From a6611baa84e3acf64f297a7919a7d390c8ba5d1e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=9A=D0=B8=D1=80=D0=B8=D0=BB=D0=BB=20=D0=A0=D0=BE=D0=BC?=
 =?UTF-8?q?=D0=B0=D0=BD=D0=BE=D0=B2?= <kirilinka320@gmail.com>
Date: Fri, 5 Jun 2026 13:07:18 +0000
Subject: [PATCH] Delete obsolete vector_store.py

---
 vector_store.py | 93 -------------------------------------------------
 1 file changed, 93 deletions(-)
 delete mode 100644 vector_store.py

diff --git a/vector_store.py b/vector_store.py
deleted file mode 100644
index 45603f7..0000000
--- a/vector_store.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""Vector store implementation using Qdrant.
-
-This module provides functions to create a Qdrant vector store backed by Ollama embeddings
-and to load documents from a directory into the store. The store is persisted in a local
-directory and can be reused across runs.
-"""
-
-from pathlib import Path
-from typing import List
-
-from langchain_qdrant import Qdrant
-from langchain_ollama import OllamaEmbeddings
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain.docstore.document import Document
-
-__all__ = ["create_vectorstore", "load_documents"]
-
-def create_vectorstore(persist_directory: str = "./qdrant_db") -> Qdrant:
-    """Create or load a Qdrant vector store.
-
-    Parameters
-    ----------
-    persist_directory: str
-        Path to the directory where Qdrant will store its data. The directory
-        will be created if it does not exist.
-
-    Returns
-    -------
-    Qdrant
-        A Qdrant vector store instance.
-    """
-    # Ensure the directory exists
-    Path(persist_directory).mkdir(parents=True, exist_ok=True)
-
-    # Use Ollama embeddings (nomic-embed-text) for semantic similarity
-    embeddings = OllamaEmbeddings(model="nomic-embed-text")
-
-    # Qdrant can run in local mode when ``location`` is provided.
-    # The ``url`` is set to the default local address.
-    return Qdrant(
-        collection_name="documents",
-        embedding=embeddings,
-        url="http://localhost:6333",  # Qdrant server address
-        location=persist_directory,
-    )
-
-def _load_text_files(directory: str) -> List[str]:
-    """Recursively read all .txt and .md files from *directory*.
-
-    Parameters
-    ----------
-    directory: str
-        Root directory to search for documents.
-
-    Returns
-    -------
-    List[str]
-        List of file contents.
-    """
-    texts: List[str] = []
-    for path in Path(directory).rglob("*"):
-        if path.suffix.lower() in {".txt", ".md"}:
-            try:
-                with open(path, "r", encoding="utf-8") as f:
-                    texts.append(f.read())
-            except Exception as exc:  # pragma: no cover - defensive
-                print(f"Could not read {path}: {exc}")
-    return texts
-
-def load_documents(directory: str, vectorstore: Qdrant) -> None:
-    """Load documents from *directory* into the provided *vectorstore*.
-
-    The function performs chunking via :class:`RecursiveCharacterTextSplitter`
-    before adding the chunks to the vector store.
-    """
-    raw_texts = _load_text_files(directory)
-    if not raw_texts:
-        print(f"No .txt or .md files found in {directory}")
-        return
-
-    # Chunk each document into manageable pieces
-    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    chunks: List[str] = []
-    for text in raw_texts:
-        chunks.extend(splitter.split_text(text))
-
-    # Convert to LangChain Document objects
-    documents = [Document(page_content=chunk) for chunk in chunks]
-
-    # Add documents to Qdrant. The underlying Qdrant client will handle
-    # persistence automatically.
-    vectorstore.add_documents(documents)
-    print(f"Loaded {len(documents)} chunks into Qdrant.")
\ No newline at end of file