Explanation on Response Synthetizers and Prompts

[hristogg]

I am using a bit customized CondensePlusContextChatEngine and am trying to understand the way it works once I use the "chat" method.

I am trying to follow all the classes and subclasses being initialized but to be honest there seems to be a high level of abstraction which makes it a bit hard for me to understand.

Could you guys support me in confirming my understanding and maybe answering a few additional questions.
If I understand correctly, the first thing that the chat method does is call the run_c3 method which take scare of condensation ,retrieval and ultimately leads to getting the response_synthetizer where things get a bit murky to me...
There seems to be called two time the function get_prefix_messages_with_context once for context and once for refinement. I do not clearly understand the refinement need here.

The call here leads us to initialization of CompactAndRefine class with the above mentioned templates.

Ultimately this CompactAndRefine's method synthetize method is being called which is taken taken from CompactAndRefine -> Refine -> BaseSynthesizer.

And if I understand correctly this is where the magic happens - with the get_response which is an abstractmethod defined in the the CompactAndRefine class, which however takes the implementation from the Refine class.
In this method we start going over some "text chunks" and if I understand correctly these text_chunks are the nodes passed to the chat method with only the content taken.

Now it seems we are iterating over every text chunk - for the first chunk doing _give_response_single and for each next doing _refine_response_single.

This whole setup is incredibly confusing to me and I am not fully clear on why this iteration is there, how it helps, how can I change it and how to control it. My assumption is that this deals with having large context and if you cannot fit the entire context to generate answer to go over it iteratively and update the answer based on each additional chunk(node).

However I do not see how it is checking what my context window is and whether it can actually fit. Can someone please help me shed a light on the logic and especially the last step - _refine_response_single which seems to do most of it :)

[dosubot[bot]]

The CondensePlusContextChatEngine's chat method indeed involves several steps and abstractions, particularly around the CompactAndRefine class and its methods. Here's a breakdown of the process:

1. run_c3 Method: This method is responsible for condensing the conversation history and retrieving relevant context nodes. It then constructs a response synthesizer, which is an instance of the CompactAndRefine class.

2. CompactAndRefine Class: This class is used to synthesize responses by iterating over text chunks. It uses two main methods:

   • _give_response_single: This method generates an initial response for the first text chunk.
   • _refine_response_single: This method refines the response for each subsequent text chunk. It uses a refine template to incorporate the query string and the existing answer, ensuring the response is as accurate and detailed as possible based on the available context ^[1].

3. Handling Large Contexts: The iteration over text chunks allows the system to handle large contexts by processing them in smaller, manageable pieces. This approach helps in updating the response iteratively as more context is considered.

4. Context Window Management: The CompactAndRefine class uses a _prompt_helper to manage context size. This helper calculates the available context size and repacks text chunks to fit within the model's constraints, ensuring efficient use of the context window ^[2][3].

Your understanding is correct that this setup deals with large contexts by iterating over text chunks and refining the response iteratively. The _refine_response_single method plays a crucial role in this process by adjusting the response based on additional context or constraints. If you want to control this process, you might need to look into how the _prompt_helper manages context size and how text chunks are generated and processed.

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}

[hristogg]

Thanks @dosu is there a way I can control the context window threshold - I am assuming that the default one is DEFAULT_CONTEXT_WINDOW in core/constant.py which is 3900 tokens which the _prompt_helper uses, however I do not see this context_window being used as an argument anywhere which makes me believe you do not have control over it in the higher levels of abstractions maybe only from the constants file?

[dosubot[bot]]

You can control the context window size without modifying the constants file by using the Settings class in the LlamaIndex framework. Here's how you can set a custom context window size:

from llama_index.core import Settings

# Set a custom context window size
Settings.context_window = 4096

Additionally, if you are using the HuggingFaceLLM class, you can set the context_window parameter directly when initializing the model:

from llama_index.llms.huggingface import HuggingFaceLLM

llm = HuggingFaceLLM(
    model_name="HuggingFaceH4/zephyr-7b-beta",
    tokenizer_name="HuggingFaceH4/zephyr-7b-beta",
    context_window=3900,
    max_new_tokens=256,
    # other parameters
)

This approach allows you to customize the context window size through configuration rather than altering the source code directly ^[1][2].

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}

[hristogg]

@dosu just to make sure I understand - if I set the context window size to a larger one, and have 5 nodes passed to the synthetizer would this mean that this would not be 5 iterations for the refine, but it would be chunked based on the new context size - for example it could be one chunk it fits into the context

[dosubot[bot]]

Yes, if you set a larger context window size, the CompactAndRefine class will adjust the number of iterations based on the new context size. The PromptHelper will repack the text chunks to fit within the specified context window. If all 5 nodes can fit into the new, larger context window, they could be processed in fewer iterations, potentially even in a single chunk if they all fit ^[1][2].

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}