gemini

name

gemini

description

Use when integrating Gemini AI models in Supabase Edge Functions or Deno. Covers Gemini 3 models (Pro, Flash, Pro-Image), thinking levels, URL Context, Google Search grounding, structured output, thought signatures, function calling, image generation, document processing, deep search, error handling, and REST API patterns. Use for AI enrichment, data extraction, analysis tasks, and image generation.

Gemini AI Integration

Build AI features using Google's Gemini 3 models with URL Context, Google Search grounding, structured outputs, thinking levels, function calling, and image generation.

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CRITICAL RULES (Audit Findings G1-G6)

These rules MUST be followed in every Gemini integration. Violating them causes crashes, degraded output, or security issues.

ID	Rule	Why
G1	Always use responseJsonSchema with responseMimeType: "application/json"	responseMimeType alone just asks the model to try JSON. Adding responseJsonSchema guarantees syntactically valid JSON matching the schema. Without it, JSON parse crashes are likely.
G2	Keep temperature at default 1.0 for Gemini 3	Setting temperature below 1.0 causes looping, repetitive output, or degraded performance on Gemini 3 models. The docs explicitly warn against this.
G3	Combine structured output with Google Search	Gemini 3 supports responseJsonSchema + googleSearch together. Use both to get valid JSON even with search grounding. Without the schema, search metadata can corrupt JSON output.
G4	Pass API key via x-goog-api-key header, NOT query param	?key= exposes the key in server logs and referrer headers. Every official REST example uses the header.
G5	Extract groundingChunks citations from Google Search	The response includes groundingChunks (source URLs/titles) and groundingSupports (inline citation mapping). Don't discard this data — use it for citations.
G6	Both googleSearch and google_search casing work	The API accepts both camelCase and snake_case via protobuf. Use camelCase for SDK consistency.

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Quick Reference

Model	Use Case	Context	Pricing
gemini-3.1-pro-preview	Latest Pro — complex reasoning, agentic, vibe coding	1M / 64k	$2-4 / $12-18
gemini-3-flash-preview	Fast responses, extraction, chat	1M / 64k	$0.50 / $3
gemini-3.1-flash-lite-preview	Cost-efficient, fastest for high-frequency tasks	1M / 64k	$0.50 / $3
gemini-3.1-flash-image-preview	Fast image generation + editing	131k / 32k	$2 / $0.134
gemini-3-pro-image-preview	Pro image generation (Nano Banana Pro)	131k / 32k	$2 / $0.134

⚠️ DEPRECATED: gemini-3-pro-preview shut down March 9, 2026 — use gemini-3.1-pro-preview instead.

All Gemini 3 models have a knowledge cutoff of January 2025.

SDK Setup (Deno Edge Functions)

import { GoogleGenAI } from "npm:@google/genai@^1.0.0";

const ai = new GoogleGenAI({ apiKey: Deno.env.get("GEMINI_API_KEY") });

IMPORTANT: Use npm:@google/genai@^1.0.0 (not esm.sh, not legacy @google/generativeai).

Thinking Levels

Control reasoning depth with thinkingLevel (Gemini 3):

Level	Models	Use Case	Latency
minimal	Flash only	Matches "no thinking" for most queries	Lowest
low	Pro + Flash	Simple instruction following	Low
medium	Flash only	Balanced tasks	Medium
high	Pro + Flash	Complex analysis, scoring (default)	Higher

// SDK pattern
const response = await ai.models.generateContent({
  model: "gemini-3-flash-preview",
  contents: prompt,
  config: {
    thinkingConfig: {
      thinkingLevel: "high"  // For complex analysis
    }
  }
});

Best Practice: Use high for scoring, readiness calculations, and complex analysis. Use low or minimal for simple extraction and chat.

WARNING: Cannot use both thinkingLevel and legacy thinkingBudget in the same request (400 error).

Built-in Tools

1. URL Context

Extract data from web URLs directly:

const response = await ai.models.generateContent({
  model: "gemini-3-flash-preview",
  contents: `Extract company info from ${url}`,
  config: {
    tools: [{ urlContext: {} }]
  }
});

Limitations:

• Up to 20 URLs per request
• Max 34MB per URL
• Cannot access: paywalled content, YouTube, Google Workspace, localhost
• Supported: HTML, JSON, PDF, images (PNG, JPEG)

Metadata access:

const metadata = response.candidates[0].urlContextMetadata;
// Check retrieval status for each URL

2. Google Search Grounding

Connect to real-time web information:

const response = await ai.models.generateContent({
  model: "gemini-3-flash-preview",
  contents: "Who won the 2024 Euro championship?",
  config: {
    tools: [{ googleSearch: {} }]
  }
});

// Access grounding metadata (G5: DON'T discard this!)
const { webSearchQueries, groundingChunks, groundingSupports } =
  response.candidates[0].groundingMetadata;

Extracting Citations (G5):

// groundingChunks contains source URLs and titles
const citations = groundingChunks?.map(chunk => ({
  url: chunk.web?.uri,
  title: chunk.web?.title
})) ?? [];

// groundingSupports maps text segments to sources
const supports = groundingSupports?.map(support => ({
  text: support.segment?.text,
  sources: support.groundingChunkIndices  // indexes into groundingChunks
})) ?? [];

Combine Tools:

config: {
  tools: [
    { urlContext: {} },
    { googleSearch: {} }
  ]
}

Structured Output

Force JSON responses matching a schema. G1: Always use responseJsonSchema — this guarantees valid JSON.

import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

const schema = z.object({
  company_name: z.string(),
  industry: z.array(z.string()),
  description: z.string(),
  founded_year: z.number().optional()
});

const response = await ai.models.generateContent({
  model: "gemini-3-flash-preview",
  contents: `Extract company info from: ${url}`,
  config: {
    tools: [{ urlContext: {} }],
    responseMimeType: "application/json",
    responseJsonSchema: zodToJsonSchema(schema)  // G1: REQUIRED for valid JSON
  }
});

const data = schema.parse(JSON.parse(response.text));

Why responseJsonSchema matters:

• responseMimeType alone = model tries to output JSON (can fail)
• responseMimeType + responseJsonSchema = guaranteed syntactically valid JSON matching the schema
• Works with Google Search, URL Context, and Code Execution tools (G3)

Supported types: string, number, integer, boolean, object, array, null

REST API Patterns

For raw fetch() calls in Edge Functions (instead of SDK):

Basic Request

const GEMINI_API_KEY = Deno.env.get("GEMINI_API_KEY");
const model = "gemini-3-flash-preview";

// G4: Use x-goog-api-key header, NOT ?key= query param
const url = `https://generativelanguage.googleapis.com/v1beta/models/${model}:generateContent`;

const response = await fetch(url, {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    "x-goog-api-key": GEMINI_API_KEY,  // G4: Header, not query param
  },
  body: JSON.stringify({
    contents: [{ parts: [{ text: prompt }] }],
    generationConfig: {
      responseMimeType: "application/json",
      responseJsonSchema: schema,  // G1: Include schema
      // G2: Do NOT set temperature below 1.0
      thinkingConfig: {
        thinkingLevel: "high"
      }
    }
  })
});

const data = await response.json();
const text = data.candidates?.[0]?.content?.parts?.[0]?.text;

With Google Search + Structured Output (G3)

const response = await fetch(url, {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    "x-goog-api-key": GEMINI_API_KEY,
  },
  body: JSON.stringify({
    contents: [{ parts: [{ text: prompt }] }],
    tools: [{ googleSearch: {} }],
    generationConfig: {
      responseMimeType: "application/json",
      responseJsonSchema: {
        type: "object",
        properties: {
          summary: { type: "string" },
          sources: {
            type: "array",
            items: {
              type: "object",
              properties: {
                url: { type: "string" },
                title: { type: "string" }
              }
            }
          }
        },
        required: ["summary", "sources"]
      }
    }
  })
});

const data = await response.json();
const result = JSON.parse(data.candidates[0].content.parts[0].text);

// G5: Also extract grounding citations
const groundingChunks = data.candidates[0].groundingMetadata?.groundingChunks ?? [];

Streaming (REST)

const url = `https://generativelanguage.googleapis.com/v1beta/models/${model}:streamGenerateContent?alt=sse`;

const response = await fetch(url, {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    "x-goog-api-key": GEMINI_API_KEY,
  },
  body: JSON.stringify({
    contents: [{ parts: [{ text: prompt }] }]
  })
});

Complete Edge Function Pattern

import { GoogleGenAI } from "npm:@google/genai@^1.0.0";
import { z } from "npm:zod@^3.23.0";
import { zodToJsonSchema } from "npm:zod-to-json-schema@^3.23.0";

const ai = new GoogleGenAI({ apiKey: Deno.env.get("GEMINI_API_KEY") });

// Enrichment with URL Context + Google Search + Structured Output
async function enrichUrl(url: string) {
  const schema = z.object({
    company_name: z.string(),
    description: z.string(),
    industry: z.array(z.string()),
    competitors: z.array(z.object({
      name: z.string(),
      differentiator: z.string()
    }))
  });

  const response = await ai.models.generateContent({
    model: "gemini-3-flash-preview",
    contents: `Analyze this company: ${url}. Find competitors.`,
    config: {
      tools: [
        { urlContext: {} },
        { googleSearch: {} }
      ],
      responseMimeType: "application/json",
      responseJsonSchema: zodToJsonSchema(schema),  // G1+G3
      thinkingConfig: {
        thinkingLevel: "medium"
      }
      // G2: No temperature override — keeps default 1.0
    }
  });

  // G5: Extract citations
  const citations = response.candidates[0]?.groundingMetadata
    ?.groundingChunks?.map(c => ({ url: c.web?.uri, title: c.web?.title })) ?? [];

  return {
    data: schema.parse(JSON.parse(response.text)),
    citations
  };
}

// Analysis with high thinking
async function calculateReadiness(data: object) {
  const schema = z.object({
    overall_score: z.number().min(0).max(100),
    category_scores: z.object({
      product: z.number(),
      market: z.number(),
      team: z.number(),
      clarity: z.number()
    }),
    recommendations: z.array(z.string())
  });

  const response = await ai.models.generateContent({
    model: "gemini-3-flash-preview",
    contents: `Calculate investor readiness score: ${JSON.stringify(data)}`,
    config: {
      responseMimeType: "application/json",
      responseJsonSchema: zodToJsonSchema(schema),  // G1
      thinkingConfig: {
        thinkingLevel: "high"  // Deep reasoning for scoring
      }
    }
  });

  return schema.parse(JSON.parse(response.text));
}

Function Calling

Declare tools the model can call:

const tools = [{
  functionDeclarations: [{
    name: "get_weather",
    description: "Get weather for a city",
    parameters: {
      type: "object",
      properties: {
        city: { type: "string", description: "City name" }
      },
      required: ["city"]
    }
  }]
}];

const response = await ai.models.generateContent({
  model: "gemini-3-flash-preview",
  contents: "What's the weather in Paris?",
  config: { tools }
});

// Handle function call
const functionCall = response.functionCalls?.[0];
if (functionCall) {
  // Execute your function, then send result back
}

Modes: AUTO (default), ANY (force tool use), NONE (disable), VALIDATED

NOTE: Combining built-in tools (Google Search, URL Context) with function calling is NOT supported in Gemini 3.

Thought Signatures (Multi-turn)

For function calling and image editing workflows, preserve thought context:

// Model response includes thoughtSignature
const response1 = await ai.models.generateContent({...});
const signature = response1.candidates[0].content.parts[0].thoughtSignature;

// Return signature in next request
const response2 = await ai.models.generateContent({
  model: "gemini-3-flash-preview",
  contents: [
    { role: "user", parts: [{ text: "Initial prompt" }] },
    {
      role: "model",
      parts: [{
        functionCall: {...},
        thoughtSignature: signature  // REQUIRED for function calling
      }]
    },
    { role: "user", parts: [{ functionResponse: {...} }] }
  ]
});

Rules:

• Required for function calling (400 error without it)
• Required for multi-turn image editing conversations (single-turn generation works without them)
• Recommended for text/chat (improves reasoning continuity)
• SDKs handle this automatically
• SDKs manage thought signatures automatically; preserve all accumulated signatures in multi-turn
• Parallel calls: only the first functionCall has the signature
• Multi-step: ALL accumulated signatures must be preserved

Image Generation

const response = await ai.models.generateContent({
  model: "gemini-3-pro-image-preview",
  contents: "Generate a logo for a tech startup",
  config: {
    tools: [{ googleSearch: {} }],  // Optional: ground in real data
    imageConfig: {
      aspectRatio: "16:9",
      imageSize: "4K"
    }
  }
});

// Extract image
const imagePart = response.candidates[0].content.parts
  .find(p => p.inlineData);
const imageData = imagePart.inlineData.data;  // base64

Key capabilities:

• 4K resolution with text rendering
• Grounded generation via Google Search
• Conversational editing (multi-turn, requires thought signatures)
• SynthID watermarks on all generated images

Document Processing

Process PDFs and documents directly:

// Inline (small docs)
const response = await ai.models.generateContent({
  model: "gemini-3-flash-preview",
  contents: [
    { text: "Summarize this document" },
    { inlineData: { mimeType: "application/pdf", data: base64Data } }
  ]
});

// Files API (large docs, recommended)
const file = await ai.files.upload({ file: "document.pdf" });
const response = await ai.models.generateContent({
  model: "gemini-3-flash-preview",
  contents: [
    { text: "Extract key findings" },
    { fileData: { fileUri: file.uri, mimeType: file.mimeType } }
  ]
});

• Supports up to 1000 pages
• Use mediaResolution: "media_resolution_medium" for standard PDFs
• Combine with structured output for typed extraction

System Instructions

const response = await ai.models.generateContent({
  model: "gemini-3-flash-preview",
  contents: "Hello there",
  config: {
    systemInstruction: "You are a startup advisor. Be concise and data-driven.",
  }
});

Temperature

G2: Always use default temperature (1.0) for Gemini 3. Lower values cause looping or degraded performance, especially for reasoning and math tasks.

// WRONG — causes issues on Gemini 3
config: { temperature: 0.4 }

// RIGHT — use default (omit temperature)
config: { /* no temperature setting */ }

Error Handling

Code	Error	Cause	Fix
400	INVALID_ARGUMENT	Bad request params, missing thought signature	Check schema, ensure thought signatures present
403	PERMISSION_DENIED	Invalid API key or restricted	Check key, ensure API enabled
404	NOT_FOUND	Model not found	Verify model ID (e.g., gemini-3-flash-preview)
429	RESOURCE_EXHAUSTED	Rate limit hit	Implement exponential backoff
500	INTERNAL	Server error	Retry with backoff
503	UNAVAILABLE	Service temporarily down	Retry with backoff

Retry pattern:

async function callGeminiWithRetry(fn: () => Promise<any>, maxRetries = 3) {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await fn();
    } catch (error) {
      if (error.status === 429 || error.status >= 500) {
        await new Promise(r => setTimeout(r, Math.pow(2, i) * 1000));
        continue;
      }
      throw error;  // Don't retry 400/403/404
    }
  }
  throw new Error("Max retries exceeded");
}

Token Counting

const { thoughtsTokenCount, candidatesTokenCount, totalTokenCount } =
  response.usageMetadata;

// For URL Context
const { toolUsePromptTokenCount } = response.usageMetadata;

Common Patterns

Extraction with Fallback

try {
  // Try URL Context first
  const result = await enrichWithUrlContext(url);
} catch (error) {
  // Fallback to Google Search
  const result = await enrichWithGoogleSearch(companyName);
}

Founder Lookup (Google Search Only)

// DON'T use URL Context for LinkedIn (private profiles)
const response = await ai.models.generateContent({
  model: "gemini-3-flash-preview",
  contents: `Find info about founder: "${name}" at "${company}"`,
  config: {
    tools: [{ googleSearch: {} }],  // Not urlContext!
    responseMimeType: "application/json",
    responseJsonSchema: founderSchema  // G1: Always include schema
  }
});

Resources

• references/gemini-3.md - Full Gemini 3 documentation
• references/text-generation.md - Text gen, system instructions, temperature
• references/structured-output.md - JSON schema output, Zod patterns
• references/google-search.md - Google Search grounding, citations
• references/function-calling.md - Tool declarations, multi-turn
• references/thinking.md - Thinking levels, budgets
• references/thought-signatures.md - Multi-turn state, function calling
• references/url-context.md - URL extraction, limitations
• references/image-generation.md - Nano Banana, 4K, SynthID
• references/document-processing.md - PDF, inline data, Files API
• references/deep-search.md - Deep Research Agent, Interactions API
• references/troubleshooting.md - Error codes, temperature warnings
• references/api-setup.md - API keys, SDKs, quickstart

Checklist

When implementing Gemini AI:

• Use npm:@google/genai@^1.0.0 (not esm.sh, not legacy SDK)
• Set GEMINI_API_KEY via Deno.env.get() in Edge Functions
• G1: Use BOTH responseMimeType AND responseJsonSchema for JSON output
• G2: Do NOT set temperature below 1.0 on Gemini 3 models
• G3: Combine responseJsonSchema with googleSearch when using both
• G4: Pass API key via x-goog-api-key header (not query param)
• G5: Extract groundingChunks citations from Google Search responses
• Choose appropriate thinking level for task complexity
• Handle URL Context failures with Google Search fallback
• Don't use URL Context for private pages (LinkedIn)
• Return thought signatures for function calling and image editing
• Implement retry with exponential backoff for 429/5xx errors