@langchain/anthropic
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Anthropic integrations for LangChain.js
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{"version":3,"file":"message_inputs.cjs","names":["HumanMessage","standardContentBlockConverter","_isAnthropicImageBlockParam","_isAnthropicThinkingBlock","_isAnthropicRedactedThinkingBlock","_isAnthropicCompactionBlock","_isAnthropicSearchResultBlock","AIMessage","_formatStandardContent"],"sources":["../../src/utils/message_inputs.ts"],"sourcesContent":["/**\n * This util file contains functions for converting LangChain messages to Anthropic messages.\n */\nimport type Anthropic from \"@anthropic-ai/sdk\";\nimport {\n type BaseMessage,\n HumanMessage,\n ToolMessage,\n MessageContentComplex,\n isDataContentBlock,\n convertToProviderContentBlock,\n parseBase64DataUrl,\n ContentBlock,\n AIMessage,\n} from \"@langchain/core/messages\";\nimport { ToolCall } from \"@langchain/core/messages/tool\";\nimport {\n AnthropicImageBlockParam,\n AnthropicMessageCreateParams,\n AnthropicTextBlockParam,\n AnthropicToolResultBlockParam,\n AnthropicToolUseBlockParam,\n AnthropicDocumentBlockParam,\n AnthropicThinkingBlockParam,\n AnthropicRedactedThinkingBlockParam,\n AnthropicServerToolUseBlockParam,\n AnthropicWebSearchToolResultBlockParam,\n AnthropicSearchResultBlockParam,\n AnthropicToolResponse,\n AnthropicContainerUploadBlockParam,\n AnthropicCompactionBlockParam,\n} from \"../types.js\";\nimport {\n _isAnthropicImageBlockParam,\n _isAnthropicRedactedThinkingBlock,\n _isAnthropicSearchResultBlock,\n _isAnthropicThinkingBlock,\n _isAnthropicCompactionBlock,\n standardContentBlockConverter,\n} from \"./content.js\";\nimport { _formatStandardContent } from \"./standard.js\";\n\nfunction _formatImage(imageUrl: string) {\n const parsed = parseBase64DataUrl({ dataUrl: imageUrl });\n if (parsed) {\n return {\n type: \"base64\",\n media_type: parsed.mime_type,\n data: parsed.data,\n };\n }\n let parsedUrl: URL;\n\n try {\n parsedUrl = new URL(imageUrl);\n } catch {\n throw new Error(\n [\n `Malformed image URL: ${JSON.stringify(\n imageUrl\n )}. Content blocks of type 'image_url' must be a valid http, https, or base64-encoded data URL.`,\n \"Example: data:image/png;base64,/9j/4AAQSk...\",\n \"Example: https://example.com/image.jpg\",\n ].join(\"\\n\\n\")\n );\n }\n\n if (parsedUrl.protocol === \"http:\" || parsedUrl.protocol === \"https:\") {\n return {\n type: \"url\",\n url: imageUrl,\n };\n }\n\n throw new Error(\n [\n `Invalid image URL protocol: ${JSON.stringify(\n parsedUrl.protocol\n )}. Anthropic only supports images as http, https, or base64-encoded data URLs on 'image_url' content blocks.`,\n \"Example: data:image/png;base64,/9j/4AAQSk...\",\n \"Example: https://example.com/image.jpg\",\n ].join(\"\\n\\n\")\n );\n}\n\nfunction _ensureMessageContents(messages: BaseMessage[]): BaseMessage[] {\n // Merge runs of human/tool messages into single human messages with content blocks.\n const updatedMsgs = [];\n for (const message of messages) {\n if (message._getType() === \"tool\") {\n if (typeof message.content === \"string\") {\n const previousMessage = updatedMsgs[updatedMsgs.length - 1];\n if (\n previousMessage?._getType() === \"human\" &&\n Array.isArray(previousMessage.content) &&\n \"type\" in previousMessage.content[0] &&\n previousMessage.content[0].type === \"tool_result\"\n ) {\n // If the previous message was a tool result, we merge this tool message into it.\n (previousMessage.content as MessageContentComplex[]).push({\n type: \"tool_result\",\n content: message.content,\n tool_use_id: (message as ToolMessage).tool_call_id,\n });\n } else {\n // If not, we create a new human message with the tool result.\n updatedMsgs.push(\n new HumanMessage({\n content: [\n {\n type: \"tool_result\",\n content: message.content,\n tool_use_id: (message as ToolMessage).tool_call_id,\n },\n ],\n })\n );\n }\n } else {\n updatedMsgs.push(\n new HumanMessage({\n content: [\n {\n type: \"tool_result\",\n // rare case: message.content could be undefined\n ...(message.content != null\n ? { content: _formatContent(message) }\n : {}),\n tool_use_id: (message as ToolMessage).tool_call_id,\n },\n ],\n })\n );\n }\n } else {\n updatedMsgs.push(message);\n }\n }\n return updatedMsgs;\n}\n\nexport function _convertLangChainToolCallToAnthropic(\n toolCall: ToolCall\n): AnthropicToolResponse {\n if (toolCall.id === undefined) {\n throw new Error(`Anthropic requires all tool calls to have an \"id\".`);\n }\n return {\n type: \"tool_use\",\n id: toolCall.id,\n name: toolCall.name,\n input: toolCall.args,\n };\n}\n\nfunction* _formatContentBlocks(\n content: ContentBlock[],\n toolCalls?: ToolCall[]\n): Generator<Anthropic.Beta.BetaContentBlockParam> {\n const toolTypes = [\n \"bash_code_execution_tool_result\",\n \"input_json_delta\",\n \"server_tool_use\",\n \"text_editor_code_execution_tool_result\",\n \"tool_result\",\n \"tool_use\",\n \"web_search_result\",\n \"web_search_tool_result\",\n ];\n const textTypes = [\"text\", \"text_delta\"];\n for (const contentPart of content) {\n if (isDataContentBlock(contentPart)) {\n yield convertToProviderContentBlock(\n contentPart,\n standardContentBlockConverter\n );\n }\n\n const cacheControl =\n \"cache_control\" in contentPart\n ? (contentPart.cache_control as Anthropic.Beta.BetaCacheControlEphemeral)\n : undefined;\n\n if (contentPart.type === \"image_url\") {\n let source;\n if (typeof contentPart.image_url === \"string\") {\n source = _formatImage(contentPart.image_url);\n } else if (\n typeof contentPart.image_url === \"object\" &&\n contentPart.image_url !== null &&\n \"url\" in contentPart.image_url &&\n typeof contentPart.image_url.url === \"string\"\n ) {\n source = _formatImage(contentPart.image_url.url);\n }\n if (source) {\n yield {\n type: \"image\" as const, // Explicitly setting the type as \"image\"\n source,\n ...(cacheControl ? { cache_control: cacheControl } : {}),\n } as Anthropic.Messages.ImageBlockParam;\n }\n } else if (_isAnthropicImageBlockParam(contentPart)) {\n yield contentPart;\n } else if (contentPart.type === \"image\") {\n // Handle new ContentBlock.Multimodal.Image format\n let source;\n\n if (\"url\" in contentPart && typeof contentPart.url === \"string\") {\n // URL-based image\n source = _formatImage(contentPart.url);\n } else if (\n \"data\" in contentPart &&\n (typeof contentPart.data === \"string\" ||\n // eslint-disable-next-line no-instanceof/no-instanceof\n contentPart.data instanceof Uint8Array)\n ) {\n // Base64-based image\n const mimeType =\n \"mimeType\" in contentPart && typeof contentPart.mimeType === \"string\"\n ? contentPart.mimeType\n : \"image/jpeg\";\n const data =\n typeof contentPart.data === \"string\"\n ? contentPart.data\n : Buffer.from(contentPart.data).toString(\"base64\");\n source = {\n type: \"base64\" as const,\n media_type: mimeType as\n | \"image/jpeg\"\n | \"image/png\"\n | \"image/gif\"\n | \"image/webp\",\n data,\n };\n } else if (\n \"fileId\" in contentPart &&\n typeof contentPart.fileId === \"string\"\n ) {\n // File ID-based image\n // Note: Anthropic supports file IDs for images that have been uploaded\n // to their servers via the Files API\n source = {\n type: \"file\" as const,\n file_id: contentPart.fileId,\n };\n }\n\n if (source) {\n yield {\n type: \"image\" as const,\n source,\n ...(cacheControl ? { cache_control: cacheControl } : {}),\n } as Anthropic.Messages.ImageBlockParam;\n }\n } else if (contentPart.type === \"file\") {\n // Handle new ContentBlock.Multimodal.File format\n let source:\n | { type: \"url\"; url: string }\n | { type: \"base64\"; media_type: string; data: string }\n | { type: \"file\"; file_id: string }\n | undefined;\n\n if (\"url\" in contentPart && typeof contentPart.url === \"string\") {\n // File with URL\n source = {\n type: \"url\" as const,\n url: contentPart.url,\n };\n } else if (\n \"data\" in contentPart &&\n (typeof contentPart.data === \"string\" ||\n // eslint-disable-next-line no-instanceof/no-instanceof\n contentPart.data instanceof Uint8Array)\n ) {\n // File with base64 data (string or Uint8Array)\n const media_type =\n \"mimeType\" in contentPart && typeof contentPart.mimeType === \"string\"\n ? contentPart.mimeType\n : \"application/pdf\";\n const data =\n typeof contentPart.data === \"string\"\n ? contentPart.data\n : Buffer.from(contentPart.data).toString(\"base64\");\n\n source = {\n type: \"base64\" as const,\n media_type,\n data,\n };\n } else if (\n \"fileId\" in contentPart &&\n typeof contentPart.fileId === \"string\"\n ) {\n // File ID from Anthropic Files API\n // https://platform.claude.com/docs/en/build-with-claude/pdf-support#option-3-files-api\n source = {\n type: \"file\" as const,\n file_id: contentPart.fileId,\n };\n }\n\n if (source) {\n yield {\n type: \"document\" as const,\n source,\n ...(cacheControl ? { cache_control: cacheControl } : {}),\n } as Anthropic.Messages.DocumentBlockParam;\n }\n } else if (contentPart.type === \"document\") {\n // PDF\n yield {\n ...contentPart,\n ...(cacheControl ? { cache_control: cacheControl } : {}),\n } as Anthropic.Messages.DocumentBlockParam;\n } else if (_isAnthropicThinkingBlock(contentPart)) {\n const block: AnthropicThinkingBlockParam = {\n type: \"thinking\" as const, // Explicitly setting the type as \"thinking\"\n thinking: contentPart.thinking,\n signature: contentPart.signature,\n ...(cacheControl ? { cache_control: cacheControl } : {}),\n };\n yield block;\n } else if (_isAnthropicRedactedThinkingBlock(contentPart)) {\n const block: AnthropicRedactedThinkingBlockParam = {\n type: \"redacted_thinking\" as const, // Explicitly setting the type as \"redacted_thinking\"\n data: contentPart.data,\n ...(cacheControl ? { cache_control: cacheControl } : {}),\n };\n yield block;\n } else if (_isAnthropicCompactionBlock(contentPart)) {\n const block: AnthropicCompactionBlockParam = {\n type: \"compaction\" as const,\n content: contentPart.content,\n ...(cacheControl ? { cache_control: cacheControl } : {}),\n };\n yield block;\n } else if (_isAnthropicSearchResultBlock(contentPart)) {\n const block: AnthropicSearchResultBlockParam = {\n type: \"search_result\" as const, // Explicitly setting the type as \"search_result\"\n title: contentPart.title,\n source: contentPart.source,\n ...(\"cache_control\" in contentPart && contentPart.cache_control\n ? { cache_control: contentPart.cache_control }\n : {}),\n ...(\"citations\" in contentPart && contentPart.citations\n ? { citations: contentPart.citations }\n : {}),\n content: contentPart.content,\n };\n yield block as Anthropic.Beta.BetaSearchResultBlockParam;\n } else if (\n textTypes.find((t) => t === contentPart.type) &&\n \"text\" in contentPart\n ) {\n // Assuming contentPart is of type MessageContentText here\n yield {\n type: \"text\" as const, // Explicitly setting the type as \"text\"\n text: contentPart.text,\n ...(cacheControl ? { cache_control: cacheControl } : {}),\n ...(\"citations\" in contentPart && contentPart.citations\n ? { citations: contentPart.citations }\n : {}),\n } as Anthropic.Messages.TextBlockParam;\n } else if (toolTypes.find((t) => t === contentPart.type)) {\n const contentPartCopy = { ...contentPart };\n\n if (contentPartCopy.type === \"input_json_delta\") {\n // `input_json_delta` type only represents yielding partial tool inputs\n // and is not a valid type for Anthropic messages.\n // These blocks appear in streaming responses and should be skipped\n // as their input data is already captured in tool_calls.\n continue;\n }\n\n if (\n contentPartCopy.type === \"tool_use\" &&\n typeof contentPartCopy.input === \"string\"\n ) {\n // First, try to get the input from the corresponding tool_call.\n // This is the most reliable source since tool_calls are properly\n // consolidated from tool_call_chunks during streaming.\n const matchingToolCall = toolCalls?.find(\n (tc) => tc.id === contentPartCopy.id\n );\n if (matchingToolCall) {\n contentPartCopy.input = matchingToolCall.args;\n } else {\n // Fallback: `tool_use` content part may be followed by `input_json_delta`\n // content parts which are chunks of a stringified JSON input,\n // so we need to collect them and merge their inputs.\n const inputDeltas = content.filter(\n (nestedContentPart) =>\n nestedContentPart.index === contentPartCopy.index &&\n nestedContentPart.type === \"input_json_delta\" &&\n typeof nestedContentPart.input === \"string\"\n );\n // If no `input_json_delta` parts are found, this line will just\n // return `contentPartCopy.input`, so no additional check is needed\n contentPartCopy.input = inputDeltas.reduce(\n (accumulator, nestedContentPart) =>\n accumulator + nestedContentPart.input,\n contentPartCopy.input\n );\n }\n }\n\n if (\"index\" in contentPartCopy) {\n // Anthropic does not support passing the index field here, so we remove it.\n delete contentPartCopy.index;\n }\n\n if (\"input\" in contentPartCopy) {\n // Anthropic tool use inputs should be valid objects, when applicable.\n if (typeof contentPartCopy.input === \"string\") {\n try {\n contentPartCopy.input = JSON.parse(contentPartCopy.input);\n } catch {\n contentPartCopy.input = {};\n }\n }\n }\n // TODO: Fix when SDK types are fixed\n yield {\n ...contentPartCopy,\n ...(cacheControl ? { cache_control: cacheControl } : {}),\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n } as any;\n } else if (contentPart.type === \"container_upload\") {\n yield {\n ...contentPart,\n ...(cacheControl ? { cache_control: cacheControl } : {}),\n } as AnthropicContainerUploadBlockParam;\n }\n\n // Note that we are intentionally dropping any blocks that we don't\n // recognize. This is to allow for cross-compatibility between different\n // providers that may have different block types. Ie if we take a message\n // output from OpenAI and send it to Anthropic, we want to drop any blocks\n // that Anthropic doesn't understand.\n }\n}\n\nfunction _formatContent(message: BaseMessage, toolCalls?: ToolCall[]) {\n const { content } = message;\n\n if (typeof content === \"string\") {\n return content;\n } else {\n return Array.from(_formatContentBlocks(content, toolCalls));\n }\n}\n\n/**\n * Formats messages as a prompt for the model.\n * Used in LangSmith, export is important here.\n * @param messages The base messages to format as a prompt.\n * @returns The formatted prompt.\n */\nexport function _convertMessagesToAnthropicPayload(\n messages: BaseMessage[]\n): AnthropicMessageCreateParams {\n const mergedMessages = _ensureMessageContents(messages);\n let system;\n if (mergedMessages.length > 0 && mergedMessages[0]._getType() === \"system\") {\n system = messages[0].content;\n }\n const conversationMessages =\n system !== undefined ? mergedMessages.slice(1) : mergedMessages;\n const formattedMessages = conversationMessages.map((message) => {\n let role;\n if (message._getType() === \"human\") {\n role = \"user\" as const;\n } else if (message._getType() === \"ai\") {\n role = \"assistant\" as const;\n } else if (message._getType() === \"tool\") {\n role = \"user\" as const;\n } else if (message._getType() === \"system\") {\n throw new Error(\n \"System messages are only permitted as the first passed message.\"\n );\n } else {\n throw new Error(`Message type \"${message.type}\" is not supported.`);\n }\n if (\n AIMessage.isInstance(message) &&\n message.response_metadata?.output_version === \"v1\"\n ) {\n return {\n role,\n content: _formatStandardContent(message),\n };\n }\n if (AIMessage.isInstance(message) && !!message.tool_calls?.length) {\n if (typeof message.content === \"string\") {\n if (message.content === \"\") {\n return {\n role,\n content: message.tool_calls.map(\n _convertLangChainToolCallToAnthropic\n ),\n };\n } else {\n return {\n role,\n content: [\n { type: \"text\", text: message.content },\n ...message.tool_calls.map(_convertLangChainToolCallToAnthropic),\n ],\n };\n }\n } else {\n const { content } = message;\n const formattedContent = _formatContent(message, message.tool_calls);\n const formattedContentArr = Array.isArray(formattedContent)\n ? formattedContent\n : [{ type: \"text\" as const, text: formattedContent }];\n const missingToolCalls = message.tool_calls.filter(\n (toolCall) =>\n !content.find(\n (contentPart) =>\n (contentPart.type === \"tool_use\" ||\n contentPart.type === \"input_json_delta\" ||\n contentPart.type === \"server_tool_use\") &&\n contentPart.id === toolCall.id\n )\n );\n return {\n role,\n content: [\n ...formattedContentArr,\n ...missingToolCalls.map(_convertLangChainToolCallToAnthropic),\n ],\n };\n }\n } else {\n return {\n role,\n content: _formatContent(\n message,\n AIMessage.isInstance(message) ? message.tool_calls : undefined\n ),\n };\n }\n });\n return {\n messages: mergeMessages(\n formattedMessages as AnthropicMessageCreateParams[\"messages\"]\n ),\n system,\n } as AnthropicMessageCreateParams;\n}\n\n/**\n * Cache control configuration for Anthropic prompt caching.\n */\ninterface CacheControl {\n type: \"ephemeral\";\n ttl?: \"5m\" | \"1h\";\n}\n\n/**\n * Applies cache_control to the last content block of the last message in the payload.\n * This is the recommended approach for prompt caching as it applies the cache_control\n * at the final formatting layer, after all message processing is complete.\n *\n * This matches the Python langchain-anthropic implementation where cache_control\n * is applied via model_settings rather than modifying message content blocks directly.\n *\n * @param payload - The formatted Anthropic message payload\n * @param cacheControl - The cache control configuration to apply\n * @returns The payload with cache_control applied to the last content block\n */\nexport function applyCacheControlToPayload(\n payload: AnthropicMessageCreateParams,\n cacheControl: CacheControl\n): AnthropicMessageCreateParams {\n if (!payload.messages || payload.messages.length === 0) {\n return payload;\n }\n\n const messages = [...payload.messages];\n const lastMessageIndex = messages.length - 1;\n const lastMessage = messages[lastMessageIndex];\n\n if (!lastMessage) {\n return payload;\n }\n\n // Handle string content - convert to text block with cache_control\n if (typeof lastMessage.content === \"string\") {\n messages[lastMessageIndex] = {\n ...lastMessage,\n content: [\n {\n type: \"text\",\n text: lastMessage.content,\n cache_control: cacheControl,\n },\n ],\n };\n return { ...payload, messages };\n }\n\n // Handle array content - add cache_control to the last block\n if (Array.isArray(lastMessage.content) && lastMessage.content.length > 0) {\n const content = [...lastMessage.content];\n const lastBlockIndex = content.length - 1;\n const lastBlock = content[lastBlockIndex];\n\n // Add cache_control to the last block\n content[lastBlockIndex] = {\n ...lastBlock,\n cache_control: cacheControl,\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n } as any;\n\n messages[lastMessageIndex] = {\n ...lastMessage,\n content,\n };\n return { ...payload, messages };\n }\n\n return payload;\n}\n\nfunction mergeMessages(messages: AnthropicMessageCreateParams[\"messages\"]) {\n if (!messages || messages.length <= 1) {\n return messages;\n }\n\n const result: AnthropicMessageCreateParams[\"messages\"] = [];\n let currentMessage = messages[0];\n\n const normalizeContent = (\n content:\n | string\n | Array<\n | AnthropicTextBlockParam\n | AnthropicImageBlockParam\n | AnthropicToolUseBlockParam\n | AnthropicToolResultBlockParam\n | AnthropicDocumentBlockParam\n | AnthropicThinkingBlockParam\n | AnthropicRedactedThinkingBlockParam\n | AnthropicServerToolUseBlockParam\n | AnthropicWebSearchToolResultBlockParam\n | AnthropicSearchResultBlockParam\n >\n ): Array<\n | AnthropicTextBlockParam\n | AnthropicImageBlockParam\n | AnthropicToolUseBlockParam\n | AnthropicToolResultBlockParam\n | AnthropicDocumentBlockParam\n | AnthropicThinkingBlockParam\n | AnthropicRedactedThinkingBlockParam\n | AnthropicServerToolUseBlockParam\n | AnthropicWebSearchToolResultBlockParam\n | AnthropicSearchResultBlockParam\n > => {\n if (typeof content === \"string\") {\n return [\n {\n type: \"text\",\n text: content,\n },\n ];\n }\n return content;\n };\n\n const isToolResultMessage = (msg: (typeof messages)[0]) => {\n if (msg.role !== \"user\") return false;\n\n if (typeof msg.content === \"string\") {\n return false;\n }\n\n return (\n Array.isArray(msg.content) &&\n msg.content.every((item) => item.type === \"tool_result\")\n );\n };\n\n for (let i = 1; i < messages.length; i += 1) {\n const nextMessage = messages[i];\n\n if (\n isToolResultMessage(currentMessage) &&\n isToolResultMessage(nextMessage)\n ) {\n // Merge the messages by combining their content arrays\n currentMessage = {\n ...currentMessage,\n content: [\n ...normalizeContent(currentMessage.content),\n ...normalizeContent(nextMessage.content),\n ],\n };\n } else {\n result.push(currentMessage);\n currentMessage = nextMessage;\n }\n }\n\n result.push(currentMessage);\n return 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