File Search Skill (Gemini RAG)

Enable Retrieval Augmented Generation (RAG) using Gemini's File Search API. Upload documents, create knowledge stores, and query with semantic search.

Features

• •Semantic Search: Find relevant info by meaning, not just keywords
• •Auto Chunking: Documents automatically split and indexed
• •Free Storage: No cost for storage or query-time embeddings
• •Citations: Responses include source references
• •Structured Output: Get typed responses (Gemini 3+)

Capabilities

Supported File Types

• •Documents: PDF, Word, Excel, PowerPoint
• •Code: Python, JS, TS, Java, Go, Rust, etc.
• •Text: Markdown, TXT, CSV, JSON, XML, YAML
• •Web: HTML, CSS

Usage Examples

Create Store and Upload Files

from rhea_noir.skills.filesearch.actions import skill as fs

# Create a knowledge store
store = fs.create_store("rhea-knowledge-base")

# Upload documents
fs.upload_file(store, "./docs/architecture.md")
fs.upload_file(store, "./docs/api-reference.pdf")

Query with Semantic Search

result = fs.query(
    store_name=store,
    question="How does the authentication system work?",
    model="gemini-3-flash-preview"
)
print(result["answer"])
print(result["citations"])

Add Metadata for Filtering

fs.upload_file(
    store,
    "./books/i-claudius.txt",
    metadata={"author": "Robert Graves", "year": 1934}
)

# Query with filter
result = fs.query(
    store_name=store,
    question="What happened to Claudius?",
    metadata_filter='author="Robert Graves"'
)

Structured Output

from pydantic import BaseModel

class Summary(BaseModel):
    title: str
    key_points: list[str]
    
result = fs.query_structured(
    store_name=store,
    question="Summarize the main architecture",
    response_schema=Summary
)

Pricing

• •Indexing: $0.15 per 1M tokens (one-time)
• •Storage: FREE
• •Query embeddings: FREE
• •Retrieved tokens: Normal Gemini pricing

Limits

[!NOTE] Keep stores under 20 GB for optimal retrieval latency.