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refactor(llm): colocate per-type factories on their namespaces (#26799)

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This commit is contained in:
Kit Langton
2026-05-11 13:05:41 -04:00
committed by GitHub
parent 52f7ba7d4d
commit 023e1c711e
13 changed files with 91 additions and 111 deletions
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10
packages/llm/AGENTS.md
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@@ -8,6 +8,10 @@
- In `Effect.gen`, yield yieldable errors directly (`return yield* new MyError(...)`) instead of `Effect.fail(new MyError(...))`.
- Use `Effect.void` instead of `Effect.succeed(undefined)` when the successful value is intentionally void.
## Conventions
Per-type constructors live on the type's namespace, not as top-level re-exports. Use `Message.user(...)`, `Message.assistant(...)`, `Message.tool(...)`, `ToolDefinition.make(...)`, `ToolCallPart.make(...)`, `ToolResultPart.make(...)`, `ToolChoice.make(...)`, `ToolChoice.named(...)`, `SystemPart.make(...)`, and `GenerationOptions.make(...)` directly. The top-level `LLM` namespace is reserved for the request-shaped call API: `LLM.request`, `LLM.generate`, `LLM.stream`, `LLM.model`, `LLM.updateRequest`, `LLM.generateObject`. Two ways to construct the same thing is one too many.
## Tests
- Use `testEffect(...)` from `test/lib/effect.ts` for tests requiring Effect layers.
@@ -166,12 +170,12 @@ If you find yourself copying a 3-to-5-line snippet between two protocols, lift i
Tool loops are represented in common messages and events:
```ts
const call = LLM.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } })
const result = LLM.toolMessage({ id: "call_1", name: "lookup", result: { forecast: "sunny" } })
const call = ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })
const result = Message.tool({ id: "call_1", name: "lookup", result: { forecast: "sunny" } })
const followUp = LLM.request({
model,
messages: [LLM.user("Weather?"), LLM.assistant([call]), result],
messages: [Message.user("Weather?"), Message.assistant([call]), result],
})
```

32
packages/llm/src/llm.ts
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@@ -44,32 +44,8 @@ export type RequestInput = Omit<
export const limits = modelLimits
export const text = Message.text
export const system = SystemPart.make
export const message = Message.make
export const user = Message.user
export const assistant = Message.assistant
export const model = modelRef
export const toolDefinition = ToolDefinition.make
export const toolCall = ToolCallPart.make
export const toolResult = ToolResultPart.make
export const toolMessage = Message.tool
export const toolChoiceName = ToolChoice.named
export const toolChoice = ToolChoice.make
export const generation = GenerationOptions.make
export const generate = LLMClient.generate
export const stream = LLMClient.stream
@@ -95,10 +71,10 @@ export const request = (input: RequestInput) => {
return new LLMRequest({
...rest,
system: SystemPart.content(requestSystem),
messages: [...(messages?.map(message) ?? []), ...(prompt === undefined ? [] : [user(prompt)])],
tools: tools?.map(toolDefinition) ?? [],
toolChoice: requestToolChoice ? toolChoice(requestToolChoice) : undefined,
generation: requestGeneration === undefined ? undefined : generation(requestGeneration),
messages: [...(messages?.map(Message.make) ?? []), ...(prompt === undefined ? [] : [Message.user(prompt)])],
tools: tools?.map(ToolDefinition.make) ?? [],
toolChoice: requestToolChoice ? ToolChoice.make(requestToolChoice) : undefined,
generation: requestGeneration === undefined ? undefined : GenerationOptions.make(requestGeneration),
providerOptions: requestProviderOptions,
http: requestHttp === undefined ? undefined : HttpOptions.make(requestHttp),
})

10
packages/llm/test/cache-policy.test.ts
View File

@@ -1,6 +1,6 @@
import { describe, expect, test } from "bun:test"
import { Effect } from "effect"
import { CacheHint, LLM } from "../src"
import { CacheHint, LLM, Message } from "../src"
import { LLMClient } from "../src/route"
import * as AnthropicMessages from "../src/protocols/anthropic-messages"
import * as BedrockConverse from "../src/protocols/bedrock-converse"
@@ -59,7 +59,7 @@ describe("applyCachePolicy", () => {
model: anthropicModel,
system: "Sys A",
tools: [{ name: "t1", description: "t1", inputSchema: { type: "object", properties: {} } }],
messages: [LLM.user("first user"), LLM.assistant("assistant reply"), LLM.user("latest user message")],
messages: [Message.user("first user"), Message.assistant("assistant reply"), Message.user("latest user message")],
cache: "auto",
}),
)
@@ -122,7 +122,7 @@ describe("applyCachePolicy", () => {
model: bedrockModel,
system: "Sys",
tools: [{ name: "t1", description: "t1", inputSchema: { type: "object", properties: {} } }],
messages: [LLM.user("first user"), LLM.assistant("reply"), LLM.user("latest user")],
messages: [Message.user("first user"), Message.assistant("reply"), Message.user("latest user")],
cache: "auto",
}),
)
@@ -221,7 +221,7 @@ describe("applyCachePolicy", () => {
const prepared = yield* LLMClient.prepare(
LLM.request({
model: anthropicModel,
messages: [LLM.user("u1"), LLM.assistant("a1"), LLM.user("u2"), LLM.assistant("a2")],
messages: [Message.user("u1"), Message.assistant("a1"), Message.user("u2"), Message.assistant("a2")],
cache: { messages: { tail: 2 } },
}),
)
@@ -239,7 +239,7 @@ describe("applyCachePolicy", () => {
const prepared = yield* LLMClient.prepare(
LLM.request({
model: anthropicModel,
messages: [LLM.user("u1"), LLM.assistant("a1"), LLM.user("u2")],
messages: [Message.user("u1"), Message.assistant("a1"), Message.user("u2")],
cache: { messages: "latest-assistant" },
}),
)

28
packages/llm/test/llm.test.ts
View File

@@ -1,6 +1,6 @@
import { describe, expect, test } from "bun:test"
import { LLM, LLMResponse } from "../src"
import { LLMRequest, Message, ModelRef, ToolChoice, ToolDefinition } from "../src/schema"
import { LLMRequest, Message, ModelRef, ToolCallPart, ToolChoice, ToolDefinition, ToolResultPart } from "../src/schema"
describe("llm constructors", () => {
test("builds canonical schema classes from ergonomic input", () => {
@@ -28,7 +28,7 @@ describe("llm constructors", () => {
})
const updated = LLM.updateRequest(base, {
generation: { maxTokens: 20 },
messages: [...base.messages, LLM.assistant("Hi.")],
messages: [...base.messages, Message.assistant("Hi.")],
})
expect(updated).toBeInstanceOf(LLMRequest)
@@ -70,7 +70,7 @@ describe("llm constructors", () => {
model: LLM.model({ id: "fake-model", provider: "fake", route: "openai-chat", baseURL: "https://fake.local" }),
prompt: "Say hello.",
})
const updated = LLMRequest.update(base, { messages: [...base.messages, LLM.assistant("Hi.")] })
const updated = LLMRequest.update(base, { messages: [...base.messages, Message.assistant("Hi.")] })
expect(updated).toBeInstanceOf(LLMRequest)
expect(updated.id).toBe("req_1")
@@ -91,18 +91,18 @@ describe("llm constructors", () => {
})
test("builds tool choices from names and tools", () => {
const tool = LLM.toolDefinition({ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } })
const tool = ToolDefinition.make({ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } })
expect(tool).toBeInstanceOf(ToolDefinition)
expect(LLM.toolChoice("lookup")).toEqual(new ToolChoice({ type: "tool", name: "lookup" }))
expect(LLM.toolChoiceName("required")).toEqual(new ToolChoice({ type: "tool", name: "required" }))
expect(LLM.toolChoice(tool)).toEqual(new ToolChoice({ type: "tool", name: "lookup" }))
expect(ToolChoice.make("lookup")).toEqual(new ToolChoice({ type: "tool", name: "lookup" }))
expect(ToolChoice.named("required")).toEqual(new ToolChoice({ type: "tool", name: "required" }))
expect(ToolChoice.make(tool)).toEqual(new ToolChoice({ type: "tool", name: "lookup" }))
})
test("builds tool choice modes from reserved strings", () => {
expect(LLM.toolChoice("auto")).toEqual(new ToolChoice({ type: "auto" }))
expect(LLM.toolChoice("none")).toEqual(new ToolChoice({ type: "none" }))
expect(LLM.toolChoice("required")).toEqual(new ToolChoice({ type: "required" }))
expect(ToolChoice.make("auto")).toEqual(new ToolChoice({ type: "auto" }))
expect(ToolChoice.make("none")).toEqual(new ToolChoice({ type: "none" }))
expect(ToolChoice.make("required")).toEqual(new ToolChoice({ type: "required" }))
expect(
LLM.request({
model: LLM.model({ id: "fake-model", provider: "fake", route: "openai-chat", baseURL: "https://fake.local" }),
@@ -113,11 +113,11 @@ describe("llm constructors", () => {
})
test("builds assistant tool calls and tool result messages", () => {
const call = LLM.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } })
const result = LLM.toolResult({ id: "call_1", name: "lookup", result: { temperature: 72 } })
const call = ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })
const result = ToolResultPart.make({ id: "call_1", name: "lookup", result: { temperature: 72 } })
expect(LLM.assistant([call]).content).toEqual([call])
expect(LLM.toolMessage(result).content).toEqual([
expect(Message.assistant([call]).content).toEqual([call])
expect(Message.tool(result).content).toEqual([
{ type: "tool-result", id: "call_1", name: "lookup", result: { type: "json", value: { temperature: 72 } } },
])
})

10
packages/llm/test/provider/anthropic-messages.recorded.test.ts
View File

@@ -1,7 +1,7 @@
import { Redactor } from "@opencode-ai/http-recorder"
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { LLM, LLMError } from "../../src"
import { LLM, LLMError, Message, ToolCallPart } from "../../src"
import { LLMClient } from "../../src/route"
import * as AnthropicMessages from "../../src/protocols/anthropic-messages"
import { weatherToolName } from "../recorded-scenarios"
@@ -16,12 +16,12 @@ const malformedToolOrderRequest = LLM.request({
id: "recorded_anthropic_malformed_tool_order",
model,
messages: [
LLM.assistant([
LLM.toolCall({ id: "call_1", name: weatherToolName, input: { city: "Paris" } }),
Message.assistant([
ToolCallPart.make({ id: "call_1", name: weatherToolName, input: { city: "Paris" } }),
{ type: "text", text: "I will check the weather." },
]),
LLM.toolMessage({ id: "call_1", name: weatherToolName, result: { temperature: "72F" } }),
LLM.user("Use that result to answer briefly."),
Message.tool({ id: "call_1", name: weatherToolName, result: { temperature: "72F" } }),
Message.user("Use that result to answer briefly."),
],
tools: [{ name: weatherToolName, description: "Get weather", inputSchema: { type: "object", properties: {} } }],
})

28
packages/llm/test/provider/anthropic-messages.test.ts
View File

@@ -1,6 +1,6 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { CacheHint, LLM, LLMError, Usage } from "../../src"
import { CacheHint, LLM, LLMError, Message, ToolCallPart, Usage } from "../../src"
import { LLMClient } from "../../src/route"
import * as AnthropicMessages from "../../src/protocols/anthropic-messages"
import { it } from "../lib/effect"
@@ -47,9 +47,9 @@ describe("Anthropic Messages route", () => {
id: "req_tool_result",
model,
messages: [
LLM.user("What is the weather?"),
LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
LLM.toolMessage({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
Message.user("What is the weather?"),
Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
Message.tool({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
],
cache: "none",
}),
@@ -77,7 +77,7 @@ describe("Anthropic Messages route", () => {
LLM.request({
model,
messages: [
LLM.assistant([
Message.assistant([
{ type: "reasoning", text: "thinking", providerMetadata: { anthropic: { signature: "sig_1" } } },
]),
],
@@ -304,8 +304,8 @@ describe("Anthropic Messages route", () => {
id: "req_round_trip",
model,
messages: [
LLM.user("Search for something."),
LLM.assistant([
Message.user("Search for something."),
Message.assistant([
{
type: "tool-call",
id: "srvtoolu_abc",
@@ -322,7 +322,7 @@ describe("Anthropic Messages route", () => {
},
{ type: "text", text: "Found it." },
]),
LLM.user("Thanks."),
Message.user("Thanks."),
],
}),
)
@@ -355,7 +355,7 @@ describe("Anthropic Messages route", () => {
id: "req_unknown_server_tool",
model,
messages: [
LLM.assistant([
Message.assistant([
{
type: "tool-result",
id: "srvtoolu_abc",
@@ -378,7 +378,7 @@ describe("Anthropic Messages route", () => {
LLM.request({
id: "req_media",
model,
messages: [LLM.user({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
messages: [Message.user({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
}),
).pipe(Effect.flip)
@@ -416,9 +416,9 @@ describe("Anthropic Messages route", () => {
},
],
messages: [
LLM.user("What's the weather?"),
LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: {} })]),
LLM.toolMessage({
Message.user("What's the weather?"),
Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: {} })]),
Message.tool({
id: "call_1",
name: "lookup",
result: { temp: 72 },
@@ -501,7 +501,7 @@ describe("Anthropic Messages route", () => {
},
],
system: [{ type: "text", text: "system-tail", cache: hint }],
messages: [LLM.user([{ type: "text", text: "message-tail", cache: hint }])],
messages: [Message.user([{ type: "text", text: "message-tail", cache: hint }])],
}),
)

32
packages/llm/test/provider/bedrock-converse.test.ts
View File

@@ -2,7 +2,7 @@ import { EventStreamCodec } from "@smithy/eventstream-codec"
import { fromUtf8, toUtf8 } from "@smithy/util-utf8"
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { CacheHint, LLM } from "../../src"
import { CacheHint, LLM, Message, ToolCallPart, ToolChoice } from "../../src"
import { LLMClient } from "../../src/route"
import * as BedrockConverse from "../../src/protocols/bedrock-converse"
import { it } from "../lib/effect"
@@ -94,7 +94,7 @@ describe("Bedrock Converse route", () => {
inputSchema: { type: "object", properties: { query: { type: "string" } }, required: ["query"] },
},
],
toolChoice: LLM.toolChoice({ type: "required" }),
toolChoice: ToolChoice.make({ type: "required" }),
}),
)
@@ -124,9 +124,9 @@ describe("Bedrock Converse route", () => {
id: "req_history",
model,
messages: [
LLM.user("What is the weather?"),
LLM.assistant([LLM.toolCall({ id: "tool_1", name: "lookup", input: { query: "weather" } })]),
LLM.toolMessage({ id: "tool_1", name: "lookup", result: { forecast: "sunny" } }),
Message.user("What is the weather?"),
Message.assistant([ToolCallPart.make({ id: "tool_1", name: "lookup", input: { query: "weather" } })]),
Message.tool({ id: "tool_1", name: "lookup", result: { forecast: "sunny" } }),
],
cache: "none",
}),
@@ -294,8 +294,8 @@ describe("Bedrock Converse route", () => {
model,
system: [{ type: "text", text: "System prefix.", cache }],
messages: [
LLM.user([{ type: "text", text: "User prefix.", cache }]),
LLM.assistant([{ type: "text", text: "Assistant prefix.", cache }]),
Message.user([{ type: "text", text: "User prefix.", cache }]),
Message.assistant([{ type: "text", text: "Assistant prefix.", cache }]),
],
generation: { maxTokens: 16, temperature: 0 },
}),
@@ -335,7 +335,7 @@ describe("Bedrock Converse route", () => {
id: "req_image",
model,
messages: [
LLM.user([
Message.user([
{ type: "text", text: "What is in this image?" },
{ type: "media", mediaType: "image/png", data: "AAAA" },
{ type: "media", mediaType: "image/jpeg", data: "BBBB" },
@@ -371,7 +371,7 @@ describe("Bedrock Converse route", () => {
LLM.request({
id: "req_image_bytes",
model,
messages: [LLM.user([{ type: "media", mediaType: "image/png", data: new Uint8Array([1, 2, 3, 4, 5]) }])],
messages: [Message.user([{ type: "media", mediaType: "image/png", data: new Uint8Array([1, 2, 3, 4, 5]) }])],
}),
)
@@ -394,7 +394,7 @@ describe("Bedrock Converse route", () => {
id: "req_doc",
model,
messages: [
LLM.user([
Message.user([
{ type: "media", mediaType: "application/pdf", data: "PDFDATA", filename: "report.pdf" },
{ type: "media", mediaType: "text/csv", data: "CSVDATA" },
]),
@@ -424,7 +424,7 @@ describe("Bedrock Converse route", () => {
LLM.request({
id: "req_bad_image",
model,
messages: [LLM.user([{ type: "media", mediaType: "image/svg+xml", data: "x" }])],
messages: [Message.user([{ type: "media", mediaType: "image/svg+xml", data: "x" }])],
}),
).pipe(Effect.flip)
@@ -438,7 +438,7 @@ describe("Bedrock Converse route", () => {
LLM.request({
id: "req_bad_doc",
model,
messages: [LLM.user([{ type: "media", mediaType: "application/x-tar", data: "x", filename: "a.tar" }])],
messages: [Message.user([{ type: "media", mediaType: "application/x-tar", data: "x", filename: "a.tar" }])],
}),
).pipe(Effect.flip)
@@ -471,9 +471,9 @@ describe("Bedrock Converse route", () => {
model,
tools: [{ name: "lookup", description: "lookup", inputSchema: { type: "object", properties: {} }, cache }],
messages: [
LLM.user("What's the weather?"),
LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: {} })]),
LLM.toolMessage({ id: "call_1", name: "lookup", result: { temp: 72 }, cache }),
Message.user("What's the weather?"),
Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: {} })]),
Message.tool({ id: "call_1", name: "lookup", result: { temp: 72 }, cache }),
],
cache: "none",
}),
@@ -583,7 +583,7 @@ describe("Bedrock Converse recorded", () => {
system: "Call tools exactly as requested.",
prompt: "Call get_weather with city exactly Paris.",
tools: [weatherTool],
toolChoice: LLM.toolChoice(weatherTool),
toolChoice: ToolChoice.make(weatherTool),
cache: "none",
generation: { maxTokens: 80, temperature: 0 },
}),

10
packages/llm/test/provider/gemini.test.ts
View File

@@ -1,6 +1,6 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { LLM, LLMError, Usage } from "../../src"
import { LLM, LLMError, Message, ToolCallPart, Usage } from "../../src"
import { LLMClient } from "../../src/route"
import * as Gemini from "../../src/protocols/gemini"
import { it } from "../lib/effect"
@@ -49,12 +49,12 @@ describe("Gemini route", () => {
],
toolChoice: { type: "tool", name: "lookup" },
messages: [
LLM.user([
Message.user([
{ type: "text", text: "What is in this image?" },
{ type: "media", mediaType: "image/png", data: "AAECAw==" },
]),
LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
LLM.toolMessage({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
Message.tool({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
],
}),
)
@@ -353,7 +353,7 @@ describe("Gemini route", () => {
LLM.request({
id: "req_media",
model,
messages: [LLM.assistant({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
messages: [Message.assistant({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
}),
).pipe(Effect.flip)

12
packages/llm/test/provider/openai-chat.test.ts
View File

@@ -1,7 +1,7 @@
import { describe, expect } from "bun:test"
import { Effect, Schema, Stream } from "effect"
import { HttpClientRequest } from "effect/unstable/http"
import { LLM, LLMError, Usage } from "../../src"
import { LLM, LLMError, Message, ToolCallPart, Usage } from "../../src"
import * as Azure from "../../src/providers/azure"
import * as OpenAI from "../../src/providers/openai"
import * as OpenAIChat from "../../src/protocols/openai-chat"
@@ -149,9 +149,9 @@ describe("OpenAI Chat route", () => {
id: "req_tool_result",
model,
messages: [
LLM.user("What is the weather?"),
LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
LLM.toolMessage({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
Message.user("What is the weather?"),
Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
Message.tool({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
],
}),
)
@@ -185,7 +185,7 @@ describe("OpenAI Chat route", () => {
LLM.request({
id: "req_media",
model,
messages: [LLM.user({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
messages: [Message.user({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
}),
).pipe(Effect.flip)
@@ -199,7 +199,7 @@ describe("OpenAI Chat route", () => {
LLM.request({
id: "req_reasoning",
model,
messages: [LLM.assistant({ type: "reasoning", text: "hidden" })],
messages: [Message.assistant({ type: "reasoning", text: "hidden" })],
}),
).pipe(Effect.flip)

8
packages/llm/test/provider/openai-compatible-chat.test.ts
View File

@@ -1,7 +1,7 @@
import { describe, expect } from "bun:test"
import { Effect, Schema } from "effect"
import { HttpClientRequest } from "effect/unstable/http"
import { LLM } from "../../src"
import { LLM, Message, ToolCallPart } from "../../src"
import { LLMClient } from "../../src/route"
import * as OpenAICompatible from "../../src/providers/openai-compatible"
import * as OpenAICompatibleChat from "../../src/protocols/openai-compatible-chat"
@@ -157,9 +157,9 @@ describe("OpenAI-compatible Chat route", () => {
],
toolChoice: "lookup",
messages: [
LLM.user("What is the weather?"),
LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
LLM.toolMessage({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
Message.user("What is the weather?"),
Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
Message.tool({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
],
}),
)

10
packages/llm/test/provider/openai-responses.test.ts
View File

@@ -1,7 +1,7 @@
import { describe, expect } from "bun:test"
import { ConfigProvider, Effect, Layer, Stream } from "effect"
import { Headers, HttpClientRequest } from "effect/unstable/http"
import { LLM, LLMError, Usage } from "../../src"
import { LLM, LLMError, Message, ToolCallPart, Usage } from "../../src"
import { Auth, LLMClient, RequestExecutor, WebSocketExecutor } from "../../src/route"
import * as Azure from "../../src/providers/azure"
import * as OpenAI from "../../src/providers/openai"
@@ -251,9 +251,9 @@ describe("OpenAI Responses route", () => {
id: "req_tool_result",
model,
messages: [
LLM.user("What is the weather?"),
LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
LLM.toolMessage({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
Message.user("What is the weather?"),
Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
Message.tool({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
],
}),
)
@@ -508,7 +508,7 @@ describe("OpenAI Responses route", () => {
LLM.request({
id: "req_media",
model,
messages: [LLM.user({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
messages: [Message.user({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
}),
).pipe(Effect.flip)

6
packages/llm/test/recorded-scenarios.ts
View File

@@ -1,6 +1,6 @@
import { expect } from "bun:test"
import { Effect, Schema, Stream } from "effect"
import { LLM, LLMEvent, LLMResponse, type LLMRequest, type ModelRef } from "../src"
import { LLM, LLMEvent, LLMResponse, ToolChoice, ToolDefinition, type LLMRequest, type ModelRef } from "../src"
import { LLMClient } from "../src/route"
import { tool } from "../src/tool"
@@ -18,7 +18,7 @@ export const LARGE_CACHEABLE_SYSTEM = (() => {
return sentence.repeat(250)
})()
export const weatherTool = LLM.toolDefinition({
export const weatherTool = ToolDefinition.make({
name: weatherToolName,
description: "Get current weather for a city.",
inputSchema: {
@@ -70,7 +70,7 @@ export const weatherToolRequest = (input: {
system: "Call tools exactly as requested.",
prompt: "Call get_weather with city exactly Paris.",
tools: [weatherTool],
toolChoice: LLM.toolChoice(weatherTool),
toolChoice: ToolChoice.make(weatherTool),
cache: "none",
generation:
input.temperature === false

6
packages/llm/test/tool-runtime.test.ts
View File

@@ -1,6 +1,6 @@
import { describe, expect } from "bun:test"
import { Effect, Schema, Stream } from "effect"
import { LLM, LLMEvent, LLMRequest, LLMResponse } from "../src"
import { GenerationOptions, LLM, LLMEvent, LLMRequest, LLMResponse, ToolChoice } from "../src"
import { LLMClient } from "../src/route"
import * as AnthropicMessages from "../src/protocols/anthropic-messages"
import * as OpenAIChat from "../src/protocols/openai-chat"
@@ -78,8 +78,8 @@ describe("LLMClient tools", () => {
yield* TestToolRuntime.runTools({
request: LLMRequest.update(baseRequest, {
generation: LLM.generation({ maxTokens: 50 }),
toolChoice: LLM.toolChoice("auto"),
generation: GenerationOptions.make({ maxTokens: 50 }),
toolChoice: ToolChoice.make("auto"),
}),
tools: { get_weather },
}).pipe(Stream.runCollect, Effect.provide(layer))