from pathlib import Path from typing import Any from fastmcp import FastMCP from pydantic import BaseModel from haiku.rag.client import HaikuRAG from haiku.rag.config import AppConfig, Config from haiku.rag.graph.research.models import ResearchReport from haiku.rag.store.models import SearchResult from haiku.rag.utils import format_citations class DocumentResult(BaseModel): id: str | None content: str uri: str | None = None title: str | None = None metadata: dict[str, Any] = {} created_at: str updated_at: str def create_mcp_server(db_path: Path, config: AppConfig = Config) -> FastMCP: """Create an MCP server with the specified database path.""" mcp = FastMCP("haiku-rag") @mcp.tool() async def add_document_from_file( file_path: str, metadata: dict[str, Any] | None = None, title: str | None = None, ) -> str | None: """Add a document to the RAG system from a file path.""" try: async with HaikuRAG(db_path, config=config) as rag: result = await rag.create_document_from_source( Path(file_path), title=title, metadata=metadata or {} ) # Handle both single document and list of documents (directories) if isinstance(result, list): return result[0].id if result else None return result.id except Exception: return None @mcp.tool() async def add_document_from_url( url: str, metadata: dict[str, Any] | None = None, title: str | None = None ) -> str | None: """Add a document to the RAG system from a URL.""" try: async with HaikuRAG(db_path, config=config) as rag: result = await rag.create_document_from_source( url, title=title, metadata=metadata or {} ) # Handle both single document and list of documents if isinstance(result, list): return result[0].id if result else None return result.id except Exception: return None @mcp.tool() async def add_document_from_text( content: str, uri: str | None = None, metadata: dict[str, Any] | None = None, title: str | None = None, ) -> str | None: """Add a document to the RAG system from text content.""" try: async with HaikuRAG(db_path, config=config) as rag: document = await rag.create_document( content, uri, title=title, metadata=metadata or {} ) return document.id except Exception: return None @mcp.tool() async def search_documents(query: str, limit: int = 5) -> list[SearchResult]: """Search the RAG system for documents using hybrid search (vector similarity + full-text search).""" try: async with HaikuRAG(db_path, config=config) as rag: return await rag.search(query, limit) except Exception: return [] @mcp.tool() async def get_document(document_id: str) -> DocumentResult | None: """Get a document by its ID.""" try: async with HaikuRAG(db_path, config=config) as rag: document = await rag.get_document_by_id(document_id) if document is None: return None return DocumentResult( id=document.id, content=document.content, uri=document.uri, title=document.title, metadata=document.metadata, created_at=str(document.created_at), updated_at=str(document.updated_at), ) except Exception: return None @mcp.tool() async def list_documents( limit: int | None = None, offset: int | None = None, filter: str | None = None, ) -> list[DocumentResult]: """List all documents with optional pagination and filtering. Args: limit: Maximum number of documents to return. offset: Number of documents to skip. filter: Optional SQL WHERE clause to filter documents. Returns: List of DocumentResult instances matching the criteria. """ try: async with HaikuRAG(db_path, config=config) as rag: documents = await rag.list_documents(limit, offset, filter) return [ DocumentResult( id=doc.id, content=doc.content, uri=doc.uri, title=doc.title, metadata=doc.metadata, created_at=str(doc.created_at), updated_at=str(doc.updated_at), ) for doc in documents ] except Exception: return [] @mcp.tool() async def delete_document(document_id: str) -> bool: """Delete a document by its ID.""" try: async with HaikuRAG(db_path, config=config) as rag: return await rag.delete_document(document_id) except Exception: return False @mcp.tool() async def ask_question( question: str, cite: bool = False, deep: bool = False, ) -> str: """Ask a question using the QA agent. Args: question: The question to ask. cite: Whether to include citations in the response. deep: Use deep multi-agent QA for complex questions that require decomposition. Returns: The answer as a string. """ try: async with HaikuRAG(db_path, config=config) as rag: if deep: from haiku.rag.graph.deep_qa.dependencies import DeepQAContext from haiku.rag.graph.deep_qa.graph import build_deep_qa_graph from haiku.rag.graph.deep_qa.state import DeepQADeps, DeepQAState graph = build_deep_qa_graph(config=config) context = DeepQAContext(original_question=question) state = DeepQAState.from_config(context=context, config=config) deps = DeepQADeps(client=rag) result = await graph.run(state=state, deps=deps) answer = result.answer citations = result.citations else: answer, citations = await rag.ask(question) if cite and citations: answer += "\n\n" + format_citations(citations) return answer except Exception as e: return f"Error answering question: {e!s}" @mcp.tool() async def research_question( question: str, ) -> ResearchReport | None: """Run multi-agent research to investigate a complex question. The research process uses multiple agents to plan, search, evaluate, and synthesize information iteratively until confidence threshold is met or max iterations reached. Args: question: The research question to investigate. Returns: A research report with findings, or None if an error occurred. """ try: from haiku.rag.graph.research.dependencies import ResearchContext from haiku.rag.graph.research.graph import build_research_graph from haiku.rag.graph.research.state import ResearchDeps, ResearchState async with HaikuRAG(db_path, config=config) as rag: graph = build_research_graph(config=config) context = ResearchContext(original_question=question) state = ResearchState.from_config(context=context, config=config) deps = ResearchDeps(client=rag) result = await graph.run(state=state, deps=deps) return result except Exception: return None return mcp