Update src/vector_store.py
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"""Vector store implementation using Qdrant and Ollama embeddings.
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"""Vector store and knowledge base implementation using Qdrant and Ollama embeddings.
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This module provides a KnowledgeBase class that wraps a QdrantVectorStore and exposes
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methods for adding documents and searching the knowledge base.
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This module defines a `KnowledgeBase` class that manages a Qdrant collection, provides methods to add documents (with chunking) and perform semantic search.
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"""
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from __future__ import annotations
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import os
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from pathlib import Path
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from typing import List, Dict, Any
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from langchain_ollama import OllamaEmbeddings
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from langchain_qdrant import QdrantVectorStore
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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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 qdrant_client import QdrantClient
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from qdrant_client.http.models import Distance, VectorParams
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# Default configuration constants
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DEFAULT_COLLECTION_NAME = "knowledge_base"
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DEFAULT_VECTOR_SIZE = 3072 # size of nomic-embed-text embeddings
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DEFAULT_DISTANCE = Distance.COSINE
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DEFAULT_QDRANT_PATH = Path("./qdrant_data")
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class KnowledgeBase:
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"""A simple wrapper around QdrantVectorStore.
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"""A thin wrapper around QdrantVectorStore.
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The class ensures that the collection is created only once and provides
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convenient methods for adding documents and performing semantic search.
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"""
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def __init__(
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self,
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collection_name: str = DEFAULT_COLLECTION_NAME,
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host: str | None = None,
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port: int | None = None,
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path: str | None = None,
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api_key: str | None = None,
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) -> None:
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"""Create or connect to a Qdrant collection.
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Parameters
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----------
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collection_name: str
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Name of the Qdrant collection to use.
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host: str, optional
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Qdrant host address. Defaults to ``localhost``.
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port: int, optional
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Qdrant port. Defaults to ``6333``.
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Name of the Qdrant collection.
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host, port: str/int
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Optional host and port for a remote Qdrant instance.
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path: str
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Path for an on‑disk Qdrant instance (used in local mode).
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api_key: str
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API key for Qdrant Cloud.
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"""
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def __init__(self, collection_name: str = "knowledge_base", host: str = "localhost", port: int = 6333):
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# Determine client connection.
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if host and port:
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self.client = QdrantClient(url=f"{host}:{port}")
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elif path:
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self.client = QdrantClient(path=path)
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else:
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# Default to a persistent on‑disk client.
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self.client = QdrantClient(path=str(DEFAULT_QDRANT_PATH))
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self.collection_name = collection_name
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self.client = QdrantClient(host=host, port=port)
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# Ensure the collection exists
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self.client.recreate_collection(
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collection_name=self.collection_name,
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vectors_config={"size": 1024, "distance": "Cosine"},
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# Create collection if it does not exist.
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if collection_name not in self.client.get_collections().collections:
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self.client.create_collection(
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collection_name=collection_name,
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vectors_config=VectorParams(size=DEFAULT_VECTOR_SIZE, distance=DEFAULT_DISTANCE),
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)
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# Embedding model from Ollama.
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self.embeddings = OllamaEmbeddings(model="nomic-embed-text")
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self.vector_store = QdrantVectorStore(
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client=self.client,
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collection_name=self.collection_name,
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embeddings=self.embeddings,
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)
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self.splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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def add_document(self, content: str, title: str) -> None:
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# Vector store wrapper.
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self.store = QdrantVectorStore(
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client=self.client,
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collection_name=collection_name,
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embedding=self.embeddings,
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)
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# Text splitter for chunking.
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self.splitter = RecursiveCharacterTextSplitter(
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chunk_size=500, chunk_overlap=50, length_function=len
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)
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# ---------------------------------------------------------------------
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# Public API
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# ---------------------------------------------------------------------
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def add_document(self, title: str, content: str) -> None:
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"""Add a document to the knowledge base.
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The document is split into chunks, embedded, and stored in Qdrant.
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The content is split into chunks, embedded, and stored.
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"""
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chunks = self.splitter.split_text(content)
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documents: List[Document] = []
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for idx, chunk in enumerate(chunks):
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meta = {"title": title, "chunk_index": idx}
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documents.append(Document(page_content=chunk, metadata=meta))
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self.vector_store.add_documents(documents)
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# Split into Document objects with metadata.
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docs = self.splitter.create_documents([content])
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for i, doc in enumerate(docs):
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# Attach metadata: title and chunk index.
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doc.metadata.update({"title": title, "chunk_index": i})
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# Add to store.
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self.store.add_documents(docs)
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def search(self, query: str, max_results: int = 5) -> List[Dict[str, Any]]:
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"""Search the knowledge base for the most relevant chunks.
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def search(self, query: str, limit: int = 5) -> List[Dict[str, Any]]:
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"""Perform a semantic search and return results.
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Returns a list of dictionaries containing the chunk content, title, and score.
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Returns a list of dictionaries containing the chunk content and metadata.
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"""
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results = self.vector_store.similarity_search_with_score(query, k=max_results)
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results = self.store.similarity_search(query, k=limit)
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output = []
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for doc, score in results:
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output.append({
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for doc in results:
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output.append(
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{
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"content": doc.page_content,
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"title": doc.metadata.get("title"),
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"chunk_index": doc.metadata.get("chunk_index"),
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"score": score,
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})
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}
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)
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return output
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def load_from_directory(self, directory: str) -> None:
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"""Load all .txt files from a directory into the knowledge base.
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def get_all_documents(self) -> List[Document]:
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"""Return all documents stored in the collection."""
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return self.store.get_all_documents()
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Parameters
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----------
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directory: str
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Path to the directory containing text files.
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"""
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path = Path(directory)
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for file_path in path.rglob("*.txt"):
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title = file_path.stem
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content = file_path.read_text(encoding="utf-8")
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self.add_document(content, title)
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# Example usage (uncomment for quick test)
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# if __name__ == "__main__":
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# kb = KnowledgeBase()
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# kb.load_from_directory("data")
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# print(kb.search("What is Python?", 3))
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# End of src/vector_store.py
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