diff --git a/.github/workflows/api-tests.yml b/.github/workflows/api-tests.yml index aa7e68dbac..4ba91c467f 100644 --- a/.github/workflows/api-tests.yml +++ b/.github/workflows/api-tests.yml @@ -89,6 +89,5 @@ jobs: pgvecto-rs pgvector chroma - myscale - name: Test Vector Stores run: poetry run -C api bash dev/pytest/pytest_vdb.sh diff --git a/api/README.md b/api/README.md index 8d2afa5e80..70ca2e86a8 100644 --- a/api/README.md +++ b/api/README.md @@ -12,7 +12,8 @@ ```bash cd ../docker cp middleware.env.example middleware.env - docker compose -f docker-compose.middleware.yaml -p dify up -d + # change the profile to other vector database if you are not using weaviate + docker compose -f docker-compose.middleware.yaml --profile weaviate -p dify up -d cd ../api ``` diff --git a/api/configs/packaging/__init__.py b/api/configs/packaging/__init__.py index 2926ee6ac5..13c55ca425 100644 --- a/api/configs/packaging/__init__.py +++ b/api/configs/packaging/__init__.py @@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings): CURRENT_VERSION: str = Field( description='Dify version', - default='0.6.14', + default='0.6.15', ) COMMIT_SHA: str = Field( diff --git a/api/controllers/console/app/conversation.py b/api/controllers/console/app/conversation.py index 6475150405..96cd9a6ea1 100644 --- a/api/controllers/console/app/conversation.py +++ b/api/controllers/console/app/conversation.py @@ -22,7 +22,7 @@ from fields.conversation_fields import ( ) from libs.helper import datetime_string from libs.login import login_required -from models.model import AppMode, Conversation, Message, MessageAnnotation +from models.model import AppMode, Conversation, EndUser, Message, MessageAnnotation class CompletionConversationApi(Resource): @@ -156,19 +156,31 @@ class ChatConversationApi(Resource): parser.add_argument('limit', type=int_range(1, 100), required=False, default=20, location='args') args = parser.parse_args() + subquery = ( + db.session.query( + Conversation.id.label('conversation_id'), + EndUser.session_id.label('from_end_user_session_id') + ) + .outerjoin(EndUser, Conversation.from_end_user_id == EndUser.id) + .subquery() + ) + query = db.select(Conversation).where(Conversation.app_id == app_model.id) if args['keyword']: + keyword_filter = '%{}%'.format(args['keyword']) query = query.join( - Message, Message.conversation_id == Conversation.id + Message, Message.conversation_id == Conversation.id, + ).join( + subquery, subquery.c.conversation_id == Conversation.id ).filter( or_( - Message.query.ilike('%{}%'.format(args['keyword'])), - Message.answer.ilike('%{}%'.format(args['keyword'])), - Conversation.name.ilike('%{}%'.format(args['keyword'])), - Conversation.introduction.ilike('%{}%'.format(args['keyword'])), + Message.query.ilike(keyword_filter), + Message.answer.ilike(keyword_filter), + Conversation.name.ilike(keyword_filter), + Conversation.introduction.ilike(keyword_filter), + subquery.c.from_end_user_session_id.ilike(keyword_filter) ), - ) account = current_user diff --git a/api/controllers/console/app/generator.py b/api/controllers/console/app/generator.py index ee02fc1846..4e228a70ff 100644 --- a/api/controllers/console/app/generator.py +++ b/api/controllers/console/app/generator.py @@ -22,17 +22,19 @@ class RuleGenerateApi(Resource): @account_initialization_required def post(self): parser = reqparse.RequestParser() - parser.add_argument('audiences', type=str, required=True, nullable=False, location='json') - parser.add_argument('hoping_to_solve', type=str, required=True, nullable=False, location='json') + parser.add_argument('instruction', type=str, required=True, nullable=False, location='json') + parser.add_argument('model_config', type=dict, required=True, nullable=False, location='json') + parser.add_argument('no_variable', type=bool, required=True, default=False, location='json') args = parser.parse_args() account = current_user try: rules = LLMGenerator.generate_rule_config( - account.current_tenant_id, - args['audiences'], - args['hoping_to_solve'] + tenant_id=account.current_tenant_id, + instruction=args['instruction'], + model_config=args['model_config'], + no_variable=args['no_variable'] ) except ProviderTokenNotInitError as ex: raise ProviderNotInitializeError(ex.description) diff --git a/api/core/app/app_config/easy_ui_based_app/dataset/manager.py b/api/core/app/app_config/easy_ui_based_app/dataset/manager.py index c10aa98dba..13da5514d1 100644 --- a/api/core/app/app_config/easy_ui_based_app/dataset/manager.py +++ b/api/core/app/app_config/easy_ui_based_app/dataset/manager.py @@ -62,7 +62,12 @@ class DatasetConfigManager: return None # dataset configs - dataset_configs = config.get('dataset_configs', {'retrieval_model': 'single'}) + if 'dataset_configs' in config and config.get('dataset_configs'): + dataset_configs = config.get('dataset_configs') + else: + dataset_configs = { + 'retrieval_model': 'multiple' + } query_variable = config.get('dataset_query_variable') if dataset_configs['retrieval_model'] == 'single': @@ -83,9 +88,10 @@ class DatasetConfigManager: retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.value_of( dataset_configs['retrieval_model'] ), - top_k=dataset_configs.get('top_k'), + top_k=dataset_configs.get('top_k', 4), score_threshold=dataset_configs.get('score_threshold'), - reranking_model=dataset_configs.get('reranking_model') + reranking_model=dataset_configs.get('reranking_model'), + weights=dataset_configs.get('weights') ) ) @@ -114,12 +120,6 @@ class DatasetConfigManager: if not isinstance(config["dataset_configs"], dict): raise ValueError("dataset_configs must be of object type") - if config["dataset_configs"]['retrieval_model'] == 'multiple': - if not config["dataset_configs"]['reranking_model']: - raise ValueError("reranking_model has not been set") - if not isinstance(config["dataset_configs"]['reranking_model'], dict): - raise ValueError("reranking_model must be of object type") - if not isinstance(config["dataset_configs"], dict): raise ValueError("dataset_configs must be of object type") diff --git a/api/core/app/app_config/entities.py b/api/core/app/app_config/entities.py index 9b7012c3fb..9133a35c08 100644 --- a/api/core/app/app_config/entities.py +++ b/api/core/app/app_config/entities.py @@ -159,7 +159,11 @@ class DatasetRetrieveConfigEntity(BaseModel): retrieve_strategy: RetrieveStrategy top_k: Optional[int] = None score_threshold: Optional[float] = None + rerank_mode: Optional[str] = 'reranking_model' reranking_model: Optional[dict] = None + weights: Optional[dict] = None + + class DatasetEntity(BaseModel): diff --git a/api/core/app/segments/__init__.py b/api/core/app/segments/__init__.py index e5cecd35fd..0179d28887 100644 --- a/api/core/app/segments/__init__.py +++ b/api/core/app/segments/__init__.py @@ -1,11 +1,12 @@ from .segment_group import SegmentGroup -from .segments import Segment +from .segments import NoneSegment, Segment from .types import SegmentType from .variables import ( ArrayVariable, FileVariable, FloatVariable, IntegerVariable, + NoneVariable, ObjectVariable, SecretVariable, StringVariable, @@ -23,5 +24,7 @@ __all__ = [ 'Variable', 'SegmentType', 'SegmentGroup', - 'Segment' + 'Segment', + 'NoneSegment', + 'NoneVariable', ] diff --git a/api/core/app/segments/factory.py b/api/core/app/segments/factory.py index 4f0b361d95..187042ec03 100644 --- a/api/core/app/segments/factory.py +++ b/api/core/app/segments/factory.py @@ -10,6 +10,7 @@ from .variables import ( FileVariable, FloatVariable, IntegerVariable, + NoneVariable, ObjectVariable, SecretVariable, StringVariable, @@ -39,6 +40,8 @@ def build_variable_from_mapping(m: Mapping[str, Any], /) -> Variable: def build_anonymous_variable(value: Any, /) -> Variable: + if value is None: + return NoneVariable(name='anonymous') if isinstance(value, str): return StringVariable(name='anonymous', value=value) if isinstance(value, int): diff --git a/api/core/app/segments/segments.py b/api/core/app/segments/segments.py index b7ca250ff2..e6bf6cc3a3 100644 --- a/api/core/app/segments/segments.py +++ b/api/core/app/segments/segments.py @@ -43,6 +43,23 @@ class Segment(BaseModel): return self.value +class NoneSegment(Segment): + value_type: SegmentType = SegmentType.NONE + value: None = None + + @property + def text(self) -> str: + return 'null' + + @property + def log(self) -> str: + return 'null' + + @property + def markdown(self) -> str: + return 'null' + + class StringSegment(Segment): value_type: SegmentType = SegmentType.STRING value: str diff --git a/api/core/app/segments/types.py b/api/core/app/segments/types.py index 517f210533..ebcbf507c6 100644 --- a/api/core/app/segments/types.py +++ b/api/core/app/segments/types.py @@ -2,16 +2,10 @@ from enum import Enum class SegmentType(str, Enum): - STRING = 'string' + NONE = 'none' NUMBER = 'number' - FILE = 'file' - + STRING = 'string' SECRET = 'secret' - - OBJECT = 'object' - ARRAY = 'array' - ARRAY_STRING = 'array[string]' - ARRAY_NUMBER = 'array[number]' - ARRAY_OBJECT = 'array[object]' - ARRAY_FILE = 'array[file]' \ No newline at end of file + OBJECT = 'object' + FILE = 'file' diff --git a/api/core/app/segments/variables.py b/api/core/app/segments/variables.py index e600b442d6..b020914d84 100644 --- a/api/core/app/segments/variables.py +++ b/api/core/app/segments/variables.py @@ -6,7 +6,7 @@ from pydantic import Field from core.file.file_obj import FileVar from core.helper import encrypter -from .segments import Segment, StringSegment +from .segments import NoneSegment, Segment, StringSegment from .types import SegmentType @@ -20,6 +20,7 @@ class Variable(Segment): description="Unique identity for variable. It's only used by environment variables now.", ) name: str + description: str = Field(default='', description='Description of the variable.') class StringVariable(StringSegment, Variable): @@ -81,3 +82,8 @@ class SecretVariable(StringVariable): @property def log(self) -> str: return encrypter.obfuscated_token(self.value) + + +class NoneVariable(NoneSegment, Variable): + value_type: SegmentType = SegmentType.NONE + value: None = None diff --git a/api/core/llm_generator/llm_generator.py b/api/core/llm_generator/llm_generator.py index 70d3befbbd..d6a4399fc7 100644 --- a/api/core/llm_generator/llm_generator.py +++ b/api/core/llm_generator/llm_generator.py @@ -3,10 +3,13 @@ import logging import re from typing import Optional -from core.llm_generator.output_parser.errors import OutputParserException from core.llm_generator.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser from core.llm_generator.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser -from core.llm_generator.prompts import CONVERSATION_TITLE_PROMPT, GENERATOR_QA_PROMPT +from core.llm_generator.prompts import ( + CONVERSATION_TITLE_PROMPT, + GENERATOR_QA_PROMPT, + WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE, +) from core.model_manager import ModelManager from core.model_runtime.entities.message_entities import SystemPromptMessage, UserPromptMessage from core.model_runtime.entities.model_entities import ModelType @@ -115,55 +118,158 @@ class LLMGenerator: return questions @classmethod - def generate_rule_config(cls, tenant_id: str, audiences: str, hoping_to_solve: str) -> dict: + def generate_rule_config(cls, tenant_id: str, instruction: str, model_config: dict, no_variable: bool) -> dict: output_parser = RuleConfigGeneratorOutputParser() + error = "" + error_step = "" + rule_config = { + "prompt": "", + "variables": [], + "opening_statement": "", + "error": "" + } + model_parameters = { + "max_tokens": 512, + "temperature": 0.01 + } + + if no_variable: + prompt_template = PromptTemplateParser( + WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE + ) + + prompt_generate = prompt_template.format( + inputs={ + "TASK_DESCRIPTION": instruction, + }, + remove_template_variables=False + ) + + prompt_messages = [UserPromptMessage(content=prompt_generate)] + + model_manager = ModelManager() + + model_instance = model_manager.get_default_model_instance( + tenant_id=tenant_id, + model_type=ModelType.LLM, + ) + + try: + response = model_instance.invoke_llm( + prompt_messages=prompt_messages, + model_parameters=model_parameters, + stream=False + ) + + rule_config["prompt"] = response.message.content + + except InvokeError as e: + error = str(e) + error_step = "generate rule config" + except Exception as e: + logging.exception(e) + rule_config["error"] = str(e) + + rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else "" + + return rule_config + + # get rule config prompt, parameter and statement + prompt_generate, parameter_generate, statement_generate = output_parser.get_format_instructions() + prompt_template = PromptTemplateParser( - template=output_parser.get_format_instructions() + prompt_generate ) - prompt = prompt_template.format( + parameter_template = PromptTemplateParser( + parameter_generate + ) + + statement_template = PromptTemplateParser( + statement_generate + ) + + # format the prompt_generate_prompt + prompt_generate_prompt = prompt_template.format( inputs={ - "audiences": audiences, - "hoping_to_solve": hoping_to_solve, - "variable": "{{variable}}", - "lanA": "{{lanA}}", - "lanB": "{{lanB}}", - "topic": "{{topic}}" + "TASK_DESCRIPTION": instruction, }, remove_template_variables=False ) + prompt_messages = [UserPromptMessage(content=prompt_generate_prompt)] + # get model instance model_manager = ModelManager() - model_instance = model_manager.get_default_model_instance( + model_instance = model_manager.get_model_instance( tenant_id=tenant_id, model_type=ModelType.LLM, + provider=model_config.get("provider") if model_config else None, + model=model_config.get("name") if model_config else None, ) - prompt_messages = [UserPromptMessage(content=prompt)] - try: - response = model_instance.invoke_llm( - prompt_messages=prompt_messages, - model_parameters={ - "max_tokens": 512, - "temperature": 0 - }, - stream=False - ) + try: + # the first step to generate the task prompt + prompt_content = model_instance.invoke_llm( + prompt_messages=prompt_messages, + model_parameters=model_parameters, + stream=False + ) + except InvokeError as e: + error = str(e) + error_step = "generate prefix prompt" + rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else "" + + return rule_config + + rule_config["prompt"] = prompt_content.message.content + + parameter_generate_prompt = parameter_template.format( + inputs={ + "INPUT_TEXT": prompt_content.message.content, + }, + remove_template_variables=False + ) + parameter_messages = [UserPromptMessage(content=parameter_generate_prompt)] + + # the second step to generate the task_parameter and task_statement + statement_generate_prompt = statement_template.format( + inputs={ + "TASK_DESCRIPTION": instruction, + "INPUT_TEXT": prompt_content.message.content, + }, + remove_template_variables=False + ) + statement_messages = [UserPromptMessage(content=statement_generate_prompt)] + + try: + parameter_content = model_instance.invoke_llm( + prompt_messages=parameter_messages, + model_parameters=model_parameters, + stream=False + ) + rule_config["variables"] = re.findall(r'"\s*([^"]+)\s*"', parameter_content.message.content) + except InvokeError as e: + error = str(e) + error_step = "generate variables" + + try: + statement_content = model_instance.invoke_llm( + prompt_messages=statement_messages, + model_parameters=model_parameters, + stream=False + ) + rule_config["opening_statement"] = statement_content.message.content + except InvokeError as e: + error = str(e) + error_step = "generate conversation opener" - rule_config = output_parser.parse(response.message.content) - except InvokeError as e: - raise e - except OutputParserException: - raise ValueError('Please give a valid input for intended audience or hoping to solve problems.') except Exception as e: logging.exception(e) - rule_config = { - "prompt": "", - "variables": [], - "opening_statement": "" - } + rule_config["error"] = str(e) + + rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else "" return rule_config diff --git a/api/core/llm_generator/output_parser/rule_config_generator.py b/api/core/llm_generator/output_parser/rule_config_generator.py index f6d4bcf11a..8856f0c685 100644 --- a/api/core/llm_generator/output_parser/rule_config_generator.py +++ b/api/core/llm_generator/output_parser/rule_config_generator.py @@ -1,14 +1,18 @@ from typing import Any from core.llm_generator.output_parser.errors import OutputParserException -from core.llm_generator.prompts import RULE_CONFIG_GENERATE_TEMPLATE +from core.llm_generator.prompts import ( + RULE_CONFIG_PARAMETER_GENERATE_TEMPLATE, + RULE_CONFIG_PROMPT_GENERATE_TEMPLATE, + RULE_CONFIG_STATEMENT_GENERATE_TEMPLATE, +) from libs.json_in_md_parser import parse_and_check_json_markdown class RuleConfigGeneratorOutputParser: - def get_format_instructions(self) -> str: - return RULE_CONFIG_GENERATE_TEMPLATE + def get_format_instructions(self) -> tuple[str, str, str]: + return RULE_CONFIG_PROMPT_GENERATE_TEMPLATE, RULE_CONFIG_PARAMETER_GENERATE_TEMPLATE, RULE_CONFIG_STATEMENT_GENERATE_TEMPLATE def parse(self, text: str) -> Any: try: diff --git a/api/core/llm_generator/prompts.py b/api/core/llm_generator/prompts.py index a1737f00c6..87361b385a 100644 --- a/api/core/llm_generator/prompts.py +++ b/api/core/llm_generator/prompts.py @@ -81,65 +81,73 @@ GENERATOR_QA_PROMPT = ( '' ) -RULE_CONFIG_GENERATE_TEMPLATE = """Given MY INTENDED AUDIENCES and HOPING TO SOLVE using a language model, please select \ -the model prompt that best suits the input. -You will be provided with the prompt, variables, and an opening statement. -Only the content enclosed in double curly braces, such as {{variable}}, in the prompt can be considered as a variable; \ -otherwise, it cannot exist as a variable in the variables. -If you believe revising the original input will result in a better response from the language model, you may \ -suggest revisions. +WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE = """ +Here is a task description for which I would like you to create a high-quality prompt template for: + +{{TASK_DESCRIPTION}} + +Based on task description, please create a well-structured prompt template that another AI could use to consistently complete the task. The prompt template should include: +- Do not inlcude or section and variables in the prompt, assume user will add them at their own will. +- Clear instructions for the AI that will be using this prompt, demarcated with tags. The instructions should provide step-by-step directions on how to complete the task using the input variables. Also Specifies in the instructions that the output should not contain any xml tag. +- Relevant examples if needed to clarify the task further, demarcated with tags. Do not include variables in the prompt. Give three pairs of input and output examples. +- Include other relevant sections demarcated with appropriate XML tags like , . +- Use the same language as task description. +- Output in ``` xml ``` and start with +Please generate the full prompt template with at least 300 words and output only the prompt template. +""" -<> -Integrate the intended audience in the prompt e.g. the audience is an expert in the field. -Break down complex tasks into a sequence of simpler prompts in an interactive conversation. -Implement example-driven prompting (Use few-shot prompting). -When formatting your prompt start with Instruction followed by either Example if relevant. \ -Subsequently present your content. Use one or more line breaks to separate instructions examples questions context and input data. -Incorporate the following phrases: “Your task is” and “You MUST”. -Incorporate the following phrases: “You will be penalized”. -Use leading words like writing “think step by step”. -Add to your prompt the following phrase “Ensure that your answer is unbiased and does not rely on stereotypes”. -Assign a role to the large language models. -Use Delimiters. -To write an essay /text /paragraph /article or any type of text that should be detailed: “Write a detailed [essay/text/paragraph] for me on [topic] in detail by adding all the information necessary”. -Clearly state the requirements that the model must follow in order to produce content in the form of the keywords regulations hint or instructions +RULE_CONFIG_PROMPT_GENERATE_TEMPLATE = """ +Here is a task description for which I would like you to create a high-quality prompt template for: + +{{TASK_DESCRIPTION}} + +Based on task description, please create a well-structured prompt template that another AI could use to consistently complete the task. The prompt template should include: +- Descriptive variable names surrounded by {{ }} (two curly brackets) to indicate where the actual values will be substituted in. Choose variable names that clearly indicate the type of value expected. Variable names have to be composed of number, english alphabets and underline and nothing else. +- Clear instructions for the AI that will be using this prompt, demarcated with tags. The instructions should provide step-by-step directions on how to complete the task using the input variables. Also Specifies in the instructions that the output should not contain any xml tag. +- Relevant examples if needed to clarify the task further, demarcated with tags. Do not use curly brackets any other than in section. +- Any other relevant sections demarcated with appropriate XML tags like , , etc. +- Use the same language as task description. +- Output in ``` xml ``` and start with +Please generate the full prompt template and output only the prompt template. +""" -<< FORMATTING >> -Return a markdown code snippet with a JSON object formatted to look like, \ -no any other string out of markdown code snippet: -```json -{{{{ - "prompt": string \\ generated prompt - "variables": list of string \\ variables - "opening_statement": string \\ an opening statement to guide users on how to ask questions with generated prompt \ -and fill in variables, with a welcome sentence, and keep TLDR. -}}}} -``` +RULE_CONFIG_PARAMETER_GENERATE_TEMPLATE = """ +I need to extract the following information from the input text. The tag specifies the 'type', 'description' and 'required' of the information to be extracted. + +variables name bounded two double curly brackets. Variable name has to be composed of number, english alphabets and underline and nothing else. + -<< EXAMPLES >> -[EXAMPLE A] -```json -{ - "prompt": "I need your help to translate the following {{Input_language}}paper paragraph into {{Target_language}}, in a style similar to a popular science magazine in {{Target_language}}. #### Rules Ensure accurate conveyance of the original text's facts and context during translation. Maintain the original paragraph format and retain technical terms and company abbreviations ", - "variables": ["Input_language", "Target_language"], - "opening_statement": " Hi. I am your translation assistant. I can help you with any translation and ensure accurate conveyance of information. " -} -``` +Step 1: Carefully read the input and understand the structure of the expected output. +Step 2: Extract relevant parameters from the provided text based on the name and description of object. +Step 3: Structure the extracted parameters to JSON object as specified in . +Step 4: Ensure that the list of variable_names is properly formatted and valid. The output should not contain any XML tags. Output an empty list if there is no valid variable name in input text. -[EXAMPLE B] -```json -{ - "prompt": "Your task is to review the provided meeting notes and create a concise summary that captures the essential information, focusing on key takeaways and action items assigned to specific individuals or departments during the meeting. Use clear and professional language, and organize the summary in a logical manner using appropriate formatting such as headings, subheadings, and bullet points. Ensure that the summary is easy to understand and provides a comprehensive but succinct overview of the meeting's content, with a particular focus on clearly indicating who is responsible for each action item.", - "variables": ["meeting_notes"], - "opening_statement": "Hi! I'm your meeting notes summarizer AI. I can help you with any meeting notes and ensure accurate conveyance of information." -} -``` +### Structure +Here is the structure of the expected output, I should always follow the output structure. +["variable_name_1", "variable_name_2"] -<< MY INTENDED AUDIENCES >> -{{audiences}} +### Input Text +Inside XML tags, there is a text that I should extract parameters and convert to a JSON object. + +{{INPUT_TEXT}} + -<< HOPING TO SOLVE >> -{{hoping_to_solve}} +### Answer +I should always output a valid list. Output nothing other than the list of variable_name. Output an empty list if there is no variable name in input text. +""" -<< OUTPUT >> -""" \ No newline at end of file +RULE_CONFIG_STATEMENT_GENERATE_TEMPLATE = """ + +Step 1: Identify the purpose of the chatbot from the variable {{TASK_DESCRIPTION}} and infer chatbot's tone (e.g., friendly, professional, etc.) to add personality traits. +Step 2: Create a coherent and engaging opening statement. +Step 3: Ensure the output is welcoming and clearly explains what the chatbot is designed to do. Do not include any XML tags in the output. +Please use the same language as the user's input language. If user uses chinese then generate opening statement in chinese, if user uses english then generate opening statement in english. +Example Input: +Provide customer support for an e-commerce website +Example Output: +Welcome! I'm here to assist you with any questions or issues you might have with your shopping experience. Whether you're looking for product information, need help with your order, or have any other inquiries, feel free to ask. I'm friendly, helpful, and ready to support you in any way I can. + +Here is the task description: {{INPUT_TEXT}} + +You just need to generate the output +""" diff --git a/api/core/model_runtime/docs/en_US/schema.md b/api/core/model_runtime/docs/en_US/schema.md index 2e55d05b0f..67f4e0879d 100644 --- a/api/core/model_runtime/docs/en_US/schema.md +++ b/api/core/model_runtime/docs/en_US/schema.md @@ -86,6 +86,9 @@ - `agent-thought` Agent reasoning, generally over 70B with thought chain capability. - `vision` Vision, i.e., image understanding. +- `tool-call` +- `multi-tool-call` +- `stream-tool-call` ### FetchFrom diff --git a/api/core/model_runtime/docs/zh_Hans/schema.md b/api/core/model_runtime/docs/zh_Hans/schema.md index f40a3f8698..681f49c435 100644 --- a/api/core/model_runtime/docs/zh_Hans/schema.md +++ b/api/core/model_runtime/docs/zh_Hans/schema.md @@ -87,6 +87,9 @@ - `agent-thought` Agent 推理，一般超过 70B 有思维链能力。 - `vision` 视觉，即：图像理解。 +- `tool-call` 工具调用 +- `multi-tool-call` 多工具调用 +- `stream-tool-call` 流式工具调用 ### FetchFrom diff --git a/api/core/model_runtime/model_providers/groq/llm/_position.yaml b/api/core/model_runtime/model_providers/groq/llm/_position.yaml new file mode 100644 index 0000000000..be115ca920 --- /dev/null +++ b/api/core/model_runtime/model_providers/groq/llm/_position.yaml @@ -0,0 +1,7 @@ +- llama-3.1-405b-reasoning +- llama-3.1-70b-versatile +- llama-3.1-8b-instant +- llama3-70b-8192 +- llama3-8b-8192 +- mixtral-8x7b-32768 +- llama2-70b-4096 diff --git a/api/core/model_runtime/model_providers/groq/llm/llama-3.1-405b-reasoning.yaml b/api/core/model_runtime/model_providers/groq/llm/llama-3.1-405b-reasoning.yaml new file mode 100644 index 0000000000..217785cea2 --- /dev/null +++ b/api/core/model_runtime/model_providers/groq/llm/llama-3.1-405b-reasoning.yaml @@ -0,0 +1,25 @@ +model: llama-3.1-405b-reasoning +label: + zh_Hans: Llama-3.1-405b-reasoning + en_US: Llama-3.1-405b-reasoning +model_type: llm +features: + - agent-thought +model_properties: + mode: chat + context_size: 131072 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: max_tokens + use_template: max_tokens + default: 512 + min: 1 + max: 8192 +pricing: + input: '0.05' + output: '0.1' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/groq/llm/llama-3.1-70b-versatile.yaml b/api/core/model_runtime/model_providers/groq/llm/llama-3.1-70b-versatile.yaml new file mode 100644 index 0000000000..ab5f6ab05e --- /dev/null +++ b/api/core/model_runtime/model_providers/groq/llm/llama-3.1-70b-versatile.yaml @@ -0,0 +1,25 @@ +model: llama-3.1-70b-versatile +label: + zh_Hans: Llama-3.1-70b-versatile + en_US: Llama-3.1-70b-versatile +model_type: llm +features: + - agent-thought +model_properties: + mode: chat + context_size: 131072 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: max_tokens + use_template: max_tokens + default: 512 + min: 1 + max: 8192 +pricing: + input: '0.05' + output: '0.1' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/groq/llm/llama-3.1-8b-instant.yaml b/api/core/model_runtime/model_providers/groq/llm/llama-3.1-8b-instant.yaml new file mode 100644 index 0000000000..a82e64532e --- /dev/null +++ b/api/core/model_runtime/model_providers/groq/llm/llama-3.1-8b-instant.yaml @@ -0,0 +1,25 @@ +model: llama-3.1-8b-instant +label: + zh_Hans: Llama-3.1-8b-instant + en_US: Llama-3.1-8b-instant +model_type: llm +features: + - agent-thought +model_properties: + mode: chat + context_size: 131072 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: max_tokens + use_template: max_tokens + default: 512 + min: 1 + max: 8192 +pricing: + input: '0.05' + output: '0.1' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/openrouter/llm/_position.yaml b/api/core/model_runtime/model_providers/openrouter/llm/_position.yaml index fd4ed1109d..7e00dd3f4b 100644 --- a/api/core/model_runtime/model_providers/openrouter/llm/_position.yaml +++ b/api/core/model_runtime/model_providers/openrouter/llm/_position.yaml @@ -12,6 +12,9 @@ - google/gemini-pro - cohere/command-r-plus - cohere/command-r +- meta-llama/llama-3.1-405b-instruct +- meta-llama/llama-3.1-70b-instruct +- meta-llama/llama-3.1-8b-instruct - meta-llama/llama-3-70b-instruct - meta-llama/llama-3-8b-instruct - mistralai/mixtral-8x22b-instruct diff --git a/api/core/model_runtime/model_providers/openrouter/llm/llama-3.1-405b-instruct.yaml b/api/core/model_runtime/model_providers/openrouter/llm/llama-3.1-405b-instruct.yaml new file mode 100644 index 0000000000..7d68e708b7 --- /dev/null +++ b/api/core/model_runtime/model_providers/openrouter/llm/llama-3.1-405b-instruct.yaml @@ -0,0 +1,23 @@ +model: meta-llama/llama-3.1-405b-instruct +label: + en_US: llama-3.1-405b-instruct +model_type: llm +model_properties: + mode: chat + context_size: 128000 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: max_tokens + use_template: max_tokens + required: true + default: 512 + min: 1 + max: 128000 +pricing: + input: "3" + output: "3" + unit: "0.000001" + currency: USD diff --git a/api/core/model_runtime/model_providers/openrouter/llm/llama-3.1-70b-instruct.yaml b/api/core/model_runtime/model_providers/openrouter/llm/llama-3.1-70b-instruct.yaml new file mode 100644 index 0000000000..78e3b45435 --- /dev/null +++ b/api/core/model_runtime/model_providers/openrouter/llm/llama-3.1-70b-instruct.yaml @@ -0,0 +1,23 @@ +model: meta-llama/llama-3.1-70b-instruct +label: + en_US: llama-3.1-70b-instruct +model_type: llm +model_properties: + mode: chat + context_size: 128000 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: max_tokens + use_template: max_tokens + required: true + default: 512 + min: 1 + max: 128000 +pricing: + input: "0.9" + output: "0.9" + unit: "0.000001" + currency: USD diff --git a/api/core/model_runtime/model_providers/openrouter/llm/llama-3.1-8b-instruct.yaml b/api/core/model_runtime/model_providers/openrouter/llm/llama-3.1-8b-instruct.yaml new file mode 100644 index 0000000000..6e69b7deb7 --- /dev/null +++ b/api/core/model_runtime/model_providers/openrouter/llm/llama-3.1-8b-instruct.yaml @@ -0,0 +1,23 @@ +model: meta-llama/llama-3.1-8b-instruct +label: + en_US: llama-3.1-8b-instruct +model_type: llm +model_properties: + mode: chat + context_size: 128000 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: max_tokens + use_template: max_tokens + required: true + default: 512 + min: 1 + max: 128000 +pricing: + input: "0.2" + output: "0.2" + unit: "0.000001" + currency: USD diff --git a/api/core/model_runtime/model_providers/tongyi/llm/llm.py b/api/core/model_runtime/model_providers/tongyi/llm/llm.py index 41d8f37aaf..1f018c4078 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/llm.py +++ b/api/core/model_runtime/model_providers/tongyi/llm/llm.py @@ -262,6 +262,10 @@ You should also complete the text started with ``` but not tell ``` directly. :param prompt_messages: prompt messages :return: llm response """ + if response.status_code != 200 and response.status_code != HTTPStatus.OK: + raise ServiceUnavailableError( + response.message + ) # transform assistant message to prompt message assistant_prompt_message = AssistantPromptMessage( content=response.output.choices[0].message.content, @@ -421,7 +425,7 @@ You should also complete the text started with ``` but not tell ``` directly. raise ValueError(f"Got unknown type {message}") return message_text - + def _convert_messages_to_prompt(self, messages: list[PromptMessage]) -> str: """ Format a list of messages into a full prompt for the Anthropic model @@ -496,6 +500,9 @@ You should also complete the text started with ``` but not tell ``` directly. tongyi_messages.append({ 'role': 'assistant', 'content': content if not rich_content else [{"text": content}], + 'tool_calls': [tool_call.model_dump() for tool_call in + prompt_message.tool_calls] if prompt_message.tool_calls else [] + }) elif isinstance(prompt_message, ToolPromptMessage): tongyi_messages.append({ diff --git a/api/core/ops/utils.py b/api/core/ops/utils.py index 2b12db0f48..3b2e04abb7 100644 --- a/api/core/ops/utils.py +++ b/api/core/ops/utils.py @@ -25,7 +25,6 @@ def measure_time(): yield timing_info finally: timing_info['end'] = datetime.now() - print(f"Execution time: {timing_info['end'] - timing_info['start']}") def replace_text_with_content(data): diff --git a/api/core/rag/data_post_processor/data_post_processor.py b/api/core/rag/data_post_processor/data_post_processor.py index a0f2947784..2ed6d74187 100644 --- a/api/core/rag/data_post_processor/data_post_processor.py +++ b/api/core/rag/data_post_processor/data_post_processor.py @@ -5,15 +5,20 @@ from core.model_runtime.entities.model_entities import ModelType from core.model_runtime.errors.invoke import InvokeAuthorizationError from core.rag.data_post_processor.reorder import ReorderRunner from core.rag.models.document import Document -from core.rag.rerank.rerank import RerankRunner +from core.rag.rerank.constants.rerank_mode import RerankMode +from core.rag.rerank.entity.weight import KeywordSetting, VectorSetting, Weights +from core.rag.rerank.rerank_model import RerankModelRunner +from core.rag.rerank.weight_rerank import WeightRerankRunner class DataPostProcessor: """Interface for data post-processing document. """ - def __init__(self, tenant_id: str, reranking_model: dict, reorder_enabled: bool = False): - self.rerank_runner = self._get_rerank_runner(reranking_model, tenant_id) + def __init__(self, tenant_id: str, reranking_mode: str, + reranking_model: Optional[dict] = None, weights: Optional[dict] = None, + reorder_enabled: bool = False): + self.rerank_runner = self._get_rerank_runner(reranking_mode, tenant_id, reranking_model, weights) self.reorder_runner = self._get_reorder_runner(reorder_enabled) def invoke(self, query: str, documents: list[Document], score_threshold: Optional[float] = None, @@ -26,19 +31,37 @@ class DataPostProcessor: return documents - def _get_rerank_runner(self, reranking_model: dict, tenant_id: str) -> Optional[RerankRunner]: - if reranking_model: - try: - model_manager = ModelManager() - rerank_model_instance = model_manager.get_model_instance( - tenant_id=tenant_id, - provider=reranking_model['reranking_provider_name'], - model_type=ModelType.RERANK, - model=reranking_model['reranking_model_name'] + def _get_rerank_runner(self, reranking_mode: str, tenant_id: str, reranking_model: Optional[dict] = None, + weights: Optional[dict] = None) -> Optional[RerankModelRunner | WeightRerankRunner]: + if reranking_mode == RerankMode.WEIGHTED_SCORE.value and weights: + return WeightRerankRunner( + tenant_id, + Weights( + weight_type=weights['weight_type'], + vector_setting=VectorSetting( + vector_weight=weights['vector_setting']['vector_weight'], + embedding_provider_name=weights['vector_setting']['embedding_provider_name'], + embedding_model_name=weights['vector_setting']['embedding_model_name'], + ), + keyword_setting=KeywordSetting( + keyword_weight=weights['keyword_setting']['keyword_weight'], + ) ) - except InvokeAuthorizationError: - return None - return RerankRunner(rerank_model_instance) + ) + elif reranking_mode == RerankMode.RERANKING_MODEL.value: + if reranking_model: + try: + model_manager = ModelManager() + rerank_model_instance = model_manager.get_model_instance( + tenant_id=tenant_id, + provider=reranking_model['reranking_provider_name'], + model_type=ModelType.RERANK, + model=reranking_model['reranking_model_name'] + ) + except InvokeAuthorizationError: + return None + return RerankModelRunner(rerank_model_instance) + return None return None def _get_reorder_runner(self, reorder_enabled) -> Optional[ReorderRunner]: diff --git a/api/core/rag/datasource/keyword/jieba/jieba_keyword_table_handler.py b/api/core/rag/datasource/keyword/jieba/jieba_keyword_table_handler.py index 5f862b8d18..ad669ef515 100644 --- a/api/core/rag/datasource/keyword/jieba/jieba_keyword_table_handler.py +++ b/api/core/rag/datasource/keyword/jieba/jieba_keyword_table_handler.py @@ -1,4 +1,5 @@ import re +from typing import Optional import jieba from jieba.analyse import default_tfidf @@ -11,7 +12,7 @@ class JiebaKeywordTableHandler: def __init__(self): default_tfidf.stop_words = STOPWORDS - def extract_keywords(self, text: str, max_keywords_per_chunk: int = 10) -> set[str]: + def extract_keywords(self, text: str, max_keywords_per_chunk: Optional[int] = 10) -> set[str]: """Extract keywords with JIEBA tfidf.""" keywords = jieba.analyse.extract_tags( sentence=text, diff --git a/api/core/rag/datasource/retrieval_service.py b/api/core/rag/datasource/retrieval_service.py index 702dcec314..abbf4a35a4 100644 --- a/api/core/rag/datasource/retrieval_service.py +++ b/api/core/rag/datasource/retrieval_service.py @@ -6,6 +6,7 @@ from flask import Flask, current_app from core.rag.data_post_processor.data_post_processor import DataPostProcessor from core.rag.datasource.keyword.keyword_factory import Keyword from core.rag.datasource.vdb.vector_factory import Vector +from core.rag.rerank.constants.rerank_mode import RerankMode from core.rag.retrieval.retrival_methods import RetrievalMethod from extensions.ext_database import db from models.dataset import Dataset @@ -26,13 +27,19 @@ class RetrievalService: @classmethod def retrieve(cls, retrival_method: str, dataset_id: str, query: str, - top_k: int, score_threshold: Optional[float] = .0, reranking_model: Optional[dict] = None): + top_k: int, score_threshold: Optional[float] = .0, + reranking_model: Optional[dict] = None, reranking_mode: Optional[str] = None, + weights: Optional[dict] = None): dataset = db.session.query(Dataset).filter( Dataset.id == dataset_id ).first() if not dataset or dataset.available_document_count == 0 or dataset.available_segment_count == 0: return [] all_documents = [] + keyword_search_documents = [] + embedding_search_documents = [] + full_text_search_documents = [] + hybrid_search_documents = [] threads = [] exceptions = [] # retrieval_model source with keyword @@ -87,7 +94,8 @@ class RetrievalService: raise Exception(exception_message) if retrival_method == RetrievalMethod.HYBRID_SEARCH.value: - data_post_processor = DataPostProcessor(str(dataset.tenant_id), reranking_model, False) + data_post_processor = DataPostProcessor(str(dataset.tenant_id), reranking_mode, + reranking_model, weights, False) all_documents = data_post_processor.invoke( query=query, documents=all_documents, @@ -143,7 +151,9 @@ class RetrievalService: if documents: if reranking_model and retrival_method == RetrievalMethod.SEMANTIC_SEARCH.value: - data_post_processor = DataPostProcessor(str(dataset.tenant_id), reranking_model, False) + data_post_processor = DataPostProcessor(str(dataset.tenant_id), + RerankMode.RERANKING_MODEL.value, + reranking_model, None, False) all_documents.extend(data_post_processor.invoke( query=query, documents=documents, @@ -175,7 +185,9 @@ class RetrievalService: ) if documents: if reranking_model and retrival_method == RetrievalMethod.FULL_TEXT_SEARCH.value: - data_post_processor = DataPostProcessor(str(dataset.tenant_id), reranking_model, False) + data_post_processor = DataPostProcessor(str(dataset.tenant_id), + RerankMode.RERANKING_MODEL.value, + reranking_model, None, False) all_documents.extend(data_post_processor.invoke( query=query, documents=documents, diff --git a/api/core/rag/datasource/vdb/qdrant/qdrant_vector.py b/api/core/rag/datasource/vdb/qdrant/qdrant_vector.py index f9a9389868..77c3f6a271 100644 --- a/api/core/rag/datasource/vdb/qdrant/qdrant_vector.py +++ b/api/core/rag/datasource/vdb/qdrant/qdrant_vector.py @@ -396,9 +396,11 @@ class QdrantVector(BaseVector): documents = [] for result in results: if result: - documents.append(self._document_from_scored_point( + document = self._document_from_scored_point( result, Field.CONTENT_KEY.value, Field.METADATA_KEY.value - )) + ) + document.metadata['vector'] = result.vector + documents.append(document) return documents diff --git a/api/core/rag/docstore/__init__.py b/api/core/rag/docstore/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/api/core/rag/rerank/constants/rerank_mode.py b/api/core/rag/rerank/constants/rerank_mode.py new file mode 100644 index 0000000000..afbb9fd89d --- /dev/null +++ b/api/core/rag/rerank/constants/rerank_mode.py @@ -0,0 +1,8 @@ +from enum import Enum + + +class RerankMode(Enum): + + RERANKING_MODEL = 'reranking_model' + WEIGHTED_SCORE = 'weighted_score' + diff --git a/api/core/rag/rerank/entity/weight.py b/api/core/rag/rerank/entity/weight.py new file mode 100644 index 0000000000..36afc89a21 --- /dev/null +++ b/api/core/rag/rerank/entity/weight.py @@ -0,0 +1,23 @@ +from pydantic import BaseModel + + +class VectorSetting(BaseModel): + vector_weight: float + + embedding_provider_name: str + + embedding_model_name: str + + +class KeywordSetting(BaseModel): + keyword_weight: float + + +class Weights(BaseModel): + """Model for weighted rerank.""" + + weight_type: str + + vector_setting: VectorSetting + + keyword_setting: KeywordSetting diff --git a/api/core/rag/rerank/rerank.py b/api/core/rag/rerank/rerank_model.py similarity index 98% rename from api/core/rag/rerank/rerank.py rename to api/core/rag/rerank/rerank_model.py index 7000f4e0ad..d9067da288 100644 --- a/api/core/rag/rerank/rerank.py +++ b/api/core/rag/rerank/rerank_model.py @@ -4,7 +4,7 @@ from core.model_manager import ModelInstance from core.rag.models.document import Document -class RerankRunner: +class RerankModelRunner: def __init__(self, rerank_model_instance: ModelInstance) -> None: self.rerank_model_instance = rerank_model_instance diff --git a/api/core/rag/rerank/weight_rerank.py b/api/core/rag/rerank/weight_rerank.py new file mode 100644 index 0000000000..d07f94adb7 --- /dev/null +++ b/api/core/rag/rerank/weight_rerank.py @@ -0,0 +1,178 @@ +import math +from collections import Counter +from typing import Optional + +import numpy as np + +from core.embedding.cached_embedding import CacheEmbedding +from core.model_manager import ModelManager +from core.model_runtime.entities.model_entities import ModelType +from core.rag.datasource.keyword.jieba.jieba_keyword_table_handler import JiebaKeywordTableHandler +from core.rag.models.document import Document +from core.rag.rerank.entity.weight import VectorSetting, Weights + + +class WeightRerankRunner: + + def __init__(self, tenant_id: str, weights: Weights) -> None: + self.tenant_id = tenant_id + self.weights = weights + + def run(self, query: str, documents: list[Document], score_threshold: Optional[float] = None, + top_n: Optional[int] = None, user: Optional[str] = None) -> list[Document]: + """ + Run rerank model + :param query: search query + :param documents: documents for reranking + :param score_threshold: score threshold + :param top_n: top n + :param user: unique user id if needed + + :return: + """ + docs = [] + doc_id = [] + unique_documents = [] + for document in documents: + if document.metadata['doc_id'] not in doc_id: + doc_id.append(document.metadata['doc_id']) + docs.append(document.page_content) + unique_documents.append(document) + + documents = unique_documents + + rerank_documents = [] + query_scores = self._calculate_keyword_score(query, documents) + + query_vector_scores = self._calculate_cosine(self.tenant_id, query, documents, self.weights.vector_setting) + for document, query_score, query_vector_score in zip(documents, query_scores, query_vector_scores): + # format document + score = self.weights.vector_setting.vector_weight * query_vector_score + \ + self.weights.keyword_setting.keyword_weight * query_score + if score_threshold and score < score_threshold: + continue + document.metadata['score'] = score + rerank_documents.append(document) + rerank_documents = sorted(rerank_documents, key=lambda x: x.metadata['score'], reverse=True) + return rerank_documents[:top_n] if top_n else rerank_documents + + def _calculate_keyword_score(self, query: str, documents: list[Document]) -> list[float]: + """ + Calculate BM25 scores + :param query: search query + :param documents: documents for reranking + + :return: + """ + keyword_table_handler = JiebaKeywordTableHandler() + query_keywords = keyword_table_handler.extract_keywords(query, None) + documents_keywords = [] + for document in documents: + # get the document keywords + document_keywords = keyword_table_handler.extract_keywords(document.page_content, None) + document.metadata['keywords'] = document_keywords + documents_keywords.append(document_keywords) + + # Counter query keywords(TF) + query_keyword_counts = Counter(query_keywords) + + # total documents + total_documents = len(documents) + + # calculate all documents' keywords IDF + all_keywords = set() + for document_keywords in documents_keywords: + all_keywords.update(document_keywords) + + keyword_idf = {} + for keyword in all_keywords: + # calculate include query keywords' documents + doc_count_containing_keyword = sum(1 for doc_keywords in documents_keywords if keyword in doc_keywords) + # IDF + keyword_idf[keyword] = math.log((1 + total_documents) / (1 + doc_count_containing_keyword)) + 1 + + query_tfidf = {} + + for keyword, count in query_keyword_counts.items(): + tf = count + idf = keyword_idf.get(keyword, 0) + query_tfidf[keyword] = tf * idf + + # calculate all documents' TF-IDF + documents_tfidf = [] + for document_keywords in documents_keywords: + document_keyword_counts = Counter(document_keywords) + document_tfidf = {} + for keyword, count in document_keyword_counts.items(): + tf = count + idf = keyword_idf.get(keyword, 0) + document_tfidf[keyword] = tf * idf + documents_tfidf.append(document_tfidf) + + def cosine_similarity(vec1, vec2): + intersection = set(vec1.keys()) & set(vec2.keys()) + numerator = sum(vec1[x] * vec2[x] for x in intersection) + + sum1 = sum(vec1[x] ** 2 for x in vec1.keys()) + sum2 = sum(vec2[x] ** 2 for x in vec2.keys()) + denominator = math.sqrt(sum1) * math.sqrt(sum2) + + if not denominator: + return 0.0 + else: + return float(numerator) / denominator + + similarities = [] + for document_tfidf in documents_tfidf: + similarity = cosine_similarity(query_tfidf, document_tfidf) + similarities.append(similarity) + + # for idx, similarity in enumerate(similarities): + # print(f"Document {idx + 1} similarity: {similarity}") + + return similarities + + def _calculate_cosine(self, tenant_id: str, query: str, documents: list[Document], + vector_setting: VectorSetting) -> list[float]: + """ + Calculate Cosine scores + :param query: search query + :param documents: documents for reranking + + :return: + """ + query_vector_scores = [] + + model_manager = ModelManager() + + embedding_model = model_manager.get_model_instance( + tenant_id=tenant_id, + provider=vector_setting.embedding_provider_name, + model_type=ModelType.TEXT_EMBEDDING, + model=vector_setting.embedding_model_name + + ) + cache_embedding = CacheEmbedding(embedding_model) + query_vector = cache_embedding.embed_query(query) + for document in documents: + # calculate cosine similarity + if 'score' in document.metadata: + query_vector_scores.append(document.metadata['score']) + else: + content_vector = document.metadata['vector'] + # transform to NumPy + vec1 = np.array(query_vector) + vec2 = np.array(document.metadata['vector']) + + # calculate dot product + dot_product = np.dot(vec1, vec2) + + # calculate norm + norm_vec1 = np.linalg.norm(vec1) + norm_vec2 = np.linalg.norm(vec2) + + # calculate cosine similarity + cosine_sim = dot_product / (norm_vec1 * norm_vec2) + query_vector_scores.append(cosine_sim) + + return query_vector_scores diff --git a/api/core/rag/retrieval/dataset_retrieval.py b/api/core/rag/retrieval/dataset_retrieval.py index c1f5e0820c..d51ea2942a 100644 --- a/api/core/rag/retrieval/dataset_retrieval.py +++ b/api/core/rag/retrieval/dataset_retrieval.py @@ -1,4 +1,6 @@ +import math import threading +from collections import Counter from typing import Optional, cast from flask import Flask, current_app @@ -14,9 +16,10 @@ from core.model_runtime.entities.model_entities import ModelFeature, ModelType from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel from core.ops.ops_trace_manager import TraceQueueManager, TraceTask, TraceTaskName from core.ops.utils import measure_time +from core.rag.data_post_processor.data_post_processor import DataPostProcessor +from core.rag.datasource.keyword.jieba.jieba_keyword_table_handler import JiebaKeywordTableHandler from core.rag.datasource.retrieval_service import RetrievalService from core.rag.models.document import Document -from core.rag.rerank.rerank import RerankRunner from core.rag.retrieval.retrival_methods import RetrievalMethod from core.rag.retrieval.router.multi_dataset_function_call_router import FunctionCallMultiDatasetRouter from core.rag.retrieval.router.multi_dataset_react_route import ReactMultiDatasetRouter @@ -132,8 +135,9 @@ class DatasetRetrieval: app_id, tenant_id, user_id, user_from, available_datasets, query, retrieve_config.top_k, retrieve_config.score_threshold, - retrieve_config.reranking_model.get('reranking_provider_name'), - retrieve_config.reranking_model.get('reranking_model_name'), + retrieve_config.rerank_mode, + retrieve_config.reranking_model, + retrieve_config.weights, message_id, ) @@ -272,7 +276,8 @@ class DatasetRetrieval: retrival_method=retrival_method, dataset_id=dataset.id, query=query, top_k=top_k, score_threshold=score_threshold, - reranking_model=reranking_model + reranking_model=reranking_model, + weights=retrieval_model_config.get('weights', None), ) self._on_query(query, [dataset_id], app_id, user_from, user_id) @@ -292,14 +297,18 @@ class DatasetRetrieval: query: str, top_k: int, score_threshold: float, - reranking_provider_name: str, - reranking_model_name: str, + reranking_mode: str, + reranking_model: Optional[dict] = None, + weights: Optional[dict] = None, + reranking_enable: bool = True, message_id: Optional[str] = None, ): threads = [] all_documents = [] dataset_ids = [dataset.id for dataset in available_datasets] + index_type = None for dataset in available_datasets: + index_type = dataset.indexing_technique retrieval_thread = threading.Thread(target=self._retriever, kwargs={ 'flask_app': current_app._get_current_object(), 'dataset_id': dataset.id, @@ -311,23 +320,24 @@ class DatasetRetrieval: retrieval_thread.start() for thread in threads: thread.join() - # do rerank for searched documents - model_manager = ModelManager() - rerank_model_instance = model_manager.get_model_instance( - tenant_id=tenant_id, - provider=reranking_provider_name, - model_type=ModelType.RERANK, - model=reranking_model_name - ) - rerank_runner = RerankRunner(rerank_model_instance) + if reranking_enable: + # do rerank for searched documents + data_post_processor = DataPostProcessor(tenant_id, reranking_mode, + reranking_model, weights, False) - with measure_time() as timer: - all_documents = rerank_runner.run( - query, all_documents, - score_threshold, - top_k - ) + with measure_time() as timer: + all_documents = data_post_processor.invoke( + query=query, + documents=all_documents, + score_threshold=score_threshold, + top_n=top_k + ) + else: + if index_type == "economy": + all_documents = self.calculate_keyword_score(query, all_documents, top_k) + elif index_type == "high_quality": + all_documents = self.calculate_vector_score(all_documents, top_k, score_threshold) self._on_query(query, dataset_ids, app_id, user_from, user_id) if all_documents: @@ -420,7 +430,8 @@ class DatasetRetrieval: score_threshold=retrieval_model['score_threshold'] if retrieval_model['score_threshold_enabled'] else None, reranking_model=retrieval_model['reranking_model'] - if retrieval_model['reranking_enable'] else None + if retrieval_model['reranking_enable'] else None, + weights=retrieval_model.get('weights', None), ) all_documents.extend(documents) @@ -513,3 +524,94 @@ class DatasetRetrieval: tools.append(tool) return tools + + def calculate_keyword_score(self, query: str, documents: list[Document], top_k: int) -> list[Document]: + """ + Calculate keywords scores + :param query: search query + :param documents: documents for reranking + + :return: + """ + keyword_table_handler = JiebaKeywordTableHandler() + query_keywords = keyword_table_handler.extract_keywords(query, None) + documents_keywords = [] + for document in documents: + # get the document keywords + document_keywords = keyword_table_handler.extract_keywords(document.page_content, None) + document.metadata['keywords'] = document_keywords + documents_keywords.append(document_keywords) + + # Counter query keywords(TF) + query_keyword_counts = Counter(query_keywords) + + # total documents + total_documents = len(documents) + + # calculate all documents' keywords IDF + all_keywords = set() + for document_keywords in documents_keywords: + all_keywords.update(document_keywords) + + keyword_idf = {} + for keyword in all_keywords: + # calculate include query keywords' documents + doc_count_containing_keyword = sum(1 for doc_keywords in documents_keywords if keyword in doc_keywords) + # IDF + keyword_idf[keyword] = math.log((1 + total_documents) / (1 + doc_count_containing_keyword)) + 1 + + query_tfidf = {} + + for keyword, count in query_keyword_counts.items(): + tf = count + idf = keyword_idf.get(keyword, 0) + query_tfidf[keyword] = tf * idf + + # calculate all documents' TF-IDF + documents_tfidf = [] + for document_keywords in documents_keywords: + document_keyword_counts = Counter(document_keywords) + document_tfidf = {} + for keyword, count in document_keyword_counts.items(): + tf = count + idf = keyword_idf.get(keyword, 0) + document_tfidf[keyword] = tf * idf + documents_tfidf.append(document_tfidf) + + def cosine_similarity(vec1, vec2): + intersection = set(vec1.keys()) & set(vec2.keys()) + numerator = sum(vec1[x] * vec2[x] for x in intersection) + + sum1 = sum(vec1[x] ** 2 for x in vec1.keys()) + sum2 = sum(vec2[x] ** 2 for x in vec2.keys()) + denominator = math.sqrt(sum1) * math.sqrt(sum2) + + if not denominator: + return 0.0 + else: + return float(numerator) / denominator + + similarities = [] + for document_tfidf in documents_tfidf: + similarity = cosine_similarity(query_tfidf, document_tfidf) + similarities.append(similarity) + + for document, score in zip(documents, similarities): + # format document + document.metadata['score'] = score + documents = sorted(documents, key=lambda x: x.metadata['score'], reverse=True) + return documents[:top_k] if top_k else documents + + def calculate_vector_score(self, all_documents: list[Document], + top_k: int, score_threshold: float) -> list[Document]: + filter_documents = [] + for document in all_documents: + if document.metadata['score'] >= score_threshold: + filter_documents.append(document) + if not filter_documents: + return [] + filter_documents = sorted(filter_documents, key=lambda x: x.metadata['score'], reverse=True) + return filter_documents[:top_k] if top_k else filter_documents + + + diff --git a/api/core/rag/splitter/__init__.py b/api/core/rag/splitter/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/api/core/tools/tool/dataset_retriever/dataset_multi_retriever_tool.py b/api/core/tools/tool/dataset_retriever/dataset_multi_retriever_tool.py index eaf58ed5bd..1a0933af16 100644 --- a/api/core/tools/tool/dataset_retriever/dataset_multi_retriever_tool.py +++ b/api/core/tools/tool/dataset_retriever/dataset_multi_retriever_tool.py @@ -7,7 +7,7 @@ from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCa from core.model_manager import ModelManager from core.model_runtime.entities.model_entities import ModelType from core.rag.datasource.retrieval_service import RetrievalService -from core.rag.rerank.rerank import RerankRunner +from core.rag.rerank.rerank_model import RerankModelRunner from core.rag.retrieval.retrival_methods import RetrievalMethod from core.tools.tool.dataset_retriever.dataset_retriever_base_tool import DatasetRetrieverBaseTool from extensions.ext_database import db @@ -72,7 +72,7 @@ class DatasetMultiRetrieverTool(DatasetRetrieverBaseTool): model=self.reranking_model_name ) - rerank_runner = RerankRunner(rerank_model_instance) + rerank_runner = RerankModelRunner(rerank_model_instance) all_documents = rerank_runner.run(query, all_documents, self.score_threshold, self.top_k) for hit_callback in self.hit_callbacks: @@ -180,7 +180,8 @@ class DatasetMultiRetrieverTool(DatasetRetrieverBaseTool): score_threshold=retrieval_model['score_threshold'] if retrieval_model['score_threshold_enabled'] else None, reranking_model=retrieval_model['reranking_model'] - if retrieval_model['reranking_enable'] else None + if retrieval_model['reranking_enable'] else None, + weights=retrieval_model.get('weights', None), ) all_documents.extend(documents) \ No newline at end of file diff --git a/api/core/tools/tool/dataset_retriever/dataset_retriever_tool.py b/api/core/tools/tool/dataset_retriever/dataset_retriever_tool.py index b1e541b8db..397ff7966e 100644 --- a/api/core/tools/tool/dataset_retriever/dataset_retriever_tool.py +++ b/api/core/tools/tool/dataset_retriever/dataset_retriever_tool.py @@ -78,7 +78,8 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool): score_threshold=retrieval_model['score_threshold'] if retrieval_model['score_threshold_enabled'] else None, reranking_model=retrieval_model['reranking_model'] - if retrieval_model['reranking_enable'] else None + if retrieval_model['reranking_enable'] else None, + weights=retrieval_model.get('weights', None), ) else: documents = [] diff --git a/api/core/workflow/nodes/knowledge_retrieval/entities.py b/api/core/workflow/nodes/knowledge_retrieval/entities.py index c8874ff22c..5a9a4a9009 100644 --- a/api/core/workflow/nodes/knowledge_retrieval/entities.py +++ b/api/core/workflow/nodes/knowledge_retrieval/entities.py @@ -13,13 +13,41 @@ class RerankingModelConfig(BaseModel): model: str +class VectorSetting(BaseModel): + """ + Vector Setting. + """ + vector_weight: float + embedding_provider_name: str + embedding_model_name: str + + +class KeywordSetting(BaseModel): + """ + Keyword Setting. + """ + keyword_weight: float + + +class WeightedScoreConfig(BaseModel): + """ + Weighted score Config. + """ + weight_type: str + vector_setting: VectorSetting + keyword_setting: KeywordSetting + + class MultipleRetrievalConfig(BaseModel): """ Multiple Retrieval Config. """ top_k: int score_threshold: Optional[float] = None + reranking_mode: str = 'reranking_model' + reranking_enable: bool = True reranking_model: RerankingModelConfig + weights: WeightedScoreConfig class ModelConfig(BaseModel): diff --git a/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py b/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py index 779986f43a..00a46b8a60 100644 --- a/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py +++ b/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py @@ -137,13 +137,38 @@ class KnowledgeRetrievalNode(BaseNode): planning_strategy=planning_strategy ) elif node_data.retrieval_mode == DatasetRetrieveConfigEntity.RetrieveStrategy.MULTIPLE.value: + if node_data.multiple_retrieval_config.reranking_mode == 'reranking_model': + reranking_model = { + 'reranking_provider_name': node_data.multiple_retrieval_config.reranking_model['provider'], + 'reranking_model_name': node_data.multiple_retrieval_config.reranking_model['name'] + } + weights = None + elif node_data.multiple_retrieval_config.reranking_mode == 'weighted_score': + reranking_model = None + weights = { + 'weight_type': node_data.multiple_retrieval_config.weights.weight_type, + 'vector_setting': { + "vector_weight": node_data.multiple_retrieval_config.weights.vector_setting.vector_weight, + "embedding_provider_name": node_data.multiple_retrieval_config.weights.vector_setting.embedding_provider_name, + "embedding_model_name": node_data.multiple_retrieval_config.weights.vector_setting.embedding_model_name, + }, + 'keyword_setting': { + "keyword_weight": node_data.multiple_retrieval_config.weights.keyword_setting.keyword_weight + } + } + else: + reranking_model = None + weights = None all_documents = dataset_retrieval.multiple_retrieve(self.app_id, self.tenant_id, self.user_id, self.user_from.value, available_datasets, query, node_data.multiple_retrieval_config.top_k, node_data.multiple_retrieval_config.score_threshold, - node_data.multiple_retrieval_config.reranking_model.provider, - node_data.multiple_retrieval_config.reranking_model.model) + node_data.multiple_retrieval_config.reranking_mode, + reranking_model, + weights, + node_data.multiple_retrieval_config.reranking_enable, + ) context_list = [] if all_documents: diff --git a/api/core/workflow/workflow_entry.py b/api/core/workflow/workflow_entry.py index 3cff6e8505..4fe2165777 100644 --- a/api/core/workflow/workflow_entry.py +++ b/api/core/workflow/workflow_entry.py @@ -31,15 +31,18 @@ logger = logging.getLogger(__name__) class WorkflowEntry: - def run_workflow(self, workflow: Workflow, - user_id: str, - user_from: UserFrom, - invoke_from: InvokeFrom, - user_inputs: Mapping[str, Any], - system_inputs: Mapping[SystemVariable, Any], - callbacks: Sequence[BaseWorkflowCallback], - call_depth: int = 0, - variable_pool: Optional[VariablePool] = None) -> None: + def run_workflow( + self, + *, + workflow: Workflow, + user_id: str, + user_from: UserFrom, + invoke_from: InvokeFrom, + user_inputs: Mapping[str, Any], + system_inputs: Mapping[SystemVariable, Any], + callbacks: Sequence[WorkflowCallback], + call_depth: int = 0 + ) -> None: """ :param workflow: Workflow instance :param user_id: user id @@ -66,12 +69,11 @@ class WorkflowEntry: raise ValueError('edges in workflow graph must be a list') # init variable pool - if not variable_pool: - variable_pool = VariablePool( - system_variables=system_inputs, - user_inputs=user_inputs, - environment_variables=workflow.environment_variables, - ) + variable_pool = VariablePool( + system_variables=system_inputs, + user_inputs=user_inputs, + environment_variables=workflow.environment_variables, + ) workflow_call_max_depth = dify_config.WORKFLOW_CALL_MAX_DEPTH if call_depth > workflow_call_max_depth: diff --git a/api/fields/dataset_fields.py b/api/fields/dataset_fields.py index 50c5f43540..120b66a92d 100644 --- a/api/fields/dataset_fields.py +++ b/api/fields/dataset_fields.py @@ -18,10 +18,28 @@ reranking_model_fields = { 'reranking_model_name': fields.String } +keyword_setting_fields = { + 'keyword_weight': fields.Float +} + +vector_setting_fields = { + 'vector_weight': fields.Float, + 'embedding_model_name': fields.String, + 'embedding_provider_name': fields.String, +} + +weighted_score_fields = { + 'weight_type': fields.String, + 'keyword_setting': fields.Nested(keyword_setting_fields), + 'vector_setting': fields.Nested(vector_setting_fields), +} + dataset_retrieval_model_fields = { 'search_method': fields.String, 'reranking_enable': fields.Boolean, + 'reranking_mode': fields.String, 'reranking_model': fields.Nested(reranking_model_fields), + 'weights': fields.Nested(weighted_score_fields, allow_null=True), 'top_k': fields.Integer, 'score_threshold_enabled': fields.Boolean, 'score_threshold': fields.Float diff --git a/api/migrations/versions/53bf8af60645_update_model.py b/api/migrations/versions/53bf8af60645_update_model.py new file mode 100644 index 0000000000..3d0928d013 --- /dev/null +++ b/api/migrations/versions/53bf8af60645_update_model.py @@ -0,0 +1,41 @@ +"""update model + +Revision ID: 53bf8af60645 +Revises: 8e5588e6412e +Create Date: 2024-07-24 08:06:55.291031 + +""" +import sqlalchemy as sa +from alembic import op + +import models as models + +# revision identifiers, used by Alembic. +revision = '53bf8af60645' +down_revision = '8e5588e6412e' +branch_labels = None +depends_on = None + + +def upgrade(): + # ### commands auto generated by Alembic - please adjust! ### + with op.batch_alter_table('embeddings', schema=None) as batch_op: + batch_op.alter_column('provider_name', + existing_type=sa.VARCHAR(length=40), + type_=sa.String(length=255), + existing_nullable=False, + existing_server_default=sa.text("''::character varying")) + + # ### end Alembic commands ### + + +def downgrade(): + # ### commands auto generated by Alembic - please adjust! ### + with op.batch_alter_table('embeddings', schema=None) as batch_op: + batch_op.alter_column('provider_name', + existing_type=sa.String(length=255), + type_=sa.VARCHAR(length=40), + existing_nullable=False, + existing_server_default=sa.text("''::character varying")) + + # ### end Alembic commands ### diff --git a/api/models/dataset.py b/api/models/dataset.py index 34dde2dcef..ca1c4583a6 100644 --- a/api/models/dataset.py +++ b/api/models/dataset.py @@ -640,7 +640,7 @@ class Embedding(db.Model): hash = db.Column(db.String(64), nullable=False) embedding = db.Column(db.LargeBinary, nullable=False) created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)')) - provider_name = db.Column(db.String(40), nullable=False, + provider_name = db.Column(db.String(255), nullable=False, server_default=db.text("''::character varying")) def set_embedding(self, embedding_data: list[float]): diff --git a/api/models/model.py b/api/models/model.py index 396cd7ec63..a6f517ea6b 100644 --- a/api/models/model.py +++ b/api/models/model.py @@ -328,7 +328,9 @@ class AppModelConfig(db.Model): return {'retrieval_model': 'single'} else: return dataset_configs - return {'retrieval_model': 'single'} + return { + 'retrieval_model': 'multiple', + } @property def file_upload_dict(self) -> dict: diff --git a/api/poetry.lock b/api/poetry.lock index 353548eea7..2a277dac2d 100644 --- a/api/poetry.lock +++ b/api/poetry.lock @@ -184,13 +184,13 @@ alibabacloud_credentials = ">=0.2.0,<1.0.0" [[package]] name = "alibabacloud-gpdb20160503" -version = "3.8.2" +version = "3.8.3" description = "Alibaba Cloud AnalyticDB for PostgreSQL (20160503) SDK Library for Python" optional = false python-versions = ">=3.6" files = [ - {file = "alibabacloud_gpdb20160503-3.8.2-py3-none-any.whl", hash = "sha256:081977cdd4174c786b303f3c5651026297d84baa0256386be8215ee997cd5c75"}, - {file = "alibabacloud_gpdb20160503-3.8.2.tar.gz", hash = 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############################################################ [tool.poetry.group.vdb.dependencies] -chromadb = "~0.5.1" +chromadb = "0.5.1" oracledb = "~2.2.1" pgvecto-rs = "0.1.4" pgvector = "0.2.5" diff --git a/api/services/app_service.py b/api/services/app_service.py index f88e824b07..e433bb59bb 100644 --- a/api/services/app_service.py +++ b/api/services/app_service.py @@ -287,8 +287,12 @@ class AppService: """ db.session.delete(app) db.session.commit() + # Trigger asynchronous deletion of app and related data - remove_app_and_related_data_task.delay(app.id) + remove_app_and_related_data_task.delay( + tenant_id=app.tenant_id, + app_id=app.id + ) def get_app_meta(self, app_model: App) -> dict: """ diff --git a/api/services/hit_testing_service.py b/api/services/hit_testing_service.py index a04ffdfbbe..69274dff09 100644 --- a/api/services/hit_testing_service.py +++ b/api/services/hit_testing_service.py @@ -38,14 +38,16 @@ class HitTestingService: if not retrieval_model: retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model - all_documents = RetrievalService.retrieve(retrival_method=retrieval_model['search_method'], + all_documents = RetrievalService.retrieve(retrival_method=retrieval_model.get('search_method', 'semantic_search'), dataset_id=dataset.id, query=cls.escape_query_for_search(query), - top_k=retrieval_model['top_k'], + top_k=retrieval_model.get('top_k', 2), score_threshold=retrieval_model['score_threshold'] if retrieval_model['score_threshold_enabled'] else None, reranking_model=retrieval_model['reranking_model'] - if retrieval_model['reranking_enable'] else None + if retrieval_model['reranking_enable'] else None, + reranking_mode=retrieval_model.get('reranking_mode', None), + weights=retrieval_model.get('weights', None), ) end = time.perf_counter() diff --git a/api/tasks/remove_app_and_related_data_task.py b/api/tasks/remove_app_and_related_data_task.py index 117ce8d923..378756e68c 100644 --- a/api/tasks/remove_app_and_related_data_task.py +++ b/api/tasks/remove_app_and_related_data_task.py @@ -3,7 +3,6 @@ import time import click from celery import shared_task -from sqlalchemy import select from sqlalchemy.exc import SQLAlchemyError from extensions.ext_database import db @@ -25,6 +24,7 @@ from models.model import ( RecommendedApp, Site, TagBinding, + TraceAppConfig, ) from models.tools import WorkflowToolProvider from models.web import PinnedConversation, SavedMessage @@ -32,122 +32,287 @@ from models.workflow import Workflow, WorkflowAppLog, WorkflowNodeExecution, Wor @shared_task(queue='app_deletion', bind=True, max_retries=3) -def remove_app_and_related_data_task(self, app_id: str): - logging.info(click.style(f'Start deleting app and related data: {app_id}', fg='green')) +def remove_app_and_related_data_task(self, tenant_id: str, app_id: str): + logging.info(click.style(f'Start deleting app and related data: {tenant_id}:{app_id}', fg='green')) start_at = time.perf_counter() try: - # Use a transaction to ensure all deletions succeed or none do - with db.session.begin_nested(): - # Delete related data - _delete_app_model_configs(app_id) - _delete_app_site(app_id) - _delete_app_api_tokens(app_id) - _delete_installed_apps(app_id) - _delete_recommended_apps(app_id) - _delete_app_annotation_data(app_id) - _delete_app_dataset_joins(app_id) - _delete_app_workflows(app_id) - _delete_app_conversations(app_id) - _delete_app_messages(app_id) - _delete_workflow_tool_providers(app_id) - _delete_app_tag_bindings(app_id) - _delete_end_users(app_id) - - # If we reach here, the transaction was successful - db.session.commit() + # Delete related data + _delete_app_model_configs(tenant_id, app_id) + _delete_app_site(tenant_id, app_id) + _delete_app_api_tokens(tenant_id, app_id) + _delete_installed_apps(tenant_id, app_id) + _delete_recommended_apps(tenant_id, app_id) + _delete_app_annotation_data(tenant_id, app_id) + _delete_app_dataset_joins(tenant_id, app_id) + _delete_app_workflows(tenant_id, app_id) + _delete_app_workflow_runs(tenant_id, app_id) + _delete_app_workflow_node_executions(tenant_id, app_id) + _delete_app_workflow_app_logs(tenant_id, app_id) + _delete_app_conversations(tenant_id, app_id) + _delete_app_messages(tenant_id, app_id) + _delete_workflow_tool_providers(tenant_id, app_id) + _delete_app_tag_bindings(tenant_id, app_id) + _delete_end_users(tenant_id, app_id) + _delete_trace_app_configs(tenant_id, app_id) end_at = time.perf_counter() logging.info(click.style(f'App and related data deleted: {app_id} latency: {end_at - start_at}', fg='green')) - except SQLAlchemyError as e: - db.session.rollback() logging.exception( click.style(f"Database error occurred while deleting app {app_id} and related data", fg='red')) raise self.retry(exc=e, countdown=60) # Retry after 60 seconds - except Exception as e: logging.exception(click.style(f"Error occurred while deleting app {app_id} and related data", fg='red')) raise self.retry(exc=e, countdown=60) # Retry after 60 seconds -def _delete_app_model_configs(app_id: str): - db.session.query(AppModelConfig).filter(AppModelConfig.app_id == app_id).delete() +def _delete_app_model_configs(tenant_id: str, app_id: str): + def del_model_config(model_config_id: str): + db.session.query(AppModelConfig).filter(AppModelConfig.id == model_config_id).delete(synchronize_session=False) + + _delete_records( + """select id from app_model_configs where app_id=:app_id limit 1000""", + {"app_id": app_id}, + del_model_config, + "app model config" + ) -def _delete_app_site(app_id: str): - db.session.query(Site).filter(Site.app_id == app_id).delete() +def _delete_app_site(tenant_id: str, app_id: str): + def del_site(site_id: str): + db.session.query(Site).filter(Site.id == site_id).delete(synchronize_session=False) + + _delete_records( + """select id from sites where app_id=:app_id limit 1000""", + {"app_id": app_id}, + del_site, + "site" + ) -def _delete_app_api_tokens(app_id: str): - db.session.query(ApiToken).filter(ApiToken.app_id == app_id).delete() +def _delete_app_api_tokens(tenant_id: str, app_id: str): + def del_api_token(api_token_id: str): + db.session.query(ApiToken).filter(ApiToken.id == api_token_id).delete(synchronize_session=False) + + _delete_records( + """select id from api_tokens where app_id=:app_id limit 1000""", + {"app_id": app_id}, + del_api_token, + "api token" + ) -def _delete_installed_apps(app_id: str): - db.session.query(InstalledApp).filter(InstalledApp.app_id == app_id).delete() +def _delete_installed_apps(tenant_id: str, app_id: str): + def del_installed_app(installed_app_id: str): + db.session.query(InstalledApp).filter(InstalledApp.id == installed_app_id).delete(synchronize_session=False) + + _delete_records( + """select id from installed_apps where tenant_id=:tenant_id and app_id=:app_id limit 1000""", + {"tenant_id": tenant_id, "app_id": app_id}, + del_installed_app, + "installed app" + ) -def _delete_recommended_apps(app_id: str): - db.session.query(RecommendedApp).filter(RecommendedApp.app_id == app_id).delete() +def _delete_recommended_apps(tenant_id: str, app_id: str): + def del_recommended_app(recommended_app_id: str): + db.session.query(RecommendedApp).filter(RecommendedApp.id == recommended_app_id).delete( + synchronize_session=False) + + _delete_records( + """select id from recommended_apps where app_id=:app_id limit 1000""", + {"app_id": app_id}, + del_recommended_app, + "recommended app" + ) -def _delete_app_annotation_data(app_id: str): - db.session.query(AppAnnotationHitHistory).filter(AppAnnotationHitHistory.app_id == app_id).delete() - db.session.query(AppAnnotationSetting).filter(AppAnnotationSetting.app_id == app_id).delete() +def _delete_app_annotation_data(tenant_id: str, app_id: str): + def del_annotation_hit_history(annotation_hit_history_id: str): + db.session.query(AppAnnotationHitHistory).filter( + AppAnnotationHitHistory.id == annotation_hit_history_id).delete(synchronize_session=False) + + _delete_records( + """select id from app_annotation_hit_histories where app_id=:app_id limit 1000""", + {"app_id": app_id}, + del_annotation_hit_history, + "annotation hit history" + ) + + def del_annotation_setting(annotation_setting_id: str): + db.session.query(AppAnnotationSetting).filter(AppAnnotationSetting.id == annotation_setting_id).delete( + synchronize_session=False) + + _delete_records( + """select id from app_annotation_settings where app_id=:app_id limit 1000""", + {"app_id": app_id}, + del_annotation_setting, + "annotation setting" + ) -def _delete_app_dataset_joins(app_id: str): - db.session.query(AppDatasetJoin).filter(AppDatasetJoin.app_id == app_id).delete() +def _delete_app_dataset_joins(tenant_id: str, app_id: str): + def del_dataset_join(dataset_join_id: str): + db.session.query(AppDatasetJoin).filter(AppDatasetJoin.id == dataset_join_id).delete(synchronize_session=False) + + _delete_records( + """select id from app_dataset_joins where app_id=:app_id limit 1000""", + {"app_id": app_id}, + del_dataset_join, + "dataset join" + ) -def _delete_app_workflows(app_id: str): - db.session.query(WorkflowRun).filter( - WorkflowRun.workflow_id.in_( - db.session.query(Workflow.id).filter(Workflow.app_id == app_id) - ) - ).delete(synchronize_session=False) - db.session.query(WorkflowNodeExecution).filter( - WorkflowNodeExecution.workflow_id.in_( - db.session.query(Workflow.id).filter(Workflow.app_id == app_id) - ) - ).delete(synchronize_session=False) - db.session.query(WorkflowAppLog).filter(WorkflowAppLog.app_id == app_id).delete(synchronize_session=False) - db.session.query(Workflow).filter(Workflow.app_id == app_id).delete(synchronize_session=False) +def _delete_app_workflows(tenant_id: str, app_id: str): + def del_workflow(workflow_id: str): + db.session.query(Workflow).filter(Workflow.id == workflow_id).delete(synchronize_session=False) + + _delete_records( + """select id from workflows where tenant_id=:tenant_id and app_id=:app_id limit 1000""", + {"tenant_id": tenant_id, "app_id": app_id}, + del_workflow, + "workflow" + ) -def _delete_app_conversations(app_id: str): - db.session.query(PinnedConversation).filter( - PinnedConversation.conversation_id.in_( - db.session.query(Conversation.id).filter(Conversation.app_id == app_id) - ) - ).delete(synchronize_session=False) - db.session.query(Conversation).filter(Conversation.app_id == app_id).delete() +def _delete_app_workflow_runs(tenant_id: str, app_id: str): + def del_workflow_run(workflow_run_id: str): + db.session.query(WorkflowRun).filter(WorkflowRun.id == workflow_run_id).delete(synchronize_session=False) + + _delete_records( + """select id from workflow_runs where tenant_id=:tenant_id and app_id=:app_id limit 1000""", + {"tenant_id": tenant_id, "app_id": app_id}, + del_workflow_run, + "workflow run" + ) -def _delete_app_messages(app_id: str): - message_ids = select(Message.id).filter(Message.app_id == app_id).scalar_subquery() - db.session.query(MessageFeedback).filter(MessageFeedback.message_id.in_(message_ids)).delete( - synchronize_session=False) - db.session.query(MessageAnnotation).filter(MessageAnnotation.message_id.in_(message_ids)).delete( - synchronize_session=False) - db.session.query(MessageChain).filter(MessageChain.message_id.in_(message_ids)).delete(synchronize_session=False) - db.session.query(MessageAgentThought).filter(MessageAgentThought.message_id.in_(message_ids)).delete( - synchronize_session=False) - db.session.query(MessageFile).filter(MessageFile.message_id.in_(message_ids)).delete(synchronize_session=False) - db.session.query(SavedMessage).filter(SavedMessage.message_id.in_(message_ids)).delete(synchronize_session=False) - db.session.query(Message).filter(Message.app_id == app_id).delete(synchronize_session=False) +def _delete_app_workflow_node_executions(tenant_id: str, app_id: str): + def del_workflow_node_execution(workflow_node_execution_id: str): + db.session.query(WorkflowNodeExecution).filter( + WorkflowNodeExecution.id == workflow_node_execution_id).delete(synchronize_session=False) + + _delete_records( + """select id from workflow_node_executions where tenant_id=:tenant_id and app_id=:app_id limit 1000""", + {"tenant_id": tenant_id, "app_id": app_id}, + del_workflow_node_execution, + "workflow node execution" + ) -def _delete_workflow_tool_providers(app_id: str): - db.session.query(WorkflowToolProvider).filter( - WorkflowToolProvider.app_id == app_id - ).delete(synchronize_session=False) +def _delete_app_workflow_app_logs(tenant_id: str, app_id: str): + def del_workflow_app_log(workflow_app_log_id: str): + db.session.query(WorkflowAppLog).filter(WorkflowAppLog.id == workflow_app_log_id).delete(synchronize_session=False) + + _delete_records( + """select id from workflow_app_logs where tenant_id=:tenant_id and app_id=:app_id limit 1000""", + {"tenant_id": tenant_id, "app_id": app_id}, + del_workflow_app_log, + "workflow app log" + ) -def _delete_app_tag_bindings(app_id: str): - db.session.query(TagBinding).filter( - TagBinding.target_id == app_id - ).delete(synchronize_session=False) +def _delete_app_conversations(tenant_id: str, app_id: str): + def del_conversation(conversation_id: str): + db.session.query(PinnedConversation).filter(PinnedConversation.conversation_id == conversation_id).delete( + synchronize_session=False) + db.session.query(Conversation).filter(Conversation.id == conversation_id).delete(synchronize_session=False) + + _delete_records( + """select id from conversations where app_id=:app_id limit 1000""", + {"app_id": app_id}, + del_conversation, + "conversation" + ) -def _delete_end_users(app_id: str): - db.session.query(EndUser).filter(EndUser.app_id == app_id).delete() +def _delete_app_messages(tenant_id: str, app_id: str): + def del_message(message_id: str): + db.session.query(MessageFeedback).filter(MessageFeedback.message_id == message_id).delete( + synchronize_session=False) + db.session.query(MessageAnnotation).filter(MessageAnnotation.message_id == message_id).delete( + synchronize_session=False) + db.session.query(MessageChain).filter(MessageChain.message_id == message_id).delete( + synchronize_session=False) + db.session.query(MessageAgentThought).filter(MessageAgentThought.message_id == message_id).delete( + synchronize_session=False) + db.session.query(MessageFile).filter(MessageFile.message_id == message_id).delete(synchronize_session=False) + db.session.query(SavedMessage).filter(SavedMessage.message_id == message_id).delete( + synchronize_session=False) + db.session.query(Message).filter(Message.id == message_id).delete() + + _delete_records( + """select id from messages where app_id=:app_id limit 1000""", + {"app_id": app_id}, + del_message, + "message" + ) + + +def _delete_workflow_tool_providers(tenant_id: str, app_id: str): + def del_tool_provider(tool_provider_id: str): + db.session.query(WorkflowToolProvider).filter(WorkflowToolProvider.id == tool_provider_id).delete( + synchronize_session=False) + + _delete_records( + """select id from tool_workflow_providers where tenant_id=:tenant_id and app_id=:app_id limit 1000""", + {"tenant_id": tenant_id, "app_id": app_id}, + del_tool_provider, + "tool workflow provider" + ) + + +def _delete_app_tag_bindings(tenant_id: str, app_id: str): + def del_tag_binding(tag_binding_id: str): + db.session.query(TagBinding).filter(TagBinding.id == tag_binding_id).delete(synchronize_session=False) + + _delete_records( + """select id from tag_bindings where tenant_id=:tenant_id and target_id=:app_id limit 1000""", + {"tenant_id": tenant_id, "app_id": app_id}, + del_tag_binding, + "tag binding" + ) + + +def _delete_end_users(tenant_id: str, app_id: str): + def del_end_user(end_user_id: str): + db.session.query(EndUser).filter(EndUser.id == end_user_id).delete(synchronize_session=False) + + _delete_records( + """select id from end_users where tenant_id=:tenant_id and app_id=:app_id limit 1000""", + {"tenant_id": tenant_id, "app_id": app_id}, + del_end_user, + "end user" + ) + + +def _delete_trace_app_configs(tenant_id: str, app_id: str): + def del_trace_app_config(trace_app_config_id: str): + db.session.query(TraceAppConfig).filter(TraceAppConfig.id == trace_app_config_id).delete( + synchronize_session=False) + + _delete_records( + """select id from trace_app_config where app_id=:app_id limit 1000""", + {"app_id": app_id}, + del_trace_app_config, + "trace app config" + ) + + +def _delete_records(query_sql: str, params: dict, delete_func: callable, name: str) -> None: + while True: + with db.engine.begin() as conn: + rs = conn.execute(db.text(query_sql), params) + if rs.rowcount == 0: + break + + for i in rs: + record_id = str(i.id) + try: + delete_func(record_id) + db.session.commit() + logging.info(click.style(f"Deleted {name} {record_id}", fg='green')) + except Exception: + logging.exception(f"Error occurred while deleting {name} {record_id}") + continue + rs.close() diff --git a/api/tests/unit_tests/app/test_variables.py b/api/tests/unit_tests/app/test_variables.py index 05b080bdcf..40872c8d53 100644 --- a/api/tests/unit_tests/app/test_variables.py +++ b/api/tests/unit_tests/app/test_variables.py @@ -2,14 +2,16 @@ import pytest from pydantic import ValidationError from core.app.segments import ( + ArrayVariable, FloatVariable, IntegerVariable, + NoneVariable, + ObjectVariable, SecretVariable, SegmentType, StringVariable, factory, ) -from core.app.segments.variables import ArrayVariable, ObjectVariable def test_string_variable(): @@ -134,3 +136,13 @@ def test_variable_to_object(): assert var.to_object() == 3.14 var = SecretVariable(name='secret', value='secret_value') assert var.to_object() == 'secret_value' + + +def test_build_a_object_variable_with_none_value(): + var = factory.build_anonymous_variable( + { + 'key1': None, + } + ) + assert isinstance(var, ObjectVariable) + assert isinstance(var.value['key1'], NoneVariable) diff --git a/dev/pytest/pytest_vdb.sh b/dev/pytest/pytest_vdb.sh index cb8aae6740..c954c528fb 100755 --- a/dev/pytest/pytest_vdb.sh +++ b/dev/pytest/pytest_vdb.sh @@ -3,7 +3,6 @@ set -x pytest api/tests/integration_tests/vdb/chroma \ api/tests/integration_tests/vdb/milvus \ - api/tests/integration_tests/vdb/myscale \ api/tests/integration_tests/vdb/pgvecto_rs \ api/tests/integration_tests/vdb/pgvector \ api/tests/integration_tests/vdb/qdrant \ diff --git a/docker-legacy/docker-compose.yaml b/docker-legacy/docker-compose.yaml index b905b72f8b..9d7039df2c 100644 --- a/docker-legacy/docker-compose.yaml +++ b/docker-legacy/docker-compose.yaml @@ -2,7 +2,7 @@ version: '3' services: # API service api: - image: langgenius/dify-api:0.6.14 + image: langgenius/dify-api:0.6.15 restart: always environment: # Startup mode, 'api' starts the API server. @@ -224,7 +224,7 @@ services: # worker service # The Celery worker for processing the queue. worker: - image: langgenius/dify-api:0.6.14 + image: langgenius/dify-api:0.6.15 restart: always environment: CONSOLE_WEB_URL: '' @@ -390,7 +390,7 @@ services: # Frontend web application. web: - image: langgenius/dify-web:0.6.14 + image: langgenius/dify-web:0.6.15 restart: always environment: # The base URL of console application api server, refers to the Console base URL of WEB service if console domain is diff --git a/docker/docker-compose.middleware.yaml b/docker/docker-compose.middleware.yaml index 3dee6efb7c..6ab003ceab 100644 --- a/docker/docker-compose.middleware.yaml +++ b/docker/docker-compose.middleware.yaml @@ -48,7 +48,7 @@ services: # ssrf_proxy server # for more information, please refer to - # https://docs.dify.ai/getting-started/install-self-hosted/install-faq#id-16.-why-is-ssrf_proxy-needed + # https://docs.dify.ai/learn-more/faq/self-host-faq#id-18.-why-is-ssrf_proxy-needed ssrf_proxy: image: ubuntu/squid:latest restart: always diff --git a/docker/docker-compose.yaml b/docker/docker-compose.yaml index 30fdf16b17..a9b7b8acb0 100644 --- a/docker/docker-compose.yaml +++ b/docker/docker-compose.yaml @@ -179,7 +179,7 @@ x-shared-env: &shared-api-worker-env services: # API service api: - image: langgenius/dify-api:0.6.14 + image: langgenius/dify-api:0.6.15 restart: always environment: # Use the shared environment variables. @@ -199,7 +199,7 @@ services: # worker service # The Celery worker for processing the queue. worker: - image: langgenius/dify-api:0.6.14 + image: langgenius/dify-api:0.6.15 restart: always environment: # Use the shared environment variables. @@ -218,7 +218,7 @@ services: # Frontend web application. web: - image: langgenius/dify-web:0.6.14 + image: langgenius/dify-web:0.6.15 restart: always environment: CONSOLE_API_URL: ${CONSOLE_API_URL:-} diff --git a/web/app/components/app-sidebar/app-info.tsx b/web/app/components/app-sidebar/app-info.tsx index 90d3717d2c..3f52e9d6f5 100644 --- a/web/app/components/app-sidebar/app-info.tsx +++ b/web/app/components/app-sidebar/app-info.tsx @@ -434,7 +434,7 @@ const AppInfo = ({ expand }: IAppInfoProps) => { {showImportDSLModal && ( setShowImportDSLModal(false)} - onBackup={onExport} + onBackup={exportCheck} /> )} {secretEnvList.length > 0 && ( diff --git a/web/app/components/app/configuration/config-prompt/advanced-prompt-input.tsx b/web/app/components/app/configuration/config-prompt/advanced-prompt-input.tsx index 641cdd7e23..fb3ceadc0d 100644 --- a/web/app/components/app/configuration/config-prompt/advanced-prompt-input.tsx +++ b/web/app/components/app/configuration/config-prompt/advanced-prompt-input.tsx @@ -43,6 +43,7 @@ type Props = { promptVariables: PromptVariable[] isContextMissing: boolean onHideContextMissingTip: () => void + noResize?: boolean } const AdvancedPromptInput: FC = ({ @@ -56,6 +57,7 @@ const AdvancedPromptInput: FC = ({ promptVariables, isContextMissing, onHideContextMissingTip, + noResize, }) => { const { t } = useTranslation() const { eventEmitter } = useEventEmitterContextContext() @@ -207,6 +209,7 @@ const AdvancedPromptInput: FC = ({
{value.length}
)} + hideResize={noResize} > void } @@ -26,7 +30,11 @@ const Prompt: FC = ({ mode, promptTemplate, promptVariables, + noTitle, + gradientBorder, readonly = false, + editorHeight, + noResize, onChange, }) => { const { t } = useTranslation() @@ -99,6 +107,10 @@ const Prompt: FC = ({ promptVariables={promptVariables} readonly={readonly} onChange={onChange} + noTitle={noTitle} + gradientBorder={gradientBorder} + editorHeight={editorHeight} + noResize={noResize} /> ) } @@ -121,6 +133,7 @@ const Prompt: FC = ({ promptVariables={promptVariables} isContextMissing={isContextMissing && !isHideContextMissTip} onHideContextMissingTip={() => setIsHideContextMissTip(true)} + noResize={noResize} /> )) ) @@ -136,6 +149,7 @@ const Prompt: FC = ({ promptVariables={promptVariables} isContextMissing={isContextMissing && !isHideContextMissTip} onHideContextMissingTip={() => setIsHideContextMissTip(true)} + noResize={noResize} /> ) } diff --git a/web/app/components/app/configuration/config-prompt/simple-prompt-input.tsx b/web/app/components/app/configuration/config-prompt/simple-prompt-input.tsx index a15f538227..2811c42402 100644 --- a/web/app/components/app/configuration/config-prompt/simple-prompt-input.tsx +++ b/web/app/components/app/configuration/config-prompt/simple-prompt-input.tsx @@ -14,6 +14,7 @@ import PromptEditorHeightResizeWrap from './prompt-editor-height-resize-wrap' import cn from '@/utils/classnames' import { type PromptVariable } from '@/models/debug' import Tooltip from '@/app/components/base/tooltip' +import type { CompletionParams } from '@/types/app' import { AppType } from '@/types/app' import { getNewVar, getVars } from '@/utils/var' import AutomaticBtn from '@/app/components/app/configuration/config/automatic/automatic-btn' @@ -28,6 +29,7 @@ import { useEventEmitterContextContext } from '@/context/event-emitter' import { ADD_EXTERNAL_DATA_TOOL } from '@/app/components/app/configuration/config-var' import { INSERT_VARIABLE_VALUE_BLOCK_COMMAND } from '@/app/components/base/prompt-editor/plugins/variable-block' import { PROMPT_EDITOR_UPDATE_VALUE_BY_EVENT_EMITTER } from '@/app/components/base/prompt-editor/plugins/update-block' +import useBreakpoints, { MediaType } from '@/hooks/use-breakpoints' export type ISimplePromptInput = { mode: AppType @@ -35,6 +37,10 @@ export type ISimplePromptInput = { promptVariables: PromptVariable[] readonly?: boolean onChange?: (promp: string, promptVariables: PromptVariable[]) => void + noTitle?: boolean + gradientBorder?: boolean + editorHeight?: number + noResize?: boolean } const Prompt: FC = ({ @@ -43,11 +49,19 @@ const Prompt: FC = ({ promptVariables, readonly = false, onChange, + noTitle, + gradientBorder, + editorHeight: initEditorHeight, + noResize, }) => { const { t } = useTranslation() + const media = useBreakpoints() + const isMobile = media === MediaType.mobile + const { eventEmitter } = useEventEmitterContextContext() const { modelConfig, + completionParams, dataSets, setModelConfig, setPrevPromptConfig, @@ -116,6 +130,11 @@ const Prompt: FC = ({ const [showAutomatic, { setTrue: showAutomaticTrue, setFalse: showAutomaticFalse }] = useBoolean(false) const handleAutomaticRes = (res: AutomaticRes) => { + // put eventEmitter in first place to prevent overwrite the configs.prompt_variables.But another problem is that prompt won't hight the prompt_variables. + eventEmitter?.emit({ + type: PROMPT_EDITOR_UPDATE_VALUE_BY_EVENT_EMITTER, + payload: res.prompt, + } as any) const newModelConfig = produce(modelConfig, (draft) => { draft.configs.prompt_template = res.prompt draft.configs.prompt_variables = res.variables.map(key => ({ key, name: key, type: 'string', required: true })) @@ -125,41 +144,41 @@ const Prompt: FC = ({ if (mode !== AppType.completion) setIntroduction(res.opening_statement) showAutomaticFalse() - eventEmitter?.emit({ - type: PROMPT_EDITOR_UPDATE_VALUE_BY_EVENT_EMITTER, - payload: res.prompt, - } as any) } - const minHeight = 228 + const minHeight = initEditorHeight || 228 const [editorHeight, setEditorHeight] = useState(minHeight) return ( -
+
-
-
-
{mode !== AppType.completion ? t('appDebug.chatSubTitle') : t('appDebug.completionSubTitle')}
- {!readonly && ( - - {t('appDebug.promptTip')} -
} - selector='config-prompt-tooltip'> - - - )} + {!noTitle && ( +
+
+
{mode !== AppType.completion ? t('appDebug.chatSubTitle') : t('appDebug.completionSubTitle')}
+ {!readonly && ( + + {t('appDebug.promptTip')} +
} + selector='config-prompt-tooltip'> + + + )} +
+
+ {!isAgent && !readonly && !isMobile && ( + + )} +
-
- {!isAgent && !readonly && ( - - )} -
-
+ )} +
{promptTemplate.length}
@@ -216,6 +235,7 @@ const Prompt: FC = ({ onBlur={() => { handleChange(promptTemplate, getVars(promptTemplate)) }} + editable={!readonly} />
@@ -232,6 +252,14 @@ const Prompt: FC = ({ {showAutomatic && ( void } - -const leftIcon = ( - - - - - - -) const AutomaticBtn: FC = ({ onClick, }) => { const { t } = useTranslation() return ( -
- {leftIcon} + {t('appDebug.operation.automatic')}
) diff --git a/web/app/components/app/configuration/config/automatic/get-automatic-res.tsx b/web/app/components/app/configuration/config/automatic/get-automatic-res.tsx index fa58253cac..1939dd3ad7 100644 --- a/web/app/components/app/configuration/config/automatic/get-automatic-res.tsx +++ b/web/app/components/app/configuration/config/automatic/get-automatic-res.tsx @@ -1,70 +1,126 @@ 'use client' import type { FC } from 'react' -import React from 'react' +import React, { useCallback } from 'react' import { useTranslation } from 'react-i18next' import { useBoolean } from 'ahooks' +import { + RiDatabase2Line, + RiFileExcel2Line, + RiGitCommitLine, + RiNewspaperLine, + RiPresentationLine, + RiRoadMapLine, + RiTerminalBoxLine, + RiTranslate, + RiUser2Line, +} from '@remixicon/react' +import cn from 'classnames' +import s from './style.module.css' import Modal from '@/app/components/base/modal' import Button from '@/app/components/base/button' import Toast from '@/app/components/base/toast' import { generateRule } from '@/service/debug' import ConfigPrompt from '@/app/components/app/configuration/config-prompt' +import type { Model } from '@/types/app' import { AppType } from '@/types/app' import ConfigVar from '@/app/components/app/configuration/config-var' import OpeningStatement from '@/app/components/app/configuration/features/chat-group/opening-statement' import GroupName from '@/app/components/app/configuration/base/group-name' import Loading from '@/app/components/base/loading' import Confirm from '@/app/components/base/confirm' + // type import type { AutomaticRes } from '@/service/debug' -import useBreakpoints, { MediaType } from '@/hooks/use-breakpoints' - -const noDataIcon = ( - - - -) +import { Generator } from '@/app/components/base/icons/src/vender/other' export type IGetAutomaticResProps = { mode: AppType + model: Model isShow: boolean onClose: () => void onFinished: (res: AutomaticRes) => void + isInLLMNode?: boolean } -const genIcon = ( - - - - - -) +const TryLabel: FC<{ + Icon: any + text: string + onClick: () => void +}> = ({ Icon, text, onClick }) => { + return ( +
+ +
{text}
+
+ ) +} const GetAutomaticRes: FC = ({ mode, + model, isShow, onClose, - // appId, + isInLLMNode, onFinished, }) => { const { t } = useTranslation() - const media = useBreakpoints() - const isMobile = media === MediaType.mobile + const tryList = [ + { + icon: RiTerminalBoxLine, + key: 'pythonDebugger', + }, + { + icon: RiTranslate, + key: 'translation', + }, + { + icon: RiPresentationLine, + key: 'meetingTakeaways', + }, + { + icon: RiNewspaperLine, + key: 'writingsPolisher', + }, + { + icon: RiUser2Line, + key: 'professionalAnalyst', + }, + { + icon: RiFileExcel2Line, + key: 'excelFormulaExpert', + }, + { + icon: RiRoadMapLine, + key: 'travelPlanning', + }, + { + icon: RiDatabase2Line, + key: 'SQLSorcerer', + }, + { + icon: RiGitCommitLine, + key: 'GitGud', + }, + ] - const [audiences, setAudiences] = React.useState('') - const [hopingToSolve, setHopingToSolve] = React.useState('') - const isValid = () => { - if (audiences.trim() === '') { - Toast.notify({ - type: 'error', - message: t('appDebug.automatic.audiencesRequired'), - }) - return false + const [instruction, setInstruction] = React.useState('') + const handleChooseTemplate = useCallback((key: string) => { + return () => { + const template = t(`appDebug.generate.template.${key}.instruction`) + setInstruction(template) } - if (hopingToSolve.trim() === '') { + }, [t]) + const isValid = () => { + if (instruction.trim() === '') { Toast.notify({ type: 'error', - message: t('appDebug.automatic.problemRequired'), + message: t('common.errorMsg.fieldRequired', { + field: t('appDebug.generate.instruction'), + }), }) return false } @@ -76,14 +132,17 @@ const GetAutomaticRes: FC = ({ const renderLoading = (
-
{t('appDebug.automatic.loading')}
+
{t('appDebug.generate.loading')}
) const renderNoData = (
- {noDataIcon} -
{t('appDebug.automatic.noData')}
+ +
+
{t('appDebug.generate.noDataLine1')}
+
{t('appDebug.generate.noDataLine2')}
+
) @@ -94,11 +153,18 @@ const GetAutomaticRes: FC = ({ return setLoadingTrue() try { - const res = await generateRule({ - audiences, - hoping_to_solve: hopingToSolve, + const { error, ...res } = await generateRule({ + instruction, + model_config: model, + no_variable: !!isInLLMNode, }) setRes(res) + if (error) { + Toast.notify({ + type: 'error', + message: error, + }) + } } finally { setLoadingFalse() @@ -107,24 +173,7 @@ const GetAutomaticRes: FC = ({ const [showConfirmOverwrite, setShowConfirmOverwrite] = React.useState(false) - const isShowAutoPromptInput = () => { - if (isMobile) { - // hide prompt panel on mobile if it is loading or has had result - if (isLoading || res) - return false - return true - } - - // always display prompt panel on desktop mode - return true - } - const isShowAutoPromptResPlaceholder = () => { - if (isMobile) { - // hide placeholder panel on mobile - return false - } - return !isLoading && !res } @@ -132,75 +181,97 @@ const GetAutomaticRes: FC = ({ -
- {isShowAutoPromptInput() &&
-
-
{t('appDebug.automatic.title')}
-
{t('appDebug.automatic.description')}
+
+
+
+
{t('appDebug.generate.title')}
+
{t('appDebug.generate.description')}
+
+
+
+
{t('appDebug.generate.tryIt')}
+
+
+
+ {tryList.map(item => ( + + ))} +
{/* inputs */} -
-
-
{t('appDebug.automatic.intendedAudience')}
- setAudiences(e.target.value)} /> -
-
-
{t('appDebug.automatic.solveProblem')}
-