feat: parsing langchain messages for mellea

[psschwei]

Misc PR

Type of PR

• Bug Fix
• New Feature
• Documentation
• Other

Description

• Link to Issue: add support for ingesting chat contexts / chat completion messages from other libraries #255

Adds support for converting LangChain messages to Mellea Message types and validating LangChain agent outputs using Mellea's native validation API, addressing #255.

Changes

• Added mellea/stdlib/components/chat_converters.py with conversion functions for LangChain message types (HumanMessage, AIMessage, SystemMessage, ToolMessage)
• Supports multimodal content (images via base64 data URIs)
• Provides two conversion strategies: direct parsing and via OpenAI intermediate format
• Added langchain-core as optional dependency: pip install "mellea[langchain]"
• Added Jupyter notebook docs/examples/library_interop/langchain_mellea_ivr.ipynb demonstrating validation of LangChain agent outputs

Usage

from langchain_core.messages import HumanMessage, AIMessage
from mellea.stdlib.components.chat_converters import langchain_messages_to_mellea
from mellea.core.base import ModelOutputThunk
from mellea.stdlib.context import ChatContext
import mellea

lc_messages = [HumanMessage(content="Hello!"), AIMessage(content="The answer is 7.")]
mellea_messages = langchain_messages_to_mellea(lc_messages[:-1])

# Validate LangChain output with Mellea's native API
ctx = ChatContext()
for msg in mellea_messages:
    ctx = ctx.add(msg)
ctx = ctx.add(ModelOutputThunk(value=lc_messages[-1].content))

m = mellea.start_session(ctx=ctx)
validations = m.validate([req("The answer must be greater than 5")])

Testing

• Tests added to the respective file if code was changed
• New code has 100% coverage if code as added
• Ensure existing tests and github automation passes (a maintainer will kick off the github automation when the rest of the PR is populated)

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