Fix logging output in evaluation test harness and examples:
- Replace print() statements with logger calls in e2e_test_harness.py
- Update copy_llm_cache_to_another_storage.py to use logger instead of print
- Remove redundant logging configuration in copy_llm_cache_to_another_storage.py
Fix path handling and typos:
- Correct makedirs() call in lightrag_openai_demo.py to create log_dir directly
- Update constants.py comments to clarify SOURCE_IDS_LIMIT_METHOD options
- Remove duplicate return statement in utils.py normalize_extracted_info()
- Fix error string formatting in chroma_impl.py with !s conversion
- Remove unused pipmaster import from chroma_impl.py
Add comprehensive test suites for prompt evaluation:
- test_prompt_accuracy.py: 365 lines testing prompt extraction accuracy
- test_prompt_quality_deep.py: 672 lines for deep quality analysis
- Refactor prompt.py to consolidate optimized variants (removed prompt_optimized.py)
- Apply ruff formatting and type hints across 30 files
- Update pyrightconfig.json for static type checking
- Modernize reproduce scripts and examples with improved type annotations
- Sync uv.lock dependencies
Format entire codebase with ruff and add type hints across all modules:
- Apply ruff formatting to all Python files (121 files, 17K insertions)
- Add type hints to function signatures throughout lightrag core and API
- Update test suite with improved type annotations and docstrings
- Add pyrightconfig.json for static type checking configuration
- Create prompt_optimized.py and test_extraction_prompt_ab.py test files
- Update ruff.toml and .gitignore for improved linting configuration
- Standardize code style across examples, reproduce scripts, and utilities
This commit renames the parameter 'llm_model_max_token_size' to 'summary_max_tokens' for better clarity, as it specifically controls the token limit for entity relation summaries.
Refactored the LLM cache to a flat Key-Value (KV) structure, replacing the previous nested format. The old structure used the 'mode' as a key and stored specific cache content as JSON nested under it. This change significantly enhances cache recall efficiency.
This pull request demonstrates how to create a new Opik project when using LiteLLM for LlamaIndex-based LLM calls. The primary goal is to enable detailed tracing, monitoring, and logging of LLM interactions in a new Opik project_name, particularly when using LiteLLM as an API proxy. This enhancement allows for better debugging, performance analysis, observability when using LightRAG with LiteLLM and Opik.
**Motivation:**
As our application's reliance on Large Language Models (LLMs) grows, robust observability becomes crucial for maintaining system health, optimizing performance, and understanding usage patterns. Integrating Opik provides the following key benefits:
1. **Improved Debugging:** Enables end-to-end tracing of requests through the LlamaIndex and LiteLLM layers, making it easier to identify and resolve issues or performance bottlenecks.
2. **Comprehensive Performance Monitoring:** Allows for the collection of vital metrics such as LLM call latency, token usage, and error rates. This data can be filtered and analyzed within Opik using project names and tags.
3. **Effective Cost Management:** Facilitates tracking of token consumption associated with specific requests or projects, leading to better cost control and optimization.
4. **Deeper Usage Insights:** Provides a clearer understanding of how different components of the application or various projects are utilizing LLM capabilities.
These changes empower developers to seamlessly add observability to their LlamaIndex-based LLM workflows, especially when leveraging LiteLLM, by passing necessary Opik metadata.
**Changes Made:**
1. **`lightrag/llm/llama_index_impl.py`:**
* Modified the `llama_index_complete_if_cache` function:
* The `**kwargs` parameter, which previously handled additional arguments, has been refined. A dedicated `chat_kwargs={}` parameter is now used to pass keyword arguments directly to the `model.achat()` method. This change ensures that vendor-specific parameters, such as LiteLLM's `litellm_params` for Opik metadata, are correctly propagated.
* The logic for retrieving `llm_instance` from `kwargs` was removed as `model` is now a direct parameter, simplifying the function.
* Updated the `llama_index_complete` function:
* Ensured that `**kwargs` (which may include `chat_kwargs` or other parameters intended for `llama_index_complete_if_cache`) are correctly passed down.
2. **`examples/unofficial-sample/lightrag_llamaindex_litellm_demo.py`:**
* This existing demo file was updated to align with the changes in `llama_index_impl.py`.
* The `llm_model_func` now passes an empty `chat_kwargs={}` by default to `llama_index_complete_if_cache` if no specific chat arguments are needed, maintaining compatibility with the updated function signature. This file serves as a baseline example without Opik integration.
3. **`examples/unofficial-sample/lightrag_llamaindex_litellm_opik_demo.py` (New File):**
* A new example script has been added to specifically demonstrate the integration of LightRAG with LlamaIndex, LiteLLM, and Opik for observability.
* The `llm_model_func` in this demo showcases how to construct the `chat_kwargs` dictionary.
* It includes `litellm_params` with a `metadata` field for Opik, containing `project_name` and `tags`. This provides a clear example of how to send observability data to Opik.
* The call to `llama_index_complete_if_cache` within `llm_model_func` passes these `chat_kwargs`, ensuring Opik metadata is included in the LiteLLM request.
These modifications provide a more robust and extensible way to pass parameters to the underlying LLM calls, specifically enabling the integration of observability tools like Opik.
Co-authored-by: Martin Perez-Guevara <8766915+MartinPerez@users.noreply.github.com>
Co-authored-by: Young Jin Kim <157011356+jidodata-ykim@users.noreply.github.com>
