AI Templates$59
RAG Pipeline Template
Build a retrieval-augmented generation pipeline that ingests documents, chunks content, creates embeddings, retrieves context, and answers with grounded responses.
Prerequisites
- Python 3.10 or newer.
- OpenAI or compatible embedding/chat model credentials.
- A vector database such as Pinecone.
- A folder, bucket, or database containing the source documents.
1
Install dependencies
Setup snippet
python -m venv .venv
pip install -r requirements.txt2
Set API keys
Setup snippet
OPENAI_API_KEY=sk-...
PINECONE_API_KEY=...
PINECONE_INDEX=knowledge-base3
Ingest documents
- Place source documents in the configured input folder.
- Run the ingestion script to parse and normalize text.
- Chunk content by headings or token windows.
- Generate embeddings and upsert them into the vector index.
4
Query the pipeline
- Embed the user question.
- Retrieve the top matching chunks from the vector database.
- Pass retrieved context into the answer prompt.
- Return the answer plus source references when available.
5
Evaluate quality
Checklist
- Create a small question set from real user questions.
- Check answer correctness, citation quality, and refusal behavior.
- Tune chunk size, retrieval count, and prompts before production.
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