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task-6a1864f78a94f887e50d46da/vectorstore.py
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2026-06-02 07:11:17 +00:00

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Python

"""Module for creating and loading a Chroma vector store with Ollama embeddings."""
from pathlib import Path
from typing import List
from langchain_ollama import OllamaEmbeddings
from langchain_chroma import Chroma
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document
def create_vectorstore(persist_directory: str = "./chroma_db") -> Chroma:
"""Create a Chroma vector store with Ollama embeddings.
Parameters
----------
persist_directory: str
Directory where the vector store will be persisted.
Returns
-------
Chroma
The created Chroma vector store.
"""
embeddings = OllamaEmbeddings(model="nomic-embed-text")
return Chroma(persist_directory=persist_directory, embedding_function=embeddings)
def load_documents(directory: str, vectorstore: Chroma, chunk_size: int = 1000, chunk_overlap: int = 200) -> None:
"""Load .txt and .md files from a directory, split them into chunks, and add to the vector store.
Parameters
----------
directory: str
Path to the directory containing documents.
vectorstore: Chroma
The vector store to add documents to.
chunk_size: int
Maximum size of each chunk.
chunk_overlap: int
Number of characters to overlap between chunks.
"""
splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
documents: List[Document] = []
for file_path in Path(directory).glob("**/*"):
if file_path.suffix.lower() in {".txt", ".md"}:
text = file_path.read_text(encoding="utf-8")
chunks = splitter.split_text(text)
for chunk in chunks:
documents.append(Document(page_content=chunk, metadata={"source": str(file_path)}))
if documents:
vectorstore.add_documents(documents)
vectorstore.persist()