Walk me through the exact architectural steps to set up a secure Retrieval-Augmented Generation pipeline using Pinecone and OpenAI embeddings for internal HR documents.
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Walk me through the exact architectural steps to set up a secure Retrieval-Augmented Generation pipeline using Pinecone and OpenAI embeddings for internal HR documents.
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
OpenAI API embeddings documentation data controls API data usage retention official Google ↗
Pinecone documentation metadata filtering namespaces security RBAC encryption private endpoints official Google ↗
- Data controls in the OpenAI platform · developers.openai.com
- Data controls in the OpenAI platform · developers.openai.com
- pinecone-ts-client/guides/data-operations/metadata-filtering.md at main · pinecone-io/pinecone-ts-client · github.com
- Trust and Security · pinecone.io
- OpenAI — Embeddings guide · developers.openai.com
- Pinecone — Creating indexes · docs.pinecone.io
- Pinecone — Metadata filtering · docs.pinecone.io
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
OpenAI API embeddings documentation data controls API data usage retention official OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Pinecone documentation metadata filtering namespaces security RBAC encryption private endpoints official OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |