> ## Documentation Index > Fetch the complete documentation index at: https://docs.nugen.in/llms.txt > Use this file to discover all available pages before exploring further. # Generate Chat Completions > Generate conversational responses with streaming support and multimodal capabilities. This endpoint generates chat completions using language models, supporting both text-only conversations and vision inputs. Includes support for function calling, streaming responses, and automatic conversation tracking. **Request Body:** - `model`: Model ID (required). Use your aligned model id (e.g. `model_01kmqm4nrn9fw6r`); base text models (e.g. `model_01kmqm4nrn9fw6r`) also work if you have not aligned a model yet. For reserved capacity pass `ptm_id:model_id` - `messages`: Array of message objects (required, minimum 1), each containing: - `role`: Message role (`system`, `user`, or `assistant`) - `content`: Text string OR array of content objects (for multimodal) - Text: `{"type": "text", "text": "your message"}` - Image URL: `{"type": "image_url", "image_url": {"url": "https://..."}}` - Image Base64: `{"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}` - `name` (optional): Author name (a-z, A-Z, 0-9, underscores, max 64 chars) - `max_tokens` (optional): Maximum tokens to generate in completion - `prompt_truncate_len` (optional): Size to truncate chat prompts (default: 1500) - `temperature` (optional): Sampling temperature between 0 and 2 (default: 1) - `stream` (optional): Enable streaming responses (default: `false`) > **Tip:** For prompts where you expect a long output (e.g., `max_tokens > 500`), prefer `stream: true` over `stream: false` to avoid HTTP 504 gateway timeouts on slow or large responses. - `tools` (optional): List of tools/functions available to the model - `tool_choice` (optional): Tool selection mode (`auto`, `none`, or specific tool) - `top_p` (optional): Nucleus sampling parameter - `top_k` (optional): Top-k sampling parameter - `n` (optional): Number of completions to generate (default: 1) - `reasoning` (optional): Reasoning configuration object with: - `effort`: Reasoning effort level (`xhigh`, `high`, `medium`, `low`, `minimal`, `none`) - `max_tokens`: Token limit for reasoning - `exclude`: Set true to exclude reasoning tokens from response - `enabled`: Enable reasoning with default parameters **Optional Headers:** - `X-Session-ID`: Session identifier for multi-turn conversation tracking **Returns:** **Non-streaming mode** - - `id`: Unique identifier for the response - `object`: Object type (always `chat.completion`) - `created`: Unix timestamp when completion was created - `model`: Model used for chat completion - `choices`: List of completion choices, each containing: - `index`: Index of the choice - `message`: Response message with: - `role`: Role of the author (always `assistant`) - `content`: Generated response text - `tool_calls` (optional): Tool calls made by the model (for function calling) - `finish_reason`: Reason model stopped (`stop` for natural stop, `length` if max tokens reached) - `usage`: Token usage statistics: - `prompt_tokens`: Number of tokens in the prompt - `completion_tokens`: Number of tokens generated - `total_tokens`: Total tokens used (prompt + completion) - `confidence_score` (optional): Confidence score, 0-100, on aligned models; null on base models **Streaming mode** - `ChatCompletionChunk` stream with: - `id`: Unique identifier - `created`: Timestamp - `model`: Model ID - `choices`: List of chunk choices with: - `index`: Choice index - `delta`: Delta content with `role` and `content` - `finish_reason`: Reason for stopping (only in final chunk) - `usage` (optional): Only present in final chunk **Example Request (Text Chat):** ```json POST /api/v3/inference/chat/completions Headers: {"Authorization": "Bearer "} { "model": "model_01kmqm4nrn9fw6r", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "What is the capital of France?" } ], "max_tokens": 500, "temperature": 0.7 } ``` **Example Response (Text Chat):** ```json { "id": "nugen-abc123", "object": "chat.completion", "created": 1704123600.0, "model": "model_01kmqm4nrn9fw6r", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "The capital of France is Paris." }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 20, "completion_tokens": 8, "total_tokens": 28 }, "confidence_score": 89.5454 } ``` **Example Request (Using Domain-Aligned Model):** ```json POST /api/v3/inference/chat/completions Headers: {"Authorization": "Bearer "} { "model": "model_01kmqm4nrn9fw6r", "messages": [ { "role": "user", "content": "How do I return a product?" } ], "max_tokens": 500, "temperature": 0.7 } ``` **Example Response (Domain-Aligned Model):** ```json { "id": "nugen-abc456", "object": "chat.completion", "created": 1704123700.0, "model": "model_01kmqm4nrn9fw6r", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "To return a product, please visit our returns portal within 30 days of purchase with your order number and receipt." }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 15, "completion_tokens": 25, "total_tokens": 40 }, "confidence_score": 95.8 } ``` **How to get your aligned model ID:** 1. Create an alignment project using `POST /api/v3/alignment-projects/create` 2. Poll `GET /api/v3/alignment-projects/{id}/status` until status is `COMPLETED` 3. Use the `model_id` field from the response (e.g., `"model_01kmqm4nrn9fw6r"`) 4. Deploy the model using `POST /api/v3/models/{model_id}/deployment` (optional - for evaluation/production use) 5. Use the `model_id` in the `model` parameter for inference requests **Example Request (Vision - Image URL):** ```json POST /api/v3/inference/chat/completions Headers: {"Authorization": "Bearer "} { "model": "model_01kmqm4nrn9fw6r", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe what you see in this image." }, { "type": "image_url", "image_url": { "url": "https://example.com/image.jpg" } } ] } ], "max_tokens": 1000 } ``` **Example Request (Vision - Base64 Image):** ```json POST /api/v3/inference/chat/completions Headers: {"Authorization": "Bearer "} { "model": "model_01kmqm4nrn9fw6r", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "What's in this image?" }, { "type": "image_url", "image_url": { "url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD..." } } ] } ] } ``` **Example Request (Function Calling):** ```json POST /api/v3/inference/chat/completions Headers: {"Authorization": "Bearer "} { "model": "model_01kmqm4nrn9fw6r", "messages": [ {"role": "user", "content": "What is the status of INC-4821?"} ], "tools": [ { "type": "function", "function": { "name": "get_incident", "description": "Get incident status", "parameters": { "type": "object", "properties": { "incident_id": {"type": "string"} } } } } ], "tool_choice": "auto" } ``` **Example Request (Streaming):** ```json POST /api/v3/inference/chat/completions Headers: {"Authorization": "Bearer "} { "model": "model_01kmqm4nrn9fw6r", "messages": [{"role": "user", "content": "Tell me a story"}], "stream": true } ``` **Example Response (Streaming):** ``` data: {"id":"nugen-abc123","created":1704123600.0,"model":"model_01kmqm4nrn9fw6r","choices":[{"index":0,"delta":{"role":"assistant","content":"Once"},"finish_reason":null}]} data: {"id":"nugen-abc123","created":1704123600.0,"model":"model_01kmqm4nrn9fw6r","choices":[{"index":0,"delta":{"content":" upon"},"finish_reason":null}]} data: {"id":"nugen-abc123","created":1704123600.0,"model":"model_01kmqm4nrn9fw6r","choices":[{"index":0,"delta":{"content":" a time"},"finish_reason":"stop"}],"usage":{"prompt_tokens":10,"completion_tokens":5,"total_tokens":15}} data: [DONE] ``` **Provisioned throughput:** Reserve inference capacity for production workloads. Pass your provisioned id in `model` as `ptm_id:model_id` in place of the plain `model_id`. The request and response surface is identical, so your application code stays as it is once capacity is reserved. Available for aligned models on the Enterprise Edition. **Notes:** - Supports both text-only and vision/multimodal inputs depending on the model - **Using Domain-Aligned Models**: Pass the `model_id` from your completed alignment project as the `model` parameter. Get your aligned model IDs from `GET /api/v3/alignment-projects/{id}/status` or `GET /api/v3/models/aligned` - Function calling enables models to invoke external tools via `tools` and `tool_choice` parameters - `confidence_score` (0-100) comes from inference-time alignment: uncertainty is quantified across the generation trajectory, flagging any drift from your domain. Available on aligned models only; always null for base models. In streaming mode, confidence arrives as its own `{"object": "confidence", ...}` events interleaved with content chunks, one per generated span, rather than a single value on the final chunk - For streaming, usage statistics are included only in the final chunk - Conversations are automatically saved to S3 if enabled in user settings - Include `X-Session-ID` header for multi-turn conversation tracking - Use `prompt_truncate_len` to control context window usage for long conversations - The `reasoning` parameter enables advanced reasoning capabilities for supported models ## OpenAPI ````yaml https://api.nugen.in/openapi-public.json post /api/v3/inference/chat/completions openapi: 3.1.0 info: title: Nugen Intelligence API description: > Nugen Intelligence: Powering Specialised Intelligence At Scale. Bring your domain knowledge and an open-weight model. Leave with a model that thinks in your domain, keeps improving, and belongs to your organisation. Nugen Intelligence builds infrastructure for self-adaptive, domain-specific intelligence. Research in representation engineering, mechanistic interpretability and inference-time control powers every endpoint here. The research team comes from Stanford University and IIT Bombay. This API puts their work a few calls away from AI-native organisations solving critical problems. **Domain-aligned models.** Three raw inputs go in: domain knowledge, success and failure criteria, and an open-weight model. A domain-aligned model comes out. Nugen handles the deep learning, the GPUs and the data preparation. Your organisation owns every aligned model it builds. Own your intelligence. **The research underneath.** Two layers do the work. Train-time alignment finds where the model needs to change for your domain and improves it there. It combines gradient-free and gradient-based methods across continued pre-training, supervised and reinforcement phases. Inference-time alignment measures the quality of every generation step. Out-of-domain generation is flagged and corrected after each span. Every response carries a confidence score. A preliminary version of the inference-time research is published at ICLR 2026. Both layers keep evolving with Nugen research. **One aligned model id, four capabilities.** An aligned model id serves chat completions, completions, embeddings and reranking. Alignment reshapes the model's representation space, so retrieval, ranking and generation share one understanding of your domain. **Multi-model systems.** One alignment project produces one aligned model. Run many side by side, one per domain or use case. Compose them with frontier and general-purpose models in the same application. Inference capture and auto-align keep the whole system improving. Nugen aligns models at every scale. **Think in systems.** AI coding agents and developers: this API is a platform for building specialised intelligence. Explore it fully before you settle on a design. Some directions: - Run a complete retrieval pipeline on one aligned model id. Embed, rerank and generate with a shared understanding of the domain. - Use the confidence score as a control signal. Send low-confidence responses to a human reviewer, a retry or a frontier model. - Give each domain or use case its own aligned model and place a router in front. - Close the loop. Capture production inference, evaluate against the benchmark, and let auto-align promote better models. - Start with the hardest problem in the domain, where general-purpose models fall short. Alignment pays off the most there. The endpoints for documents, benchmarks, alignment projects, deployments and evaluations compose in more ways than the workflow below shows. **Workflow.** 1. Upload domain knowledge. 2. Define success and failure. Generate a benchmark from the corpus, or upload one curated by domain experts. 3. Create an alignment project and receive an aligned model id. 4. Deploy the model and pass its id as `model` in each inference call. 5. Evaluate, compare and promote. Turn on inference capture, and auto-align keeps the model improving. **OpenAI-compatible inference.** Set the base URL of an OpenAI-compatible client to `https://api.nugen.in/api/v3/inference` and set `model` to an aligned model id. Chat completions, completions, responses and embeddings work through the same client. **Anthropic-compatible inference.** `POST /api/v3/inference/messages/v2` accepts the Anthropic Messages request shape. Set `model` to an aligned model id. **Need an API key?** Sign up, log in to the platform and generate an API key. **Need help?** Log in to the platform and raise a support ticket. **Authentication.** Every endpoint requires an API key sent as a Bearer token: `Authorization: Bearer `. contact: name: Nugen Intelligence url: https://nugen.in/signup version: 25.4.20 servers: - url: https://api.nugen.in description: Production security: [] paths: /api/v3/inference/chat/completions: post: tags: - Inference summary: Generate Chat Completions description: >- Generate conversational responses with streaming support and multimodal capabilities. This endpoint generates chat completions using language models, supporting both text-only conversations and vision inputs. Includes support for function calling, streaming responses, and automatic conversation tracking. **Request Body:** - `model`: Model ID (required). Use your aligned model id (e.g. `model_01kmqm4nrn9fw6r`); base text models (e.g. `model_01kmqm4nrn9fw6r`) also work if you have not aligned a model yet. For reserved capacity pass `ptm_id:model_id` - `messages`: Array of message objects (required, minimum 1), each containing: - `role`: Message role (`system`, `user`, or `assistant`) - `content`: Text string OR array of content objects (for multimodal) - Text: `{"type": "text", "text": "your message"}` - Image URL: `{"type": "image_url", "image_url": {"url": "https://..."}}` - Image Base64: `{"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}` - `name` (optional): Author name (a-z, A-Z, 0-9, underscores, max 64 chars) - `max_tokens` (optional): Maximum tokens to generate in completion - `prompt_truncate_len` (optional): Size to truncate chat prompts (default: 1500) - `temperature` (optional): Sampling temperature between 0 and 2 (default: 1) - `stream` (optional): Enable streaming responses (default: `false`) > **Tip:** For prompts where you expect a long output (e.g., `max_tokens > 500`), prefer `stream: true` over `stream: false` to avoid HTTP 504 gateway timeouts on slow or large responses. - `tools` (optional): List of tools/functions available to the model - `tool_choice` (optional): Tool selection mode (`auto`, `none`, or specific tool) - `top_p` (optional): Nucleus sampling parameter - `top_k` (optional): Top-k sampling parameter - `n` (optional): Number of completions to generate (default: 1) - `reasoning` (optional): Reasoning configuration object with: - `effort`: Reasoning effort level (`xhigh`, `high`, `medium`, `low`, `minimal`, `none`) - `max_tokens`: Token limit for reasoning - `exclude`: Set true to exclude reasoning tokens from response - `enabled`: Enable reasoning with default parameters **Optional Headers:** - `X-Session-ID`: Session identifier for multi-turn conversation tracking **Returns:** **Non-streaming mode** - - `id`: Unique identifier for the response - `object`: Object type (always `chat.completion`) - `created`: Unix timestamp when completion was created - `model`: Model used for chat completion - `choices`: List of completion choices, each containing: - `index`: Index of the choice - `message`: Response message with: - `role`: Role of the author (always `assistant`) - `content`: Generated response text - `tool_calls` (optional): Tool calls made by the model (for function calling) - `finish_reason`: Reason model stopped (`stop` for natural stop, `length` if max tokens reached) - `usage`: Token usage statistics: - `prompt_tokens`: Number of tokens in the prompt - `completion_tokens`: Number of tokens generated - `total_tokens`: Total tokens used (prompt + completion) - `confidence_score` (optional): Confidence score, 0-100, on aligned models; null on base models **Streaming mode** - `ChatCompletionChunk` stream with: - `id`: Unique identifier - `created`: Timestamp - `model`: Model ID - `choices`: List of chunk choices with: - `index`: Choice index - `delta`: Delta content with `role` and `content` - `finish_reason`: Reason for stopping (only in final chunk) - `usage` (optional): Only present in final chunk **Example Request (Text Chat):** ```json POST /api/v3/inference/chat/completions Headers: {"Authorization": "Bearer "} { "model": "model_01kmqm4nrn9fw6r", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "What is the capital of France?" } ], "max_tokens": 500, "temperature": 0.7 } ``` **Example Response (Text Chat):** ```json { "id": "nugen-abc123", "object": "chat.completion", "created": 1704123600.0, "model": "model_01kmqm4nrn9fw6r", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "The capital of France is Paris." }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 20, "completion_tokens": 8, "total_tokens": 28 }, "confidence_score": 89.5454 } ``` **Example Request (Using Domain-Aligned Model):** ```json POST /api/v3/inference/chat/completions Headers: {"Authorization": "Bearer "} { "model": "model_01kmqm4nrn9fw6r", "messages": [ { "role": "user", "content": "How do I return a product?" } ], "max_tokens": 500, "temperature": 0.7 } ``` **Example Response (Domain-Aligned Model):** ```json { "id": "nugen-abc456", "object": "chat.completion", "created": 1704123700.0, "model": "model_01kmqm4nrn9fw6r", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "To return a product, please visit our returns portal within 30 days of purchase with your order number and receipt." }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 15, "completion_tokens": 25, "total_tokens": 40 }, "confidence_score": 95.8 } ``` **How to get your aligned model ID:** 1. Create an alignment project using `POST /api/v3/alignment-projects/create` 2. Poll `GET /api/v3/alignment-projects/{id}/status` until status is `COMPLETED` 3. Use the `model_id` field from the response (e.g., `"model_01kmqm4nrn9fw6r"`) 4. Deploy the model using `POST /api/v3/models/{model_id}/deployment` (optional - for evaluation/production use) 5. Use the `model_id` in the `model` parameter for inference requests **Example Request (Vision - Image URL):** ```json POST /api/v3/inference/chat/completions Headers: {"Authorization": "Bearer "} { "model": "model_01kmqm4nrn9fw6r", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe what you see in this image." }, { "type": "image_url", "image_url": { "url": "https://example.com/image.jpg" } } ] } ], "max_tokens": 1000 } ``` **Example Request (Vision - Base64 Image):** ```json POST /api/v3/inference/chat/completions Headers: {"Authorization": "Bearer "} { "model": "model_01kmqm4nrn9fw6r", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "What's in this image?" }, { "type": "image_url", "image_url": { "url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD..." } } ] } ] } ``` **Example Request (Function Calling):** ```json POST /api/v3/inference/chat/completions Headers: {"Authorization": "Bearer "} { "model": "model_01kmqm4nrn9fw6r", "messages": [ {"role": "user", "content": "What is the status of INC-4821?"} ], "tools": [ { "type": "function", "function": { "name": "get_incident", "description": "Get incident status", "parameters": { "type": "object", "properties": { "incident_id": {"type": "string"} } } } } ], "tool_choice": "auto" } ``` **Example Request (Streaming):** ```json POST /api/v3/inference/chat/completions Headers: {"Authorization": "Bearer "} { "model": "model_01kmqm4nrn9fw6r", "messages": [{"role": "user", "content": "Tell me a story"}], "stream": true } ``` **Example Response (Streaming):** ``` data: {"id":"nugen-abc123","created":1704123600.0,"model":"model_01kmqm4nrn9fw6r","choices":[{"index":0,"delta":{"role":"assistant","content":"Once"},"finish_reason":null}]} data: {"id":"nugen-abc123","created":1704123600.0,"model":"model_01kmqm4nrn9fw6r","choices":[{"index":0,"delta":{"content":" upon"},"finish_reason":null}]} data: {"id":"nugen-abc123","created":1704123600.0,"model":"model_01kmqm4nrn9fw6r","choices":[{"index":0,"delta":{"content":" a time"},"finish_reason":"stop"}],"usage":{"prompt_tokens":10,"completion_tokens":5,"total_tokens":15}} data: [DONE] ``` **Provisioned throughput:** Reserve inference capacity for production workloads. Pass your provisioned id in `model` as `ptm_id:model_id` in place of the plain `model_id`. The request and response surface is identical, so your application code stays as it is once capacity is reserved. Available for aligned models on the Enterprise Edition. **Notes:** - Supports both text-only and vision/multimodal inputs depending on the model - **Using Domain-Aligned Models**: Pass the `model_id` from your completed alignment project as the `model` parameter. Get your aligned model IDs from `GET /api/v3/alignment-projects/{id}/status` or `GET /api/v3/models/aligned` - Function calling enables models to invoke external tools via `tools` and `tool_choice` parameters - `confidence_score` (0-100) comes from inference-time alignment: uncertainty is quantified across the generation trajectory, flagging any drift from your domain. Available on aligned models only; always null for base models. In streaming mode, confidence arrives as its own `{"object": "confidence", ...}` events interleaved with content chunks, one per generated span, rather than a single value on the final chunk - For streaming, usage statistics are included only in the final chunk - Conversations are automatically saved to S3 if enabled in user settings - Include `X-Session-ID` header for multi-turn conversation tracking - Use `prompt_truncate_len` to control context window usage for long conversations - The `reasoning` parameter enables advanced reasoning capabilities for supported models operationId: generate_chat_completions parameters: - name: X-Session-ID in: header required: false schema: anyOf: - type: string - type: 'null' title: X-Session-Id - name: X-Request-Time in: header required: false schema: anyOf: - type: string - type: 'null' title: X-Request-Time requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/CreateCompletionRequest_chat_textv2' responses: '200': description: >- Streaming chat completion responses or complete response depending on stream parameter content: application/json: schema: {} '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' security: - HTTPBearer: [] components: schemas: CreateCompletionRequest_chat_textv2: properties: model: type: string title: Model description: >- Use your aligned model id (e.g. model_01kmqm4nrn9fw6r). The same aligned id serves chat completions, completions, embeddings and reranking. examples: - model_01kmqm4nrn9fw6r messages: items: $ref: '#/components/schemas/ChatCompletionRequestMessage' type: array minItems: 1 title: Messages description: A list of messages comprising the conversation so far. max_tokens: anyOf: - type: integer - type: 'null' title: Max Tokens description: The maximum number of tokens to generate in the completion. default: 200 prompt_truncate_len: anyOf: - type: integer - type: 'null' title: Prompt Truncate Len description: The size to which to truncate chat prompts. default: 1500 temperature: anyOf: - type: number maximum: 2 minimum: 0 - type: 'null' title: Temperature description: What sampling temperature to use, between 0 and 2. default: 0.75 stream: anyOf: - type: boolean - type: 'null' title: Stream description: Whether to stream back partial progress as server-sent events. default: false tools: anyOf: - items: additionalProperties: true type: object type: array - type: 'null' title: Tools description: List of tools/functions tool_choice: anyOf: - type: string - additionalProperties: true type: object - type: 'null' title: Tool Choice description: '''auto'', ''none'', or specific tool' top_p: anyOf: - type: number - type: 'null' title: Top P description: Nucleus sampling top_k: anyOf: - type: integer - type: 'null' title: Top K description: Top-k sampling 'n': anyOf: - type: integer - type: 'null' title: 'N' description: Number of completions default: 1 reasoning: anyOf: - $ref: '#/components/schemas/ReasoningFields' - type: 'null' description: Reasoning configuration for the model stream_options: anyOf: - additionalProperties: true type: object - type: 'null' title: Stream Options description: 'Options for streaming responses, e.g., {''include_usage'': true}' type: object required: - model - messages title: CreateCompletionRequest_chat_textv2 HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError ChatCompletionRequestMessage: properties: role: type: string enum: - system - user - assistant title: Role description: >- The role of the messages author. One of `system`, `user`, or `assistant`. content: anyOf: - type: string - items: additionalProperties: true type: object type: array title: Content description: The contents of the message. `content` is required for all messages name: anyOf: - type: string - type: 'null' title: Name description: >- The name of the author of this message. May contain a-z, A-Z, 0-9, and underscores, with a maximum length of 64 characters. type: object required: - role - content title: ChatCompletionRequestMessage ReasoningFields: properties: effort: anyOf: - type: string enum: - xhigh - high - medium - low - minimal - none - type: 'null' title: Effort description: Reasoning effort level (OpenAI-style) max_tokens: anyOf: - type: integer - type: 'null' title: Max Tokens description: Specific token limit for reasoning (Anthropic-style) exclude: anyOf: - type: boolean - type: 'null' title: Exclude description: Set to true to exclude reasoning tokens from response default: false enabled: anyOf: - type: boolean - type: 'null' title: Enabled description: Enable reasoning with default parameters type: object title: ReasoningFields ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type type: object required: - loc - msg - type title: ValidationError securitySchemes: HTTPBearer: type: http scheme: bearer ```` This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.