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.. | ||
data | ||
models | ||
.gitignore | ||
docker-compose.yml | ||
query.py | ||
README.md | ||
store.py |
Data query example
This example makes use of Llama-Index to enable question answering on a set of documents.
It loosely follows the quickstart.
Requirements
For this in order to work, you will need a model compatible with the llama.cpp
backend. This is will not work with gpt4all.
The example uses WizardLM
. Edit the config files in models/
accordingly to specify the model you use (change HERE
).
You will also need a training data set. Copy that over data
.
Setup
Start the API:
# Clone LocalAI
git clone https://github.com/go-skynet/LocalAI
cd LocalAI/examples/query_data
# Copy your models, edit config files accordingly
# start with docker-compose
docker-compose up -d --build
Create a storage:
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
export OPENAI_API_BASE=http://localhost:8080/v1
export OPENAI_API_KEY=sk-
python query.py