glide-the 6865d60133
Fix/feat: Implementation of Minute-Based Rate Limiting in CommunityReportsExtractor Using asyncio and async_mode (#373)
* RateLimiter: The original TpmRpmLLMLimiter strategy did not account for minute-based rate limiting when scheduled. The RateLimiter was introduced to ensure that the CommunityReportsExtractor could be scheduled to adhere to rate configurations on a per-minute basis.

RateLimiter scheduled: using asyncio and async_mode

Additionally, some key loading issues for rpm = "REQUESTS_PER_MINUTE" and tpm = "TOKENS_PER_MINUTE" were fixed. Configuration loading was also enhanced to include temperature = "TEMPERATURE" and top_p = "TOP_P" settings.

* RateLimiter scheduled: using asyncio and async_mode

* Additionally, some key loading issues for rpm = "REQUESTS_PER_MINUTE" and tpm = "TOKENS_PER_MINUTE" were fixed. Configuration loading was also enhanced to include temperature = "TEMPERATURE" and top_p = "TOP_P" settings.

* Format

* Semversioner

* Format and cleanup

---------

Co-authored-by: Alonso Guevara <alonsog@microsoft.com>
2024-07-05 13:41:11 -06:00
2024-07-01 23:40:54 +00:00
2024-07-01 15:25:30 -06:00
2024-07-03 12:38:36 -06:00
2024-07-01 15:25:30 -06:00
2024-07-01 15:25:30 -06:00
2024-07-01 15:25:30 -06:00
2024-07-01 15:25:30 -06:00
2024-07-01 15:25:30 -06:00
2024-07-01 15:25:30 -06:00
2024-07-01 15:25:30 -06:00
2024-07-01 15:25:30 -06:00
2024-07-01 15:25:30 -06:00
2024-07-01 15:25:30 -06:00
2024-07-03 10:01:18 -06:00
2024-07-01 15:25:30 -06:00
2024-07-01 15:25:30 -06:00
2024-07-01 16:55:22 -06:00
2024-07-01 15:25:30 -06:00
2024-07-03 10:13:16 -06:00
2024-07-01 15:25:30 -06:00
2024-07-01 15:25:30 -06:00

GraphRAG

👉 Use the GraphRAG Accelerator solution
👉 Microsoft Research Blog Post
👉 Read the docs
👉 GraphRAG Arxiv

Overview

The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs.

To learn more about GraphRAG and how it can be used to enhance your LLMs ability to reason about your private data, please visit the Microsoft Research Blog Post.

Quickstart

To get started with the GraphRAG system we recommend trying the Solution Accelerator package. This provides a user-friendly end-to-end experience with Azure resources.

Repository Guidance

This repository presents a methodology for using knowledge graph memory structures to enhance LLM outputs. Please note that the provided code serves as a demonstration and is not an officially supported Microsoft offering.

Diving Deeper

Prompt Tuning

Using GraphRAG with your data out of the box may not yield the best possible results. We strongly recommend to fine-tune your prompts following the Prompt Tuning Guide in our documentation.

Responsible AI FAQ

See RAI_TRANSPARENCY.md

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Privacy

Microsoft Privacy Statement

Description
A modular graph-based Retrieval-Augmented Generation (RAG) system
Readme MIT 280 MiB
Languages
Python 96%
Jupyter Notebook 3.9%