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|
package executor
import (
"context"
"encoding/json"
"fmt"
"os/exec"
"strings"
)
type Classification struct {
AgentType string `json:"agent_type"`
Model string `json:"model"`
Reason string `json:"reason"`
}
type SystemStatus struct {
ActiveTasks map[string]int
RateLimited map[string]bool
}
type Classifier struct {
GeminiBinaryPath string
}
const classificationPrompt = `
You are a task classifier for Claudomator.
Given a task description and system status, select the best agent (claude or gemini) and model to use.
Agent Types:
- claude: Best for complex coding, reasoning, and tool use.
- gemini: Best for large context, fast reasoning, and multimodal tasks.
Available Models:
Claude:
- claude-3-5-sonnet-latest (balanced)
- claude-3-5-sonnet-20241022 (stable)
- claude-3-opus-20240229 (most powerful, expensive)
- claude-3-5-haiku-20241022 (fast, cheap)
Gemini:
- gemini-2.5-flash-lite (fastest, most efficient, best for simple tasks)
- gemini-3-flash-preview (fast, multimodal)
- gemini-1.5-flash (fast, balanced)
- gemini-1.5-pro (more powerful, larger context)
Selection Criteria:
- Agent: You MUST prefer an agent that is NOT rate limited. If an agent is rate limited, do NOT select it unless all available agents are rate limited.
- Model: Select based on task complexity. Use powerful models (opus, pro, pro-preview) for complex reasoning/coding, flash-lite/flash/haiku for simple tasks.
Task:
Name: %s
Instructions: %s
System Status:
%s
Respond with ONLY a JSON object:
{
"agent_type": "claude" | "gemini",
"model": "model-name",
"reason": "brief reason"
}
`
func (c *Classifier) Classify(ctx context.Context, taskName, instructions string, status SystemStatus) (*Classification, error) {
statusStr := ""
for agent, active := range status.ActiveTasks {
statusStr += fmt.Sprintf("- Agent %s: %d active tasks, Rate Limited: %t\n", agent, active, status.RateLimited[agent])
}
prompt := fmt.Sprintf(classificationPrompt,
taskName, instructions, statusStr,
)
binary := c.GeminiBinaryPath
if binary == "" {
binary = "gemini"
}
// Use a minimal model for classification to be fast and cheap.
args := []string{
"--prompt", prompt,
"--model", "gemini-2.5-flash-lite",
"--output-format", "json",
}
cmd := exec.CommandContext(ctx, binary, args...)
out, err := cmd.Output()
if err != nil {
if exitErr, ok := err.(*exec.ExitError); ok {
return nil, fmt.Errorf("classifier failed (%v): %s", err, string(exitErr.Stderr))
}
return nil, fmt.Errorf("classifier failed: %w", err)
}
// 1. Parse the JSON envelope from the gemini CLI.
var cliOut struct {
Response string `json:"response"`
}
if err := json.Unmarshal(out, &cliOut); err != nil {
// If it's not JSON, it might be raw text (though we requested JSON).
// This can happen if the CLI prints "Loaded cached credentials" or other info.
cliOut.Response = string(out)
}
// 2. Extract the model response from the "response" field if present.
// If it was already raw text, cliOut.Response will have it.
cleanOut := strings.TrimSpace(cliOut.Response)
// 3. Clean up "Loaded cached credentials" or other noise that might be in the string
// if we fell back to string(out).
if strings.Contains(cleanOut, "Loaded cached credentials.") {
lines := strings.Split(cleanOut, "\n")
var modelLines []string
for _, line := range lines {
if !strings.Contains(line, "Loaded cached credentials.") {
modelLines = append(modelLines, line)
}
}
cleanOut = strings.TrimSpace(strings.Join(modelLines, "\n"))
}
// 4. Gemini might wrap the JSON in markdown code blocks.
cleanOut = strings.TrimPrefix(cleanOut, "```json")
cleanOut = strings.TrimPrefix(cleanOut, "```") // fallback
cleanOut = strings.TrimSuffix(cleanOut, "```")
cleanOut = strings.TrimSpace(cleanOut)
var cls Classification
if err := json.Unmarshal([]byte(cleanOut), &cls); err != nil {
return nil, fmt.Errorf("failed to parse classification JSON: %w\nOriginal Output: %s\nCleaned Output: %s", err, string(out), cleanOut)
}
return &cls, nil
}
|
