/** * Defines the shared service for language model providers. * * The `LanguageModel` service lets application code ask for generated text, * streamed text, or structured output without depending on a specific provider. * Requests can include tools, and the service can resolve tool calls while the * model is generating a response. This module contains the service contract, * request and response types, structured-output support, and the constructor * used by provider packages to adapt their own generate and stream functions to * the shared interface. * * @stability unstable * @since 4.0.0 */ import type * as Cause from "../Cause.ts" import * as Context from "../Context.ts" import * as Effect from "../Effect.ts" import * as FiberSet from "../FiberSet.ts" import { constFalse, identity, pipe } from "../Function.ts" import type * as JsonSchema from "../JsonSchema.ts" import * as Option from "../Option.ts" import * as Predicate from "../Predicate.ts" import * as Queue from "../Queue.ts" import * as Schema from "../Schema.ts" import * as SchemaAST from "../SchemaAST.ts" import * as Semaphore from "../Semaphore.ts" import * as Sink from "../Sink.ts" import * as Stream from "../Stream.ts" import type { Span } from "../Tracer.ts" import type { Concurrency, Mutable, NoExcessProperties } from "../Types.ts" import * as AiError from "./AiError.ts" import { defaultIdGenerator, IdGenerator } from "./IdGenerator.ts" import * as InternalCodecTransformer from "./internal/codec-transformer.ts" import * as Prompt from "./Prompt.ts" import * as Response from "./Response.ts" import * as ResponseIdTracker from "./ResponseIdTracker.ts" import type { SpanTransformer } from "./Telemetry.ts" import { CurrentSpanTransformer } from "./Telemetry.ts" import type * as Tool from "./Tool.ts" import * as Toolkit from "./Toolkit.ts" /** * Service key for text generation, structured output, and tool calls. * * **Example** (Accessing the language model service) * * ```ts import.meta.vitest * import { Effect, Layer, Stream } from "effect" * import { LanguageModel } from "effect/ai" * * const FakeLanguageModel = Layer.effect( * LanguageModel.LanguageModel, * LanguageModel.make({ * generateText: () => * Effect.succeed([{ * type: "text", * text: "Machine learning finds patterns in data." * }]), * streamText: () => Stream.empty * }) * ) * * const program = Effect.gen(function*() { * const model = yield* LanguageModel.LanguageModel * const response = yield* model.generateText({ * prompt: "What is machine learning?" * }) * return response.text * }) * * await Effect.runPromise(program.pipe(Effect.provide(FakeLanguageModel))) // => "Machine learning finds patterns in data." * ``` * * @stability unstable * @category services * @since 4.0.0 */ export const LanguageModel: Context.Service = Context.Service( "effect/ai/LanguageModel" ) /** * Brand type for `LanguageModel`. * * @stability unstable * @category type IDs * @since 4.0.0 */ export type TypeId = "~effect/ai/LanguageModel" /** * Brand for `LanguageModel` implementations. * * @stability unstable * @category type IDs * @since 4.0.0 */ export const TypeId: TypeId = "~effect/ai/LanguageModel" /** * Text generation, streaming, and structured output operations. * * @stability unstable * @category models * @since 4.0.0 */ export interface LanguageModel { readonly [TypeId]: TypeId /** * Whether the effective provider configuration supports system messages in * history. Evaluate in the request's context to respect scoped overrides. * Placement restrictions may apply; an absent capability means unknown support. */ readonly supportsSystemMessagesInHistory?: Effect.Effect | undefined /** * Generate text using the language model. */ readonly generateText: { // No toolkit: force `{}` instead of falling back to `Record`. >( options: Options & GenerateTextOptionsWithoutToolkit ): Effect.Effect< GenerateTextResponse<{}>, ExtractError, ExtractServices > // Generic toolkit: preserve caller-supplied `Tools` in helpers like `(toolkit: WithHandler) => ...`. < Tools extends Record, Options extends NoExcessProperties< GenerateTextOptions & { readonly toolkit: ToolkitInput }, Options > >( options: Options & GenerateTextOptions & { readonly toolkit: ToolkitInput } ): Effect.Effect< GenerateTextResponse>, ExtractError, ExtractServices > // Toolkit unions: recover distributive `ExtractTools` inference for `toolkitA | toolkitB` call sites. < Options extends { readonly toolkit: ToolkitOption } & NoExcessProperties, Options> >( options: Options & GenerateTextOptions> & { readonly toolkit: Options["toolkit"] } ): Effect.Effect< GenerateTextResponse, ExtractToolParametersMode>, ExtractError, ExtractServices > } /** * Generate a structured object from a schema using the language model. */ readonly generateObject: < ObjectEncoded extends Record, StructuredOutputSchema extends Schema.Encoder, Options extends NoExcessProperties< GenerateObjectOptions, Options >, Tools extends Record = {} >( options: Options & GenerateObjectOptions ) => Effect.Effect< GenerateObjectResponse>, ExtractError, ExtractServices | StructuredOutputSchema["DecodingServices"] > /** * Generate text using the language model with streaming output. */ readonly streamText: { // No toolkit: force `{}` instead of falling back to `Record`. >( options: Options & GenerateTextOptionsWithoutToolkit ): Stream.Stream< Response.StreamPart<{}>, ExtractError, ExtractServices > // Generic toolkit: preserve caller-supplied `Tools` in helpers like `(toolkit: WithHandler) => ...`. < Tools extends Record, Options extends NoExcessProperties< GenerateTextOptions & { readonly toolkit: ToolkitInput }, Options > >( options: Options & GenerateTextOptions & { readonly toolkit: ToolkitInput } ): Stream.Stream< Response.StreamPart>, ExtractError, ExtractServices > // Toolkit unions: recover distributive `ExtractTools` inference for `toolkitA | toolkitB` call sites. < Options extends { readonly toolkit: ToolkitOption } & NoExcessProperties, Options> >( options: Options & GenerateTextOptions> & { readonly toolkit: Options["toolkit"] } ): Stream.Stream< Response.StreamPart, ExtractToolParametersMode>, ExtractError, ExtractServices > } } /** * A function that transforms a `Schema.Codec` into a provider-compatible form for structured output generation. * * **Details** * * Different language model providers have varying constraints on the JSON * schemas they accept. A `CodecTransformer` rewrites a codec's encoded side to * satisfy those constraints while preserving the decoded type. A provider * schema may be less restrictive than the codec when the provider cannot * express every constraint; the returned codec remains authoritative for * validating model output. * * @stability unstable * @category utility types * @since 4.0.0 */ export type CodecTransformer = (schema: Schema.ConstraintCodec) => { readonly codec: Schema.ConstraintCodec readonly jsonSchema: JsonSchema.JsonSchema } /** * The default codec transformer that passes schemas through without * provider-specific rewrites. * * **When to use** * * Use as the codec transformer for provider implementations when the provider * accepts the JSON Schema generated from an `Effect` Schema codec without * provider-specific rewrites. * * **Details** * * The transformer returns the original codec, resolves a top-level `$ref`, and * copies schema definitions into `$defs`. * * @see {@link CodecTransformer} for the structured-output transformer contract * @see {@link make} for where this transformer is used as the default * * @stability unstable * @category services * @since 4.0.0 */ export const defaultCodecTransformer: CodecTransformer = InternalCodecTransformer.defaultCodecTransformer /** * Configuration options for text generation. * * @stability unstable * @category options * @since 4.0.0 */ export interface GenerateTextOptions> { /** * The prompt input to use to generate text. */ readonly prompt: Prompt.RawInput /** * A toolkit containing both the tools and the tool call handler to use to * augment text generation. */ readonly toolkit?: ToolkitInput | undefined /** * The tool choice mode for the language model. * - `auto` (default): The model can decide whether or not to call tools, as * well as which tools to call. * - `required`: The model **must** call a tool but can decide which tool will * be called. * - `none`: The model **must not** call a tool. * - `{ tool: }`: The model must call the specified tool. * - `{ mode?: "auto" (default) | "required", "oneOf": [] }`: The * model is restricted to the subset of tools specified by `oneOf`. When * `mode` is `"auto"` or omitted, the model can decide whether or not a tool * from the allowed subset of tools can be called. When `mode` is * `"required"`, the model **must** call one tool from the allowed subset of * tools. */ readonly toolChoice?: | ToolChoice<{ [Name in keyof Tools]: Tools[Name]["name"] }[keyof Tools]> | undefined /** * The concurrency level for resolving tool calls. */ readonly concurrency?: Concurrency | undefined /** * When set to `true`, tool calls requested by the large language model are not auto-resolved by the framework. * * **When to use** * * Use when you want to include tool call definitions from an `AiToolkit` * in requests to the large language model, while controlling tool call * resolver execution yourself. */ readonly disableToolCallResolution?: boolean | undefined } type GenerateTextOptionsWithoutToolkit = Omit, "toolkit"> & { readonly toolkit?: undefined } /** * Configuration options for structured object generation. * * @stability unstable * @category options * @since 4.0.0 */ export interface GenerateObjectOptions< Tools extends Record, StructuredOutputSchema extends Schema.Top > extends GenerateTextOptions { /** * The name of the structured output that should be generated. Used by some * large language model providers to provide additional guidance to the model. */ readonly objectName?: string | undefined /** * The schema to be used to specify the structure of the object to generate. */ readonly schema: StructuredOutputSchema } /** * The tool choice mode for the language model. * - `auto` (default): The model can decide whether or not to call tools, as * well as which tools to call. * - `required`: The model **must** call a tool but can decide which tool will * be called. * - `none`: The model **must not** call a tool. * - `{ tool: }`: The model must call the specified tool. * - `{ mode?: "auto" (default) | "required", "oneOf": [] }`: The * model is restricted to the subset of tools specified by `oneOf`. When * `mode` is `"auto"` or omitted, the model can decide whether or not a tool * from the allowed subset of tools can be called. When `mode` is * `"required"`, the model **must** call one tool from the allowed subset of * tools. * * @stability unstable * @category models * @since 4.0.0 */ export type ToolChoice = | "auto" | "none" | "required" | { readonly tool: ToolName } | { readonly mode?: "auto" | "required" readonly oneOf: ReadonlyArray } /** * Response class for text generation operations, with accessors for extracting text, tool calls, usage information, and other response parts from generated content. * * **Example** (Inspecting a text response) * * ```ts import.meta.vitest * import { LanguageModel, Response } from "effect/ai" * * const response = new LanguageModel.GenerateTextResponse([ * Response.makePart("text", { text: "Plants convert light into energy." }) * ]) * * const result = [response.text, response.finishReason] // => ["Plants convert light into energy.", "unknown"] * ``` * * @stability unstable * @category models * @since 4.0.0 */ export class GenerateTextResponse< Tools extends Record, ParametersMode extends Response.ToolParametersMode = "decoded" > { readonly content: Array> constructor(content: Array>) { this.content = content } /** * Extracts and concatenates all text parts from the response. */ get text(): string { const text: Array = [] for (const part of this.content) { if (part.type === "text") { text.push(part.text) } } return text.join("") } /** * Returns all reasoning parts from the response. */ get reasoning(): Array { return this.content.filter((part) => part.type === "reasoning") } /** * Extracts and concatenates all reasoning text, or undefined if none exists. */ get reasoningText(): string | undefined { const text: Array = [] for (const part of this.content) { if (part.type === "reasoning") { text.push(part.text) } } return text.length === 0 ? undefined : text.join("") } /** * Returns all tool call parts from the response. */ get toolCalls(): Array> { return this.content.filter((part) => part.type === "tool-call") } /** * Returns all tool result parts from the response. */ get toolResults(): Array> { return this.content.filter((part) => part.type === "tool-result") } /** * The reason why text generation finished. */ get finishReason(): Response.FinishReason { const finishPart = this.content.find((part) => part.type === "finish") return Predicate.isUndefined(finishPart) ? "unknown" : finishPart.reason } /** * Token usage statistics for the generation request. */ get usage(): Response.Usage { const finishPart = this.content.find((part) => part.type === "finish") if (Predicate.isUndefined(finishPart)) { return new Response.Usage({ inputTokens: { uncached: undefined, total: undefined, cacheRead: undefined, cacheWrite: undefined }, outputTokens: { total: undefined, text: undefined, reasoning: undefined } }) } return finishPart.usage } } /** * Response class for structured object generation operations. * * **Example** (Inspecting an object response) * * ```ts import.meta.vitest * import { LanguageModel, Response } from "effect/ai" * * const response = new LanguageModel.GenerateObjectResponse( * { name: "John Doe", email: "john@example.com" }, * [Response.makePart("text", { text: '{"name":"John Doe","email":"john@example.com"}' })] * ) * * response.value // => { name: "John Doe", email: "john@example.com" } * response.text // => '{"name":"John Doe","email":"john@example.com"}' * ``` * * @stability unstable * @category models * @since 4.0.0 */ export class GenerateObjectResponse< Tools extends Record, A, ParametersMode extends Response.ToolParametersMode = "decoded" > extends GenerateTextResponse { /** * The parsed structured object that conforms to the provided schema. */ readonly value: A constructor(value: A, content: Array>) { super(content) this.value = value } } // ============================================================================= // Utility Types // ============================================================================= /** * The supported toolkit option shapes for language model operations. * * @stability unstable * @category utility types * @since 4.0.0 */ export type ToolkitOption< Tools extends Record, E = never, R = any > = Tools extends any ? ( | Toolkit.WithHandler | Effect.Effect< Toolkit.WithHandler, E, R > ) : never /** * The supported toolkit input shapes for language model operation options. * * **Details** * * Unlike `ToolkitOption`, this type does not distribute over unions. It is * intended for call-site assignability, while `ToolkitOption` remains the * distributive helper used for extraction and inference. * * @stability unstable * @category utility types * @since 4.0.0 */ export type ToolkitInput< Tools extends Record, E = never, R = any > = | ToolkitOption | Toolkit.WithHandler | Effect.Effect< Toolkit.WithHandler, E, R > type ExtractToolsFromToolkitOption = ToolkitValue extends Toolkit.WithHandler ? Tools : ToolkitValue extends Effect.Effect< Toolkit.WithHandler, infer _E, infer _R > ? _Tools : never /** * Utility type that extracts the toolset from LanguageModel options. * * @stability unstable * @category utility types * @since 4.0.0 */ export type ExtractTools = Options extends { readonly toolkit: infer ToolkitValue } ? ExtractToolsFromToolkitOption> : {} /** * Resolves to `"encoded"` when tool call resolution is * disabled, otherwise `"opaque"`. * * @stability unstable * @category utility types * @since 4.0.0 */ export type ExtractToolParametersMode = Options extends { readonly disableToolCallResolution: true } ? "encoded" : "opaque" type ExtractErrorFromToolkitOption = ToolkitValue extends Toolkit.WithHandler ? | AiError.AiError | (DisableToolCallResolution extends true ? never : Tool.HandlerError) : ToolkitValue extends Effect.Effect< Toolkit.WithHandler, infer E, infer _R > ? AiError.AiError | E | (DisableToolCallResolution extends true ? never : Tool.HandlerError<_Tools[keyof _Tools]>) : AiError.AiError type ExtractServicesFromToolkitOption = ToolkitValue extends Toolkit.WithHandler ? | Tool.HandlerServices | Tool.ResultDecodingServices | Tool.ParametersEncodingServices : ToolkitValue extends Effect.Effect< Toolkit.WithHandler, infer _E, infer R > ? | Tool.HandlerServices | Tool.ResultDecodingServices | Tool.ParametersEncodingServices | R : never // Disabled resolution needs parameter encoding but not handlers or result // decoding. Match Toolkit before Effect to omit its handler requirement. type ExtractDisabledResolutionServicesFromToolkitOption = ToolkitValue extends Toolkit.WithHandler ? Tool.ParametersEncodingServices : ToolkitValue extends Toolkit.Toolkit ? Tool.ParametersEncodingServices : ToolkitValue extends Effect.Effect< Toolkit.WithHandler, infer _E, infer R > ? Tool.ParametersEncodingServices | R : never type ExtractToolkitResolutionError = ToolkitValue extends Effect.Effect< Toolkit.WithHandler, infer E, infer _R > ? E : never type ExtractToolkitResolutionServices = ToolkitValue extends Effect.Effect< Toolkit.WithHandler, infer _E, infer R > ? R : never /** * Utility type that extracts the error type from LanguageModel options. * * **Details** * * Automatically infers the possible error types based on toolkit configuration * and tool call resolution settings. * * @stability unstable * @category utility types * @since 4.0.0 */ export type ExtractError = Options extends { readonly disableToolCallResolution: true readonly toolkit: infer ToolkitValue } ? ExtractErrorFromToolkitOption, true> : Options extends { readonly toolkit: infer ToolkitValue } ? ExtractErrorFromToolkitOption, false> : Options extends { readonly disableToolCallResolution: true } ? AiError.AiError : AiError.AiError /** * Utility type that extracts the context requirements from LanguageModel options. * * **Details** * * Automatically infers the required services based on the toolkit configuration. * * @stability unstable * @category utility types * @since 4.0.0 */ export type ExtractServices = Options extends { readonly disableToolCallResolution: true } ? Options extends { readonly toolkit: infer ToolkitValue } ? ExtractDisabledResolutionServicesFromToolkitOption> : never : Options extends { readonly toolkit: infer ToolkitValue } ? ExtractServicesFromToolkitOption> : never // ============================================================================= // Service Constructor // ============================================================================= /** * Configuration options passed along to language model provider implementations. * * **Details** * * This interface defines the normalized options that are passed to the * underlying provider implementation, regardless of the specific provider being * used. * * @stability unstable * @category options * @since 4.0.0 */ export interface ProviderOptions { /** * The prompt messages to use to generate text. */ readonly prompt: Prompt.Prompt /** * The tools that the large language model will have available to provide * additional information which can be incorporated into its text generation. */ readonly tools: ReadonlyArray /** * The format the response should be provided in. * * **Details** * * If `"text"` is specified, the large language model response is returned as * text. If `"json"` is specified, the large language model response is * provided as a JSON object that conforms to the shape of the specified schema. * The default is `{ type: "text" }`. */ readonly responseFormat: | { readonly type: "text" } | { readonly type: "json" readonly objectName: string readonly schema: Schema.Top } /** * The tool choice mode for the language model. * - `auto` (default): The model can decide whether or not to call tools, as * well as which tools to call. * - `required`: The model **must** call a tool but can decide which tool will * be called. * - `none`: The model **must not** call a tool. * - `{ tool: }`: The model must call the specified tool. * - `{ mode?: "auto" (default) | "required", "oneOf": [] }`: The * model is restricted to the subset of tools specified by `oneOf`. When * `mode` is `"auto"` or omitted, the model can decide whether or not a tool * from the allowed subset of tools can be called. When `mode` is * `"required"`, the model **must** call one tool from the allowed subset of * tools. */ readonly toolChoice: ToolChoice /** * The span to use to trace interactions with the large language model. */ readonly span: Span /** * The previous response identifier for incremental provider calls. */ readonly previousResponseId: string | undefined /** * The prompt reduced to messages not yet seen by the provider. */ readonly incrementalPrompt: Prompt.Prompt | undefined /** * Whether this request is retrying an incremental request with the full * prompt after the provider rejected the incremental request. */ readonly incrementalFallback?: boolean | undefined } /** * Creates a LanguageModel service from provider-specific text generation and * streaming implementations. * * **When to use** * * Use when you are implementing a provider adapter and need to expose the * standard language-model service while keeping provider-specific request hooks * behind it. * * **Details** * * The returned service implements `generateText`, `generateObject`, and * `streamText`. It prepares `ProviderOptions` for each request, including the * normalized prompt, tools, tool choice, response format, tracing span, and * incremental response fields, before calling the supplied provider hook. * Structured object generation uses the `generateText` hook and the configured * `codecTransformer`, or `defaultCodecTransformer` when none is supplied. * * **Gotchas** * * Provider hooks must return encoded response parts that match the toolkit and * response format prepared in `ProviderOptions`; invalid parts fail decoding as * `AiError.InvalidOutputError`. * * @see {@link LanguageModel} for the returned service contract * @see {@link ProviderOptions} for the normalized options passed to provider hooks * @see {@link defaultCodecTransformer} for the default structured-output schema transformer * * @stability unstable * @category constructors * @since 4.0.0 */ export const make: (params: { /** * A method that requests text generation from the large language model provider and returns the final result when generation finishes. */ readonly generateText: ( options: ProviderOptions ) => Effect.Effect, AiError.AiError, IdGenerator> /** * A method that requests text generation from the large language model provider and streams intermediate results. */ readonly streamText: ( options: ProviderOptions ) => Stream.Stream /** * A function that transforms a `Schema.Codec` into a provider-compatible form * for structured output generation. */ readonly codecTransformer?: CodecTransformer | undefined /** Whether the effective provider configuration supports system messages in history. */ readonly supportsSystemMessagesInHistory?: Effect.Effect | undefined }) => Effect.Effect = Effect.fnUntraced(function*(params) { const codecTransformer = params.codecTransformer ?? defaultCodecTransformer const parentSpanTransformer = yield* Effect.serviceOption( CurrentSpanTransformer ) const getSpanTransformer = Effect.serviceOption( CurrentSpanTransformer ).pipe(Effect.map(Option.orElse(() => parentSpanTransformer))) const idGenerator = yield* Effect.serviceOption(IdGenerator).pipe( Effect.map(Option.getOrElse(() => defaultIdGenerator)) ) const generateText = < Options extends NoExcessProperties, Options>, Tools extends Record = {} >( options: Options & GenerateTextOptions ): Effect.Effect< GenerateTextResponse, ExtractError, ExtractServices > => Effect.useSpan( "LanguageModel.generateText", { kind: "client" }, Effect.fnUntraced( function*(span) { const spanTransformer = yield* getSpanTransformer const providerOptions: Mutable = { prompt: Prompt.make(options.prompt), tools: [], toolChoice: "none", responseFormat: { type: "text" }, span, previousResponseId: undefined, incrementalPrompt: undefined } const content = yield* generateContent(options, providerOptions) applySpanTransformer( spanTransformer, content as any, providerOptions ) return new GenerateTextResponse(content) }, Effect.catchTag("SchemaError", (error) => Effect.fail( AiError.make({ module: "LanguageModel", method: "generateText", reason: AiError.InvalidOutputError.fromSchemaError(error) }) )), (effect, span) => Effect.withParentSpan(effect, span, { captureStackTrace: false }), Effect.provideService(IdGenerator, idGenerator) ) ) as any const generateObject = < ObjectEncoded extends Record, StructuredOutputSchema extends Schema.Encoder, Options extends NoExcessProperties< GenerateObjectOptions, Options >, Tools extends Record = {} >( options: Options & GenerateObjectOptions ): Effect.Effect< GenerateObjectResponse>, ExtractError, ExtractServices | StructuredOutputSchema["DecodingServices"] > => { const objectName = getObjectName(options.objectName, options.schema) return Effect.useSpan( "LanguageModel.generateObject", { kind: "client", attributes: { "effect.ai.object_name": objectName } }, Effect.fnUntraced( function*(span) { const spanTransformer = yield* getSpanTransformer const providerOptions: Mutable = { prompt: Prompt.make(options.prompt), tools: [], toolChoice: "none", responseFormat: { type: "json", objectName, schema: options.schema }, span, previousResponseId: undefined, incrementalPrompt: undefined } const content = yield* generateContent(options, providerOptions) applySpanTransformer( spanTransformer, content as any, providerOptions ) const { codec } = yield* Effect.try({ try: () => codecTransformer(options.schema), catch: (error) => AiError.make({ module: "LanguageModel", method: "generateObject", reason: new AiError.UnsupportedSchemaError({ description: error instanceof Error ? error.message : String(error) }) }) }) const value = yield* resolveStructuredOutput(content as any, codec) return new GenerateObjectResponse(value, content) }, Effect.catchTag("SchemaError", (error) => Effect.fail( AiError.make({ module: "LanguageModel", method: "generateObject", reason: AiError.InvalidOutputError.fromSchemaError(error) }) )), (effect, span) => Effect.withParentSpan(effect, span, { captureStackTrace: false }), Effect.provideService(IdGenerator, idGenerator) ) ) as any } const streamText: < Options extends NoExcessProperties, Options>, Tools extends Record = {} >( options: Options & GenerateTextOptions ) => Stream.Stream< Response.StreamPart, ExtractError, ExtractServices > = Effect.fnUntraced( function*< Tools extends Record, Options extends NoExcessProperties, Options> >(options: Options & GenerateTextOptions) { const span = yield* Effect.makeSpanScoped("LanguageModel.streamText", { kind: "client" }) const providerOptions: Mutable = { prompt: Prompt.make(options.prompt), tools: [], toolChoice: "none", responseFormat: { type: "text" }, span, previousResponseId: undefined, incrementalPrompt: undefined } // Resolve the content stream for the request const stream = yield* streamContent(options, providerOptions) // Return the stream immediately if there is no span transformer const spanTransformer = yield* getSpanTransformer if (Option.isNone(spanTransformer)) { return stream } // Otherwise aggregate generated content and apply the span transformer // when the stream is finished const content: Array> = [] return stream.pipe( Stream.mapArray((parts) => { content.push(...parts) return parts }), Stream.ensuring( Effect.sync(() => { spanTransformer.value({ ...providerOptions, response: content as any }) }) ) ) }, Stream.unwrap, Stream.mapError((error) => Schema.isSchemaError(error) ? AiError.make({ module: "LanguageModel", method: "streamText", reason: AiError.InvalidOutputError.fromSchemaError(error) }) : error ), Stream.provideService(IdGenerator, idGenerator) ) as any const generateContent: < Options extends NoExcessProperties, Options>, Tools extends Record = {} >( options: Options & GenerateTextOptions, providerOptions: Mutable ) => Effect.Effect< Array>, AiError.AiError | Schema.SchemaError, IdGenerator > = Effect.fnUntraced(function*< Tools extends Record, Options extends NoExcessProperties, Options> >( options: Options & GenerateTextOptions, providerOptions: Mutable ) { const tracker = Option.getOrUndefined(yield* Effect.serviceOption(ResponseIdTracker.ResponseIdTracker)) const toolChoice = options.toolChoice ?? "auto" const concurrency = options.concurrency ?? "unbounded" annotateRequestOptions(providerOptions, toolChoice, concurrency) const generateWithNonIncrementalFallback = () => { const requestOptions: ProviderOptions = { ...providerOptions } const fallbackPrompt = requestOptions.prompt const fallbackOptions: ProviderOptions = { ...requestOptions, prompt: fallbackPrompt, incrementalPrompt: undefined, previousResponseId: undefined, incrementalFallback: true } return requestOptions.incrementalPrompt ? params.generateText(requestOptions).pipe( Effect.catchReason("AiError", "InvalidRequestError", (_) => params.generateText(fallbackOptions)) ) : params.generateText(requestOptions) } // Check for pending approvals that need resolution const { approved, denied } = collectToolApprovals( providerOptions.prompt.content, { excludeResolved: true } ) const hasPendingApprovals = approved.length > 0 || denied.length > 0 // If there is no toolkit, the generated content can be returned immediately if (Predicate.isUndefined(options.toolkit)) { // But first check if we have pending approvals that require a toolkit if (hasPendingApprovals) { return yield* AiError.make({ module: "LanguageModel", method: "generateText", reason: new AiError.ToolkitRequiredError({ pendingApprovals: [...approved, ...denied] .map((result) => result.toolCall?.name) .filter(Predicate.isNotUndefined) }) }) } if (tracker) { const prepared = tracker.prepareUnsafe(providerOptions.prompt) if (Option.isSome(prepared)) { providerOptions.previousResponseId = prepared.value.previousResponseId providerOptions.incrementalPrompt = prepared.value.prompt } } const ResponseSchema = Schema.mutable( Schema.Array(Response.Part(Toolkit.empty)) ) const rawContent = yield* generateWithNonIncrementalFallback() const content = yield* Schema.decodeEffect(ResponseSchema)(rawContent) if (tracker) { const responseMetadata = content.find((part) => part.type === "response-metadata") if (Predicate.isNotUndefined(responseMetadata) && Predicate.isNotUndefined(responseMetadata.id)) { tracker.markParts(providerOptions.prompt.content, responseMetadata.id) } } return content as Array> } // If there is a toolkit resolve and apply it to the provider options const toolkit = yield* resolveToolkit(options.toolkit) // If the resolved toolkit is empty, return the generated content immediately if (Object.values(toolkit.tools).length === 0) { // But first check if we have pending approvals that require a toolkit if (hasPendingApprovals) { return yield* AiError.make({ module: "LanguageModel", method: "generateText", reason: new AiError.ToolkitRequiredError({ pendingApprovals: [...approved, ...denied] .map((result) => result.toolCall?.name) .filter(Predicate.isNotUndefined) }) }) } if (tracker) { const prepared = tracker.prepareUnsafe(providerOptions.prompt) if (Option.isSome(prepared)) { providerOptions.previousResponseId = prepared.value.previousResponseId providerOptions.incrementalPrompt = prepared.value.prompt } } const ResponseSchema = Schema.mutable( Schema.Array(Response.Part(Toolkit.empty)) ) const rawContent = yield* generateWithNonIncrementalFallback() const content = yield* Schema.decodeEffect(ResponseSchema)(rawContent) if (tracker) { const responseMetadata = content.find((part) => part.type === "response-metadata") if (Predicate.isNotUndefined(responseMetadata) && Predicate.isNotUndefined(responseMetadata.id)) { tracker.markParts(providerOptions.prompt.content, responseMetadata.id) } } return content as Array> } // Pre-resolve pending tool approvals before calling the LLM let preResolvedResults: Array> = [] if (hasPendingApprovals) { for (const approval of approved) { if (approval.toolCall && !toolkit.tools[approval.toolCall.name]) { return yield* AiError.make({ module: "LanguageModel", method: "generateText", reason: new AiError.ToolNotFoundError({ toolName: approval.toolCall.name, availableTools: Object.keys(toolkit.tools) }) }) } } const approvedResults = yield* executeApprovedToolCalls( approved, toolkit, concurrency ) const deniedResults = createDenialResults(denied) preResolvedResults = [...approvedResults, ...deniedResults] if (preResolvedResults.length > 0) { providerOptions.prompt = Prompt.fromMessages([ ...providerOptions.prompt.content, ...Prompt.fromResponseParts(preResolvedResults).content ]) } } // Strip all resolved approval artifacts (both current and from previous // rounds) in a single pass before sending to the provider. { const { approved: allResolved, denied: allDenied } = collectToolApprovals( providerOptions.prompt.content ) if (allResolved.length > 0 || allDenied.length > 0) { providerOptions.prompt = stripResolvedApprovals( providerOptions.prompt, allResolved, allDenied ) } } const tools = typeof toolChoice === "object" && "oneOf" in toolChoice ? Object.values(toolkit.tools).filter((tool) => toolChoice.oneOf.includes(tool.name)) : Object.values(toolkit.tools) providerOptions.tools = tools providerOptions.toolChoice = toolChoice if (tracker) { const prepared = tracker.prepareUnsafe(providerOptions.prompt) if (Option.isSome(prepared)) { providerOptions.previousResponseId = prepared.value.previousResponseId providerOptions.incrementalPrompt = prepared.value.prompt } } const ResponseSchema = Schema.mutable(Schema.Array(Response.Part( options.disableToolCallResolution === true ? makeToolkitWithEncodedParameters(toolkit) : makeToolkitWithOpaqueParameters(toolkit) ))) // If tool call resolution is disabled, return the response without // resolving the tool calls that were generated if (options.disableToolCallResolution === true) { const rawContent = yield* generateWithNonIncrementalFallback() const content = yield* Schema.decodeEffect(ResponseSchema)(rawContent) if (tracker) { const responseMetadata = content.find((part) => part.type === "response-metadata") if (Predicate.isNotUndefined(responseMetadata) && Predicate.isNotUndefined(responseMetadata.id)) { tracker.markParts(providerOptions.prompt.content, responseMetadata.id) } } return [...preResolvedResults, ...content] as Array> } const rawContent = yield* generateWithNonIncrementalFallback() // Validate before running tool handlers. const content = yield* Schema.decodeEffect(ResponseSchema)(rawContent) yield* validateProviderExecutedToolCalls(toolkit, rawContent) // Resolve the generated tool calls. When the finish reason indicates an // incomplete response, handlers do not run and every executable tool call // gets a synthesized failure result instead. const incompleteFinishReason = findIncompleteFinishReason(rawContent) const toolResults = incompleteFinishReason !== undefined ? rawContent .filter((part): part is Response.ToolCallPartEncoded => part.type === "tool-call" && part.providerExecuted !== true && toolkit.tools[part.name] !== undefined ) .map((part) => makeInterruptedToolResult(part, incompleteFinishReason) as ToolResolutionResult) : yield* resolveToolCalls( rawContent, toolkit, providerOptions.prompt.content, concurrency ).pipe( Stream.filter( (result) => result.type === "tool-approval-request" || result.preliminary === false ), Stream.runCollect ) if (tracker) { const responseMetadata = content.find((part) => part.type === "response-metadata") if (Predicate.isNotUndefined(responseMetadata) && Predicate.isNotUndefined(responseMetadata.id)) { tracker.markParts(providerOptions.prompt.content, responseMetadata.id) } } // Retain pre-resolved results so Chat can recognize completed approvals on later turns. return [...preResolvedResults, ...content, ...toolResults] as Array> }) const streamContent: < Options extends NoExcessProperties, Options>, Tools extends Record = {} >( options: Options & GenerateTextOptions, providerOptions: Mutable ) => Effect.Effect< Stream.Stream< Response.StreamPart, AiError.AiError | Schema.SchemaError, IdGenerator >, Options extends { readonly toolkit: infer ToolkitValue } ? ExtractToolkitResolutionError> : never, Options extends { readonly toolkit: infer ToolkitValue } ? ExtractToolkitResolutionServices> : never > = Effect.fnUntraced(function*< Tools extends Record, Options extends NoExcessProperties, Options> >( options: Options & GenerateTextOptions, providerOptions: Mutable ) { const tracker = Option.getOrUndefined(yield* Effect.serviceOption(ResponseIdTracker.ResponseIdTracker)) const toolChoice = options.toolChoice ?? "auto" const concurrency = options.concurrency ?? "unbounded" annotateRequestOptions(providerOptions, toolChoice, concurrency) const streamWithNonIncrementalFallback = () => { const requestOptions: ProviderOptions = { ...providerOptions } const fallbackPrompt = requestOptions.prompt const fallbackOptions: ProviderOptions = { ...requestOptions, prompt: fallbackPrompt, incrementalPrompt: undefined, previousResponseId: undefined, incrementalFallback: true } // Only response parts with content count as emitted - a lone // response-metadata part must not disable the full-prompt fallback. let emitted = false return requestOptions.incrementalPrompt ? params.streamText(requestOptions).pipe( Stream.tap((part) => Effect.sync(() => { if (part.type !== "response-metadata") emitted = true }) ), Stream.catchReason( "AiError", "InvalidRequestError", (_, error) => emitted ? Stream.fail(error) : params.streamText(fallbackOptions) ) ) : params.streamText(requestOptions) } // Check for pending approvals that need resolution const { approved: pendingApproved, denied: pendingDenied } = collectToolApprovals(providerOptions.prompt.content, { excludeResolved: true }) const hasPendingApprovals = pendingApproved.length > 0 || pendingDenied.length > 0 // If there is no toolkit, return immediately if (Predicate.isUndefined(options.toolkit)) { // But first check if we have pending approvals that require a toolkit if (hasPendingApprovals) { return yield* AiError.make({ module: "LanguageModel", method: "streamText", reason: new AiError.ToolkitRequiredError({ pendingApprovals: [...pendingApproved, ...pendingDenied] .map((a) => a.toolCall?.name) .filter(Predicate.isNotUndefined) }) }) } if (tracker) { const prepared = tracker.prepareUnsafe(providerOptions.prompt) if (Option.isSome(prepared)) { providerOptions.previousResponseId = prepared.value.previousResponseId providerOptions.incrementalPrompt = prepared.value.prompt } } const schema = Schema.NonEmptyArray(Response.StreamPart(Toolkit.empty)) const decodeParts = Schema.decodeEffect(schema) return pipe( streamWithNonIncrementalFallback(), Stream.mapArrayEffect((parts) => decodeParts(parts).pipe( tracker ? Effect.tap((decodedParts) => { for (const part of decodedParts) { if (part.type === "response-metadata" && Predicate.isNotUndefined(part.id)) { tracker.markParts(providerOptions.prompt.content, part.id) } } return Effect.void }) : identity ) ) ) as Stream.Stream< Response.StreamPart, AiError.AiError | Schema.SchemaError, IdGenerator > } // If there is a toolkit resolve and apply it to the provider options const toolkit = yield* resolveToolkit(options.toolkit) // If the toolkit is empty, return immediately if (Object.values(toolkit.tools).length === 0) { // But first check if we have pending approvals that require a toolkit if (hasPendingApprovals) { return yield* AiError.make({ module: "LanguageModel", method: "streamText", reason: new AiError.ToolkitRequiredError({ pendingApprovals: [...pendingApproved, ...pendingDenied] .map((a) => a.toolCall?.name) .filter(Predicate.isNotUndefined) }) }) } if (tracker) { const prepared = tracker.prepareUnsafe(providerOptions.prompt) if (Option.isSome(prepared)) { providerOptions.previousResponseId = prepared.value.previousResponseId providerOptions.incrementalPrompt = prepared.value.prompt } } const schema = Schema.NonEmptyArray(Response.StreamPart(Toolkit.empty)) const decodeParts = Schema.decodeEffect(schema) return pipe( streamWithNonIncrementalFallback(), Stream.mapArrayEffect((parts) => decodeParts(parts).pipe( tracker ? Effect.tap((decodedParts) => { for (const part of decodedParts) { if (part.type === "response-metadata" && part.id) { tracker.markParts(providerOptions.prompt.content, part.id) } } return Effect.void }) : identity ) ) ) as Stream.Stream< Response.StreamPart, AiError.AiError | Schema.SchemaError, IdGenerator > } // Pre-resolve pending tool approvals before calling the LLM let preResolvedStreamParts: Array> = [] if (hasPendingApprovals) { for (const approval of pendingApproved) { if (approval.toolCall && !toolkit.tools[approval.toolCall.name]) { return yield* AiError.make({ module: "LanguageModel", method: "streamText", reason: new AiError.ToolNotFoundError({ toolName: approval.toolCall.name, availableTools: Object.keys(toolkit.tools) }) }) } } const approvedResults = yield* executeApprovedToolCalls( pendingApproved, toolkit, concurrency ) const deniedResults = createDenialResults(pendingDenied) const preResolvedResults = [...approvedResults, ...deniedResults] if (preResolvedResults.length > 0) { providerOptions.prompt = Prompt.fromMessages([ ...providerOptions.prompt.content, ...Prompt.fromResponseParts(preResolvedResults).content ]) } // Emit pre-resolved tool-results as stream parts so Chat.streamText // persists them to history. This lets collectToolApprovals find them // on subsequent rounds and skip the now-resolved approvals. // Preserve the existing encoded result representation for streaming approvals. for (const r of preResolvedResults) { preResolvedStreamParts.push( Response.makePart("tool-result", { id: r.id, name: r.name, providerExecuted: false, preliminary: false, result: r.encodedResult, encodedResult: r.encodedResult, isFailure: r.isFailure }) as Response.StreamPart ) } } // Strip all resolved approval artifacts (both current and from previous // rounds) in a single pass before sending to the provider. const { approved: allResolved, denied: allDenied } = collectToolApprovals( providerOptions.prompt.content ) if (allResolved.length > 0 || allDenied.length > 0) { providerOptions.prompt = stripResolvedApprovals( providerOptions.prompt, allResolved, allDenied ) } const tools = typeof toolChoice === "object" && "oneOf" in toolChoice ? Object.values(toolkit.tools).filter((tool) => toolChoice.oneOf.includes(tool.name)) : Object.values(toolkit.tools) providerOptions.tools = tools providerOptions.toolChoice = toolChoice if (tracker) { const prepared = tracker.prepareUnsafe(providerOptions.prompt) if (Option.isSome(prepared)) { providerOptions.previousResponseId = prepared.value.previousResponseId providerOptions.incrementalPrompt = prepared.value.prompt } } const ResponseSchema = Schema.NonEmptyArray(Response.StreamPart( options.disableToolCallResolution === true ? makeToolkitWithEncodedParameters(toolkit) : makeToolkitWithOpaqueParameters(toolkit) )) const decodeParts = Schema.decodeEffect(ResponseSchema) // If tool call resolution is disabled, return the response without // resolving the tool calls that were generated if (options.disableToolCallResolution === true) { return streamWithNonIncrementalFallback().pipe( Stream.mapArrayEffect((parts) => decodeParts(parts).pipe( tracker ? Effect.tap((decodedParts) => { for (const part of decodedParts) { if (part.type === "response-metadata" && Predicate.isNotUndefined(part.id)) { tracker.markParts(providerOptions.prompt.content, part.id) } } return Effect.void }) : identity ) ) ) as Stream.Stream< Response.StreamPart, AiError.AiError | Schema.SchemaError, IdGenerator > } // Queue for decoded parts and tool results const queue = yield* Queue.make< Response.StreamPart, | AiError.AiError | Cause.Done | Schema.SchemaError >() const deferredFinishParts: Array> = [] // Emit pre-resolved tool results so Chat.streamText persists them to // history. This ensures collectToolApprovals({ excludeResolved }) can // find the corresponding tool-results on future rounds. if (preResolvedStreamParts.length > 0) { yield* Queue.offerAll(queue, preResolvedStreamParts) } // FiberSet to track concurrent tool call handlers const toolCallFibers = yield* FiberSet.make() const toolCallSemaphore = concurrency === "unbounded" ? undefined : yield* Semaphore.make(concurrency) // Tool calls that have been observed but not yet resolved with a final // result or approval request (id -> tool name) const pendingToolCalls = new Map() // One-chunk lookahead buffer: a tool call's handler only starts once the // stream has moved past the call (the next chunk arrives, or the stream // ends with a complete finish). Providers emit a truncating finish // back-to-back with the last tool call, so this window is what lets an // incomplete finish prevent handlers from starting at all. const bufferedToolCalls: Array = [] // Helper function to handle tool calls with approval logic const handleToolCall = Effect.fnUntraced(function*(part: Response.ToolCallPartEncoded) { const tool = toolkit.tools[part.name] if (!tool) return const needsApproval = yield* isApprovalNeeded( tool, part, providerOptions.prompt.content ) if (needsApproval) { const idGen = yield* IdGenerator const approvalId = yield* idGen.generateId() const approvalPart = Response.makePart("tool-approval-request", { approvalId, toolCallId: part.id }) as Response.StreamPart Queue.offerUnsafe(queue, approvalPart) pendingToolCalls.delete(part.id) return } yield* toolkit.handle(part.name, part.params as any, part.id).pipe( Stream.unwrap, Stream.runForEach((result) => { const toolResultPart = Response.makePart("tool-result", { id: part.id, name: part.name, providerExecuted: false, result: result.result, encodedResult: result.encodedResult, isFailure: result.isFailure, preliminary: result.preliminary }) as Response.StreamPart return Effect.sync(() => { Queue.offerUnsafe(queue, toolResultPart) if (result.preliminary !== true) { pendingToolCalls.delete(part.id) } }) }) ) }) const forkBufferedToolCalls = Effect.suspend(() => { if (bufferedToolCalls.length === 0) return Effect.void return Effect.forEach( bufferedToolCalls.splice(0), (part) => { const effect = handleToolCall(part) return FiberSet.run( toolCallFibers, toolCallSemaphore ? toolCallSemaphore.withPermit(effect) : effect ) }, { discard: true } ) }) yield* streamWithNonIncrementalFallback().pipe( Stream.runForEachArray( Effect.fnUntraced(function*(chunk) { const parts = (yield* decodeParts(chunk)) as ReadonlyArray> yield* validateProviderExecutedToolCalls(toolkit, chunk) if (tracker) { for (const part of parts) { if (part.type === "response-metadata" && part.id) { tracker.markParts(providerOptions.prompt.content, part.id) } } } // Defer finish parts until all tool handlers complete. This guarantees // tool results are emitted before finish in streaming mode. const immediateParts: Array> = [] for (const part of parts) { if (part.type === "finish") { deferredFinishParts.push(part) } else { immediateParts.push(part) } } if (immediateParts.length > 0) { yield* Queue.offerAll(queue, immediateParts) } // The stream has moved past any previously buffered tool calls, so // start their handlers now - unless a finish part (possibly // recorded just above) reports an incomplete response, in which // case they stay pending and resolve with synthesized failure // results. if (findIncompleteFinishReason(deferredFinishParts) === undefined) { yield* forkBufferedToolCalls } // Buffer this chunk's tool calls until the next chunk or the end of // the stream - use the raw chunk for encoded params for (const part of chunk) { if (part.type === "tool-call" && part.providerExecuted !== true) { if (toolkit.tools[part.name] !== undefined) { pendingToolCalls.set(part.id, part.name) } bufferedToolCalls.push(part) } } }) ), // Wait for all tool calls to either: // - complete (FiberSet.awaitEmpty) // - fail (FiberSet.join) // If the provider reported an incomplete finish, interrupt any handlers // that are still running instead and emit synthesized failure results // for the tool calls left without one, so the response leaves no tool // call unanswered. Effect.andThen( Effect.suspend(() => { const incompleteFinishReason = findIncompleteFinishReason(deferredFinishParts) if (incompleteFinishReason === undefined) { return forkBufferedToolCalls.pipe( Effect.andThen(Effect.raceFirst( FiberSet.join(toolCallFibers), FiberSet.awaitEmpty(toolCallFibers) )) ) } return FiberSet.clear(toolCallFibers).pipe( Effect.andThen(Effect.suspend(() => Queue.offerAll( queue, Array.from(pendingToolCalls, ([id, name]) => makeInterruptedToolResult({ id, name }, incompleteFinishReason) as Response.StreamPart) ) )) ) }) ), Effect.andThen( Queue.offerAll(queue, deferredFinishParts) ), // And then end the queue Effect.andThen(Queue.end(queue)), Effect.tapCause((cause) => Queue.failCause(queue, cause)), Effect.forkScoped ) return Stream.fromQueue(queue) }) as any return LanguageModel.of({ [TypeId]: TypeId, supportsSystemMessagesInHistory: params.supportsSystemMessagesInHistory, generateText: generateText as LanguageModel["generateText"], generateObject: generateObject as LanguageModel["generateObject"], streamText: streamText as LanguageModel["streamText"] }) }) // ============================================================================= // Accessors // ============================================================================= /** * Generates text using a language model. * * **Example** (Generating text with options) * * ```ts import.meta.vitest * import { Effect, Layer, Stream } from "effect" * import { LanguageModel } from "effect/ai" * * const FakeLanguageModel = Layer.effect( * LanguageModel.LanguageModel, * LanguageModel.make({ * generateText: (options) => * Effect.succeed([ * { * type: "text", * text: options.toolChoice === "none" * ? "Code flows through types / Errors become values / Programs stay composed" * : "Unexpected tool choice" * }, * { * type: "finish", * reason: "stop", * usage: { * inputTokens: { total: 6 }, * outputTokens: { total: 12 } * } * } * ]), * streamText: () => Stream.empty * }) * ) * * const program = LanguageModel.generateText({ * prompt: "Write a haiku about programming", * toolChoice: "none" * }).pipe( * Effect.map((response) => [response.text, response.usage.inputTokens.total]), * Effect.provide(FakeLanguageModel) * ) * * await Effect.runPromise(program) // => ["Code flows through types / Errors become values / Programs stay composed", 6] * ``` * * @stability unstable * @category text generation * @since 4.0.0 */ export const generateText: { // No toolkit: force `{}` instead of falling back to `Record`. < Options extends NoExcessProperties >( options: Options & GenerateTextOptionsWithoutToolkit ): Effect.Effect< GenerateTextResponse<{}>, ExtractError, LanguageModel | ExtractServices > // Generic toolkit: preserve caller-supplied `Tools` in helpers like `(toolkit: WithHandler) => ...`. < Tools extends Record, Options extends NoExcessProperties & { readonly toolkit: ToolkitInput }, Options> >( options: Options & GenerateTextOptions & { readonly toolkit: ToolkitInput } ): Effect.Effect< GenerateTextResponse>, ExtractError, LanguageModel | ExtractServices > // Toolkit unions: recover distributive `ExtractTools` inference for `toolkitA | toolkitB` call sites. < Options extends { readonly toolkit: ToolkitOption } & NoExcessProperties, Options> >( options: Options & GenerateTextOptions> & { readonly toolkit: Options["toolkit"] } ): Effect.Effect< GenerateTextResponse, ExtractToolParametersMode>, ExtractError, ExtractServices | LanguageModel > } = (options: GenerateTextOptions): Effect.Effect< GenerateTextResponse, AiError.AiError, LanguageModel > => Effect.flatMap( Effect.service(LanguageModel), (model) => model.generateText(options as any) ) /** * Generates a structured object from a schema using a language model. * * **Example** (Generating an object) * * ```ts import.meta.vitest * import { Effect, Layer, Schema, Stream } from "effect" * import { LanguageModel } from "effect/ai" * * const EventSchema = Schema.Struct({ * title: Schema.String, * date: Schema.String, * location: Schema.String * }) * * const FakeLanguageModel = Layer.effect( * LanguageModel.LanguageModel, * LanguageModel.make({ * generateText: () => * Effect.succeed([{ * type: "text", * text: '{"title":"Tech Conference","date":"March 15th","location":"San Francisco"}' * }]), * streamText: () => Stream.empty * }) * ) * * const program = LanguageModel.generateObject({ * prompt: "Extract event info: Tech Conference on March 15th in San Francisco", * schema: EventSchema, * objectName: "event" * }).pipe( * Effect.map((response) => response.value), * Effect.provide(FakeLanguageModel) * ) * * await Effect.runPromise(program) // => { title: "Tech Conference", date: "March 15th", location: "San Francisco" } * ``` * * @stability unstable * @category generators * @since 4.0.0 */ export const generateObject = < ObjectEncoded extends Record, StructuredOutputSchema extends Schema.Encoder, Options extends NoExcessProperties< GenerateObjectOptions, Options > >( options: Options & GenerateObjectOptions, StructuredOutputSchema> ): Effect.Effect< GenerateObjectResponse< ExtractTools, StructuredOutputSchema["Type"], ExtractToolParametersMode >, ExtractError, ExtractServices | StructuredOutputSchema["DecodingServices"] | LanguageModel > => Effect.flatMap( Effect.service(LanguageModel), (model) => model.generateObject(options as any) ) as any /** * Generates text using a language model with streaming output. * * **Details** * * Returns a stream of response parts that are emitted as soon as they are * available from the model, enabling real-time text generation experiences. * * **Example** (Streaming text deltas) * * ```ts import.meta.vitest * import { Effect, Layer, Stream } from "effect" * import { LanguageModel } from "effect/ai" * * const FakeLanguageModel = Layer.effect( * LanguageModel.LanguageModel, * LanguageModel.make({ * generateText: () => Effect.succeed([]), * streamText: () => * Stream.make( * { type: "text-delta", id: "story", delta: "The explorer reached orbit." }, * { type: "text-delta", id: "story", delta: " Earth glowed below." } * ) * }) * ) * * const program = LanguageModel.streamText({ * prompt: "Write a story about a space explorer" * }).pipe( * Stream.runFold(() => "", (text, part) => part.type === "text-delta" ? text + part.delta : text), * Effect.provide(FakeLanguageModel) * ) * * await Effect.runPromise(program) // => "The explorer reached orbit. Earth glowed below." * ``` * * @stability unstable * @category text generation * @since 4.0.0 */ export const streamText: { // No toolkit: force `{}` instead of falling back to `Record`. < Options extends NoExcessProperties >( options: Options & GenerateTextOptionsWithoutToolkit ): Stream.Stream< Response.StreamPart<{}>, ExtractError, ExtractServices | LanguageModel > // Generic toolkit: preserve caller-supplied `Tools` in helpers like `(toolkit: WithHandler) => ...`. < Tools extends Record, Options extends NoExcessProperties & { readonly toolkit: ToolkitInput }, Options> >( options: Options & GenerateTextOptions & { readonly toolkit: ToolkitInput } ): Stream.Stream< Response.StreamPart>, ExtractError, ExtractServices | LanguageModel > // Toolkit unions: recover distributive `ExtractTools` inference for `toolkitA | toolkitB` call sites. < Options extends { readonly toolkit: ToolkitOption } & NoExcessProperties, Options> >( options: Options & GenerateTextOptions> & { readonly toolkit: Options["toolkit"] } ): Stream.Stream< Response.StreamPart, ExtractToolParametersMode>, ExtractError, ExtractServices | LanguageModel > } = (options: GenerateTextOptions): Stream.Stream< Response.StreamPart<{}>, AiError.AiError, LanguageModel > => Stream.unwrap(Effect.map( Effect.service(LanguageModel), (model) => model.streamText(options as any) )) as any // ============================================================================= // Tool Approval Helpers // ============================================================================= interface ApprovalResult { readonly approvalId: string readonly toolCallId: string readonly approved: boolean readonly reason?: string | undefined readonly toolCall?: Prompt.ToolCallPart | undefined } interface CollectToolApprovalsOptions { readonly excludeResolved?: boolean } const collectToolApprovals = ( messages: ReadonlyArray, options?: CollectToolApprovalsOptions ): { readonly approved: Array readonly denied: Array } => { const requests = new Map< string, Pick >() const responses: Array> = [] const toolCallsById = new Map() const toolResultIds = new Set() // Collect all tool approval requests, responses, tool calls, and tool results for (const message of messages) { if (message.role === "assistant") { for (const part of message.content) { if (part.type === "tool-approval-request") { requests.set(part.approvalId, { approvalId: part.approvalId, toolCallId: part.toolCallId }) } if (part.type === "tool-call") { toolCallsById.set(part.id, part) } } } if (message.role === "tool") { for (const part of message.content) { if (part.type === "tool-approval-response") { responses.push({ approvalId: part.approvalId, approved: part.approved, reason: part.reason }) } if (part.type === "tool-result") { toolResultIds.add(part.id) } } } } const approved: Array = [] const denied: Array = [] for (const response of responses) { const request = requests.get(response.approvalId) if (Predicate.isNotUndefined(request)) { // Skip if already resolved if (options?.excludeResolved && toolResultIds.has(request.toolCallId)) { continue } const result: ApprovalResult = { ...response, toolCallId: request.toolCallId, toolCall: toolCallsById.get(request.toolCallId) } if (response.approved) { approved.push(result) } else { denied.push(result) } } } return { approved, denied } } /** * Strip resolved approval artifacts from the prompt before sending to the * provider. After pre-resolving approvals (executing approved tools and * creating denial results), the original `tool-approval-request` parts in * assistant messages and `tool-approval-response` parts in tool messages are * no longer needed. Leaving them in causes provider-specific errors (e.g. * OpenAI rejects `mcp_approval_response` items that reference approval * requests it never issued). */ const stripResolvedApprovals = ( prompt: Prompt.Prompt, approved: ReadonlyArray, denied: ReadonlyArray ): Prompt.Prompt => { const resolvedApprovalIds = new Set() for (const a of approved) resolvedApprovalIds.add(a.approvalId) for (const d of denied) resolvedApprovalIds.add(d.approvalId) const cleanedMessages: Array = [] for (const message of prompt.content) { if (message.role === "assistant") { const filteredContent = message.content.filter( (part) => part.type !== "tool-approval-request" || !resolvedApprovalIds.has(part.approvalId) ) if (filteredContent.length > 0) { cleanedMessages.push( Prompt.makeMessage("assistant", { content: filteredContent, options: message.options }) ) } } else if (message.role === "tool") { const filteredContent = message.content.filter( (part) => part.type !== "tool-approval-response" || !resolvedApprovalIds.has(part.approvalId) ) if (filteredContent.length > 0) { cleanedMessages.push( Prompt.makeMessage("tool", { content: filteredContent, options: message.options }) ) } } else { cleanedMessages.push(message) } } return Prompt.fromMessages(cleanedMessages) } const isApprovalNeeded = Effect.fnUntraced(function*( tool: T, toolCall: Response.ToolCallPartEncoded, messages: ReadonlyArray ): Effect.fn.Return> { if (Predicate.isUndefined(tool.needsApproval)) { return false } if (typeof tool.needsApproval === "function") { const params = yield* Schema.decodeUnknownEffect(tool.parametersSchema)( toolCall.params ) as any const result = tool.needsApproval(params, { toolCallId: toolCall.id, messages }) return Effect.isEffect(result) ? yield* result : result } return tool.needsApproval }, Effect.orElseSucceed(constFalse)) const executeApprovedToolCalls = >( approvals: ReadonlyArray, toolkit: Toolkit.WithHandler, concurrency: Concurrency ): Effect.Effect< Array>, Tool.HandlerError | AiError.AiError, Tool.HandlerServices > => { const executeTool = Effect.fnUntraced(function*(approval: ApprovalResult) { const toolCall = approval.toolCall if (Predicate.isUndefined(toolCall)) { return yield* Effect.die("Approval missing tool call reference") } const tool = toolkit.tools[toolCall.name] if (Predicate.isUndefined(tool)) { return yield* AiError.make({ module: "LanguageModel", method: "generateText", reason: new AiError.ToolNotFoundError({ toolName: toolCall.name, availableTools: Object.keys(toolkit.tools) }) }) } const resultStream = yield* toolkit.handle( toolCall.name, toolCall.params as any, approval.toolCallId ) const terminalResult = yield* resultStream.pipe( Stream.filter((result) => result.preliminary === false), Stream.run(Sink.last()), Effect.flatMap( Option.match({ onNone: () => Effect.die("Tool handler did not produce a final result"), onSome: Effect.succeed }) ) ) return Response.makePart("tool-result", { id: approval.toolCallId, name: toolCall.name, result: terminalResult.result, encodedResult: terminalResult.encodedResult, isFailure: terminalResult.isFailure, preliminary: terminalResult.preliminary, providerExecuted: false }) }) return Effect.forEach(approvals, executeTool, { concurrency }) } const createDenialResults = ( denials: ReadonlyArray ): ReadonlyArray> => { const results: Array> = [] for (const denial of denials) { if (Predicate.isNotUndefined(denial.toolCall)) { results.push( Response.makePart("tool-result", { id: denial.toolCallId, name: denial.toolCall.name, isFailure: true, result: { type: "execution-denied", reason: denial.reason }, encodedResult: { type: "execution-denied", reason: denial.reason }, preliminary: false, providerExecuted: false }) ) } } return results } // ============================================================================= // Tool Call Resolution // ============================================================================= // Finish reasons that indicate the provider completed the response. Anything // else (including "unknown", "other", and future reasons) fails safe and // prevents tool handlers from running. const completeFinishReasons: ReadonlyArray = ["stop", "tool-calls", "pause"] const findIncompleteFinishReason = ( content: ReadonlyArray<{ readonly type: string; readonly reason?: unknown }> ): string | undefined => { for (const part of content) { if ( part.type === "finish" && !completeFinishReasons.includes(part.reason as Response.FinishReason) ) { return typeof part.reason === "string" ? part.reason : "unknown" } } return undefined } // Synthesized failure result for a tool call whose handler was interrupted or // never started because the response finished with an incomplete reason. This // keeps the tool call resolved so the conversation history stays well-formed // for subsequent provider requests. const makeInterruptedToolResult = ( toolCall: { readonly id: string; readonly name: string }, finishReason: string ) => { const result = { type: "execution-interrupted", reason: `Tool call execution was interrupted because the response finished with reason "${finishReason}"` } return Response.makePart("tool-result", { id: toolCall.id, name: toolCall.name, providerExecuted: false, preliminary: false, result, encodedResult: result, isFailure: true }) } type ToolResolutionResult> = | Response.ToolResultPart< Tool.Name, Tool.Success, Tool.Failure > | Response.ToolApprovalRequestPart const resolveToolCalls = >( content: ReadonlyArray, toolkit: Toolkit.WithHandler, messages: ReadonlyArray, concurrency: Concurrency ): Stream.Stream< ToolResolutionResult, Tool.HandlerError | AiError.AiError, Tool.HandlerServices | IdGenerator > => { const toolCalls: Array = [] for (const part of content) { if (part.type === "tool-call") { if (part.providerExecuted === true) { continue } toolCalls.push(part) } } const { approved, denied } = collectToolApprovals(messages) const approvedToolCallIds = new Set( approved.map((approval) => approval.toolCallId) ) const deniedByToolCallId = new Map( denied.map((denial) => [denial.toolCallId, denial]) ) const streams = toolCalls.map((toolCall) => Effect.gen(function*() { const tool = toolkit.tools[toolCall.name] if (!tool) { return Stream.empty } if (deniedByToolCallId.has(toolCall.id)) { const denial = deniedByToolCallId.get(toolCall.id)! return Stream.succeed( Response.makePart("tool-result", { id: toolCall.id, name: toolCall.name, providerExecuted: false, isFailure: true, result: { type: "execution-denied", reason: denial.reason }, encodedResult: { type: "execution-denied", reason: denial.reason }, preliminary: false }) as ToolResolutionResult ) } if (approvedToolCallIds.has(toolCall.id)) { return toolkit.handle(toolCall.name, toolCall.params as any, toolCall.id).pipe( Stream.unwrap, Stream.map( (result) => Response.makePart("tool-result", { id: toolCall.id, name: toolCall.name, providerExecuted: false, result: result.result, encodedResult: result.encodedResult, isFailure: result.isFailure, preliminary: result.preliminary }) as ToolResolutionResult ) ) } const needsApproval = yield* isApprovalNeeded(tool, toolCall, messages) if (needsApproval) { const generator = yield* IdGenerator const approvalId = yield* generator.generateId() return Stream.succeed( Response.makePart("tool-approval-request", { approvalId, toolCallId: toolCall.id }) as ToolResolutionResult ) } return toolkit.handle(toolCall.name, toolCall.params as any, toolCall.id).pipe( Stream.unwrap, Stream.map( (result) => Response.makePart("tool-result", { id: toolCall.id, name: toolCall.name, providerExecuted: false, result: result.result, encodedResult: result.encodedResult, isFailure: result.isFailure, preliminary: result.preliminary }) as ToolResolutionResult ) ) }).pipe(Stream.unwrap) ) return Stream.mergeAll(streams, { concurrency }) } // ============================================================================= // Utilities // ============================================================================= // Stable parameter-mode copies let Response reuse each tool's part schemas. const parameterModeTools = new WeakMap() const withParameterMode = (tool: Tool.Any, mode: "encoded" | "opaque"): Tool.Any => { let tools = parameterModeTools.get(tool) if (tools === undefined) { tools = {} parameterModeTools.set(tool, tools) } return tools[mode] ??= tool.setParameters( mode === "encoded" ? Schema.toEncoded(tool.parametersSchema) : Schema.Unknown ) } const makeToolkitWithEncodedParameters = >( toolkit: Toolkit.WithHandler ): Toolkit.Any => Toolkit.make( ...Object.values(toolkit.tools).map((tool) => withParameterMode(tool, "encoded")) ) const makeToolkitWithOpaqueParameters = >( toolkit: Toolkit.WithHandler ): Toolkit.Any => Toolkit.make( ...Object.values(toolkit.tools).map((tool) => withParameterMode(tool, "opaque")) ) // Provider-executed tools bypass Toolkit, so validate their parameters here. const validateProviderExecutedToolCalls = >( toolkit: Toolkit.WithHandler, parts: ReadonlyArray ): Effect.Effect => Effect.forEach( parts, (part) => { if (part.type !== "tool-call" || part.providerExecuted !== true) { return Effect.void } const tool = toolkit.tools[part.name] if (Predicate.isUndefined(tool) || !Schema.isSchema(tool.parametersSchema)) { return Effect.void } return Effect.asVoid( Schema.decodeUnknownEffect(tool.parametersSchema)(part.params) ) as Effect.Effect }, { discard: true } ) const resolveToolkit = , E, R>( toolkit: ToolkitInput ): Effect.Effect, E, R> => (Effect.isEffect(toolkit) ? toolkit : Effect.succeed(toolkit as unknown as Toolkit.WithHandler)) as any const annotateRequestOptions = ( { responseFormat, span }: ProviderOptions, toolChoice: ToolChoice, concurrency: Concurrency ): void => { span.attribute("gen_ai.output.type", responseFormat.type) span.attribute("effect.ai.tool_choice", typeof toolChoice === "string" ? toolChoice : JSON.stringify(toolChoice)) span.attribute("effect.ai.concurrency", concurrency) } /** * @internal */ export const getObjectName = ( objectName: string | undefined, schema: StructuredOutputSchema ): string => { if (Predicate.isNotUndefined(objectName)) { return objectName } if ("identifier" in schema && typeof schema.identifier === "string") { return schema.identifier } const identifier = SchemaAST.resolveIdentifier(schema.ast) if (typeof identifier === "string") { return identifier } return "generateObject" } const resolveStructuredOutput = Effect.fnUntraced(function*< StructuredOutputSchema extends Schema.Constraint >(response: ReadonlyArray>, schema: StructuredOutputSchema) { const texts: Array = [] for (const part of response) { if (part.type === "text") { texts.push(part.text) } } const text = texts.join("") if (text.length === 0) { return yield* AiError.make({ module: "LanguageModel", method: "generateObject", reason: new AiError.StructuredOutputError({ description: "No text content in response", responseText: text }) }) } const decode = Schema.decodeEffect(Schema.fromJsonString(schema)) return yield* Effect.mapError(decode(text), (error) => AiError.make({ module: "LanguageModel", method: "generateObject", reason: AiError.StructuredOutputError.fromSchemaError(error, text) })) }) const applySpanTransformer = ( transformer: Option.Option, response: ReadonlyArray>, options: ProviderOptions ): void => { if (Option.isSome(transformer)) { transformer.value({ ...options, response: response as any }) } }