develop

app.py

@@ -1,53 +1,39 @@
 import os
 import gradio as gr
-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
-)
+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
+
 OPENAI_API_KEY=os.environ["OPENAI_API_KEY"]
-embedding = OpenAIEmbeddings()
-vectorstore = Chroma(persist_directory='vectorstore', embedding_function=embedding)
-retriever = vectorstore.as_retriever()
-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}")
+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)
+
+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
+    )
 ]
-prompt = ChatPromptTemplate.from_messages(messages)
-qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), retriever, return_source_documents=True, qa_prompt=prompt)
+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, chat_historyhistory=[]):
-    response = qa({"question":input, "chat_history":history})
-    return response, chat_history
+def predict(input, history=[]):
+    response = agent_chain.run(input)
+    history = history + [(input, response)]
+    response = history
+    # response = [response] 
+    # return response, response
+    return response, response
 
 with gr.Blocks() as demo:
     chatbot = gr.Chatbot()

requirements.txt

@@ -1,2 +1,3 @@
+pinecone-client
 langchain
-chromadb
+llama-index

vectorstore/id_to_uuid_4a7cd045-8fcd-408a-bdb3-8a9fc28f86f1.pkl

@@ -1,3 +0,0 @@
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-size 13822

vectorstore/index_4a7cd045-8fcd-408a-bdb3-8a9fc28f86f1.bin

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vectorstore/index_metadata_4a7cd045-8fcd-408a-bdb3-8a9fc28f86f1.pkl

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vectorstore/uuid_to_id_4a7cd045-8fcd-408a-bdb3-8a9fc28f86f1.pkl

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