Start the Pathway Vector DB Server¶

Set the environment variables for Pathway, and the embedding model.

Note: If you are using TEI_EMBEDDING_ENDPOINT, make sure embedding service is already running. See the instructions under here

export PATHWAY_HOST=0.0.0.0
export PATHWAY_PORT=8666
# TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"  # uncomment if you want to use the hosted embedding service, example: "http://127.0.0.1:6060"

Configuration¶

Setting up the Pathway data sources¶

Pathway can listen to many sources simultaneously, such as local files, S3 folders, cloud storage, and any data stream. Whenever a new file is added or an existing file is modified, Pathway parses, chunks and indexes the documents in real-time.

See pathway-io for more information.

You can easily connect to the data inside the folder with the Pathway file system connector. The data will automatically be updated by Pathway whenever the content of the folder changes. In this example, we create a single data source that reads the files under the ./data folder.

You can manage your data sources by configuring the data_sources in vectorstore_pathway.py.

import pathway as pw

data = pw.io.fs.read(
    "./data",
    format="binary",
    mode="streaming",
    with_metadata=True,
)  # This creates a Pathway connector that tracks
# all the files in the ./data directory

data_sources = [data]

Other configs (parser, splitter and the embedder)¶

Pathway vectorstore handles the ingestion and processing of the documents. This allows you to configure the parser, splitter and the embedder. Whenever a file is added or modified in one of the sources, Pathway will automatically ingest the file.

By default, ParseUnstructured parser, langchain.text_splitter.CharacterTextSplitter splitter and BAAI/bge-base-en-v1.5 embedder are used.

For more information, see the relevant Pathway docs:

Building and running¶

Build the Docker and run the Pathway Vector Store:

cd comps/vectorstores/langchain/pathway/docker

docker build --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -t opea/vectorstore-pathway:latest .

# with locally loaded model, you may add `EMBED_MODEL` env variable to configure the model.
docker run -e PATHWAY_HOST=${PATHWAY_HOST} -e PATHWAY_PORT=${PATHWAY_PORT} -e http_proxy=$http_proxy -e https_proxy=$https_proxy -v ./data:/app/data -p ${PATHWAY_PORT}:${PATHWAY_PORT} opea/vectorstore-pathway:latest

# with the hosted embedder (network argument is needed for the vector server to reach to the embedding service)
docker run -e PATHWAY_HOST=${PATHWAY_HOST} -e PATHWAY_PORT=${PATHWAY_PORT} -e TEI_EMBEDDING_ENDPOINT=${TEI_EMBEDDING_ENDPOINT} -e http_proxy=$http_proxy -e https_proxy=$https_proxy -v ./data:/app/data -p ${PATHWAY_PORT}:${PATHWAY_PORT} --network="host" opea/vectorstore-pathway:latest

Health check the vector store¶

Wait until the server finishes indexing the docs, and send the following request to check it.

curl -X 'POST' \
  "http://$PATHWAY_HOST:$PATHWAY_PORT/v1/statistics" \
  -H 'accept: */*' \
  -H 'Content-Type: application/json'

This should respond with something like:

{"file_count": 1, "last_indexed": 1724325093, "last_modified": 1724317365}