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Add hub entrypoint with automatic dynamic entrypoint of models
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,59 @@ | ||
| import { Runnable } from "@langchain/core/runnables"; | ||
| import type { BaseLanguageModel } from "@langchain/core/language_models/base"; | ||
| import { load } from "../load/index.js"; | ||
| import { basePush, basePull, generateModelImportMap } from "./base.js"; | ||
|
|
||
| export { basePush as push }; | ||
|
|
||
| /** | ||
| * Pull a prompt from the hub. | ||
| * NOTE: If you are in a Node environment and want to include an instantiated model with your pulled prompt, | ||
| * you can instead import this function from "langchain/hub/node" and pass "includeModel: true". | ||
| * @param ownerRepoCommit The name of the repo containing the prompt, as well as an optional commit hash separated by a slash. | ||
| * @param options | ||
| * @returns | ||
| */ | ||
| export async function pull<T extends Runnable>( | ||
| ownerRepoCommit: string, | ||
| options?: { | ||
| apiKey?: string; | ||
| apiUrl?: string; | ||
| includeModel?: boolean; | ||
| // eslint-disable-next-line @typescript-eslint/no-explicit-any | ||
| modelClass?: new (...args: any[]) => BaseLanguageModel; | ||
| } | ||
| ) { | ||
| const promptObject = await basePull(ownerRepoCommit, options); | ||
| try { | ||
| const loadedPrompt = await load<T>( | ||
| JSON.stringify(promptObject.manifest), | ||
| undefined, | ||
| undefined, | ||
| generateModelImportMap(options?.modelClass) | ||
| ); | ||
| return loadedPrompt; | ||
| // eslint-disable-next-line @typescript-eslint/no-explicit-any | ||
| } catch (e: any) { | ||
| if (options?.includeModel) { | ||
| throw new Error( | ||
| [ | ||
| e.message, | ||
| "", | ||
| `To load prompts with an associated non-OpenAI model, you must use the "langchain/hub/node" entrypoint, or pass a "modelClass" parameter like this:`, | ||
| "", | ||
| "```", | ||
| `import { pull } from "langchain/hub";`, | ||
| `import { ChatAnthropic } from "@langchain/anthropic";`, | ||
| "", | ||
| `const prompt = await pull("my-prompt", {`, | ||
| ` includeModel: true,`, | ||
| ` modelClass: ChatAnthropic,`, | ||
| `});`, | ||
| "```", | ||
| ].join("\n") | ||
| ); | ||
| } else { | ||
| throw e; | ||
| } | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,56 @@ | ||
| import { Runnable } from "@langchain/core/runnables"; | ||
| import { basePush, basePull, generateModelImportMap } from "./base.js"; | ||
| import { load } from "../load/index.js"; | ||
|
|
||
| export { basePush as push }; | ||
|
|
||
| /** | ||
| * Pull a prompt from the hub. | ||
| * @param ownerRepoCommit The name of the repo containing the prompt, as well as an optional commit hash separated by a slash. | ||
| * @param options | ||
| * @returns | ||
| */ | ||
| export async function pull<T extends Runnable>( | ||
| ownerRepoCommit: string, | ||
| options?: { | ||
| apiKey?: string; | ||
| apiUrl?: string; | ||
| includeModel?: boolean; | ||
| } | ||
| ) { | ||
| const promptObject = await basePull(ownerRepoCommit, options); | ||
| let modelClass; | ||
| if (options?.includeModel) { | ||
| if (Array.isArray(promptObject.manifest.kwargs?.last?.kwargs?.bound?.id)) { | ||
| const modelName = | ||
| promptObject.manifest.kwargs?.last?.kwargs?.bound?.id.at(-1); | ||
| if (modelName === "ChatAnthropic") { | ||
| modelClass = (await import("@langchain/anthropic")).ChatAnthropic; | ||
| } else if (modelName === "ChatAzureOpenAI") { | ||
| modelClass = (await import("@langchain/openai")).AzureChatOpenAI; | ||
| } else if (modelName === "ChatGoogleVertexAI") { | ||
| modelClass = (await import("@langchain/google-vertexai")).ChatVertexAI; | ||
| } else if (modelName === "ChatGoogleGenerativeAI") { | ||
| modelClass = (await import("@langchain/google-genai")) | ||
| .ChatGoogleGenerativeAI; | ||
| } else if (modelName === "ChatBedrockConverse") { | ||
| modelClass = (await import("@langchain/aws")).ChatBedrockConverse; | ||
| } else if (modelName === "ChatMistral") { | ||
| modelClass = (await import("@langchain/mistralai")).ChatMistralAI; | ||
| } else if (modelName === "ChatGroq") { | ||
| modelClass = (await import("@langchain/groq")).ChatGroq; | ||
| } else if (modelName !== undefined) { | ||
| console.warn( | ||
| `Received unknown model name from prompt hub: "${modelName}"` | ||
| ); | ||
| } | ||
| } | ||
| } | ||
| const loadedPrompt = await load<T>( | ||
| JSON.stringify(promptObject.manifest), | ||
| undefined, | ||
| undefined, | ||
| generateModelImportMap(modelClass) | ||
| ); | ||
| return loadedPrompt; | ||
| } |
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