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# Retrieval Augmented Generation
RAG with ollama (and optionally cohere) and qdrant. This is basically a glorified
(bloated) `ctrl+f`.
## Usage
### Setup
#### 1. Environment Variables
Create a .env file or set the following parameters:
```.env
CHUNK_SIZE=4096
CHUNK_OVERLAP=256
ENCODER_MODEL=nomic-embed-text
EMBEDDING_DIM=768
RETRIEVER_TOP_K=15
RETRIEVER_SCORE_THRESHOLD=0.5
RERANK_MODEL=mixedbread-ai/mxbai-rerank-large-v1
RERANK_TOP_K=5
GENERATOR_MODEL=llama3
DOCUMENT_DB_NAME=rag
DOCUMENT_DB_USER=aktersnurra
QDRANT_URL=http://localhost:6333
QDRANT_COLLECTION_NAME=knowledge-base
COHERE_API_KEY = <COHERE_API_KEY> # OPTIONAL
COHERE_RERANK_MODEL = "rerank-english-v3.0"
```
#### 2. Install Python Dependencies
```
poetry install
```
#### 3. 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
```
#### 4. Qdrant
Qdrant is used to store the embeddings of the chunks from the documents.
Download and run qdrant.
#### 5. Postgres
Postgres is used to save hashes of the document to prevent documents from
being added to the vector db more than ones.
Download and run qdrant.
#### 6. Cohere
Get an API from their website, but is optional.
### Running
Activate the poetry shell:
```sh
poetry shell
```
Use the cli:
```sh
python rag/cli.py
```
or the ui using a browser:
```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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