{"version":3,"file":"transform-messages.d.ts","sourceRoot":"","sources":["../../src/providers/transform-messages.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EACX,GAAG,EACH,gBAAgB,EAEhB,OAAO,EACP,KAAK,EAIL,MAAM,aAAa,CAAC;AAiDrB;;;;GAIG;AACH,wBAAgB,iBAAiB,CAAC,IAAI,SAAS,GAAG,EACjD,QAAQ,EAAE,OAAO,EAAE,EACnB,KAAK,EAAE,KAAK,CAAC,IAAI,CAAC,EAClB,mBAAmB,CAAC,EAAE,CAAC,EAAE,EAAE,MAAM,EAAE,KAAK,EAAE,KAAK,CAAC,IAAI,CAAC,EAAE,MAAM,EAAE,gBAAgB,KAAK,MAAM,GACxF,OAAO,EAAE,CAwJX","sourcesContent":["import type {\n\tApi,\n\tAssistantMessage,\n\tImageContent,\n\tMessage,\n\tModel,\n\tTextContent,\n\tToolCall,\n\tToolResultMessage,\n} from \"../types.ts\";\n\nconst NON_VISION_USER_IMAGE_PLACEHOLDER = \"(image omitted: model does not support images)\";\nconst NON_VISION_TOOL_IMAGE_PLACEHOLDER = \"(tool image omitted: model does not support images)\";\n\nfunction replaceImagesWithPlaceholder(content: (TextContent | ImageContent)[], placeholder: string): TextContent[] {\n\tconst result: TextContent[] = [];\n\tlet previousWasPlaceholder = false;\n\n\tfor (const block of content) {\n\t\tif (block.type === \"image\") {\n\t\t\tif (!previousWasPlaceholder) {\n\t\t\t\tresult.push({ type: \"text\", text: placeholder });\n\t\t\t}\n\t\t\tpreviousWasPlaceholder = true;\n\t\t\tcontinue;\n\t\t}\n\n\t\tresult.push(block);\n\t\tpreviousWasPlaceholder = block.text === placeholder;\n\t}\n\n\treturn result;\n}\n\nfunction downgradeUnsupportedImages<TApi extends Api>(messages: Message[], model: Model<TApi>): Message[] {\n\tif (model.input.includes(\"image\")) {\n\t\treturn messages;\n\t}\n\n\treturn messages.map((msg) => {\n\t\tif (msg.role === \"user\" && Array.isArray(msg.content)) {\n\t\t\treturn {\n\t\t\t\t...msg,\n\t\t\t\tcontent: replaceImagesWithPlaceholder(msg.content, NON_VISION_USER_IMAGE_PLACEHOLDER),\n\t\t\t};\n\t\t}\n\n\t\tif (msg.role === \"toolResult\") {\n\t\t\treturn {\n\t\t\t\t...msg,\n\t\t\t\tcontent: replaceImagesWithPlaceholder(msg.content, NON_VISION_TOOL_IMAGE_PLACEHOLDER),\n\t\t\t};\n\t\t}\n\n\t\treturn msg;\n\t});\n}\n\n/**\n * Normalize tool call ID for cross-provider compatibility.\n * OpenAI Responses API generates IDs that are 450+ chars with special characters like `|`.\n * Anthropic APIs require IDs matching ^[a-zA-Z0-9_-]+$ (max 64 chars).\n */\nexport function transformMessages<TApi extends Api>(\n\tmessages: Message[],\n\tmodel: Model<TApi>,\n\tnormalizeToolCallId?: (id: string, model: Model<TApi>, source: AssistantMessage) => string,\n): Message[] {\n\t// Build a map of original tool call IDs to normalized IDs\n\tconst toolCallIdMap = new Map<string, string>();\n\tconst imageAwareMessages = downgradeUnsupportedImages(messages, model);\n\n\t// First pass: transform messages (unsupported image downgrade, thinking blocks, tool call ID normalization)\n\tconst transformed = imageAwareMessages.map((msg) => {\n\t\t// User messages pass through unchanged\n\t\tif (msg.role === \"user\") {\n\t\t\treturn msg;\n\t\t}\n\n\t\t// Handle toolResult messages - normalize toolCallId if we have a mapping\n\t\tif (msg.role === \"toolResult\") {\n\t\t\tconst normalizedId = toolCallIdMap.get(msg.toolCallId);\n\t\t\tif (normalizedId && normalizedId !== msg.toolCallId) {\n\t\t\t\treturn { ...msg, toolCallId: normalizedId };\n\t\t\t}\n\t\t\treturn msg;\n\t\t}\n\n\t\t// Assistant messages need transformation check\n\t\tif (msg.role === \"assistant\") {\n\t\t\tconst assistantMsg = msg as AssistantMessage;\n\t\t\tconst isSameModel =\n\t\t\t\tassistantMsg.provider === model.provider &&\n\t\t\t\tassistantMsg.api === model.api &&\n\t\t\t\tassistantMsg.model === model.id;\n\n\t\t\tconst transformedContent = assistantMsg.content.flatMap((block) => {\n\t\t\t\tif (block.type === \"thinking\") {\n\t\t\t\t\t// Redacted thinking is opaque encrypted content, only valid for the same model.\n\t\t\t\t\t// Drop it for cross-model to avoid API errors.\n\t\t\t\t\tif (block.redacted) {\n\t\t\t\t\t\treturn isSameModel ? block : [];\n\t\t\t\t\t}\n\t\t\t\t\t// For same model: keep thinking blocks with signatures (needed for replay)\n\t\t\t\t\t// even if the thinking text is empty (OpenAI encrypted reasoning)\n\t\t\t\t\tif (isSameModel && block.thinkingSignature) return block;\n\t\t\t\t\t// Skip empty thinking blocks, convert others to plain text\n\t\t\t\t\tif (!block.thinking || block.thinking.trim() === \"\") return [];\n\t\t\t\t\tif (isSameModel) return block;\n\t\t\t\t\treturn {\n\t\t\t\t\t\ttype: \"text\" as const,\n\t\t\t\t\t\ttext: block.thinking,\n\t\t\t\t\t};\n\t\t\t\t}\n\n\t\t\t\tif (block.type === \"text\") {\n\t\t\t\t\tif (isSameModel) return block;\n\t\t\t\t\treturn {\n\t\t\t\t\t\ttype: \"text\" as const,\n\t\t\t\t\t\ttext: block.text,\n\t\t\t\t\t};\n\t\t\t\t}\n\n\t\t\t\tif (block.type === \"toolCall\") {\n\t\t\t\t\tconst toolCall = block as ToolCall;\n\t\t\t\t\tlet normalizedToolCall: ToolCall = toolCall;\n\n\t\t\t\t\tif (!isSameModel && toolCall.thoughtSignature) {\n\t\t\t\t\t\tnormalizedToolCall = { ...toolCall };\n\t\t\t\t\t\tdelete (normalizedToolCall as { thoughtSignature?: string }).thoughtSignature;\n\t\t\t\t\t}\n\n\t\t\t\t\tif (!isSameModel && normalizeToolCallId) {\n\t\t\t\t\t\tconst normalizedId = normalizeToolCallId(toolCall.id, model, assistantMsg);\n\t\t\t\t\t\tif (normalizedId !== toolCall.id) {\n\t\t\t\t\t\t\ttoolCallIdMap.set(toolCall.id, normalizedId);\n\t\t\t\t\t\t\tnormalizedToolCall = { ...normalizedToolCall, id: normalizedId };\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\treturn normalizedToolCall;\n\t\t\t\t}\n\n\t\t\t\treturn block;\n\t\t\t});\n\n\t\t\treturn {\n\t\t\t\t...assistantMsg,\n\t\t\t\tcontent: transformedContent,\n\t\t\t};\n\t\t}\n\t\treturn msg;\n\t});\n\n\t// Second pass: insert synthetic empty tool results for orphaned tool calls\n\t// This preserves thinking signatures and satisfies API requirements\n\tconst result: Message[] = [];\n\tlet pendingToolCalls: ToolCall[] = [];\n\tlet existingToolResultIds = new Set<string>();\n\tconst insertSyntheticToolResults = () => {\n\t\tif (pendingToolCalls.length > 0) {\n\t\t\tfor (const tc of pendingToolCalls) {\n\t\t\t\tif (!existingToolResultIds.has(tc.id)) {\n\t\t\t\t\tresult.push({\n\t\t\t\t\t\trole: \"toolResult\",\n\t\t\t\t\t\ttoolCallId: tc.id,\n\t\t\t\t\t\ttoolName: tc.name,\n\t\t\t\t\t\tcontent: [{ type: \"text\", text: \"No result provided\" }],\n\t\t\t\t\t\tisError: true,\n\t\t\t\t\t\ttimestamp: Date.now(),\n\t\t\t\t\t} as ToolResultMessage);\n\t\t\t\t}\n\t\t\t}\n\t\t\tpendingToolCalls = [];\n\t\t\texistingToolResultIds = new Set();\n\t\t}\n\t};\n\n\tfor (let i = 0; i < transformed.length; i++) {\n\t\tconst msg = transformed[i];\n\n\t\tif (msg.role === \"assistant\") {\n\t\t\t// If we have pending orphaned tool calls from a previous assistant, insert synthetic results now\n\t\t\tinsertSyntheticToolResults();\n\n\t\t\t// Skip errored/aborted assistant messages entirely.\n\t\t\t// These are incomplete turns that shouldn't be replayed:\n\t\t\t// - May have partial content (reasoning without message, incomplete tool calls)\n\t\t\t// - Replaying them can cause API errors (e.g., OpenAI \"reasoning without following item\")\n\t\t\t// - The model should retry from the last valid state\n\t\t\tconst assistantMsg = msg as AssistantMessage;\n\t\t\tif (assistantMsg.stopReason === \"error\" || assistantMsg.stopReason === \"aborted\") {\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\t// Track tool calls from this assistant message\n\t\t\tconst toolCalls = assistantMsg.content.filter((b) => b.type === \"toolCall\") as ToolCall[];\n\t\t\tif (toolCalls.length > 0) {\n\t\t\t\tpendingToolCalls = toolCalls;\n\t\t\t\texistingToolResultIds = new Set();\n\t\t\t}\n\n\t\t\tresult.push(msg);\n\t\t} else if (msg.role === \"toolResult\") {\n\t\t\texistingToolResultIds.add(msg.toolCallId);\n\t\t\tresult.push(msg);\n\t\t} else if (msg.role === \"user\") {\n\t\t\t// User message interrupts tool flow - insert synthetic results for orphaned calls\n\t\t\tinsertSyntheticToolResults();\n\t\t\tresult.push(msg);\n\t\t} else {\n\t\t\tresult.push(msg);\n\t\t}\n\t}\n\n\t// If the conversation ends with unresolved tool calls, synthesize results now.\n\tinsertSyntheticToolResults();\n\n\treturn result;\n}\n"]}