Update app.py

app.py

@@ -1,39 +1,55 @@
 import os
 import gradio as gr
-import pinecone
-from llama_index import GPTIndexMemory, GPTPineconeIndex
-from langchain.agents import Tool
-from langchain.chains.conversation.memory import ConversationBufferMemory
-from langchain import OpenAI
-from langchain.agents import initialize_agent
+from langchain.vectorstores import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain.embeddings.openai import OpenAIEmbeddings
+# convo chain lib
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.llms import OpenAI
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    AIMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from langchain.schema import (
+    AIMessage,
+    HumanMessage,
+    SystemMessage
+)
 
-OPENAI_API_KEY=os.environ["OPENAI_API_KEY"]
-PINECONE_API_KEY=os.environ["PINECONE_API_KEY"]
-
-pinecone.init(api_key=PINECONE_API_KEY, environment="us-east1-gcp")
 
-pindex=pinecone.Index("sejarah")
-indexed_pinecone=GPTPineconeIndex([], pinecone_index=pindex)
+OPENAI_API_KEY=os.environ["OPENAI_API_KEY"]
+embedding = OpenAIEmbedding()
+vectorstore = Chroma(persist_directory='/vectorstore', embedding_function=embedding)
 
-tools = [
-    Tool(
-        name = "GPT Index",
-        func=lambda q: str(indexed_pinecone.query(q)),
-        description="useful for when you want to answer questions about the author. The input to this tool should be a complete english sentence.",
-        return_direct=True
-    )
+aisyah_template="""
+Answer each question truthfully using the Malaysia's Form 1 History data provided. Your answers should be concise and straight to the point.
+For questions that are open-ended, which require subjective judgment or opinion, you may not find a definitive answer in the textbook. 
+However, you should still address the question's directive based on the data's context. Ideally, your answer should provide 3 points that support your response. 
+You are encouraged to better provide positive suggestions for concepts that are less ethical.
+Please keep in mind that the scope of the data provided is limited to the content covered in the Malaysia's Form 1 History textbook.
+---------------
+{context}"""
+##If you don't know the answer, just say that you don't know, don't try to make up an answer.
+system_template="""Use the following pieces of context to answer the users question. 
+----------------
+{context}"""
+##If you don't know the answer, just say that you don't know, don't try to make up an answer.
+messages = [
+    SystemMessagePromptTemplate.from_template(aisyah_template),
+    HumanMessagePromptTemplate.from_template("{question}")
 ]
-memory = GPTIndexMemory(index=indexed_pinecone, memory_key="chat_history", query_kwargs={"response_mode": "compact"})
-llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo")
-agent_chain = initialize_agent(tools, llm, agent="conversational-react-description", memory=memory, verbose=True)
-
-def predict(input, history=[]):
-    response = agent_chain.run(input)
-    history = history + [(input, response)]
-    response = history
-    # response = [response] 
-    # return response, response
-    return response, response
+prompt = ChatPromptTemplate.from_messages(messages)
+qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), retriever, return_source_documents=True, qa_prompt=prompt)
+
+def predict(input, chat_historyhistory=[]):
+    response = qa({"question":input, "chat_history":history})
+    return response, chat_history
 
 with gr.Blocks() as demo:
     chatbot = gr.Chatbot()