fromdify_clientimportCompletionClientapi_key="your_api_key"# Initialize CompletionClientcompletion_client=CompletionClient(api_key) # Create Completion Message using CompletionClientcompletion_response=completion_client.create_completion_message(inputs={"query": "What's the weather like today?"}, response_mode="blocking", user="user_id") completion_response.raise_for_status() result=completion_response.json() print(result.get('answer'))

fromdify_clientimportCompletionClientapi_key="your_api_key"# Initialize CompletionClientcompletion_client=CompletionClient(api_key) files= [{"type": "image", "transfer_method": "remote_url", "url": "your_image_url" }] # files = [{# "type": "image",# "transfer_method": "local_file",# "upload_file_id": "your_file_id"# }]# Create Completion Message using CompletionClientcompletion_response=completion_client.create_completion_message(inputs={"query": "Describe the picture."}, response_mode="blocking", user="user_id", files=files) completion_response.raise_for_status() result=completion_response.json() print(result.get('answer'))

importjsonfromdify_clientimportChatClientapi_key="your_api_key"# Initialize ChatClientchat_client=ChatClient(api_key) # Create Chat Message using ChatClientchat_response=chat_client.create_chat_message(inputs={}, query="Hello", user="user_id", response_mode="streaming") chat_response.raise_for_status() forlineinchat_response.iter_lines(decode_unicode=True): line=line.split('data:', 1)[-1] ifline.strip(): line=json.loads(line.strip()) print(line.get('answer'))

fromdify_clientimportChatClientapi_key="your_api_key"# Initialize ChatClientchat_client=ChatClient(api_key) files= [{"type": "image", "transfer_method": "remote_url", "url": "your_image_url" }] # files = [{# "type": "image",# "transfer_method": "local_file",# "upload_file_id": "your_file_id"# }]# Create Chat Message using ChatClientchat_response=chat_client.create_chat_message(inputs={}, query="Describe the picture.", user="user_id", response_mode="blocking", files=files) chat_response.raise_for_status() result=chat_response.json() print(result.get("answer"))

fromdify_clientimportDifyClientapi_key="your_api_key"# Initialize Clientdify_client=DifyClient(api_key) file_path="your_image_file_path"file_name="panda.jpeg"mime_type="image/jpeg"withopen(file_path, "rb") asfile: files={"file": (file_name, file, mime_type) } response=dify_client.file_upload("user_id", files) result=response.json() print(f'upload_file_id: {result.get("id")}')

fromdify_clientimportChatClientapi_key="your_api_key"# Initialize Clientclient=ChatClient(api_key) # Get App parametersparameters=client.get_application_parameters(user="user_id") parameters.raise_for_status() print('[parameters]') print(parameters.json()) # Get Conversation List (only for chat)conversations=client.get_conversations(user="user_id") conversations.raise_for_status() print('[conversations]') print(conversations.json()) # Get Message List (only for chat)messages=client.get_conversation_messages(user="user_id", conversation_id="conversation_id") messages.raise_for_status() print('[messages]') print(messages.json()) # Rename Conversation (only for chat)rename_conversation_response=client.rename_conversation(conversation_id="conversation_id", name="new_name", user="user_id") rename_conversation_response.raise_for_status() print('[rename result]') print(rename_conversation_response.json())

importjsonimportrequestsfromdify_clientimportWorkflowClientapi_key="your_api_key"# Initialize Workflow Clientclient=WorkflowClient(api_key) # Prepare parameters for Workflow Clientuser_id="your_user_id"context="previous user interaction / metadata"user_prompt="What is the capital of France?"inputs={"context": context, "user_prompt": user_prompt, # Add other input fields expected by your workflow (e.g., additional context, task parameters) } # Set response mode (default: streaming)response_mode="blocking"# Run the workflowresponse=client.run(inputs=inputs, response_mode=response_mode, user=user_id) response.raise_for_status() # Parse resultresult=json.loads(response.text) answer=result.get("data").get("outputs") print(answer["answer"])

fromdify_clientimportKnowledgeBaseClientapi_key="your_api_key"dataset_id="your_dataset_id"# Use context manager to ensure proper resource cleanupwithKnowledgeBaseClient(api_key, dataset_id) askb_client: # Get dataset informationdataset_info=kb_client.get_dataset() dataset_info.raise_for_status() print(dataset_info.json()) # Update dataset configurationupdate_response=kb_client.update_dataset( name="Updated Dataset Name", description="Updated description", indexing_technique="high_quality" ) update_response.raise_for_status() print(update_response.json()) # Batch update document statusbatch_response=kb_client.batch_update_document_status( action="enable", document_ids=["doc_id_1", "doc_id_2", "doc_id_3"] ) batch_response.raise_for_status() print(batch_response.json())

fromdify_clientimportChatClientapi_key="your_api_key"# Use context manager to ensure proper resource cleanupwithChatClient(api_key) aschat_client: # Get all conversation variablesvariables=chat_client.get_conversation_variables( conversation_id="conversation_id", user="user_id" ) variables.raise_for_status() print(variables.json()) # Update a specific conversation variableupdate_var=chat_client.update_conversation_variable( conversation_id="conversation_id", variable_id="variable_id", value="new_value", user="user_id" ) update_var.raise_for_status() print(update_var.json())

importasynciofromdify_clientimportAsyncChatClientapi_key="your_api_key"asyncdefmain(): # Use async context manager for proper resource cleanupasyncwithAsyncChatClient(api_key) asclient: response=awaitclient.create_chat_message( inputs={}, query="Hello, how are you?", user="user_id", response_mode="blocking" ) response.raise_for_status() result=response.json() print(result.get('answer')) # Run the async functionasyncio.run(main())

importasyncioimportjsonfromdify_clientimportAsyncCompletionClientapi_key="your_api_key"asyncdefmain(): asyncwithAsyncCompletionClient(api_key) asclient: response=awaitclient.create_completion_message( inputs={"query": "What's the weather?"}, response_mode="streaming", user="user_id" ) response.raise_for_status() # Stream the responseasyncforlineinresponse.aiter_lines(): ifline.startswith('data:'): data=line[5:].strip() ifdata: chunk=json.loads(data) print(chunk.get('answer', ''), end='', flush=True) asyncio.run(main())

importasynciofromdify_clientimportAsyncWorkflowClientapi_key="your_api_key"asyncdefmain(): asyncwithAsyncWorkflowClient(api_key) asclient: response=awaitclient.run( inputs={"query": "What is machine learning?"}, response_mode="blocking", user="user_id" ) response.raise_for_status() result=response.json() print(result.get("data").get("outputs")) asyncio.run(main())

importasynciofromdify_clientimportAsyncKnowledgeBaseClientapi_key="your_api_key"dataset_id="your_dataset_id"asyncdefmain(): asyncwithAsyncKnowledgeBaseClient(api_key, dataset_id) askb_client: # Get dataset informationdataset_info=awaitkb_client.get_dataset() dataset_info.raise_for_status() print(dataset_info.json()) # List documentsdocs=awaitkb_client.list_documents(page=1, page_size=10) docs.raise_for_status() print(docs.json()) asyncio.run(main())