TAPD Data Fetcher

# 创建文本对话请求 Creates a model response for the given chat conversation. ## POST ```url https://api.siliconflow.cn/v1/chat/completions ``` ### cURL ```curl curl --request POST \ --url https://api.siliconflow.cn/v1/chat/completions \ --header 'Authorization: Bearer <token>' \ --header 'Content-Type: application/json' \ --data '{ "model": "Qwen/QwQ-32B", "messages": [ { "role": "user", "content": "What opportunities and challenges will the Chinese large model industry face in 2025?" } ], "stream": false, "max_tokens": 512, "enable_thinking": false, "thinking_budget": 4096, "min_p": 0.05, "stop": null, "temperature": 0.7, "top_p": 0.7, "top_k": 50, "frequency_penalty": 0.5, "n": 1, "response_format": { "type": "text" }, "tools": [ { "type": "function", "function": { "description": "<string>", "name": "<string>", "parameters": {}, "strict": false } } ] }' ``` ### Python ```python import requests url = "https://api.siliconflow.cn/v1/chat/completions" payload = { "model": "Qwen/QwQ-32B", "messages": [ { "role": "user", "content": "What opportunities and challenges will the Chinese large model industry face in 2025?" } ], "stream": False, "max_tokens": 512, "enable_thinking": False, "thinking_budget": 4096, "min_p": 0.05, "stop": None, "temperature": 0.7, "top_p": 0.7, "top_k": 50, "frequency_penalty": 0.5, "n": 1, "response_format": {"type": "text"}, "tools": [ { "type": "function", "function": { "description": "<string>", "name": "<string>", "parameters": {}, "strict": False } } ] } headers = { "Authorization": "Bearer <token>", "Content-Type": "application/json" } response = requests.request("POST", url, json=payload, headers=headers) print(response.text) ``` ### JavaScript ```JavaScript const options = { method: 'POST', headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'}, body: '{"model":"Qwen/QwQ-32B","messages":[{"role":"user","content":"What opportunities and challenges will the Chinese large model industry face in 2025?"}],"stream":false,"max_tokens":512,"enable_thinking":false,"thinking_budget":4096,"min_p":0.05,"stop":null,"temperature":0.7,"top_p":0.7,"top_k":50,"frequency_penalty":0.5,"n":1,"response_format":{"type":"text"},"tools":[{"type":"function","function":{"description":"<string>","name":"<string>","parameters":{},"strict":false}}]}' }; fetch('https://api.siliconflow.cn/v1/chat/completions', options) .then(response => response.json()) .then(response => console.log(response)) .catch(err => console.error(err)); ``` ## Authorization ### Authorization `string` `header` `required` Use the following format for authentication: `Bearerg <your api key>` ## Body application/json ### LLM #### model `enum<string>` `default:Qwen/QwQ-32B` `required` Corresponding Model Name. To better enhance service quality, we will make periodic changes to the models provided by this service, including but not limited to model on/offlining and adjustments to model service capabilities. We will notify you of such changes through appropriate means such as announcements or message pushes where feasible. Available options: * baidu/ERNIE-4.5-300B-A47B * moonshotai/Kimi-K2-Instruct * ascend-tribe/pangu-pro-moe * tencent/Hunyuan-A13B-Instruct * MiniMaxAI/MiniMax-M1-80k * Tongyi-Zhiwen/QwenLong-L1-32B * Qwen/Qwen3-30B-A3B * Qwen/Qwen3-32B * Qwen/Qwen3-14B * Qwen/Qwen3-8B * Qwen/Qwen3-235B-A22B * THUDM/GLM-Z1-32B-0414 * THUDM/GLM-4-32B-0414 * THUDM/GLM-Z1-Rumination-32B-0414 * THUDM/GLM-4-9B-0414 * THUDM/GLM-4-9B-0414 * Qwen/QwQ-32B * Pro/deepseek-ai/DeepSeek-R1 * Pro/deepseek-ai/DeepSeek-V3 * deepseek-ai/DeepSeek-R1 * deepseek-ai/DeepSeek-V3 * deepseek-ai/DeepSeek-R1-0528-Qwen3-8B * deepseek-ai/DeepSeek-R1-Distill-Qwen-32B * deepseek-ai/DeepSeek-R1-Distill-Qwen-14B * deepseek-ai/DeepSeek-R1-Distill-Qwen-7B * Pro/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B * deepseek-ai/DeepSeek-V2.5 * Qwen/Qwen2.5-72B-Instruct-128K * Qwen/Qwen2.5-72B-Instruct * Qwen/Qwen2.5-32B-Instruct * Qwen/Qwen2.5-14B-Instruct * Qwen/Qwen2.5-7B-Instruct * Qwen/Qwen2.5-Coder-32B-Instruct * Qwen/Qwen2.5-Coder-7B-Instruct * Qwen/Qwen2-7B-Instruct * TeleAI/TeleChat2 * THUDM/glm-4-9b-chat * Vendor-A/Qwen/Qwen2.5-72B-Instruct * internlm/internlm2_5-7b-chat * Pro/Qwen/Qwen2.5-7B-Instruct * Pro/Qwen/Qwen2-7B-Instruct * Pro/THUDM/glm-4-9b-chat **Example:** "Qwen/QwQ-32B" #### messages `object[]` `required` A list of messages comprising the conversation so far. Required array length: `1 - 10` elements ##### role `enum<string>` `default:user` `required` The role of the messages author. Choice between: system, user, or assistant. Available options: `user`, `assistant`, `system` **Example:** "user" ##### content `string` `default:What opportunities and challenges will the Chinese large model industry face in 2025?` `required` The contents of the message. **Example:** "What opportunities and challenges will the Chinese large model industry face in 2025?" ​ #### stream `boolean` `default:true` If set, tokens are returned as Server-Sent Events as they are made available. Stream terminates with `data: [DONE]` **Example:** false #### max_tokens `integer` `default:512` The maximum number of tokens to generate. Required range: `1 <= x <= 16384` **Example:** 512 #### enable_thinking `boolean` `default:true` Switches between thinking and non-thinking modes. Default is True. This field only applies to Qwen3. **Example:** false #### thinking_budget `integer` `default:4096` Maximum number of tokens for chain-of-thought output. This field applies to all Reasoning models. Required range: `128 <= x <= 32768` **Example:** 4096 #### min_p `number` `default:0.05` Dynamic filtering threshold that adapts based on token probabilities.This field only applies to Qwen3. Required range: `0 <= x <= 1` **Example:** 0.05 #### stop `string | null` or `string[] | null` Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence. **Example:** null #### temperature `number` `default:0.7` Determines the degree of randomness in the response. **Example:** 0.7 #### top_p `number` `default:0.7` The `top_p` (nucleus) parameter is used to dynamically adjust the number of choices for each predicted token based on the cumulative probabilities. **Example:** 0.7 #### top_k `number` `default:50` **Example:** 50 #### frequency_penalty `number` `default:0.5` **Example:** 0.5 #### n `integer` `default:1` Number of generations to return **Example:** 1 #### response_format `object` An object specifying the format that the model must output. ##### response_format.type `string` The type of the response format. **Example:** "text" #### tools `object[]` A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. A max of 128 functions are supported. ##### type `enum<string>` `required` The type of the tool. Currently, only `function` is supported. **Available options:** function ##### function `object` `required` ###### function.name `string` `required` The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64. ###### function.description `string` A description of what the function does, used by the model to choose when and how to call the function. ###### function.parameters `object` The parameters the functions accepts, described as a JSON Schema object. See the [guide](https://docs.siliconflow.cn/guides/function_calling) for examples, and the [JSON Schema reference](https://json-schema.org/understanding-json-schema/) for documentation about the format. Omitting `parameters` defines a function with an empty parameter list. ###### function.strict `boolean | null` `default:false` Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the `parameters` field. Only a subset of JSON Schema is supported when `strict` is `true`. Learn more about Structured Outputs in the [function calling guide](https://docs.siliconflow.cn/cn/api-reference/chat-completions/docs/guides/function-calling). --- ## Response ### 200 ```json { "id": "<string>", "choices": [ { "message": { "role": "assistant", "content": "<string>", "reasoning_content": "<string>", "tool_calls": [ { "id": "<string>", "type": "function", "function": { "name": "<string>", "arguments": "<string>" } } ] }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 123, "completion_tokens": 123, "total_tokens": 123 }, "created": 123, "model": "<string>", "object": "chat.completion" } ``` ### 400 ```json { "code": 20012, "message": "<string>", "data": "<string>" } ``` ### 401 ```json "Invalid token" ``` ### 404 ```json "404 page not found" ``` ### 429 ```json { "message": "Request was rejected due to rate limiting. If you want more, please contact contact@siliconflow.cn. Details:TPM limit reached.", "data": "<string>" } ``` ### 503 ```json { "code": 50505, "message": "Model service overloaded. Please try again later.", "data": "<string>" } ``` ### 504 ```json "<string>" ```