Update vectorstore.py

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2026-06-03 10:25:50 +00:00
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"""Vector store utilities for ChromaDB. """Vector store utilities for ChromaDB with Ollama embeddings.
This module provides functions to create a persistent ChromaDB vector store using This module provides functions to create a persistent Chroma vector store and
Ollama embeddings and to load documents from a directory into the store. load documents from a directory into it. Documents are split into chunks using
`RecursiveCharacterTextSplitter` and stored in the Chroma collection.
""" """
import os
from pathlib import Path from pathlib import Path
from typing import List
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_chroma import Chroma from langchain_chroma import Chroma
from langchain_ollama import OllamaEmbeddings from langchain_ollama import OllamaEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.documents import Document
# ---------------------------------------------------------------------------
# Vector store creation
# ---------------------------------------------------------------------------
def create_vectorstore(persist_directory: str = "./chroma_db") -> Chroma: def create_vectorstore(persist_directory: str = "./chroma_db") -> Chroma:
"""Create a Chroma vector store with Ollama embeddings. """Create or load a Chroma vector store.
Parameters Parameters
---------- ----------
persist_directory: str persist_directory: str
Directory where the vector store will be persisted. Directory where the Chroma DB files are stored.
Returns Returns
------- -------
Chroma Chroma
A Chroma vector store instance. A Chroma vector store instance.
""" """
# Ensure directory exists
Path(persist_directory).mkdir(parents=True, exist_ok=True)
# Use Ollama embeddings
embeddings = OllamaEmbeddings(model="nomic-embed-text") embeddings = OllamaEmbeddings(model="nomic-embed-text")
return Chroma(persist_directory=persist_directory, embedding_function=embeddings) # Create Chroma store
vectorstore = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
return vectorstore
# ---------------------------------------------------------------------------
# Document loading
# ---------------------------------------------------------------------------
def _load_text_files(directory: str) -> List[str]:
"""Load all .txt and .md files from a directory into a list of strings."""
texts = []
for root, _, files in os.walk(directory):
for file in files:
if file.lower().endswith(('.txt', '.md')):
path = Path(root) / file
try:
content = path.read_text(encoding="utf-8")
texts.append(content)
except Exception as e:
print(f"Failed to read {path}: {e}")
return texts
def load_documents(directory: str, vectorstore: Chroma) -> None: def load_documents(directory: str, vectorstore: Chroma, chunk_size: int = 1000, chunk_overlap: int = 200) -> None:
"""Load .txt and .md files from *directory* into *vectorstore*. """Load documents from a directory into the provided vector store.
The documents are split into chunks using ``RecursiveCharacterTextSplitter`` Parameters
before being added to the vector store. ----------
directory: str
Path to the directory containing .txt/.md files.
vectorstore: Chroma
The vector store to add documents to.
chunk_size: int, optional
Maximum size of each chunk.
chunk_overlap: int, optional
Number of characters to overlap between chunks.
""" """
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) texts = _load_text_files(directory)
if not texts:
print("No text files found in the directory.")
return
splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
docs = [] docs = []
for file_path in Path(directory).glob("*"): for text in texts:
if file_path.suffix.lower() not in {".txt", ".md"}: docs.extend(splitter.split_text(text))
continue
with open(file_path, "r", encoding="utf-8") as f: # Add documents to Chroma
text = f.read() vectorstore.add_texts(docs)
chunks = splitter.split_text(text) print(f"Loaded {len(docs)} chunks into the vector store.")
docs.extend([Document(page_content=c, metadata={"source": str(file_path)}) for c in chunks])
if docs: # ---------------------------------------------------------------------------
vectorstore.add_documents(docs) # Example usage (uncomment to run directly)
print(f"Loaded {len(docs)} chunks from {directory} into ChromaDB.") # ---------------------------------------------------------------------------
else: # if __name__ == "__main__":
print(f"No .txt/.md files found in {directory}.") # store = create_vectorstore()
# load_documents("documents", store)
""