[Question]: Can I use a chat engine with streamlit? #8403
Replies: 13 comments 1 reply
-
|
🤖 Hello, Yes, you can use a chat engine with Streamlit in the LlamaIndex framework. The LlamaIndex framework provides several chat engines, including Here is a basic example of how you might set up the 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 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 Please note that you will need to replace I hope this helps! If you have any other questions, feel free to ask. Best, Sources
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. |
Beta Was this translation helpful? Give feedback.
-
|
@dosu-beta I tried implementing something that uses an index, but I would like you to point out what is wrong. |
Beta Was this translation helpful? Give feedback.
-
|
🤖 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:
if not isinstance(service_context.llm_predictor, LLMPredictor):
raise ValueError(f"llm_predictor must be a LLMPredictor instance, not {type(service_context.llm_predictor)}")
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()
I hope these suggestions help you improve your code. If you have any further questions, feel free to ask. Best, Sources
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. |
Beta Was this translation helpful? Give feedback.
-
|
@dosu-bot |
Beta Was this translation helpful? Give feedback.
-
|
🤖 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 For example, in the 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 I hope this helps! If you have any other questions, feel free to ask. Best, Sources
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. |
Beta Was this translation helpful? Give feedback.
-
|
@dosu-bot 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. |
Beta Was this translation helpful? Give feedback.
-
|
🤖 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 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 You can find more details about the To handle meta questions and questions about the index itself, you can modify the 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 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 I hope this helps! If you have any other questions, feel free to ask. Sources
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. |
Beta Was this translation helpful? Give feedback.
-
|
@dosu-bot The source is below. |
Beta Was this translation helpful? Give feedback.
-
|
🤖 Hello, The error message In your code, it seems like you're trying to directly use 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 For more information, you can refer to the following sources:
I hope this helps! If you have any other questions, feel free to ask. Sources
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. |
Beta Was this translation helpful? Give feedback.
-
|
@dosu-bot 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? |
Beta Was this translation helpful? Give feedback.
-
|
🤖 Hello, Yes, you can use Here is how you can correctly implement it: from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.memory import ChatMemoryBuffer
# Load data and build index
data = SimpleDirectoryReader(input_dir="../data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(data)
# Configure chat engine
memory = ChatMemoryBuffer.from_defaults(token_limit=1500)
chat_engine = index.as_chat_engine(
chat_mode="context",
memory=memory,
system_prompt=(
"You are a chatbot, able to have normal interactions, as well as talk"
" about an essay discussing Paul Grahams life."
),
)
# Now you can chat with your data
response = chat_engine.chat("Hello!")
print(response)In this code, the This solution is based on the example provided in the chat_engine_context.ipynb notebook in the LlamaIndex repository. As for the I hope this helps! Let me know if you have any other questions. Sources
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. |
Beta Was this translation helpful? Give feedback.
-
|
🤖 Hello, Yes, you can integrate the 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 Please replace The I hope this helps! Let me know if you have any other questions. SourcesThis 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. |
Beta Was this translation helpful? Give feedback.
-
|
@dosu-bot |
Beta Was this translation helpful? Give feedback.
-
|
🤖 Hi there, The error you're encountering is due to the fact that the To resolve this issue, you need to create a subclass of 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
passIn this example, You can then pass an instance of 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 The I hope this helps! Let me know if you have any other questions. Sources
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. |
Beta Was this translation helpful? Give feedback.
-
Question Validation
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.
Beta Was this translation helpful? Give feedback.
All reactions