From 7d0c6ff35cc900c90c7158061792b4eb660a6aeb 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?= <gurevich1104@mail.ru>
Date: Sat, 27 Jun 2026 13:56:39 +0000
Subject: [PATCH] add: main.py

---
 main.py | 135 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 135 insertions(+)
 create mode 100644 main.py

diff --git a/main.py b/main.py
new file mode 100644
index 0000000..cce0e01
--- /dev/null
+++ b/main.py
@@ -0,0 +1,135 @@
+import os
+import asyncio
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_chroma import Chroma
+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_core.messages import HumanMessage
+from tavily import TavilySearchResults
+
+# ---------------------
+# Configuration
+# ---------------------
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
+
+# ---------------------
+# Vector store utilities
+# ---------------------
+
+def create_vectorstore(persist_directory: str = "./chroma_db") -> Chroma:
+    """Create or load a Chroma vector store with OpenAI embeddings."""
+    embeddings = OpenAIEmbeddings(
+        model="text-embedding-3-small",
+        base_url="https://openrouter.ai/api/v1",
+        api_key=OPENAI_API_KEY,
+    )
+    return Chroma(
+        collection_name="knowledge",
+        embedding_function=embeddings,
+        persist_directory=persist_directory,
+    )
+
+
+def load_documents(directory: str, vectorstore: Chroma) -> None:
+    """Load .txt and .md files from *directory* into *vectorstore* using chunking."""
+    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    docs = []
+    for root, _, files in os.walk(directory):
+        for fname in files:
+            if fname.lower().endswith(('.txt', '.md')):
+                path = os.path.join(root, fname)
+                with open(path, "r", encoding="utf-8") as f:
+                    text = f.read()
+                # Create Document objects
+                docs.extend(
+                    [Document(page_content=chunk, metadata={"source": path}) for chunk in splitter.split_text(text)]
+                )
+    if docs:
+        vectorstore.add_documents(docs)
+        vectorstore.persist()
+
+# ---------------------
+# Tools
+# ---------------------
+
+vectorstore = create_vectorstore()
+
+# Ensure we have some data loaded – load from ./documents if collection empty
+if len(vectorstore.get_all_documents()) == 0:
+    load_documents("./documents", vectorstore)
+
+@tool
+def search_local_kb(query: str, top_k: int = 3) -> str:
+    """Search the local knowledge base for relevant passages."""
+    docs = vectorstore.similarity_search(query, k=top_k)
+    if not docs:
+        return "[Local KB] No relevant information found."
+    result = "\n\n".join([f"{d.metadata.get('source', 'unknown')}\n{d.page_content}" for d in docs])
+    return f"[Local KB]\n{result}"
+
+@tool
+def web_search(query: str) -> str:
+    """Perform a web search using Tavily and return top results."""
+    results = TavilySearchResults(query=query, max_results=3, api_key=TAVILY_API_KEY)
+    if not results.results:
+        return "[Web Search] No results found."
+    snippets = []
+    for r in results.results:
+        snippets.append(f"{r.get('title', 'No title')}\n{r.get('content', 'No content')}\nURL: {r.get('url', '')}")
+    return f"[Web Search]\n\n".join(snippets)
+
+# ---------------------
+# Agent setup
+# ---------------------
+
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=OPENAI_API_KEY,
+    temperature=0.0,
+)
+
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+system_prompt = (
+    "You are a helpful RAG agent. For questions about local documents, use the tool `search_local_kb`. "
+    "For current news or facts that may not be in your local knowledge base, use `web_search`. "
+    "Return the answer prefixed with either `[Local KB]` or `[Web Search]` to indicate the source."
+)
+
+agent = create_deep_agent(
+    model=llm,
+    tools=[search_local_kb, web_search],
+    backend=backend,
+    system_prompt=system_prompt,
+)
+
+# ---------------------
+# CLI loop
+# ---------------------
+
+async def main():
+    print("RAG Agent ready. Type your question (or 'exit' to quit).")
+    while True:
+        user_input = input("\nQuery: ")
+        if user_input.lower() in {"exit", "quit", "q"}:
+            print("Goodbye!")
+            break
+        # Invoke agent
+        result = await agent.ainvoke(
+            {"messages": [HumanMessage(content=user_input)]},
+            {"configurable": {"thread_id": "session-1"}},
+        )
+        # The agent returns a list of messages; get the last one
+        reply = result["messages"][-1].content
+        print(f"\n{reply}")
+
+if __name__ == "__main__":
+    asyncio.run(main())