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LLM Connections API Endpoints

Base URL

/ruuter-private/llm/connections

1. Create LLM Connection

Endpoint

POST /ruuter-private/llm/connections/create

Request Body

{
  "llmPlatform": "OpenAI",
  "llmModel": "GPT-4o",
  "embeddingPlatform": "OpenAI",
  "embeddingModel": "text-embedding-3-small",
  "monthlyBudget": 1000.00,
  "deploymentEnvironment": "Testing",
  // Azure credentials (optional)
  "deploymentName": "my-deployment",
  "targetUri": "https://my-endpoint.azure.com",
  "apiKey": "azure-api-key",
  // AWS Bedrock credentials (optional)
  "secretKey": "aws-secret-key",
  "accessKey": "aws-access-key",
  // Embedding model credentials (optional)
  "embeddingModelApiKey": "embedding-api-key"
}

Response (201 Created)

{
  "id": 1,
  "llmPlatform": "OpenAI",
  "llmModel": "GPT-4o",
  "embeddingPlatform": "OpenAI",
  "embeddingModel": "text-embedding-3-small",
  "monthlyBudget": 1000.00,
  "usedBudget": 0.00,
  "deploymentEnvironment": "Testing",
  "status": "active",
  "createdAt": "2025-09-02T10:15:30.000Z",
  // Azure credentials (if provided)
  "deploymentName": "my-deployment",
  "targetUri": "https://my-endpoint.azure.com",
  "apiKey": "azure-api-key",
  // AWS Bedrock credentials (if provided)  
  "secretKey": "aws-secret-key",
  "accessKey": "aws-access-key",
  // Embedding model credentials (if provided)
  "embeddingModelApiKey": "embedding-api-key"
}

2. Update LLM Connection

Endpoint

POST /ruuter-private/llm/connections/update

Request Body

{
  "connectionId": 1,
  "llmPlatform": "Azure AI",
  "llmModel": "GPT-4o-mini",
  "embeddingPlatform": "Azure AI", 
  "embeddingModel": "text-embedding-ada-002",
  "monthlyBudget": 2000.00,
  "deploymentEnvironment": "Production",
  // Azure credentials (optional)
  "deploymentName": "updated-deployment",
  "targetUri": "https://updated-endpoint.azure.com",
  "apiKey": "updated-azure-api-key",
  // AWS Bedrock credentials (optional)
  "secretKey": "updated-aws-secret-key",
  "accessKey": "updated-aws-access-key",
  // Embedding model credentials (optional)
  "embeddingModelApiKey": "updated-embedding-api-key"
}

Response (200 OK)

{
  "id": 1,
  "llmPlatform": "Azure AI",
  "llmModel": "GPT-4o-mini",
  "embeddingPlatform": "Azure AI",
  "embeddingModel": "text-embedding-ada-002",
  "monthlyBudget": 2000.00,
  "usedBudget": 150.75,
  "deploymentEnvironment": "Production",
  "status": "active",
  "createdAt": "2025-09-02T10:15:30.000Z",
  // Azure credentials (if provided)
  "deploymentName": "updated-deployment",
  "targetUri": "https://updated-endpoint.azure.com",
  "apiKey": "updated-azure-api-key",
  // AWS Bedrock credentials (if provided)  
  "secretKey": "updated-aws-secret-key",
  "accessKey": "updated-aws-access-key",
  // Embedding model credentials (if provided)
  "embeddingModelApiKey": "updated-embedding-api-key"
}

3. Get LLM Connections (Paginated List)

Endpoint

POST /ruuter-private/rag-search/llm-connections/list

Request Body

{
  "page": 1,
  "page_size": 10,
  "sorting": "created_at desc"
}

Request Parameters

Parameter Type Required Description Default
page number No Page number (1-based) 1
page_size number No Number of items per page 10
sorting string No Sorting criteria "created_at desc"

Sorting Options

  • llm_platform asc/desc
  • llm_model asc/desc
  • embedding_platform asc/desc
  • embedding_model asc/desc
  • monthly_budget asc/desc
  • environment asc/desc
  • status asc/desc
  • created_at asc/desc
  • updated_at asc/desc

Response (200 OK)

[
  {
    "id": 1,
    "llmPlatform": "OpenAI",
    "llmModel": "GPT-4o",
    "embeddingPlatform": "OpenAI",
    "embeddingModel": "text-embedding-3-small",
    "monthlyBudget": 1000.00,
    "environment": "Testing",
    "status": "active",
    "createdAt": "2025-09-02T10:15:30.000Z",
    "updatedAt": "2025-09-02T10:15:30.000Z",
    "totalPages": 3
  },
  {
    "id": 2,
    "llmPlatform": "Azure AI",
    "llmModel": "GPT-4o-mini",
    "embeddingPlatform": "Azure AI",
    "embeddingModel": "Ada-200-1",
    "monthlyBudget": 2000.00,
    "environment": "Production",
    "status": "active",
    "createdAt": "2025-09-02T09:30:15.000Z",
    "updatedAt": "2025-09-02T11:00:00.000Z",
    "totalPages": 3
  }
]

4. Get Single LLM Connection

Endpoint

POST /ruuter-private/rag-search/llm-connections/get

Request Body

{
  "connection_id": 1
}

Response (200 OK)

{
  "id": 1,
  "llmPlatform": "OpenAI",
  "llmModel": "GPT-4o",
  "embeddingPlatform": "OpenAI",
  "embeddingModel": "text-embedding-3-small",
  "monthlyBudget": 1000.00,
  "environment": "Testing",
  "status": "active",
  "createdAt": "2025-09-02T10:15:30.000Z",
  "updatedAt": "2025-09-02T10:15:30.000Z"
}

Response (404 Not Found)

"error: connection not found"

5. Add New LLM Connection

Endpoint

POST /ruuter-private/rag-search/llm-connections/add

Request Body

{
  "llm_platform": "OpenAI",
  "llm_model": "GPT-4o",
  "embedding_platform": "OpenAI",
  "embedding_model": "text-embedding-3-small",
  "monthly_budget": 1000.00,
  "environment": "Testing"
}

Request Parameters

Parameter Type Required Description
llm_platform string Yes LLM platform (e.g., "Azure AI", "OpenAI")
llm_model string Yes LLM model (e.g., "GPT-4o")
embedding_platform string Yes Embedding platform
embedding_model string Yes Embedding model
monthly_budget number Yes Monthly budget amount
environment string Yes "Testing" or "Production"

Response (200 OK)

{
  "id": 3,
  "llm_platform": "OpenAI",
  "llm_model": "GPT-4o",
  "embedding_platform": "OpenAI",
  "embedding_model": "text-embedding-3-small",
  "monthly_budget": 1000.00,
  "environment": "Testing",
  "status": "active",
  "created_at": "2025-09-02T12:00:00.000Z",
  "updated_at": "2025-09-02T12:00:00.000Z"
}

Response (400 Bad Request)

"error: environment must be 'Testing' or 'Production'"

6. Update LLM Connection

Endpoint

POST /ruuter-private/rag-search/llm-connections/edit

Request Body

{
  "connection_id": 1,
  "llm_platform": "Azure AI",
  "llm_model": "GPT-4o-mini",
  "embedding_platform": "Azure AI",
  "embedding_model": "Ada-200-1",
  "monthly_budget": 2000.00,
  "environment": "Production"
}

Response (200 OK)

{
  "id": 1,
  "llm_platform": "Azure AI",
  "llm_model": "GPT-4o-mini",
  "embedding_platform": "Azure AI",
  "embedding_model": "Ada-200-1",
  "monthly_budget": 2000.00,
  "environment": "Production",
  "status": "active",
  "created_at": "2025-09-02T10:15:30.000Z",
  "updated_at": "2025-09-02T12:30:00.000Z"
}

Response (404 Not Found)

"error: connection not found"

7. Delete LLM Connection

Endpoint

POST /ruuter-private/rag-search/llm-connections/delete

Request Body

{
  "connection_id": 1
}

Response (200 OK)

"LLM connection deleted successfully"

Response (404 Not Found)

"error: connection not found"

4. List All LLM Connections

Endpoint

GET /ruuter-private/llm/connections/list

Query Parameters (Optional for filtering)

Parameter Type Description
llmPlatform string Filter by LLM platform
llmModel string Filter by LLM model
deploymentEnvironment string Filter by environment (Testing / Production)
pageNumber number Page number (1-based)
pageSize number Number of items per page
sortBy string Field to sort by
sortOrder string Sort order: 'asc' or 'desc'

Example Request

GET /ruuter-private/llm/connections/list?llmPlatform=OpenAI&deploymentEnvironment=Testing&model=GPT4

5. Get Production LLM Connection (with filters)

Endpoint

GET /ruuter-private/llm/connections/production

Query Parameters (Optional for filtering)

Parameter Type Description
llmPlatform string Filter by LLM platform
llmModel string Filter by LLM model
embeddingPlatform string Filter by embedding platform
embeddingModel string Filter by embedding model
connectionStatus string Filter by connection status
sortBy string Field to sort by
sortOrder string Sort order: 'asc' or 'desc'

Example Request

GET /ruuter-private/llm/connections/production?llmPlatform=OpenAI&connectionStatus=active

Response (200 OK)

[
  {
    "id": 1,
    "llmPlatform": "OpenAI",
    "llmModel": "GPT-4o",
    "embeddingPlatform": "OpenAI",
    "embeddingModel": "text-embedding-3-small",
    "monthlyBudget": 1000.00,
    "deploymentEnvironment": "Testing",
    "status": "active",
    "createdAt": "2025-09-02T10:15:30.000Z",
    "updatedAt": "2025-09-02T10:15:30.000Z"
  }
]

5. Get Single LLM Connection

Endpoint

GET /ruuter-private/llm/connections/overview

Response (200 OK)

{
  "id": 1,
  "llmPlatform": "OpenAI",
  "llmModel": "GPT-4o",
  "embeddingPlatform": "OpenAI",
  "embeddingModel": "text-embedding-3-small",
  "monthlyBudget": 1000.00,
  "deploymentEnvironment": "Testing",
  "status": "active",
  "createdAt": "2025-09-02T10:15:30.000Z",
  "updatedAt": "2025-09-02T10:15:30.000Z"
}

Inference Results API Endpoints

Base URL

/ruuter-private/inference/results

1. Store Test Inference Result

Endpoint

POST /ruuter-private/inference/results/test/store

Request Body

{
  "llm_connection_id": 1,
  "user_question": "What are the benefits of using LLMs?",
  "final_answer": "LLMs can improve productivity by summarizing large documents, enabling Q&A, and enhancing automation."
}

Request Parameters

Parameter Type Required Description
llm_connection_id number Yes ID of the LLM connection
user_question string Yes User's raw question/input
final_answer string Yes LLM's final generated answer

Response (200 OK)

{
  "data": {
    "id": 10,
    "llm_connection_id": 1,
    "chat_id": null,
    "user_question": "What are the benefits of using LLMs?",
    "refined_questions": null,
    "conversation_history": null,
    "ranked_chunks": null,
    "embedding_scores": null,
    "final_answer": "LLMs can improve productivity by summarizing large documents, enabling Q&A, and enhancing automation.",
    "environment": "testing",
    "created_at": "2025-09-25T12:15:00.000Z"
  },
  "operationSuccess": true,
  "statusCode": 200
}

Response (400 Bad Request)

{
  "data": "[]",
  "operationSuccess": false,
  "statusCode": 400
}

Response (404 Not Found)

"error: LLM connection not found"

2. Store Production Inference Result

Endpoint

POST /ruuter-private/inference/results/production/store

Request Body

{
  "chat_id": "chat-12345",
  "user_question": "What are the benefits of using LLMs?",
  "refined_questions": [
    "How do LLMs improve productivity?",
    "What are practical use cases of LLMs?"
  ],
  "conversation_history": [
    { "role": "user", "content": "Hello" },
    { "role": "assistant", "content": "Hi! How can I help you?" }
  ],
  "ranked_chunks": [
    { "id": "chunk_1", "content": "LLMs help in summarization", "rank": 1 },
    { "id": "chunk_2", "content": "They improve Q&A systems", "rank": 2 }
  ],
  "embedding_scores": [0.92, 0.85, 0.78],
  "final_answer": "LLMs can improve productivity by summarizing large documents, enabling Q&A, and enhancing automation."
}

Request Parameters

Parameter Type Required Description
chat_id string No Optional chat session ID
user_question string Yes User's raw question/input
refined_questions object No List of refined questions (LLM-generated)
conversation_history object No Prior messages array of {role, content}
ranked_chunks object No Retrieved chunks ranked with metadata
embedding_scores object No Distance scores for each chunk
final_answer string Yes LLM's final generated answer

Response (200 OK)

{
  "data": {
    "id": 15,
    "llm_connection_id": null,
    "chat_id": "chat-12345",
    "user_question": "What are the benefits of using LLMs?",
    "refined_questions": [
      "How do LLMs improve productivity?",
      "What are practical use cases of LLMs?"
    ],
    "conversation_history": [
      { "role": "user", "content": "Hello" },
      { "role": "assistant", "content": "Hi! How can I help you?" }
    ],
    "ranked_chunks": [
      { "id": "chunk_1", "content": "LLMs help in summarization", "rank": 1 },
      { "id": "chunk_2", "content": "They improve Q&A systems", "rank": 2 }
    ],
    "embedding_scores": [0.92, 0.85, 0.78],
    "final_answer": "LLMs can improve productivity by summarizing large documents, enabling Q&A, and enhancing automation.",
    "environment": "production",
    "created_at": "2025-09-25T12:15:00.000Z"
  },
  "operationSuccess": true,
  "statusCode": 200
}

Response (400 Bad Request)

{
  "data": "[]",
  "operationSuccess": false,
  "statusCode": 400
}

3. View/get Inference Result

Endpoint

POST /ruuter-private/inference/results/test/store

Request Body

{
  "llmConnectionId": 1,
  "userQuestion": "What are the benefits of using LLMs?",
  "finalAnswer": "LLMs can improve productivity by summarizing large documents, enabling Q&A, and enhancing automation."
}

Response (201 Created)

{
  "data": {
    "id": 15,
    "llmConnectionId": 1,
    "userQuestion": "What are the benefits of using LLMs?",
    "finalAnswer": "LLMs can improve productivity by summarizing large documents, enabling Q&A, and enhancing automation.",
    "environment": "testing",
    "createdAt": "2025-09-25T10:15:30.000Z"
  },
  "operationSuccess": true,
  "statusCode": 200
}

4. Inquiry from chatbot to llm orchestration service

Endpoint

POST /ruuter-private/inference/results/production/store

Request Body

{
  "llmConnectionId": 1,
  "chatId": "chat-session-12345",
  "userQuestion": "What are the benefits of using LLMs?",
  "refinedQuestions": [
    "How do LLMs improve productivity?",
    "What are practical use cases of LLMs?"
  ],
  "conversationHistory": [
    { "role": "user", "content": "Hello" },
    { "role": "assistant", "content": "Hi! How can I help you?" }
  ],
  "rankedChunks": [
    { "id": "chunk_1", "content": "LLMs help in summarization", "rank": 1 },
    { "id": "chunk_2", "content": "They improve Q&A systems", "rank": 2 }
  ],
  "embeddingScores": {
    "chunk_1": 0.92,
    "chunk_2": 0.85
  },
  "finalAnswer": "LLMs can improve productivity by summarizing large documents, enabling Q&A, and enhancing automation."
}

Response (201 Created)

{
  "id": 20,
  "llmConnectionId": 1,
  "chatId": "chat-session-12345",
  "userQuestion": "What are the benefits of using LLMs?",
  "refinedQuestions": [
    "How do LLMs improve productivity?",
    "What are practical use cases of LLMs?"
  ],
  "conversationHistory": [
    { "role": "user", "content": "Hello" },
    { "role": "assistant", "content": "Hi! How can I help you?" }
  ],
  "rankedChunks": [
    { "id": "chunk_1", "content": "LLMs help in summarization", "rank": 1 },
    { "id": "chunk_2", "content": "They improve Q&A systems", "rank": 2 }
  ],
  "embeddingScores": {
    "chunk_1": 0.92,
    "chunk_2": 0.85
  },
  "finalAnswer": "LLMs can improve productivity by summarizing large documents, enabling Q&A, and enhancing automation.",
  "environment": "production",
  "createdAt": "2025-09-25T10:15:30.000Z"
}