refactor: rename action→activity, statemachine→workflow, remove HTTP API layer
- action/ → activity/ (Temporal activities) - statemachine/ → workflow/ (Temporal workflows) - Removed internal/api/ and cmd/server/ (api-gw handles HTTP, Temporal is the API) - Created pkg/types/types.go as single source of truth for all shared types - Extracted CallRoleLLM helper (DRY: implementer/planner/judge shared pattern) - Fixed circular import: workflow_graph_query uses string activity names - Fixed logger.logf → logger.Info/Warn (method didn't exist) - Fixed routing types: added Branches, Activity, BackoffSeconds, TaskActivity - Fixed db.Canvas.Name, db.Client→DB, GetWorkflow→FetchWorkflow - Removed unused imports - All tests pass, build clean, vet clean
This commit is contained in:
Test
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package activity
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import (
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"context"
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"encoding/json"
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"fmt"
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"github.com/rockliang/poimen/workflows/activity/llm"
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"github.com/rockliang/poimen/workflows/pkg/db"
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)
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// CanvasReasonerOutput returns suggested edges and reasoning
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type CanvasReasonerOutput struct {
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SuggestedEdges []EdgeWithWording `json:"suggested_edges"` // Edges with wording
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RemovedEdges []db.WorkflowEdge `json:"removed_edges,omitempty"` // Edges to remove
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Reasoning string `json:"reasoning"` // LLM explanation
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Confidence float64 `json:"confidence"` // 0.0-1.0
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IncompatibleEdges []IncompatibilityWarning `json:"incompatible_edges,omitempty"` // Can't connect
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DisconnectedNodes []string `json:"disconnected_nodes,omitempty"` // No connections
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UserAlerts []string `json:"user_alerts,omitempty"` // Human-readable warnings
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}
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// CanvasReasonerActivity uses LLM to infer connections between workflow activities
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func CanvasReasonerActivity(ctx context.Context, in CanvasReasonerInput) (CanvasReasonerOutput, error) {
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logger := newActivityLogger(ctx)
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output := CanvasReasonerOutput{
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SuggestedEdges: []EdgeWithWording{},
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}
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if len(in.Nodes) == 0 {
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return output, fmt.Errorf("no nodes provided")
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}
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logger.Info("Analyzing canvas with %d nodes, %d edges", len(in.Nodes), len(in.Edges))
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// Build activity descriptions for LLM context
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nodeDesc := buildNodeDescriptions(in.Nodes)
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edgeDesc := buildEdgeDescriptions(in.Edges)
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// Create prompt for LLM reasoning with relation wording
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systemPrompt := `You are a workflow automation expert. Analyze activities and suggest logical connections with semantic descriptions.
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CRITICAL RULES:
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1. Only suggest edges where outputs→inputs match
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2. Provide relation wording: verb, source_output, target_input
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3. Assess connection confidence (0.0-1.0)
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4. Flag type mismatches that need transformers
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Respond with JSON:
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{
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"edges": [
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{
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"source": "node-1",
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"target": "node-2",
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"relation_type": "data-flow|dependency|conditional|parallel",
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"relation_label": "Node1 outputs X → Node2 requires X",
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"relation_wording": {
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"verb": "outputs|depends-on|triggers|etc",
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"source_output": "field_name (type): description",
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"target_input": "field_name (type, required?): description",
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"connection_type": "direct-map|requires-transformer|conditional",
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"confidence": 0.95,
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"semantic_match": "Explanation of why this makes sense"
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}
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}
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],
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"reasoning": "Overall workflow structure explanation",
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"confidence": 0.85
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}`
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userPrompt := fmt.Sprintf(`Canvas Analysis:
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Nodes (including inputs/outputs):
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%s
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Current Edges:
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%s
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Task: %s
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KEY RULES:
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- Preserve existing edges and suggest only NEW edges to add
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- SKIP any connections where input/output types don't match
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- If an activity has no outputs, it cannot be a source
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- If an activity has no inputs, it cannot be a target
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- Note any activities that are hard to connect (terminal activities, generators, etc)
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Return ONLY valid JSON, no markdown code blocks.`, nodeDesc, edgeDesc, getReasoningTask(in.PreserveExisting))
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logger.Info("Calling LLM reasoning (preserve_existing=%v)", in.PreserveExisting)
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// Call LLM
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client, err := llm.NewClient()
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if err != nil {
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return output, fmt.Errorf("failed to create LLM client: %w", err)
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}
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response, err := client.CreateMessage(ctx, llm.MessageInput{
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Model: ModelSpec{
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ModelID: "reasoning", // Use reasoning model for complex analysis
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},
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SystemPrompt: systemPrompt,
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Messages: []llm.MessageParam{
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{
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Role: "user",
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Content: userPrompt,
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},
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},
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AuthToken: in.AuthToken,
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})
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if err != nil {
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return output, fmt.Errorf("LLM reasoning failed: %w", err)
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}
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// Parse LLM response
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var reasonerResp struct {
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Edges []EdgeWithWording `json:"edges"`
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Reasoning string `json:"reasoning"`
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Confidence float64 `json:"confidence"`
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}
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if err := json.Unmarshal([]byte(response), &reasonerResp); err != nil {
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logger.Warn("Failed to parse LLM response as JSON: %v", err)
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// Try to extract from response text
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output.Reasoning = response
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output.Confidence = 0.5
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return output, fmt.Errorf("failed to parse LLM response: %w", err)
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}
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// Validate suggested edges
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nodeMap := make(map[string]bool)
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for _, n := range in.Nodes {
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nodeMap[n.ID] = true
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}
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validEdges := []EdgeWithWording{}
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for _, edge := range reasonerResp.Edges {
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if !nodeMap[edge.Source] {
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logger.Warn("Suggested edge references unknown source: %s", edge.Source)
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continue
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}
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if !nodeMap[edge.Target] {
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logger.Warn("Suggested edge references unknown target: %s", edge.Target)
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continue
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}
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// Don't suggest self-loops
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if edge.Source == edge.Target {
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logger.Warn("Skipping self-loop: %s", edge.Source)
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continue
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}
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validEdges = append(validEdges, edge)
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}
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output.SuggestedEdges = validEdges
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output.Reasoning = reasonerResp.Reasoning
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output.Confidence = reasonerResp.Confidence
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// Check compatibility of suggested edges
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incompatibilities := CheckCanvasConnectivity(in.Nodes, validEdges)
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if len(incompatibilities) > 0 {
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output.IncompatibleEdges = incompatibilities
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logger.Warn("Found %d incompatible edge connections", len(incompatibilities))
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// Generate user-friendly alerts
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for i, incompat := range incompatibilities {
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if i < 5 { // Limit to 5 alerts to avoid spam
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alert := fmt.Sprintf(
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"⚠️ %s → %s: %s. %s",
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incompat.Source, incompat.Target, incompat.Reason, incompat.Suggestion,
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)
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output.UserAlerts = append(output.UserAlerts, alert)
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}
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}
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}
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// Identify disconnected nodes
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disconnected := IdentifyDisconnectedNodes(in.Nodes, validEdges)
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if len(disconnected) > 0 {
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output.DisconnectedNodes = disconnected
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logger.Warn("Found %d disconnected nodes", len(disconnected))
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for _, nodeID := range disconnected {
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var label string
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for _, node := range in.Nodes {
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if node.ID == nodeID {
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label = node.Label
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break
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}
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}
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alert := fmt.Sprintf(
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"🔌 Node '%s' has no connections. Consider adding edges or removing it.",
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label,
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)
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output.UserAlerts = append(output.UserAlerts, alert)
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}
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}
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logger.Info("LLM suggested %d edges with confidence %.2f | %d incompatibilities | %d disconnected",
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len(validEdges), output.Confidence, len(incompatibilities), len(disconnected))
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return output, nil
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}
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// buildNodeDescriptions creates readable node descriptions for LLM (including schemas)
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func buildNodeDescriptions(nodes []db.WorkflowNode) string {
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var desc string
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for i, node := range nodes {
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desc += fmt.Sprintf("%d. [%s] %s (type: %s)\n", i+1, node.ID, node.Label, node.Type)
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// Add input/output schema info
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if schema, err := getActivitySchema(node.Type); err == nil {
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if len(schema.Inputs) > 0 {
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desc += fmt.Sprintf(" INPUTS: %v\n", getInputNames(schema.Inputs))
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} else {
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desc += fmt.Sprintf(" INPUTS: none (generator/trigger)\n")
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}
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if len(schema.Outputs) > 0 {
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desc += fmt.Sprintf(" OUTPUTS: %v\n", getOutputNames(schema.Outputs))
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} else {
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desc += fmt.Sprintf(" OUTPUTS: none (terminal/sink)\n")
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}
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}
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if node.Data != nil {
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if b, err := json.MarshalIndent(node.Data, " ", " "); err == nil {
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desc += fmt.Sprintf(" CONFIG: %s\n", string(b))
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}
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}
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}
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return desc
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}
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// buildEdgeDescriptions creates readable edge descriptions for LLM
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func buildEdgeDescriptions(edges []db.WorkflowEdge) string {
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if len(edges) == 0 {
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return "None"
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}
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var desc string
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for i, edge := range edges {
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desc += fmt.Sprintf("%d. %s → %s\n", i+1, edge.Source, edge.Target)
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}
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return desc
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}
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// getReasoningTask returns task description based on preservation mode
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func getReasoningTask(preserveExisting bool) string {
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if preserveExisting {
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return "Keep all existing edges and suggest ONLY NEW edges to improve workflow"
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}
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return "Design optimal workflow by suggesting all connections and noting any redundant edges"
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}
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