langchain/dist/agents/tests/utils.cjs.map

Typescript bindings for langchain

{"version":3,"file":"utils.cjs","names":["BaseChatModel","value: number","tools: StructuredTool[]","_schema: any","RunnableLambda","messages: BaseMessage[]","_options?: this[\"ParsedCallOptions\"]","_runManager?: CallbackManagerForLLMRun","HumanMessage","AIMessage"],"sources":["../../../src/agents/tests/utils.ts"],"sourcesContent":["/* eslint-disable @typescript-eslint/no-explicit-any */\n/* eslint-disable import/no-extraneous-dependencies */\nimport { expect } from \"vitest\";\nimport { CallbackManagerForLLMRun } from \"@langchain/core/callbacks/manager\";\nimport {\n  BaseChatModel,\n  BaseChatModelParams,\n  BaseChatModelCallOptions,\n  BindToolsInput,\n  ToolChoice,\n} from \"@langchain/core/language_models/chat_models\";\nimport { StructuredTool } from \"@langchain/core/tools\";\nimport {\n  BaseMessage,\n  AIMessage,\n  HumanMessage,\n  BaseMessageFields,\n  AIMessageFields,\n  ToolMessage,\n  ToolMessageFields,\n} from \"@langchain/core/messages\";\nimport { ChatResult } from \"@langchain/core/outputs\";\nimport {\n  Runnable,\n  RunnableConfig,\n  RunnableLambda,\n  RunnableBinding,\n} from \"@langchain/core/runnables\";\nimport {\n  MemorySaver,\n  Checkpoint,\n  CheckpointMetadata,\n  type BaseCheckpointSaver,\n} from \"@langchain/langgraph-checkpoint\";\nimport { LanguageModelLike } from \"@langchain/core/language_models/base\";\nimport { z } from \"zod/v3\";\n\nexport class _AnyIdAIMessage extends AIMessage {\n  get lc_id() {\n    return [\"langchain_core\", \"messages\", \"AIMessage\"];\n  }\n\n  constructor(fields: AIMessageFields | string) {\n    let fieldsWithJestMatcher: Partial<AIMessageFields> = {\n      id: expect.any(String) as unknown as string,\n    };\n    if (typeof fields === \"string\") {\n      fieldsWithJestMatcher = {\n        content: fields,\n        ...fieldsWithJestMatcher,\n      };\n    } else {\n      fieldsWithJestMatcher = {\n        ...fields,\n        ...fieldsWithJestMatcher,\n      };\n    }\n    super(fieldsWithJestMatcher as AIMessageFields);\n  }\n}\n\nexport class _AnyIdHumanMessage extends HumanMessage {\n  get lc_id() {\n    return [\"langchain_core\", \"messages\", \"HumanMessage\"];\n  }\n\n  constructor(fields: BaseMessageFields | string) {\n    let fieldsWithJestMatcher: Partial<BaseMessageFields> = {\n      id: expect.any(String) as unknown as string,\n    };\n    if (typeof fields === \"string\") {\n      fieldsWithJestMatcher = {\n        content: fields,\n        ...fieldsWithJestMatcher,\n      };\n    } else {\n      fieldsWithJestMatcher = {\n        ...fields,\n        ...fieldsWithJestMatcher,\n      };\n    }\n    super(fieldsWithJestMatcher as BaseMessageFields);\n  }\n}\n\nexport class _AnyIdToolMessage extends ToolMessage {\n  get lc_id() {\n    return [\"langchain_core\", \"messages\", \"ToolMessage\"];\n  }\n\n  constructor(fields: ToolMessageFields) {\n    const fieldsWithJestMatcher: Partial<ToolMessageFields> = {\n      id: expect.any(String) as unknown as string,\n      ...fields,\n    };\n    super(fieldsWithJestMatcher as ToolMessageFields);\n  }\n}\n\nexport class FakeConfigurableModel extends BaseChatModel {\n  _queuedMethodOperations: Record<string, any> = {};\n\n  _chatModel: LanguageModelLike;\n\n  constructor(\n    fields: {\n      model: LanguageModelLike;\n    } & BaseChatModelParams\n  ) {\n    super(fields);\n    this._chatModel = fields.model;\n  }\n\n  _llmType() {\n    return \"fake_configurable\";\n  }\n\n  async _generate(\n    _messages: BaseMessage[],\n    _options: this[\"ParsedCallOptions\"],\n    _runManager?: CallbackManagerForLLMRun\n  ): Promise<ChatResult> {\n    throw new Error(\"Not implemented\");\n  }\n\n  async _model() {\n    return this._chatModel;\n  }\n\n  bindTools(tools: BindToolsInput[]) {\n    const modelWithTools = new FakeConfigurableModel({\n      model: (this._chatModel as FakeToolCallingChatModel).bindTools(tools),\n    });\n    modelWithTools._queuedMethodOperations.bindTools = tools;\n    return modelWithTools;\n  }\n}\n\nexport class FakeToolCallingChatModel extends BaseChatModel {\n  sleep?: number = 50;\n\n  responses?: BaseMessage[];\n\n  thrownErrorString?: string;\n\n  idx: number;\n\n  toolStyle: \"openai\" | \"anthropic\" | \"bedrock\" | \"google\" = \"openai\";\n\n  structuredResponse?: Record<string, unknown>;\n\n  // Track messages passed to structured output calls\n  structuredOutputMessages: BaseMessage[][] = [];\n\n  constructor(\n    fields: {\n      sleep?: number;\n      responses?: BaseMessage[];\n      thrownErrorString?: string;\n      toolStyle?: \"openai\" | \"anthropic\" | \"bedrock\" | \"google\";\n      structuredResponse?: Record<string, unknown>;\n    } & BaseChatModelParams\n  ) {\n    super(fields);\n    this.sleep = fields.sleep ?? this.sleep;\n    this.responses = fields.responses;\n    this.thrownErrorString = fields.thrownErrorString;\n    this.idx = 0;\n    this.toolStyle = fields.toolStyle ?? this.toolStyle;\n    this.structuredResponse = fields.structuredResponse;\n    this.structuredOutputMessages = [];\n  }\n\n  _llmType() {\n    return \"fake\";\n  }\n\n  async _generate(\n    messages: BaseMessage[],\n    _options: this[\"ParsedCallOptions\"],\n    runManager?: CallbackManagerForLLMRun\n  ): Promise<ChatResult> {\n    if (this.thrownErrorString) {\n      throw new Error(this.thrownErrorString);\n    }\n    if (this.sleep !== undefined) {\n      await new Promise((resolve) => setTimeout(resolve, this.sleep));\n    }\n    const responses = this.responses?.length ? this.responses : messages;\n    const msg = responses[this.idx % responses.length];\n    const generation: ChatResult = {\n      generations: [\n        {\n          text: \"\",\n          message: msg,\n        },\n      ],\n    };\n    this.idx += 1;\n\n    if (typeof msg.content === \"string\") {\n      await runManager?.handleLLMNewToken(msg.content);\n    }\n    return generation;\n  }\n\n  bindTools(tools: BindToolsInput[]): Runnable<any> {\n    const toolDicts = [];\n    const serverTools = [];\n    for (const tool of tools) {\n      if (!(\"name\" in tool)) {\n        serverTools.push(tool);\n        continue;\n      }\n\n      // NOTE: this is a simplified tool spec for testing purposes only\n      if (this.toolStyle === \"openai\") {\n        toolDicts.push({\n          type: \"function\",\n          function: {\n            name: tool.name,\n          },\n        });\n      } else if ([\"anthropic\", \"google\"].includes(this.toolStyle)) {\n        toolDicts.push({\n          name: tool.name,\n        });\n      } else if (this.toolStyle === \"bedrock\") {\n        toolDicts.push({\n          toolSpec: {\n            name: tool.name,\n          },\n        });\n      }\n    }\n    let toolsToBind: BindToolsInput[] = toolDicts;\n    if (this.toolStyle === \"google\") {\n      toolsToBind = [{ functionDeclarations: toolDicts }];\n    }\n    return this.withConfig({\n      tools: [...toolsToBind, ...serverTools],\n    } as BaseChatModelCallOptions);\n  }\n\n  withStructuredOutput<\n    RunOutput extends Record<string, any> = Record<string, any>\n  >(_: unknown): Runnable<any> {\n    if (!this.structuredResponse) {\n      throw new Error(\"No structured response provided\");\n    }\n    // Create a runnable that returns the proper structured format\n    return RunnableLambda.from(async (messages: BaseMessage[]) => {\n      if (this.sleep) {\n        await new Promise((resolve) => setTimeout(resolve, this.sleep));\n      }\n\n      // Store the messages that were sent to generate structured output\n      this.structuredOutputMessages.push([...messages]);\n\n      // Return in the format expected: { raw: BaseMessage, parsed: RunOutput }\n      return this.structuredResponse as RunOutput;\n    });\n  }\n}\n\nexport class MemorySaverAssertImmutable extends MemorySaver {\n  storageForCopies: Record<string, Record<string, Uint8Array>> = {};\n\n  constructor() {\n    super();\n    this.storageForCopies = {};\n  }\n\n  async put(\n    config: RunnableConfig,\n    checkpoint: Checkpoint,\n    metadata: CheckpointMetadata\n  ): Promise<RunnableConfig> {\n    const thread_id = config.configurable?.thread_id;\n    this.storageForCopies[thread_id] ??= {};\n\n    // assert checkpoint hasn't been modified since last written\n    const saved = await this.get(config);\n    if (saved) {\n      const savedId = saved.id;\n      if (this.storageForCopies[thread_id][savedId]) {\n        const loaded = await this.serde.loadsTyped(\n          \"json\",\n          this.storageForCopies[thread_id][savedId]\n        );\n\n        expect(\n          saved,\n          `Checkpoint [${savedId}] has been modified since last written`\n        ).toEqual(loaded);\n      }\n    }\n    const [, serializedCheckpoint] = await this.serde.dumpsTyped(checkpoint);\n    // save a copy of the checkpoint\n    this.storageForCopies[thread_id][checkpoint.id] = serializedCheckpoint;\n\n    return super.put(config, checkpoint, metadata);\n  }\n}\n\ninterface ToolCall {\n  name: string;\n  args: Record<string, any>;\n  id: string;\n  type?: \"tool_call\";\n}\n\ninterface FakeToolCallingModelFields {\n  toolCalls?: ToolCall[][];\n  toolStyle?: \"openai\" | \"anthropic\";\n  index?: number;\n  structuredResponse?: any;\n}\n\n// Helper function to create checkpointer\nexport function createCheckpointer(): BaseCheckpointSaver {\n  return new MemorySaver();\n}\n\n/**\n * Fake chat model for testing tool calling functionality\n */\nexport class FakeToolCallingModel extends BaseChatModel {\n  toolCalls: ToolCall[][];\n\n  toolStyle: \"openai\" | \"anthropic\";\n\n  // Use a shared reference object so the index persists across bindTools calls\n  private indexRef: { current: number };\n\n  structuredResponse?: any;\n\n  private tools: StructuredTool[] = [];\n\n  constructor({\n    toolCalls = [],\n    toolStyle = \"openai\",\n    index = 0,\n    structuredResponse,\n    indexRef,\n    ...rest\n  }: FakeToolCallingModelFields & { indexRef?: { current: number } } = {}) {\n    super(rest);\n    this.toolCalls = toolCalls;\n    this.toolStyle = toolStyle;\n    // Share the same index reference across instances\n    this.indexRef = indexRef ?? { current: index };\n    this.structuredResponse = structuredResponse;\n  }\n\n  // Getter/setter for backwards compatibility\n  get index(): number {\n    return this.indexRef.current;\n  }\n\n  set index(value: number) {\n    this.indexRef.current = value;\n  }\n\n  _llmType(): string {\n    return \"fake-tool-calling\";\n  }\n\n  _combineLLMOutput() {\n    return [];\n  }\n\n  bindTools(\n    tools: StructuredTool[]\n  ):\n    | FakeToolCallingModel\n    | RunnableBinding<\n        any,\n        any,\n        any & { tool_choice?: ToolChoice | undefined }\n      > {\n    const newInstance = new FakeToolCallingModel({\n      toolCalls: this.toolCalls,\n      toolStyle: this.toolStyle,\n      structuredResponse: this.structuredResponse,\n      // Pass the same indexRef so all instances share the same index state\n      indexRef: this.indexRef,\n    });\n    newInstance.tools = [...this.tools, ...tools];\n    return newInstance;\n  }\n\n  withStructuredOutput(_schema: any) {\n    return new RunnableLambda({\n      func: async () => {\n        return this.structuredResponse;\n      },\n    });\n  }\n\n  async _generate(\n    messages: BaseMessage[],\n    _options?: this[\"ParsedCallOptions\"],\n    _runManager?: CallbackManagerForLLMRun\n  ): Promise<ChatResult> {\n    const lastMessage = messages[messages.length - 1];\n    let content = lastMessage.content as string;\n\n    // Handle prompt concatenation\n    if (messages.length > 1) {\n      const parts = messages.map((m) => m.content).filter(Boolean);\n      content = parts.join(\"-\");\n    }\n\n    // Reset index at the start of a new conversation (only human message)\n    // This allows the model to be reused across multiple agent.invoke() calls\n    const isStartOfConversation =\n      messages.length === 1 ||\n      (messages.length === 2 && messages.every(HumanMessage.isInstance));\n    if (isStartOfConversation && this.index !== 0) {\n      this.index = 0;\n    }\n\n    const currentToolCalls = this.toolCalls[this.index] || [];\n    const messageId = this.index.toString();\n\n    // Move to next set of tool calls for subsequent invocations\n    this.index = (this.index + 1) % Math.max(1, this.toolCalls.length);\n\n    const message = new AIMessage({\n      content,\n      id: messageId,\n      tool_calls:\n        currentToolCalls.length > 0\n          ? currentToolCalls.map((tc) => ({\n              ...tc,\n              type: \"tool_call\" as const,\n            }))\n          : undefined,\n    });\n\n    return {\n      generations: [\n        {\n          text: content,\n          message,\n        },\n      ],\n      llmOutput: {},\n    };\n  }\n}\n\nexport class SearchAPI extends StructuredTool {\n  name = 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