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# Retrieval Augmented Generation
RAG with ollama and qdrant.
## Usage
### Setup
#### Environment Variables
Create a .env file or set the following parameters:
```.env
CHUNK_SIZE = <CHUNK_SIZE>
CHUNK_OVERLAP = <CHUNK_OVERLAP>
ENCODER_MODEL = <ENCODER_MODEL>
EMBEDDING_DIM = <EMBEDDING_DIM>
GENERATOR_MODEL = <GENERATOR_MODEL>
DOCUMENT_DB_NAME = <DOCUMENT_DB_NAME>
DOCUMENT_DB_USER = <DOCUMENT_DB_USER>
QDRANT_URL = <QDRANT_URL>
QDRANT_COLLECTION_NAME = <QDRANT_COLLECTION_NAME>
COHERE_API_KEY = <COHERE_API_KEY> # OPTIONAL
```
### Ollama
Make sure ollama is running:
```sh
ollama serve
```
Download the encoder and generator models with ollama:
```sh
ollama pull $GENERATOR_MODEL
ollama pull $ENCODER_MODEL
```
### Qdrant
Qdrant will is used to store the embeddings of the chunks from the documents.
Download and run qdrant.
### Postgres
Postgres is used to save hashes of the document chunks to prevent document chunks from
being added to the vector db more than ones.
Download and run qdrant.
### Cohere
Get an API from their website.
### Running
#### Prerequisites
##### Python Environment
Activate the poetry shell:
```sh
poetry shell
```
#### CLI
```sh
python rag/cli.py
```
#### UI
Run the web app with streamlit:
```sh
streamlit run rag/ui.py
```
### Notes
Yes, it is inefficient/dumb to use ollama when you can just load the models with python
in the same process.
### Inspiration
I took some inspiration from these tutorials:
[rag-openai-qdrant](https://colab.research.google.com/github/qdrant/examples/blob/master/rag-openai-qdrant/rag-openai-qdrant.ipynb)
[building-rag-application-using-langchain-openai-faiss](https://medium.com/@solidokishore/building-rag-application-using-langchain-openai-faiss-3b2af23d98ba)
[knowledge_gpt](https://github.com/mmz-001/knowledge_gpt)
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