[Question]: How to force "Answer" to be the same as "Observation"?(prompt does not work)

lchustc commented 7 months ago

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

Question

I want the "Answer" to be the same as the （last）"Observation".

dosubot[bot] commented 7 months ago

To ensure that the "Answer" section exactly matches the "Observation" section in your conversational AI's debugging interface, you need to modify the prompt templates in a way that explicitly instructs the AI to mirror the observation in its response. This can be achieved by adjusting the content of the ChatMessage objects that define the prompts.

Here's how you can modify the content attribute of the ChatMessage objects to achieve this:

TEXT_QA_PROMPT_TMPL_MSGS and TREE_SUMMARIZE_PROMPT_TMPL_MSGS: Modify the ending of the prompt to instruct the AI to repeat the observation as the answer.
CHAT_REFINE_PROMPT_TMPL_MSGS: Since this involves refining an existing answer, ensure the instruction clearly states to use the observation if it directly matches the query.
CHAT_REFINE_TABLE_CONTEXT_TMPL_MSGS: Similar to the CHAT_REFINE_PROMPT, but with an additional focus on the table schema and context information.

Here's an example modification for the TEXT_QA_PROMPT_TMPL_MSGS:

TEXT_QA_PROMPT_TMPL_MSGS = [
    TEXT_QA_SYSTEM_PROMPT,
    ChatMessage(
        content=(
            "Context information is below.\n"
            "---------------------\n"
            "{context_str}\n"
            "---------------------\n"
            "Given the context information and not prior knowledge, "
            "answer the query.\n"
            "Query: {query_str}\n"
            "Observation: {observation_str}\n"  # Assuming you have an observation variable
            "Answer: Repeat the observation as the answer."
        ),
        role=MessageRole.USER,
    ),
]

For this modification to work, you need to ensure that {observation_str} (or however you choose to name the variable holding the observation text) is properly populated with the observation data you want the AI to mirror in its answer. This approach assumes you have a mechanism to capture or define the observation related to each query.

If the observation data is not directly available as a variable or if the structure of your data does not allow for a straightforward inclusion of observations in the prompt, you might need to adjust the data flow or structure to accommodate this requirement.

Remember, the key to making this work is ensuring that the AI's instructions within the prompt are clear and unambiguous, and that the data it needs to follow those instructions (in this case, the observation data) is readily available within the context of the prompt.

You can find more details about this in the llama-index-legacy/llama_index/legacy/prompts/chat_prompts.py file.

The LlamaIndex codebase does contain a conversational AI debugging interface, as evident from the chat_component function in the llama-index-packs/llama-index-packs-panel-chatbot/llama_index/packs/panel_chatbot/app.py file.

The LlamaIndex codebase also contains a mechanism to capture or define the observation related to each query in the conversational AI debugging interface. This is facilitated through the observability feature, which allows for one-click integration with observability/evaluation tools. This feature enables users to view LLM/prompt inputs/outputs, ensure component outputs are performing as expected, and view call traces for both indexing and querying. You can find more details about this in the docs/docs/understanding/tracing_and_debugging/tracing_and_debugging.md file.

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lchustc commented 7 months ago

Doesn't work, I hope the Answer to be the same as the tool Obeservation. These include functions and QA tools

logan-markewich commented 7 months ago

Hate to say it, but this is fundamental to how agents work, prompting is really the only way....

However, I just merged a fix into main that will be in the next release

tool = FunctionTool.from_defaults(...., return_direct=True)

You can declare tools as return_direct -- but be aware, this has limitations!

If a tool with return_direct is used, it will end the loop (no more tool calls)
If more than one tool is invoked (this really only applies to function calling/openai agents), then return_direct is ignored

run-llama / llama_index