vscode-chat-langchain-bridge/dist/index.cjs.map

Create VS Code Chat participants (agents) with LangChain/LangGraph: tool-calling and streaming bridge for LanguageModelChat.

{"version":3,"sources":["../src/index.ts","../src/chat_models.ts","../src/utils.ts","../src/tools.ts"],"sourcesContent":["export { ChatVSCode } from './chat_models.js';\nexport type * from './types.js';\nexport { toVSCodeChatTool,convertVscodeHistory } from './utils.js';","import { CallbackManagerForLLMRun } from '@langchain/core/callbacks/manager';\nimport { BaseLanguageModelInput } from '@langchain/core/language_models/base';\nimport { BaseChatModel } from '@langchain/core/language_models/chat_models';\nimport { AIMessage, AIMessageChunk, BaseMessage, ToolCall, ToolMessage } from '@langchain/core/messages';\nimport { ChatGeneration, ChatGenerationChunk, ChatResult } from '@langchain/core/outputs';\nimport { Runnable } from '@langchain/core/runnables';\nimport { CancellationToken, ChatResponseProgressPart, ChatResponseStream, LanguageModelChat, LanguageModelChatMessage, LanguageModelTextPart, LanguageModelToolCallPart } from 'vscode';\nimport { convertBaseMessage, toVSCodeChatTool } from './utils.js';\nimport { ChatVSCodeCallOptions, ChatVSCodeFields, ChatVSCodeToolType } from './types.js';\n\n\nexport class ChatVSCode extends BaseChatModel<\n    ChatVSCodeCallOptions,\n    AIMessageChunk\n> {\n    protected model: LanguageModelChat;\n    token: CancellationToken;\n    responseStream: ChatResponseStream;\n    constructor(fields: ChatVSCodeFields) {\n        super(fields ?? {});\n        this.model = fields.model;\n        this.token = fields.token;\n        this.responseStream = fields.responseStream;\n    }\n\n    static lc_name() {\n        return \"ChatVSCode\";\n    }\n    _llmType() {\n        return \"vscode\";\n    }\n\n    async _generate(\n        messages: BaseMessage[],\n        options: this[\"ParsedCallOptions\"],\n        runManager?: CallbackManagerForLLMRun\n    ): Promise<ChatResult> {\n        let vscodeMessages: LanguageModelChatMessage[] = messages.map(\n            (message): LanguageModelChatMessage => {\n                return convertBaseMessage(message);\n            }\n        );\n\n        const lastMessage = messages.at(-1);\n\n        if (messages.length > 0 && ToolMessage.isInstance(lastMessage)) {\n            // If the last message is a ToolMessage, we need to handle it differently\n            // because it indicates a tool result from the user side.\n            // Here, we can implement any specific logic needed for tool results.\n            vscodeMessages.push(LanguageModelChatMessage.User(`\n                Above is the result from one or more tool calls. The user cannot see the results, so you should use this information to continue the conversation.\n                `));\n        }\n\n        const response = await this.model.sendRequest(\n            vscodeMessages,\n            {\n                tools: options.tools?.map((tool) => {\n                    return {\n                        name: tool.name,\n                        description: tool.description,\n                        inputSchema: tool.inputSchema,\n                    };\n                }),\n            },\n            this.token\n        );\n\n        let text = '';\n        const toolCalls: ToolCall[] = [];\n\n        for await (const part of response.stream) {\n            if (part instanceof LanguageModelTextPart) {\n                text += part.value;\n                runManager?.handleLLMNewToken(part.value);\n            } else if (part instanceof LanguageModelToolCallPart) {\n                const toolCall: ToolCall = {\n                    id: part.callId,\n                    name: part.name,\n                    args: part.input,\n                    type: 'tool_call'\n                };\n                toolCalls.push(toolCall);\n            } else {\n                console.warn(`Unknown part type received from model stream:`);\n                console.warn(part);\n                const progressText = new ChatResponseProgressPart((part as any).value);\n                this.responseStream?.push(progressText);\n            }\n        }\n\n        let result: ChatResult = {\n            generations: []\n        };\n\n        const message = new AIMessage(text);\n        message.tool_calls = toolCalls;\n\n        const generation: ChatGeneration = {\n            text,\n            message\n        };\n\n        result.generations.push(generation);\n\n        return result;\n    }\n\n    async *_streamResponseChunks(\n        messages: BaseMessage[],\n        options: this['ParsedCallOptions'],\n        runManager?: CallbackManagerForLLMRun\n    ): AsyncGenerator<ChatGenerationChunk> {\n        const copilotMessages = messages.map(convertBaseMessage);\n\n        const response = this.model.sendRequest(copilotMessages, {\n            tools: options.tools?.map((tool) => {\n                return {\n                    name: tool.name,\n                    description: tool.description,\n                    inputSchema: tool.inputSchema,\n                };\n            }),\n        },\n            this.token\n        );\n        for await (const part of (await response).stream) {\n            if (this.token.isCancellationRequested) {\n                break;\n            }\n\n            if (part instanceof LanguageModelTextPart) {\n                // console.log(\"Received text part from model stream:\", part);\n                const chunk = new ChatGenerationChunk({\n                    text: part.value,\n                    message: new AIMessageChunk(part.value),\n                });\n                runManager?.handleLLMNewToken(part.value);\n                yield chunk;\n            } else if (part instanceof LanguageModelToolCallPart) {\n                // console.log(\"Received tool call part from model stream:\", part);\n                const chunkMessage = new AIMessageChunk({\n                    tool_call_chunks: [\n                        {\n                            name: part.name,\n                            args: JSON.stringify(part.input), // Chunk the input as a JSON string\n                            id: part.callId,\n                            type: 'tool_call_chunk',\n                        }\n                    ]\n                });\n                const chunk = new ChatGenerationChunk({\n                    text: '',\n                    message: chunkMessage,\n                });\n                yield chunk;\n            } else {\n                console.log(\"Is ChatResponseProgressPart:\", part instanceof ChatResponseProgressPart);\n                console.warn(`Unknown part type received from model stream:`);\n                console.warn(part);\n            }\n        }\n    }\n\n    bindTools(\n        tools: ChatVSCodeToolType[],\n        kwargs?: Partial<ChatVSCodeCallOptions> | undefined\n    ): Runnable<BaseLanguageModelInput, AIMessageChunk, ChatVSCodeCallOptions> {\n        if (kwargs && 'strict' in kwargs) {\n            delete kwargs.strict;\n        }\n\n        return this.withConfig({\n            tools: tools.map((tool) => toVSCodeChatTool(tool)),\n            ...kwargs,\n        })\n    }\n}","import { AIMessage, BaseMessage, ContentBlock, HumanMessage, MessageContent, SystemMessage, ToolCall, ToolMessage } from '@langchain/core/messages';\nimport { DynamicStructuredTool, StructuredTool } from '@langchain/core/tools';\nimport { ChatContext, ChatRequestTurn, ChatResponseAnchorPart, ChatResponseMarkdownPart, ChatResponseTurn, LanguageModelChatMessage, LanguageModelTextPart, LanguageModelToolCallPart, LanguageModelToolResultPart, Uri } from 'vscode';\nimport { ChatVSCodeTool } from './tools.js';\nimport { ChatVSCodeToolInput, ChatVSCodeToolType } from './types.js';\n\n/**\n * Ensures every LangChain tool is wrapped in a ChatVSCodeTool instance that\n * exposes the schema and invoke function expected by the VS Code chat bridge.\n */\nexport function toVSCodeChatTool(tool: ChatVSCodeToolType): ChatVSCodeTool {\n  if (tool instanceof ChatVSCodeTool) {\n    return tool as ChatVSCodeTool\n  }\n  if (isDynamicStructuredTool(tool)) {\n    return new ChatVSCodeTool({\n      name: tool.name,\n      description: tool.description,\n      schema: tool.schema as ChatVSCodeToolInput['schema'],\n      func: (input, runManager?, config?) => {\n        return tool.invoke(input, config)\n      },\n    })\n  }\n  if (isStructuredTool(tool)) {\n    return new ChatVSCodeTool({\n      name: tool.name,\n      description: tool.description,\n      schema: tool.schema as ChatVSCodeToolInput['schema'],\n      func: (input, runManager?, config?) => {\n        return tool.invoke(input, config);\n      },\n    })\n  }\n  throw new Error('Invalid tool type');\n}\n\n/**\n * Type guard for plain text content blocks.\n */\nexport function\n  isTextContentBlock(part: ContentBlock): part is ContentBlock.Text {\n  return part.type === 'text';\n}\n\n/**\n * Type guard for multimodal image content blocks.\n */\nexport function\n  isImageContentBlock(part: ContentBlock): part is ContentBlock.Multimodal.Image {\n  return part.type === 'image';\n}\n\n/**\n * Type guard for multimodal plain text blocks.\n */\nexport function\n  isMultiModalPlainText(part: ContentBlock): part is ContentBlock.Multimodal.PlainText {\n  return part.type === 'text-plain';\n}\n\n/**\n * Converts a MessageContent to an array of LanguageModelTextPart.\n * Supports plain text and image content blocks.\n * @param content The MessageContent to convert.\n * @returns The converted array of LanguageModelTextPart.\n */\nexport function\n  toTextContent(content: MessageContent): Array<LanguageModelTextPart> {\n  if (typeof content === 'string') {\n    return [new LanguageModelTextPart(content)];\n  }\n  if (Array.isArray(content)) {\n    return content.map((part: ContentBlock) => {\n      if (isTextContentBlock(part)) {\n        return new LanguageModelTextPart(part.text);\n      } else if (isImageContentBlock(part)) {\n        if (part.url) {\n          return new LanguageModelTextPart(part.url);\n        }\n\n        if (part.data && part.mimeType) {\n          // Some APIs require the Base64 `data:<mimeType>;base64,<data>` format.\n          const base64String = typeof part.data === 'string'\n            ? part.data\n            : Buffer.from(part.data).toString('base64');\n\n          const dataUrl = `data:${part.mimeType};base64,${base64String}`;\n\n          // Send the data URL or the structured data, depending on the SDK expectations.\n          return new LanguageModelTextPart(dataUrl);\n\n        }\n\n        // Handle the 'fileId' case if the SDK supports it directly.\n        else if (part.fileId) {\n          // If the model supports it, you can send the ID or throw an error.\n          throw new Error(`Sending image via fileId not yet supported by this parser.`);\n        } else {\n          throw new Error(`message part type not supported: ${part}`);\n        }\n\n      } else {\n        throw new Error(`message part type not supported: ${part}`);\n      }\n    })\n  } else {\n    throw new Error('Unknown message content type');\n  }\n}\n\n/**\n * Converts a BaseMessage to a LanguageModelChatMessage.\n * @param message The BaseMessage to convert.\n * @returns The converted LanguageModelChatMessage.\n */\nexport function\n  convertBaseMessage(message: BaseMessage): LanguageModelChatMessage {\n  // if this is an AI message\n  if (AIMessage.isInstance(message)) {\n    if (!!message.tool_calls?.length) {\n      const aiMessage = message as AIMessage & { tool_calls: ToolCall[] };\n\n      const toolCallParts = aiMessage.tool_calls.map(\n        (toolCall): LanguageModelToolCallPart => {\n          return new LanguageModelToolCallPart(\n            toolCall.id || '',\n            toolCall.name,\n            toolCall.args\n          );\n        });\n      return LanguageModelChatMessage.Assistant(\n        toolCallParts,\n        aiMessage.name);\n    }\n    // if this is a text AI message\n    return LanguageModelChatMessage.Assistant(\n      toTextContent(message.content),\n      message.name\n    );\n  }\n  if (HumanMessage.isInstance(message)) {\n    const humanMessage = message;\n    return LanguageModelChatMessage.User(\n      toTextContent(humanMessage.content),\n      humanMessage.name\n    );\n  }\n\n  if (ToolMessage.isInstance(message)) {\n    const toolResult = new LanguageModelToolResultPart(\n      message.tool_call_id,\n      toTextContent(message.content)\n    );\n    return LanguageModelChatMessage.User(\n      [toolResult],\n      message.name\n    );\n  }\n\n  if (SystemMessage.isInstance(message)) {\n    return LanguageModelChatMessage.User(\n      toTextContent(message.content),\n      message.name\n    );\n  }\n\n  throw new Error(`Unsupported message type: ${message}`);\n}\n\n/**\n * Utility function that converts a VS Code chat history into the LangChain\n * message format.\n */\nexport function convertVscodeHistory(chatContext: ChatContext): BaseMessage[] {\n  const result = [];\n  for(const message of chatContext.history) {\n    if (message instanceof ChatRequestTurn) {\n            result.push(new HumanMessage({\n              content: message.prompt\n            }));\n    } else if (message instanceof ChatResponseTurn) {\n      result.push(new AIMessage({\n        content: chatResponseToString(message)\n      }));\n    }\n  }\n  return result;\n}\n\n/**\n * Extracts the textual representation of a ChatResponseTurn for LangChain.\n */\nexport function chatResponseToString(response: ChatResponseTurn): string {\n\treturn response.response\n\t\t.map(r => {\n\t\t\tif (r instanceof ChatResponseMarkdownPart) {\n\t\t\t\treturn r.value.value;\n\t\t\t} else if (r instanceof ChatResponseAnchorPart) {\n\t\t\t\tif (r.value instanceof Uri) {\n\t\t\t\t\treturn r.value.fsPath;\n\t\t\t\t} else {\n\t\t\t\t\treturn r.value.uri.fsPath;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\treturn '';\n\t\t})\n\t\t.join('');\n}\n\nfunction isDynamicStructuredTool(\n  tool: ChatVSCodeToolType\n): tool is DynamicStructuredTool {\n  return (\n    tool instanceof DynamicStructuredTool\n  );\n}\n\nfunction isStructuredTool(\n  tool: ChatVSCodeToolType\n): tool is StructuredTool {\n  return (\n    tool instanceof StructuredTool\n  );\n}\n\n","import { ToolCall } from '@langchain/core/messages';\nimport { RunnableConfig } from '@langchain/core/runnables';\nimport { DynamicStructuredTool } from '@langchain/core/tools';\nimport {\n  CancellationToken,\n  LanguageModelChatTool,\n  LanguageModelTool,\n  LanguageModelToolInvocationOptions,\n  LanguageModelToolResult,\n  ProviderResult,\n} from 'vscode';\nimport type { z } from 'zod/v3';\n\nimport { zodToJsonSchema } from 'zod-to-json-schema';\nimport { ChatVSCodeToolInput, ZodObjectAny } from './types.js';\n\nexport class ChatVSCodeTool<\n  T extends ZodObjectAny | Record<string, any> = ZodObjectAny\n>\n  extends DynamicStructuredTool<T extends ZodObjectAny ? T : ZodObjectAny>\n  implements\n  LanguageModelChatTool,\n  LanguageModelTool<z.infer<T extends ZodObjectAny ? T : ZodObjectAny>> {\n  inputSchema?: Record<string, unknown>;\n\n  constructor(fields: ChatVSCodeToolInput<T>) {\n    super(fields);\n    this.inputSchema = zodToJsonSchema(fields.schema);\n  }\n\n  static lc_name(): string {\n    return 'ChatVSCodeTool';\n  }\n\n  invoke(\n    input: string | ToolCall | { [x: string]: any },\n    config?: RunnableConfig\n  ): Promise<any>\n  invoke(\n    options: LanguageModelToolInvocationOptions<\n      z.infer<T extends ZodObjectAny ? T : ZodObjectAny>\n    >,\n    token: CancellationToken\n  ): ProviderResult<LanguageModelToolResult>\n\n  invoke(\n    inputOrOptions:\n      | string\n      | ToolCall\n      | { [x: string]: any }\n      | LanguageModelToolInvocationOptions<ZodObjectAny>,\n    configOrToken?: RunnableConfig | CancellationToken\n  ): Promise<any> | ProviderResult<LanguageModelToolResult> {\n    if (\n      inputOrOptions &&\n      typeof inputOrOptions === 'object' &&\n      'input' in inputOrOptions\n    ) {\n      // Second overload - VSCode tool invocation\n      const options =\n        inputOrOptions as LanguageModelToolInvocationOptions<ZodObjectAny>;\n      return super.invoke(options.input as any)\n    } else {\n      // First overload - Standard LangChain invocation\n      const input = inputOrOptions as string | ToolCall | { [x: string]: any }\n      const config = configOrToken as RunnableConfig\n      return super.invoke(input as any, config);\n    }\n  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