query expansion

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Contents

• Note
  • [[#Note#Pattern visualization|Pattern visualization]]
  • [[#Note#Use-cases and products|Use-cases and products]]
  • [[#Note#Prompt example|Prompt example]]
• Resources

Note

• ask an LLM to generate one or multiple queries based on your initial query; use auto-generated queries for retrieving relevant chunks from vector database
• The idea is that expanded queries are better suitable for information retrieval than the original human-written request
• Query expansion can become core component to an AI product like SQL assistant or No-code UI generator
• Can be done using a zero-shot learning prompt, but if good output query examples are available, these can be added

Pattern visualization

Figure

Use-cases and products

• Multi-language documents in the database
• SQL assistant: generating standardized query language under the hood (SQL, Cypher)
• Specific vocabulary, terms, which user might not know
• Any other specialized info that might help the Assistant set up a better search

Prompt example

Prompt example

You are an AI search assistant at {company_name}.
You can search for any information in the database using full-text search engine.
Information in the database is in English and {another_language}.
Given the original user query below, generate five different search phrases in order to most efficiently search in the database. 
By generating multiple perspectives on the user query, your goal is to help overcome limitations of the distance-based similarity search.
Provide these alternative questions separated by newlines.
Take into account {company_name} domain specialization
# {company_name} domain specialization
// Brief info about the industry and primary business targets.
Take into account data source specialization
# data source specialization
// Brief info about documents, database, specific terms, anything non-standard.
# original user query
"{query}"

Resources

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