Multi-modal Optimizer + Context for Optimization

[allenanie]

Adding multi-modal support. Also introducing a context section.

For context, the design intention is that if the user provides context, it will appear in the user message; if no context is provided, the section will not be there.

[Copilot]

Pull Request Overview

This PR implements multi-modal support for optimizers and introduces a context section to provide additional information during optimization. The changes enable image input handling, context passing, and improved structure for optimization prompts.

Key changes include:

• Multi-modal payload support for handling images alongside text queries
• Context section implementation for passing additional optimization context
• Optimizer API enhancements to support image and context inputs

Reviewed Changes

Copilot reviewed 8 out of 8 changed files in this pull request and generated 5 comments.

Show a summary per file

File	Description
tests/unit_tests/test_priority_search.py	Added multi-modal message handling for test compatibility
opto/optimizers/utils.py	Added image encoding utility for base64 conversion
opto/optimizers/optoprime_v2.py	Main multi-modal and context implementation with API changes
opto/optimizers/opro_v2.py	Extended OPRO optimizer with context support
opto/features/flows/types.py	Added multi-modal payload types and query normalization
opto/features/flows/compose.py	Updated TracedLLM to handle multi-modal payloads
docs/tutorials/minibatch.ipynb	Updated escape sequences in notebook output
.github/workflows/ci.yml	Commented out optimizer test suite

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[allenanie]

TODO:

1. Support Image URL directly loading (Adith)
2. Add support for in-memory image (like RGB/numpy) (Ching-An)
3. Node supports image (multi-modal) -- when we traverse it, we add the image as payload into the optimizer. Add a function to node to determine if it's image.

[allenanie]

@copilot open a new pull request to apply changes based on the comments in this thread

[Copilot]

@allenanie I've opened a new pull request, #54, to work on those changes. Once the pull request is ready, I'll request review from you.

[Copilot]

Pull Request Overview

Copilot reviewed 8 out of 8 changed files in this pull request and generated 14 comments.

Comments suppressed due to low confidence (1)

opto/optimizers/optoprime_v2.py:236

• Call to method OptoPrime.extract_llm_suggestion with too few arguments; should be no fewer than 2.

        return OptoPrime.extract_llm_suggestion(response)

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[allenanie]

To increase backward compatibility, the llm.py is designed in the following way:
(mm_beta means multi-modal beta version):

When mm_beta (multimodal) is enabled, we either use:

1. LiteLLM's response API (most compatible with OpenAI models, but also can work with others)

When mm_beta is disabled, for backward compatibility, we use:

1. LiteLLM's completion API (default)

For any Google models (starts with gemini in the model name), we use:
Google's generate_content API (LiteLLM's Gemini support is insufficient for our use case)

Even with this small change, a lot of details were handled:

1. OpenAI returns base64 string. Google GenAI library returns bytes.
2. Google GenAI library expects system_instruction explicitly passed in. OpenAI uses message role role="system".
3. (and other small quality of life updates)

In addition to llm.py changes, we update the AssistantTurn construction. Now it can take a raw response from the LLM API call and directly map the returned result into our class construct.

This is not strictly necessary, but this helps us simplify Optimizer's design since it no longer needs to interact with raw LLM API response object.

[allenanie]

Multiturn conversation is tested.

See test test_real_google_genai_multi_turn_with_images_updated in test_optimizer_backbone.py

We store conversation history as structured data in AssistantTurn and UserTurn. They are added to ConversationHistory object. When we need to pass them back into the LLM API call, we call history.to_messages() to automatically get the input, or explicitly call history.to_gemini_format() or history.to_litellm_format().

to_messages() will do a smart check to see the model used by the last AssistantTurn and automatically determine which format function is use. However, this is not 100% reliable (for example, if CustomLLM backend is used but a Gemini model is called, then this automatic conversion will fail because CustomLLM expects an OpenAI-compatible server).

[allenanie]

So far, all supporting functions for multi-modal capabilities are finished:

1. backbone.py
2. llm.py

Tests are finished:

1. test_llm.py
2. test_optimizer_backbone.py

Remaining todos:

1. Integrate this into the optimizer class (i.e., support image parameter extraction)
2. Write a notebook to demonstrate usage of the backbone as well as new optimizer.

[allenanie]

@chinganc I think this is ready for the first round of code review...

Can you see if this notebook runs for you?
https://github.com/AgentOpt/OpenTrace/blob/9ea88c9079abc16558c139b0262f224620ffa751/examples/multimodal_html_example/example.ipynb

My plan for the 2nd round:

1. Add a new notebook for the new llm.py usage
2. Add a new notebook for the backbone.py usage

[allenanie]

Chat now is a multi-modal multi-turn chat session management class with auto-truncation and tool support.