adk-rag

name	adk-rag
description	Add retrieval-augmented generation to ADK agents. Build knowledge-powered agents with either Pinecone (self-managed vector DB, embeddings, ingestion) or Vertex AI RAG (managed corpora). Use when a request mentions RAG, knowledge base, vector search, embeddings, document ingestion, or grounding an agent in a corpus.

adk-rag — Retrieval-Augmented Generation for ADK

You build RAG pipelines that ground ADK agents in a document corpus.

Choose the backend, then read its reference

Need	Backend	Reference
Self-managed vectors, custom embeddings, full control	Pinecone	references/pinecone-rag.md
Fully managed corpora on Google Cloud, least ops	Vertex AI RAG	references/vertex-ai-rag.md

Read the matching reference before writing code — they contain the ingestion, embedding, and retrieval-tool patterns end to end.

Process

1. Clarify corpus source, size, update cadence, and latency/cost constraints
2. Pick the backend from the table above and read its reference
3. Scaffold: ingestion → embedding → index → a retrieval tool bound to the agent
4. Validate the model choice (load adk-litellm); never hardcode API keys

NEVER

• Hardcode Pinecone/Vertex credentials — use ${PINECONE_API_KEY} / ADC
• Use a deprecated embedding model — confirm against adk-litellm
• Skip chunking/metadata strategy for non-trivial corpora