1Enterprise AI platform:
2- Visual workflow builder
3- LLM orchestration
4- Knowledge bases
5- Team collaboration
6- Enterprise security

1✅ Visual AI workflow builder
2✅ Multiple LLM support
3✅ Document processing
4✅ RAG (Retrieval Augmented Generation)
5✅ API endpoints
6✅ Team collaboration
7✅ Enterprise security
8✅ Pre-built templates

1- Document Q&A systems
2- Customer support agents
3- Data analysis workflows
4- Content generation
5- Code assistance
6- Research automation

11. Go to stack-ai.com
22. Sign up (free tier available)
33. Create workspace
44. Create first project

1┌────────────────────────────────────────────────────┐
2│ [Project]              [Deploy] [Share] [Settings] │
3├──────────┬─────────────────────────────────────────┤
4│ Nodes    │                                         │
5│ ──────── │         WORKFLOW CANVAS                │
6│ Input    │                                         │
7│ LLM      │    ┌────┐    ┌────┐    ┌────┐         │
8│ Output   │    │In  │───▶│LLM │───▶│Out │         │
9│ Logic    │    └────┘    └────┘    └────┘         │
10│ Data     │                                         │
11│ Tools    │                                         │
12└──────────┴─────────────────────────────────────────┘

1Input Nodes:
2- Text Input: User query
3- File Input: Documents
4- API Input: External data
5 
6Processing Nodes:
7- LLM: Generate responses
8- Vector DB: Search documents
9- Code: Custom logic
10- Condition: Branching
11 
12Output Nodes:
13- Text Output: Responses
14- API Output: Send data
15- File Output: Generate files

1Data flows through connections:
2- Drag from output port
3- Connect to input port
4- Data transforms between nodes

1Pass data between nodes:
2- {{input.text}}: User input
3- {{llm.output}}: LLM response
4- {{search.results}}: Search results

1Create knowledge base:
21. Go to Knowledge section
32. Click "New Knowledge Base"
43. Name it (e.g., "Company Docs")
54. Upload documents:
6   - PDF
7   - Word
8   - Text
9   - Web pages
105. Process documents

1Workflow structure:
2Input → Search KB → LLM → Output
3 
41. Add Text Input node
52. Add Knowledge Base Search node
63. Add LLM node
74. Add Text Output node
85. Connect nodes

1Knowledge Base Search node:
2- Select knowledge base
3- Query: {{input.text}}
4- Top K: 5 (number of results)
5- Threshold: 0.7 (relevance cutoff)

1LLM node:
2Model: gpt-4o-mini (or preferred)
3 
4System prompt:
5"You are a helpful assistant. 
6Answer questions based on the provided context.
7If the answer isn't in the context, say so.
8 
9Context:
10{{search.results}}"
11 
12User prompt:
13"{{input.text}}"

1Test:
21. Click "Test" button
32. Enter sample question
43. Check response quality
54. Adjust parameters
6 
7Deploy:
81. Click "Deploy"
92. Get API endpoint
103. Integrate into app

1Complex task breakdown:
2 
3Input → Analyze Intent
4           ↓
5      ┌────┴────┐
6      ↓         ↓
7   Question   Action
8      ↓         ↓
9   Search    Execute
10      ↓         ↓
11    LLM      Confirm
12      ↓         ↓
13      └────┬────┘
14           ↓
15        Output

1Give LLM access to tools:
2 
31. LLM decides what tool to use
42. Tool executes
53. Result back to LLM
64. LLM generates final response
7 
8Tools:
9- Search knowledge base
10- Query database
11- Call external API
12- Calculate
13- Generate content

1Condition node:
2If {{llm.confidence}} > 0.8
3  → Direct response
4Else
5  → Ask clarifying question
6 
7If {{intent}} == "complaint"
8  → Escalation flow
9Else
10  → Standard flow

1Document pipeline:
21. Upload documents
32. Extract text (OCR if needed)
43. Chunk into sections
54. Generate embeddings
65. Store in vector DB

1Supported:
2- PDF (text & scanned)
3- Word documents
4- PowerPoint
5- Excel/CSV
6- HTML
7- Plain text
8- Images (with OCR)

1Chunking:
2- Size: 500-1000 tokens
3- Overlap: 100-200 tokens
4- By paragraph/section
5 
6Embedding:
7- OpenAI embeddings
8- Cohere
9- Custom models

1HTTP Request node:
2- URL: API endpoint
3- Method: GET/POST
4- Headers: Authentication
5- Body: Request data
6 
7Parse response:
8- JSON parsing
9- Extract fields
10- Error handling

1Start workflow from external event:
21. Add Webhook Input node
32. Get webhook URL
43. Configure in external system
54. Workflow triggers on webhook

1Expose workflow as API:
21. Deploy workflow
32. Get API endpoint
43. Call from your app
5 
6Request:
7POST /api/workflow/{id}
8{
9  "input": "User question here"
10}
11 
12Response:
13{
14  "output": "Answer here"
15}

1Organize:
2- Create workspaces per team
3- Invite members
4- Set permissions
5- Share workflows

1Roles:
2- Owner: Full access
3- Editor: Build & modify
4- Viewer: Run only
5- API: API access only

1Enterprise security:
2- SSO integration
3- SOC 2 compliance
4- Data encryption
5- Private cloud option
6- Audit logs

1Bring your own:
2- Azure OpenAI
3- AWS Bedrock
4- Custom fine-tuned models
5- On-premise deployment

1Standard RAG:
21. User question
32. Search knowledge base
43. Retrieve relevant docs
54. Include in prompt
65. Generate answer
76. Cite sources

1ReAct agent:
21. Receive task
32. Think: What do I need to do?
43. Act: Use appropriate tool
54. Observe: Review result
65. Repeat until complete
76. Return final answer

1Sequential processing:
21. Summarize document
32. Extract key points
43. Generate action items
54. Format output
65. Send notification