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Refactor API
This commit is contained in:
Saoud Rizwan
202
src/api/transform/openai-format.ts
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202
src/api/transform/openai-format.ts
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import { Anthropic } from "@anthropic-ai/sdk"
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import OpenAI from "openai"
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export function convertToOpenAiMessages(
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anthropicMessages: Anthropic.Messages.MessageParam[]
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): OpenAI.Chat.ChatCompletionMessageParam[] {
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const openAiMessages: OpenAI.Chat.ChatCompletionMessageParam[] = []
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for (const anthropicMessage of anthropicMessages) {
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if (typeof anthropicMessage.content === "string") {
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openAiMessages.push({ role: anthropicMessage.role, content: anthropicMessage.content })
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} else {
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// image_url.url is base64 encoded image data
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// ensure it contains the content-type of the image: data:image/png;base64,
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/*
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{ role: "user", content: "" | { type: "text", text: string } | { type: "image_url", image_url: { url: string } } },
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// content required unless tool_calls is present
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{ role: "assistant", content?: "" | null, tool_calls?: [{ id: "", function: { name: "", arguments: "" }, type: "function" }] },
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{ role: "tool", tool_call_id: "", content: ""}
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*/
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if (anthropicMessage.role === "user") {
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const { nonToolMessages, toolMessages } = anthropicMessage.content.reduce<{
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nonToolMessages: (Anthropic.TextBlockParam | Anthropic.ImageBlockParam)[]
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toolMessages: Anthropic.ToolResultBlockParam[]
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}>(
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(acc, part) => {
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if (part.type === "tool_result") {
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acc.toolMessages.push(part)
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} else if (part.type === "text" || part.type === "image") {
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acc.nonToolMessages.push(part)
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} // user cannot send tool_use messages
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return acc
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},
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{ nonToolMessages: [], toolMessages: [] }
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)
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// Process tool result messages FIRST since they must follow the tool use messages
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let toolResultImages: Anthropic.Messages.ImageBlockParam[] = []
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toolMessages.forEach((toolMessage) => {
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// The Anthropic SDK allows tool results to be a string or an array of text and image blocks, enabling rich and structured content. In contrast, the OpenAI SDK only supports tool results as a single string, so we map the Anthropic tool result parts into one concatenated string to maintain compatibility.
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let content: string
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if (typeof toolMessage.content === "string") {
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content = toolMessage.content
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} else {
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content =
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toolMessage.content
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?.map((part) => {
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if (part.type === "image") {
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toolResultImages.push(part)
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return "(see following user message for image)"
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}
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return part.text
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})
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.join("\n") ?? ""
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}
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openAiMessages.push({
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role: "tool",
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tool_call_id: toolMessage.tool_use_id,
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content: content,
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})
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})
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// If tool results contain images, send as a separate user message
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// I ran into an issue where if I gave feedback for one of many tool uses, the request would fail.
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// "Messages following `tool_use` blocks must begin with a matching number of `tool_result` blocks."
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// Therefore we need to send these images after the tool result messages
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// NOTE: it's actually okay to have multiple user messages in a row, the model will treat them as a continuation of the same input (this way works better than combining them into one message, since the tool result specifically mentions (see following user message for image)
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if (toolResultImages.length > 0) {
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openAiMessages.push({
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role: "user",
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content: toolResultImages.map((part) => ({
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type: "image_url",
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image_url: { url: `data:${part.source.media_type};base64,${part.source.data}` },
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})),
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})
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}
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// Process non-tool messages
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if (nonToolMessages.length > 0) {
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openAiMessages.push({
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role: "user",
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content: nonToolMessages.map((part) => {
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if (part.type === "image") {
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return {
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type: "image_url",
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image_url: { url: `data:${part.source.media_type};base64,${part.source.data}` },
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}
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}
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return { type: "text", text: part.text }
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}),
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})
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}
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} else if (anthropicMessage.role === "assistant") {
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const { nonToolMessages, toolMessages } = anthropicMessage.content.reduce<{
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nonToolMessages: (Anthropic.TextBlockParam | Anthropic.ImageBlockParam)[]
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toolMessages: Anthropic.ToolUseBlockParam[]
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}>(
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(acc, part) => {
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if (part.type === "tool_use") {
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acc.toolMessages.push(part)
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} else if (part.type === "text" || part.type === "image") {
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acc.nonToolMessages.push(part)
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} // assistant cannot send tool_result messages
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return acc
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},
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{ nonToolMessages: [], toolMessages: [] }
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)
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// Process non-tool messages
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let content: string | undefined
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if (nonToolMessages.length > 0) {
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content = nonToolMessages
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.map((part) => {
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if (part.type === "image") {
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return "" // impossible as the assistant cannot send images
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}
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return part.text
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})
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.join("\n")
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}
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// Process tool use messages
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let tool_calls: OpenAI.Chat.ChatCompletionMessageToolCall[] = toolMessages.map((toolMessage) => ({
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id: toolMessage.id,
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type: "function",
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function: {
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name: toolMessage.name,
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// json string
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arguments: JSON.stringify(toolMessage.input),
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},
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}))
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openAiMessages.push({
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role: "assistant",
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content,
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// Cannot be an empty array. API expects an array with minimum length 1, and will respond with an error if it's empty
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tool_calls: tool_calls.length > 0 ? tool_calls : undefined,
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})
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}
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}
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}
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return openAiMessages
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}
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// Convert OpenAI response to Anthropic format
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export function convertToAnthropicMessage(
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completion: OpenAI.Chat.Completions.ChatCompletion
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): Anthropic.Messages.Message {
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const openAiMessage = completion.choices[0].message
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const anthropicMessage: Anthropic.Messages.Message = {
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id: completion.id,
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type: "message",
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role: openAiMessage.role, // always "assistant"
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content: [
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{
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type: "text",
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text: openAiMessage.content || "",
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},
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],
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model: completion.model,
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stop_reason: (() => {
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switch (completion.choices[0].finish_reason) {
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case "stop":
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return "end_turn"
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case "length":
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return "max_tokens"
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case "tool_calls":
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return "tool_use"
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case "content_filter": // Anthropic doesn't have an exact equivalent
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default:
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return null
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}
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})(),
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stop_sequence: null, // which custom stop_sequence was generated, if any (not applicable if you don't use stop_sequence)
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usage: {
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input_tokens: completion.usage?.prompt_tokens || 0,
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output_tokens: completion.usage?.completion_tokens || 0,
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},
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}
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if (openAiMessage.tool_calls && openAiMessage.tool_calls.length > 0) {
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anthropicMessage.content.push(
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...openAiMessage.tool_calls.map((toolCall): Anthropic.ToolUseBlock => {
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let parsedInput = {}
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try {
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parsedInput = JSON.parse(toolCall.function.arguments || "{}")
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} catch (error) {
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console.error("Failed to parse tool arguments:", error)
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}
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return {
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type: "tool_use",
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id: toolCall.id,
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name: toolCall.function.name,
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input: parsedInput,
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}
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})
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)
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}
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return anthropicMessage
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}
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