# RAG-Chat

Chat with your Documents/Knowledgebase


## Usage

Clone the repo, enter it and run:

```
podman-compose -f docker-compose.yaml up
```

## Structure

### Object Storage
* Minio (http://localhost:9001/)

### Vector Storage
* OpenSearch (Dashboard under: http://localhost:5601/)

### RAG-Backend
* API-Docs http://localhost:8000/docs


## Development

```
nodemon --ext '*' --exec "podman stop rag-chat-backend; podman rm rag-chat-backend; podman-compose -f docker-compose.yaml up --build"
```

### Ollama (CLI)

<details>
<summary><b>Show Models from Ollama</b></summary>

    curl http://localhost:11434/api/tags | jq

</details>


<details>
<summary><b>Run Chat Completion</b></summary>

    curl http://localhost:11434/v1/chat/completions \
        -H "Content-Type: application/json" \
        -d '{
            "model": "phi3:latest",
            "messages": [
                {
                    "role": "system",
                    "content": "You are a helpful assistant."
                },
                {
                    "role": "user",
                    "content": "Hello!"
                }
            ]
        }'

</details>


### VectorDB (CLI)

<details>
<summary><b>Quere Verctoriezed content</b></summary>

    curl -X 'POST' \
        'http://localhost:8000/api/search-engine?query=HTML%20of%20your%20question' \
        -H 'accept: application/json' | jq

</details>


### VectorDB (Opensearch Dashboard)

Run these at http://localhost:5601/app/dev_tools#/console

<details>
<summary><b>View Chunked Vectors</b></summary>

    GET /my-project-name/_search
    {
        "query": {
            "match_all": {}
        }
    }


</details>


<details>
<summary><b>Get the three best documents with theire embeddings</b></summary>

    GET /my-project-name/_search
    {
        "size": 0,
        "query": {
            "bool": {"must": [{"match": {"content": "Enter your Question here..."}}]}
        },
        "aggs": {
            "group_by_source": {
                "terms": {
                    "field": "metadata.source.keyword",
                    "size": 100000
                    
                },
                "aggs": {
                    "top_entries": {
                        "top_hits": {
                            "size": 3,
                            "sort": [{"_score": {"order": "desc"}}],
                            "_source": {"excludes": ["embedding_vector"]}
                        }
                    }
                }
            }
        }
    }

</details>


## TODO