learningcircuit--local-deep-research/examples/llm_integration

LLM Integration Examples

This directory contains examples of integrating custom LangChain LLMs with Local Deep Research.

Examples

1. Basic Custom LLM (basic_custom_llm.py)

Shows the simplest way to create and use a custom LLM with LDR.

2. Advanced Custom LLM (advanced_custom_llm.py)

Demonstrates advanced features like:

• Factory functions with configuration
• Multiple LLM registration
• Combining with custom retrievers
• Error handling and retry logic

3. Fine-tuned Model Integration (finetuned_model_example.py)

Example of using a fine-tuned model for domain-specific research.

4. Mock LLM for Testing (mock_llm_example.py)

Shows how to create mock LLMs for testing your research pipelines without API costs.

5. Rate-Limited Wrapper (rate_limited_llm.py)

Demonstrates wrapping any LLM with rate limiting to avoid API limits.

Running the Examples

1. Install Local Deep Research:

pip install local-deep-research

2. Run an example:

python examples/llm_integration/basic_custom_llm.py

Key Concepts

• BaseChatModel: All custom LLMs must inherit from langchain_core.language_models.BaseChatModel
• Factory Functions: Can be used to create LLMs with dynamic configuration
• Registration: LLMs are registered via the llms parameter in API functions
• Provider Selection: Use the registered name as the provider parameter

Common Use Cases

1. Fine-tuned Models: Use models trained on your specific domain
2. Custom Wrappers: Add logging, retry logic, or preprocessing
3. Mock Testing: Test research flows without real LLM calls
4. Rate Limiting: Manage API quotas effectively
5. Multi-Model Pipelines: Use different models for different research phases