fix: import error

api/controllers/service_api/app/completion.py

@@ -2,11 +2,8 @@ import json
 import logging
 from typing import Union, Generator
 
-from datasets import Dataset
 from flask import stream_with_context, Response
 from flask_restful import reqparse
-from ragas import evaluate
-from ragas.metrics import answer_relevancy, context_precision, faithfulness
 from werkzeug.exceptions import NotFound, InternalServerError
 
 import services
@@ -79,84 +76,6 @@ class CompletionApi(AppApiResource):
             raise InternalServerError()
 
 
-class CompletionEvaluateApi(AppApiResource):
-    def post(self, app_model, end_user):
-        if app_model.mode != 'chat':
-            raise AppUnavailableError()
-
-        parser = reqparse.RequestParser()
-        parser.add_argument('inputs', type=dict, required=True, location='json')
-        parser.add_argument('queries', type=list, location='json', default='')
-        parser.add_argument('files', type=list, required=False, location='json')
-        parser.add_argument('response_mode', type=str, choices=['blocking', 'streaming'], location='json')
-        parser.add_argument('user', type=str, location='json')
-        parser.add_argument('retriever_from', type=str, required=False, default='dev', location='json')
-
-        args = parser.parse_args()
-
-        streaming = args['response_mode'] == 'streaming'
-
-        if end_user is None and args['user'] is not None:
-            end_user = create_or_update_end_user_for_user_id(app_model, args['user'])
-
-        args['auto_generate_name'] = False
-        queries = args['queries']
-        try:
-            questions = []
-            answers = []
-            contexts = []
-            for query in queries:
-                args['query'] = query
-                response = CompletionService.completion(
-                    app_model=app_model,
-                    user=end_user,
-                    args=args,
-                    from_source='api',
-                    streaming=streaming,
-                )
-                questions.append(query)
-                answers.append(response['answer'])
-                context = []
-                metadata = response['metadata']
-                if 'retriever_resources' in metadata and metadata['retriever_resources']:
-                    retriever_resources = response['metadata']['retriever_resources']
-                    for retriever_resource in retriever_resources:
-                        context.append(retriever_resource['content'])
-                else:
-                    context.append('')
-                contexts.append(context)
-            ds = Dataset.from_dict(
-                {
-                    "question": questions,
-                    "answer": answers,
-                    "contexts": contexts,
-                }
-            )
-            result = evaluate(ds, [answer_relevancy, context_precision, faithfulness])
-            return result
-        except services.errors.conversation.ConversationNotExistsError:
-            raise NotFound("Conversation Not Exists.")
-        except services.errors.conversation.ConversationCompletedError:
-            raise ConversationCompletedError()
-        except services.errors.app_model_config.AppModelConfigBrokenError:
-            logging.exception("App model config broken.")
-            raise AppUnavailableError()
-        except ProviderTokenNotInitError as ex:
-            raise ProviderNotInitializeError(ex.description)
-        except QuotaExceededError:
-            raise ProviderQuotaExceededError()
-        except ModelCurrentlyNotSupportError:
-            raise ProviderModelCurrentlyNotSupportError()
-        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
-                LLMRateLimitError, LLMAuthorizationError) as e:
-            raise CompletionRequestError(str(e))
-        except ValueError as e:
-            raise e
-        except Exception as e:
-            logging.exception("internal server error.")
-            raise InternalServerError()
-
-
 class CompletionStopApi(AppApiResource):
     def post(self, app_model, end_user, task_id):
         if app_model.mode != 'completion':
@@ -269,7 +188,6 @@ def compact_response(response: Union[dict | Generator]) -> Response:
 
 
 api.add_resource(CompletionApi, '/completion-messages')
-api.add_resource(CompletionEvaluateApi, '/chat-evaluate')
 api.add_resource(CompletionStopApi, '/completion-messages/<string:task_id>/stop')
 api.add_resource(ChatApi, '/chat-messages')
 api.add_resource(ChatStopApi, '/chat-messages/<string:task_id>/stop')

api/core/orchestrator_rule_parser.py

-import json
-import threading
 from typing import Optional, List
 
 from langchain.callbacks.manager import Callbacks
@@ -8,9 +6,6 @@ from langchain.tools import BaseTool, Tool, WikipediaQueryRun
 from langchain.utilities.wikipedia import WikipediaAPIWrapper
 from pydantic import BaseModel, Field
 
-from core.agent.agent.multi_dataset_router_agent import MultiDatasetRouterAgent
-from core.agent.agent.output_parser.structured_chat import StructuredChatOutputParser
-from core.agent.agent.structed_multi_dataset_router_agent import StructuredMultiDatasetRouterAgent
 from core.agent.agent_executor import AgentExecutor, PlanningStrategy, AgentConfiguration
 from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
 from core.callback_handler.dataset_tool_callback_handler import DatasetToolCallbackHandler

api/core/third_party/langchain/llms/anthropic_llm.py

 from typing import Dict
 
 from langchain.chat_models import ChatAnthropic
-from langchain.llms.anthropic import _to_secret
+from langchain.llms.anthropic import convert_to_secret_str
 from langchain.schema import ChatMessage, BaseMessage, HumanMessage, AIMessage, SystemMessage
 from langchain.utils import get_from_dict_or_env, check_package_version
 from pydantic import root_validator
@@ -11,7 +11,7 @@ class AnthropicLLM(ChatAnthropic):
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
-        values["anthropic_api_key"] = _to_secret(
+        values["anthropic_api_key"] = convert_to_secret_str(
             get_from_dict_or_env(values, "anthropic_api_key", "ANTHROPIC_API_KEY")
         )
         # Get custom api url from environment.

api/requirements.txt

@@ -10,7 +10,7 @@ flask-session2==1.3.1
 flask-cors==3.0.10
 gunicorn~=21.2.0
 gevent~=22.10.2
-langchain==0.0.311
+langchain==0.0.340
 openai~=0.28.0
 psycopg2-binary~=2.9.6
 pycryptodome==3.17
@@ -44,7 +44,7 @@ google-search-results==2.4.2
 replicate~=0.9.0
 websocket-client~=1.6.1
 dashscope~=1.11.0
-huggingface_hub~=0.16.4
+huggingface_hub~=0.19.4
 transformers~=4.31.0
 stripe~=5.5.0
 pandas==1.5.3
@@ -55,18 +55,3 @@ werkzeug==2.3.7
 pymilvus==2.3.0
 qdrant-client==1.6.4
 cohere~=4.32
\ No newline at end of file
-pydantic~=1.10.12
-requests~=2.31.0
-regex~=2023.8.8
-jieba3k~=0.35.1
-numpy~=1.25.2
-httpx~=0.24.1
-dataclasses~=0.6
-click~=8.1.6
-blinker~=1.6.2
-botocore~=1.31.17
-alembic~=1.11.2
-tqdm~=4.66.1
-pytz~=2022.7.1
-datasets~=2.15.0
-ragas~=0.0.20
\ No newline at end of file