@ai-sdk/google
Version:
The **[Google Generative AI provider](https://ai-sdk.dev/providers/ai-sdk-providers/google-generative-ai)** for the [AI SDK](https://ai-sdk.dev/docs) contains language model support for the [Google Generative AI](https://ai.google/discover/generativeai/)
1,466 lines (1,451 loc) • 67.9 kB
JavaScript
// src/google-provider.ts
import {
generateId as generateId2,
loadApiKey,
withoutTrailingSlash,
withUserAgentSuffix
} from "@ai-sdk/provider-utils";
// src/version.ts
var VERSION = true ? "3.0.10" : "0.0.0-test";
// src/google-generative-ai-embedding-model.ts
import {
TooManyEmbeddingValuesForCallError
} from "@ai-sdk/provider";
import {
combineHeaders,
createJsonResponseHandler,
lazySchema as lazySchema3,
parseProviderOptions,
postJsonToApi,
resolve,
zodSchema as zodSchema3
} from "@ai-sdk/provider-utils";
import { z as z3 } from "zod/v4";
// src/google-error.ts
import {
createJsonErrorResponseHandler,
lazySchema,
zodSchema
} from "@ai-sdk/provider-utils";
import { z } from "zod/v4";
var googleErrorDataSchema = lazySchema(
() => zodSchema(
z.object({
error: z.object({
code: z.number().nullable(),
message: z.string(),
status: z.string()
})
})
)
);
var googleFailedResponseHandler = createJsonErrorResponseHandler({
errorSchema: googleErrorDataSchema,
errorToMessage: (data) => data.error.message
});
// src/google-generative-ai-embedding-options.ts
import {
lazySchema as lazySchema2,
zodSchema as zodSchema2
} from "@ai-sdk/provider-utils";
import { z as z2 } from "zod/v4";
var googleGenerativeAIEmbeddingProviderOptions = lazySchema2(
() => zodSchema2(
z2.object({
/**
* Optional. Optional reduced dimension for the output embedding.
* If set, excessive values in the output embedding are truncated from the end.
*/
outputDimensionality: z2.number().optional(),
/**
* Optional. Specifies the task type for generating embeddings.
* Supported task types:
* - SEMANTIC_SIMILARITY: Optimized for text similarity.
* - CLASSIFICATION: Optimized for text classification.
* - CLUSTERING: Optimized for clustering texts based on similarity.
* - RETRIEVAL_DOCUMENT: Optimized for document retrieval.
* - RETRIEVAL_QUERY: Optimized for query-based retrieval.
* - QUESTION_ANSWERING: Optimized for answering questions.
* - FACT_VERIFICATION: Optimized for verifying factual information.
* - CODE_RETRIEVAL_QUERY: Optimized for retrieving code blocks based on natural language queries.
*/
taskType: z2.enum([
"SEMANTIC_SIMILARITY",
"CLASSIFICATION",
"CLUSTERING",
"RETRIEVAL_DOCUMENT",
"RETRIEVAL_QUERY",
"QUESTION_ANSWERING",
"FACT_VERIFICATION",
"CODE_RETRIEVAL_QUERY"
]).optional()
})
)
);
// src/google-generative-ai-embedding-model.ts
var GoogleGenerativeAIEmbeddingModel = class {
constructor(modelId, config) {
this.specificationVersion = "v3";
this.maxEmbeddingsPerCall = 2048;
this.supportsParallelCalls = true;
this.modelId = modelId;
this.config = config;
}
get provider() {
return this.config.provider;
}
async doEmbed({
values,
headers,
abortSignal,
providerOptions
}) {
const googleOptions = await parseProviderOptions({
provider: "google",
providerOptions,
schema: googleGenerativeAIEmbeddingProviderOptions
});
if (values.length > this.maxEmbeddingsPerCall) {
throw new TooManyEmbeddingValuesForCallError({
provider: this.provider,
modelId: this.modelId,
maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,
values
});
}
const mergedHeaders = combineHeaders(
await resolve(this.config.headers),
headers
);
if (values.length === 1) {
const {
responseHeaders: responseHeaders2,
value: response2,
rawValue: rawValue2
} = await postJsonToApi({
url: `${this.config.baseURL}/models/${this.modelId}:embedContent`,
headers: mergedHeaders,
body: {
model: `models/${this.modelId}`,
content: {
parts: [{ text: values[0] }]
},
outputDimensionality: googleOptions == null ? void 0 : googleOptions.outputDimensionality,
taskType: googleOptions == null ? void 0 : googleOptions.taskType
},
failedResponseHandler: googleFailedResponseHandler,
successfulResponseHandler: createJsonResponseHandler(
googleGenerativeAISingleEmbeddingResponseSchema
),
abortSignal,
fetch: this.config.fetch
});
return {
warnings: [],
embeddings: [response2.embedding.values],
usage: void 0,
response: { headers: responseHeaders2, body: rawValue2 }
};
}
const {
responseHeaders,
value: response,
rawValue
} = await postJsonToApi({
url: `${this.config.baseURL}/models/${this.modelId}:batchEmbedContents`,
headers: mergedHeaders,
body: {
requests: values.map((value) => ({
model: `models/${this.modelId}`,
content: { role: "user", parts: [{ text: value }] },
outputDimensionality: googleOptions == null ? void 0 : googleOptions.outputDimensionality,
taskType: googleOptions == null ? void 0 : googleOptions.taskType
}))
},
failedResponseHandler: googleFailedResponseHandler,
successfulResponseHandler: createJsonResponseHandler(
googleGenerativeAITextEmbeddingResponseSchema
),
abortSignal,
fetch: this.config.fetch
});
return {
warnings: [],
embeddings: response.embeddings.map((item) => item.values),
usage: void 0,
response: { headers: responseHeaders, body: rawValue }
};
}
};
var googleGenerativeAITextEmbeddingResponseSchema = lazySchema3(
() => zodSchema3(
z3.object({
embeddings: z3.array(z3.object({ values: z3.array(z3.number()) }))
})
)
);
var googleGenerativeAISingleEmbeddingResponseSchema = lazySchema3(
() => zodSchema3(
z3.object({
embedding: z3.object({ values: z3.array(z3.number()) })
})
)
);
// src/google-generative-ai-language-model.ts
import {
combineHeaders as combineHeaders2,
createEventSourceResponseHandler,
createJsonResponseHandler as createJsonResponseHandler2,
generateId,
lazySchema as lazySchema5,
parseProviderOptions as parseProviderOptions2,
postJsonToApi as postJsonToApi2,
resolve as resolve2,
zodSchema as zodSchema5
} from "@ai-sdk/provider-utils";
import { z as z5 } from "zod/v4";
// src/convert-google-generative-ai-usage.ts
function convertGoogleGenerativeAIUsage(usage) {
var _a, _b, _c, _d;
if (usage == null) {
return {
inputTokens: {
total: void 0,
noCache: void 0,
cacheRead: void 0,
cacheWrite: void 0
},
outputTokens: {
total: void 0,
text: void 0,
reasoning: void 0
},
raw: void 0
};
}
const promptTokens = (_a = usage.promptTokenCount) != null ? _a : 0;
const candidatesTokens = (_b = usage.candidatesTokenCount) != null ? _b : 0;
const cachedContentTokens = (_c = usage.cachedContentTokenCount) != null ? _c : 0;
const thoughtsTokens = (_d = usage.thoughtsTokenCount) != null ? _d : 0;
return {
inputTokens: {
total: promptTokens,
noCache: promptTokens - cachedContentTokens,
cacheRead: cachedContentTokens,
cacheWrite: void 0
},
outputTokens: {
total: candidatesTokens + thoughtsTokens,
text: candidatesTokens,
reasoning: thoughtsTokens
},
raw: usage
};
}
// src/convert-json-schema-to-openapi-schema.ts
function convertJSONSchemaToOpenAPISchema(jsonSchema, isRoot = true) {
if (jsonSchema == null) {
return void 0;
}
if (isEmptyObjectSchema(jsonSchema)) {
if (isRoot) {
return void 0;
}
if (typeof jsonSchema === "object" && jsonSchema.description) {
return { type: "object", description: jsonSchema.description };
}
return { type: "object" };
}
if (typeof jsonSchema === "boolean") {
return { type: "boolean", properties: {} };
}
const {
type,
description,
required,
properties,
items,
allOf,
anyOf,
oneOf,
format,
const: constValue,
minLength,
enum: enumValues
} = jsonSchema;
const result = {};
if (description) result.description = description;
if (required) result.required = required;
if (format) result.format = format;
if (constValue !== void 0) {
result.enum = [constValue];
}
if (type) {
if (Array.isArray(type)) {
const hasNull = type.includes("null");
const nonNullTypes = type.filter((t) => t !== "null");
if (nonNullTypes.length === 0) {
result.type = "null";
} else {
result.anyOf = nonNullTypes.map((t) => ({ type: t }));
if (hasNull) {
result.nullable = true;
}
}
} else {
result.type = type;
}
}
if (enumValues !== void 0) {
result.enum = enumValues;
}
if (properties != null) {
result.properties = Object.entries(properties).reduce(
(acc, [key, value]) => {
acc[key] = convertJSONSchemaToOpenAPISchema(value, false);
return acc;
},
{}
);
}
if (items) {
result.items = Array.isArray(items) ? items.map((item) => convertJSONSchemaToOpenAPISchema(item, false)) : convertJSONSchemaToOpenAPISchema(items, false);
}
if (allOf) {
result.allOf = allOf.map(
(item) => convertJSONSchemaToOpenAPISchema(item, false)
);
}
if (anyOf) {
if (anyOf.some(
(schema) => typeof schema === "object" && (schema == null ? void 0 : schema.type) === "null"
)) {
const nonNullSchemas = anyOf.filter(
(schema) => !(typeof schema === "object" && (schema == null ? void 0 : schema.type) === "null")
);
if (nonNullSchemas.length === 1) {
const converted = convertJSONSchemaToOpenAPISchema(
nonNullSchemas[0],
false
);
if (typeof converted === "object") {
result.nullable = true;
Object.assign(result, converted);
}
} else {
result.anyOf = nonNullSchemas.map(
(item) => convertJSONSchemaToOpenAPISchema(item, false)
);
result.nullable = true;
}
} else {
result.anyOf = anyOf.map(
(item) => convertJSONSchemaToOpenAPISchema(item, false)
);
}
}
if (oneOf) {
result.oneOf = oneOf.map(
(item) => convertJSONSchemaToOpenAPISchema(item, false)
);
}
if (minLength !== void 0) {
result.minLength = minLength;
}
return result;
}
function isEmptyObjectSchema(jsonSchema) {
return jsonSchema != null && typeof jsonSchema === "object" && jsonSchema.type === "object" && (jsonSchema.properties == null || Object.keys(jsonSchema.properties).length === 0) && !jsonSchema.additionalProperties;
}
// src/convert-to-google-generative-ai-messages.ts
import {
UnsupportedFunctionalityError
} from "@ai-sdk/provider";
import { convertToBase64 } from "@ai-sdk/provider-utils";
function convertToGoogleGenerativeAIMessages(prompt, options) {
var _a, _b, _c;
const systemInstructionParts = [];
const contents = [];
let systemMessagesAllowed = true;
const isGemmaModel = (_a = options == null ? void 0 : options.isGemmaModel) != null ? _a : false;
const providerOptionsName = (_b = options == null ? void 0 : options.providerOptionsName) != null ? _b : "google";
for (const { role, content } of prompt) {
switch (role) {
case "system": {
if (!systemMessagesAllowed) {
throw new UnsupportedFunctionalityError({
functionality: "system messages are only supported at the beginning of the conversation"
});
}
systemInstructionParts.push({ text: content });
break;
}
case "user": {
systemMessagesAllowed = false;
const parts = [];
for (const part of content) {
switch (part.type) {
case "text": {
parts.push({ text: part.text });
break;
}
case "file": {
const mediaType = part.mediaType === "image/*" ? "image/jpeg" : part.mediaType;
parts.push(
part.data instanceof URL ? {
fileData: {
mimeType: mediaType,
fileUri: part.data.toString()
}
} : {
inlineData: {
mimeType: mediaType,
data: convertToBase64(part.data)
}
}
);
break;
}
}
}
contents.push({ role: "user", parts });
break;
}
case "assistant": {
systemMessagesAllowed = false;
contents.push({
role: "model",
parts: content.map((part) => {
var _a2;
const providerOpts = (_a2 = part.providerOptions) == null ? void 0 : _a2[providerOptionsName];
const thoughtSignature = (providerOpts == null ? void 0 : providerOpts.thoughtSignature) != null ? String(providerOpts.thoughtSignature) : void 0;
switch (part.type) {
case "text": {
return part.text.length === 0 ? void 0 : {
text: part.text,
thoughtSignature
};
}
case "reasoning": {
return part.text.length === 0 ? void 0 : {
text: part.text,
thought: true,
thoughtSignature
};
}
case "file": {
if (part.data instanceof URL) {
throw new UnsupportedFunctionalityError({
functionality: "File data URLs in assistant messages are not supported"
});
}
return {
inlineData: {
mimeType: part.mediaType,
data: convertToBase64(part.data)
},
thoughtSignature
};
}
case "tool-call": {
return {
functionCall: {
name: part.toolName,
args: part.input
},
thoughtSignature
};
}
}
}).filter((part) => part !== void 0)
});
break;
}
case "tool": {
systemMessagesAllowed = false;
const parts = [];
for (const part of content) {
if (part.type === "tool-approval-response") {
continue;
}
const output = part.output;
if (output.type === "content") {
for (const contentPart of output.value) {
switch (contentPart.type) {
case "text":
parts.push({
functionResponse: {
name: part.toolName,
response: {
name: part.toolName,
content: contentPart.text
}
}
});
break;
case "image-data":
parts.push(
{
inlineData: {
mimeType: contentPart.mediaType,
data: contentPart.data
}
},
{
text: "Tool executed successfully and returned this image as a response"
}
);
break;
default:
parts.push({ text: JSON.stringify(contentPart) });
break;
}
}
} else {
parts.push({
functionResponse: {
name: part.toolName,
response: {
name: part.toolName,
content: output.type === "execution-denied" ? (_c = output.reason) != null ? _c : "Tool execution denied." : output.value
}
}
});
}
}
contents.push({
role: "user",
parts
});
break;
}
}
}
if (isGemmaModel && systemInstructionParts.length > 0 && contents.length > 0 && contents[0].role === "user") {
const systemText = systemInstructionParts.map((part) => part.text).join("\n\n");
contents[0].parts.unshift({ text: systemText + "\n\n" });
}
return {
systemInstruction: systemInstructionParts.length > 0 && !isGemmaModel ? { parts: systemInstructionParts } : void 0,
contents
};
}
// src/get-model-path.ts
function getModelPath(modelId) {
return modelId.includes("/") ? modelId : `models/${modelId}`;
}
// src/google-generative-ai-options.ts
import { lazySchema as lazySchema4, zodSchema as zodSchema4 } from "@ai-sdk/provider-utils";
import { z as z4 } from "zod/v4";
var googleGenerativeAIProviderOptions = lazySchema4(
() => zodSchema4(
z4.object({
responseModalities: z4.array(z4.enum(["TEXT", "IMAGE"])).optional(),
thinkingConfig: z4.object({
thinkingBudget: z4.number().optional(),
includeThoughts: z4.boolean().optional(),
// https://ai.google.dev/gemini-api/docs/gemini-3?thinking=high#thinking_level
thinkingLevel: z4.enum(["minimal", "low", "medium", "high"]).optional()
}).optional(),
/**
* Optional.
* The name of the cached content used as context to serve the prediction.
* Format: cachedContents/{cachedContent}
*/
cachedContent: z4.string().optional(),
/**
* Optional. Enable structured output. Default is true.
*
* This is useful when the JSON Schema contains elements that are
* not supported by the OpenAPI schema version that
* Google Generative AI uses. You can use this to disable
* structured outputs if you need to.
*/
structuredOutputs: z4.boolean().optional(),
/**
* Optional. A list of unique safety settings for blocking unsafe content.
*/
safetySettings: z4.array(
z4.object({
category: z4.enum([
"HARM_CATEGORY_UNSPECIFIED",
"HARM_CATEGORY_HATE_SPEECH",
"HARM_CATEGORY_DANGEROUS_CONTENT",
"HARM_CATEGORY_HARASSMENT",
"HARM_CATEGORY_SEXUALLY_EXPLICIT",
"HARM_CATEGORY_CIVIC_INTEGRITY"
]),
threshold: z4.enum([
"HARM_BLOCK_THRESHOLD_UNSPECIFIED",
"BLOCK_LOW_AND_ABOVE",
"BLOCK_MEDIUM_AND_ABOVE",
"BLOCK_ONLY_HIGH",
"BLOCK_NONE",
"OFF"
])
})
).optional(),
threshold: z4.enum([
"HARM_BLOCK_THRESHOLD_UNSPECIFIED",
"BLOCK_LOW_AND_ABOVE",
"BLOCK_MEDIUM_AND_ABOVE",
"BLOCK_ONLY_HIGH",
"BLOCK_NONE",
"OFF"
]).optional(),
/**
* Optional. Enables timestamp understanding for audio-only files.
*
* https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/audio-understanding
*/
audioTimestamp: z4.boolean().optional(),
/**
* Optional. Defines labels used in billing reports. Available on Vertex AI only.
*
* https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/add-labels-to-api-calls
*/
labels: z4.record(z4.string(), z4.string()).optional(),
/**
* Optional. If specified, the media resolution specified will be used.
*
* https://ai.google.dev/api/generate-content#MediaResolution
*/
mediaResolution: z4.enum([
"MEDIA_RESOLUTION_UNSPECIFIED",
"MEDIA_RESOLUTION_LOW",
"MEDIA_RESOLUTION_MEDIUM",
"MEDIA_RESOLUTION_HIGH"
]).optional(),
/**
* Optional. Configures the image generation aspect ratio for Gemini models.
*
* https://ai.google.dev/gemini-api/docs/image-generation#aspect_ratios
*/
imageConfig: z4.object({
aspectRatio: z4.enum([
"1:1",
"2:3",
"3:2",
"3:4",
"4:3",
"4:5",
"5:4",
"9:16",
"16:9",
"21:9"
]).optional(),
imageSize: z4.enum(["1K", "2K", "4K"]).optional()
}).optional(),
/**
* Optional. Configuration for grounding retrieval.
* Used to provide location context for Google Maps and Google Search grounding.
*
* https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/grounding-with-google-maps
*/
retrievalConfig: z4.object({
latLng: z4.object({
latitude: z4.number(),
longitude: z4.number()
}).optional()
}).optional()
})
)
);
// src/google-prepare-tools.ts
import {
UnsupportedFunctionalityError as UnsupportedFunctionalityError2
} from "@ai-sdk/provider";
function prepareTools({
tools,
toolChoice,
modelId
}) {
var _a;
tools = (tools == null ? void 0 : tools.length) ? tools : void 0;
const toolWarnings = [];
const isLatest = [
"gemini-flash-latest",
"gemini-flash-lite-latest",
"gemini-pro-latest"
].some((id) => id === modelId);
const isGemini2orNewer = modelId.includes("gemini-2") || modelId.includes("gemini-3") || isLatest;
const supportsDynamicRetrieval = modelId.includes("gemini-1.5-flash") && !modelId.includes("-8b");
const supportsFileSearch = modelId.includes("gemini-2.5") || modelId.includes("gemini-3");
if (tools == null) {
return { tools: void 0, toolConfig: void 0, toolWarnings };
}
const hasFunctionTools = tools.some((tool) => tool.type === "function");
const hasProviderTools = tools.some((tool) => tool.type === "provider");
if (hasFunctionTools && hasProviderTools) {
toolWarnings.push({
type: "unsupported",
feature: `combination of function and provider-defined tools`
});
}
if (hasProviderTools) {
const googleTools2 = [];
const ProviderTools = tools.filter((tool) => tool.type === "provider");
ProviderTools.forEach((tool) => {
switch (tool.id) {
case "google.google_search":
if (isGemini2orNewer) {
googleTools2.push({ googleSearch: {} });
} else if (supportsDynamicRetrieval) {
googleTools2.push({
googleSearchRetrieval: {
dynamicRetrievalConfig: {
mode: tool.args.mode,
dynamicThreshold: tool.args.dynamicThreshold
}
}
});
} else {
googleTools2.push({ googleSearchRetrieval: {} });
}
break;
case "google.enterprise_web_search":
if (isGemini2orNewer) {
googleTools2.push({ enterpriseWebSearch: {} });
} else {
toolWarnings.push({
type: "unsupported",
feature: `provider-defined tool ${tool.id}`,
details: "Enterprise Web Search requires Gemini 2.0 or newer."
});
}
break;
case "google.url_context":
if (isGemini2orNewer) {
googleTools2.push({ urlContext: {} });
} else {
toolWarnings.push({
type: "unsupported",
feature: `provider-defined tool ${tool.id}`,
details: "The URL context tool is not supported with other Gemini models than Gemini 2."
});
}
break;
case "google.code_execution":
if (isGemini2orNewer) {
googleTools2.push({ codeExecution: {} });
} else {
toolWarnings.push({
type: "unsupported",
feature: `provider-defined tool ${tool.id}`,
details: "The code execution tools is not supported with other Gemini models than Gemini 2."
});
}
break;
case "google.file_search":
if (supportsFileSearch) {
googleTools2.push({ fileSearch: { ...tool.args } });
} else {
toolWarnings.push({
type: "unsupported",
feature: `provider-defined tool ${tool.id}`,
details: "The file search tool is only supported with Gemini 2.5 models and Gemini 3 models."
});
}
break;
case "google.vertex_rag_store":
if (isGemini2orNewer) {
googleTools2.push({
retrieval: {
vertex_rag_store: {
rag_resources: {
rag_corpus: tool.args.ragCorpus
},
similarity_top_k: tool.args.topK
}
}
});
} else {
toolWarnings.push({
type: "unsupported",
feature: `provider-defined tool ${tool.id}`,
details: "The RAG store tool is not supported with other Gemini models than Gemini 2."
});
}
break;
case "google.google_maps":
if (isGemini2orNewer) {
googleTools2.push({ googleMaps: {} });
} else {
toolWarnings.push({
type: "unsupported",
feature: `provider-defined tool ${tool.id}`,
details: "The Google Maps grounding tool is not supported with Gemini models other than Gemini 2 or newer."
});
}
break;
default:
toolWarnings.push({
type: "unsupported",
feature: `provider-defined tool ${tool.id}`
});
break;
}
});
return {
tools: googleTools2.length > 0 ? googleTools2 : void 0,
toolConfig: void 0,
toolWarnings
};
}
const functionDeclarations = [];
for (const tool of tools) {
switch (tool.type) {
case "function":
functionDeclarations.push({
name: tool.name,
description: (_a = tool.description) != null ? _a : "",
parameters: convertJSONSchemaToOpenAPISchema(tool.inputSchema)
});
break;
default:
toolWarnings.push({
type: "unsupported",
feature: `function tool ${tool.name}`
});
break;
}
}
if (toolChoice == null) {
return {
tools: [{ functionDeclarations }],
toolConfig: void 0,
toolWarnings
};
}
const type = toolChoice.type;
switch (type) {
case "auto":
return {
tools: [{ functionDeclarations }],
toolConfig: { functionCallingConfig: { mode: "AUTO" } },
toolWarnings
};
case "none":
return {
tools: [{ functionDeclarations }],
toolConfig: { functionCallingConfig: { mode: "NONE" } },
toolWarnings
};
case "required":
return {
tools: [{ functionDeclarations }],
toolConfig: { functionCallingConfig: { mode: "ANY" } },
toolWarnings
};
case "tool":
return {
tools: [{ functionDeclarations }],
toolConfig: {
functionCallingConfig: {
mode: "ANY",
allowedFunctionNames: [toolChoice.toolName]
}
},
toolWarnings
};
default: {
const _exhaustiveCheck = type;
throw new UnsupportedFunctionalityError2({
functionality: `tool choice type: ${_exhaustiveCheck}`
});
}
}
}
// src/map-google-generative-ai-finish-reason.ts
function mapGoogleGenerativeAIFinishReason({
finishReason,
hasToolCalls
}) {
switch (finishReason) {
case "STOP":
return hasToolCalls ? "tool-calls" : "stop";
case "MAX_TOKENS":
return "length";
case "IMAGE_SAFETY":
case "RECITATION":
case "SAFETY":
case "BLOCKLIST":
case "PROHIBITED_CONTENT":
case "SPII":
return "content-filter";
case "MALFORMED_FUNCTION_CALL":
return "error";
case "FINISH_REASON_UNSPECIFIED":
case "OTHER":
default:
return "other";
}
}
// src/google-generative-ai-language-model.ts
var GoogleGenerativeAILanguageModel = class {
constructor(modelId, config) {
this.specificationVersion = "v3";
var _a;
this.modelId = modelId;
this.config = config;
this.generateId = (_a = config.generateId) != null ? _a : generateId;
}
get provider() {
return this.config.provider;
}
get supportedUrls() {
var _a, _b, _c;
return (_c = (_b = (_a = this.config).supportedUrls) == null ? void 0 : _b.call(_a)) != null ? _c : {};
}
async getArgs({
prompt,
maxOutputTokens,
temperature,
topP,
topK,
frequencyPenalty,
presencePenalty,
stopSequences,
responseFormat,
seed,
tools,
toolChoice,
providerOptions
}) {
var _a;
const warnings = [];
const providerOptionsName = this.config.provider.includes("vertex") ? "vertex" : "google";
let googleOptions = await parseProviderOptions2({
provider: providerOptionsName,
providerOptions,
schema: googleGenerativeAIProviderOptions
});
if (googleOptions == null && providerOptionsName !== "google") {
googleOptions = await parseProviderOptions2({
provider: "google",
providerOptions,
schema: googleGenerativeAIProviderOptions
});
}
if ((tools == null ? void 0 : tools.some(
(tool) => tool.type === "provider" && tool.id === "google.vertex_rag_store"
)) && !this.config.provider.startsWith("google.vertex.")) {
warnings.push({
type: "other",
message: `The 'vertex_rag_store' tool is only supported with the Google Vertex provider and might not be supported or could behave unexpectedly with the current Google provider (${this.config.provider}).`
});
}
const isGemmaModel = this.modelId.toLowerCase().startsWith("gemma-");
const { contents, systemInstruction } = convertToGoogleGenerativeAIMessages(
prompt,
{ isGemmaModel, providerOptionsName }
);
const {
tools: googleTools2,
toolConfig: googleToolConfig,
toolWarnings
} = prepareTools({
tools,
toolChoice,
modelId: this.modelId
});
return {
args: {
generationConfig: {
// standardized settings:
maxOutputTokens,
temperature,
topK,
topP,
frequencyPenalty,
presencePenalty,
stopSequences,
seed,
// response format:
responseMimeType: (responseFormat == null ? void 0 : responseFormat.type) === "json" ? "application/json" : void 0,
responseSchema: (responseFormat == null ? void 0 : responseFormat.type) === "json" && responseFormat.schema != null && // Google GenAI does not support all OpenAPI Schema features,
// so this is needed as an escape hatch:
// TODO convert into provider option
((_a = googleOptions == null ? void 0 : googleOptions.structuredOutputs) != null ? _a : true) ? convertJSONSchemaToOpenAPISchema(responseFormat.schema) : void 0,
...(googleOptions == null ? void 0 : googleOptions.audioTimestamp) && {
audioTimestamp: googleOptions.audioTimestamp
},
// provider options:
responseModalities: googleOptions == null ? void 0 : googleOptions.responseModalities,
thinkingConfig: googleOptions == null ? void 0 : googleOptions.thinkingConfig,
...(googleOptions == null ? void 0 : googleOptions.mediaResolution) && {
mediaResolution: googleOptions.mediaResolution
},
...(googleOptions == null ? void 0 : googleOptions.imageConfig) && {
imageConfig: googleOptions.imageConfig
}
},
contents,
systemInstruction: isGemmaModel ? void 0 : systemInstruction,
safetySettings: googleOptions == null ? void 0 : googleOptions.safetySettings,
tools: googleTools2,
toolConfig: (googleOptions == null ? void 0 : googleOptions.retrievalConfig) ? {
...googleToolConfig,
retrievalConfig: googleOptions.retrievalConfig
} : googleToolConfig,
cachedContent: googleOptions == null ? void 0 : googleOptions.cachedContent,
labels: googleOptions == null ? void 0 : googleOptions.labels
},
warnings: [...warnings, ...toolWarnings],
providerOptionsName
};
}
async doGenerate(options) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i;
const { args, warnings, providerOptionsName } = await this.getArgs(options);
const mergedHeaders = combineHeaders2(
await resolve2(this.config.headers),
options.headers
);
const {
responseHeaders,
value: response,
rawValue: rawResponse
} = await postJsonToApi2({
url: `${this.config.baseURL}/${getModelPath(
this.modelId
)}:generateContent`,
headers: mergedHeaders,
body: args,
failedResponseHandler: googleFailedResponseHandler,
successfulResponseHandler: createJsonResponseHandler2(responseSchema),
abortSignal: options.abortSignal,
fetch: this.config.fetch
});
const candidate = response.candidates[0];
const content = [];
const parts = (_b = (_a = candidate.content) == null ? void 0 : _a.parts) != null ? _b : [];
const usageMetadata = response.usageMetadata;
let lastCodeExecutionToolCallId;
for (const part of parts) {
if ("executableCode" in part && ((_c = part.executableCode) == null ? void 0 : _c.code)) {
const toolCallId = this.config.generateId();
lastCodeExecutionToolCallId = toolCallId;
content.push({
type: "tool-call",
toolCallId,
toolName: "code_execution",
input: JSON.stringify(part.executableCode),
providerExecuted: true
});
} else if ("codeExecutionResult" in part && part.codeExecutionResult) {
content.push({
type: "tool-result",
// Assumes a result directly follows its corresponding call part.
toolCallId: lastCodeExecutionToolCallId,
toolName: "code_execution",
result: {
outcome: part.codeExecutionResult.outcome,
output: part.codeExecutionResult.output
}
});
lastCodeExecutionToolCallId = void 0;
} else if ("text" in part && part.text != null && part.text.length > 0) {
content.push({
type: part.thought === true ? "reasoning" : "text",
text: part.text,
providerMetadata: part.thoughtSignature ? {
[providerOptionsName]: {
thoughtSignature: part.thoughtSignature
}
} : void 0
});
} else if ("functionCall" in part) {
content.push({
type: "tool-call",
toolCallId: this.config.generateId(),
toolName: part.functionCall.name,
input: JSON.stringify(part.functionCall.args),
providerMetadata: part.thoughtSignature ? {
[providerOptionsName]: {
thoughtSignature: part.thoughtSignature
}
} : void 0
});
} else if ("inlineData" in part) {
content.push({
type: "file",
data: part.inlineData.data,
mediaType: part.inlineData.mimeType,
providerMetadata: part.thoughtSignature ? {
[providerOptionsName]: {
thoughtSignature: part.thoughtSignature
}
} : void 0
});
}
}
const sources = (_d = extractSources({
groundingMetadata: candidate.groundingMetadata,
generateId: this.config.generateId
})) != null ? _d : [];
for (const source of sources) {
content.push(source);
}
return {
content,
finishReason: {
unified: mapGoogleGenerativeAIFinishReason({
finishReason: candidate.finishReason,
// Only count client-executed tool calls for finish reason determination.
hasToolCalls: content.some(
(part) => part.type === "tool-call" && !part.providerExecuted
)
}),
raw: (_e = candidate.finishReason) != null ? _e : void 0
},
usage: convertGoogleGenerativeAIUsage(usageMetadata),
warnings,
providerMetadata: {
[providerOptionsName]: {
promptFeedback: (_f = response.promptFeedback) != null ? _f : null,
groundingMetadata: (_g = candidate.groundingMetadata) != null ? _g : null,
urlContextMetadata: (_h = candidate.urlContextMetadata) != null ? _h : null,
safetyRatings: (_i = candidate.safetyRatings) != null ? _i : null,
usageMetadata: usageMetadata != null ? usageMetadata : null
}
},
request: { body: args },
response: {
// TODO timestamp, model id, id
headers: responseHeaders,
body: rawResponse
}
};
}
async doStream(options) {
const { args, warnings, providerOptionsName } = await this.getArgs(options);
const headers = combineHeaders2(
await resolve2(this.config.headers),
options.headers
);
const { responseHeaders, value: response } = await postJsonToApi2({
url: `${this.config.baseURL}/${getModelPath(
this.modelId
)}:streamGenerateContent?alt=sse`,
headers,
body: args,
failedResponseHandler: googleFailedResponseHandler,
successfulResponseHandler: createEventSourceResponseHandler(chunkSchema),
abortSignal: options.abortSignal,
fetch: this.config.fetch
});
let finishReason = {
unified: "other",
raw: void 0
};
let usage = void 0;
let providerMetadata = void 0;
const generateId3 = this.config.generateId;
let hasToolCalls = false;
let currentTextBlockId = null;
let currentReasoningBlockId = null;
let blockCounter = 0;
const emittedSourceUrls = /* @__PURE__ */ new Set();
let lastCodeExecutionToolCallId;
return {
stream: response.pipeThrough(
new TransformStream({
start(controller) {
controller.enqueue({ type: "stream-start", warnings });
},
transform(chunk, controller) {
var _a, _b, _c, _d, _e, _f, _g;
if (options.includeRawChunks) {
controller.enqueue({ type: "raw", rawValue: chunk.rawValue });
}
if (!chunk.success) {
controller.enqueue({ type: "error", error: chunk.error });
return;
}
const value = chunk.value;
const usageMetadata = value.usageMetadata;
if (usageMetadata != null) {
usage = usageMetadata;
}
const candidate = (_a = value.candidates) == null ? void 0 : _a[0];
if (candidate == null) {
return;
}
const content = candidate.content;
const sources = extractSources({
groundingMetadata: candidate.groundingMetadata,
generateId: generateId3
});
if (sources != null) {
for (const source of sources) {
if (source.sourceType === "url" && !emittedSourceUrls.has(source.url)) {
emittedSourceUrls.add(source.url);
controller.enqueue(source);
}
}
}
if (content != null) {
const parts = (_b = content.parts) != null ? _b : [];
for (const part of parts) {
if ("executableCode" in part && ((_c = part.executableCode) == null ? void 0 : _c.code)) {
const toolCallId = generateId3();
lastCodeExecutionToolCallId = toolCallId;
controller.enqueue({
type: "tool-call",
toolCallId,
toolName: "code_execution",
input: JSON.stringify(part.executableCode),
providerExecuted: true
});
} else if ("codeExecutionResult" in part && part.codeExecutionResult) {
const toolCallId = lastCodeExecutionToolCallId;
if (toolCallId) {
controller.enqueue({
type: "tool-result",
toolCallId,
toolName: "code_execution",
result: {
outcome: part.codeExecutionResult.outcome,
output: part.codeExecutionResult.output
}
});
lastCodeExecutionToolCallId = void 0;
}
} else if ("text" in part && part.text != null && part.text.length > 0) {
if (part.thought === true) {
if (currentTextBlockId !== null) {
controller.enqueue({
type: "text-end",
id: currentTextBlockId
});
currentTextBlockId = null;
}
if (currentReasoningBlockId === null) {
currentReasoningBlockId = String(blockCounter++);
controller.enqueue({
type: "reasoning-start",
id: currentReasoningBlockId,
providerMetadata: part.thoughtSignature ? {
[providerOptionsName]: {
thoughtSignature: part.thoughtSignature
}
} : void 0
});
}
controller.enqueue({
type: "reasoning-delta",
id: currentReasoningBlockId,
delta: part.text,
providerMetadata: part.thoughtSignature ? {
[providerOptionsName]: {
thoughtSignature: part.thoughtSignature
}
} : void 0
});
} else {
if (currentReasoningBlockId !== null) {
controller.enqueue({
type: "reasoning-end",
id: currentReasoningBlockId
});
currentReasoningBlockId = null;
}
if (currentTextBlockId === null) {
currentTextBlockId = String(blockCounter++);
controller.enqueue({
type: "text-start",
id: currentTextBlockId,
providerMetadata: part.thoughtSignature ? {
[providerOptionsName]: {
thoughtSignature: part.thoughtSignature
}
} : void 0
});
}
controller.enqueue({
type: "text-delta",
id: currentTextBlockId,
delta: part.text,
providerMetadata: part.thoughtSignature ? {
[providerOptionsName]: {
thoughtSignature: part.thoughtSignature
}
} : void 0
});
}
} else if ("inlineData" in part) {
controller.enqueue({
type: "file",
mediaType: part.inlineData.mimeType,
data: part.inlineData.data
});
}
}
const toolCallDeltas = getToolCallsFromParts({
parts: content.parts,
generateId: generateId3,
providerOptionsName
});
if (toolCallDeltas != null) {
for (const toolCall of toolCallDeltas) {
controller.enqueue({
type: "tool-input-start",
id: toolCall.toolCallId,
toolName: toolCall.toolName,
providerMetadata: toolCall.providerMetadata
});
controller.enqueue({
type: "tool-input-delta",
id: toolCall.toolCallId,
delta: toolCall.args,
providerMetadata: toolCall.providerMetadata
});
controller.enqueue({
type: "tool-input-end",
id: toolCall.toolCallId,
providerMetadata: toolCall.providerMetadata
});
controller.enqueue({
type: "tool-call",
toolCallId: toolCall.toolCallId,
toolName: toolCall.toolName,
input: toolCall.args,
providerMetadata: toolCall.providerMetadata
});
hasToolCalls = true;
}
}
}
if (candidate.finishReason != null) {
finishReason = {
unified: mapGoogleGenerativeAIFinishReason({
finishReason: candidate.finishReason,
hasToolCalls
}),
raw: candidate.finishReason
};
providerMetadata = {
[providerOptionsName]: {
promptFeedback: (_d = value.promptFeedback) != null ? _d : null,
groundingMetadata: (_e = candidate.groundingMetadata) != null ? _e : null,
urlContextMetadata: (_f = candidate.urlContextMetadata) != null ? _f : null,
safetyRatings: (_g = candidate.safetyRatings) != null ? _g : null
}
};
if (usageMetadata != null) {
providerMetadata[providerOptionsName].usageMetadata = usageMetadata;
}
}
},
flush(controller) {
if (currentTextBlockId !== null) {
controller.enqueue({
type: "text-end",
id: currentTextBlockId
});
}
if (currentReasoningBlockId !== null) {
controller.enqueue({
type: "reasoning-end",
id: currentReasoningBlockId
});
}
controller.enqueue({
type: "finish",
finishReason,
usage: convertGoogleGenerativeAIUsage(usage),
providerMetadata
});
}
})
),
response: { headers: responseHeaders },
request: { body: args }
};
}
};
function getToolCallsFromParts({
parts,
generateId: generateId3,
providerOptionsName
}) {
const functionCallParts = parts == null ? void 0 : parts.filter(
(part) => "functionCall" in part
);
return functionCallParts == null || functionCallParts.length === 0 ? void 0 : functionCallParts.map((part) => ({
type: "tool-call",
toolCallId: generateId3(),
toolName: part.functionCall.name,
args: JSON.stringify(part.functionCall.args),
providerMetadata: part.thoughtSignature ? {
[providerOptionsName]: {
thoughtSignature: part.thoughtSignature
}
} : void 0
}));
}
function extractSources({
groundingMetadata,
generateId: generateId3
}) {
var _a, _b, _c, _d, _e;
if (!(groundingMetadata == null ? void 0 : groundingMetadata.groundingChunks)) {
return void 0;
}
const sources = [];
for (const chunk of groundingMetadata.groundingChunks) {
if (chunk.web != null) {
sources.push({
type: "source",
sourceType: "url",
id: generateId3(),
url: chunk.web.uri,
title: (_a = chunk.web.title) != null ? _a : void 0
});
} else if (chunk.retrievedContext != null) {
const uri = chunk.retrievedContext.uri;
const fileSearchStore = chunk.retrievedContext.fileSearchStore;
if (uri && (uri.startsWith("http://") || uri.startsWith("https://"))) {
sources.push({
type: "source",
sourceType: "url",
id: generateId3(),
url: uri,
title: (_b = chunk.retrievedContext.title) != null ? _b : void 0
});
} else if (uri) {
const title = (_c = chunk.retrievedContext.title) != null ? _c : "Unknown Document";
let mediaType = "application/octet-stream";
let filename = void 0;
if (uri.endsWith(".pdf")) {
mediaType = "application/pdf";
filename = uri.split("/").pop();
} else if (uri.endsWith(".txt")) {
mediaType = "text/plain";
filename = uri.split("/").pop();
} else if (uri.endsWith(".docx")) {
mediaType = "application/vnd.openxmlformats-officedocument.wordprocessingml.document";
filename = uri.split("/").pop();
} else if (uri.endsWith(".doc")) {
mediaType = "application/msword";
filename = uri.split("/").pop();
} else if (uri.match(/\.(md|markdown)$/)) {
mediaType = "text/markdown";
filename = uri.split("/").pop();
} else {
filename = uri.split("/").pop();
}
sources.push({
type: "source",
sourceType: "document",
id: generateId3(),
mediaType,
title,
filename
});
} else if (fileSearchStore) {
const title = (_d = chunk.retrievedContext.title) != null ? _d : "Unknown Document";
sources.push({
type: "source",
sourceType: "document",