file-ai

Manages files, provides/retrieves data from AI

Setup Instructions

Prerequisites

• Python 3.8+
• MongoDB Atlas account
• Ollama installed locally

MongoDB Atlas Setup

1. Create a MongoDB Atlas cluster if you haven't already
2. Create a database and collection for storing vectors
3. Create a Vector Search Index on your collection with the following configuration:

{
  "fields": [
    {
      "numDimensions": 768,
      "path": "embedding",
      "similarity": "cosine",
      "type": "vector"
    },
    {
      "path": "user_id",
      "type": "filter"
    }
  ]
}

Environment Setup

1. Create a .env file in the project root with the following variables:

MONGO_URI=your_mongodb_connection_string
EMBED_MODEL_NAME=nomic-embed-text
LLM_MODEL_NAME=llama2
DB_NAME=your_database_name
COLLECTION_NAME=your_collection_name
VECTOR_SEARCH_INDEX=your_vector_search_index_name
STORAGE_PATH=path_to_store_uploaded_files

Ollama Setup

1. Install Ollama from ollama.ai
2. Pull required models:

ollama pull nomic-embed-text
ollama pull llama2

3. Start Ollama server:

ollama serve

Project Setup

1. Install dependencies:

pip install -r requirements.txt

2. Run unit tests:

python -m pytest tests/unit_tests.py

Test Run Screenshot

3. Run the project (make sure Ollama server is running):

uvicorn main:app --port 8001

The API will be available at http://localhost:8001

API Documentation

Once the server is running, you can access the API documentation at:

• Swagger UI: http://localhost:8001/docs
• ReDoc: http://localhost:8001/redoc