Claude RAG MCP Pipeline

import chromadb from chromadb.config import Settings import uuid class VectorStore: def __init__(self, persist_directory="./chroma_db"): self.client = chromadb.PersistentClient(path=persist_directory) self.collection_name = "claude_document_collection" # Create or get collection try: self.collection = self.client.get_collection(self.collection_name) print(f"Loaded existing collection: {self.collection_name}") except: self.collection = self.client.create_collection(self.collection_name) print(f"Created new collection: {self.collection_name}") def add_documents(self, chunks, embeddings): """Add document chunks with embeddings to vector store""" ids = [chunk['chunk_id'] for chunk in chunks] documents = [chunk['text'] for chunk in chunks] metadatas = [{'source': chunk['source'], 'file_path': chunk['file_path']} for chunk in chunks] self.collection.add( embeddings=embeddings, documents=documents, metadatas=metadatas, ids=ids ) print(f"Added {len(chunks)} chunks to vector store") def query(self, query_text, embedding_service, n_results=5): """Search for similar documents""" query_embedding = embedding_service.get_embedding(query_text) results = self.collection.query( query_embeddings=[query_embedding], n_results=n_results ) return { 'documents': results['documents'][0], 'metadatas': results['metadatas'][0], 'distances': results['distances'][0] } def get_stats(self): """Get collection statistics""" return { 'total_documents': self.collection.count(), 'collection_name': self.collection_name } def clear_collection(self): """Clear all documents from collection (useful for testing)""" self.client.delete_collection(self.collection_name) self.collection = self.client.create_collection(self.collection_name) print("Cleared collection")