From 764e1ff90ac59f6cce6b26aba30ff27d08064cff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=98=D0=BB=D1=8C=D1=8F=205f1b81b8-4f5d-11e8-9c2d-fa7ae01?= =?UTF-8?q?bbebc?= Date: Sat, 27 Jun 2026 13:55:36 +0000 Subject: [PATCH] add: main.py --- main.py | 138 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 138 insertions(+) create mode 100644 main.py diff --git a/main.py b/main.py new file mode 100644 index 0000000..613ec29 --- /dev/null +++ b/main.py @@ -0,0 +1,138 @@ +import os +import asyncio +from pathlib import Path + +from dotenv import load_dotenv +from langchain_openai import ChatOpenAI, OpenAIEmbeddings +from langchain_core.documents import Document +from langchain_text_splitters import RecursiveCharacterTextSplitter +from langchain_qdrant import QdrantVectorStore +from langchain.tools import tool +from deepagents import create_deep_agent +from deepagents.backends import CompositeBackend, LocalShellBackend, FilesystemBackend + +# Load environment variables (OPENAI_API_KEY) +load_dotenv() + +# --------------------------------------------------------------------------- +# LLM and embeddings configuration (OpenRouter) +# --------------------------------------------------------------------------- +llm = ChatOpenAI( + model="openai/gpt-oss-20b:free", + base_url="https://openrouter.ai/api/v1", + api_key=os.getenv("OPENAI_API_KEY"), + temperature=0.0, +) + +embeddings = OpenAIEmbeddings( + model="text-embedding-3-small", + base_url="https://openrouter.ai/api/v1", + api_key=os.getenv("OPENAI_API_KEY"), +) + +# --------------------------------------------------------------------------- +# Qdrant vector store setup +# --------------------------------------------------------------------------- +from qdrant_client import QdrantClient + +qdrant_client = QdrantClient(url="http://localhost:6333") # Qdrant must be running locally +vector_store = QdrantVectorStore( + embedding_function=embeddings, + client=qdrant_client, + collection_name="knowledge", +) + +# --------------------------------------------------------------------------- +# Text splitter for chunking documents +# --------------------------------------------------------------------------- +text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) + +# --------------------------------------------------------------------------- +# Tools for the agent +# --------------------------------------------------------------------------- +@tool +async def search_knowledge_base(query: str, max_results: int = 5) -> str: + """Perform a semantic search in the knowledge base.""" + docs = vector_store.similarity_search(query, k=max_results) + if not docs: + return "No relevant documents found." + return "\n\n".join([f"Title: {doc.metadata.get('title', 'N/A')}\n{doc.page_content}" for doc in docs]) + +@tool +async def add_to_knowledge_base(content: str, title: str = "Unnamed Document") -> str: + """Add a document to the knowledge base after chunking.""" + chunks = text_splitter.split_text(content) + docs = [Document(page_content=chunk, metadata={"title": title}) for chunk in chunks] + vector_store.add_documents(docs) + return f"Document '{title}' added with {len(chunks)} chunks." + +# --------------------------------------------------------------------------- +# Backend configuration for DeepAgents +# --------------------------------------------------------------------------- +backend = CompositeBackend([ + LocalShellBackend(workspace_dir="./workspace"), + FilesystemBackend(), +]) + +# --------------------------------------------------------------------------- +# DeepAgent definition +# --------------------------------------------------------------------------- +agent = create_deep_agent( + model=llm, + tools=[search_knowledge_base, add_to_knowledge_base], + backend=backend, + system_prompt="You are a helpful assistant with access to a knowledge base. Use the provided tools to search and add documents. Respond concisely.", +) + +# --------------------------------------------------------------------------- +# Helper function to load all .txt files from a directory into the vector store +# --------------------------------------------------------------------------- +async def load_documents_from_directory(directory: str): + dir_path = Path(directory) + for txt_file in dir_path.rglob("*.txt"): + content = txt_file.read_text(encoding="utf-8") + title = txt_file.stem + await add_to_knowledge_base(content, title) + print(f"Loaded {len(list(dir_path.rglob('*.txt')))} documents from {directory}.") + +# --------------------------------------------------------------------------- +# Interactive CLI client +# --------------------------------------------------------------------------- +async def interactive_loop(): + print("Welcome to the RAG Agent CLI. Commands: /add title content | /search query | /quit") + while True: + user_input = input("> ") + if user_input.lower() == "/quit": + print("Goodbye!") + break + if user_input.startswith("/add "): + try: + _, rest = user_input.split("/add ", 1) + title, content = rest.split(" ", 1) + except ValueError: + print("Usage: /add title content") + continue + human_msg = f"Please add a document titled '{title}' with content: {content}" + elif user_input.startswith("/search "): + query = user_input[len("/search "):] + human_msg = f"Please search for: {query}" + else: + print("Unknown command. Use /add, /search, or /quit.") + continue + + result = await agent.ainvoke( + {"messages": [HumanMessage(content=human_msg)]}, + {"configurable": {"thread_id": "session-1"}}, + ) + print(result["messages"][-1].content) + +# --------------------------------------------------------------------------- +# Main entry point +# --------------------------------------------------------------------------- +async def main(): + # Optionally load documents from a directory on startup + # await load_documents_from_directory("./data") + await interactive_loop() + +if __name__ == "__main__": + asyncio.run(main())