Data query example

This example makes use of langchain and chroma to enable question answering on a set of documents.

Setup

Download the models and start the API:

# Clone LocalAI
git clone https://github.com/go-skynet/LocalAI

cd LocalAI/examples/langchain-chroma

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

# configure your .env
# NOTE: ensure that THREADS does not exceed your machine's CPU cores
mv .env.example .env

# start with docker-compose
docker-compose up -d --build

# tail the logs & wait until the build completes
docker logs -f langchain-chroma-api-1

Python requirements

pip install -r requirements.txt

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.

export OPENAI_API_BASE=http://localhost:8080/v1
export OPENAI_API_KEY=sk-

wget https://raw.githubusercontent.com/hwchase17/chat-your-data/master/state_of_the_union.txt
python store.py

After it finishes, a directory "db" will be created with the vector index database.

Query

We can now query the dataset.

export OPENAI_API_BASE=http://localhost:8080/v1
export OPENAI_API_KEY=sk-

python query.py
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