This PR adds a Gemini-based direct demo for LightRAG, mirroring the
existing llamaindex direct demo.
Features:
- Uses gemini-2.0-flash for LLM inference
- Uses text-embedding-004 for embeddings
- Demonstrates naive, local, global, and hybrid query modes
- Includes required storage initialization
- Keeps a minimal, dependency-free setup
This serves as a reference example for users who want to use
LightRAG with Google Gemini without external frameworks.
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>
