@langchain/anthropic/dist/utils/message_outputs.cjs.map

Anthropic integrations for LangChain.js

{"version":3,"file":"message_outputs.cjs","names":["AIMessageChunk","_isAnthropicCompactionBlock","AIMessage","extractToolCalls"],"sources":["../../src/utils/message_outputs.ts"],"sourcesContent":["/**\n * This util file contains functions for converting Anthropic messages to LangChain messages.\n */\nimport Anthropic from \"@anthropic-ai/sdk\";\nimport {\n  AIMessage,\n  AIMessageChunk,\n  UsageMetadata,\n} from \"@langchain/core/messages\";\nimport type { ToolCallChunk } from \"@langchain/core/messages/tool\";\nimport { ChatGeneration } from \"@langchain/core/outputs\";\nimport { AnthropicMessageResponse } from \"../types.js\";\nimport { extractToolCalls } from \"../output_parsers.js\";\nimport { _isAnthropicCompactionBlock } from \"./content.js\";\n\nexport function _makeMessageChunkFromAnthropicEvent(\n  data: Anthropic.Beta.Messages.BetaRawMessageStreamEvent,\n  fields: {\n    streamUsage: boolean;\n    coerceContentToString: boolean;\n  }\n): {\n  chunk: AIMessageChunk;\n} | null {\n  const response_metadata = { model_provider: \"anthropic\" };\n  if (data.type === \"message_start\") {\n    const { content, usage, ...additionalKwargs } = data.message;\n    // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n    const filteredAdditionalKwargs: Record<string, any> = {};\n    for (const [key, value] of Object.entries(additionalKwargs)) {\n      if (value !== undefined && value !== null) {\n        filteredAdditionalKwargs[key] = value;\n      }\n    }\n    // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n    const { input_tokens, output_tokens, ...rest }: Record<string, any> =\n      usage ?? {};\n    const usageMetadata = buildUsageMetadata(usage);\n    return {\n      chunk: new AIMessageChunk({\n        content: fields.coerceContentToString ? \"\" : [],\n        additional_kwargs: filteredAdditionalKwargs,\n        usage_metadata: fields.streamUsage ? usageMetadata : undefined,\n        response_metadata: {\n          ...response_metadata,\n          usage: {\n            ...rest,\n          },\n        },\n        id: data.message.id,\n      }),\n    };\n  } else if (data.type === \"message_delta\") {\n    const usageMetadata: UsageMetadata = {\n      input_tokens: 0,\n      output_tokens: data.usage.output_tokens,\n      total_tokens: data.usage.output_tokens,\n    };\n    const responseMetadata =\n      \"context_management\" in data.delta\n        ? { context_management: data.delta.context_management }\n        : undefined;\n    const cost = (data.usage as typeof data.usage & { cost?: unknown })?.cost;\n    return {\n      chunk: new AIMessageChunk({\n        content: fields.coerceContentToString ? \"\" : [],\n        response_metadata:\n          typeof cost === \"number\"\n            ? { ...responseMetadata, usage: { cost } }\n            : responseMetadata,\n        additional_kwargs: { ...data.delta },\n        usage_metadata: fields.streamUsage ? usageMetadata : undefined,\n      }),\n    };\n  } else if (\n    data.type === \"content_block_start\" &&\n    [\n      \"tool_use\",\n      \"document\",\n      \"server_tool_use\",\n      \"web_search_tool_result\",\n    ].includes(data.content_block.type)\n  ) {\n    const contentBlock = data.content_block;\n    let toolCallChunks: ToolCallChunk[];\n    if (contentBlock.type === \"tool_use\") {\n      toolCallChunks = [\n        {\n          id: contentBlock.id,\n          index: data.index,\n          name: contentBlock.name,\n          args: \"\",\n        },\n      ];\n    } else {\n      toolCallChunks = [];\n    }\n    return {\n      chunk: new AIMessageChunk({\n        content: fields.coerceContentToString\n          ? \"\"\n          : [\n              {\n                index: data.index,\n                ...data.content_block,\n                input:\n                  contentBlock.type === \"server_tool_use\" ||\n                  contentBlock.type === \"tool_use\"\n                    ? \"\"\n                    : undefined,\n              },\n            ],\n        response_metadata,\n        additional_kwargs: {},\n        tool_call_chunks: toolCallChunks,\n      }),\n    };\n  } else if (\n    data.type === \"content_block_delta\" &&\n    [\n      \"text_delta\",\n      \"citations_delta\",\n      \"thinking_delta\",\n      \"signature_delta\",\n    ].includes(data.delta.type)\n  ) {\n    if (fields.coerceContentToString && \"text\" in data.delta) {\n      return {\n        chunk: new AIMessageChunk({\n          content: data.delta.text,\n        }),\n      };\n    } else {\n      // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n      const contentBlock: Record<string, any> = data.delta;\n      if (\"citation\" in contentBlock) {\n        contentBlock.citations = [contentBlock.citation];\n        delete contentBlock.citation;\n      }\n      if (\n        contentBlock.type === \"thinking_delta\" ||\n        contentBlock.type === \"signature_delta\"\n      ) {\n        return {\n          chunk: new AIMessageChunk({\n            content: [{ index: data.index, ...contentBlock, type: \"thinking\" }],\n            response_metadata,\n          }),\n        };\n      }\n\n      return {\n        chunk: new AIMessageChunk({\n          content: [{ index: data.index, ...contentBlock, type: \"text\" }],\n          response_metadata,\n        }),\n      };\n    }\n  } else if (\n    data.type === \"content_block_delta\" &&\n    data.delta.type === \"input_json_delta\"\n  ) {\n    return {\n      chunk: new AIMessageChunk({\n        content: fields.coerceContentToString\n          ? \"\"\n          : [\n              {\n                index: data.index,\n                input: data.delta.partial_json,\n                type: data.delta.type,\n              },\n            ],\n        response_metadata,\n        additional_kwargs: {},\n        tool_call_chunks: [\n          {\n            index: data.index,\n            args: data.delta.partial_json,\n          },\n        ],\n      }),\n    };\n  } else if (\n    data.type === \"content_block_start\" &&\n    data.content_block.type === \"text\"\n  ) {\n    const content = data.content_block?.text;\n    if (content !== undefined) {\n      return {\n        chunk: new AIMessageChunk({\n          content: fields.coerceContentToString\n            ? content\n            : [\n                {\n                  index: data.index,\n                  ...data.content_block,\n                },\n              ],\n          response_metadata,\n          additional_kwargs: {},\n        }),\n      };\n    }\n  } else if (\n    data.type === \"content_block_start\" &&\n    data.content_block.type === \"redacted_thinking\"\n  ) {\n    return {\n      chunk: new AIMessageChunk({\n        content: fields.coerceContentToString\n          ? \"\"\n          : [{ index: data.index, ...data.content_block }],\n        response_metadata,\n      }),\n    };\n  } else if (\n    data.type === \"content_block_start\" &&\n    data.content_block.type === \"thinking\"\n  ) {\n    const content = data.content_block.thinking;\n    return {\n      chunk: new AIMessageChunk({\n        content: fields.coerceContentToString\n          ? content\n          : [{ index: data.index, ...data.content_block }],\n        response_metadata,\n      }),\n    };\n  } else if (\n    data.type === \"content_block_start\" &&\n    _isAnthropicCompactionBlock(data.content_block)\n  ) {\n    return {\n      chunk: new AIMessageChunk({\n        content: fields.coerceContentToString\n          ? \"\"\n          : [{ index: data.index, ...data.content_block }],\n        response_metadata,\n      }),\n    };\n  } else if (\n    data.type === \"content_block_delta\" &&\n    data.delta.type === \"compaction_delta\"\n  ) {\n    return {\n      chunk: new AIMessageChunk({\n        content: fields.coerceContentToString\n          ? \"\"\n          : [\n              {\n                index: data.index,\n                ...data.delta,\n                type: \"compaction\",\n              },\n            ],\n        response_metadata,\n      }),\n    };\n  }\n  return null;\n}\n\nexport function anthropicResponseToChatMessages(\n  messages: AnthropicMessageResponse[],\n  additionalKwargs: Record<string, unknown>\n): ChatGeneration[] {\n  const response_metadata = {\n    ...additionalKwargs,\n    model_provider: \"anthropic\",\n  };\n  const usage: Record<string, number> | null | undefined =\n    additionalKwargs.usage as Record<string, number> | null | undefined;\n  const usageMetadata = usage != null ? buildUsageMetadata(usage) : undefined;\n  if (messages.length === 1 && messages[0].type === \"text\") {\n    return [\n      {\n        text: messages[0].text,\n        message: new AIMessage({\n          content: messages[0].text,\n          additional_kwargs: additionalKwargs,\n          usage_metadata: usageMetadata,\n          response_metadata,\n          id: additionalKwargs.id as string,\n        }),\n      },\n    ];\n  } else {\n    const toolCalls = extractToolCalls(messages);\n    const generations: ChatGeneration[] = [\n      {\n        text: \"\",\n        message: new AIMessage({\n          // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n          content: messages as any,\n          additional_kwargs: additionalKwargs,\n          tool_calls: toolCalls,\n          usage_metadata: usageMetadata,\n          response_metadata,\n          id: additionalKwargs.id as string,\n        }),\n      },\n    ];\n    return generations;\n  }\n}\n\nfunction buildUsageMetadata(\n  usage: Anthropic.Messages.Usage | Record<string, number>\n): UsageMetadata {\n  const cacheCreationInputTokens = usage.cache_creation_input_tokens ?? 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