How to add a chat engine for text-to-sql pipeline?

[sohanasarah]

I want to build a chatbot that can interact with a SQL table. I am following this example to build a text-to-sql pipeline:
https://docs.llamaindex.ai/en/stable/examples/pipeline/query_pipeline_sql/?h=advanced+text

I want to use Streamlit as the UI. How do I wrap the pipeline qp to the chat engine? what should be the index here?

    st.session_state.chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True)

[dosubot[bot]]

Hey @sohanasarah, great to see you diving into another challenge with LlamaIndex! Let's get that chatbot chatting with SQL in no time. 🚀

Based on the context you've provided, it seems like you're trying to integrate the text-to-SQL pipeline into a chat engine using Streamlit. The index in your code snippet index.as_chat_engine(chat_mode="condense_question", verbose=True) should be an instance of one of the chat engine classes imported from llama_index.legacy.chat_engine.

Given your specific requirement to use chat_mode="condense_question", you should create an instance of CondenseQuestionChatEngine. Here's how you can do it:

from llama_index.legacy.chat_engine.condense_question import CondenseQuestionChatEngine

# Create an instance of CondenseQuestionChatEngine
index = CondenseQuestionChatEngine()

# Use the instance with the specified method and parameters
index.as_chat_engine(chat_mode="condense_question", verbose=True)

This code snippet correctly initializes index as an instance of CondenseQuestionChatEngine, which is suitable for your requirement to use the condense_question chat mode in the Streamlit chat engine. You can find more details about this in the llama-index-legacy/llama_index/legacy/chat_engine/init.py file.

However, it's important to note that the CondenseQuestionChatEngine class in the provided context does not explicitly define a method named as_chat_engine. Therefore, it's not possible to describe the purpose and functionality of a method that does not exist in the given context. You might want to double-check the method name or the class you're using.

The CondenseQuestionChatEngine class in the LlamaIndex codebase is designed to process a conversation between a human and an assistant, along with a follow-up message from the human, and generate a standalone question that encapsulates all relevant context from the conversation. This standalone question is then used to query a query engine for a response. The purpose of this process is to ensure that the query engine receives a clear, contextually rich question, potentially leading to more accurate and relevant responses. You can find more details about this in the llama-index-core/llama_index/core/chat_engine/condense_question.py file.

I hope this helps! If you have any more questions or need further clarification, feel free to ask.

Sources

• llama-index-legacy/llama_index/legacy/chat_engine/init.py
• llama-index-core/llama_index/core/chat_engine/condense_question.py

About Dosu

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[dosubot]

To connect your text-to-SQL query pipeline to a query engine using LlamaIndex, you'll primarily work with the SQLAutoVectorQueryEngine class. This class is adept at querying both SQL databases and vector databases, making intelligent decisions on querying sequences and augmentations based on the context. Here's how you can set it up:

1. Prepare SQL and Vector Query Engines: Ensure you have instances of your SQL query engine (like BaseSQLTableQueryEngine or NLSQLTableQueryEngine) and vector query engine (typically a RetrieverQueryEngine with a VectorIndexAutoRetriever).

2. Create Query Engine Tools: Utilize the QueryEngineTool.from_defaults method to create tools for both SQL and vector querying. These tools encapsulate your query engines, providing names and descriptions for each.

3. Instantiate SQLAutoVectorQueryEngine: With your query tools ready, instantiate the SQLAutoVectorQueryEngine. This step may include specifying additional parameters such as a selector (e.g., LLMSingleSelector), a service context, and whether to enable SQL vector synthesis.

4. Alternative Instantiation Method: The SQLAutoVectorQueryEngine can also be instantiated using the from_sql_and_vector_query_engines class method. This method directly accepts your SQL and vector query engines, along with their names, descriptions, and any other relevant parameters.

Here's a simplified example to illustrate the process:

# Assuming your SQL and vector query engines are initialized
sql_query_engine = ...  # Instance of your SQL query engine
vector_query_engine = ...  # Instance of your vector query engine

# Create SQL and Vector Query Tools
sql_query_tool = QueryEngineTool.from_defaults(
    sql_query_engine, name="SQL Tool", description="SQL Query Tool"
)
vector_query_tool = QueryEngineTool.from_defaults(
    vector_query_engine, name="Vector Tool", description="Vector Query Tool"
)

# Instantiate SQLAutoVectorQueryEngine
sql_auto_vector_query_engine = SQLAutoVectorQueryEngine(
    sql_query_tool=sql_query_tool,
    vector_query_tool=vector_query_tool,
    # Optionally, specify other parameters
)

# Alternative instantiation
sql_auto_vector_query_engine = SQLAutoVectorQueryEngine.from_sql_and_vector_query_engines(
    sql_query_engine=sql_query_engine,
    sql_tool_name="SQL Tool",
    sql_tool_description="SQL Query Tool",
    vector_auto_retriever=vector_query_engine,
    vector_tool_name="Vector Tool",
    vector_tool_description="Vector Query Tool",
    # Optionally, specify other parameters
)

This setup enables your text-to-SQL pipeline to effectively leverage both SQL and vector store queries, enhancing the versatility and accuracy of your query engine. For more detailed information on the SQLAutoVectorQueryEngine class, you can refer to the sql_vector_query_engine.py file.

Sources

• llama-index-legacy/llama_index/legacy/query_engine/sql_vector_query_engine.py
• llama-index-core/llama_index/core/query_engine/sql_vector_query_engine.py
• llama-index-core/llama_index/core/tools/query_engine.py

About Dosu

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.

[sohanasarah]

@dosu-bot, can you guide me on how to use a query pipeline in chat conversation?

[dosubot]

To integrate your text-to-SQL query pipeline into a chat conversation, you can create a custom chat engine that leverages your query pipeline for processing messages. Here's a way to do it:

First, define a custom chat engine by subclassing BaseChatEngine. This custom chat engine will utilize your query pipeline to process incoming chat messages and generate responses:

from llama_index.legacy.chat_engine import BaseChatEngine

class CustomChatEngine(BaseChatEngine):
    def __init__(self, query_pipeline, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.query_pipeline = query_pipeline

    def chat(self, message):
        # Use your query pipeline to process the message and generate a response
        response = self.query_pipeline.process(message)
        return response

With CustomChatEngine, you initialize it with your query pipeline. This setup allows the chat method to process chat messages using the query pipeline, facilitating the generation of responses based on the processed message.

Next, integrate this custom chat engine into your Streamlit app like so:

st.session_state.chat_engine = CustomChatEngine(qp, chat_mode="condense_question", verbose=True)

This approach ensures that when a chat message is received, the chat method of CustomChatEngine is invoked. This method then leverages your query pipeline to process the message and generate an appropriate response.

Keep in mind, this example is simplified and might require adjustments to fit your specific needs. You may need to implement additional methods or handle various types of messages differently. For further guidance and ideas, consider exploring the source code of existing chat engines in the llama_index.legacy.chat_engine module.

For more detailed examples and ideas, you can refer to:

• llama-index-legacy/llama_index/legacy/chat_engine/init.py
• llama-index-legacy/llama_index/legacy/chat_engine/base.py

This setup should help you effectively integrate your text-to-SQL query pipeline into a chat conversation, enhancing the interactivity and functionality of your chatbot.

Sources

• llama-index-legacy/llama_index/legacy/chat_engine/init.py
• llama-index-core/llama_index/core/chat_engine/condense_question.py
• llama-index-legacy/llama_index/legacy/indices/base.py

About Dosu

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.

[sohanasarah]

@dosu-bot, I get the following error when defining a custom chat engine by subclassing BaseChatEngine.

TypeError: Can't instantiate abstract class CustomChatEngine with abstract methods achat, astream_chat, chat, chat_history, reset, stream_chat

[dosubot]

Based on the error message you're seeing, it appears that you need to implement the abstract methods achat, astream_chat, chat_history, reset, and stream_chat in your CustomChatEngine subclass. These methods are part of the BaseChatEngine class in the LlamaIndex framework and are required for any subclass.

Here's a brief overview of what each method does:

• achat and astream_chat: These are the asynchronous versions of the chat and stream_chat methods. They are designed to handle chat interactions in an asynchronous manner, which can be beneficial in environments that support or require asynchronous operations.

• chat_history: This method should return the entire chat history as a list of ChatMessage objects. If your chat engine does not maintain a history, you can return an empty list.

• reset: This method should reset the conversation state. If your chat engine does not maintain a conversation state or if resetting is not applicable, you can implement this method to simply pass.

• stream_chat: This method is designed for streaming chat interactions. If your application does not require streaming responses, you can implement this method to immediately return a response without actual streaming.

Here's how you can implement these methods in your CustomChatEngine class:

from llama_index.legacy.chat_engine import BaseChatEngine
from typing import List, Optional
from llama_index.core.chat_engine.types import ChatMessage, AgentChatResponse, StreamingAgentChatResponse

class CustomChatEngine(BaseChatEngine):
    def __init__(self, query_pipeline, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.query_pipeline = query_pipeline

    def chat(self, message):
        # Intended use of the query pipeline to process the message and generate a response
        response = self.query_pipeline.process(message)
        return response

    def reset(self) -> None:
        # Reset conversation state if applicable
        pass

    async def achat(self, message: str, chat_history: Optional[List[ChatMessage]] = None) -> AgentChatResponse:
        # Implement your async chat logic here
        return AgentChatResponse(response="Your async response here")

    def stream_chat(self, message: str, chat_history: Optional[List[ChatMessage]] = None) -> StreamingAgentChatResponse:
        # Implement your streaming chat logic here
        return StreamingAgentChatResponse(response="Streaming response")

    async def astream_chat(self, message: str, chat_history: Optional[List[ChatMessage]] = None) -> StreamingAgentChatResponse:
        # Implement your async streaming chat logic here
        return StreamingAgentChatResponse(response="Async streaming response")

    @property
    def chat_history(self) -> List[ChatMessage]:
        # Return chat history if applicable
        return []

Please note that the AgentChatResponse and StreamingAgentChatResponse are placeholder responses in the example above. You should replace them with the actual responses generated by your chat engine.

For more information about these methods, you can refer to the LlamaIndex core chat engine types and context source code.

I hope this helps! If you have any more questions or need further clarification, feel free to ask.

Sources

• llama-index-core/llama_index/core/chat_engine/types.py
• llama-index-core/llama_index/core/chat_engine/context.py

About Dosu

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.