Update functions.py

functions.py

@@ -30,10 +30,7 @@ from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceBgeEmbeddings
 from langchain.vectorstores import FAISS
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.chat_models import ChatOpenAI
-from langchain.callbacks import StdOutCallbackHandler
-from langchain.chains import ConversationalRetrievalChain, QAGenerationChain, LLMChain
-from langchain.memory import ConversationBufferMemory
-from langchain.chains.question_answering import load_qa_chain
+from langchain.chains import QAGenerationChain
 
 from langchain.callbacks import StreamlitCallbackHandler
 from langchain.agents import OpenAIFunctionsAgent, AgentExecutor
@@ -66,43 +63,6 @@ time_str = time.strftime("%d%m%Y-%H%M%S")
 HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; 
 margin-bottom: 2.5rem">{}</div> """
 
-memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True, output_key='answer')
-
-
-#Stuff Chain Type Prompt template
-
-@st.cache_data
-def load_prompt():
-
- system_template="""Use only the following pieces of earnings context to answer the users question accurately.
- Do not use any information not provided in the earnings context and remember you are a to speak like a finance expert.
- If you don't know the answer, just say 'There is no relevant answer in the given earnings call transcript', 
- don't try to make up an answer.
- 
- ALWAYS return a "SOURCES" part in your answer.
- The "SOURCES" part should be a reference to the source of the document from which you got your answer.
- 
- Remember, do not reference any information not given in the context.
-
- If the answer is not available in the given context just say 'There is no relevant answer in the given earnings call transcript'
- 
- Follow the below format when answering:
- 
- Question: {question}
- SOURCES: [xyz]
- 
- Begin!
- ----------------
- {context}"""
- 
- messages = [
- SystemMessagePromptTemplate.from_template(system_template),
- HumanMessagePromptTemplate.from_template("{question}")
- ]
- prompt = ChatPromptTemplate.from_messages(messages)
- 
- return prompt
-
 
 ###################### Functions #######################################################################################
 
@@ -597,7 +557,8 @@ def create_vectorstore(corpus, title, embedding_model, chunk_size=1000, overlap=
  vectorstore = FAISS.from_texts(texts, embeddings, metadatas=[{"source": i} for i in range(len(texts))])
 
  return vectorstore
- 
+
+@st.cache_data
 def create_memory_and_agent(query,_docsearch):
  
  '''Embed text and generate semantic search scores'''