"parser_config": { "raptor": {"user_raptor": True} } }) # 然后解析文档 dataset.async_parse_documents(doc_ids) 聊天助手高级配置 def create_legal_assistant(rag_object, dataset_id, assistant_name="疾控法规助手"): # 创建具有高级配置的聊天助手 assistant = rag_object.create_chat( name=assis...
public_key = CharField(max_length=255, null=True, index=True) llm_id = CharField(max_length=128, null=False, help_text="default llm ID", index=True) embd_id = CharField(max_length=128, null=False, help_text="default embedding model ID", index=True) asr_id = CharField(max_length=1...
--url http://{address}/api/v1/agents/{agent_id}/sessions?user_id={user_id} \ @@ -2215,7 +2221,7 @@ curl --request POST \ - `agent_id`: (*Path parameter*) The ID of the associated agent. - `user_id`: (*Filter parameter*), string The optional user-defined ID for parsi...
role:"user"or"assistant". content: The text content of user or assistant. The citations are in a format like##0$$. The number in the middle, 0 in this case, indicates which part in data.reference.chunks it refers to. user_id: This is set by the caller. ...
Specifically, we need to detect the user intent and pass it as input to the agent: 具体来说,我们需要检测用户意图并将其作为输入传递给代理: AI检测代码解析 detect_intent_texts(project_id, session_id, texts, language_code): """Returns the result of detect intent with texts as inputs. ...
USER root WORKDIR /ragflow # install dependencies from uv.lock file COPY pyproject.toml uv.lock ./ # https://github.com/astral-sh/uv/issues/10462 # uv records index url into uv.lock but doesn't failover among multiple indexes RUN --mount=type=cache,id=ragflow_uv,target=/root...
, "role": "assistant" } ], "reference": [], "tokens": 0, "update_date": "Fri, 12 Apr 2024 17:26:21 GMT", "update_time": 1712913981857, "user_id": "<USER_ID_SET_BY_THE_CALLER>" }, "retcode": 0, "retmsg": "success" } Get conversation history This method retrieves ...
Introduction: Recently, small language models have made significant progress in terms of quality and context size. These advancements have enabled new possibilities, making it increasingly viable t... \n \n\n Meta-Llama-3-8B-Instruct \n\n ...
user:<contexts>{{contexts}}Human:<question>{{question}}AI: 9.Prompt flow -Evaluation and benchmark: We have prepared the Q&A evaluation data from step 1 and two SLMs. To compare how well they perform, we can use the evaluation feature in Prompt flow, which makes...
'sas'# container_url: 'container_url'# sas_token: 'sas_token'# azure:# auth_type: 'spn'# account_url: 'account_url'# client_id: 'client_id'# secret: 'secret'# tenant_id: 'tenant_id'# container_name: 'container_name'# user_default_llm:# factory: 'Tongyi-Qianwen'# api_key: ...