Our vector search accuracy in Pinecone is completely tanking on long technical documents right now, what are the exact chunking strategies and embedding models I should test to fix this before my demo at two o'clock?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Our vector search accuracy in Pinecone is completely tanking on long technical documents right now, what are the exact chunking strategies and embedding models I should test to fix this before my demo at two o'clock?
3 real searches · 1 real-search pass
Pinecone documentation chunking strategies semantic chunking hierarchical parent child technical documents embedding models 2026Google ↗
OBSERVED
Pinecone integrated embedding models multilingual-e5-large text-embedding-3-large voyage-code-3 documentation dimensionsGoogle ↗
OBSERVED
OpenAI embeddings text-embedding-3-large dimensions pricing official documentationGoogle ↗
OBSERVED
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 3 queries…
Searches and sources
ChatGPT3 real searches · gpt-6
Pinecone documentation chunking strategies semantic chunking hierarchical parent child technical documents embedding models 2026 Google ↗
Pinecone integrated embedding models multilingual-e5-large text-embedding-3-large voyage-code-3 documentation dimensions Google ↗
OpenAI embeddings text-embedding-3-large dimensions pricing official documentation Google ↗
- Chunking Strategies for LLM Applications · pinecone.io
- Integrated embedding on the Documents API · pinecone.io
- text-embedding-3-large Model · developers.openai.com
- The Practitioner's Guide To E5 · pinecone.io
- Generating embeddings · sdk.pinecone.io
- Introducing cascading retrieval: Unifying dense and sparse with reranking · pinecone.io
- Reranking results · sdk.pinecone.io
- Introducing integrated inference: Embed, rerank, and retrieve your data with a single API · pinecone.io
Queries
3 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Pinecone documentation chunking strategies semantic chunking hierarchical parent child technical documents embedding models 2026 OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Pinecone integrated embedding models multilingual-e5-large text-embedding-3-large voyage-code-3 documentation dimensions OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
OpenAI embeddings text-embedding-3-large dimensions pricing official documentation OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |