Files
agents_and_robots/shell/llm/openai.go
T
egutierrez 5697b92ab8 feat: integrar structured logging en todos los componentes del shell
Se propaga *slog.Logger a todos los componentes impuros del shell:
- shell/bus/ — logs de subscribe, send, reply, timeout, unsubscribe
- shell/effects/ — duración y resultado de cada action ejecutada
- shell/llm/ (anthropic, openai, factory) — request/response con tokens, duración, fallback
- shell/memory/sqlite — open, save, recall, close con detalles
- shell/ssh/ — inicio, fin, errores y duración de comandos SSH
- tools/registry — registro, ejecución y errores de herramientas

Se usa el paquete shell/logger para field names consistentes (FieldDurationMS, FieldTokensUsed, etc.).
Cada componente recibe el logger por inyección de dependencias, sin globals.
Las firmas de New/FromConfig se actualizan para aceptar *slog.Logger.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-06 21:53:31 +00:00

170 lines
4.6 KiB
Go

package llm
import (
"context"
"encoding/json"
"fmt"
"log/slog"
"os"
"time"
openai "github.com/sashabaranov/go-openai"
coretypes "github.com/enmanuel/agents/pkg/llm"
"github.com/enmanuel/agents/shell/logger"
)
// NewOpenAIComplete returns a CompleteFunc backed by the OpenAI-compatible API.
// Works with OpenAI, Ollama, vLLM, LMStudio — just change baseURL.
func NewOpenAIComplete(apiKeyEnv, baseURL string, log *slog.Logger) coretypes.CompleteFunc {
return func(ctx context.Context, req coretypes.CompletionRequest) (coretypes.CompletionResponse, error) {
apiKey := os.Getenv(apiKeyEnv)
if apiKey == "" {
apiKey = "ollama" // Ollama doesn't require a real key
}
cfg := openai.DefaultConfig(apiKey)
if baseURL != "" {
cfg.BaseURL = baseURL
}
client := openai.NewClientWithConfig(cfg)
msgs := make([]openai.ChatCompletionMessage, 0, len(req.Messages)+1)
if req.SystemPrompt != "" {
msgs = append(msgs, openai.ChatCompletionMessage{
Role: openai.ChatMessageRoleSystem,
Content: req.SystemPrompt,
})
}
for _, m := range req.Messages {
msgs = append(msgs, toOpenAIMessage(m))
}
openReq := openai.ChatCompletionRequest{
Model: req.Model,
Messages: msgs,
MaxTokens: req.MaxTokens,
Temperature: float32(req.Temperature),
}
// Add tools if present
if len(req.Tools) > 0 {
openReq.Tools = toOpenAITools(req.Tools)
}
log.Info("llm_request",
"provider", "openai",
"model", req.Model,
"messages", len(req.Messages),
"tools", len(req.Tools),
)
start := time.Now()
resp, err := client.CreateChatCompletion(ctx, openReq)
if err != nil {
ms := time.Since(start).Milliseconds()
log.Error("llm_error", "provider", "openai", logger.FieldDurationMS, ms, "err", err)
return coretypes.CompletionResponse{}, fmt.Errorf("openai completion: %w", err)
}
ms := time.Since(start).Milliseconds()
if len(resp.Choices) == 0 {
log.Error("llm_error", "provider", "openai", logger.FieldDurationMS, ms, "err", "empty choices")
return coretypes.CompletionResponse{}, fmt.Errorf("openai: empty choices")
}
choice := resp.Choices[0]
var toolCalls []coretypes.ToolCall
for _, tc := range choice.Message.ToolCalls {
toolCalls = append(toolCalls, coretypes.ToolCall{
ID: tc.ID,
Name: tc.Function.Name,
Arguments: tc.Function.Arguments,
})
}
log.Info("llm_response",
"provider", "openai",
"model", req.Model,
logger.FieldDurationMS, ms,
logger.FieldTokensUsed, resp.Usage.TotalTokens,
"input_tokens", resp.Usage.PromptTokens,
"output_tokens", resp.Usage.CompletionTokens,
"tool_calls", len(toolCalls),
"finish_reason", string(choice.FinishReason),
)
return coretypes.CompletionResponse{
Content: choice.Message.Content,
ToolCalls: toolCalls,
FinishReason: string(choice.FinishReason),
Usage: coretypes.TokenUsage{
InputTokens: resp.Usage.PromptTokens,
OutputTokens: resp.Usage.CompletionTokens,
TotalTokens: resp.Usage.TotalTokens,
},
}, nil
}
}
// toOpenAIMessage converts a core Message to an OpenAI ChatCompletionMessage.
func toOpenAIMessage(m coretypes.Message) openai.ChatCompletionMessage {
role := openai.ChatMessageRoleUser
switch m.Role {
case coretypes.RoleAssistant:
role = openai.ChatMessageRoleAssistant
case coretypes.RoleSystem:
role = openai.ChatMessageRoleSystem
case coretypes.RoleTool:
role = openai.ChatMessageRoleTool
}
msg := openai.ChatCompletionMessage{
Role: role,
Content: m.Content,
ToolCallID: m.ToolCallID,
}
// Assistant messages with tool calls
if m.Role == coretypes.RoleAssistant && len(m.ToolCalls) > 0 {
msg.ToolCalls = make([]openai.ToolCall, len(m.ToolCalls))
for i, tc := range m.ToolCalls {
msg.ToolCalls[i] = openai.ToolCall{
ID: tc.ID,
Type: openai.ToolTypeFunction,
Function: openai.FunctionCall{
Name: tc.Name,
Arguments: tc.Arguments,
},
}
}
}
return msg
}
// toOpenAITools converts core ToolSpecs to OpenAI Tool format.
func toOpenAITools(specs []coretypes.ToolSpec) []openai.Tool {
tools := make([]openai.Tool, len(specs))
for i, s := range specs {
tools[i] = openai.Tool{
Type: openai.ToolTypeFunction,
Function: &openai.FunctionDefinition{
Name: s.Name,
Description: s.Description,
Parameters: json.RawMessage(marshalSchema(s.InputSchema)),
},
}
}
return tools
}
// marshalSchema marshals a JSON schema map to bytes. Falls back to empty object.
func marshalSchema(schema map[string]any) []byte {
b, err := json.Marshal(schema)
if err != nil {
return []byte("{}")
}
return b
}