105 lines
3.6 KiB
Python
105 lines
3.6 KiB
Python
from langchain_openai import ChatOpenAI
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from pydantic import SecretStr
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from langchain.tools import tool
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from langchain_qdrant import QdrantVectorStore
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from qdrant_client import QdrantClient
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from qdrant_client.http.models import Distance, VectorParams
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from langchain_ollama import OllamaEmbeddings
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from langchain_core.documents import Document
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain.agents import create_agent
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import os
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# ---------- LLM ----------
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b",
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base_url='https://platform.brojs.ru/jrnl-bh/api/inference/v1',
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api_key=SecretStr("jrnl_30283ab953615cbb6846ff9940a1eedce0b76d7b2f59a2394f29e74643e6a90d"),
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temperature=0.7,
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)
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# ---------- Vector Store ----------
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client = QdrantClient(":memory:")
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client.create_collection(
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collection_name="knowledge_base",
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vectors_config=VectorParams(size=1024, distance=Distance.COSINE),
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)
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embeddings = OllamaEmbeddings(model="nomic-embed-text")
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vector_store = QdrantVectorStore(
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client=client,
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collection_name="knowledge_base",
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embedding=embeddings,
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)
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# ---------- Text Splitter ----------
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splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
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# ---------- Tools ----------
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@tool
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def add_to_knowledge_base(content: str, title: str) -> str:
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"""Add a document to the knowledge base."""
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docs = splitter.split_text(content)
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documents = [Document(page_content=c, metadata={"title": title}) for c in docs]
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vector_store.add_documents(documents)
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return f"Added {len(docs)} chunks under title '{title}'."
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@tool
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def search_knowledge_base(query: str, max_results: int = 5) -> str:
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"""Search the knowledge base for relevant information."""
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results = vector_store.similarity_search_with_score(query, k=max_results)
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if not results:
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return "No relevant documents found."
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out_lines = []
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for doc, score in results:
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title = doc.metadata.get("title", "Untitled")
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out_lines.append(f"[{score:.2f}] {title}: {doc.page_content[:200]}...")
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return "\n".join(out_lines)
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# ---------- Agent ----------
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system_prompt = """
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You are an assistant with access to a knowledge base.
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Use the tools `add_to_knowledge_base` and `search_knowledge_base` as needed.
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Respond concisely. If you need more info, ask the user.
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"""
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agent = create_agent(
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model=llm,
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tools=[add_to_knowledge_base, search_knowledge_base],
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system_prompt=system_prompt,
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)
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# ---------- CLI ----------
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def main():
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print("RAG Agent CLI. Commands: /add <title> <content>, /search <query>, /quit")
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while True:
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try:
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inp = input("> ").strip()
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except EOFError:
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break
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if not inp:
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continue
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if inp.lower() in ("exit", "quit", "/quit"):
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print("Bye!")
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break
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if inp.startswith("/add "):
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parts = inp[5:].split(None, 1)
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if len(parts) != 2:
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print("Usage: /add <title> <content>")
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continue
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title, content = parts
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res = add_to_knowledge_base(content=content, title=title)
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print(res)
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elif inp.startswith("/search "):
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query = inp[8:].strip()
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if not query:
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print("Usage: /search <query>")
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continue
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res = search_knowledge_base(query=query, max_results=5)
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print(res)
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else:
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# Regular chat with agent
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result = agent.invoke({"messages": [{"role": "human", "content": inp}]})
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ai_msg = result["messages"][-1]
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print(ai_msg.content)
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if __name__ == "__main__":
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main() |