Added custom is_finished_parser logic to Google Vertex AI customizati…

[cnemri]

Title: Added a custom completion detection parser for Gemini models

Description

This PR updates Ragas model customization how to guide. It adds proper completion detection support for Google's Vertex AI Gemini models. Currently, Ragas systematically raises LLMDidNotFinishException with Gemini models because it doesn't correctly interpret Gemini's completion signals.

Problem

The current implementation in LangchainLLMWrapper doesn't properly handle Gemini's completion signals:

• Gemini uses "STOP" and "MAX_TOKENS" as valid completion reasons
• The completion status can be found in either generation_info or response_metadata
• The current logic doesn't account for these Gemini-specific patterns

Solution

1. Added a custom completion detection parser for Gemini models
2. Added the custom parser to LangchainLLMWrapper call

Changes

1. Modified ragas/docs/howtos/customizations/customize_models.md:

from langchain_core.outputs import LLMResult, ChatGeneration

def gemini_is_finished_parser(response: LLMResult) -> bool:
    is_finished_list = []
    for g in response.flatten():
        resp = g.generations[0][0]
        
        # Check generation_info first
        if resp.generation_info is not None:
            finish_reason = resp.generation_info.get("finish_reason")
            if finish_reason is not None:
                is_finished_list.append(
                    finish_reason in ["STOP", "MAX_TOKENS"]
                )
                continue
                
        # Check response_metadata as fallback
        if isinstance(resp, ChatGeneration) and resp.message is not None:
            metadata = resp.message.response_metadata
            if metadata.get("finish_reason"):
                is_finished_list.append(
                    metadata["finish_reason"] in ["STOP", "MAX_TOKENS"]
                )
            elif metadata.get("stop_reason"):
                is_finished_list.append(
                    metadata["stop_reason"] in ["STOP", "MAX_TOKENS"] 
                )
        
        # If no finish reason found, default to True
        if not is_finished_list:
            is_finished_list.append(True)
            
    return all(is_finished_list)

vertextai_llm = LangchainLLMWrapper(vertextai_llm, is_finished_parser=gemini_is_finished_parser)

[cnemri]

Hi, @shahules786 Is there further clarification needed on this PR?

[jjmachan]

@cnemri thanks again for this contribution 🙂