Update vectorstore.py

This commit is contained in:
2026-06-05 11:21:25 +00:00
parent 2e634d011f
commit 7250b26b9b
+41 -36
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@@ -1,19 +1,20 @@
"""Vector store utilities using ChromaDB and Ollama embeddings. """Vector store utilities using ChromaDB and Ollama embeddings.
This module provides functions to create a persistent Chroma vector store This module provides functions to create a persistent Chroma vector store and load
and to load documents from a directory into the store. text documents from a directory into it. The store is exposed via the global
``store`` variable so that other modules (e.g. tools) can access it.
""" """
from pathlib import Path from pathlib import Path
from typing import Iterable from typing import List
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_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.documents import Document
# --------------------------------------------------------------------------- # Global store that will be initialised in ``create_vectorstore``.
# Create a persistent Chroma vector store. store: Chroma | None = None
# ---------------------------------------------------------------------------
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 a Chroma vector store with Ollama embeddings.
@@ -21,41 +22,45 @@ def create_vectorstore(persist_directory: str = "./chroma_db") -> Chroma:
Parameters Parameters
---------- ----------
persist_directory: str persist_directory: str
Directory where the Chroma database will be stored. Directory where the vector data will be persisted.
Returns
-------
Chroma
A Chroma vector store instance.
""" """
global store
embeddings = OllamaEmbeddings(model="nomic-embed-text") embeddings = OllamaEmbeddings(model="nomic-embed-text")
return Chroma(persist_directory=persist_directory, embedding_function=embeddings) store = Chroma(
collection_name="rag_collection",
embedding_function=embeddings,
persist_directory=persist_directory,
)
return store
# --------------------------------------------------------------------------- def load_documents(directory: str | Path, vectorstore: Chroma) -> None:
# Load documents from a directory into the vector store.
# ---------------------------------------------------------------------------
def load_documents(directory: str, vectorstore: Chroma) -> None:
"""Load all .txt and .md files from *directory* into *vectorstore*. """Load all .txt and .md files from *directory* into *vectorstore*.
The documents are split into chunks using a RecursiveCharacterTextSplitter The documents are split using ``RecursiveCharacterTextSplitter`` before
before being added to the vector store. being added to the collection.
""" """
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) dir_path = Path(directory)
docs = [] txt_files = list(dir_path.rglob("*.txt")) + list(dir_path.rglob("*.md"))
for path in Path(directory).rglob("*.txt"): if not txt_files:
docs.append(path.read_text(encoding="utf-8")) print(f"No .txt or .md files found in {dir_path}")
for path in Path(directory).rglob("*.md"):
docs.append(path.read_text(encoding="utf-8"))
if not docs:
return return
# Split the documents into chunks.
chunks = splitter.split_text("\n\n".join(docs))
# Create LangChain Document objects.
from langchain.docstore.document import Document
documents = [Document(page_content=chunk) for chunk in chunks]
vectorstore.add_documents(documents)
# --------------------------------------------------------------------------- splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
# End of vectorstore.py docs: List[Document] = []
# --------------------------------------------------------------------------- for file_path in txt_files:
text = file_path.read_text(encoding="utf-8")
docs.extend(splitter.create_documents([text], metadata={"source": str(file_path)}))
vectorstore.add_documents(docs)
# Persist the collection to disk.
vectorstore.persist()
print(f"Loaded {len(docs)} documents from {dir_path} into Chroma.")
# Helper to get the global store.
def get_vectorstore() -> Chroma:
if store is None:
raise RuntimeError("Vector store has not been initialised. Call create_vectorstore() first.")
return store
"""End of vectorstore.py"""