Add main.py
This commit is contained in:
@@ -1,163 +1,73 @@
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import os
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import asyncio
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import json
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import httpx
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from pathlib import Path
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from typing import List
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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from langchain_ollama import ChatOllama
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from langchain_chroma import Chroma
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from langchain_core.documents import Document
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from langchain_core.messages import HumanMessage
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from langchain.tools import tool
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from deepagents import create_deep_agent
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from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
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from langchain.embeddings import OllamaEmbeddings
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from langchain.schema import Document
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from langchain.prompts import ChatPromptTemplate
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from langchain.chains import LLMChain
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# ---------------------------------------------------------------------------
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# Configuration
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# ---------------------------------------------------------------------------
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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if not OPENAI_API_KEY:
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raise RuntimeError("OPENAI_API_KEY not set in environment")
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# 1. Load FAQ into Chroma
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# LLM via OpenRouter
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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base_url="https://openrouter.ai/api/v1",
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api_key=OPENAI_API_KEY,
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temperature=0.0,
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)
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def load_faq_to_chroma(md_path: str, persist_dir: str = "./chroma_faq"):
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from langchain.document_loaders import TextLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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# Embeddings via OpenRouter
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embeddings = OpenAIEmbeddings(
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model="text-embedding-3-small",
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base_url="https://openrouter.ai/api/v1",
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api_key=OPENAI_API_KEY,
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)
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loader = TextLoader(md_path)
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docs = loader.load()
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splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
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texts = splitter.split_documents(docs)
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embeddings = OllamaEmbeddings(model="nomic-embed-text")
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chroma = Chroma.from_documents(texts, embeddings, persist_directory=persist_dir)
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chroma.persist()
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return chroma
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# ---------------------------------------------------------------------------
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# Chroma persistence
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# ---------------------------------------------------------------------------
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CHROMA_DIR = Path("./chroma_faq")
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CHROMA_DIR.mkdir(parents=True, exist_ok=True)
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vector_store = Chroma(
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collection_name="faq_collection",
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embedding_function=embeddings,
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persist_directory=str(CHROMA_DIR),
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)
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# 2. Search function
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# ---------------------------------------------------------------------------
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# Utility: load markdown files into Chroma
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# ---------------------------------------------------------------------------
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@tool
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def load_faq_to_chroma() -> str:
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"""Load all .md files from data/ into the Chroma vector store.
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This tool is idempotent – it will overwrite existing collection.
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"""
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data_dir = Path("data")
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if not data_dir.exists():
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return "Data directory not found."
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docs: List[Document] = []
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for md_file in data_dir.glob("*.md"):
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text = md_file.read_text(encoding="utf-8")
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docs.append(Document(page_content=text, metadata={"source": md_file.name}))
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if docs:
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vector_store.delete_collection()
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vector_store.add_documents(docs)
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vector_store.persist()
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return f"Loaded {len(docs)} documents into Chroma."
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return "No markdown files found."
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def search_course_docs(query: str, k: int = 3):
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chroma = Chroma(persist_directory="./chroma_faq", embedding_function=OllamaEmbeddings(model="nomic-embed-text"))
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results = chroma.similarity_search(query, k=k)
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return [doc.page_content for doc in results]
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# ---------------------------------------------------------------------------
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# Tool: search knowledge base
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# ---------------------------------------------------------------------------
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@tool
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def search_course_docs(query: str, k: int = 3) -> str:
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"""Search the FAQ collection for relevant passages.
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Returns a string with the top k passages and a source tag.
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"""
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docs = vector_store.similarity_search(query, k=k)
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if not docs:
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return "No relevant information found in the FAQ."
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results = "\n\n".join(f"{i+1}. {doc.page_content}" for i, doc in enumerate(docs))
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return f"source: chroma\n{results}"
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# 3. MCP-style tool
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# ---------------------------------------------------------------------------
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# MCP-style tool: fetch course metadata
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# ---------------------------------------------------------------------------
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# For simplicity we use a static JSON file in the repo. In production this
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# would be an HTTP call to an MCP server.
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METADATA_FILE = Path("course_meta.json")
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def fetch_course_meta(query: str):
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# For demo, use static JSON file
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meta_path = Path("meta.json")
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if not meta_path.exists():
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return {"error": "Meta not found"}
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meta = json.loads(meta_path.read_text())
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# simple search by key
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return meta.get(query, {})
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@tool
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def fetch_course_meta(query: str) -> str:
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"""Return metadata that matches the query.
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The function performs a simple keyword search in the static JSON.
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"""
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if not METADATA_FILE.exists():
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return "Metadata file not found."
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data = json.loads(METADATA_FILE.read_text(encoding="utf-8"))
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matches = [item for item in data if query.lower() in item.get("title", "").lower()]
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if not matches:
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return "No metadata matches the query."
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return f"source: mcp_meta\n" + json.dumps(matches, indent=2)
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# 4. Agent logic
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# ---------------------------------------------------------------------------
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# Backend setup
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# ---------------------------------------------------------------------------
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backend = CompositeBackend([
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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])
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# ---------------------------------------------------------------------------
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# Agent definition
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# ---------------------------------------------------------------------------
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agent = create_deep_agent(
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model=llm,
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tools=[load_faq_to_chroma, search_course_docs, fetch_course_meta],
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backend=backend,
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system_prompt=(
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"You are a helpful FAQ bot for a course. "
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"Use the search_course_docs tool for questions about lecture materials. "
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"Use fetch_course_meta for questions about schedule or metadata. "
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"Do not call both tools unless necessary. "
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"Always prefix your answer with the source tag (chroma or mcp_meta)."
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),
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)
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# ---------------------------------------------------------------------------
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# CLI helpers
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# ---------------------------------------------------------------------------
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PRESET_QUESTIONS = [
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"What is the deadline for the final project?", # should hit metadata
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"Explain the concept of polymorphism in OOP.", # should hit FAQ
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"How many lectures are there in the first module?", # metadata
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]
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async def run_agent(question: str, thread_id: str = "session-1"):
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=question)]},
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{"configurable": {"thread_id": thread_id}},
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)
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return result["messages"][-1].content
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async def main():
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# Ensure FAQ is loaded once
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await agent.ainvoke(
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{"messages": [HumanMessage(content="load_faq_to_chroma()")]},
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{"configurable": {"thread_id": "init"}},
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)
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print("\n--- Preset questions ---")
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for q in PRESET_QUESTIONS:
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ans = await run_agent(q)
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print(f"Q: {q}\nA: {ans}\n")
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print("\n--- Interactive mode (type 'exit' to quit) ---")
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while True:
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user_input = input("> ")
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if user_input.lower() in {"exit", "quit"}:
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break
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ans = await run_agent(user_input)
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print(ans)
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def answer_question(question: str):
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# Simple heuristic: if question contains "schedule" or "метаданные" use meta
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if any(word in question.lower() for word in ["schedule", "расписание", "метаданные"]):
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source = "mcp_meta"
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answer = fetch_course_meta(question)
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else:
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source = "chroma"
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answer = search_course_docs(question, k=1)[0]
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return {"answer": answer, "source": source}
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if __name__ == "__main__":
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asyncio.run(main())
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# CLI with preset questions
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preset = [
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"Что такое ChromaDB?",
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"Как подключить Ollama embeddings?",
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"Когда будет расписание следующего занятия?"
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]
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for q in preset:
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res = answer_question(q)
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print(f"Q: {q}\nA: {res['answer']}\nSource: {res['source']}\n")
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# interactive
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while True:
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q = input("Ask a question (or 'exit'): ")
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if q.lower() == "exit":
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break
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res = answer_question(q)
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print(f"A: {res['answer']} (source: {res['source']})")
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