Add lang chain lite llm integration support

[Naseem77]

Summary by CodeRabbit

• New Features

  • LangChain LiteLLM integration: configurable model, chat sessions with persistent history, streaming responses, and JSON import/export.

• Documentation

  • Added “Using LangChain LiteLLM Integration” with setup steps, when to use LangChain vs direct LiteLLM, examples, and an OpenRouter proxy walkthrough.

• Tests

  • Tests for initialization, serialization, chat sessions, messaging, streaming, and message deletion (with gated integration tests).

• Chores

  • Added LangChain-related dependencies.

[coderabbitai[bot]]

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Walkthrough

Adds a LangChain-backed LiteLLM wrapper and chat session classes, documentation for using LangChain LiteLLM, tests for initialization/serialization/chat flow/streaming, and new runtime dependencies (langchain-litellm, langchain-core). No public API signatures of existing modules were modified.

Changes

Cohort / File(s)	Summary of changes
Documentation README.md	Added "Using LangChain LiteLLM Integration" section with setup, usage examples, decision guidance, and an OpenRouter LiteLLM proxy example.
Model integration graphrag_sdk/models/langchain_litellm.py	New LangChainLiteModel and LangChainLiteModelChatSession classes: lazy-imports langchain-litellm, initializes ChatLiteLLM with merged params, provides start_chat, send_message, send_message_stream, chat history management, response parsing, and JSON (de)serialization; raises informative errors on missing deps or request failures.
Dependencies pyproject.toml, requirements.txt	Added dependencies: langchain-litellm and langchain-core.
Tests tests/test_langchain_litellm.py	New tests for model initialization, to_json/from_json, chat session creation, history retrieval/deletion, and env-gated integration tests for messaging and streaming.
Wordlist .wordlist.txt	Added terms: LangChain, ChatLiteLLM, OpenRouter, LangChain's, LiteLLM's.

Sequence Diagram(s)

sequenceDiagram
  autonumber
  actor User
  participant Model as LangChainLiteModel
  participant Session as LangChainLiteModelChatSession
  participant LC as LangChain ChatLiteLLM
  participant Provider as Provider API

  User->>Model: start_chat(system_instruction?)
  Model->>Model: lazy-import ChatLiteLLM
  Model->>LC: init(model_name, api_key, api_base, params, gen_config)
  Model-->>User: ChatSession

  User->>Session: send_message(content)
  Session->>Session: append HumanMessage
  Session->>LC: generate(messages, config)
  LC->>Provider: HTTP request
  Provider-->>LC: response
  LC-->>Session: AIMessage
  Session->>Session: append AIMessage
  Session-->>User: GenerationResponse

  opt Streaming
    User->>Session: send_message_stream(content)
    Session->>LC: stream(messages, config)
    loop tokens
      LC-->>Session: partial token
      Session-->>User: yield partial
    end
    Session->>Session: append full AIMessage
  end

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~25 minutes

Suggested reviewers

• gkorland
• swilly22

Poem

A hop, I scaffold chat and bind the stream,
I tuck system prompts where bright tokens gleam.
I nibble configs, stitch each generated seam,
History cozy beneath moonbeam cream.
(\/) Tests twinkle — (••) Ready to beam.

Pre-merge checks and finishing touches

✅ Passed checks (3 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title Check	✅ Passed	The pull request title clearly and succinctly states the primary change—adding LangChain LiteLLM integration support—and aligns directly with the modified files without extraneous or vague wording.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.

✨ Finishing touches

• [ ] 📝 Generate docstrings

🧪 Generate unit tests (beta)

• [ ] Create PR with unit tests
• [ ] Post copyable unit tests in a comment
• [ ] Commit unit tests in branch Add-LangChain-LiteLLM-integration-support

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