https://github.com/mfkimbell/ai-rag-pdf
https://github.com/mfkimbell/ai-rag-pdf
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/mfkimbell/ai-rag-pdf
- Owner: mfkimbell
- Created: 2024-09-17T19:23:38.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-09-20T20:18:19.000Z (10 months ago)
- Last Synced: 2025-01-22T22:31:18.319Z (6 months ago)
- Language: Python
- Size: 17.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ai-rag-pdf
### Architechture
### Demo
### Tools Used
* `PyPDFLoader` - Lanchain library for loading pdf data
* `FAISS` - Vector Storage and simliarity search through Langchain
* `Titan Text v1` - Creating text embeddings
* `Bedrock` - LangChain module for integrating with AWS Bedrock for LLM interactions.
* `Streamlit` - Framework for building interactive web applications (particularly in data science)
* `Docker` - Containerization platform used for running the application locally.
* `Claude-v2` - Large language model used
### Imporant to note
* Chunking size mattered a lot here, apparenlty 300-500 is recommended for resumes to give it more context
### Local Testing
To test the application locally, follow these steps:
1. **Clone the repo:**
```bash
git clone https://github.com/mfkimbell/ai-rag-hr.git
```
2. **Pull the Docker Image:**
```bash
docker pull mfkimbell/ai-rag-doc:latest
```
2. **Run the Docker Container:**
```bash
docker run --env-file .env -p 8501:8501 mfkimbell/ai-rag-doc:latest
```
Ensure that you have a `.env` file with the necessary environment variables.
`AWS_ACCESS_KEY_ID`
`AWS_SECRET_ACCESS_KEY`
`AWS_DEFAULT_REGION`
3. **Access Webapp**
```http://0.0.0.0:8501/```
or
```http://localhost:8501/```