delated storage

core/storage.py

@@ -1,58 +0,0 @@
-import chromadb
-import os
-from mistralai import Mistral
-import config
-
-# Initialize ChromaDB client (persistent directory can be set via CHROMA_DB_DIR)
-chroma_db_path = os.getenv("CHROMA_DB_DIR", "db/")
-client = chromadb.Client()
-collection = client.get_or_create_collection("documents")
-
-# Use Mistral API for embeddings
-
-def get_mistral_embedding(text: str) -> list[float]:
-    """
-    Get embedding for the given text using Mistral API.
-    """
-    with Mistral(api_key=config.MISTRAL_API_KEY) as client:
-        response = client.embeddings.create(
-            model="mistral-embed",
-            input=text
-        )
-        # The API returns a list of embeddings (one per input)
-        return response['data'][0]['embedding']
-
-
-def add_document(doc_id: str, text: str, metadata: dict):
-    """
-    Add a document's text and metadata to the ChromaDB collection.
-    """
-    embedding = get_mistral_embedding(text)
-    collection.add(ids=[doc_id], embeddings=[embedding], documents=[text], metadatas=[metadata])
-    # Persist to disk
-    client.persist()
-    return True
-
-
-def search_documents(query: str, top_k: int = 5) -> dict:
-    """
-    Search for documents semantically similar to the query.
-    Returns a dictionary of top results.
-    """
-    query_vec = get_mistral_embedding(query)
-    results = collection.query(query_embeddings=[query_vec], n_results=top_k,
-                               include=['ids','distances','documents','metadatas'])
-    return results
-
-
-def get_all_documents() -> list:
-    """
-    Retrieve metadata for all documents in the collection.
-    """
-    all_ids = collection.get()['ids']
-    docs = []
-    for doc_id in all_ids:
-        res = collection.get(ids=[doc_id])
-        if res and res['metadatas']:
-            docs.append({"id": doc_id, "metadata": res['metadatas'][0]})
-    return docs