Add vectorstore.py

vectorstore.py

@@ -0,0 +1,56 @@
+"""
+Vector store utilities for ChromaDB with Ollama embeddings.
+"""
+
+import os
+from pathlib import Path
+
+from langchain_chroma import Chroma
+from langchain_ollama import OllamaEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.docstore.document import Document
+
+def create_vectorstore(persist_directory: str = "./chroma_db") -> Chroma:
+    """Create or load a Chroma vector store.
+
+    Parameters
+    ----------
+    persist_directory: str
+        Directory where the Chroma database is persisted.
+
+    Returns
+    -------
+    Chroma
+        A Chroma vector store instance.
+    """
+    embeddings = OllamaEmbeddings(model="nomic-embed-text")
+    return Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+
+def load_documents(directory: str, vectorstore: Chroma) -> None:
+    """Load text and markdown files from *directory*, chunk them, and add to *vectorstore*.
+
+    Parameters
+    ----------
+    directory: str
+        Path to the folder containing .txt and .md files.
+    vectorstore: Chroma
+        The Chroma vector store to which documents will be added.
+    """
+    docs = []
+    for file_path in Path(directory).rglob("*.*"):
+        if file_path.suffix.lower() in ".txt .md".split():
+            try:
+                text = file_path.read_text(encoding="utf-8")
+            except Exception as e:
+                print(f"Failed to read {file_path}: {e}")
+                continue
+            docs.append(Document(page_content=text, metadata={"source": str(file_path)}))
+
+    if not docs:
+        print("No documents found to load.")
+        return
+
+    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    chunks = splitter.split_documents(docs)
+    vectorstore.add_documents(chunks)
+    print(f"Loaded {len(chunks)} chunks into the vector store.")