#1432#1447
This PR adds support for the GROQ LLM (Large Language Model).
Groq is an AI solutions company delivering ultra-low latency inference
with the first-ever LPU™ Inference Engine. The Groq API enables
developers to integrate state-of-the-art LLMs, such as Llama-2 and
llama3-70b-8192, into low latency applications with the request limits
specified below. Learn more at [groq.com](https://groq.com/).
Supported Models
| ID | Requests per Minute | Requests per Day | Tokens per Minute |
|----------------------|---------------------|------------------|-------------------|
| gemma-7b-it | 30 | 14,400 | 15,000 |
| gemma2-9b-it | 30 | 14,400 | 15,000 |
| llama3-70b-8192 | 30 | 14,400 | 6,000 |
| llama3-8b-8192 | 30 | 14,400 | 30,000 |
| mixtral-8x7b-32768 | 30 | 14,400 | 5,000 |
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Co-authored-by: paresh0628 <paresh.tuvoc@gmail.com>
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
### What problem does this PR solve?
#1036
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Co-authored-by: Zhedong Cen <cenzhedong2@126.com>
### What problem does this PR solve?
#308
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: KevinHuSh <kevinhu.sh@gmail.com>
### What problem does this PR solve?
Support displaying tables in the chunks of pdf file when using QA parser
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Support extracting questions and answers from PDF files
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
_Briefly describe what this PR aims to solve. Include background context
that will help reviewers understand the purpose of the PR._
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Let file in knowledgebases visible in file manager.
#162
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Optimize task broker and executor for reduce memory usage and deployment
complexity.
### Type of change
- [x] Performance Improvement
- [x] Refactoring
### Change Log
- Enhance redis utils for message queue(use stream)
- Modify task broker logic via message queue (1.get parse event from
message queue 2.use ThreadPoolExecutor async executor )
- Modify the table column name of document and task (process_duation ->
process_duration maybe just a spelling mistake)
- Reformat some code style(just what i see)
- Add requirement_dev.txt for developer
- Add redis container on docker compose
---------
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
