90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
from pathlib import Path
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# LLM and embeddings via Ollama
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from langchain_ollama import ChatOllama, OllamaEmbeddings
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from langchain.tools import tool
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from langchain.agents import create_agent
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from langchain_core.documents import Document
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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_core.messages import HumanMessage
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# ---------- Qdrant setup ----------
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client = QdrantClient(":memory:")
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client.create_collection(
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collection_name="knowledge",
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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",
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embedding=embeddings,
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)
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# ---------- Tools ----------
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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 documents."""
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docs_with_score = vector_store.similarity_search_with_score(query, k=max_results)
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if not docs_with_score:
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return "No results found."
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return "\n".join(
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f"{i+1}. {doc.page_content[:200]}..."
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for i, (doc, _) in enumerate(docs_with_score)
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)
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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 new document to the knowledge base."""
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doc = Document(page_content=content, metadata={"title": title})
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vector_store.add_documents([doc])
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return f"Document '{title}' added."
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# ---------- Agent ----------
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llm = ChatOllama(model="llama3")
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agent = create_agent(
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model=llm,
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tools=[search_knowledge_base, add_to_knowledge_base],
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system_prompt="You are a helpful assistant that can search and store knowledge.",
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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 ("quit", "/quit"):
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print("Bye!")
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break
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# Add document
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if inp.startswith("/add "):
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_, rest = inp.split(maxsplit=1)
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try:
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title, content = rest.split("|", 1)
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except ValueError:
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print("Usage: /add <title> | <content>")
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continue
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res = agent.invoke({"messages": [HumanMessage(content=f"Add document {title}")]})
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print(res.messages[-1].content)
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# Search documents
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elif inp.startswith("/search "):
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query = inp[len("/search "):]
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res = agent.invoke({"messages": [HumanMessage(content=f"Search for {query}")]})
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print(res.messages[-1].content)
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# General chat
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else:
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res = agent.invoke({"messages": [HumanMessage(content=inp)]})
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print(res.messages[-1].content)
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if __name__ == "__main__":
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main() |