- Step1: Clone the Cognita GitHub repository or install via pip.
- Step2: Define your data sources and ingest documents into a vector store.
- Step3: Configure embedding provider (OpenAI, TrueFoundry Embeddings, etc.).
- Step4: Create a retrieval pipeline using YAML or Python DSL.
- Step5: Launch the built-in frontend to test and refine query results.
- Step6: Use provided deployment templates to deploy on Kubernetes or serverless.
- Step7: Monitor performance and adjust retrieval parameters as needed.