Update app.py

app.py

@@ -49,7 +49,7 @@ if "messages" not in st.session_state:
 if "memory" not in st.session_state:
     st.session_state.memory = ConversationBufferWindowMemory(k=2, memory_key="chat_history",return_messages=True) 
 
-embeddings = HuggingFaceEmbeddings(model_name="nomic-ai/nomic-embed-text-v1",model_kwargs={"trust_remote_code":True,"revision":"289f532e14dbbbd5a04753fa58739e9ba766f3c7"})
+embeddings = HuggingFaceEmbeddings(model_name="nomic-ai/nomic-embed-text-v1.5",model_kwargs={"trust_remote_code":True,"revision":"289f532e14dbbbd5a04753fa58739e9ba766f3c7"})
 db = FAISS.load_local("ipc_vector_db", embeddings, allow_dangerous_deserialization=True)
 
 db_retriever = db.as_retriever(search_type="similarity",search_kwargs={"k": 4})
@@ -65,7 +65,7 @@ ANSWER:
 prompt = PromptTemplate(template=prompt_template,
                         input_variables=['context', 'question', 'chat_history'])
 
-# You can also use other LLMs options from https://python.langchain.com/docs/integrations/llms. Here I have used TogetherAI API
+
 os.environ["TOGETHER_API_KEY"] = "ce91a607b153fc2fa7a63f6081686beee67b30a6ff2a7e8e8d07d522b361bc55"
 TOGETHER_AI_API = "ce91a607b153fc2fa7a63f6081686beee67b30a6ff2a7e8e8d07d522b361bc55"
 llm = Together(