Delete obsolete vector_store.py

vector_store.py

@@ -1,72 +0,0 @@
-"""Vector store implementation using Qdrant and Ollama embeddings.
-
-This module provides a simple wrapper around QdrantVectorStore that handles
-- Initialization of the Qdrant client and collection.
-- Chunking of documents using RecursiveCharacterTextSplitter.
-- Adding documents with embeddings from Ollama.
-- Semantic search.
-"""
-
-from pathlib import Path
-from typing import List, Dict, Any
-
-from langchain_ollama import OllamaEmbeddings
-from langchain_qdrant import QdrantVectorStore
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain.schema import Document
-
-# Global configuration
-QDRANT_HOST = "localhost"
-QDRANT_PORT = 6333
-COLLECTION_NAME = "knowledge_base"
-EMBEDDING_MODEL = "nomic-embed-text"
-
-# Initialize embeddings and splitter
-embeddings = OllamaEmbeddings(model=EMBEDDING_MODEL)
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-
-# Create or connect to Qdrant collection
-vector_store = QdrantVectorStore(
-    client_kwargs={"host": QDRANT_HOST, "port": QDRANT_PORT},
-    collection_name=COLLECTION_NAME,
-    embeddings=embeddings,
-)
-
-# Ensure collection exists
-if not vector_store.client.has_collection(COLLECTION_NAME):
-    vector_store.client.create_collection(COLLECTION_NAME)
-
-def add_documents(docs: List[Dict[str, str]]) -> None:
-    """Add a list of documents to the vector store.
-
-    Each document dict must contain ``title`` and ``content`` keys.
-    The content is split into chunks before being stored.
-    """
-    documents: List[Document] = []
-    for doc in docs:
-        title = doc.get("title", "")
-        content = doc.get("content", "")
-        # Split content into chunks
-        chunks = text_splitter.split_text(content)
-        for i, chunk in enumerate(chunks):
-            meta = {"title": title, "chunk_index": i}
-            documents.append(Document(page_content=chunk, metadata=meta))
-    vector_store.add_documents(documents)
-
-def search(query: str, k: int = 5) -> List[Document]:
-    """Semantic search in the vector store.
-
-    Returns a list of Documents ordered by relevance.
-    """
-    return vector_store.similarity_search(query, k=k)
-
-# Convenience: add a single document
-
-def add_document(title: str, content: str) -> None:
-    add_documents([{"title": title, "content": content}])
-
-# Convenience: search and return plain strings
-
-def search_text(query: str, k: int = 5) -> List[str]:
-    docs = search(query, k)
-    return [f"{doc.metadata.get('title', 'Untitled')} (chunk {doc.metadata.get('chunk_index', 0)}): {doc.page_content[:200]}..." for doc in docs]