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feat(js): Add support for formatting LangChain prompts as OpenAI and Anthropic payloads #1038
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,57 @@ | ||
| /* eslint-disable import/no-extraneous-dependencies */ | ||
| import type { BasePromptValue } from "@langchain/core/prompt_values"; | ||
| import * as langChainAnthropicImports from "@langchain/anthropic"; | ||
| import Anthropic from "@anthropic-ai/sdk"; | ||
|
|
||
| /** | ||
| * Convert a formatted LangChain prompt (e.g. pulled from the hub) into | ||
| * a format expected by Anthropic's JS SDK. | ||
| * | ||
| * Requires the "@langchain/anthropic" package to be installed in addition | ||
| * to the Anthropic SDK. | ||
| * | ||
| * @example | ||
| * ```ts | ||
| * import { convertPromptToAnthropic } from "langsmith/utils/hub/anthropic"; | ||
| * import { pull } from "langchain/hub"; | ||
| * | ||
| * import Anthropic from '@anthropic-ai/sdk'; | ||
| * | ||
| * const prompt = await pull("jacob/joke-generator"); | ||
| * const formattedPrompt = await prompt.invoke({ | ||
| * topic: "cats", | ||
| * }); | ||
| * | ||
| * const { system, messages } = convertPromptToAnthropic(formattedPrompt); | ||
| * | ||
| * const anthropicClient = new Anthropic({ | ||
| * apiKey: 'your_api_key', | ||
| * }); | ||
| * | ||
| * const anthropicResponse = await anthropicClient.messages.create({ | ||
| * model: "claude-3-5-sonnet-20240620", | ||
| * max_tokens: 1024, | ||
| * stream: false, | ||
| * system, | ||
| * messages, | ||
| * }); | ||
| * ``` | ||
| * @param formattedPrompt | ||
| * @returns A partial Anthropic payload. | ||
| */ | ||
| export function convertPromptToAnthropic( | ||
| formattedPrompt: BasePromptValue | ||
| ): Anthropic.Messages.MessageCreateParams { | ||
| const messages = formattedPrompt.toChatMessages(); | ||
| const { _convertMessagesToAnthropicPayload } = langChainAnthropicImports; | ||
| if (typeof _convertMessagesToAnthropicPayload !== "function") { | ||
| throw new Error( | ||
| `Please update your version of "@langchain/anthropic" to 0.3.2 or higher.` | ||
| ); | ||
| } | ||
| const anthropicBody = _convertMessagesToAnthropicPayload(messages); | ||
| if (anthropicBody.messages === undefined) { | ||
| anthropicBody.messages = []; | ||
| } | ||
| return anthropicBody; | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,52 @@ | ||
| /* eslint-disable import/no-extraneous-dependencies */ | ||
| import type { BasePromptValue } from "@langchain/core/prompt_values"; | ||
| import * as langChainOpenAIImports from "@langchain/openai"; | ||
| import type { OpenAI } from "openai"; | ||
|
|
||
| /** | ||
| * Convert a formatted LangChain prompt (e.g. pulled from the hub) into | ||
| * a format expected by OpenAI's JS SDK. | ||
| * | ||
| * Requires the "@langchain/openai" package to be installed in addition | ||
| * to the OpenAI SDK. | ||
| * | ||
| * @example | ||
| * ```ts | ||
| * import { convertPromptToOpenAI } from "langsmith/utils/hub/openai"; | ||
| * import { pull } from "langchain/hub"; | ||
| * | ||
| * import OpenAI from 'openai'; | ||
| * | ||
| * const prompt = await pull("jacob/joke-generator"); | ||
| * const formattedPrompt = await prompt.invoke({ | ||
| * topic: "cats", | ||
| * }); | ||
| * | ||
| * const { messages } = convertPromptToOpenAI(formattedPrompt); | ||
| * | ||
| * const openAIClient = new OpenAI(); | ||
| * | ||
| * const openaiResponse = await openAIClient.chat.completions.create({ | ||
| * model: "gpt-4o", | ||
| * messages, | ||
| * }); | ||
| * ``` | ||
| * @param formattedPrompt | ||
| * @returns A partial OpenAI payload. | ||
| */ | ||
| export function convertPromptToOpenAI(formattedPrompt: BasePromptValue): { | ||
| messages: OpenAI.Chat.ChatCompletionMessageParam[]; | ||
| } { | ||
| const messages = formattedPrompt.toChatMessages(); | ||
| const { _convertMessagesToOpenAIParams } = langChainOpenAIImports; | ||
| if (typeof _convertMessagesToOpenAIParams !== "function") { | ||
| throw new Error( | ||
| `Please update your version of "@langchain/openai" to 0.3.1 or higher.` | ||
| ); | ||
| } | ||
| return { | ||
| messages: _convertMessagesToOpenAIParams( | ||
| messages | ||
| ) as OpenAI.Chat.ChatCompletionMessageParam[], | ||
| }; | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,25 @@ | ||
| import Anthropic from "@anthropic-ai/sdk"; | ||
| import { pull } from "langchain/hub"; | ||
|
|
||
| import { convertPromptToAnthropic } from "../anthropic.js"; | ||
|
|
||
| test("basic traceable implementation", async () => { | ||
| const prompt = await pull("jacob/joke-generator"); | ||
| const formattedPrompt = await prompt.invoke({ | ||
| topic: "cats", | ||
| }); | ||
|
|
||
| const { system, messages } = convertPromptToAnthropic(formattedPrompt); | ||
|
|
||
| const anthropicClient = new Anthropic(); | ||
|
|
||
| const anthropicResponse = await anthropicClient.messages.create({ | ||
| model: "claude-3-haiku-20240307", | ||
| system, | ||
| messages: messages, | ||
| max_tokens: 1024, | ||
| stream: false, | ||
| }); | ||
|
|
||
| expect(anthropicResponse.content).toBeDefined(); | ||
| }); | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,22 @@ | ||
| import OpenAI from "openai"; | ||
| import { pull } from "langchain/hub"; | ||
|
|
||
| import { convertPromptToOpenAI } from "../openai.js"; | ||
|
|
||
| test("basic traceable implementation", async () => { | ||
| const prompt = await pull("jacob/joke-generator"); | ||
| const formattedPrompt = await prompt.invoke({ | ||
| topic: "cats", | ||
| }); | ||
|
|
||
| const { messages } = convertPromptToOpenAI(formattedPrompt); | ||
|
|
||
| const openAIClient = new OpenAI(); | ||
|
|
||
| const openAIResponse = await openAIClient.chat.completions.create({ | ||
| model: "gpt-4o-mini", | ||
| messages, | ||
| }); | ||
|
|
||
| expect(openAIResponse.choices.length).toBeGreaterThan(0); | ||
| }); |
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maybe silly question, but what's the need to do this in the SDK if we are pulling from langchain/hub anyway?
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Optics and consistency with Python?