Update app.py

app.py

@@ -5,11 +5,12 @@ import os
 from langchain_google_genai import GoogleGenerativeAIEmbeddings
 import google.generativeai as genai
 from langchain_community.vectorstores import FAISS
-from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain.chains.question_answering import load_qa_chain
 from langchain.prompts import PromptTemplate
 from dotenv import load_dotenv
 import traceback
+from transformers import pipeline
+from langchain.embeddings import HuggingFaceEmbeddings
 
 # Load environment variables
 load_dotenv()
@@ -46,8 +47,16 @@ def get_text_chunks(text):
 # Function to create an in-memory FAISS vector store
 def get_vector_store(text_chunks):
     try:
-        embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001",api_key=google_api_key)
-        vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+        # Create a pipeline for feature extraction with the specified model
+        feature_extractor = pipeline("feature-extraction", model="jinaai/jina-embeddings-v2-base-code", trust_remote_code=True)
+
+        # Define a function to generate embeddings using the pipeline
+        def embedding_function(text):
+            # The pipeline returns nested lists, so we flatten it by taking the first item.
+            return feature_extractor(text)[0]
+        
+        # Using FAISS to create vector store with the new embeddings function
+        vector_store = FAISS.from_texts(text_chunks, embedding=embedding_function)
         return vector_store
     except Exception as e:
         st.error(f"Error creating vector store: {e}")