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chat_models.ts
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chat_models.ts
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import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";
import { NewTokenIndices } from "@langchain/core/callbacks/base";
import { CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager";
import {
BaseChatModel,
BaseChatModelCallOptions,
type BaseChatModelParams,
} from "@langchain/core/language_models/chat_models";
import {
AIMessage,
AIMessageChunk,
BaseMessage,
ChatMessage,
ChatMessageChunk,
HumanMessageChunk,
SystemMessageChunk,
ToolMessage,
OpenAIToolCall,
isAIMessage,
} from "@langchain/core/messages";
import {
ChatGeneration,
ChatGenerationChunk,
ChatResult,
} from "@langchain/core/outputs";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import {
type OpenAICoreRequestOptions,
type OpenAIClient,
} from "@langchain/openai";
import { isZodSchema } from "@langchain/core/utils/types";
import Groq from "groq-sdk";
import { ChatCompletionChunk } from "groq-sdk/lib/chat_completions_ext";
import {
ChatCompletion,
ChatCompletionCreateParams,
ChatCompletionCreateParamsNonStreaming,
ChatCompletionCreateParamsStreaming,
} from "groq-sdk/resources/chat/completions";
import {
Runnable,
RunnableInterface,
RunnablePassthrough,
RunnableSequence,
} from "@langchain/core/runnables";
import {
BaseLanguageModelInput,
FunctionDefinition,
StructuredOutputMethodOptions,
} from "@langchain/core/language_models/base";
import {
BaseLLMOutputParser,
JsonOutputParser,
StructuredOutputParser,
} from "@langchain/core/output_parsers";
import {
JsonOutputKeyToolsParser,
parseToolCall,
makeInvalidToolCall,
convertLangChainToolCallToOpenAI,
} from "@langchain/core/output_parsers/openai_tools";
import { StructuredToolInterface } from "@langchain/core/tools";
import { convertToOpenAITool } from "@langchain/core/utils/function_calling";
export interface ChatGroqCallOptions extends BaseChatModelCallOptions {
headers?: Record<string, string>;
tools?: OpenAIClient.ChatCompletionTool[];
tool_choice?: OpenAIClient.ChatCompletionToolChoiceOption;
response_format?: { type: "json_object" };
}
export interface ChatGroqInput extends BaseChatModelParams {
/**
* The Groq API key to use for requests.
* @default process.env.GROQ_API_KEY
*/
apiKey?: string;
/**
* The name of the model to use.
* Alias for `model`
* @default "llama2-70b-4096"
*/
modelName?: string;
/**
* The name of the model to use.
* @default "llama2-70b-4096"
*/
model?: string;
/**
* Up to 4 sequences where the API will stop generating further tokens. The
* returned text will not contain the stop sequence.
* Alias for `stopSequences`
*/
stop?: string | null | Array<string>;
/**
* Up to 4 sequences where the API will stop generating further tokens. The
* returned text will not contain the stop sequence.
*/
stopSequences?: Array<string>;
/**
* Whether or not to stream responses.
*/
streaming?: boolean;
/**
* The temperature to use for sampling.
* @default 0.7
*/
temperature?: number;
/**
* The maximum number of tokens that the model can process in a single response.
* This limits ensures computational efficiency and resource management.
*/
maxTokens?: number;
}
type GroqRoleEnum = "system" | "assistant" | "user" | "function";
interface TokenUsage {
completionTokens?: number;
promptTokens?: number;
totalTokens?: number;
}
export function messageToGroqRole(message: BaseMessage): GroqRoleEnum {
const type = message._getType();
switch (type) {
case "system":
return "system";
case "ai":
return "assistant";
case "human":
return "user";
case "function":
return "function";
case "tool":
// Not yet supported as a type
return "tool" as GroqRoleEnum;
default:
throw new Error(`Unknown message type: ${type}`);
}
}
function convertMessagesToGroqParams(
messages: BaseMessage[]
): Array<ChatCompletion.Choice.Message> {
return messages.map((message): ChatCompletion.Choice.Message => {
if (typeof message.content !== "string") {
throw new Error("Non string message content not supported");
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
const completionParam: Record<string, any> = {
role: messageToGroqRole(message),
content: message.content,
name: message.name,
function_call: message.additional_kwargs.function_call,
tool_calls: message.additional_kwargs.tool_calls,
tool_call_id: (message as ToolMessage).tool_call_id,
};
if (isAIMessage(message) && !!message.tool_calls?.length) {
completionParam.tool_calls = message.tool_calls.map(
convertLangChainToolCallToOpenAI
);
} else {
if (message.additional_kwargs.tool_calls != null) {
completionParam.tool_calls = message.additional_kwargs.tool_calls;
}
if ((message as ToolMessage).tool_call_id != null) {
completionParam.tool_call_id = (message as ToolMessage).tool_call_id;
}
}
return completionParam as ChatCompletion.Choice.Message;
});
}
function groqResponseToChatMessage(
message: ChatCompletion.Choice.Message
): BaseMessage {
const rawToolCalls: OpenAIToolCall[] | undefined = message.tool_calls as
| OpenAIToolCall[]
| undefined;
switch (message.role) {
case "assistant": {
const toolCalls = [];
const invalidToolCalls = [];
for (const rawToolCall of rawToolCalls ?? []) {
try {
toolCalls.push(parseToolCall(rawToolCall, { returnId: true }));
// eslint-disable-next-line @typescript-eslint/no-explicit-any
} catch (e: any) {
invalidToolCalls.push(makeInvalidToolCall(rawToolCall, e.message));
}
}
return new AIMessage({
content: message.content || "",
additional_kwargs: { tool_calls: rawToolCalls },
tool_calls: toolCalls,
invalid_tool_calls: invalidToolCalls,
});
}
default:
return new ChatMessage(message.content || "", message.role ?? "unknown");
}
}
function _convertDeltaToMessageChunk(
// eslint-disable-next-line @typescript-eslint/no-explicit-any
delta: Record<string, any>
) {
const { role } = delta;
const content = delta.content ?? "";
let additional_kwargs;
if (delta.function_call) {
additional_kwargs = {
function_call: delta.function_call,
};
} else if (delta.tool_calls) {
additional_kwargs = {
tool_calls: delta.tool_calls,
};
} else {
additional_kwargs = {};
}
if (role === "user") {
return new HumanMessageChunk({ content });
} else if (role === "assistant") {
return new AIMessageChunk({ content, additional_kwargs });
} else if (role === "system") {
return new SystemMessageChunk({ content });
} else {
return new ChatMessageChunk({ content, role });
}
}
/**
* Wrapper around Groq API for large language models fine-tuned for chat
*
* Groq API is compatible to the OpenAI API with some limitations. View the
* full API ref at:
* @link {https://docs.api.groq.com/md/openai.oas.html}
*
* To use, you should have the `GROQ_API_KEY` environment variable set.
* @example
* ```typescript
* const model = new ChatGroq({
* temperature: 0.9,
* apiKey: process.env.GROQ_API_KEY,
* });
*
* const response = await model.invoke([new HumanMessage("Hello there!")]);
* console.log(response);
* ```
*/
export class ChatGroq extends BaseChatModel<
ChatGroqCallOptions,
AIMessageChunk
> {
client: Groq;
modelName = "llama2-70b-4096";
model = "llama2-70b-4096";
temperature = 0.7;
stop?: string[];
stopSequences?: string[];
maxTokens?: number;
streaming = false;
static lc_name() {
return "ChatGroq";
}
_llmType() {
return "groq";
}
get lc_secrets(): { [key: string]: string } | undefined {
return {
apiKey: "GROQ_API_KEY",
};
}
lc_serializable = true;
constructor(fields?: ChatGroqInput) {
super(fields ?? {});
const apiKey = fields?.apiKey || getEnvironmentVariable("GROQ_API_KEY");
if (!apiKey) {
throw new Error(
`Groq API key not found. Please set the GROQ_API_KEY environment variable or provide the key into "apiKey"`
);
}
this.client = new Groq({
apiKey,
dangerouslyAllowBrowser: true,
});
this.temperature = fields?.temperature ?? this.temperature;
this.modelName = fields?.model ?? fields?.modelName ?? this.model;
this.model = this.modelName;
this.streaming = fields?.streaming ?? this.streaming;
this.stop =
fields?.stopSequences ??
(typeof fields?.stop === "string" ? [fields.stop] : fields?.stop) ??
[];
this.stopSequences = this.stop;
this.maxTokens = fields?.maxTokens;
}
async completionWithRetry(
request: ChatCompletionCreateParamsStreaming,
options?: OpenAICoreRequestOptions
): Promise<AsyncIterable<ChatCompletionChunk>>;
async completionWithRetry(
request: ChatCompletionCreateParamsNonStreaming,
options?: OpenAICoreRequestOptions
): Promise<ChatCompletion>;
async completionWithRetry(
request: ChatCompletionCreateParams,
options?: OpenAICoreRequestOptions
): Promise<AsyncIterable<ChatCompletionChunk> | ChatCompletion> {
return this.caller.call(async () =>
this.client.chat.completions.create(request, options)
);
}
invocationParams(
options: this["ParsedCallOptions"]
): ChatCompletionCreateParams {
const params = super.invocationParams(options);
if (options.tool_choice !== undefined) {
params.tool_choice = options.tool_choice;
}
if (options.tools !== undefined) {
params.tools = options.tools;
}
if (options.response_format !== undefined) {
params.response_format = options.response_format;
}
return {
...params,
stop: options.stop ?? this.stopSequences,
model: this.model,
temperature: this.temperature,
max_tokens: this.maxTokens,
};
}
override bindTools(
tools: (Record<string, unknown> | StructuredToolInterface)[],
kwargs?: Partial<ChatGroqCallOptions>
): RunnableInterface<
BaseLanguageModelInput,
AIMessageChunk,
ChatGroqCallOptions
> {
return this.bind({
tools: tools.map(convertToOpenAITool),
...kwargs,
});
}
override async *_streamResponseChunks(
messages: BaseMessage[],
options: this["ParsedCallOptions"],
runManager?: CallbackManagerForLLMRun
): AsyncGenerator<ChatGenerationChunk> {
const params = this.invocationParams(options);
const messagesMapped = convertMessagesToGroqParams(messages);
if (options.tools !== undefined && options.tools.length > 0) {
const result = await this._generateNonStreaming(
messages,
options,
runManager
);
const generationMessage = result.generations[0].message as AIMessage;
if (
generationMessage === undefined ||
typeof generationMessage.content !== "string"
) {
throw new Error("Could not parse Groq output.");
}
const toolCallChunks = generationMessage.tool_calls?.map(
(toolCall, i) => ({
name: toolCall.name,
args: JSON.stringify(toolCall.args),
id: toolCall.id,
index: i,
})
);
yield new ChatGenerationChunk({
message: new AIMessageChunk({
content: generationMessage.content,
additional_kwargs: generationMessage.additional_kwargs,
tool_call_chunks: toolCallChunks,
}),
text: generationMessage.content,
});
} else {
const response = await this.completionWithRetry(
{
...params,
messages: messagesMapped,
stream: true,
},
{
signal: options?.signal,
headers: options?.headers,
}
);
for await (const data of response) {
const choice = data?.choices[0];
if (!choice) {
continue;
}
const chunk = new ChatGenerationChunk({
message: _convertDeltaToMessageChunk(choice.delta ?? {}),
text: choice.delta.content ?? "",
generationInfo: {
finishReason: choice.finish_reason,
},
});
yield chunk;
void runManager?.handleLLMNewToken(chunk.text ?? "");
}
if (options.signal?.aborted) {
throw new Error("AbortError");
}
}
}
override async _generate(
messages: BaseMessage[],
options: this["ParsedCallOptions"],
runManager?: CallbackManagerForLLMRun
): Promise<ChatResult> {
if (this.streaming) {
const tokenUsage: TokenUsage = {};
const stream = this._streamResponseChunks(messages, options, runManager);
const finalChunks: Record<number, ChatGenerationChunk> = {};
for await (const chunk of stream) {
const index =
(chunk.generationInfo as NewTokenIndices)?.completion ?? 0;
if (finalChunks[index] === undefined) {
finalChunks[index] = chunk;
} else {
finalChunks[index] = finalChunks[index].concat(chunk);
}
}
const generations = Object.entries(finalChunks)
.sort(([aKey], [bKey]) => parseInt(aKey, 10) - parseInt(bKey, 10))
.map(([_, value]) => value);
return { generations, llmOutput: { estimatedTokenUsage: tokenUsage } };
} else {
return this._generateNonStreaming(messages, options, runManager);
}
}
async _generateNonStreaming(
messages: BaseMessage[],
options: this["ParsedCallOptions"],
_runManager?: CallbackManagerForLLMRun
): Promise<ChatResult> {
const tokenUsage: TokenUsage = {};
const params = this.invocationParams(options);
const messagesMapped = convertMessagesToGroqParams(messages);
const data = await this.completionWithRetry(
{
...params,
stream: false,
messages: messagesMapped,
},
{
signal: options?.signal,
headers: options?.headers,
}
);
if ("usage" in data && data.usage) {
const {
completion_tokens: completionTokens,
prompt_tokens: promptTokens,
total_tokens: totalTokens,
} = data.usage as ChatCompletion.Usage;
if (completionTokens) {
tokenUsage.completionTokens =
(tokenUsage.completionTokens ?? 0) + completionTokens;
}
if (promptTokens) {
tokenUsage.promptTokens = (tokenUsage.promptTokens ?? 0) + promptTokens;
}
if (totalTokens) {
tokenUsage.totalTokens = (tokenUsage.totalTokens ?? 0) + totalTokens;
}
}
const generations: ChatGeneration[] = [];
if ("choices" in data && data.choices) {
for (const part of (data as ChatCompletion).choices) {
const text = part.message?.content ?? "";
const generation: ChatGeneration = {
text,
message: groqResponseToChatMessage(
part.message ?? { role: "assistant" }
),
};
generation.generationInfo = {
...(part.finish_reason ? { finish_reason: part.finish_reason } : {}),
...(part.logprobs ? { logprobs: part.logprobs } : {}),
};
generations.push(generation);
}
}
return {
generations,
llmOutput: { tokenUsage },
};
}
withStructuredOutput<
// eslint-disable-next-line @typescript-eslint/no-explicit-any
RunOutput extends Record<string, any> = Record<string, any>
>(
outputSchema:
| z.ZodType<RunOutput>
// eslint-disable-next-line @typescript-eslint/no-explicit-any
| Record<string, any>,
config?: StructuredOutputMethodOptions<false>
): Runnable<BaseLanguageModelInput, RunOutput>;
withStructuredOutput<
// eslint-disable-next-line @typescript-eslint/no-explicit-any
RunOutput extends Record<string, any> = Record<string, any>
>(
outputSchema:
| z.ZodType<RunOutput>
// eslint-disable-next-line @typescript-eslint/no-explicit-any
| Record<string, any>,
config?: StructuredOutputMethodOptions<true>
): Runnable<BaseLanguageModelInput, { raw: BaseMessage; parsed: RunOutput }>;
withStructuredOutput<
// eslint-disable-next-line @typescript-eslint/no-explicit-any
RunOutput extends Record<string, any> = Record<string, any>
>(
outputSchema:
| z.ZodType<RunOutput>
// eslint-disable-next-line @typescript-eslint/no-explicit-any
| Record<string, any>,
config?: StructuredOutputMethodOptions<boolean>
):
| Runnable<BaseLanguageModelInput, RunOutput>
| Runnable<
BaseLanguageModelInput,
{ raw: BaseMessage; parsed: RunOutput }
> {
// eslint-disable-next-line @typescript-eslint/no-explicit-any
const schema: z.ZodType<RunOutput> | Record<string, any> = outputSchema;
const name = config?.name;
const method = config?.method;
const includeRaw = config?.includeRaw;
let functionName = name ?? "extract";
let outputParser: BaseLLMOutputParser<RunOutput>;
let llm: Runnable<BaseLanguageModelInput>;
if (method === "jsonMode") {
llm = this.bind({
response_format: { type: "json_object" },
});
if (isZodSchema(schema)) {
outputParser = StructuredOutputParser.fromZodSchema(schema);
} else {
outputParser = new JsonOutputParser<RunOutput>();
}
} else {
if (isZodSchema(schema)) {
const asJsonSchema = zodToJsonSchema(schema);
llm = this.bind({
tools: [
{
type: "function" as const,
function: {
name: functionName,
description: asJsonSchema.description,
parameters: asJsonSchema,
},
},
],
tool_choice: {
type: "function" as const,
function: {
name: functionName,
},
},
});
outputParser = new JsonOutputKeyToolsParser({
returnSingle: true,
keyName: functionName,
zodSchema: schema,
});
} else {
let openAIFunctionDefinition: FunctionDefinition;
if (
typeof schema.name === "string" &&
typeof schema.parameters === "object" &&
schema.parameters != null
) {
openAIFunctionDefinition = schema as FunctionDefinition;
functionName = schema.name;
} else {
functionName = schema.title ?? functionName;
openAIFunctionDefinition = {
name: functionName,
description: schema.description ?? "",
parameters: schema,
};
}
llm = this.bind({
tools: [
{
type: "function" as const,
function: openAIFunctionDefinition,
},
],
tool_choice: {
type: "function" as const,
function: {
name: functionName,
},
},
});
outputParser = new JsonOutputKeyToolsParser<RunOutput>({
returnSingle: true,
keyName: functionName,
});
}
}
if (!includeRaw) {
return llm.pipe(outputParser).withConfig({
runName: "ChatGroqStructuredOutput",
});
}
const parserAssign = RunnablePassthrough.assign({
// eslint-disable-next-line @typescript-eslint/no-explicit-any
parsed: (input: any, config) => outputParser.invoke(input.raw, config),
});
const parserNone = RunnablePassthrough.assign({
parsed: () => null,
});
const parsedWithFallback = parserAssign.withFallbacks({
fallbacks: [parserNone],
});
return RunnableSequence.from<
BaseLanguageModelInput,
{ raw: BaseMessage; parsed: RunOutput }
>([
{
raw: llm,
},
parsedWithFallback,
]).withConfig({
runName: "ChatGroqStructuredOutput",
});
}
}