modified chroma db

src/backend/data/chroma_db.py

@@ -1,73 +1,73 @@
-import chromadb
-import asyncio
-from utils import logger
-from chromadb.utils import embedding_functions
-default_ef = embedding_functions.DefaultEmbeddingFunction()
+# import chromadb
+# import asyncio
+# from utils import logger
+# from chromadb.utils import embedding_functions
+# default_ef = embedding_functions.DefaultEmbeddingFunction()
 
-logger = logger.get_logger()
+# logger = logger.get_logger()
 
-# Constants
-COLLECTION_NAME = "care_companion_ai_vectors"
-DB_PATH = "./src/backend/vector-db"
+# # Constants
+# COLLECTION_NAME = "care_companion_ai_vectors"
+# DB_PATH = "./src/backend/vector-db"
 
-# Initialize ChromaDB Client
-client = chromadb.PersistentClient(path=DB_PATH)
+# # Initialize ChromaDB Client
+# client = chromadb.PersistentClient(path=DB_PATH)
 
 
 
-# chroma_client = chromadb.HttpClient(host='localhost', port=8000)
-# client = chromadb.AsyncHttpClient()
+# # chroma_client = chromadb.HttpClient(host='localhost', port=8000)
+# # client = chromadb.AsyncHttpClient()
 
-collection = client.get_or_create_collection(
-    name=COLLECTION_NAME,
-    embedding_function=default_ef,
-    metadata={
-    "description": "yuvabe care companion ai chroma collection",
-    "hnsw:space": "cosine",
-    "hnsw:search_ef": 100
-    })
+# collection = client.get_or_create_collection(
+#     name=COLLECTION_NAME,
+#     embedding_function=default_ef,
+#     metadata={
+#     "description": "yuvabe care companion ai chroma collection",
+#     "hnsw:space": "cosine",
+#     "hnsw:search_ef": 100
+#     })
 
-def add_data_to_vector_store(df):
-    try:
-        logger.info("Started upserting the data to database")
-        for index, row in df.iterrows():
-            input_text = row['input']
-            output_text = row['output']
-            instruction_text = row['instruction']
-            if not isinstance(input_text, str) or not input_text.strip():
-                logger.warning(f"Skipping row {index} due to empty or invalid input text.")
-                continue
-            row_dict = {
-                "question": input_text,
-                "answer" : output_text,
-                "instruction": instruction_text
-            }
-            collection.upsert(
-                documents=input_text,
-                metadatas=row_dict,
-                ids=f"id{index}"
-            )
-            logger.info(f"Successfully upserted {index} records.")
-        logger.info("Successfully upserted all the records.")
-    except Exception as e:
-        logger.exception(f"Unable to upsert the data to the database: {e}")
+# def add_data_to_vector_store(df):
+#     try:
+#         logger.info("Started upserting the data to database")
+#         for index, row in df.iterrows():
+#             input_text = row['input']
+#             output_text = row['output']
+#             instruction_text = row['instruction']
+#             if not isinstance(input_text, str) or not input_text.strip():
+#                 logger.warning(f"Skipping row {index} due to empty or invalid input text.")
+#                 continue
+#             row_dict = {
+#                 "question": input_text,
+#                 "answer" : output_text,
+#                 "instruction": instruction_text
+#             }
+#             collection.upsert(
+#                 documents=input_text,
+#                 metadatas=row_dict,
+#                 ids=f"id{index}"
+#             )
+#             logger.info(f"Successfully upserted {index} records.")
+#         logger.info("Successfully upserted all the records.")
+#     except Exception as e:
+#         logger.exception(f"Unable to upsert the data to the database: {e}")
 
-def search_vector_store(query, n_result : int = 3):
-    try:
-        logger.info("Trying to fetch the data from database")
-        response = collection.query(
-        query_texts=[query],
-        n_results=n_result,
-        include=["metadatas","distances","documents"]
-        )
-        logger.info("Successfully fetched the data from database")
-        return response
-    except Exception as e:
-        logger.exception("Failed to fetch the data from database")
+# def search_vector_store(query, n_result : int = 3):
+#     try:
+#         logger.info("Trying to fetch the data from database")
+#         response = collection.query(
+#         query_texts=[query],
+#         n_results=n_result,
+#         include=["metadatas","distances","documents"]
+#         )
+#         logger.info("Successfully fetched the data from database")
+#         return response
+#     except Exception as e:
+#         logger.exception("Failed to fetch the data from database")
 
-def get_retrieved_context(prompt: str) -> str:
-    response = search_vector_store(prompt)
-    if response and "metadatas" in response and response["metadatas"]:
-        retrieved_contexts = [metadata["answer"] for metadata in response["metadatas"][0]]
-        return "\n".join(retrieved_contexts[:3])
-    return "No relevant information found in the database."
+# def get_retrieved_context(prompt: str) -> str:
+#     response = search_vector_store(prompt)
+#     if response and "metadatas" in response and response["metadatas"]:
+#         retrieved_contexts = [metadata["answer"] for metadata in response["metadatas"][0]]
+#         return "\n".join(retrieved_contexts[:3])
+#     return "No relevant information found in the database."

src/backend/models/llm_model.py

@@ -1,7 +1,7 @@
 import os
 from groq import Groq
 from utils import logger
-from data import chroma_db
+# from data import chroma_db
 from data import pinecone_db
 from dotenv import load_dotenv