools.""" - return [ - Tool( - name="openrag_chat", - description=( - "Send a message to OpenRAG and get a RAG-enhanced response. " - "The response is informed by documents in your knowledge base. " - "Use chat_id to continue a previous conversation, or filter_id " - "to apply a knowledge filter." - ), - inputSchema={ - "type": "object", - "properties": { - "message": { - "type": "string", - "description": "Your question or message to send to OpenRAG", - }, - "chat_id": { - "type": "string", - "description": "Optional conversation ID to continue a previous chat", - }, - "filter_id": { - "type": "string", - "description": "Optional knowledge filter ID to apply", - }, - "limit": { - "type": "integer", - "description": "Maximum number of sources to retrieve (default: 10)", - "default": 10, - }, - "score_threshold": { - "type": "number", - "description": "Minimum relevance score threshold (default: 0)", - "default": 0, - }, - }, - "required": ["message"], +# Tool definition +CHAT_TOOL = Tool( + name="openrag_chat", + description=( + "Send a message to OpenRAG and get a RAG-enhanced response. " + "The response is informed by documents in your knowledge base. " + "Use chat_id to continue a previous conversation, or filter_id " + "to apply a knowledge filter." + ), + inputSchema={ + "type": "object", + "properties": { + "message": { + "type": "string", + "description": "Your question or message to send to OpenRAG", }, - ), - ] + "chat_id": { + "type": "string", + "description": "Optional conversation ID to continue a previous chat", + }, + "filter_id": { + "type": "string", + "description": "Optional knowledge filter ID to apply", + }, + "limit": { + "type": "integer", + "description": "Maximum number of sources to retrieve (default: 10)", + "default": 10, + }, + "score_threshold": { + "type": "number", + "description": "Minimum relevance score threshold (default: 0)", + "default": 0, + }, + }, + "required": ["message"], + }, +) -async def handle_chat_tool(name: str, arguments: dict) -> list[TextContent] | None: - """Handle chat tool calls. Returns None if tool not handled.""" - if name != "openrag_chat": - return None - +async def handle_chat(arguments: dict) -> list[TextContent]: + """Handle openrag_chat tool calls.""" message = arguments.get("message", "") chat_id = arguments.get("chat_id") filter_id = arguments.get("filter_id") @@ -115,3 +110,7 @@ async def handle_chat_tool(name: str, arguments: dict) -> list[TextContent] | No except Exception as e: logger.error(f"Chat error: {e}") return [TextContent(type="text", text=f"Error: {str(e)}")] + + +# Register the tool +register_tool(CHAT_TOOL, handle_chat) diff --git a/sdks/mcp/src/openrag_mcp/tools/documents.py b/sdks/mcp/src/openrag_mcp/tools/documents.py index ca679cd4..40e0ee61 100644 --- a/sdks/mcp/src/openrag_mcp/tools/documents.py +++ b/sdks/mcp/src/openrag_mcp/tools/documents.py @@ -15,126 +15,42 @@ from openrag_sdk import ( ) from openrag_mcp.config import get_openrag_client +from openrag_mcp.tools.registry import register_tool logger = logging.getLogger("openrag-mcp.documents") -def get_document_tools() -> list[Tool]: - """Return document-related tools.""" - return [ - Tool( - name="openrag_ingest_file", - description=( - "Ingest a local file into the OpenRAG knowledge base. " - "Supported formats: PDF, DOCX, TXT, MD, HTML, and more. " - "By default waits for ingestion to complete. Set wait=false to return immediately." - ), - inputSchema={ - "type": "object", - "properties": { - "file_path": { - "type": "string", - "description": "Absolute path to the file to ingest", - }, - "wait": { - "type": "boolean", - "description": "Wait for ingestion to complete (default: true). Set to false to return immediately with task_id.", - "default": True, - }, - }, - "required": ["file_path"], +# ============================================================================ +# Tool: openrag_ingest_file +# ============================================================================ + +INGEST_FILE_TOOL = Tool( + name="openrag_ingest_file", + description=( + "Ingest a local file into the OpenRAG knowledge base. " + "Supported formats: PDF, DOCX, TXT, MD, HTML, and more. " + "By default waits for ingestion to complete. Set wait=false to return immediately." + ), + inputSchema={ + "type": "object", + "properties": { + "file_path": { + "type": "string", + "description": "Absolute path to the file to ingest", }, - ), - Tool( - name="openrag_ingest_url", - description=( - "Ingest content from a URL into the OpenRAG knowledge base. " - "The URL content will be fetched, processed, and stored." - ), - inputSchema={ - "type": "object", - "properties": { - "url": { - "type": "string", - "description": "The URL to fetch and ingest", - }, - }, - "required": ["url"], + "wait": { + "type": "boolean", + "description": "Wait for ingestion to complete (default: true). Set to false to return immediately with task_id.", + "default": True, }, - ), - Tool( - name="openrag_get_task_status", - description=( - "Check the status of an ingestion task. " - "Use the task_id returned from openrag_ingest_file when wait=false." - ), - inputSchema={ - "type": "object", - "properties": { - "task_id": { - "type": "string", - "description": "The task ID to check status for", - }, - }, - "required": ["task_id"], - }, - ), - Tool( - name="openrag_wait_for_task", - description=( - "Wait for an ingestion task to complete. " - "Polls the task status until it completes or fails." - ), - inputSchema={ - "type": "object", - "properties": { - "task_id": { - "type": "string", - "description": "The task ID to wait for", - }, - "timeout": { - "type": "number", - "description": "Maximum seconds to wait (default: 300)", - "default": 300, - }, - }, - "required": ["task_id"], - }, - ), - Tool( - name="openrag_delete_document", - description="Delete a document from the OpenRAG knowledge base.", - inputSchema={ - "type": "object", - "properties": { - "filename": { - "type": "string", - "description": "Name of the file to delete", - }, - }, - "required": ["filename"], - }, - ), - ] + }, + "required": ["file_path"], + }, +) -async def handle_document_tool(name: str, arguments: dict) -> list[TextContent] | None: - """Handle document tool calls. Returns None if tool not handled.""" - if name == "openrag_ingest_file": - return await _ingest_file(arguments) - elif name == "openrag_ingest_url": - return await _ingest_url(arguments) - elif name == "openrag_get_task_status": - return await _get_task_status(arguments) - elif name == "openrag_wait_for_task": - return await _wait_for_task(arguments) - elif name == "openrag_delete_document": - return await _delete_document(arguments) - return None - - -async def _ingest_file(arguments: dict) -> list[TextContent]: - """Ingest a local file into OpenRAG using the SDK.""" +async def handle_ingest_file(arguments: dict) -> list[TextContent]: + """Handle openrag_ingest_file tool calls.""" file_path = arguments.get("file_path", "") wait = arguments.get("wait", True) @@ -154,11 +70,10 @@ async def _ingest_file(arguments: dict) -> list[TextContent]: response = await client.documents.ingest(file_path=path, wait=wait) if wait: - # Response is IngestTaskStatus when wait=True status = response.status successful = response.successful_files failed = response.failed_files - + if status == "completed": result = f"Successfully ingested '{path.name}'." result += f"\nStatus: {status}" @@ -170,7 +85,6 @@ async def _ingest_file(arguments: dict) -> list[TextContent]: result += f"\nSuccessful files: {successful}" result += f"\nFailed files: {failed}" else: - # Response is IngestResponse when wait=False result = f"Successfully queued '{response.filename or path.name}' for ingestion." if response.task_id: result += f"\nTask ID: {response.task_id}" @@ -201,11 +115,31 @@ async def _ingest_file(arguments: dict) -> list[TextContent]: return [TextContent(type="text", text=f"Error ingesting file: {str(e)}")] -async def _ingest_url(arguments: dict) -> list[TextContent]: - """Ingest content from a URL into OpenRAG. +# ============================================================================ +# Tool: openrag_ingest_url +# ============================================================================ - Note: This uses the SDK's chat to trigger URL ingestion via the agent. - """ +INGEST_URL_TOOL = Tool( + name="openrag_ingest_url", + description=( + "Ingest content from a URL into the OpenRAG knowledge base. " + "The URL content will be fetched, processed, and stored." + ), + inputSchema={ + "type": "object", + "properties": { + "url": { + "type": "string", + "description": "The URL to fetch and ingest", + }, + }, + "required": ["url"], + }, +) + + +async def handle_ingest_url(arguments: dict) -> list[TextContent]: + """Handle openrag_ingest_url tool calls.""" url = arguments.get("url", "") if not url: @@ -215,7 +149,6 @@ async def _ingest_url(arguments: dict) -> list[TextContent]: return [TextContent(type="text", text="Error: url must start with http:// or https://")] try: - # Use chat with a special prompt to trigger URL ingestion via the agent client = get_openrag_client() response = await client.chat.create( message=f"Please ingest the content from this URL into the knowledge base: {url}", @@ -237,8 +170,31 @@ async def _ingest_url(arguments: dict) -> list[TextContent]: return [TextContent(type="text", text=f"Error ingesting URL: {str(e)}")] -async def _get_task_status(arguments: dict) -> list[TextContent]: - """Get the status of an ingestion task.""" +# ============================================================================ +# Tool: openrag_get_task_status +# ============================================================================ + +GET_TASK_STATUS_TOOL = Tool( + name="openrag_get_task_status", + description=( + "Check the status of an ingestion task. " + "Use the task_id returned from openrag_ingest_file when wait=false." + ), + inputSchema={ + "type": "object", + "properties": { + "task_id": { + "type": "string", + "description": "The task ID to check status for", + }, + }, + "required": ["task_id"], + }, +) + + +async def handle_get_task_status(arguments: dict) -> list[TextContent]: + """Handle openrag_get_task_status tool calls.""" task_id = arguments.get("task_id", "") if not task_id: @@ -276,8 +232,36 @@ async def _get_task_status(arguments: dict) -> list[TextContent]: return [TextContent(type="text", text=f"Error getting task status: {str(e)}")] -async def _wait_for_task(arguments: dict) -> list[TextContent]: - """Wait for an ingestion task to complete.""" +# ============================================================================ +# Tool: openrag_wait_for_task +# ============================================================================ + +WAIT_FOR_TASK_TOOL = Tool( + name="openrag_wait_for_task", + description=( + "Wait for an ingestion task to complete. " + "Polls the task status until it completes or fails." + ), + inputSchema={ + "type": "object", + "properties": { + "task_id": { + "type": "string", + "description": "The task ID to wait for", + }, + "timeout": { + "type": "number", + "description": "Maximum seconds to wait (default: 300)", + "default": 300, + }, + }, + "required": ["task_id"], + }, +) + + +async def handle_wait_for_task(arguments: dict) -> list[TextContent]: + """Handle openrag_wait_for_task tool calls.""" task_id = arguments.get("task_id", "") timeout = arguments.get("timeout", 300) @@ -318,8 +302,28 @@ async def _wait_for_task(arguments: dict) -> list[TextContent]: return [TextContent(type="text", text=f"Error waiting for task: {str(e)}")] -async def _delete_document(arguments: dict) -> list[TextContent]: - """Delete a document from the knowledge base using the SDK.""" +# ============================================================================ +# Tool: openrag_delete_document +# ============================================================================ + +DELETE_DOCUMENT_TOOL = Tool( + name="openrag_delete_document", + description="Delete a document from the OpenRAG knowledge base.", + inputSchema={ + "type": "object", + "properties": { + "filename": { + "type": "string", + "description": "Name of the file to delete", + }, + }, + "required": ["filename"], + }, +) + + +async def handle_delete_document(arguments: dict) -> list[TextContent]: + """Handle openrag_delete_document tool calls.""" filename = arguments.get("filename", "") if not filename: @@ -355,3 +359,14 @@ async def _delete_document(arguments: dict) -> list[TextContent]: except Exception as e: logger.error(f"Delete document error: {e}") return [TextContent(type="text", text=f"Error deleting document: {str(e)}")] + + +# ============================================================================ +# Register all tools +# ============================================================================ + +register_tool(INGEST_FILE_TOOL, handle_ingest_file) +register_tool(INGEST_URL_TOOL, handle_ingest_url) +register_tool(GET_TASK_STATUS_TOOL, handle_get_task_status) +register_tool(WAIT_FOR_TASK_TOOL, handle_wait_for_task) +register_tool(DELETE_DOCUMENT_TOOL, handle_delete_document) diff --git a/sdks/mcp/src/openrag_mcp/tools/registry.py b/sdks/mcp/src/openrag_mcp/tools/registry.py new file mode 100644 index 00000000..851496ab --- /dev/null +++ b/sdks/mcp/src/openrag_mcp/tools/registry.py @@ -0,0 +1,37 @@ +"""Tool registry for OpenRAG MCP server.""" + +from collections.abc import Awaitable, Callable +from dataclasses import dataclass + +from mcp.types import TextContent, Tool + +# Type alias for tool handlers +ToolHandler = Callable[[dict], Awaitable[list[TextContent]]] + + +@dataclass +class ToolEntry: + """A tool definition with its handler.""" + tool: Tool + handler: ToolHandler + + +# Global registry: tool_name -> ToolEntry +_registry: dict[str, ToolEntry] = {} + + +def register_tool(tool: Tool, handler: ToolHandler) -> None: + """Register a tool with its handler.""" + _registry[tool.name] = ToolEntry(tool=tool, handler=handler) + + +def get_all_tools() -> list[Tool]: + """Get all registered tools.""" + return [entry.tool for entry in _registry.values()] + + +def get_handler(name: str) -> ToolHandler | None: + """Get the handler for a tool by name.""" + entry = _registry.get(name) + return entry.handler if entry else None + diff --git a/sdks/mcp/src/openrag_mcp/tools/search.py b/sdks/mcp/src/openrag_mcp/tools/search.py index 3185be97..2f87c8ef 100644 --- a/sdks/mcp/src/openrag_mcp/tools/search.py +++ b/sdks/mcp/src/openrag_mcp/tools/search.py @@ -14,63 +14,58 @@ from openrag_sdk import ( ) from openrag_mcp.config import get_openrag_client +from openrag_mcp.tools.registry import register_tool logger = logging.getLogger("openrag-mcp.search") -def get_search_tools() -> list[Tool]: - """Return search-related tools.""" - return [ - Tool( - name="openrag_search", - description=( - "Search the OpenRAG knowledge base using semantic search. " - "Returns matching document chunks with relevance scores. " - "Optionally filter by data sources or document types." - ), - inputSchema={ - "type": "object", - "properties": { - "query": { - "type": "string", - "description": "The search query", - }, - "limit": { - "type": "integer", - "description": "Maximum number of results (default: 10)", - "default": 10, - }, - "score_threshold": { - "type": "number", - "description": "Minimum relevance score threshold (default: 0)", - "default": 0, - }, - "filter_id": { - "type": "string", - "description": "Optional knowledge filter ID to apply", - }, - "data_sources": { - "type": "array", - "items": {"type": "string"}, - "description": "Optional list of filenames to filter by", - }, - "document_types": { - "type": "array", - "items": {"type": "string"}, - "description": "Optional list of MIME types to filter by (e.g., 'application/pdf')", - }, - }, - "required": ["query"], +# Tool definition +SEARCH_TOOL = Tool( + name="openrag_search", + description=( + "Search the OpenRAG knowledge base using semantic search. " + "Returns matching document chunks with relevance scores. " + "Optionally filter by data sources or document types." + ), + inputSchema={ + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "The search query", }, - ), - ] + "limit": { + "type": "integer", + "description": "Maximum number of results (default: 10)", + "default": 10, + }, + "score_threshold": { + "type": "number", + "description": "Minimum relevance score threshold (default: 0)", + "default": 0, + }, + "filter_id": { + "type": "string", + "description": "Optional knowledge filter ID to apply", + }, + "data_sources": { + "type": "array", + "items": {"type": "string"}, + "description": "Optional list of filenames to filter by", + }, + "document_types": { + "type": "array", + "items": {"type": "string"}, + "description": "Optional list of MIME types to filter by (e.g., 'application/pdf')", + }, + }, + "required": ["query"], + }, +) -async def handle_search_tool(name: str, arguments: dict) -> list[TextContent] | None: - """Handle search tool calls. Returns None if tool not handled.""" - if name != "openrag_search": - return None - +async def handle_search(arguments: dict) -> list[TextContent]: + """Handle openrag_search tool calls.""" query = arguments.get("query", "") limit = arguments.get("limit", 10) score_threshold = arguments.get("score_threshold", 0) @@ -138,3 +133,7 @@ async def handle_search_tool(name: str, arguments: dict) -> list[TextContent] | except Exception as e: logger.error(f"Search error: {e}") return [TextContent(type="text", text=f"Error: {str(e)}")] + + +# Register the tool +register_tool(SEARCH_TOOL, handle_search) From 41838bf64686ad9a8465d31e2a1adff75f052806 Mon Sep 17 00:00:00 2001 From: Edwin Jose Date: Fri, 16 Jan 2026 10:29:47 -0600 Subject: [PATCH 5/5] Add OpenRAG MCP documentation and sequence diagrams Introduces mcp.md and sequence.md to document OpenRAG MCP usage, configuration, features, and protocol flows. These files provide setup instructions, integration examples, and detailed sequence diagrams for all supported MCP tools and error handling. --- sdks/mcp/src/openrag_mcp/mcp.md | 205 +++++++++++ sdks/mcp/src/openrag_mcp/sequence.md | 501 +++++++++++++++++++++++++++ 2 files changed, 706 insertions(+) create mode 100644 sdks/mcp/src/openrag_mcp/mcp.md create mode 100644 sdks/mcp/src/openrag_mcp/sequence.md diff --git a/sdks/mcp/src/openrag_mcp/mcp.md b/sdks/mcp/src/openrag_mcp/mcp.md new file mode 100644 index 00000000..15f888f9 --- /dev/null +++ b/sdks/mcp/src/openrag_mcp/mcp.md @@ -0,0 +1,205 @@ +# OpenRAG MCP + +OpenRAG MCP is a [Model Context Protocol](https://modelcontextprotocol.io/) server that exposes your OpenRAG knowledge base to AI assistants. It enables seamless integration between OpenRAG's RAG capabilities and MCP-compatible applications like Cursor, Claude Desktop, and IBM Watson Orchestrate. + +--- + +## Quick Start + +Run the MCP server using `uvx` (no installation required): + +```bash +uvx openrag-mcp +``` + +For a specific version: + +```bash +uvx --from openrag-mcp==0.1.1 openrag-mcp +``` + +### Prerequisites + +- Python 3.10+ +- A running OpenRAG instance +- An OpenRAG API key (create one in **Settings > API Keys**) + +--- + +## Environment Variables + +| Variable | Description | Required | Default | +|:---------|:------------|:--------:|:--------| +| `OPENRAG_API_KEY` | Your OpenRAG API key | Yes | - | +| `OPENRAG_URL` | URL of your OpenRAG instance | No | `http://localhost:3000` | + +--- + +## Features + +| Feature | Tool | Status | Description | +|:--------|:-----|:------:|:------------| +| **Chat** | `openrag_chat` | GA | RAG-enhanced conversations with your knowledge base | +| **Search** | `openrag_search` | WIP | Semantic search over documents with relevance scoring | +| **File Ingestion** | `openrag_ingest_file` | WIP | Ingest local files (PDF, DOCX, TXT, MD, HTML) | +| **URL Ingestion** | `openrag_ingest_url` | WIP | Ingest content from web URLs | +| **Delete Document** | `openrag_delete_document` | WIP | Remove documents from knowledge base | +| **Task Status** | `openrag_get_task_status` | WIP | Check async ingestion task progress | +| **Wait for Task** | `openrag_wait_for_task` | WIP | Poll until ingestion completes | + +--- + +## Use Cases + +### Why OpenRAG MCP? + +OpenRAG MCP acts as a **universal connectivity layer** between your knowledge base and AI-powered applications. Instead of building custom integrations for each platform, MCP provides a standardized protocol that works everywhere. + +**Key Benefits:** + +- **Zero Custom Integration** - No need to build platform-specific APIs or plugins. One MCP server works across all compatible applications. + +- **Agentic AI Enablement** - Empowers AI agents to autonomously search, retrieve, and reason over your organizational knowledge without human intervention. + +- **Future-Proof Architecture** - MCP is an open standard backed by Anthropic and adopted by major AI platforms. Your integration investment carries forward as the ecosystem grows. + +- **Centralized Knowledge Access** - Single point of entry to your knowledge base for all AI applications, ensuring consistent answers and reducing knowledge silos. + +- **Secure by Design** - API key authentication ensures only authorized applications access your knowledge. No data leaves your infrastructure without explicit permission. + +- **Multi-Agent Workflows** - Enable complex workflows where multiple AI agents collaborate using shared knowledge, such as a coding agent consulting documentation while a review agent validates compliance. + +- **Reduced Hallucination** - RAG-enhanced responses are grounded in your actual documents, reducing AI hallucinations with verifiable, sourced answers. + +- **Enterprise Knowledge Democratization** - Make institutional knowledge accessible to every team member through their preferred AI tool, from developers in Cursor to analysts in Claude. + +- **Real-Time Knowledge Sync** - As you ingest new documents into OpenRAG, all connected AI applications immediately gain access to the updated knowledge. + +- **Minimal Infrastructure Overhead** - Runs as a lightweight subprocess spawned by the AI application. No separate servers, containers, or complex deployment required. + +### Agentic Application Integration + +| Platform | Use Case | +|:---------|:---------| +| **Cursor IDE** | Context-aware code assistance powered by your documentation, design specs, and internal wikis | +| **Claude Desktop** | Enhanced conversations that leverage your organization's knowledge base for accurate, sourced answers | +| **IBM Watson Orchestrate** | Workflow automation with RAG capabilities for enterprise knowledge retrieval | +| **Custom MCP Clients** | Build your own integrations using the open MCP protocol | + +### Example Scenarios + +- **Developer Productivity**: Query internal API docs, architecture decisions, and runbooks directly from your IDE +- **Customer Support**: AI assistants that reference your product documentation and knowledge base +- **Research & Analysis**: Search and synthesize information across multiple document sources +- **Workflow Automation**: Trigger document ingestion and knowledge queries as part of automated pipelines + +--- + +## Platform Configuration + +### Cursor IDE + +Add to `~/.cursor/mcp.json`: + +```json +{ + "mcpServers": { + "openrag": { + "command": "uvx", + "args": ["openrag-mcp"], + "env": { + "OPENRAG_URL": "https://your-openrag-instance.com", + "OPENRAG_API_KEY": "orag_your_api_key_here" + } + } + } +} +``` + +### Claude Desktop + +**macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json` +**Windows**: `%APPDATA%\Claude\claude_desktop_config.json` + +```json +{ + "mcpServers": { + "openrag": { + "command": "uvx", + "args": ["openrag-mcp"], + "env": { + "OPENRAG_URL": "https://your-openrag-instance.com", + "OPENRAG_API_KEY": "orag_your_api_key_here" + } + } + } +} +``` + +--- + +## Architecture + +```mermaid +flowchart LR + subgraph clients [AI Applications] + Cursor[Cursor IDE] + Claude[Claude Desktop] + Watson[IBM Watson Orchestrate] + Other[Other MCP Clients] + end + + subgraph mcp [MCP Server] + Server[openrag-mcp] + end + + subgraph backend [Backend] + OpenRAG[OpenRAG Instance] + KB[(Knowledge Base)] + end + + Cursor <-->|stdio / JSON-RPC| Server + Claude <-->|stdio / JSON-RPC| Server + Watson <-->|stdio / JSON-RPC| Server + Other <-->|stdio / JSON-RPC| Server + + Server <-->|HTTPS / X-API-Key| OpenRAG + OpenRAG <--> KB +``` + +--- + +## Example Prompts + +Once configured, interact with OpenRAG through natural language: + +- *"Search my knowledge base for authentication best practices"* +- *"What documents do I have about deployment procedures?"* +- *"Chat with OpenRAG about the Q4 roadmap"* +- *"Ingest this PDF: /path/to/architecture-overview.pdf"* +- *"Delete the outdated spec document from my knowledge base"* + +--- + +## Troubleshooting + +| Issue | Solution | +|:------|:---------| +| "OPENRAG_API_KEY environment variable is required" | Ensure `OPENRAG_API_KEY` is set in the `env` section of your MCP config | +| "Connection refused" errors | Verify OpenRAG is running and `OPENRAG_URL` is correct | +| Tools not appearing | Restart your AI application after config changes | + +--- + +## Resources + +- [OpenRAG Documentation](https://docs.openr.ag) +- [MCP SDK README](sdks/mcp/README.md) +- [Model Context Protocol Specification](https://modelcontextprotocol.io/) + +--- + +## License + +Apache 2.0 - See [LICENSE](LICENSE) for details. + diff --git a/sdks/mcp/src/openrag_mcp/sequence.md b/sdks/mcp/src/openrag_mcp/sequence.md new file mode 100644 index 00000000..c75a67a3 --- /dev/null +++ b/sdks/mcp/src/openrag_mcp/sequence.md @@ -0,0 +1,501 @@ +# OpenRAG MCP Sequence Diagrams + +This document illustrates the interaction flows between AI applications and OpenRAG through the Model Context Protocol (MCP) server. + +## Participants + +| Participant | Description | +|:------------|:------------| +| **AI App** | MCP-compatible application (Cursor IDE, Claude Desktop, Watson Orchestrate) | +| **MCP Server** | OpenRAG MCP server (`openrag-mcp`) running as subprocess | +| **OpenRAG SDK** | Python SDK client within MCP server | +| **OpenRAG API** | OpenRAG FastAPI backend server | +| **Knowledge Base** | OpenSearch vector database storing document embeddings | + +--- + +## 1. Server Initialization + +```mermaid +sequenceDiagram + participant AIApp as AI Application + participant MCP as MCP Server + participant SDK as OpenRAG SDK + participant API as OpenRAG API + + Note over AIApp,MCP: Startup Phase + + AIApp->>MCP: Spawn subprocess (uvx openrag-mcp) + activate MCP + MCP->>MCP: Load OPENRAG_API_KEY from env + MCP->>MCP: Load OPENRAG_URL from env + MCP->>SDK: Initialize client with credentials + activate SDK + SDK-->>MCP: Client ready + deactivate SDK + MCP->>MCP: Register tools (chat, search, ingest, delete) + MCP-->>AIApp: Server initialized (stdio ready) + + Note over AIApp,MCP: Tool Discovery + + AIApp->>MCP: JSON-RPC: tools/list + MCP-->>AIApp: Available tools array +``` + +--- + +## 2. Chat Flow (openrag_chat) + +RAG-enhanced conversation using the knowledge base. + +```mermaid +sequenceDiagram + participant AIApp as AI Application + participant MCP as MCP Server + participant SDK as OpenRAG SDK + participant API as OpenRAG API + participant KB as Knowledge Base + + AIApp->>MCP: JSON-RPC: tools/call
name: openrag_chat
args: {message, chat_id?, filter_id?, limit?, score_threshold?} + activate MCP + + MCP->>MCP: Validate message not empty + MCP->>SDK: client.chat.create(...) + activate SDK + + SDK->>API: POST /api/chat
X-API-Key: orag_xxx + activate API + + Note over API,KB: RAG Pipeline + + API->>KB: Semantic search for relevant chunks + activate KB + KB-->>API: Matching document chunks with scores + deactivate KB + + API->>API: Build context from chunks + API->>API: Generate LLM response with context + API-->>SDK: {response, sources[], chat_id} + deactivate API + + SDK-->>MCP: ChatResponse object + deactivate SDK + + MCP->>MCP: Format response with sources + MCP-->>AIApp: TextContent with answer + sources + chat_id + deactivate MCP + + Note over AIApp,MCP: Conversation Continuation + + AIApp->>MCP: JSON-RPC: tools/call
name: openrag_chat
args: {message, chat_id: "prev_id"} + MCP->>SDK: client.chat.create(chat_id=prev_id) + SDK->>API: POST /api/chat (with chat_id) + API->>API: Load conversation history + API->>KB: Search with conversation context + KB-->>API: Relevant chunks + API-->>SDK: Contextual response + SDK-->>MCP: ChatResponse + MCP-->>AIApp: Continued conversation response +``` + +--- + +## 3. Search Flow (openrag_search) + +Semantic search over the knowledge base. + +```mermaid +sequenceDiagram + participant AIApp as AI Application + participant MCP as MCP Server + participant SDK as OpenRAG SDK + participant API as OpenRAG API + participant KB as Knowledge Base + + AIApp->>MCP: JSON-RPC: tools/call
name: openrag_search
args: {query, limit?, score_threshold?, filter_id?, data_sources?, document_types?} + activate MCP + + MCP->>MCP: Validate query not empty + MCP->>MCP: Build SearchFilters if data_sources or document_types provided + MCP->>SDK: client.search.query(...) + activate SDK + + SDK->>API: POST /api/search
X-API-Key: orag_xxx + activate API + + API->>API: Generate query embedding + API->>KB: k-NN vector search with filters + activate KB + KB-->>API: Scored document chunks + deactivate KB + + API->>API: Apply score_threshold filter + API-->>SDK: {results[{filename, text, score, page}]} + deactivate API + + SDK-->>MCP: SearchResponse object + deactivate SDK + + alt Results found + MCP->>MCP: Format results with scores
Truncate content > 500 chars + MCP-->>AIApp: TextContent with formatted results + else No results + MCP-->>AIApp: "No results found." + end + deactivate MCP +``` + +--- + +## 4. File Ingestion Flow (openrag_ingest_file) + +Ingest local files into the knowledge base with sync or async modes. + +```mermaid +sequenceDiagram + participant AIApp as AI Application + participant MCP as MCP Server + participant SDK as OpenRAG SDK + participant API as OpenRAG API + participant KB as Knowledge Base + + Note over AIApp,KB: Synchronous Ingestion (wait=true, default) + + AIApp->>MCP: JSON-RPC: tools/call
name: openrag_ingest_file
args: {file_path, wait: true} + activate MCP + + MCP->>MCP: Validate file exists and is a file + MCP->>SDK: client.documents.ingest(file_path, wait=true) + activate SDK + + SDK->>API: POST /api/documents/upload
multipart/form-data + activate API + + API->>API: Parse document (Docling) + API->>API: Chunk document + API->>API: Generate embeddings + API->>KB: Index chunks with vectors + activate KB + KB-->>API: Indexing complete + deactivate KB + + API-->>SDK: {status: "completed", successful_files, failed_files} + deactivate API + + SDK-->>MCP: IngestResponse + deactivate SDK + + MCP-->>AIApp: "Successfully ingested 'filename'" + deactivate MCP + + Note over AIApp,KB: Asynchronous Ingestion (wait=false) + + AIApp->>MCP: JSON-RPC: tools/call
name: openrag_ingest_file
args: {file_path, wait: false} + activate MCP + + MCP->>MCP: Validate file exists + MCP->>SDK: client.documents.ingest(file_path, wait=false) + activate SDK + + SDK->>API: POST /api/documents/upload + activate API + API-->>SDK: {task_id: "abc123", filename} + deactivate API + + SDK-->>MCP: IngestResponse with task_id + deactivate SDK + + MCP-->>AIApp: "Queued for ingestion. Task ID: abc123" + deactivate MCP + + Note over API,KB: Background Processing + API--)KB: Async: Parse, chunk, embed, index +``` + +--- + +## 5. URL Ingestion Flow (openrag_ingest_url) + +Ingest content from web URLs. + +```mermaid +sequenceDiagram + participant AIApp as AI Application + participant MCP as MCP Server + participant SDK as OpenRAG SDK + participant API as OpenRAG API + participant KB as Knowledge Base + + AIApp->>MCP: JSON-RPC: tools/call
name: openrag_ingest_url
args: {url} + activate MCP + + MCP->>MCP: Validate URL starts with http:// or https:// + MCP->>SDK: client.chat.create(message with URL) + activate SDK + + SDK->>API: POST /api/chat
X-API-Key: orag_xxx + activate API + + Note over API: Agent processes URL ingestion request + + API->>API: Fetch URL content + API->>API: Parse HTML/content + API->>API: Chunk and embed + API->>KB: Index content + activate KB + KB-->>API: Indexed + deactivate KB + + API-->>SDK: {response: "URL ingested successfully"} + deactivate API + + SDK-->>MCP: ChatResponse + deactivate SDK + + MCP-->>AIApp: "URL ingestion requested. [response]" + deactivate MCP +``` + +--- + +## 6. Task Status Flow (openrag_get_task_status) + +Check the status of an async ingestion task. + +```mermaid +sequenceDiagram + participant AIApp as AI Application + participant MCP as MCP Server + participant SDK as OpenRAG SDK + participant API as OpenRAG API + + AIApp->>MCP: JSON-RPC: tools/call
name: openrag_get_task_status
args: {task_id} + activate MCP + + MCP->>MCP: Validate task_id not empty + MCP->>SDK: client.documents.get_task_status(task_id) + activate SDK + + SDK->>API: GET /api/documents/tasks/{task_id}
X-API-Key: orag_xxx + activate API + + API-->>SDK: {status, task_id, total_files, processed_files, successful_files, failed_files, files} + deactivate API + + SDK-->>MCP: TaskStatus object + deactivate SDK + + MCP->>MCP: Format status report + MCP-->>AIApp: TextContent with task status details + deactivate MCP +``` + +--- + +## 7. Wait for Task Flow (openrag_wait_for_task) + +Poll until an ingestion task completes. + +```mermaid +sequenceDiagram + participant AIApp as AI Application + participant MCP as MCP Server + participant SDK as OpenRAG SDK + participant API as OpenRAG API + + AIApp->>MCP: JSON-RPC: tools/call
name: openrag_wait_for_task
args: {task_id, timeout?} + activate MCP + + MCP->>MCP: Validate task_id not empty + MCP->>SDK: client.documents.wait_for_task(task_id, timeout) + activate SDK + + loop Poll until complete or timeout + SDK->>API: GET /api/documents/tasks/{task_id} + activate API + API-->>SDK: {status: "processing", ...} + deactivate API + SDK->>SDK: Sleep interval + end + + SDK->>API: GET /api/documents/tasks/{task_id} + activate API + API-->>SDK: {status: "completed", ...} + deactivate API + + SDK-->>MCP: TaskStatus (final) + deactivate SDK + + alt Task completed + MCP-->>AIApp: "Task Completed: [status details]" + else Timeout + MCP-->>AIApp: "Task did not complete within X seconds" + end + deactivate MCP +``` + +--- + +## 8. Delete Document Flow (openrag_delete_document) + +Remove a document from the knowledge base. + +```mermaid +sequenceDiagram + participant AIApp as AI Application + participant MCP as MCP Server + participant SDK as OpenRAG SDK + participant API as OpenRAG API + participant KB as Knowledge Base + + AIApp->>MCP: JSON-RPC: tools/call
name: openrag_delete_document
args: {filename} + activate MCP + + MCP->>MCP: Validate filename not empty + MCP->>SDK: client.documents.delete(filename) + activate SDK + + SDK->>API: DELETE /api/documents/{filename}
X-API-Key: orag_xxx + activate API + + API->>KB: Delete all chunks for filename + activate KB + KB-->>API: Deleted chunk count + deactivate KB + + API-->>SDK: {success: true, deleted_chunks: N} + deactivate API + + SDK-->>MCP: DeleteResponse + deactivate SDK + + alt Success + MCP-->>AIApp: "Successfully deleted 'filename' (N chunks removed)" + else Not found + MCP-->>AIApp: "Document not found: [message]" + end + deactivate MCP +``` + +--- + +## 9. Error Handling + +All tools implement consistent error handling. + +```mermaid +sequenceDiagram + participant AIApp as AI Application + participant MCP as MCP Server + participant SDK as OpenRAG SDK + participant API as OpenRAG API + + AIApp->>MCP: JSON-RPC: tools/call + activate MCP + MCP->>SDK: API call + activate SDK + SDK->>API: HTTPS request + activate API + + alt AuthenticationError (401) + API-->>SDK: 401 Unauthorized + SDK-->>MCP: AuthenticationError + MCP-->>AIApp: "Authentication error: [message]" + else ValidationError (400) + API-->>SDK: 400 Bad Request + SDK-->>MCP: ValidationError + MCP-->>AIApp: "Invalid request: [message]" + else RateLimitError (429) + API-->>SDK: 429 Too Many Requests + SDK-->>MCP: RateLimitError + MCP-->>AIApp: "Rate limited: [message]" + else ServerError (5xx) + API-->>SDK: 500 Internal Server Error + SDK-->>MCP: ServerError + MCP-->>AIApp: "Server error: [message]" + else NotFoundError (404) + API-->>SDK: 404 Not Found + SDK-->>MCP: NotFoundError + MCP-->>AIApp: "Not found: [message]" + else Success + API-->>SDK: 200 OK with data + SDK-->>MCP: Response object + MCP-->>AIApp: Formatted success response + end + + deactivate API + deactivate SDK + deactivate MCP +``` + +--- + +## Complete Architecture Overview + +```mermaid +sequenceDiagram + participant User as User/AI Agent + participant AIApp as AI Application + participant MCP as MCP Server + participant SDK as OpenRAG SDK + participant API as OpenRAG API + participant KB as Knowledge Base + + Note over User,KB: End-to-End Knowledge Workflow + + User->>AIApp: "Ingest this PDF" + AIApp->>MCP: openrag_ingest_file + MCP->>SDK: documents.ingest() + SDK->>API: POST /api/documents/upload + API->>KB: Index embeddings + KB-->>API: Done + API-->>SDK: Success + SDK-->>MCP: IngestResponse + MCP-->>AIApp: "Ingested successfully" + AIApp-->>User: Document added + + User->>AIApp: "What does it say about X?" + AIApp->>MCP: openrag_chat + MCP->>SDK: chat.create() + SDK->>API: POST /api/chat + API->>KB: Semantic search + KB-->>API: Relevant chunks + API->>API: LLM generates response + API-->>SDK: Response + sources + SDK-->>MCP: ChatResponse + MCP-->>AIApp: Answer with citations + AIApp-->>User: RAG-enhanced answer + + User->>AIApp: "Find all references to Y" + AIApp->>MCP: openrag_search + MCP->>SDK: search.query() + SDK->>API: POST /api/search + API->>KB: Vector search + KB-->>API: Scored results + API-->>SDK: SearchResponse + SDK-->>MCP: Results + MCP-->>AIApp: Formatted results + AIApp-->>User: Search results with scores +``` + +--- + +## Protocol Details + +| Layer | Protocol | Format | +|:------|:---------|:-------| +| AI App ↔ MCP Server | stdio | JSON-RPC 2.0 | +| MCP Server ↔ OpenRAG API | HTTPS | REST + JSON | +| Authentication | Header | `X-API-Key: orag_xxx` | + +## Tool Summary + +| Tool | Purpose | Key Parameters | +|:-----|:--------|:---------------| +| `openrag_chat` | RAG conversation | message, chat_id, filter_id, limit, score_threshold | +| `openrag_search` | Semantic search | query, limit, score_threshold, filter_id, data_sources, document_types | +| `openrag_ingest_file` | Ingest local file | file_path, wait | +| `openrag_ingest_url` | Ingest from URL | url | +| `openrag_get_task_status` | Check task status | task_id | +| `openrag_wait_for_task` | Wait for completion | task_id, timeout | +| `openrag_delete_document` | Remove document | filename |