How can I adjust chunking strategies to improve vector search accuracy when querying long-form legal and financial PDFs?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How can I adjust chunking strategies to improve vector search accuracy when querying long-form legal and financial PDFs?
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
best practices semantic chunking long legal documents PDFs vector search hierarchical retrieval parent child chunks tables metadata overlap Google ↗
OpenAI retrieval chunking strategy vector stores chunk size overlap documentation Google ↗
- Chunk Documents - Azure AI Search · learn.microsoft.com
- How to parse PDF docs for RAG · developers.openai.com
- Retrieval · developers.openai.com
- Create vector store file · developers.openai.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
best practices semantic chunking long legal documents PDFs vector search hierarchical retrieval parent child chunks tables metadata overlap OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
OpenAI retrieval chunking strategy vector stores chunk size overlap documentation OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |