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@langchain/community

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Third-party integrations for LangChain.js

github.com/langchain-ai/langchainjs/tree/main/libs/langchain-community/

langchain-ai/langchainjs

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{"version":3,"file":"zhipuai.cjs","names":["message: BaseMessage","ChatMessage","rawToolCalls: ToolCall[]","e: any","BaseChatModel","fields: Partial<ChatZhipuAIParams> & BaseChatModelParams","options?: this[\"ParsedCallOptions\"]","messages: BaseMessage[]","runManager?: CallbackManagerForLLMRun","messagesMapped: ZhipuMessage[]","response: ChatCompletionResponse","data: ChatCompletionResponse","data","text","AIMessage","tools: BindToolsInput[]","kwargs?: Partial<this[\"ParsedCallOptions\"]>","request: ChatCompletionRequest","stream: boolean","signal?: AbortSignal","onmessage?: (event: MessageEvent) => void","encodeApiKey","value","convertEventStreamToIterableReadableDataStream","json: string","ChatGenerationChunk","AIMessageChunk"],"sources":["../../src/chat_models/zhipuai.ts"],"sourcesContent":["import {\n BaseChatModel,\n BaseChatModelCallOptions,\n BindToolsInput,\n type BaseChatModelParams,\n} from \"@langchain/core/language_models/chat_models\";\nimport {\n AIMessage,\n type BaseMessage,\n ChatMessage,\n AIMessageChunk,\n} from \"@langchain/core/messages\";\nimport { ChatGenerationChunk, type ChatResult } from \"@langchain/core/outputs\";\nimport { type CallbackManagerForLLMRun } from \"@langchain/core/callbacks/manager\";\nimport { getEnvironmentVariable } from \"@langchain/core/utils/env\";\n\nimport { encodeApiKey } from \"../utils/zhipuai.js\";\nimport { convertEventStreamToIterableReadableDataStream } from \"../utils/event_source_parse.js\";\nimport { Runnable } from \"@langchain/core/runnables\";\nimport {\n BaseLanguageModelInput,\n ToolDefinition,\n} from \"@langchain/core/language_models/base\";\nimport { convertToOpenAITool } from \"@langchain/core/utils/function_calling\";\nimport {\n makeInvalidToolCall,\n parseToolCall,\n} from \"@langchain/core/output_parsers/openai_tools\";\n\nexport type ZhipuMessageRole = \"system\" | \"assistant\" | \"user\";\n\ninterface ZhipuMessage {\n role: ZhipuMessageRole;\n content: string;\n}\n\n/**\n * Interface representing a request for a chat completion.\n *\n * See https://open.bigmodel.cn/dev/howuse/model\n */\ntype ModelName =\n | (string & NonNullable<unknown>)\n // will be deprecated models\n | \"chatglm_pro\" // deprecated in 2024-12-31T23:59:59+0800，point to glm-4\n | \"chatglm_std\" // deprecated in 2024-12-31T23:59:59+0800，point to glm-3-turbo\n | \"chatglm_lite\" // deprecated in 2024-12-31T23:59:59+0800，point to glm-3-turbo\n // GLM-4 more powerful on Q/A and text generation, suitable for complex dialog interactions and deep content creation design.\n | \"glm-4\" // context size: 128k\n | \"glm-4v\" // context size: 2k\n // ChatGLM-Turbo\n | \"glm-3-turbo\" // context size: 128k\n | \"chatglm_turbo\"; // context size: 32k\n\nexport interface ChatZhipuAICallOptions extends BaseChatModelCallOptions {\n tools?: BindToolsInput[];\n}\n\ninterface ChatCompletionRequest {\n model: ModelName;\n messages?: ZhipuMessage[];\n tools?: ToolDefinition[];\n do_sample?: boolean;\n stream?: boolean;\n request_id?: string;\n max_tokens?: number | null;\n top_p?: number | null;\n top_k?: number | null;\n temperature?: number | null;\n stop?: string[];\n}\n\ninterface BaseResponse {\n code?: string;\n message?: string;\n}\n\ninterface ChoiceMessage {\n role: string;\n content?: string;\n tool_calls?: ToolCall[];\n}\n\ninterface ToolCall {\n id: string;\n type: string;\n index: number;\n function: {\n name: string;\n arguments: string; // Model-generated function call parameters list in JSON format.\n };\n}\n\ninterface ResponseChoice {\n index: number;\n finish_reason: \"stop\" | \"tool_calls\" | \"length\" | \"null\" | null;\n delta: ChoiceMessage;\n message: ChoiceMessage;\n}\n\ninterface ZhipuAIError {\n error: BaseResponse;\n}\n\n/**\n * Interface representing a response from a chat completion.\n */\ninterface ChatCompletionResponse extends ZhipuAIError {\n choices: ResponseChoice[];\n created: number;\n id: string;\n model: string;\n request_id: string;\n usage: {\n completion_tokens: number;\n prompt_tokens: number;\n total_tokens: number;\n };\n output: {\n text: string;\n finish_reason: \"stop\" | \"tool_calls\" | \"length\" | \"null\" | null;\n tool_calls?: ToolCall[];\n };\n}\n\n/**\n * Interface defining the input to the ZhipuAIChatInput class.\n */\nexport interface ChatZhipuAIParams {\n /**\n * @default \"glm-3-turbo\"\n * Alias for `model`\n */\n modelName: ModelName;\n /**\n * @default \"glm-3-turbo\"\n */\n model: ModelName;\n\n /** Whether to stream the results or not. Defaults to false. */\n streaming?: boolean;\n\n /** Messages to pass as a prefix to the prompt */\n messages?: ZhipuMessage[];\n\n /**\n * API key to use when making requests. Defaults to the value of\n * `ZHIPUAI_API_KEY` environment variable.\n * Alias for `apiKey`\n */\n zhipuAIApiKey?: string;\n\n /**\n * API key to use when making requests. Defaults to the value of\n * `ZHIPUAI_API_KEY` environment variable.\n */\n apiKey?: string;\n\n /** Amount of randomness injected into the response. Ranges\n * from 0 to 1 (0 is not included). Use temp closer to 0 for analytical /\n * multiple choice, and temp closer to 1 for creative\n * and generative tasks. Defaults to 0.95\n */\n temperature?: number;\n\n /** Total probability mass of tokens to consider at each step. Range\n * from 0 to 1 Defaults to 0.7\n */\n topP?: number;\n\n /**\n * Unique identifier for the request. Defaults to a random UUID.\n */\n requestId?: string;\n\n /**\n * turn on sampling strategy when do_sample is true,\n * do_sample is false, temperature、top_p will not take effect\n */\n doSample?: boolean;\n\n /**\n * max value is 8192，defaults to 1024\n */\n maxTokens?: number;\n\n stop?: string[];\n}\n\nfunction messageToRole(message: BaseMessage): ZhipuMessageRole {\n const type = message._getType();\n switch (type) {\n case \"ai\":\n return \"assistant\";\n case \"human\":\n return \"user\";\n case \"system\":\n return \"system\";\n case \"function\":\n throw new Error(\"Function messages not supported yet\");\n case \"generic\": {\n if (!ChatMessage.isInstance(message)) {\n throw new Error(\"Invalid generic chat message\");\n }\n if ([\"system\", \"assistant\", \"user\"].includes(message.role)) {\n return message.role as ZhipuMessageRole;\n }\n throw new Error(`Unknown message type: ${type}`);\n }\n default:\n throw new Error(`Unknown message type: ${type}`);\n }\n}\n\nfunction parseRawToolCalls(rawToolCalls: ToolCall[]) {\n const toolCalls = [];\n const invalidToolCalls = [];\n for (const rawToolCall of rawToolCalls) {\n try {\n toolCalls.push(parseToolCall(rawToolCall, { returnId: true }));\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n } catch (e: any) {\n invalidToolCalls.push(makeInvalidToolCall(rawToolCall, e.message));\n }\n }\n return { toolCalls, invalidToolCalls };\n}\n\nexport class ChatZhipuAI\n extends BaseChatModel<ChatZhipuAICallOptions>\n implements ChatZhipuAIParams\n{\n static lc_name() {\n return \"ChatZhipuAI\";\n }\n\n get callKeys() {\n return [\"stop\", \"signal\", \"options\"];\n }\n\n get lc_secrets() {\n return {\n zhipuAIApiKey: \"ZHIPUAI_API_KEY\",\n apiKey: \"ZHIPUAI_API_KEY\",\n };\n }\n\n get lc_aliases() {\n return undefined;\n }\n\n zhipuAIApiKey?: string;\n\n apiKey?: string;\n\n streaming: boolean;\n\n doSample?: boolean;\n\n messages?: ZhipuMessage[];\n\n requestId?: string;\n\n modelName: ChatCompletionRequest[\"model\"];\n\n model: ChatCompletionRequest[\"model\"];\n\n apiUrl: string;\n\n maxTokens?: number | undefined;\n\n temperature?: number | undefined;\n\n topP?: number | undefined;\n\n stop?: string[];\n\n constructor(fields: Partial<ChatZhipuAIParams> & BaseChatModelParams = {}) {\n super(fields);\n\n this.zhipuAIApiKey =\n fields?.apiKey ??\n fields?.zhipuAIApiKey ??\n getEnvironmentVariable(\"ZHIPUAI_API_KEY\");\n if (!this.zhipuAIApiKey) {\n throw new Error(\"ZhipuAI API key not found\");\n }\n\n this.apiUrl = \"https://open.bigmodel.cn/api/paas/v4/chat/completions\";\n this.streaming = fields.streaming ?? false;\n this.messages = fields.messages ?? [];\n this.temperature = fields.temperature ?? 0.95;\n this.topP = fields.topP ?? 0.7;\n this.stop = fields.stop;\n this.maxTokens = fields.maxTokens;\n this.modelName = fields?.model ?? fields.modelName ?? \"glm-3-turbo\";\n this.model = this.modelName;\n this.doSample = fields.doSample;\n }\n\n /**\n * Get the parameters used to invoke the model\n */\n invocationParams(\n options?: this[\"ParsedCallOptions\"]\n ): Omit<ChatCompletionRequest, \"messages\"> {\n return {\n model: this.model,\n request_id: this.requestId,\n do_sample: this.doSample,\n stream: this.streaming,\n temperature: this.temperature,\n top_p: this.topP,\n max_tokens: this.maxTokens,\n stop: this.stop,\n tools: options?.tools?.map((tool) => convertToOpenAITool(tool)) ?? [],\n };\n }\n\n /**\n * Get the identifying parameters for the model\n */\n identifyingParams(): Omit<ChatCompletionRequest, \"messages\"> {\n return this.invocationParams();\n }\n\n /** @ignore */\n async _generate(\n messages: BaseMessage[],\n options?: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): Promise<ChatResult> {\n const parameters = this.invocationParams(options);\n\n const messagesMapped: ZhipuMessage[] = messages.map((message) => ({\n role: messageToRole(message),\n content: message.content as string,\n }));\n\n const data = parameters.stream\n ? await new Promise<ChatCompletionResponse>((resolve, reject) => {\n let response: ChatCompletionResponse;\n let rejected = false;\n let resolved = false;\n this.completionWithRetry(\n {\n ...parameters,\n messages: messagesMapped,\n },\n true,\n options?.signal,\n (event) => {\n const data: ChatCompletionResponse = JSON.parse(event.data);\n if (data?.error?.code) {\n if (rejected) {\n return;\n }\n rejected = true;\n reject(new Error(data?.error?.message));\n return;\n }\n\n const { delta, finish_reason } = data.choices[0];\n const text = delta.content ?? \"\";\n const tool_calls = delta.tool_calls ?? [];\n\n if (!response) {\n response = {\n ...data,\n output: { text, finish_reason, tool_calls },\n };\n } else {\n response.output.text += text;\n response.output.finish_reason = finish_reason;\n response.output.tool_calls =\n response.output.tool_calls?.concat(tool_calls) ?? tool_calls;\n response.usage = data.usage;\n }\n\n // eslint-disable-next-line no-void\n void runManager?.handleLLMNewToken(text ?? \"\");\n if (finish_reason && finish_reason !== \"null\") {\n if (resolved || rejected) return;\n resolved = true;\n resolve(response);\n }\n }\n ).catch((error) => {\n if (!rejected) {\n rejected = true;\n reject(error);\n }\n });\n })\n : await this.completionWithRetry(\n {\n ...parameters,\n messages: messagesMapped,\n },\n false,\n options?.signal\n ).then<ChatCompletionResponse>((data: ChatCompletionResponse) => {\n if (data?.error?.code) {\n throw new Error(data?.error?.message);\n }\n const { finish_reason, message } = data.choices[0];\n const text = message.content ?? \"\";\n return {\n ...data,\n output: { text, finish_reason, tool_calls: message.tool_calls },\n };\n });\n\n const {\n prompt_tokens = 0,\n completion_tokens = 0,\n total_tokens = 0,\n } = data.usage;\n\n const { text, tool_calls: rawToolCalls } = data.output;\n const { toolCalls, invalidToolCalls } = parseRawToolCalls(\n rawToolCalls ?? []\n );\n\n return {\n generations: [\n {\n text,\n message: new AIMessage({\n content: text,\n tool_calls: toolCalls,\n invalid_tool_calls: invalidToolCalls,\n }),\n },\n ],\n llmOutput: {\n tokenUsage: {\n promptTokens: prompt_tokens,\n completionTokens: completion_tokens,\n totalTokens: total_tokens,\n },\n },\n };\n }\n\n bindTools(\n tools: BindToolsInput[],\n kwargs?: Partial<this[\"ParsedCallOptions\"]>\n ): Runnable<\n BaseLanguageModelInput,\n AIMessageChunk,\n BaseChatModelCallOptions\n > {\n return this.withConfig({\n tools: tools.map((tool) => convertToOpenAITool(tool)),\n ...kwargs,\n });\n }\n\n /** @ignore */\n async completionWithRetry(\n request: ChatCompletionRequest,\n stream: boolean,\n signal?: AbortSignal,\n onmessage?: (event: MessageEvent) => void\n ) {\n const makeCompletionRequest = async () => {\n const response = await fetch(this.apiUrl, {\n method: \"POST\",\n headers: {\n ...(stream ? { Accept: \"text/event-stream\" } : {}),\n Authorization: `Bearer ${encodeApiKey(this.zhipuAIApiKey)}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(request),\n signal,\n });\n\n if (!stream) {\n return response.json();\n }\n\n if (response.body) {\n // response will not be a stream if an error occurred\n if (\n !response.headers.get(\"content-type\")?.startsWith(\"text/event-stream\")\n ) {\n onmessage?.(\n new MessageEvent(\"message\", {\n data: await response.text(),\n })\n );\n return;\n }\n const reader = response.body.getReader();\n const decoder = new TextDecoder(\"utf-8\");\n let data = \"\";\n let continueReading = true;\n while (continueReading) {\n const { done, value } = await reader.read();\n if (done) {\n continueReading = false;\n break;\n }\n data += decoder.decode(value);\n let continueProcessing = true;\n while (continueProcessing) {\n const newlineIndex = data.indexOf(\"\\n\");\n if (newlineIndex === -1) {\n continueProcessing = false;\n break;\n }\n const line = data.slice(0, newlineIndex);\n data = data.slice(newlineIndex + 1);\n if (line.startsWith(\"data:\")) {\n const value = line.slice(\"data:\".length).trim();\n if (value === \"[DONE]\") {\n continueReading = false;\n break;\n }\n const event = new MessageEvent(\"message\", { data: value });\n onmessage?.(event);\n }\n }\n }\n }\n };\n\n return this.caller.call(makeCompletionRequest);\n }\n\n private async createZhipuStream(\n request: ChatCompletionRequest,\n signal?: AbortSignal\n ) {\n const response = await fetch(this.apiUrl, {\n method: \"POST\",\n headers: {\n Accept: \"text/event-stream\",\n Authorization: `Bearer ${encodeApiKey(this.zhipuAIApiKey)}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(request),\n signal,\n });\n\n if (!response.body) {\n throw new Error(\n \"Could not begin Zhipu stream. Please check the given URL and try again.\"\n );\n }\n\n return convertEventStreamToIterableReadableDataStream(response.body);\n }\n\n private _deserialize(json: string) {\n try {\n return JSON.parse(json);\n } catch {\n console.warn(`Received a non-JSON parseable chunk: ${json}`);\n }\n }\n\n async *_streamResponseChunks(\n messages: BaseMessage[],\n options?: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): AsyncGenerator<ChatGenerationChunk> {\n const parameters = {\n ...this.invocationParams(options),\n stream: true,\n };\n\n const messagesMapped: ZhipuMessage[] = messages.map((message) => ({\n role: messageToRole(message),\n content: message.content as string,\n }));\n\n const stream = await this.caller.call(async () =>\n this.createZhipuStream(\n {\n ...parameters,\n messages: messagesMapped,\n },\n options?.signal\n )\n );\n\n for await (const chunk of stream) {\n if (chunk !== \"[DONE]\") {\n const deserializedChunk = this._deserialize(chunk);\n const { choices, usage, id } = deserializedChunk;\n const text = choices[0]?.delta?.content ?? \"\";\n const rawToolCalls = choices[0]?.delta?.tool_calls ?? [];\n const { toolCalls, invalidToolCalls } = parseRawToolCalls(rawToolCalls);\n const finished = !!choices[0]?.finish_reason;\n const isToolCall = rawToolCalls.length > 0;\n yield new ChatGenerationChunk({\n text,\n // In stream mode, ZhipuAI's delta chunk includes\n // either `tool_calls` or `content` field, but not both\n message: isToolCall\n ? new AIMessageChunk({\n tool_calls: toolCalls,\n invalid_tool_calls: invalidToolCalls,\n })\n : new AIMessageChunk({ content: text }),\n generationInfo: finished\n ? {\n finished,\n request_id: id,\n // The usage of tokens is counted at the end of the stream\n usage,\n }\n : undefined,\n });\n await runManager?.handleLLMNewToken(text);\n } else {\n continue;\n }\n }\n }\n\n _llmType(): string {\n return \"zhipuai\";\n }\n\n /** @ignore */\n _combineLLMOutput() {\n return [];\n 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