[Question]: Can I use a chat engine with streamlit?

[Ma-Fukudama]

Question Validation

• [X] I have searched both the documentation and discord for an answer.

Question

Currently, the system uses a query engine to create answers, but I would like to create answers that also reflect the contents of the conversation history. I'm thinking of using a chat engine for that, but is it possible to use a chat engine with streamlit? There is no such usage in the usage pattern of llamaindex, so I don't know how to set it up.

The following is what I am currently creating with the query engine.

import os, sys, json, site, time, logging, openai
from dotenv import load_dotenv
import streamlit as st
from streamlit_chat import message
# site.addsitedir('/home/doyukai/.local/lib/python3.11/site-packages')
import tiktoken
from llama_index import (
    download_loader,
    LLMPredictor,
    VectorStoreIndex,
    ServiceContext,
    QuestionAnswerPrompt,
    StorageContext,
    load_index_from_storage,
    SimpleDirectoryReader,
    ListIndex
)
from langchain import OpenAI
from langchain.chat_models import ChatOpenAI
from llama_index.indices.list.base import ListRetrieverMode
load_dotenv()

openai.api_key = os.environ["OPENAI_API_KEY"] 

load_dotenv()
# ログレベルの設定
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG, force=True)

class QAResponseGenerator:
    def __init__(self, selected_model):
        self.llm_predictor = LLMPredictor(llm=OpenAI(temperature=1, model_name="gpt-3.5-turbo-0613"))
        self.QA_PROMPT_TMPL = (
            "下記の情報が与えられています。 \n"
            "---------------------\n"
            "{context_str}"
            "\n---------------------\n"
            "この情報を参照してできるだけ詳しく次の質問に答えてください: {query_str}\n"
            "回答には引用したファイル名を追記してください\n"
            "URLの場合はリンク先を追記してください"
        )
        self.service_context = ServiceContext.from_defaults(llm_predictor=self.llm_predictor)

    def generate(self, question):
        start = time.time()  
        storage_context = StorageContext.from_defaults(persist_dir="～/index")
        index = load_index_from_storage(storage_context)
        elapsed_time = time.time() - start
        print("load_elapsed_time:{0}".format(elapsed_time) + "[sec]")
        start = time.time()
        engine = index.as_query_engine(text_qa_template=QuestionAnswerPrompt(self.QA_PROMPT_TMPL), retriever_mode=ListRetrieverMode.EMBEDDING, similarity_top_k=1, vector_store_query_mode="default")
        result = engine.query(question)
        elapsed_time = time.time() - start
        print("query_time:{0}".format(elapsed_time) + "[sec]")
        return result.response.replace("\n", ""), result.get_formatted_sources(1000)

def display_chat(chat_history):
    for i, chat in enumerate(reversed(chat_history)):
        if "user" in chat:
            message(chat["user"], is_user=True, key=str(i)) 
        else:
            message(chat["bot"], key="bot_"+str(i))

def main():
    question = st.text_input("Your question")

    # メインの画面に質問送信ボタンを設定
    submit_question = st.button("質問")
    clear_chat = st.button("履歴消去")

    # チャット履歴を保存
    if "chat_history" not in st.session_state:
        st.session_state["chat_history"] = []

    if clear_chat:
        st.session_state["chat_history"] = []

    # pdf_reader = PDFReader()
    response_generator = QAResponseGenerator("gpt-3.5-turbo-0613")
    # ボタンがクリックされた場合の処理
    if submit_question:
        if question:  # 質問が入力されている場合
            response, source = response_generator.generate(question)
            # if choice == "表示する":
            #     response += f"\n\n参照した情報は次の通りです:\n{source}"

            # 質問と応答をチャット履歴に追加
            st.session_state["chat_history"].append({"user": question})
            st.session_state["chat_history"].append({"bot": response})

    display_chat(st.session_state["chat_history"])

if __name__ == "__main__":
    main()

[dosubot[bot]]

🤖

Hello,

Yes, you can use a chat engine with Streamlit in the LlamaIndex framework. The LlamaIndex framework provides several chat engines, including SimpleChatEngine, CondenseQuestionChatEngine, and ContextChatEngine.

Here is a basic example of how you might set up the SimpleChatEngine with Streamlit:

import streamlit as st
from llama_index.chat_engine.simple import SimpleChatEngine
from llama_index.llms.base import LLM, ChatMessage
from llama_index.memory import BaseMemory

# Initialize the chat engine
llm = LLM() # Replace with your LLM
memory = BaseMemory() # Replace with your memory
prefix_messages = [ChatMessage(content="Hello", role="system")] # Replace with your prefix messages

chat_engine = SimpleChatEngine(llm, memory, prefix_messages)

# Create a text input for the user to enter their question
user_input = st.text_input("Enter your question:")

# Use the chat engine to generate a response to the user's question
response = chat_engine.chat(user_input)

# Display the chat engine's response
st.write(response.response)

This code creates a Streamlit application where the user can enter a question into a text input. The SimpleChatEngine then generates a response to the user's question, which is displayed on the screen. The SimpleChatEngine uses an LLM to generate a response, for a fluid chat experience.

In the context shared, it's also worth noting that the LlamaIndex framework supports both synchronous and asynchronous chat interfaces, as well as streaming chat responses. This can be useful for handling more complex chat scenarios or for improving performance in certain situations.

The context shared also shows that the SimpleChatEngine can be initialized with a default service context and an optional chat history. The chat history is a list of tuples, where each tuple contains a human message and an AI message. This can be useful for initializing the chat engine with a pre-existing conversation. The SimpleChatEngine also provides a reset method, which can be used to reset the chat history.

Please note that you will need to replace LLM(), BaseMemory(), and prefix_messages with your own LLM, memory, and prefix messages respectively.

I hope this helps! If you have any other questions, feel free to ask.

Best, Dosu

Sources

• Support to add conversation history in the prompt
• llama_index/chat_engine/init.py
• llama_index/chat_engine/context.py
• llama_index/chat_engine/types.py
• llama_index/chat_engine/simple.py
• tests/chat_engine/test_simple.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[Ma-Fukudama]

@dosu-beta I don't use indexes in the examples either, but can't I use indexes?

I tried implementing something that uses an index, but I would like you to point out what is wrong.

import streamlit as st
import os, sys, json, site, time, logging, openai
from llama_index.llms.base import LLM, ChatMessage
from llama_index.llms import OpenAI
from dotenv import load_dotenv
from streamlit_chat import message
from llama_index import (
    download_loader,
    LLMPredictor,
    VectorStoreIndex,
    ServiceContext,
    QuestionAnswerPrompt,
    StorageContext,
    load_index_from_storage,
    SimpleDirectoryReader,
    ListIndex
)
from llama_index.indices.list.base import ListRetrieverMode

load_dotenv()

openai.api_key = os.environ["OPENAI_API_KEY"] 

# llm=OpenAI(temperature=0.0, model="gpt-3.5-turbo-0613")
# # ログレベルの設定
# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG, force=True)

class QAResponseGenerator:
    def __init__(self, selected_model):
        self.llm_predictor = LLMPredictor(llm=OpenAI(temperature=1, model_name="gpt-3.5-turbo-0613"))
        self.QA_PROMPT_TMPL = (
            "下記の情報が与えられています。 \n"
            "---------------------\n"
            "{context_str}"
            "\n---------------------\n"
            "この情報を参照してできるだけ詳しく次の質問に答えてください: {query_str}\n"
            "回答には引用したファイル名を追記してください\n"
            "URLの場合はリンク先を追記してください"
        )
        self.service_context = ServiceContext.from_defaults(llm_predictor=self.llm_predictor)

    def generate(self, question):
        start = time.time()  
        storage_context = StorageContext.from_defaults(persist_dir="～/index")
        index = load_index_from_storage(storage_context)
        elapsed_time = time.time() - start
        print("load_elapsed_time:{0}".format(elapsed_time) + "[sec]")
        start = time.time()
        engine = index.as_chat_engine(chat_mode="openai", verbose=True, text_qa_template=QuestionAnswerPrompt(self.QA_PROMPT_TMPL), vector_store_query_mode="default")
        result = engine.chat(question)
        elapsed_time = time.time() - start
        print("query_time:{0}".format(elapsed_time) + "[sec]")
        return result.response.replace("\n", "")

def display_chat(chat_history):
    for i, chat in enumerate(reversed(chat_history)):
        if "user" in chat:
            message(chat["user"], is_user=True, key=str(i)) 
        else:
            message(chat["bot"], key="bot_"+str(i))

def main():

    question = st.text_input("Your question")

    # メインの画面に質問送信ボタンを設定
    submit_question = st.button("質問")
    clear_chat = st.button("履歴消去")

    # チャット履歴を保存
    if "chat_history" not in st.session_state:
        st.session_state["chat_history"] = []

    if clear_chat:
        st.session_state["chat_history"] = []

    # pdf_reader = PDFReader()
    response_generator = QAResponseGenerator("gpt-3.5-turbo-0613")
    # ボタンがクリックされた場合の処理
    if submit_question:
        if question:  # 質問が入力されている場合
            response = response_generator.generate(question)
            # if choice == "表示する":
            #     response += f"\n\n参照した情報は次の通りです:\n{source}"

            # 質問と応答をチャット履歴に追加
            st.session_state["chat_history"].append({"user": question})
            st.session_state["chat_history"].append({"bot": response})

    display_chat(st.session_state["chat_history"])

if __name__ == "__main__":
    main()

[dosubot[bot]]

🤖

Hello,

Thank you for sharing your code. It's well-structured and you've done a good job of integrating the LlamaIndex framework into your Streamlit application. However, there are a few areas where you could make improvements:

1. Error Handling: It's important to provide as much information as possible when raising errors. For example, in the from_defaults method, you could include the type of the provided llm_predictor in the error message to help with debugging. Here's an example:

if not isinstance(service_context.llm_predictor, LLMPredictor):
    raise ValueError(f"llm_predictor must be a LLMPredictor instance, not {type(service_context.llm_predictor)}")

2. Code Duplication: The chat, stream_chat, achat, and astream_chat methods have a lot of duplicated code. You could create a helper method to handle the common parts. Here's an example:

def prepare_chat(self, message: str, chat_history: Optional[List[ChatMessage]] = None) -> List[ChatMessage]:
    if chat_history is not None:
        self._memory.set(chat_history)
    self._memory.put(ChatMessage(content=message, role="user"))
    return self._prefix_messages + self._memory.get()

3. Thread Safety: In the stream_chat and astream_chat methods, you start a new thread to write the response to history. If multiple threads are writing to the memory at the same time, this could lead to race conditions. Consider using a thread-safe data structure or adding locks to ensure thread safety.

4. Type Annotations: You have done a good job of using type annotations, but there are a few places where they could be added or clarified. For example, the prefix_messages parameter in the __init__ method could be annotated as List[ChatMessage] instead of just List.

5. Docstrings: Adding docstrings to your methods would make your code easier to understand. This is especially important for public methods like chat, stream_chat, etc.

6. Data Retrieval: Make sure you're properly utilizing the LlamaIndex's capabilities to retrieve and query data. For example, you can use the as_query_engine method to create a query engine for each index and then use these query engines in your chatbot to answer user queries.

7. Chatbot Loop: Consider handling exceptions in your chatbot loop. If an error occurs while processing a user's input, the loop will break and the chatbot will stop. You could add a try-except block around the agent_chain.run(input=text_input) line to catch and handle any exceptions that occur.

8. Testing: Consider adding more comprehensive tests. For example, you could test how your chatbot handles invalid inputs or how it responds to queries that require data from multiple indices.

9. Chat Engine Configuration: Consider adding a mechanism to handle meta questions like "what did I ask you before?".

10. Resetting Conversation State: Consider adding a mechanism to save the conversation history before resetting, so that you can review past conversations if needed.

I hope these suggestions help you improve your code. If you have any further questions, feel free to ask.

Best, Dosu

Sources

• llama_index/chat_engine/simple.py
• llama_index/chat_engine/context.py
• docs/end_to_end_tutorials/chatbots/building_a_chatbot.md
• docs/examples/chat_engine/chat_engine_condense_question.ipynb
• llama_index/llm_predictor/base.py
• llama_index/llm_predictor/mock.py
• llama_index/llm_predictor/structured.py
• llama_index/llm_predictor/vellum/predictor.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[Ma-Fukudama]

@dosu-bot I was able to confirm that it works without any errors using the source below, but when I ask about the contents of the index, I get an answer that they don't know. Also, where should I put error handling?

import streamlit as st
import os, sys, json, site, time, logging, openai
from llama_index.llms.base import LLM, ChatMessage
from llama_index.llms import OpenAI
from dotenv import load_dotenv
from streamlit_chat import message
from llama_index import (
    download_loader,
    LLMPredictor,
    VectorStoreIndex,
    ServiceContext,
    QuestionAnswerPrompt,
    StorageContext,
    load_index_from_storage,
    SimpleDirectoryReader,
    ListIndex
)
from llama_index.indices.list.base import ListRetrieverMode

load_dotenv()

openai.api_key = os.environ["OPENAI_API_KEY"] 

class QAResponseGenerator:
    def __init__(self, selected_model):
        self.llm_predictor = LLMPredictor(llm=OpenAI(temperature=1, model_name="gpt-3.5-turbo-0613"))
        self.QA_PROMPT_TMPL = (
            "下記の情報が与えられています。 \n"
            "---------------------\n"
            "{context_str}"
            "\n---------------------\n"
            "この情報を参照してできるだけ詳しく次の質問に答えてください: {query_str}\n"
            "回答には引用したファイル名を追記してください\n"
            "URLの場合はリンク先を追記してください"
        )
        self.service_context = ServiceContext.from_defaults(llm_predictor=self.llm_predictor)

    def generate(self, question):
        start = time.time()  
        storage_context = StorageContext.from_defaults(persist_dir="～/index")
        index = load_index_from_storage(storage_context)
        elapsed_time = time.time() - start
        print("load_elapsed_time:{0}".format(elapsed_time) + "[sec]")
        start = time.time()
        engine = index.as_chat_engine(chat_mode="openai", verbose=True, text_qa_template=QuestionAnswerPrompt(self.QA_PROMPT_TMPL), vector_store_query_mode="default")
        result = engine.chat(question)
        elapsed_time = time.time() - start
        print("query_time:{0}".format(elapsed_time) + "[sec]")
        return result.response.replace("\n", "")

    def prepare_chat(self, message: str, chat_history: Optional[List[ChatMessage]] = None) -> List[ChatMessage]:
        if chat_history is not None:
            self._memory.set(chat_history)
        self._memory.put(ChatMessage(content=message, role="user"))
        return self._prefix_messages + self._memory.get()

    def display_chat(chat_history):
        for i, chat in enumerate(reversed(chat_history)):
            if "user" in chat:
                message(chat["user"], is_user=True, key=str(i)) 
            else:
                message(chat["bot"], key="bot_"+str(i))

def main():

    question = st.text_input("Your question")

    # メインの画面に質問送信ボタンを設定
    submit_question = st.button("質問")
    clear_chat = st.button("履歴消去")

    # チャット履歴を保存
    if "chat_history" not in st.session_state:
        st.session_state["chat_history"] = []

    if clear_chat:
        st.session_state["chat_history"] = []

    # pdf_reader = PDFReader()
    response_generator = QAResponseGenerator("gpt-3.5-turbo-0613")
    # ボタンがクリックされた場合の処理
    if submit_question:
        if question:  # 質問が入力されている場合
            response = response_generator.generate(question)
            # if choice == "表示する":
            #     response += f"\n\n参照した情報は次の通りです:\n{source}"

            # 質問と応答をチャット履歴に追加
            st.session_state["chat_history"].append({"user": question})
            st.session_state["chat_history"].append({"bot": response})

    display_chat(st.session_state["chat_history"])

if __name__ == "__main__":
    main()

[dosubot[bot]]

🤖

Hello,

Based on the code you've shared, it seems like your chatbot is unable to answer questions about the index because it's operating in "condense_question" mode. In this mode, the chatbot generates a standalone question from the conversation context and the last message, then queries the query engine with the condensed question for a response. This approach works for questions directly related to the knowledge base, but it always queries the knowledge base, which can cause difficulty in answering meta questions like "what did I ask you before?" or questions about the index itself.

As for error handling, it should be implemented in the methods where the chatbot interacts with the LLM and generates responses. These methods include chat and stream_chat.

For example, in the chat method, you could add a try-except block around the code that interacts with the LLM:

def chat(self, message: str):
    try:
        response = self.chat_engine.chat(message)
        return response
    except Exception as e:
        # Handle or log the error here
        print(f"Error occurred: {e}")

This will catch any exceptions that occur during the response generation process, allowing you to handle them appropriately. In the context shared, the stream_chat method is also used, so similar error handling should be implemented there as well.

I hope this helps! If you have any other questions, feel free to ask.

Best, Dosu

Sources

• llama_index/chat_engine/context.py
• docs/end_to_end_tutorials/chatbots/building_a_chatbot.md
• llama_index/chat_engine/simple.py
• llama_index/agent/react/base.py
• docs/examples/chat_engine/chat_engine_condense_question.ipynb
• tests/chat_engine/test_condense_question.py
• llama_index/chat_engine/condense_question.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[Ma-Fukudama]

@dosu-bot How can I make it answer questions about indexes and questions in a conversation context?

I'm not particularly particular about the chat engine, so if there's another way to create an answer based on the contents of the index and the conversation context, I'd like to implement it that way.

[dosubot[bot]]

🤖

Based on your requirements, it seems like you want to make your chatbot capable of answering meta questions and questions about the index itself, based on the contents of the index and the conversation context.

To achieve this, you can use the ContextChatEngine class from the LlamaIndex framework. This class is designed to facilitate a fluid chat experience by using a retriever to fetch a context, setting the context in the system prompt, and then using an LLM (Language Model) to generate a response.

Here is a simplified example of how you can use it:

from llama_index.chat_engine.context import ContextChatEngine
from llama_index.indices.base_retriever import BaseRetriever
from llama_index.llms.base import LLM, ChatMessage
from llama_index.memory import BaseMemory
from llama_index.indices.service_context import ServiceContext

# Initialize your components
retriever = BaseRetriever()  # Replace with your actual retriever
llm = LLM()  # Replace with your actual LLM
memory = BaseMemory()  # Replace with your actual memory
service_context = ServiceContext()  # Replace with your actual service context
prefix_messages = [ChatMessage(role="system", content="Your system prompt here")]  # Replace with your actual prefix messages

# Initialize the chat engine
chat_engine = ContextChatEngine(
    retriever=retriever,
    llm=llm,
    memory=memory,
    prefix_messages=prefix_messages,
)

# Use the chat method
message = "Your message here"
chat_history = [ChatMessage(role="user", content="Previous message")]  # Replace with your actual chat history
response = chat_engine.chat(message, chat_history)

print(response)

Please replace the placeholders with your actual components. The chat() method will return an AgentChatResponse object, which contains the response from the LLM and the sources of the response.

You can find more details about the ContextChatEngine class in the LlamaIndex codebase.

To handle meta questions and questions about the index itself, you can modify the chat method of the ContextChatEngine class to check if a message is a meta question or a question about the index, and if so, generate an appropriate response. Here's a simple example of how you might implement this:

def handle_meta_question(self, message: str) -> Optional[AgentChatResponse]:
    """Handle meta questions and questions about the index."""
    # Check if the message is a meta question or a question about the index
    if self.is_meta_question(message):
        # Generate a response to the meta question
        response = self.generate_meta_response(message)
        return AgentChatResponse(response=response, sources=[], source_nodes=[])
    return None

def is_meta_question(self, message: str) -> bool:
    """Check if a message is a meta question or a question about the index."""
    # This is a simple example. You would need to implement a more robust method
    # for identifying meta questions and questions about the index.
    return message.lower().startswith("what is") or "how does" in message.lower()

def generate_meta_response(self, message: str) -> str:
    """Generate a response to a meta question or a question about the index."""
    # This is a simple example. You would need to implement a more robust method
    # for generating responses to meta questions and questions about the index.
    if "what is" in message.lower():
        return "LlamaIndex is a data framework for your LLM application."
    elif "how does" in message.lower():
        return "LlamaIndex works by retrieving a context, setting the context in the system prompt, and then using an LLM to generate a response."
    else:
        return "I'm sorry, I don't understand your question."

Then, in the chat method, you would call handle_meta_question before generating the context and if it returns a response, return that response immediately:

def chat(
    self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> AgentChatResponse:
    chat_history = chat_history or self._memory.get()

    # Handle meta questions and questions about the index
    meta_response = self.handle_meta_question(message)
    if meta_response is not None:
        return meta_response

    # Continue with the existing chat logic...

This is a basic example and would need to be expanded upon to handle a wider range of meta questions and questions about the index. The is_meta_question and generate_meta_response methods would need to be implemented with a more sophisticated method for identifying and responding to these types of questions.

I hope this helps! If you have any other questions, feel free to ask.

Sources

• [Question]: How to add previous Chat context when querying an index?
• Till when we can except the memory in house support in Llama-index Q&A ?
• Support to add conversation history in the prompt
• llama_index/chat_engine/context.py
• llama_index/agent/react/base.py
• llama_index/chat_engine/condense_question.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[Ma-Fukudama]

@dosu-bot I modified the source in my own way, but the following error occurred. AttributeError: 'VectorStoreIndex' object has no attribute 'ContextChatEngine'

The source is below.

import streamlit as st
import os, sys, json, site, time, logging, openai
from llama_index.llms import OpenAI
from dotenv import load_dotenv
from streamlit_chat import message
from llama_index import (
    download_loader,
    LLMPredictor,
    VectorStoreIndex,
    ServiceContext,
    QuestionAnswerPrompt,
    StorageContext,
    load_index_from_storage,
    SimpleDirectoryReader,
)

from llama_index.chat_engine.types import (
    AGENT_CHAT_RESPONSE_TYPE,
    AgentChatResponse,
    ChatResponseMode,
    StreamingAgentChatResponse,
)

from llama_index.chat_engine.context import ContextChatEngine
from llama_index.indices.base_retriever import BaseRetriever
from llama_index.llms.base import LLM, ChatMessage
from llama_index.memory import BaseMemory
from llama_index.indices.service_context import ServiceContext
from llama_index.indices.list.base import ListRetrieverMode
from typing import Optional, List

load_dotenv()

openai.api_key = os.environ["OPENAI_API_KEY"] 

# Initialize your components
# retriever = BaseRetriever()  # Replace with your actual retriever
prefix_messages = [ChatMessage(role="system", content="Your system prompt here")]  # Replace with your actual prefix messages

class QAResponseGenerator:
    def __init__(self, selected_model):
        self.llm_predictor = LLMPredictor(llm=OpenAI(temperature=1, model_name="gpt-3.5-turbo-0613"))
        self.QA_PROMPT_TMPL = (
            "下記の情報が与えられています。 \n"
            "---------------------\n"
            "{context_str}"
            "\n---------------------\n"
            "この情報を参照してできるだけ詳しく次の質問に答えてください: {query_str}\n"
            "回答には引用したファイル名を追記してください\n"
            "URLの場合はリンク先を追記してください"
        )
        self.service_context = ServiceContext.from_defaults(llm_predictor=self.llm_predictor)

    def generate(self, question):
        start = time.time()  
        storage_context = StorageContext.from_defaults(persist_dir="～/index")
        index = load_index_from_storage(storage_context)
        elapsed_time = time.time() - start
        print("load_elapsed_time:{0}".format(elapsed_time) + "[sec]")
        chat_history = [ChatMessage(role="user", content="Previous message")]  # Replace with your actual chat history
        start = time.time()
        engine = index.ContextChatEngine(text_qa_template=QuestionAnswerPrompt(self.QA_PROMPT_TMPL), retriever=ListRetrieverMode.EMBEDDING, prefix_messages=prefix_messages)
        result = engine.chat(question, chat_history)
        elapsed_time = time.time() - start
        print("query_time:{0}".format(elapsed_time) + "[sec]")
        return result.response.replace("\n", "")

    def prepare_chat(self, message: str, chat_history: Optional[List[ChatMessage]] = None) -> List[ChatMessage]:
        if chat_history is not None:
            self._memory.set(chat_history)
        self._memory.put(ChatMessage(content=message, role="user"))
        return self._prefix_messages + self._memory.get()

    def handle_meta_question(self, message: str) -> Optional[AgentChatResponse]:
        """Handle meta questions and questions about the index."""
        # Check if the message is a meta question or a question about the index
        if self.is_meta_question(message):
            # Generate a response to the meta question
            response = self.generate_meta_response(message)
            return AgentChatResponse(response=response, sources=[], source_nodes=[])
        return None

    def is_meta_question(self, message: str) -> bool:
        """Check if a message is a meta question or a question about the index."""
        # This is a simple example. You would need to implement a more robust method
        # for identifying meta questions and questions about the index.
        return message.lower().startswith("what is") or "how does" in message.lower()

    def generate_meta_response(self, message: str) -> str:
        """Generate a response to a meta question or a question about the index."""
        # This is a simple example. You would need to implement a more robust method
        # for generating responses to meta questions and questions about the index.
        if "what is" in message.lower():
            return "LlamaIndex is a data framework for your LLM application."
        elif "how does" in message.lower():
            return "LlamaIndex works by retrieving a context, setting the context in the system prompt, and then using an LLM to generate a response."
        else:
            return "I'm sorry, I don't understand your question."

    def chat(
        self, message: str, chat_history: Optional[List[ChatMessage]] = None
    ) -> AgentChatResponse:
        chat_history = chat_history or self._memory.get()

        # Handle meta questions and questions about the index
        meta_response = self.handle_meta_question(message)
        if meta_response is not None:
            return meta_response

def display_chat(chat_history):
    for i, chat in enumerate(reversed(chat_history)):
        if "user" in chat:
            message(chat["user"], is_user=True, key=str(i)) 
        else:
            message(chat["bot"], key="bot_"+str(i))

def main():

    question = st.text_input("Your question")

    # メインの画面に質問送信ボタンを設定
    submit_question = st.button("質問")
    clear_chat = st.button("履歴消去")

    # チャット履歴を保存
    if "chat_history" not in st.session_state:
        st.session_state["chat_history"] = []

    if clear_chat:
        st.session_state["chat_history"] = []

    # pdf_reader = PDFReader()
    response_generator = QAResponseGenerator("gpt-3.5-turbo-0613")
    # ボタンがクリックされた場合の処理
    if submit_question:
        if question:  # 質問が入力されている場合
            response = response_generator.generate(question)
            # if choice == "表示する":
            #     response += f"\n\n参照した情報は次の通りです:\n{source}"

            # 質問と応答をチャット履歴に追加
            st.session_state["chat_history"].append({"user": question})
            st.session_state["chat_history"].append({"bot": response})

    display_chat(st.session_state["chat_history"])

if __name__ == "__main__":
    main()

[dosubot[bot]]

🤖

Hello,

The error message AttributeError: 'VectorStoreIndex' object has no attribute 'ContextChatEngine' suggests that you are trying to access the ContextChatEngine attribute from an object of the VectorStoreIndex class. However, the VectorStoreIndex class does not have this attribute.

In your code, it seems like you're trying to directly use ContextChatEngine from VectorStoreIndex which is not correct. Instead, you should use the as_chat_engine method of the VectorStoreIndex object to create a chat engine. Here is an example of how you can do this:

from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
from llama_index.llms import OpenAI

# Necessary to use the latest OpenAI models that support function calling API
service_context = ServiceContext.from_defaults(llm=OpenAI(model="gpt-3.5-turbo-0613"))
data = SimpleDirectoryReader(input_dir="../data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(data, service_context=service_context)

chat_engine = index.as_chat_engine(chat_mode="openai", verbose=True)

After creating the chat engine, you can use the chat method of the chat_engine object to interact with the chatbot. If you are still facing issues, please ensure that you are using the correct version of the LlamaIndex framework and that the as_chat_engine method is available in the version you are using.

For more information, you can refer to the following sources:

• llama_index/chat_engine/context.py
• docs/examples/chat_engine/chat_engine_context.ipynb
• docs/examples/chat_engine/chat_engine_condense_question.ipynb
• docs/examples/chat_engine/chat_engine_openai.ipynb

I hope this helps! If you have any other questions, feel free to ask.

Sources

• LLama Toolkit integration seems to be broken for Vector index.
• llama_index/chat_engine/context.py
• docs/examples/chat_engine/chat_engine_context.ipynb
• docs/examples/chat_engine/chat_engine_condense_question.ipynb
• docs/examples/chat_engine/chat_engine_openai.ipynb

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[Ma-Fukudama]

@dosu-bot If you want to create an answer based on the contents of the index and the conversation context, you need to do it with ContextChatEngine, right?

In the case of VectorStoreIndex, if it is necessary to create a VectorStoreIndex chat engine with the as_chat_engine method, does that mean that ContextChatEngine cannot be used?

[dosubot[bot]]

🤖

Hello,

Yes, you can integrate the ContextChatEngine with the VectorStoreIndex in the LlamaIndex framework. The as_chat_engine method you're currently using creates a chat engine from the VectorStoreIndex object, but it doesn't prevent you from using the ContextChatEngine.

Here's an example of how you can do this:

from llama_index import VectorStoreIndex, SimpleDirectoryReader, StorageContext
from llama_index.vector_stores import DeepLakeVectorStore
from llama_index.chat_engine.context import ContextChatEngine
from llama_index.indices.base_retriever import BaseRetriever
from llama_index.llms.base import LLM
from llama_index.memory import BaseMemory
from llama_index.indices.service_context import ServiceContext

# construct vector store and customize storage context
storage_context = StorageContext.from_defaults(
    vector_store = DeepLakeVectorStore(dataset_path="<dataset_path>")
)

# Load documents and build index
documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

# Create chat engine
chat_engine = index.as_chat_engine()

# Create ContextChatEngine
context_chat_engine = ContextChatEngine.from_defaults(
    retriever=BaseRetriever(index),
    service_context=ServiceContext.from_defaults(),
    memory=BaseMemory.from_defaults()
)

In this example, we first create a DeepLakeVectorStore and use it to customize the StorageContext. We then load the documents and build the VectorStoreIndex using the from_documents method. After that, we create the chat engine using the as_chat_engine method on the VectorStoreIndex object. Finally, we create the ContextChatEngine using the from_defaults method and passing the BaseRetriever with the index, the ServiceContext from defaults, and the BaseMemory from defaults.

Please replace '<dataset_path>' with the actual path to your dataset.

The ContextChatEngine class in the LlamaIndex framework is designed to facilitate a fluid chat experience. It uses a retriever to fetch a context, sets the context in the system prompt, and then uses an LLM (Language Model) to generate a response. You can find more details about the ContextChatEngine class in the source code.

I hope this helps! Let me know if you have any other questions.

Sources

• docs/community/integrations/vector_stores.md
• llama_index/chat_engine/context.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[Ma-Fukudama]

@dosu-bot The following error occurred. Does BaseRetriever have no arguments? TypeError: BaseRetriever() takes no arguments　

[dosubot[bot]]

🤖

Hi there,

The error you're encountering is due to the fact that the BaseRetriever class in the LlamaIndex framework does not have an __init__ method defined, which means it does not accept any arguments when creating an instance of the class.

To resolve this issue, you need to create a subclass of BaseRetriever and define an __init__ method in your subclass. This __init__ method should accept the index argument and any other arguments you need. Here's an example:

from llama_index.indices.base_retriever import BaseRetriever
from llama_index.query_bundle import QueryBundle
from llama_index.node_with_score import NodeWithScore
from typing import List

class MyRetriever(BaseRetriever):
    def __init__(self, index):
        self._index = index

    def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        # Implement your retrieval logic here
        pass

In this example, MyRetriever is a subclass of BaseRetriever that accepts an index argument when creating an instance. The index is stored in the _index attribute, which is used in the get_service_context method of the BaseRetriever class.

You can then pass an instance of MyRetriever to the ContextChatEngine:

my_retriever = MyRetriever(index)
context_chat_engine = ContextChatEngine.from_defaults(
    retriever=my_retriever,
    service_context=ServiceContext.from_defaults(),
    memory=BaseMemory.from_defaults()
)

This way, you're not directly instantiating BaseRetriever (which doesn't accept arguments), but a subclass that does.

The BaseRetriever class in the LlamaIndex framework serves as an abstract base class for creating retriever classes. Its main purpose is to define the interface for retrieving nodes given a query. The actual retrieval logic is implemented in the _retrieve and _aretrieve methods, which are meant to be overridden by subclasses.

I hope this helps! Let me know if you have any other questions.

Sources

• llama_index/indices/base_retriever.py
• llama_index/retrievers/router_retriever.py
• llama_index/retrievers/recursive_retriever.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.