File size: 2,403 Bytes
f722ac6
 
 
 
 
 
 
 
 
 
 
 
 
4a9aa3a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
---
title: Medivocate
emoji: 🐒
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false
license: apache-2.0
short_description: Medivocate is an AI-driven platform leveraging Retrieval-Aug
---

# Medivocate

An AI-driven platform empowering users with trustworthy, personalized history guidance to combat misinformation and promote equitable history.

## Follows us [here](https://github.com/KameniAlexNea/medivocate)

* [**Alex Kameni**](https://www.linkedin.com/in/elie-alex-kameni-ngangue/)
* [**Esdras Fandio**](https://www.linkedin.com/in/esdras-fandio/)
* [**Patric Zeufack**](https://www.linkedin.com/in/zeufack-patric-hermann-7a9256143/)

## Project Overview

**Medivocate** is structured for modular development and ease of scalability, as seen in its directory layout:

```
πŸ“¦ ./
β”œβ”€β”€ πŸ“ docs/
β”œβ”€β”€ πŸ“ src/
β”‚   β”œβ”€β”€ πŸ“ ocr/
β”‚   β”œβ”€β”€ πŸ“ preprocessing/
β”‚   β”œβ”€β”€ πŸ“ chunking/
β”‚   β”œβ”€β”€ πŸ“ vector_store/
β”‚   β”œβ”€β”€ πŸ“ rag_pipeline/
β”‚   β”œβ”€β”€ πŸ“ llm_integration/
β”‚   └── πŸ“ prompt_engineering/
β”œβ”€β”€ πŸ“ tests/
β”‚   β”œβ”€β”€ πŸ“ unit/
β”‚   └── πŸ“ integration/
β”œβ”€β”€ πŸ“ examples/
β”œβ”€β”€ πŸ“ notebooks/
β”œβ”€β”€ πŸ“ config/
β”œβ”€β”€ πŸ“„ README.md
β”œβ”€β”€ πŸ“„ CONTRIBUTING.md
β”œβ”€β”€ πŸ“„ requirements.txt
β”œβ”€β”€ πŸ“„ .gitignore
└── πŸ“„ LICENSE
```

### Key Features

1. **Trustworthy Information Access** : Using RAG (Retrieval-Augmented Generation) pipelines to deliver fact-based responses.
2. **Advanced Document Handling** : Leveraging OCR, preprocessing, and chunking for scalable document ingestion.
3. **Integrated Tools** : Supports integration with vector databases (e.g., Chroma), LLMs, and advanced prompt engineering techniques.

### Recommendations for Integration

* **Groq** : Utilize Groq APIs for free-tier LLM support, perfect for prototyping RAG applications.
* **LangChain + LangSmith** : Build and monitor intelligent agents with LangChain and enhance debugging and evaluation using LangSmith.
* **Hugging Face Datasets** : For one-liner dataset loading and preprocessing, supporting efficient ML training pipelines.
* **Search Index** : Include Chroma for robust semantic search capabilities in RAG.

This modular design and extensive integration make Medivocate a powerful tool for historical education and research.