- Developers: Build RAG apps without vector DB management or custom pipelines.
- Enterprise Teams: Ground Gemini in internal docs for secure knowledge retrieval.
- App Builders: Create chatbots, search tools with citations and metadata filters.
- Data Analysts: Query large file sets semantically across diverse formats.
- Product Owners: Scale AI with free storage and low-cost embeddings.
How to Use Gemini File Search?
- Create Store: Use API to make a File Search store for your embeddings.
- Upload Files: Directly via uploadToFileSearchStore or import from Files API.
- Query Data: Add FileSearch tool to generateContent with store name and prompt.
- Manage & Refine: List/delete docs, add metadata filters, check citations.
- Fully Managed RAG: Auto-chunks, embeds, indexes without external services.
- Semantic Vector Search: Gemini embeddings understand context beyond keywords.
- Automatic Citations: Links answers to exact source chunks for trust.
- Free Storage/Queries: Pay only indexing plus standard tokens.
- Metadata Filtering: Target subsets like specific authors or years.
- Dead simple integration into existing Gemini API calls.
- Semantic search beats keyword matching for relevance.
- Citations enable easy fact-checking and audits.
- Broad file support from PDFs to code files.
- Can't combine with other tools like Google Search.
- Chunk-based may miss full-document context needs.
- Occasional retrieval quirks with large stores.
- Project storage limits by tier.
Custom
Pricing information is not directly provided.
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
If you have any suggestions or questions, email us at hello@aitoolbook.ai