# Data query example
This example makes use of [Llama-Index ](https://gpt-index.readthedocs.io/en/stable/getting_started/installation.html ) to enable question answering on a set of documents.
It loosely follows [the quickstart ](https://gpt-index.readthedocs.io/en/stable/guides/primer/usage_pattern.html ).
Summary of the steps:
- prepare the dataset (and store it into `data` )
- prepare a vector index database to run queries on
- run queries
## Requirements
You will need a training data set. Copy that over `data` .
## Setup
Start the API:
```bash
# Clone LocalAI
git clone https://github.com/go-skynet/LocalAI
cd LocalAI/examples/query_data
wget https://huggingface.co/skeskinen/ggml/resolve/main/all-MiniLM-L6-v2/ggml-model-q4_0.bin -O models/bert
wget https://gpt4all.io/models/ggml-gpt4all-j.bin -O models/ggml-gpt4all-j
# start with docker-compose
docker-compose up -d --build
```
### Create a storage
In this step we will create a local vector database from our document set, so later we can ask questions on it with the LLM.
Note: **OPENAI_API_KEY** is not required. However the library might fail if no API_KEY is passed by, so an arbitrary string can be used.
```bash
export OPENAI_API_BASE=http://localhost:8080/v1
export OPENAI_API_KEY=sk-
python store.py
```
After it finishes, a directory "storage" will be created with the vector index database.
## Query
We can now query the dataset.
```bash
export OPENAI_API_BASE=http://localhost:8080/v1
export OPENAI_API_KEY=sk-
python query.py
```
## Update
To update our vector database, run `update.py`
```bash
export OPENAI_API_BASE=http://localhost:8080/v1
export OPENAI_API_KEY=sk-
python update.py
```
