mirror of
https://github.com/mudler/LocalAI.git
synced 2026-01-06 02:29:54 -06:00
149 lines
4.6 KiB
Go
149 lines
4.6 KiB
Go
package openai
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import (
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"context"
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"encoding/json"
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"errors"
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"fmt"
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"net"
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"time"
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"github.com/labstack/echo/v4"
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"github.com/mudler/LocalAI/core/config"
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mcpTools "github.com/mudler/LocalAI/core/http/endpoints/mcp"
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"github.com/mudler/LocalAI/core/http/middleware"
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"github.com/google/uuid"
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"github.com/mudler/LocalAI/core/schema"
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"github.com/mudler/LocalAI/core/templates"
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"github.com/mudler/LocalAI/pkg/model"
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"github.com/mudler/cogito"
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"github.com/rs/zerolog/log"
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)
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// MCPCompletionEndpoint is the OpenAI Completion API endpoint https://platform.openai.com/docs/api-reference/completions
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// @Summary Generate completions for a given prompt and model.
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// @Param request body schema.OpenAIRequest true "query params"
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// @Success 200 {object} schema.OpenAIResponse "Response"
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// @Router /mcp/v1/completions [post]
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func MCPCompletionEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator *templates.Evaluator, appConfig *config.ApplicationConfig) echo.HandlerFunc {
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// We do not support streaming mode (Yet?)
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return func(c echo.Context) error {
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created := int(time.Now().Unix())
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ctx := c.Request().Context()
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// Handle Correlation
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id := c.Request().Header.Get("X-Correlation-ID")
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if id == "" {
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id = uuid.New().String()
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}
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input, ok := c.Get(middleware.CONTEXT_LOCALS_KEY_LOCALAI_REQUEST).(*schema.OpenAIRequest)
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if !ok || input.Model == "" {
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return echo.ErrBadRequest
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}
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config, ok := c.Get(middleware.CONTEXT_LOCALS_KEY_MODEL_CONFIG).(*config.ModelConfig)
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if !ok || config == nil {
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return echo.ErrBadRequest
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}
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if config.MCP.Servers == "" && config.MCP.Stdio == "" {
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return fmt.Errorf("no MCP servers configured")
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}
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// Get MCP config from model config
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remote, stdio, err := config.MCP.MCPConfigFromYAML()
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if err != nil {
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return fmt.Errorf("failed to get MCP config: %w", err)
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}
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// Check if we have tools in cache, or we have to have an initial connection
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sessions, err := mcpTools.SessionsFromMCPConfig(config.Name, remote, stdio)
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if err != nil {
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return fmt.Errorf("failed to get MCP sessions: %w", err)
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}
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if len(sessions) == 0 {
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return fmt.Errorf("no working MCP servers found")
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}
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fragment := cogito.NewEmptyFragment()
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for _, message := range input.Messages {
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fragment = fragment.AddMessage(message.Role, message.StringContent)
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}
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_, port, err := net.SplitHostPort(appConfig.APIAddress)
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if err != nil {
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return err
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}
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apiKey := ""
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if appConfig.ApiKeys != nil {
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apiKey = appConfig.ApiKeys[0]
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}
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ctxWithCancellation, cancel := context.WithCancel(ctx)
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defer cancel()
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// TODO: instead of connecting to the API, we should just wire this internally
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// and act like completion.go.
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// We can do this as cogito expects an interface and we can create one that
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// we satisfy to just call internally ComputeChoices
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defaultLLM := cogito.NewOpenAILLM(config.Name, apiKey, "http://127.0.0.1:"+port)
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// Build cogito options using the consolidated method
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cogitoOpts := config.BuildCogitoOptions()
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cogitoOpts = append(
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cogitoOpts,
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cogito.WithContext(ctxWithCancellation),
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cogito.WithMCPs(sessions...),
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cogito.WithStatusCallback(func(s string) {
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log.Debug().Msgf("[model agent] [model: %s] Status: %s", config.Name, s)
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}),
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cogito.WithReasoningCallback(func(s string) {
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log.Debug().Msgf("[model agent] [model: %s] Reasoning: %s", config.Name, s)
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}),
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cogito.WithToolCallBack(func(t *cogito.ToolChoice, state *cogito.SessionState) cogito.ToolCallDecision {
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log.Debug().Msgf("[model agent] [model: %s] Tool call: %s, reasoning: %s, arguments: %+v", config.Name, t.Name, t.Reasoning, t.Arguments)
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return cogito.ToolCallDecision{
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Approved: true,
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}
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}),
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cogito.WithToolCallResultCallback(func(t cogito.ToolStatus) {
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log.Debug().Msgf("[model agent] [model: %s] Tool call result: %s, result: %s, tool arguments: %+v", config.Name, t.Name, t.Result, t.ToolArguments)
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}),
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)
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f, err := cogito.ExecuteTools(
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defaultLLM, fragment,
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cogitoOpts...,
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)
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if err != nil && !errors.Is(err, cogito.ErrNoToolSelected) {
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return err
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}
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f, err = defaultLLM.Ask(ctx, f)
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if err != nil {
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return err
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}
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resp := &schema.OpenAIResponse{
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ID: id,
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Created: created,
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Model: input.Model, // we have to return what the user sent here, due to OpenAI spec.
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Choices: []schema.Choice{{Message: &schema.Message{Role: "assistant", Content: &f.LastMessage().Content}}},
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Object: "text_completion",
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}
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jsonResult, _ := json.Marshal(resp)
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log.Debug().Msgf("Response: %s", jsonResult)
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// Return the prediction in the response body
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return c.JSON(200, resp)
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}
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}
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