Backend-Controlled, UI-Compatible API Flow

warning

This tutorial is a community contribution and is not supported by the Open WebUI team. It serves only as a demonstration on how to customize Open WebUI for your specific use case. Want to contribute? Check out the contributing tutorial.

Backend-Controlled, UI-Compatible API Flow

This tutorial demonstrates how to implement server-side orchestration of Open WebUI conversations while ensuring that assistant replies appear properly in the frontend UI. This approach requires zero frontend involvement and allows complete backend control over the chat flow. This tutorial has been verified to work with Open WebUI version v0.6.15. Future versions may introduce changes in behavior or API structure.

Prerequisites

Before following this tutorial, ensure you have:

A running Open WebUI instance
Valid API authentication token
Access to the Open WebUI backend APIs
Basic understanding of REST APIs and JSON
Command-line tools: curl, jq (optional for JSON parsing)

Overview

This tutorial describes a comprehensive 7-step process that enables server-side orchestration of Open WebUI conversations while ensuring that assistant replies appear properly in the frontend UI.

Process Flow

The essential steps are:

Create a new chat with a user message - Initialize the conversation with the user's input
Enrich the chat response with an assistant message - Add assistant message to the response object in memory
Update chat with assistant message - Send the enriched chat state to the server
Trigger the assistant completion - Generate the actual AI response (with optional knowledge integration)
Wait for response completion - Monitor the assistant response until fully generated
Complete the assistant message - Mark the response as completed
Fetch and process the final chat - Retrieve and parse the completed conversation

This enables server-side orchestration while still making replies show up in the frontend UI exactly as if they were generated through normal user interaction.

Implementation Guide

Critical Step: Enrich Chat Response with Assistant Message

The assistant message needs to be added to the chat response object in memory as a critical prerequisite before triggering the completion. This step is essential because the Open WebUI frontend expects assistant messages to exist in a specific structure.

The assistant message must appear in both locations:

chat.messages[] - The main message array
chat.history.messages[<assistantId>] - The indexed message history

Expected structure of the assistant message:

{
  "id": "<uuid>",
  "role": "assistant",
  "content": "",
  "parentId": "<user-msg-id>",
  "modelName": "gpt-4o",
  "modelIdx": 0,
  "timestamp": "<currentTimestamp>"
}

Without this enrichment, the assistant's response will not appear in the frontend interface, even if the completion is successful.

Step-by-Step Implementation

Step 1: Create Chat with User Message

This starts the chat and returns a chatId that will be used in subsequent requests.

curl -X POST https://<host>/api/v1/chats/new \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{
    "chat": {
      "title": "New Chat",
      "models": ["gpt-4o"],
      "messages": [
        {
          "id": "user-msg-id",
          "role": "user",
          "content": "Hi, what is the capital of France?",
          "timestamp": 1720000000000,
          "models": ["gpt-4o"]
        }
      ],
      "history": {
        "current_id": "user-msg-id",
        "messages": {
          "user-msg-id": {
            "id": "user-msg-id",
            "role": "user",
            "content": "Hi, what is the capital of France?",
            "timestamp": 1720000000000,
            "models": ["gpt-4o"]
          }
        }
      }
    }
  }'

Step 2: Enrich Chat Response with Assistant Message

Add the assistant message to the chat response object in memory. Note that this can be combined with Step 1 by including the assistant message in the initial chat creation:

// Example implementation in Java
public void enrichChatWithAssistantMessage(OWUIChatResponse chatResponse, String model) {
    OWUIMessage assistantOWUIMessage = buildAssistantMessage(chatResponse, model, "assistant", "");
    assistantOWUIMessage.setParentId(chatResponse.getChat().getMessages().get(0).getId());

    chatResponse.getChat().getMessages().add(assistantOWUIMessage);
    chatResponse.getChat().getHistory().getMessages().put(assistantOWUIMessage.getId(), assistantOWUIMessage);
}

note

Note: This step can be performed in memory on the response object, or combined with Step 1 by including both user and empty assistant messages in the initial chat creation.

Step 3: Update Chat with Assistant Message

Send the enriched chat state containing both user and assistant messages to the server:

curl -X POST https://<host>/api/v1/chats/<chatId> \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{
    "chat": {
      "id": "<chatId>",
      "title": "New Chat",
      "models": ["gpt-4o"],
      "messages": [
        {
          "id": "user-msg-id",
          "role": "user",
          "content": "Hi, what is the capital of France?",
          "timestamp": 1720000000000,
          "models": ["gpt-4o"]
        },
        {
          "id": "assistant-msg-id",
          "role": "assistant",
          "content": "",
          "parentId": "user-msg-id",
          "modelName": "gpt-4o",
          "modelIdx": 0,
          "timestamp": 1720000001000
        }
      ],
      "history": {
        "current_id": "assistant-msg-id",
        "messages": {
          "user-msg-id": {
            "id": "user-msg-id",
            "role": "user",
            "content": "Hi, what is the capital of France?",
            "timestamp": 1720000000000,
            "models": ["gpt-4o"]
          },
          "assistant-msg-id": {
            "id": "assistant-msg-id",
            "role": "assistant",
            "content": "",
            "parentId": "user-msg-id",
            "modelName": "gpt-4o",
            "modelIdx": 0,
            "timestamp": 1720000001000
          }
        }
      }
    }
  }'

Step 4: Trigger Assistant Completion

Generate the actual AI response using the completion endpoint:

curl -X POST https://<host>/api/chat/completions \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{
    "chat_id": "<chatId>",
    "id": "assistant-msg-id",
    "messages": [
      {
        "role": "user",
        "content": "Hi, what is the capital of France?"
      }
    ],
    "model": "gpt-4o",
    "stream": true,
    "background_tasks": {
      "title_generation": true,
      "tags_generation": false,
      "follow_up_generation": false
    },
    "features": {
      "code_interpreter": false,
      "web_search": false,
      "image_generation": false,
      "memory": false
    },
    "variables": {
      "{{USER_NAME}}": "",
      "{{USER_LANGUAGE}}": "en-US",
      "{{CURRENT_DATETIME}}": "2025-07-14T12:00:00Z",
      "{{CURRENT_TIMEZONE}}": "Europe"
    },
    "session_id": "session-id"
  }'

Step 4.1: Trigger Assistant Completion with Knowledge Integration (RAG)

For advanced use cases involving knowledge bases or document collections, include knowledge files in the completion request:

curl -X POST https://<host>/api/chat/completions \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{
    "chat_id": "<chatId>",
    "id": "assistant-msg-id",
    "messages": [
      {
        "role": "user",
        "content": "Hi, what is the capital of France?"
      }
    ],
    "model": "gpt-4o",
    "stream": true,
    "files": [
      {
        "id": "knowledge-collection-id",
        "type": "collection",
        "status": "processed"
      }
    ],
    "background_tasks": {
      "title_generation": true,
      "tags_generation": false,
      "follow_up_generation": false
    },
    "features": {
      "code_interpreter": false,
      "web_search": false,
      "image_generation": false,
      "memory": false
    },
    "variables": {
      "{{USER_NAME}}": "",
      "{{USER_LANGUAGE}}": "en-US",
      "{{CURRENT_DATETIME}}": "2025-07-14T12:00:00Z",
      "{{CURRENT_TIMEZONE}}": "Europe"
    },
    "session_id": "session-id"
  }'

Step 5: Wait for Assistant Response Completion

Assistant responses can be handled in two ways depending on your implementation needs:

Option A: Stream Processing (Recommended)

If using stream: true in the completion request, you can process the streamed response in real-time and wait for the stream to complete. This is the approach used by the OpenWebUI web interface and provides immediate feedback.

Option B: Polling Approach

For implementations that cannot handle streaming, poll the chat endpoint until the response is ready. Use a retry mechanism with exponential backoff:

// Example implementation in Java
@Retryable(
    retryFor = AssistantResponseNotReadyException.class,
    maxAttemptsExpression = "#{${webopenui.retries:50}}",
    backoff = @Backoff(delayExpression = "#{${webopenui.backoffmilliseconds:2000}}")
)
public String getAssistantResponseWhenReady(String chatId, ChatCompletedRequest chatCompletedRequest) {
    OWUIChatResponse response = owuiService.fetchFinalChatResponse(chatId);
    Optional<OWUIMessage> assistantMsg = extractAssistantResponse(response);

    if (assistantMsg.isPresent() && !assistantMsg.get().getContent().isBlank()) {
        owuiService.completeAssistantMessage(chatCompletedRequest);
        return assistantMsg.get().getContent();
    }

    throw new AssistantResponseNotReadyException("Assistant response not ready yet for chatId: " + chatId);
}

For manual polling, you can use:

# Poll every few seconds until assistant content is populated
while true; do
  response=$(curl -s -X GET https://<host>/api/v1/chats/<chatId> \
    -H "Authorization: Bearer <token>")

  # Check if assistant message has content (response is ready)
  if echo "$response" | jq '.chat.messages[] | select(.role=="assistant" and .id=="assistant-msg-id") | .content' | grep -v '""' > /dev/null; then
    echo "Assistant response is ready!"
    break
  fi

  echo "Waiting for assistant response..."
  sleep 2
done

Step 6: Complete Assistant Message

Once the assistant response is ready, mark it as completed:

curl -X POST https://<host>/api/chat/completed \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{
    "chat_id": "<chatId>",
    "id": "assistant-msg-id",
    "session_id": "session-id",
    "model": "gpt-4o"
  }'

Step 7: Fetch Final Chat

Retrieve the completed conversation:

curl -X GET https://<host>/api/v1/chats/<chatId> \
  -H "Authorization: Bearer <token>"

Additional API Endpoints

Fetch Knowledge Collection

Retrieve knowledge base information for RAG integration:

curl -X GET https://<host>/api/v1/knowledge/<knowledge-id> \
  -H "Authorization: Bearer <token>"

Fetch Model Information

Get details about a specific model:

curl -X GET https://<host>/api/v1/models/model?id=<model-name> \
  -H "Authorization: Bearer <token>"

Send Additional Messages to Chat

For multi-turn conversations, you can send additional messages to an existing chat:

curl -X POST https://<host>/api/v1/chats/<chatId> \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{
    "chat": {
      "id": "<chatId>",
      "messages": [
        {
          "id": "new-user-msg-id",
          "role": "user",
          "content": "Can you tell me more about this?",
          "timestamp": 1720000002000,
          "models": ["gpt-4o"]
        }
      ],
      "history": {
        "current_id": "new-user-msg-id",
        "messages": {
          "new-user-msg-id": {
            "id": "new-user-msg-id",
            "role": "user",
            "content": "Can you tell me more about this?",
            "timestamp": 1720000002000,
            "models": ["gpt-4o"]
          }
        }
      }
    }
  }'

Response Processing

Parsing Assistant Responses

Assistant responses may be wrapped in markdown code blocks. Here's how to clean them:

# Example raw response from assistant
raw_response='```json
{
  "result": "The capital of France is Paris.",
  "confidence": 0.99
}
```'

# Clean the response (remove markdown wrappers)
cleaned_response=$(echo "$raw_response" | sed 's/^```json//' | sed 's/```$//' | sed 's/^[[:space:]]*//' | sed 's/[[:space:]]*$//')

echo "$cleaned_response" | jq '.'

This cleaning process handles:

Removal of ````json` prefix
Removal of ```` suffix
Trimming whitespace
JSON validation

API Reference

DTO Structures

Chat DTO (Complete Structure)

{
  "id": "chat-uuid-12345",
  "title": "New Chat",
  "models": ["gpt-4o"],
  "files": [],
  "tags": [
    {
      "id": "tag-id",
      "name": "important",
      "color": "#FF5733"
    }
  ],
  "params": {
    "temperature": 0.7,
    "max_tokens": 1000
  },
  "timestamp": 1720000000000,
  "messages": [
    {
      "id": "user-msg-id",
      "role": "user",
      "content": "Hi, what is the capital of France?",
      "timestamp": 1720000000000,
      "models": ["gpt-4o"]
    },
    {
      "id": "assistant-msg-id",
      "role": "assistant",
      "content": "",
      "parentId": "user-msg-id",
      "modelName": "gpt-4o",
      "modelIdx": 0,
      "timestamp": 1720000001000
    }
  ],
  "history": {
    "current_id": "assistant-msg-id",
    "messages": {
      "user-msg-id": {
        "id": "user-msg-id",
        "role": "user",
        "content": "Hi, what is the capital of France?",
        "timestamp": 1720000000000,
        "models": ["gpt-4o"]
      },
      "assistant-msg-id": {
        "id": "assistant-msg-id",
        "role": "assistant",
        "content": "",
        "parentId": "user-msg-id",
        "modelName": "gpt-4o",
        "modelIdx": 0,
        "timestamp": 1720000001000
      }
    }
  },
  "currentId": "assistant-msg-id"
}

ChatCompletionsRequest DTO

{
  "chat_id": "chat-uuid-12345",
  "id": "assistant-msg-id",
  "messages": [
    {
      "role": "user",
      "content": "Hi, what is the capital of France?"
    }
  ],
  "model": "gpt-4o",
  "stream": true,
  "background_tasks": {
    "title_generation": true,
    "tags_generation": false,
    "follow_up_generation": false
  },
  "features": {
    "code_interpreter": false,
    "web_search": false,
    "image_generation": false,
    "memory": false
  },
  "variables": {
    "{{USER_NAME}}": "",
    "{{USER_LANGUAGE}}": "en-US",
    "{{CURRENT_DATETIME}}": "2025-07-14T12:00:00Z",
    "{{CURRENT_TIMEZONE}}": "Europe"
  },
  "session_id": "session-uuid-67890",
  "filter_ids": [],
  "files": [
    {
      "id": "knowledge-collection-id",
      "type": "collection",
      "status": "processed"
    }
  ]
}

ChatCompletedRequest DTO

{
  "model": "gpt-4o",
  "chat_id": "chat-uuid-12345",
  "id": "assistant-msg-id",
  "session_id": "session-uuid-67890",
  "messages": [
    {
      "id": "user-msg-id",
      "role": "user",
      "content": "Hi, what is the capital of France?",
      "timestamp": 1720000000000,
      "models": ["gpt-4o"]
    },
    {
      "id": "assistant-msg-id",
      "role": "assistant",
      "content": "The capital of France is Paris.",
      "parentId": "user-msg-id",
      "modelName": "gpt-4o",
      "modelIdx": 0,
      "timestamp": 1720000001000
    }
  ]
}

ChatCompletionMessage DTO

{
  "role": "user",
  "content": "Hi, what is the capital of France?"
}

History DTO

{
  "current_id": "assistant-msg-id",
  "messages": {
    "user-msg-id": {
      "id": "user-msg-id",
      "role": "user",
      "content": "Hi, what is the capital of France?",
      "timestamp": 1720000000000,
      "models": ["gpt-4o"]
    },
    "assistant-msg-id": {
      "id": "assistant-msg-id",
      "role": "assistant",
      "content": "The capital of France is Paris.",
      "parentId": "user-msg-id",
      "modelName": "gpt-4o",
      "modelIdx": 0,
      "timestamp": 1720000001000
    }
  }
}

Message DTO (Complete Structure)

{
  "id": "msg-id",
  "role": "user",
  "content": "Hi, what is the capital of France?",
  "timestamp": 1720000000000,
  "models": ["gpt-4o"]
}

{
  "id": "assistant-msg-id",
  "role": "assistant",
  "content": "The capital of France is Paris.",
  "parentId": "user-msg-id",
  "modelName": "gpt-4o",
  "modelIdx": 0,
  "timestamp": 1720000001000
}

Response Examples

Create Chat Response

{
  "success": true,
  "chat": {
    "id": "chat-uuid-12345",
    "title": "New Chat",
    "models": ["gpt-4o"],
    "files": [],
    "tags": [],
    "params": {},
    "timestamp": 1720000000000,
    "messages": [
      {
        "id": "user-msg-id",
        "role": "user",
        "content": "Hi, what is the capital of France?",
        "timestamp": 1720000000000,
        "models": ["gpt-4o"]
      }
    ],
    "history": {
      "current_id": "user-msg-id",
      "messages": {
        "user-msg-id": {
          "id": "user-msg-id",
          "role": "user",
          "content": "Hi, what is the capital of France?",
          "timestamp": 1720000000000,
          "models": ["gpt-4o"]
        }
      }
    },
    "currentId": "user-msg-id"
  }
}

Final Chat Response (After Completion)

{
  "id": "chat-uuid-12345",
  "title": "Capital of France Discussion",
  "models": ["gpt-4o"],
  "files": [],
  "tags": [
    {
      "id": "auto-tag-1",
      "name": "geography",
      "color": "#4CAF50"
    }
  ],
  "params": {},
  "timestamp": 1720000000000,
  "messages": [
    {
      "id": "user-msg-id",
      "role": "user",
      "content": "Hi, what is the capital of France?",
      "timestamp": 1720000000000,
      "models": ["gpt-4o"]
    },
    {
      "id": "assistant-msg-id",
      "role": "assistant",
      "content": "The capital of France is Paris. Paris is not only the capital but also the most populous city in France, known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral.",
      "parentId": "user-msg-id",
      "modelName": "gpt-4o",
      "modelIdx": 0,
      "timestamp": 1720000001000
    }
  ],
  "history": {
    "current_id": "assistant-msg-id",
    "messages": {
      "user-msg-id": {
        "id": "user-msg-id",
        "role": "user",
        "content": "Hi, what is the capital of France?",
        "timestamp": 1720000000000,
        "models": ["gpt-4o"]
      },
      "assistant-msg-id": {
        "id": "assistant-msg-id",
        "role": "assistant",
        "content": "The capital of France is Paris. Paris is not only the capital but also the most populous city in France, known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral.",
        "parentId": "user-msg-id",
        "modelName": "gpt-4o",
        "modelIdx": 0,
        "timestamp": 1720000001000
      }
    }
  },
  "currentId": "assistant-msg-id"
}

Tag DTO

{
  "id": "tag-uuid-123",
  "name": "geography",
  "color": "#4CAF50"
}

OWUIKnowledge DTO (Knowledge Collection)

{
  "id": "knowledge-collection-id",
  "type": "collection",
  "status": "processed",
  "name": "Geography Knowledge Base",
  "description": "Contains information about world geography and capitals",
  "created_at": 1720000000000,
  "updated_at": 1720000001000
}

Knowledge Collection Response

{
  "id": "knowledge-collection-id",
  "name": "Geography Knowledge Base",
  "description": "Contains information about world geography and capitals",
  "type": "collection",
  "status": "processed",
  "files_count": 15,
  "total_size": 2048576,
  "created_at": 1720000000000,
  "updated_at": 1720000001000,
  "metadata": {
    "indexing_status": "complete",
    "last_indexed": 1720000001000
  }
}

Model Information Response

{
  "id": "gpt-4o",
  "name": "GPT-4 Optimized",
  "model": "gpt-4o",
  "base_model_id": "gpt-4o",
  "meta": {
    "description": "Most advanced GPT-4 model optimized for performance",
    "capabilities": ["text", "vision", "function_calling"],
    "context_length": 128000,
    "max_output_tokens": 4096
  },
  "params": {
    "temperature": 0.7,
    "top_p": 1.0,
    "frequency_penalty": 0.0,
    "presence_penalty": 0.0
  },
  "created_at": 1720000000000,
  "updated_at": 1720000001000
}

Field Reference Guide

Required vs Optional Fields

Chat Creation - Required Fields:

title - Chat title (string)
models - Array of model names (string[])
messages - Initial message array

Chat Creation - Optional Fields:

files - Knowledge files for RAG (defaults to empty array)
tags - Chat tags (defaults to empty array)
params - Model parameters (defaults to empty object)

Message Structure - User Message:

Required: id, role, content, timestamp, models
Optional: parentId (for threading)

Message Structure - Assistant Message:

Required: id, role, content, parentId, modelName, modelIdx, timestamp
Optional: Additional metadata fields

ChatCompletionsRequest - Required Fields:

chat_id - Target chat ID
id - Assistant message ID
messages - Array of ChatCompletionMessage
model - Model identifier
session_id - Session identifier

ChatCompletionsRequest - Optional Fields:

stream - Enable streaming (defaults to false)
background_tasks - Control automatic tasks
features - Enable/disable features
variables - Template variables
filter_ids - Pipeline filters
files - Knowledge collections for RAG

Field Constraints

Timestamps:

Format: Unix timestamp in milliseconds
Example: 1720000000000 (July 4, 2024, 00:00:00 UTC)

UUIDs:

All ID fields should use valid UUID format
Example: 550e8400-e29b-41d4-a716-446655440000

Model Names:

Must match available models in your Open WebUI instance
Common examples: gpt-4o, gpt-3.5-turbo, claude-3-sonnet

Session IDs:

Can be any unique string identifier
Recommendation: Use UUID format for consistency

Knowledge File Status:

Valid values: "processed", "processing", "error"
Only use "processed" files for completions

Important Notes

This workflow is compatible with Open WebUI + backend orchestration scenarios
Critical: The assistant message enrichment must be done in memory on the response object, not via API call
Alternative Approach: You can include both user and assistant messages in the initial chat creation (Step 1) instead of doing Step 2 separately
No frontend code changes are required for this approach
The stream: true parameter allows for real-time response streaming if needed
Response Monitoring: Use streaming for real-time processing or polling for simpler implementations that cannot handle streams
Background tasks like title generation can be controlled via the background_tasks object
Session IDs help maintain conversation context across requests
Knowledge Integration: Use the files array to include knowledge collections for RAG capabilities
Response Parsing: Handle JSON responses that may be wrapped in markdown code blocks
Error Handling: Implement proper retry mechanisms for network timeouts and server errors

Summary

Use the Open WebUI backend APIs to:

Start a chat - Create the initial conversation with user input
Enrich with assistant message - Add assistant placeholder to the response object in memory (can be combined with Step 1)
Update chat state - Send the enriched chat to the server
Trigger a reply - Generate the AI response (with optional knowledge integration)
Monitor completion - Wait for the assistant response using streaming or polling
Complete the message - Mark the response as completed
Fetch the final chat - Retrieve and parse the completed conversation

Enhanced Capabilities:

RAG Integration - Include knowledge collections for context-aware responses
Asynchronous Processing - Handle long-running AI operations with streaming or polling
Response Parsing - Clean and validate JSON responses from the assistant
Session Management - Maintain conversation context across requests

This enables backend-controlled workflows that still appear properly in the Web UI frontend chat interface, providing seamless integration between programmatic control and user experience.

The key advantage of this approach is that it maintains full compatibility with the Open WebUI frontend while allowing complete backend orchestration of the conversation flow, including advanced features like knowledge integration and asynchronous response handling.

Testing

You can test your implementation by following the step-by-step CURL examples provided above. Make sure to replace placeholder values with your actual:

Host URL
Authentication token
Chat IDs
Message IDs
Model names

tip

Start with a simple user message and gradually add complexity like knowledge integration and advanced features once the basic flow is working.

Backend-Controlled, UI-Compatible API Flow

Prerequisites​

Overview​

Process Flow​

Implementation Guide​

Critical Step: Enrich Chat Response with Assistant Message​

Step-by-Step Implementation​

Step 1: Create Chat with User Message​

Step 2: Enrich Chat Response with Assistant Message​

Step 3: Update Chat with Assistant Message​

Step 4: Trigger Assistant Completion​

Step 4.1: Trigger Assistant Completion with Knowledge Integration (RAG)​

Step 5: Wait for Assistant Response Completion​

Option A: Stream Processing (Recommended)​

Option B: Polling Approach​

Step 6: Complete Assistant Message​

Step 7: Fetch Final Chat​

Additional API Endpoints​

Fetch Knowledge Collection​

Fetch Model Information​

Send Additional Messages to Chat​

Response Processing​

Parsing Assistant Responses​

API Reference​

DTO Structures​

Chat DTO (Complete Structure)​

ChatCompletionsRequest DTO​

ChatCompletedRequest DTO​

ChatCompletionMessage DTO​

History DTO​

Message DTO (Complete Structure)​

Response Examples​

Create Chat Response​

Final Chat Response (After Completion)​

Tag DTO​

OWUIKnowledge DTO (Knowledge Collection)​

Knowledge Collection Response​

Model Information Response​

Field Reference Guide​

Required vs Optional Fields​

Field Constraints​

Important Notes​

Summary​

Testing​

Prerequisites

Overview

Process Flow

Implementation Guide

Critical Step: Enrich Chat Response with Assistant Message

Step-by-Step Implementation

Step 1: Create Chat with User Message

Step 2: Enrich Chat Response with Assistant Message

Step 3: Update Chat with Assistant Message

Step 4: Trigger Assistant Completion

Step 4.1: Trigger Assistant Completion with Knowledge Integration (RAG)

Step 5: Wait for Assistant Response Completion

Option A: Stream Processing (Recommended)

Option B: Polling Approach

Step 6: Complete Assistant Message

Step 7: Fetch Final Chat

Additional API Endpoints

Fetch Knowledge Collection

Fetch Model Information

Send Additional Messages to Chat

Response Processing

Parsing Assistant Responses

API Reference

DTO Structures

Chat DTO (Complete Structure)

ChatCompletionsRequest DTO

ChatCompletedRequest DTO

ChatCompletionMessage DTO

History DTO

Message DTO (Complete Structure)

Response Examples

Create Chat Response

Final Chat Response (After Completion)

Tag DTO

OWUIKnowledge DTO (Knowledge Collection)

Knowledge Collection Response

Model Information Response

Field Reference Guide

Required vs Optional Fields

Field Constraints

Important Notes

Summary

Testing