weaviate

Weaviate RAG Sample

This sample demonstrates how to use Weaviate as a vector database for RAG (Retrieval Augmented Generation) workflows with Genkit.

Prerequisites

1. Weaviate Instance: Either local or cloud-hosted
2. Google GenAI API Key: For embeddings and LLM

Running Weaviate Locally

docker run -d \
  --name weaviate \
  -p 8080:8080 \
  -p 50051:50051 \
  -e QUERY_DEFAULTS_LIMIT=25 \
  -e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true \
  -e PERSISTENCE_DATA_PATH=/var/lib/weaviate \
  -e DEFAULT_VECTORIZER_MODULE=none \
  -e CLUSTER_HOSTNAME=node1 \
  cr.weaviate.io/semitechnologies/weaviate:1.27.0

Environment Variables

Variable	Required	Description
GEMINI_API_KEY	Yes	Google GenAI API key
WEAVIATE_HOST	No	Weaviate host (default: localhost)
WEAVIATE_PORT	No	Weaviate HTTP port (default: 8080)
WEAVIATE_GRPC_PORT	No	Weaviate gRPC port (default: 50051)
WEAVIATE_API_KEY	No	API key for Weaviate Cloud

Running the Sample

Using .env file (Recommended)

1. Copy the example environment file:

   cp .env.example .env

2. Edit .env and set your API key:

   GEMINI_API_KEY=your-api-key
   

3. Run the sample:

   ./run.sh

Using Environment Variables

# Set required environment variables
export GEMINI_API_KEY="your-api-key"

# Optional: For Weaviate Cloud
# export WEAVIATE_HOST="your-cluster.weaviate.network"
# export WEAVIATE_API_KEY="your-weaviate-api-key"

# Run the sample
./run.sh

Or with Maven directly:

mvn exec:java -Dexec.mainClass="com.google.genkit.samples.weaviate.WeaviateRAGSample"

Available Flows

indexDocuments

Indexes sample documents about famous films into Weaviate.

curl -X POST http://localhost:4000/api/flows/indexDocuments \
  -H 'Content-Type: application/json' \
  -d '{}'

retrieveDocuments

Retrieves documents matching a semantic query.

curl -X POST http://localhost:4000/api/flows/retrieveDocuments \
  -H 'Content-Type: application/json' \
  -d '{"data": "sci-fi movies"}'

ragQuery

Answers questions using RAG with retrieved context.

curl -X POST http://localhost:4000/api/flows/ragQuery \
  -H 'Content-Type: application/json' \
  -d '{"data": "What Christopher Nolan films are mentioned?"}'

Example Workflow

1. Start the sample application
2. Index the sample documents:

   curl -X POST http://localhost:4000/api/flows/indexDocuments -H 'Content-Type: application/json' -d '{}'

3. Query for relevant documents:

   curl -X POST http://localhost:4000/api/flows/retrieveDocuments -H 'Content-Type: application/json' -d '{"data": "movies about dreams"}'

4. Ask questions using RAG:

   curl -X POST http://localhost:4000/api/flows/ragQuery -H 'Content-Type: application/json' -d '{"data": "Which films were directed by Christopher Nolan and what are they about?"}'

Configuration

The sample uses:

• Embedder: googleai/text-embedding-004 (768 dimensions)
• LLM: googleai/gemini-2.0-flash
• Distance Metric: Cosine similarity
• Collection Name: Films

You can modify these settings in WeaviateRAGSample.java.