add: main.py

main.py

@@ -0,0 +1,147 @@
+"""
+# main.py – RAG‑agent with Qdrant, OpenRouter, and deepagents
+# ----------------------------------------------------------
+# 1.  Imports and configuration
+# 2.  Qdrant vector store wrapper (embedding, add, search)
+# 3.  Text splitter (RecursiveCharacterTextSplitter)
+# 4.  LangChain tools: search_knowledge_base, add_to_knowledge_base
+# 5.  DeepAgent creation (create_deep_agent)
+# 6.  CLI client for /add, /search, /quit
+# ----------------------------------------------------------
+"""
+import os
+import asyncio
+import json
+from pathlib import Path
+from typing import List
+
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_core.documents import Document
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.tools import tool
+from deepagents import create_deep_agent
+from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
+from langchain_qdrant import QdrantVectorStore
+
+# ------------------------------------------------------------------
+# 1. Configuration
+# ------------------------------------------------------------------
+# Load environment variables (e.g. OPENAI_API_KEY)
+from dotenv import load_dotenv
+load_dotenv()
+
+# LLM – OpenRouter (free tier)
+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 – OpenAI via OpenRouter
+embeddings = OpenAIEmbeddings(
+    model="text-embedding-3-small",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+)
+
+# Qdrant client – assumes Qdrant is running locally on default port
+qdrant_url = os.getenv("QDRANT_URL", "http://localhost:6333")
+collection_name = "knowledge_base"
+vector_store = QdrantVectorStore(
+    url=qdrant_url,
+    collection_name=collection_name,
+    embeddings=embeddings,
+)
+
+# Text splitter – 1000 chars max, 200 overlap
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+
+# ------------------------------------------------------------------
+# 2. Tools
+# ------------------------------------------------------------------
+@tool
+def search_knowledge_base(query: str, max_results: int = 3) -> str:
+    """Semantic search in the Qdrant knowledge base."""
+    docs: List[Document] = vector_store.similarity_search(query, k=max_results)
+    if not docs:
+        return "No relevant documents found."
+    return "\n\n---\n\n".join([f"{doc.metadata.get('title', 'Untitled')}:\n{doc.page_content}" for doc in docs])
+
+@tool
+def add_to_knowledge_base(content: str, title: str = "Untitled") -> str:
+    """Add a new document to the knowledge base.
+    The content is split into chunks before being stored.
+    """
+    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"Added {len(docs)} chunks for document '{title}'."
+
+# ------------------------------------------------------------------
+# 3. DeepAgent setup
+# ------------------------------------------------------------------
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+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 information.",
+)
+
+# ------------------------------------------------------------------
+# 4. CLI client
+# ------------------------------------------------------------------
+async def run_cli():
+    print("Welcome to the RAG Agent CLI. Commands: /add <title> <file>, /search <query>, /quit")
+    thread_id = "cli-session"
+    while True:
+        try:
+            user_input = input("> ")
+        except EOFError:
+            break
+        if not user_input:
+            continue
+        if user_input.startswith("/quit"):
+            print("Goodbye!")
+            break
+        if user_input.startswith("/add"):
+            parts = user_input.split(maxsplit=2)
+            if len(parts) < 3:
+                print("Usage: /add <title> <file_path>")
+                continue
+            title, file_path = parts[1], parts[2]
+            try:
+                content = Path(file_path).read_text(encoding="utf-8")
+            except Exception as e:
+                print(f"Error reading file: {e}")
+                continue
+            # Invoke tool directly
+            result = add_to_knowledge_base(content, title)
+            print(result)
+            continue
+        if user_input.startswith("/search"):
+            query = user_input[len("/search"):].strip()
+            if not query:
+                print("Usage: /search <query>")
+                continue
+            # Use agent to perform search via tool
+            response = await agent.ainvoke(
+                {"messages": [{"role": "user", "content": f"search {query}"}]},
+                {"configurable": {"thread_id": thread_id}},
+            )
+            print(response["messages"][-1].content)
+            continue
+        # Default: treat as normal user message
+        response = await agent.ainvoke(
+            {"messages": [{"role": "user", "content": user_input}]},
+            {"configurable": {"thread_id": thread_id}},
+        )
+        print(response["messages"][-1].content)
+
+if __name__ == "__main__":
+    asyncio.run(run_cli())