454 lines
13 KiB
TypeScript
454 lines
13 KiB
TypeScript
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import type { BaseChatModelParams, LangSmithParams } from "@langchain/core/language_models/chat_models";
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import { type OpenAIClient, type ChatOpenAICallOptions, type OpenAIChatInput, type OpenAICoreRequestOptions, ChatOpenAI } from "@langchain/openai";
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type FireworksUnsupportedArgs = "frequencyPenalty" | "presencePenalty" | "logitBias" | "functions";
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type FireworksUnsupportedCallOptions = "functions" | "function_call";
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export type ChatFireworksCallOptions = Partial<Omit<ChatOpenAICallOptions, FireworksUnsupportedCallOptions>>;
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/**
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* Wrapper around Fireworks API for large language models fine-tuned for chat
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*
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* Fireworks API is compatible to the OpenAI API with some limitations described in
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* https://readme.fireworks.ai/docs/openai-compatibility.
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*
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* To use, you should have the `FIREWORKS_API_KEY` environment variable set.
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*
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* Setup:
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* Install `@langchain/community` and set a environment variable called `FIREWORKS_API_KEY`.
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*
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* ```bash
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* npm install @langchain/community
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* export FIREWORKS_API_KEY="your-api-key"
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* ```
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*
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* ## [Constructor args](https://api.js.langchain.com/classes/langchain_community_chat_models_fireworks.ChatFireworks.html#constructor)
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*
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* ## [Runtime args](https://api.js.langchain.com/interfaces/langchain_openai.ChatOpenAICallOptions.html)
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*
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* Because the Fireworks API extends OpenAI's, the call option type is the same.
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*
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* Runtime args can be passed as the second argument to any of the base runnable methods `.invoke`. `.stream`, `.batch`, etc.
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* They can also be passed via `.bind`, or the second arg in `.bindTools`, like shown in the examples below:
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*
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* ```typescript
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* // When calling `.bind`, call options should be passed via the first argument
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* const llmWithArgsBound = llm.bind({
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* stop: ["\n"],
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* tools: [...],
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* });
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*
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* // When calling `.bindTools`, call options should be passed via the second argument
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* const llmWithTools = llm.bindTools(
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* [...],
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* {
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* stop: ["\n"],
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* }
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* );
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* ```
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*
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* ## Examples
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*
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* <details open>
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* <summary><strong>Instantiate</strong></summary>
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*
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* ```typescript
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* import { ChatFireworks } from '@langchain/community/chat_models/fireworks';
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*
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* const llm = new ChatFireworks({
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* model: "command-r-plus",
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* temperature: 0,
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* // other params...
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* });
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Invoking</strong></summary>
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*
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* ```typescript
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* const input = `Translate "I love programming" into French.`;
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*
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* // Models also accept a list of chat messages or a formatted prompt
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* const result = await llm.invoke(input);
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* console.log(result);
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* ```
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*
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* ```txt
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* AIMessage {
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* "id": "dbc233df-532e-4aaa-8995-9d6ea65fea15",
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* "content": "The translation of \"I love programming\" into French is:\n\n\"J'adore la programmation.\"\n\nHere's a breakdown of the translation:\n\n* \"I\" is translated to \"Je\" (but in informal writing, it's common to use \"J'\" instead of \"Je\" when it's followed by a vowel)\n* \"love\" is translated to \"adore\"\n* \"programming\" is translated to \"la programmation\"\n\nSo, the complete translation is \"J'adore la programmation.\"",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "tokenUsage": {
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* "completionTokens": 105,
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* "promptTokens": 19,
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* "totalTokens": 124
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* },
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* "finish_reason": "stop"
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* },
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* "tool_calls": [],
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* "invalid_tool_calls": [],
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* "usage_metadata": {
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* "input_tokens": 19,
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* "output_tokens": 105,
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* "total_tokens": 124
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* }
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* }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Streaming Chunks</strong></summary>
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*
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* ```typescript
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* for await (const chunk of await llm.stream(input)) {
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* console.log(chunk);
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* }
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* ```
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*
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* ```txt
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* AIMessageChunk {
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* "id": "ed5fc403-b7ed-4447-819f-f9645ea0277c",
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* "content": "",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "ed5fc403-b7ed-4447-819f-f9645ea0277c",
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* "content": "The translation",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "ed5fc403-b7ed-4447-819f-f9645ea0277c",
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* "content": " of \"",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "ed5fc403-b7ed-4447-819f-f9645ea0277c",
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* "content": "I love",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "ed5fc403-b7ed-4447-819f-f9645ea0277c",
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* "content": " programming\"",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "ed5fc403-b7ed-4447-819f-f9645ea0277c",
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* "content": " into French",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "ed5fc403-b7ed-4447-819f-f9645ea0277c",
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* "content": " is:\n\n",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "ed5fc403-b7ed-4447-819f-f9645ea0277c",
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* "content": "\"J",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* ...
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* AIMessageChunk {
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* "id": "ed5fc403-b7ed-4447-819f-f9645ea0277c",
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* "content": "ation.\"",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "ed5fc403-b7ed-4447-819f-f9645ea0277c",
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* "content": "",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": "stop"
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "content": "",
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* "additional_kwargs": {},
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* "response_metadata": {},
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": [],
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* "usage_metadata": {
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* "input_tokens": 19,
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* "output_tokens": 105,
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* "total_tokens": 124
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* }
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* }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Aggregate Streamed Chunks</strong></summary>
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*
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* ```typescript
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* import { AIMessageChunk } from '@langchain/core/messages';
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* import { concat } from '@langchain/core/utils/stream';
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*
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* const stream = await llm.stream(input);
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* let full: AIMessageChunk | undefined;
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* for await (const chunk of stream) {
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* full = !full ? chunk : concat(full, chunk);
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* }
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* console.log(full);
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* ```
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*
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* ```txt
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* AIMessageChunk {
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* "id": "9b80e5af-0f50-4fb7-b700-6d431a819556",
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* "content": "The translation of \"I love programming\" into French is:\n\n\"J'adore la programmation.\"\n\nHere's a breakdown of the translation:\n\n* \"I\" is translated to \"Je\" (but in informal writing, it's common to use \"J'\" instead of \"Je\" when it's followed by a vowel)\n* \"love\" is translated to \"adore\"\n* \"programming\" is translated to \"la programmation\"\n\nSo, the complete translation is \"J'adore la programmation.\"",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": "stop"
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": [],
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* "usage_metadata": {
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|
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* "input_tokens": 19,
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* "output_tokens": 105,
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* "total_tokens": 124
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* }
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* }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Bind tools</strong></summary>
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*
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* ```typescript
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* import { z } from 'zod';
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*
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* const llmForToolCalling = new ChatFireworks({
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* // Use a model with tool calling capability
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* model: "accounts/fireworks/models/firefunction-v2",
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* temperature: 0,
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* // other params...
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* });
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* const GetWeather = {
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* name: "GetWeather",
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* description: "Get the current weather in a given location",
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* schema: z.object({
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* location: z.string().describe("The city and state, e.g. San Francisco, CA")
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* }),
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* }
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*
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* const GetPopulation = {
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* name: "GetPopulation",
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* description: "Get the current population in a given location",
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* schema: z.object({
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* location: z.string().describe("The city and state, e.g. San Francisco, CA")
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* }),
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* }
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*
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* const llmWithTools = llmForToolCalling.bindTools([GetWeather, GetPopulation]);
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* const aiMsg = await llmWithTools.invoke(
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* "Which city is hotter today and which is bigger: LA or NY?"
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* );
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* console.log(aiMsg.tool_calls);
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* ```
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*
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* ```txt
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* [
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* {
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* name: 'GetWeather',
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* args: { location: 'Los Angeles, CA' },
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* type: 'tool_call',
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* id: 'call_9DE0WnhgKDbxu6HyHOkDQFub'
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* },
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* {
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* name: 'GetWeather',
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* args: { location: 'New York, NY' },
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* type: 'tool_call',
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* id: 'call_58lcAPTqQyiqepxynwARhGs8'
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* },
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* {
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* name: 'GetPopulation',
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* args: { location: 'Los Angeles, CA' },
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* type: 'tool_call',
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* id: 'call_r0m6AFoqaMvPp4Zt5aEAc0oE'
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* },
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* {
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* name: 'GetPopulation',
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* args: { location: 'New York, NY' },
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* type: 'tool_call',
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|
|
* id: 'call_mENaPG1ryOF44BmaW4VkBaSi'
|
||
|
|
* }
|
||
|
|
* ]
|
||
|
|
* ```
|
||
|
|
* </details>
|
||
|
|
*
|
||
|
|
* <br />
|
||
|
|
*
|
||
|
|
* <details>
|
||
|
|
* <summary><strong>Structured Output</strong></summary>
|
||
|
|
*
|
||
|
|
* ```typescript
|
||
|
|
* import { z } from 'zod';
|
||
|
|
*
|
||
|
|
* const Joke = z.object({
|
||
|
|
* setup: z.string().describe("The setup of the joke"),
|
||
|
|
* punchline: z.string().describe("The punchline to the joke"),
|
||
|
|
* rating: z.number().optional().describe("How funny the joke is, from 1 to 10")
|
||
|
|
* }).describe('Joke to tell user.');
|
||
|
|
*
|
||
|
|
* const structuredLlm = llmForToolCalling.withStructuredOutput(Joke, { name: "Joke" });
|
||
|
|
* const jokeResult = await structuredLlm.invoke("Tell me a joke about cats");
|
||
|
|
* console.log(jokeResult);
|
||
|
|
* ```
|
||
|
|
*
|
||
|
|
* ```txt
|
||
|
|
* {
|
||
|
|
* setup: 'Why did the cat join a band?',
|
||
|
|
* punchline: 'Because it wanted to be the purr-cussionist!',
|
||
|
|
* rating: 8
|
||
|
|
* }
|
||
|
|
* ```
|
||
|
|
* </details>
|
||
|
|
*
|
||
|
|
* <br />
|
||
|
|
*
|
||
|
|
* <details>
|
||
|
|
*
|
||
|
|
* <summary><strong>Usage Metadata</strong></summary>
|
||
|
|
*
|
||
|
|
* ```typescript
|
||
|
|
* const aiMsgForMetadata = await llm.invoke(input);
|
||
|
|
* console.log(aiMsgForMetadata.usage_metadata);
|
||
|
|
* ```
|
||
|
|
*
|
||
|
|
* ```txt
|
||
|
|
* { input_tokens: 277, output_tokens: 8, total_tokens: 285 }
|
||
|
|
* ```
|
||
|
|
* </details>
|
||
|
|
*
|
||
|
|
* <br />
|
||
|
|
*
|
||
|
|
* <details>
|
||
|
|
* <summary><strong>Response Metadata</strong></summary>
|
||
|
|
*
|
||
|
|
* ```typescript
|
||
|
|
* const aiMsgForResponseMetadata = await llm.invoke(input);
|
||
|
|
* console.log(aiMsgForResponseMetadata.response_metadata);
|
||
|
|
* ```
|
||
|
|
*
|
||
|
|
* ```txt
|
||
|
|
* {
|
||
|
|
* tokenUsage: { completionTokens: 8, promptTokens: 277, totalTokens: 285 },
|
||
|
|
* finish_reason: 'stop'
|
||
|
|
* }
|
||
|
|
* ```
|
||
|
|
* </details>
|
||
|
|
*
|
||
|
|
* <br />
|
||
|
|
*/
|
||
|
|
export declare class ChatFireworks extends ChatOpenAI<ChatFireworksCallOptions> {
|
||
|
|
static lc_name(): string;
|
||
|
|
_llmType(): string;
|
||
|
|
get lc_secrets(): {
|
||
|
|
[key: string]: string;
|
||
|
|
} | undefined;
|
||
|
|
lc_serializable: boolean;
|
||
|
|
fireworksApiKey?: string;
|
||
|
|
apiKey?: string;
|
||
|
|
constructor(fields?: Partial<Omit<OpenAIChatInput, "openAIApiKey" | FireworksUnsupportedArgs>> & BaseChatModelParams & {
|
||
|
|
/**
|
||
|
|
* Prefer `apiKey`
|
||
|
|
*/
|
||
|
|
fireworksApiKey?: string;
|
||
|
|
/**
|
||
|
|
* The Fireworks API key to use.
|
||
|
|
*/
|
||
|
|
apiKey?: string;
|
||
|
|
});
|
||
|
|
getLsParams(options: this["ParsedCallOptions"]): LangSmithParams;
|
||
|
|
toJSON(): import("@langchain/core/load/serializable").Serialized;
|
||
|
|
completionWithRetry(request: OpenAIClient.Chat.ChatCompletionCreateParamsStreaming, options?: OpenAICoreRequestOptions): Promise<AsyncIterable<OpenAIClient.Chat.Completions.ChatCompletionChunk>>;
|
||
|
|
completionWithRetry(request: OpenAIClient.Chat.ChatCompletionCreateParamsNonStreaming, options?: OpenAICoreRequestOptions): Promise<OpenAIClient.Chat.Completions.ChatCompletion>;
|
||
|
|
}
|
||
|
|
export {};
|