from langchain.tools import tool
from qdrant_client import QdrantClient
from qdrant_client.http import models
from langchain_ollama import OllamaEmbeddings
from langchain_text_splitter import RecursiveCharacterTextSplitter

# Initialize global store
client = QdrantClient(url="http://localhost:6333")
collection_name = "rag_collection"
# Ensure collection exists
if collection_name not in client.get_collections().collections:
    client.recreate_collection(
        collection_name=collection_name,
        vectors_config=models.VectorParams(size=384, distance=models.Distance.COSINE),
    )

embeddings = OllamaEmbeddings(model="nomic-embed-text")
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

@tool("search_knowledge_base")
def search_knowledge_base(query: str, max_results: int = 5):
    """Semantic search in the knowledge base."""
    query_vec = embeddings.embed_query(query)
    results = client.search(
        collection_name=collection_name,
        query_vector=query_vec,
        limit=max_results,
        with_payload=True,
    )
    return [r.payload for r in results]

@tool("add_to_knowledge_base")
def add_to_knowledge_base(content: str, title: str):
    """Add a document to the knowledge base."""
    chunks = splitter.split_text(content)
    vectors = embeddings.embed_documents(chunks)
    points = []
    for i, (chunk, vec) in enumerate(zip(chunks, vectors)):
        points.append(models.PointStruct(id=i, vector=vec, payload={"title": title, "chunk": chunk}))
    client.upsert(collection_name=collection_name, points=models.Batch(points=points))
    return f"Added {len(chunks)} chunks to the knowledge base."