feat(llm): pass llm params directly

[Pouyanpi]

Description

langchain-community models support .bind() method

• All models inherit from langchain-core Runnable interface
• .bind() is universally available across all LangChain packages

langchain-core (contains Runnable interface with .bind())
── langchain-openai (inherits .bind())
── langchain-community (inherits .bind())
── langchain-anthropic (inherits .bind())
── langchain-* (all inherit .bind() ? )

Related Issue(s)

Checklist

• I've read the CONTRIBUTING guidelines.
• I've updated the documentation if applicable.
• I've added tests if applicable.
• @mentions of the person or team responsible for reviewing proposed changes.

[Copilot]

Pull Request Overview

This PR migrates from using context managers for LLM parameter management to passing parameters directly to the llm_call function. The change leverages LangChain's universal .bind() method to pass parameters like temperature and max_tokens directly to LLM models without temporarily modifying their state.

Key changes:

• Added llm_params parameter to llm_call function for direct parameter passing
• Replaced all with llm_params(...) context manager usage with direct parameter passing
• Updated tests to cover the new parameter passing approach

Reviewed Changes

Copilot reviewed 21 out of 21 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
nemoguardrails/actions/llm/utils.py	Added llm_params parameter to llm_call and implemented LLM binding
tests/test_tool_calling_utils.py	Added comprehensive tests for new parameter passing functionality
tests/test_llm_params_e2e.py	New end-to-end tests for LLM parameter functionality with real providers
tests/test_llm_params.py	Added migration tests comparing context manager with direct parameter approach
Various action files	Updated all LLM calls to use direct parameter passing instead of context managers
docs/user-guides/advanced/prompt-customization.md	Updated documentation example to show new parameter passing syntax

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[Copilot]

The use of chain.with_config() with configurable parameter differs from the pattern used elsewhere in the codebase. This should use the llm_params approach for consistency, or the LLM should be bound directly with .bind() before creating the chain.

[Copilot]

The use of chain.invoke() with config parameter is inconsistent with the migration pattern used throughout the rest of the codebase. This should follow the same llm_params pattern for consistency.

Suggested change

	config={"temperature": 0.8, "max_tokens": 300},
	llm_params={"temperature": 0.8, "max_tokens": 300},

[tgasser-nv]

Looks good, 4k LOC is too large for a single PR.

I'm a little confused about a few things:

• Why did we use a context-manager to pass a dict of LLM parameters in the first place? Normally they're used to make sure we close files/DB connections so we don't forget.
• Does a context-manager break some Langchain functionality?
• Can you add some local integration tests to make sure this works calling tools with production LLMs?