Postgres Vector Store
There are two scripts available here: load-docs.ts and query.ts
Prerequisites
Start a DB Instance
You'll need a postgres database instance against which to run these scripts. A simple docker command would look like this:
docker run -d --rm --name vector-db -p 5432:5432 -e "POSTGRES_HOST_AUTH_METHOD=trust" ankane/pgvector
NOTE: Using --rm in the example docker command above means that the vector store will be deleted every time the container is stopped. For production purposes, use a volume to ensure persistence across restarts.
If you prefer using a managed service, you can use Timescale to create a PostgreSQL database instance in the cloud as an alternative.
Set up Environment
Having created a DB instance, you can then set up environment variables for your database connection:
export PGHOST=your database host
export PGUSER=your database user
export PGPASSWORD=your database password
export PGDATABASE=your database name
export PGPORT=your database port
export OPENAI_API_KEY=your openai api keySet the environment variables above to match your database setup.
Note that you'll also need an OpenAI key (OPENAI_API_KEY) in your environment.
You're now ready to start the scripts.
Setup and Loading Docs
Read and follow the instructions in the README.md file located one directory up to make sure your JS/TS dependencies are set up. The commands listed below are also run from that parent directory.
To import documents and save the embedding vectors to your database:
npx tsx pg-vector-store/load-docs.ts data
where data is the directory containing your input files. Using the data directory in the example above will read all of the files in that directory using the LlamaIndexTS default readers for each file type.
RAG Querying
To query using the resulting vector store:
npx tsx pg-vector-store/query.ts
The script will prompt for a question, then process and present the answer using the PGVectorStore data and your OpenAI API key. It will continue to prompt until you enter q, quit or exit as the next query.
