--- title: "會話記錄" url: "/docs/proxy/ui_logs_sessions" canonical_url: "https://docs.litellm.ai/docs/proxy/ui_logs_sessions" type: "docs" last_updated: "2026-10-08" related: - "/docs/proxy/ui_spend_log_settings" - "/docs/proxy/deleted_keys_teams" --- # 會話記錄 > Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt 將請求分組為會話。這可讓您將相關請求分組在一起。 ## 用法 {#usage} ### `/chat/completions` {#chatcompletions} 若要將多個請求分組成單一會話，請在每個請求的中繼資料中傳入相同的 `litellm_session_id`。做法如下： **OpenAI Python v1.0.0+** **請求 1** 使用唯一的 ID 建立新會話，並發出第一個請求。會話 ID 將用於追蹤所有相關請求。 ```python showLineNumbers import openai import uuid # Create a session ID session_id = str(uuid.uuid4()) client = openai.OpenAI( api_key="", base_url="http://0.0.0.0:4000" ) # First request in session response1 = client.chat.completions.create( model="gpt-5.6-terra", messages=[ { "role": "user", "content": "Write a short story about a robot" } ], extra_body={ "litellm_session_id": session_id # Pass the session ID } ) ``` **請求 2** 使用相同的會話 ID 發出另一個請求，將其與先前的請求連結。這可讓您將相關請求一起追蹤。 ```python showLineNumbers # Second request using same session ID response2 = client.chat.completions.create( model="gpt-5.6-terra", messages=[ { "role": "user", "content": "Now write a poem about that robot" } ], extra_body={ "litellm_session_id": session_id # Reuse the same session ID } ) ``` **Langchain** **請求 1** 以唯一的 ID 初始化新會話，並建立一個 chat model 實例以發出請求。會話 ID 會嵌入模型的設定中。 ```python showLineNumbers from langchain.chat_models import ChatOpenAI import uuid # Create a session ID session_id = str(uuid.uuid4()) chat = ChatOpenAI( openai_api_base="http://0.0.0.0:4000", api_key="", model="gpt-5.6-terra", extra_body={ "litellm_session_id": session_id # Pass the session ID } ) # First request in session response1 = chat.invoke("Write a short story about a robot") ``` **請求 2** 使用相同的 chat model 實例發出另一個請求，透過先前設定的會話 ID 自動維持會話內容。 ```python showLineNumbers # Second request using same chat object and session ID response2 = chat.invoke("Now write a poem about that robot") ``` **Curl** **請求 1** 產生新的會話 ID 並發出初始 API 呼叫。中繼資料中的會話 ID 將用於追蹤這段對話。 ```bash showLineNumbers # Create a session ID SESSION_ID=$(uuidgen) # Store your API key API_KEY="" # First request in session curl --location 'http://0.0.0.0:4000/chat/completions' \ --header 'Content-Type: application/json' \ --header "Authorization: Bearer $API_KEY" \ --data '{ "model": "gpt-5.6-terra", "messages": [ { "role": "user", "content": "Write a short story about a robot" } ], "litellm_session_id": "'$SESSION_ID'" }' ``` **請求 2** 使用相同的會話 ID 發出後續請求，以維持對話內容與追蹤。 ```bash showLineNumbers # Second request using same session ID curl --location 'http://0.0.0.0:4000/chat/completions' \ --header 'Content-Type: application/json' \ --header "Authorization: Bearer $API_KEY" \ --data '{ "model": "gpt-5.6-terra", "messages": [ { "role": "user", "content": "Now write a poem about that robot" } ], "litellm_session_id": "'$SESSION_ID'" }' ``` **LiteLLM Python SDK** **請求 1** 透過建立唯一的 ID 並發出初始請求來開始新會話。此會話 ID 將用於將相關請求分組在一起。 ```python showLineNumbers import litellm import uuid # Create a session ID session_id = str(uuid.uuid4()) # First request in session response1 = litellm.completion( model="gpt-5.6-terra", messages=[{"role": "user", "content": "Write a short story about a robot"}], api_base="http://0.0.0.0:4000", api_key="", metadata={ "litellm_session_id": session_id # Pass the session ID } ) ``` **請求 2** 使用相同的會話 ID 發出另一個請求，將其連結到先前的互動，以延續對話。 ```python showLineNumbers # Second request using same session ID response2 = litellm.completion( model="gpt-5.6-terra", messages=[{"role": "user", "content": "Now write a poem about that robot"}], api_base="http://0.0.0.0:4000", api_key="", metadata={ "litellm_session_id": session_id # Reuse the same session ID } ) ``` ### `/responses` {#responses} 對於 `/responses` 端點，請使用 `previous_response_id` 將請求分組成會話。每個請求回應都會回傳 `previous_response_id`。 **OpenAI Python v1.0.0+** **請求 1** 發出初始請求並儲存回應 ID，以便連結後續請求。 ```python showLineNumbers from openai import OpenAI client = OpenAI( api_key="", base_url="http://0.0.0.0:4000" ) # First request in session response1 = client.responses.create( model="anthropic/claude-sonnet-5", input="Write a short story about a robot" ) # Store the response ID for the next request response_id = response1.id ``` **請求 2** 使用先前的回應 ID 發出後續請求，以維持對話內容。 ```python showLineNumbers # Second request using previous response ID response2 = client.responses.create( model="anthropic/claude-sonnet-5", input="Now write a poem about that robot", previous_response_id=response_id # Link to previous request ) ``` **Curl** **請求 1** 發出初始請求。回應將包含可用於連結後續請求的 ID。 ```bash showLineNumbers # Store your API key API_KEY="" # First request in session curl http://localhost:4000/v1/responses \ --header 'Content-Type: application/json' \ --header "Authorization: Bearer $API_KEY" \ --data '{ "model": "anthropic/claude-sonnet-5", "input": "Write a short story about a robot" }' # Response will include an 'id' field that you'll use in the next request ``` **請求 2** 使用先前的回應 ID 發出後續請求，以維持對話內容。 ```bash showLineNumbers # Second request using previous response ID curl http://localhost:4000/v1/responses \ --header 'Content-Type: application/json' \ --header "Authorization: Bearer $API_KEY" \ --data '{ "model": "anthropic/claude-sonnet-5", "input": "Now write a poem about that robot", "previous_response_id": "resp_abc123..." # Replace with actual response ID from previous request }' ``` **LiteLLM Python SDK** **請求 1** 發出初始請求並儲存回應 ID，以便連結後續請求。 ```python showLineNumbers import litellm # First request in session response1 = litellm.responses( model="anthropic/claude-sonnet-5", input="Write a short story about a robot", api_base="http://0.0.0.0:4000", api_key="" ) # Store the response ID for the next request response_id = response1.id ``` **請求 2** 使用先前的回應 ID 發出後續請求，以維持對話內容。 ```python showLineNumbers # Second request using previous response ID response2 = litellm.responses( model="anthropic/claude-sonnet-5", input="Now write a poem about that robot", api_base="http://0.0.0.0:4000", api_key="", previous_response_id=response_id # Link to previous request ) ``` ## Related pages - [UI 支出記錄設定](https://docs.litellm.ai/docs/proxy/ui_spend_log_settings.md) - [已刪除金鑰與團隊稽核記錄](https://docs.litellm.ai/docs/proxy/deleted_keys_teams.md)