mirror of
https://github.com/router-for-me/CLIProxyAPI.git
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Add openai codex support
This commit is contained in:
Luis Pater
239
internal/translator/gemini-cli/openai/cli_openai_request.go
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239
internal/translator/gemini-cli/openai/cli_openai_request.go
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// Package openai provides request translation functionality for OpenAI API.
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// It handles the conversion of OpenAI-compatible request formats to the internal
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// format expected by the backend client, including parsing messages, roles,
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// content types (text, image, file), and tool calls.
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package openai
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import (
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"encoding/json"
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"strings"
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"github.com/luispater/CLIProxyAPI/internal/client"
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"github.com/luispater/CLIProxyAPI/internal/misc"
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log "github.com/sirupsen/logrus"
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"github.com/tidwall/gjson"
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)
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// ConvertOpenAIChatRequestToCli translates a raw JSON request from an OpenAI-compatible format
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// to the internal format expected by the backend client. It parses messages,
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// roles, content types (text, image, file), and tool calls.
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//
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// This function handles the complex task of converting between the OpenAI message
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// format and the internal format used by the Gemini client. It processes different
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// message types (system, user, assistant, tool) and content types (text, images, files).
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//
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// Parameters:
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// - rawJSON: The raw JSON bytes of the OpenAI-compatible request
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//
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// Returns:
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// - string: The model name to use
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// - *client.Content: System instruction content (if any)
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// - []client.Content: The conversation contents in internal format
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// - []client.ToolDeclaration: Tool declarations from the request
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func ConvertOpenAIChatRequestToCli(rawJSON []byte) (string, *client.Content, []client.Content, []client.ToolDeclaration) {
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// Extract the model name from the request, defaulting to "gemini-2.5-pro".
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modelName := "gemini-2.5-pro"
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modelResult := gjson.GetBytes(rawJSON, "model")
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if modelResult.Type == gjson.String {
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modelName = modelResult.String()
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}
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// Initialize data structures for processing conversation messages
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// contents: stores the processed conversation history
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// systemInstruction: stores system-level instructions separate from conversation
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contents := make([]client.Content, 0)
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var systemInstruction *client.Content
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messagesResult := gjson.GetBytes(rawJSON, "messages")
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// Pre-process tool responses to create a lookup map
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// This first pass collects all tool responses so they can be matched with their corresponding calls
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toolItems := make(map[string]*client.FunctionResponse)
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if messagesResult.IsArray() {
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messagesResults := messagesResult.Array()
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for i := 0; i < len(messagesResults); i++ {
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messageResult := messagesResults[i]
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roleResult := messageResult.Get("role")
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if roleResult.Type != gjson.String {
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continue
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}
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contentResult := messageResult.Get("content")
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// Extract tool responses for later matching with function calls
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if roleResult.String() == "tool" {
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toolCallID := messageResult.Get("tool_call_id").String()
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if toolCallID != "" {
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var responseData string
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// Handle both string and object-based tool response formats
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if contentResult.Type == gjson.String {
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responseData = contentResult.String()
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} else if contentResult.IsObject() && contentResult.Get("type").String() == "text" {
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responseData = contentResult.Get("text").String()
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}
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// Clean up tool call ID by removing timestamp suffix
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// This normalizes IDs for consistent matching between calls and responses
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toolCallIDs := strings.Split(toolCallID, "-")
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strings.Join(toolCallIDs, "-")
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newToolCallID := strings.Join(toolCallIDs[:len(toolCallIDs)-1], "-")
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// Create function response object with normalized ID and response data
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functionResponse := client.FunctionResponse{Name: newToolCallID, Response: map[string]interface{}{"result": responseData}}
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toolItems[toolCallID] = &functionResponse
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}
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}
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}
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}
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if messagesResult.IsArray() {
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messagesResults := messagesResult.Array()
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for i := 0; i < len(messagesResults); i++ {
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messageResult := messagesResults[i]
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roleResult := messageResult.Get("role")
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contentResult := messageResult.Get("content")
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if roleResult.Type != gjson.String {
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continue
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}
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switch roleResult.String() {
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// System messages are converted to a user message followed by a model's acknowledgment.
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case "system":
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if contentResult.Type == gjson.String {
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systemInstruction = &client.Content{Role: "user", Parts: []client.Part{{Text: contentResult.String()}}}
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} else if contentResult.IsObject() {
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// Handle object-based system messages.
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if contentResult.Get("type").String() == "text" {
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systemInstruction = &client.Content{Role: "user", Parts: []client.Part{{Text: contentResult.Get("text").String()}}}
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}
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}
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// User messages can contain simple text or a multi-part body.
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case "user":
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if contentResult.Type == gjson.String {
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contents = append(contents, client.Content{Role: "user", Parts: []client.Part{{Text: contentResult.String()}}})
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} else if contentResult.IsArray() {
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// Handle multi-part user messages (text, images, files).
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contentItemResults := contentResult.Array()
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parts := make([]client.Part, 0)
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for j := 0; j < len(contentItemResults); j++ {
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contentItemResult := contentItemResults[j]
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contentTypeResult := contentItemResult.Get("type")
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switch contentTypeResult.String() {
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case "text":
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parts = append(parts, client.Part{Text: contentItemResult.Get("text").String()})
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case "image_url":
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// Parse data URI for images.
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imageURL := contentItemResult.Get("image_url.url").String()
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if len(imageURL) > 5 {
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imageURLs := strings.SplitN(imageURL[5:], ";", 2)
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if len(imageURLs) == 2 && len(imageURLs[1]) > 7 {
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parts = append(parts, client.Part{InlineData: &client.InlineData{
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MimeType: imageURLs[0],
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Data: imageURLs[1][7:],
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}})
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}
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}
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case "file":
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// Handle file attachments by determining MIME type from extension.
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filename := contentItemResult.Get("file.filename").String()
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fileData := contentItemResult.Get("file.file_data").String()
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ext := ""
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if split := strings.Split(filename, "."); len(split) > 1 {
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ext = split[len(split)-1]
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}
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if mimeType, ok := misc.MimeTypes[ext]; ok {
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parts = append(parts, client.Part{InlineData: &client.InlineData{
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MimeType: mimeType,
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Data: fileData,
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}})
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} else {
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log.Warnf("Unknown file name extension '%s' at index %d, skipping file", ext, j)
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}
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}
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}
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contents = append(contents, client.Content{Role: "user", Parts: parts})
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}
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// Assistant messages can contain text responses or tool calls
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// In the internal format, assistant messages are converted to "model" role
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case "assistant":
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if contentResult.Type == gjson.String {
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// Simple text response from the assistant
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contents = append(contents, client.Content{Role: "model", Parts: []client.Part{{Text: contentResult.String()}}})
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} else if !contentResult.Exists() || contentResult.Type == gjson.Null {
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// Handle complex tool calls made by the assistant
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// This processes function calls and matches them with their responses
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functionIDs := make([]string, 0)
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toolCallsResult := messageResult.Get("tool_calls")
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if toolCallsResult.IsArray() {
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parts := make([]client.Part, 0)
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tcsResult := toolCallsResult.Array()
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// Process each tool call in the assistant's message
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for j := 0; j < len(tcsResult); j++ {
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tcResult := tcsResult[j]
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// Extract function call details
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functionID := tcResult.Get("id").String()
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functionIDs = append(functionIDs, functionID)
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functionName := tcResult.Get("function.name").String()
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functionArgs := tcResult.Get("function.arguments").String()
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// Parse function arguments from JSON string to map
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var args map[string]any
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if err := json.Unmarshal([]byte(functionArgs), &args); err == nil {
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parts = append(parts, client.Part{
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FunctionCall: &client.FunctionCall{
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Name: functionName,
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Args: args,
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},
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})
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}
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}
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// Add the model's function calls to the conversation
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if len(parts) > 0 {
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contents = append(contents, client.Content{
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Role: "model", Parts: parts,
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})
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// Create a separate tool response message with the collected responses
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// This matches function calls with their corresponding responses
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toolParts := make([]client.Part, 0)
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for _, functionID := range functionIDs {
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if functionResponse, ok := toolItems[functionID]; ok {
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toolParts = append(toolParts, client.Part{FunctionResponse: functionResponse})
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}
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}
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// Add the tool responses as a separate message in the conversation
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contents = append(contents, client.Content{Role: "tool", Parts: toolParts})
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}
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}
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}
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}
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}
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}
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// Translate the tool declarations from the request.
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var tools []client.ToolDeclaration
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toolsResult := gjson.GetBytes(rawJSON, "tools")
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if toolsResult.IsArray() {
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tools = make([]client.ToolDeclaration, 1)
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tools[0].FunctionDeclarations = make([]any, 0)
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toolsResults := toolsResult.Array()
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for i := 0; i < len(toolsResults); i++ {
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toolResult := toolsResults[i]
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if toolResult.Get("type").String() == "function" {
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functionTypeResult := toolResult.Get("function")
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if functionTypeResult.Exists() && functionTypeResult.IsObject() {
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var functionDeclaration any
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if err := json.Unmarshal([]byte(functionTypeResult.Raw), &functionDeclaration); err == nil {
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tools[0].FunctionDeclarations = append(tools[0].FunctionDeclarations, functionDeclaration)
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}
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}
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}
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}
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} else {
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tools = make([]client.ToolDeclaration, 0)
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}
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return modelName, systemInstruction, contents, tools
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}
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