feat: bump version to 0.3.32 (#1620)

Co-authored-by: jyong <jyong@dify.ai>

.github/workflows/build-api-image.yml

@@ -34,9 +34,7 @@ jobs:
           type=raw,value=latest,enable=${{ startsWith(github.ref, 'refs/tags/') }}
           type=ref,event=branch
           type=sha,enable=true,priority=100,prefix=,suffix=,format=long
-          type=semver,pattern={{major}}.{{minor}}.{{patch}}
-          type=semver,pattern={{major}}.{{minor}}
-          type=semver,pattern={{major}}
+          type=raw,value=${{ github.ref_name }},enable=${{ startsWith(github.ref, 'refs/tags/') }}
 
     - name: Build and push
       uses: docker/build-push-action@v4

.github/workflows/build-web-image.yml

@@ -34,9 +34,7 @@ jobs:
           type=raw,value=latest,enable=${{ startsWith(github.ref, 'refs/tags/') }}
           type=ref,event=branch
           type=sha,enable=true,priority=100,prefix=,suffix=,format=long
-          type=semver,pattern={{major}}.{{minor}}.{{patch}}
-          type=semver,pattern={{major}}.{{minor}}
-          type=semver,pattern={{major}}
+          type=raw,value=${{ github.ref_name }},enable=${{ startsWith(github.ref, 'refs/tags/') }}
 
     - name: Build and push
       uses: docker/build-push-action@v4

api/config.py

@@ -91,7 +91,7 @@ class Config:
         # ------------------------
         # General Configurations.
         # ------------------------
-        self.CURRENT_VERSION = "0.3.31"
+        self.CURRENT_VERSION = "0.3.32"
         self.COMMIT_SHA = get_env('COMMIT_SHA')
         self.EDITION = "SELF_HOSTED"
         self.DEPLOY_ENV = get_env('DEPLOY_ENV')

api/controllers/console/app/statistic.py

@@ -62,16 +62,15 @@ class DailyConversationStatistic(Resource):
 
         sql_query += ' GROUP BY date order by date'
 
-        with db.engine.begin() as conn:
-            rs = conn.execute(db.text(sql_query), arg_dict)
-
         response_data = []
 
-        for i in rs:
-            response_data.append({
-                'date': str(i.date),
-                'conversation_count': i.conversation_count
-            })
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)
+            for i in rs:
+                response_data.append({
+                    'date': str(i.date),
+                    'conversation_count': i.conversation_count
+                })
 
         return jsonify({
             'data': response_data
@@ -124,16 +123,15 @@ class DailyTerminalsStatistic(Resource):
 
         sql_query += ' GROUP BY date order by date'
 
-        with db.engine.begin() as conn:
-            rs = conn.execute(db.text(sql_query), arg_dict)
-
         response_data = []
 
-        for i in rs:
-            response_data.append({
-                'date': str(i.date),
-                'terminal_count': i.terminal_count
-            })
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)            
+            for i in rs:
+                response_data.append({
+                    'date': str(i.date),
+                    'terminal_count': i.terminal_count
+                })
 
         return jsonify({
             'data': response_data
@@ -187,18 +185,17 @@ class DailyTokenCostStatistic(Resource):
 
         sql_query += ' GROUP BY date order by date'
 
-        with db.engine.begin() as conn:
-            rs = conn.execute(db.text(sql_query), arg_dict)
-
         response_data = []
 
-        for i in rs:
-            response_data.append({
-                'date': str(i.date),
-                'token_count': i.token_count,
-                'total_price': i.total_price,
-                'currency': 'USD'
-            })
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)
+            for i in rs:
+                response_data.append({
+                    'date': str(i.date),
+                    'token_count': i.token_count,
+                    'total_price': i.total_price,
+                    'currency': 'USD'
+                })
 
         return jsonify({
             'data': response_data
@@ -256,16 +253,15 @@ LEFT JOIN conversations c on c.id=subquery.conversation_id
 GROUP BY date
 ORDER BY date"""
 
+        response_data = []
+        
         with db.engine.begin() as conn:
             rs = conn.execute(db.text(sql_query), arg_dict)
-
-        response_data = []
-
-        for i in rs:
-            response_data.append({
-                'date': str(i.date),
-                'interactions': float(i.interactions.quantize(Decimal('0.01')))
-            })
+            for i in rs:
+                response_data.append({
+                    'date': str(i.date),
+                    'interactions': float(i.interactions.quantize(Decimal('0.01')))
+                })
 
         return jsonify({
             'data': response_data
@@ -320,20 +316,19 @@ class UserSatisfactionRateStatistic(Resource):
 
         sql_query += ' GROUP BY date order by date'
 
-        with db.engine.begin() as conn:
-            rs = conn.execute(db.text(sql_query), arg_dict)
-
         response_data = []
 
-        for i in rs:
-            response_data.append({
-                'date': str(i.date),
-                'rate': round((i.feedback_count * 1000 / i.message_count) if i.message_count > 0 else 0, 2),
-            })
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)
+            for i in rs:
+                response_data.append({
+                    'date': str(i.date),
+                    'rate': round((i.feedback_count * 1000 / i.message_count) if i.message_count > 0 else 0, 2),
+                })
 
         return jsonify({
-                'data': response_data
-            })
+            'data': response_data
+        })
 
 
 class AverageResponseTimeStatistic(Resource):
@@ -383,16 +378,15 @@ class AverageResponseTimeStatistic(Resource):
 
         sql_query += ' GROUP BY date order by date'
 
-        with db.engine.begin() as conn:
-            rs = conn.execute(db.text(sql_query), arg_dict)
-
         response_data = []
 
-        for i in rs:
-            response_data.append({
-                'date': str(i.date),
-                'latency': round(i.latency * 1000, 4)
-            })
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)            
+            for i in rs:
+                response_data.append({
+                    'date': str(i.date),
+                    'latency': round(i.latency * 1000, 4)
+                })
 
         return jsonify({
             'data': response_data
@@ -447,16 +441,15 @@ WHERE app_id = :app_id'''
 
         sql_query += ' GROUP BY date order by date'
 
-        with db.engine.begin() as conn:
-            rs = conn.execute(db.text(sql_query), arg_dict)
-
         response_data = []
 
-        for i in rs:
-            response_data.append({
-                'date': str(i.date),
-                'tps': round(i.tokens_per_second, 4)
-            })
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)
+            for i in rs:
+                response_data.append({
+                    'date': str(i.date),
+                    'tps': round(i.tokens_per_second, 4)
+                })
 
         return jsonify({
             'data': response_data

api/controllers/console/workspace/model_providers.py

@@ -115,7 +115,7 @@ class ModelProviderModelValidateApi(Resource):
         parser = reqparse.RequestParser()
         parser.add_argument('model_name', type=str, required=True, nullable=False, location='json')
         parser.add_argument('model_type', type=str, required=True, nullable=False,
-                            choices=['text-generation', 'embeddings', 'speech2text'], location='json')
+                            choices=['text-generation', 'embeddings', 'speech2text', 'reranking'], location='json')
         parser.add_argument('config', type=dict, required=True, nullable=False, location='json')
         args = parser.parse_args()
 
@@ -155,7 +155,7 @@ class ModelProviderModelUpdateApi(Resource):
         parser = reqparse.RequestParser()
         parser.add_argument('model_name', type=str, required=True, nullable=False, location='json')
         parser.add_argument('model_type', type=str, required=True, nullable=False,
-                            choices=['text-generation', 'embeddings', 'speech2text'], location='json')
+                            choices=['text-generation', 'embeddings', 'speech2text', 'reranking'], location='json')
         parser.add_argument('config', type=dict, required=True, nullable=False, location='json')
         args = parser.parse_args()
 
@@ -184,7 +184,7 @@ class ModelProviderModelUpdateApi(Resource):
         parser = reqparse.RequestParser()
         parser.add_argument('model_name', type=str, required=True, nullable=False, location='args')
         parser.add_argument('model_type', type=str, required=True, nullable=False,
-                            choices=['text-generation', 'embeddings', 'speech2text'], location='args')
+                            choices=['text-generation', 'embeddings', 'speech2text', 'reranking'], location='args')
         args = parser.parse_args()
 
         provider_service = ProviderService()

api/core/index/vector_index/weaviate_vector_index.py

@@ -111,7 +111,7 @@ class WeaviateVectorIndex(BaseVectorIndex):
         if self._vector_store:
             return self._vector_store
 
-        attributes = ['doc_id', 'dataset_id', 'document_id']
+        attributes = ['doc_id', 'dataset_id', 'document_id', 'doc_hash']
         if self._is_origin():
             attributes = ['doc_id']
 

api/core/model_providers/models/llm/chatglm_model.py

@@ -1,27 +1,45 @@
-import decimal
+import logging
 from typing import List, Optional, Any
 
+import openai
 from langchain.callbacks.manager import Callbacks
-from langchain.llms import ChatGLM
-from langchain.schema import LLMResult
+from langchain.schema import LLMResult, get_buffer_string
 
-from core.model_providers.error import LLMBadRequestError
+from core.model_providers.error import LLMBadRequestError, LLMRateLimitError, LLMAuthorizationError, \
+    LLMAPIUnavailableError, LLMAPIConnectionError
 from core.model_providers.models.llm.base import BaseLLM
 from core.model_providers.models.entity.message import PromptMessage, MessageType
 from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs
+from core.third_party.langchain.llms.chat_open_ai import EnhanceChatOpenAI
 
 
 class ChatGLMModel(BaseLLM):
-    model_mode: ModelMode = ModelMode.COMPLETION
+    model_mode: ModelMode = ModelMode.CHAT
 
     def _init_client(self) -> Any:
         provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs)
-        return ChatGLM(
+
+        extra_model_kwargs = {
+            'top_p': provider_model_kwargs.get('top_p')
+        }
+
+        if provider_model_kwargs.get('max_length') is not None:
+            extra_model_kwargs['max_length'] = provider_model_kwargs.get('max_length')
+
+        client = EnhanceChatOpenAI(
+            model_name=self.name,
+            temperature=provider_model_kwargs.get('temperature'),
+            max_tokens=provider_model_kwargs.get('max_tokens'),
+            model_kwargs=extra_model_kwargs,
+            streaming=self.streaming,
             callbacks=self.callbacks,
-            endpoint_url=self.credentials.get('api_base'),
-            **provider_model_kwargs
+            request_timeout=60,
+            openai_api_key="1",
+            openai_api_base=self.credentials['api_base'] + '/v1'
         )
 
+        return client
+
     def _run(self, messages: List[PromptMessage],
              stop: Optional[List[str]] = None,
              callbacks: Callbacks = None,
@@ -45,19 +63,40 @@ class ChatGLMModel(BaseLLM):
         :return:
         """
         prompts = self._get_prompt_from_messages(messages)
-        return max(self._client.get_num_tokens(prompts), 0)
+        return max(sum([self._client.get_num_tokens(get_buffer_string([m])) for m in prompts]) - len(prompts), 0)
 
     def get_currency(self):
         return 'RMB'
 
     def _set_model_kwargs(self, model_kwargs: ModelKwargs):
         provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs)
-        for k, v in provider_model_kwargs.items():
-            if hasattr(self.client, k):
-                setattr(self.client, k, v)
+        extra_model_kwargs = {
+            'top_p': provider_model_kwargs.get('top_p')
+        }
+
+        self.client.temperature = provider_model_kwargs.get('temperature')
+        self.client.max_tokens = provider_model_kwargs.get('max_tokens')
+        self.client.model_kwargs = extra_model_kwargs
 
     def handle_exceptions(self, ex: Exception) -> Exception:
-        if isinstance(ex, ValueError):
-            return LLMBadRequestError(f"ChatGLM: {str(ex)}")
+        if isinstance(ex, openai.error.InvalidRequestError):
+            logging.warning("Invalid request to ChatGLM API.")
+            return LLMBadRequestError(str(ex))
+        elif isinstance(ex, openai.error.APIConnectionError):
+            logging.warning("Failed to connect to ChatGLM API.")
+            return LLMAPIConnectionError(ex.__class__.__name__ + ":" + str(ex))
+        elif isinstance(ex, (openai.error.APIError, openai.error.ServiceUnavailableError, openai.error.Timeout)):
+            logging.warning("ChatGLM service unavailable.")
+            return LLMAPIUnavailableError(ex.__class__.__name__ + ":" + str(ex))
+        elif isinstance(ex, openai.error.RateLimitError):
+            return LLMRateLimitError(str(ex))
+        elif isinstance(ex, openai.error.AuthenticationError):
+            return LLMAuthorizationError(str(ex))
+        elif isinstance(ex, openai.error.OpenAIError):
+            return LLMBadRequestError(ex.__class__.__name__ + ":" + str(ex))
         else:
             return ex
+
+    @classmethod
+    def support_streaming(cls):
+        return True

api/core/model_providers/models/reranking/xinference_reranking.py

@@ -0,0 +1,58 @@
+import logging
+from typing import Optional, List
+
+from langchain.schema import Document
+from xinference_client.client.restful.restful_client import Client
+
+from core.model_providers.error import LLMBadRequestError
+from core.model_providers.models.reranking.base import BaseReranking
+from core.model_providers.providers.base import BaseModelProvider
+
+
+class XinferenceReranking(BaseReranking):
+
+    def __init__(self, model_provider: BaseModelProvider, name: str):
+        self.credentials = model_provider.get_model_credentials(
+            model_name=name,
+            model_type=self.type
+        )
+
+        client = Client(self.credentials['server_url'])
+
+        super().__init__(model_provider, client, name)
+
+    def rerank(self, query: str, documents: List[Document], score_threshold: Optional[float], top_k: Optional[int]) -> Optional[List[Document]]:
+        docs = []
+        doc_id = []
+        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)
+
+        model = self.client.get_model(self.credentials['model_uid'])
+        response = model.rerank(query=query, documents=docs, top_n=top_k)
+        rerank_documents = []
+
+        for idx, result in enumerate(response['results']):
+            # format document
+            index = result['index']
+            rerank_document = Document(
+                page_content=result['document'],
+                metadata={
+                    "doc_id": documents[index].metadata['doc_id'],
+                    "doc_hash": documents[index].metadata['doc_hash'],
+                    "document_id": documents[index].metadata['document_id'],
+                    "dataset_id": documents[index].metadata['dataset_id'],
+                    'score': result['relevance_score']
+                }
+            )
+            # score threshold check
+            if score_threshold is not None:
+                if result.relevance_score >= score_threshold:
+                    rerank_documents.append(rerank_document)
+            else:
+                rerank_documents.append(rerank_document)
+        return rerank_documents
+
+    def handle_exceptions(self, ex: Exception) -> Exception:
+        return LLMBadRequestError(f"Xinference rerank: {str(ex)}")

api/core/model_providers/providers/anthropic_provider.py

@@ -32,9 +32,12 @@ class AnthropicProvider(BaseModelProvider):
         if model_type == ModelType.TEXT_GENERATION:
             return [
                 {
-                    'id': 'claude-instant-1',
-                    'name': 'claude-instant-1',
+                    'id': 'claude-2.1',
+                    'name': 'claude-2.1',
                     'mode': ModelMode.CHAT.value,
+                    'features': [
+                        ModelFeature.AGENT_THOUGHT.value
+                    ]
                 },
                 {
                     'id': 'claude-2',
@@ -44,6 +47,11 @@ class AnthropicProvider(BaseModelProvider):
                         ModelFeature.AGENT_THOUGHT.value
                     ]
                 },
+                {
+                    'id': 'claude-instant-1',
+                    'name': 'claude-instant-1',
+                    'mode': ModelMode.CHAT.value,
+                },
             ]
         else:
             return []
@@ -73,12 +81,18 @@ class AnthropicProvider(BaseModelProvider):
         :param model_type:
         :return:
         """
+        model_max_tokens = {
+            'claude-instant-1': 100000,
+            'claude-2': 100000,
+            'claude-2.1': 200000,
+        }
+
         return ModelKwargsRules(
             temperature=KwargRule[float](min=0, max=1, default=1, precision=2),
             top_p=KwargRule[float](min=0, max=1, default=0.7, precision=2),
             presence_penalty=KwargRule[float](enabled=False),
             frequency_penalty=KwargRule[float](enabled=False),
-            max_tokens=KwargRule[int](alias="max_tokens_to_sample", min=10, max=100000, default=256, precision=0),
+            max_tokens=KwargRule[int](alias="max_tokens_to_sample", min=10, max=model_max_tokens.get(model_name, 100000), default=256, precision=0),
         )
 
     @classmethod

api/core/model_providers/providers/chatglm_provider.py

@@ -2,6 +2,7 @@ import json
 from json import JSONDecodeError
 from typing import Type
 
+import requests
 from langchain.llms import ChatGLM
 
 from core.helper import encrypter
@@ -25,21 +26,26 @@ class ChatGLMProvider(BaseModelProvider):
         if model_type == ModelType.TEXT_GENERATION:
             return [
                 {
-                    'id': 'chatglm2-6b',
-                    'name': 'ChatGLM2-6B',
-                    'mode': ModelMode.COMPLETION.value,
+                    'id': 'chatglm3-6b',
+                    'name': 'ChatGLM3-6B',
+                    'mode': ModelMode.CHAT.value,
                 },
                 {
-                    'id': 'chatglm-6b',
-                    'name': 'ChatGLM-6B',
-                    'mode': ModelMode.COMPLETION.value,
+                    'id': 'chatglm3-6b-32k',
+                    'name': 'ChatGLM3-6B-32K',
+                    'mode': ModelMode.CHAT.value,
+                },
+                {
+                    'id': 'chatglm2-6b',
+                    'name': 'ChatGLM2-6B',
+                    'mode': ModelMode.CHAT.value,
                 }
             ]
         else:
             return []
 
     def _get_text_generation_model_mode(self, model_name) -> str:
-        return ModelMode.COMPLETION.value
+        return ModelMode.CHAT.value
 
     def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]:
         """
@@ -64,16 +70,19 @@ class ChatGLMProvider(BaseModelProvider):
         :return:
         """
         model_max_tokens = {
-            'chatglm-6b': 2000,
-            'chatglm2-6b': 32000,
+            'chatglm3-6b-32k': 32000,
+            'chatglm3-6b': 8000,
+            'chatglm2-6b': 8000,
         }
 
+        max_tokens_alias = 'max_length' if model_name == 'chatglm2-6b' else 'max_tokens'
+
         return ModelKwargsRules(
             temperature=KwargRule[float](min=0, max=2, default=1, precision=2),
             top_p=KwargRule[float](min=0, max=1, default=0.7, precision=2),
             presence_penalty=KwargRule[float](enabled=False),
             frequency_penalty=KwargRule[float](enabled=False),
-            max_tokens=KwargRule[int](alias='max_token', min=10, max=model_max_tokens.get(model_name), default=2048, precision=0),
+            max_tokens=KwargRule[int](alias=max_tokens_alias, min=10, max=model_max_tokens.get(model_name), default=2048, precision=0),
         )
 
     @classmethod
@@ -85,16 +94,10 @@ class ChatGLMProvider(BaseModelProvider):
             raise CredentialsValidateFailedError('ChatGLM Endpoint URL must be provided.')
 
         try:
-            credential_kwargs = {
-                'endpoint_url': credentials['api_base']
-            }
+            response = requests.get(f"{credentials['api_base']}/v1/models", timeout=5)
 
-            llm = ChatGLM(
-                max_token=10,
-                **credential_kwargs
-            )
-
-            llm("ping")
+            if response.status_code != 200:
+                raise Exception('ChatGLM Endpoint URL is invalid.')
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 

api/core/model_providers/providers/xinference_provider.py

@@ -2,11 +2,13 @@ import json
 from typing import Type
 
 import requests
+from xinference_client.client.restful.restful_client import Client
 
 from core.helper import encrypter
 from core.model_providers.models.embedding.xinference_embedding import XinferenceEmbedding
 from core.model_providers.models.entity.model_params import KwargRule, ModelKwargsRules, ModelType, ModelMode
 from core.model_providers.models.llm.xinference_model import XinferenceModel
+from core.model_providers.models.reranking.xinference_reranking import XinferenceReranking
 from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError
 
 from core.model_providers.models.base import BaseProviderModel
@@ -40,6 +42,8 @@ class XinferenceProvider(BaseModelProvider):
             model_class = XinferenceModel
         elif model_type == ModelType.EMBEDDINGS:
             model_class = XinferenceEmbedding
+        elif model_type == ModelType.RERANKING:
+            model_class = XinferenceReranking
         else:
             raise NotImplementedError
 
@@ -113,6 +117,10 @@ class XinferenceProvider(BaseModelProvider):
                 )
 
                 embedding.embed_query("ping")
+            elif model_type == ModelType.RERANKING:
+                rerank_client = Client(credential_kwargs['server_url'])
+                model = rerank_client.get_model(credential_kwargs['model_uid'])
+                model.rerank(query="ping", documents=["ping", "pong"], top_n=2)
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 

api/core/model_providers/rules/anthropic.json

@@ -23,8 +23,14 @@
             "currency": "USD"
         },
         "claude-2": {
-            "prompt": "11.02",
-            "completion": "32.68",
+            "prompt": "8.00",
+            "completion": "24.00",
+            "unit": "0.000001",
+            "currency": "USD"
+        },
+        "claude-2.1": {
+            "prompt": "8.00",
+            "completion": "24.00",
             "unit": "0.000001",
             "currency": "USD"
         }

api/core/model_providers/rules/xinference.json

@@ -6,6 +6,7 @@
     "model_flexibility": "configurable",
     "supported_model_types": [
         "text-generation",
-        "embeddings"
+        "embeddings",
+        "reranking"
     ]
 }

api/core/orchestrator_rule_parser.py

@@ -207,22 +207,22 @@ class OrchestratorRuleParser:
             ).first()
 
             if not dataset:
-                return None
+                continue
 
             if dataset and dataset.available_document_count == 0 and dataset.available_document_count == 0:
-                return None
+                continue
             dataset_ids.append(dataset.id)
             if retrieval_model == 'single':
-                retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
-                top_k = retrieval_model['top_k']
+                retrieval_model_config = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
+                top_k = retrieval_model_config['top_k']
 
                 # dynamically adjust top_k when the remaining token number is not enough to support top_k
                 # top_k = self._dynamic_calc_retrieve_k(dataset=dataset, top_k=top_k, rest_tokens=rest_tokens)
 
                 score_threshold = None
-                score_threshold_enable = retrieval_model.get("score_threshold_enable")
+                score_threshold_enable = retrieval_model_config.get("score_threshold_enable")
                 if score_threshold_enable:
-                    score_threshold = retrieval_model.get("score_threshold")
+                    score_threshold = retrieval_model_config.get("score_threshold")
 
                 tool = DatasetRetrieverTool.from_dataset(
                     dataset=dataset,

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

@@ -1,7 +1,7 @@
 from typing import Dict
 
-from httpx import Limits
 from langchain.chat_models import ChatAnthropic
+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
 
@@ -29,8 +29,7 @@ class AnthropicLLM(ChatAnthropic):
                 base_url=values["anthropic_api_url"],
                 api_key=values["anthropic_api_key"],
                 timeout=values["default_request_timeout"],
-                max_retries=0,
-                connection_pool_limits=Limits(max_connections=200, max_keepalive_connections=100),
+                max_retries=0
             )
             values["async_client"] = anthropic.AsyncAnthropic(
                 base_url=values["anthropic_api_url"],
@@ -46,3 +45,16 @@ class AnthropicLLM(ChatAnthropic):
                 "Please it install it with `pip install anthropic`."
             )
         return values
+
+    def _convert_one_message_to_text(self, message: BaseMessage) -> str:
+        if isinstance(message, ChatMessage):
+            message_text = f"\n\n{message.role.capitalize()}: {message.content}"
+        elif isinstance(message, HumanMessage):
+            message_text = f"{self.HUMAN_PROMPT} {message.content}"
+        elif isinstance(message, AIMessage):
+            message_text = f"{self.AI_PROMPT} {message.content}"
+        elif isinstance(message, SystemMessage):
+            message_text = f"{message.content}"
+        else:
+            raise ValueError(f"Got unknown type {message}")
+        return message_text

api/core/tool/dataset_multi_retriever_tool.py

@@ -192,7 +192,7 @@ class DatasetMultiRetrieverTool(BaseTool):
                         'search_method'] == 'hybrid_search':
                         embedding_thread = threading.Thread(target=RetrievalService.embedding_search, kwargs={
                             'flask_app': current_app._get_current_object(),
-                            'dataset': dataset,
+                            'dataset_id': str(dataset.id),
                             'query': query,
                             'top_k': self.top_k,
                             'score_threshold': self.score_threshold,
@@ -210,7 +210,7 @@ class DatasetMultiRetrieverTool(BaseTool):
                         full_text_index_thread = threading.Thread(target=RetrievalService.full_text_index_search,
                                                                   kwargs={
                                                                       'flask_app': current_app._get_current_object(),
-                                                                      'dataset': dataset,
+                                                                      'dataset_id': str(dataset.id),
                                                                       'query': query,
                                                                       'search_method': 'hybrid_search',
                                                                       'embeddings': embeddings,

api/core/tool/dataset_retriever_tool.py

@@ -106,7 +106,7 @@ class DatasetRetrieverTool(BaseTool):
                 if retrieval_model['search_method'] == 'semantic_search' or retrieval_model['search_method'] == 'hybrid_search':
                     embedding_thread = threading.Thread(target=RetrievalService.embedding_search, kwargs={
                         'flask_app': current_app._get_current_object(),
-                        'dataset': dataset,
+                        'dataset_id': str(dataset.id),
                         'query': query,
                         'top_k': self.top_k,
                         'score_threshold': retrieval_model['score_threshold'] if retrieval_model[
@@ -124,7 +124,7 @@ class DatasetRetrieverTool(BaseTool):
                 if retrieval_model['search_method'] == 'full_text_search' or retrieval_model['search_method'] == 'hybrid_search':
                     full_text_index_thread = threading.Thread(target=RetrievalService.full_text_index_search, kwargs={
                         'flask_app': current_app._get_current_object(),
-                        'dataset': dataset,
+                        'dataset_id': str(dataset.id),
                         'query': query,
                         'search_method': retrieval_model['search_method'],
                         'embeddings': embeddings,

api/core/vector_store/vector/weaviate.py

@@ -60,7 +60,7 @@ def _create_weaviate_client(**kwargs: Any) -> Any:
 
 
 def _default_score_normalizer(val: float) -> float:
-    return 1 - 1 / (1 + np.exp(val))
+    return 1 - val
 
 
 def _json_serializable(value: Any) -> Any:
@@ -243,7 +243,8 @@ class Weaviate(VectorStore):
             query_obj = query_obj.with_where(kwargs.get("where_filter"))
         if kwargs.get("additional"):
             query_obj = query_obj.with_additional(kwargs.get("additional"))
-        result = query_obj.with_bm25(query=content).with_limit(k).do()
+        properties = ['text', 'dataset_id', 'doc_hash', 'doc_id', 'document_id']
+        result = query_obj.with_bm25(query=query, properties=properties).with_limit(k).do()
         if "errors" in result:
             raise ValueError(f"Error during query: {result['errors']}")
         docs = []
@@ -380,14 +381,14 @@ class Weaviate(VectorStore):
             result = (
                 query_obj.with_near_vector(vector)
                 .with_limit(k)
-                .with_additional("vector")
+                .with_additional(["vector", "distance"])
                 .do()
             )
         else:
             result = (
                 query_obj.with_near_text(content)
                 .with_limit(k)
-                .with_additional("vector")
+                .with_additional(["vector", "distance"])
                 .do()
             )
 
@@ -397,7 +398,7 @@ class Weaviate(VectorStore):
         docs_and_scores = []
         for res in result["data"]["Get"][self._index_name]:
             text = res.pop(self._text_key)
-            score = np.dot(res["_additional"]["vector"], embedded_query)
+            score = res["_additional"]["distance"]
             docs_and_scores.append((Document(page_content=text, metadata=res), score))
         return docs_and_scores
 

api/core/vector_store/weaviate_vector_store.py

@@ -1,4 +1,4 @@
-from langchain.vectorstores import Weaviate
+from core.vector_store.vector.weaviate import Weaviate
 
 
 class WeaviateVectorStore(Weaviate):

api/requirements.txt

@@ -35,7 +35,7 @@ docx2txt==0.8
 pypdfium2==4.16.0
 resend~=0.5.1
 pyjwt~=2.6.0
-anthropic~=0.3.4
+anthropic~=0.7.2
 newspaper3k==0.2.8
 google-api-python-client==2.90.0
 wikipedia==1.4.0
@@ -48,7 +48,7 @@ huggingface_hub~=0.16.4
 transformers~=4.31.0
 stripe~=5.5.0
 pandas==1.5.3
-xinference-client~=0.5.4
+xinference-client~=0.6.4
 safetensors==0.3.2
 zhipuai==1.0.7
 werkzeug==2.3.7

api/services/completion_service.py

@@ -232,7 +232,7 @@ class CompletionService:
                 logging.exception("Unknown Error in completion")
                 PubHandler.pub_error(user, generate_task_id, e)
             finally:
-                db.session.commit()
+                db.session.remove()
 
     @classmethod
     def countdown_and_close(cls, flask_app: Flask, worker_thread, pubsub, detached_user,
@@ -242,22 +242,25 @@ class CompletionService:
 
         def close_pubsub():
             with flask_app.app_context():
-                user = db.session.merge(detached_user)
+                try:
+                    user = db.session.merge(detached_user)
 
-                sleep_iterations = 0
-                while sleep_iterations < timeout and worker_thread.is_alive():
-                    if sleep_iterations > 0 and sleep_iterations % 10 == 0:
-                        PubHandler.ping(user, generate_task_id)
+                    sleep_iterations = 0
+                    while sleep_iterations < timeout and worker_thread.is_alive():
+                        if sleep_iterations > 0 and sleep_iterations % 10 == 0:
+                            PubHandler.ping(user, generate_task_id)
 
-                    time.sleep(1)
-                    sleep_iterations += 1
+                        time.sleep(1)
+                        sleep_iterations += 1
 
-                if worker_thread.is_alive():
-                    PubHandler.stop(user, generate_task_id)
-                    try:
-                        pubsub.close()
-                    except Exception:
-                        pass
+                    if worker_thread.is_alive():
+                        PubHandler.stop(user, generate_task_id)
+                        try:
+                            pubsub.close()
+                        except Exception:
+                            pass
+                finally:
+                    db.session.remove()
 
         countdown_thread = threading.Thread(target=close_pubsub)
         countdown_thread.start()
@@ -394,7 +397,7 @@ class CompletionService:
                     logging.exception(e)
                     raise
             finally:
-                db.session.commit()
+                db.session.remove()
 
                 try:
                     pubsub.unsubscribe(generate_channel)
@@ -436,7 +439,7 @@ class CompletionService:
                         logging.exception(e)
                         raise
                 finally:
-                    db.session.commit()
+                    db.session.remove()
 
                     try:
                         pubsub.unsubscribe(generate_channel)

api/services/hit_testing_service.py

@@ -61,7 +61,7 @@ class HitTestingService:
         if retrieval_model['search_method'] == 'semantic_search' or retrieval_model['search_method'] == 'hybrid_search':
             embedding_thread = threading.Thread(target=RetrievalService.embedding_search, kwargs={
                 'flask_app': current_app._get_current_object(),
-                'dataset': dataset,
+                'dataset_id': str(dataset.id),
                 'query': query,
                 'top_k': retrieval_model['top_k'],
                 'score_threshold': retrieval_model['score_threshold'] if retrieval_model['score_threshold_enable'] else None,
@@ -77,7 +77,7 @@ class HitTestingService:
         if retrieval_model['search_method'] == 'full_text_search' or retrieval_model['search_method'] == 'hybrid_search':
             full_text_index_thread = threading.Thread(target=RetrievalService.full_text_index_search, kwargs={
                 'flask_app': current_app._get_current_object(),
-                'dataset': dataset,
+                'dataset_id': str(dataset.id),
                 'query': query,
                 'search_method': retrieval_model['search_method'],
                 'embeddings': embeddings,

api/services/retrieval_service.py

@@ -4,6 +4,7 @@ from flask import current_app, Flask
 from langchain.embeddings.base import Embeddings
 from core.index.vector_index.vector_index import VectorIndex
 from core.model_providers.model_factory import ModelFactory
+from extensions.ext_database import db
 from models.dataset import Dataset
 
 default_retrieval_model = {
@@ -21,10 +22,13 @@ default_retrieval_model = {
 class RetrievalService:
 
     @classmethod
-    def embedding_search(cls, flask_app: Flask, dataset: Dataset, query: str,
+    def embedding_search(cls, flask_app: Flask, dataset_id: str, query: str,
                          top_k: int, score_threshold: Optional[float], reranking_model: Optional[dict],
                          all_documents: list, search_method: str, embeddings: Embeddings):
         with flask_app.app_context():
+            dataset = db.session.query(Dataset).filter(
+                Dataset.id == dataset_id
+            ).first()
 
             vector_index = VectorIndex(
                 dataset=dataset,
@@ -56,10 +60,13 @@ class RetrievalService:
                     all_documents.extend(documents)
 
     @classmethod
-    def full_text_index_search(cls, flask_app: Flask, dataset: Dataset, query: str,
+    def full_text_index_search(cls, flask_app: Flask, dataset_id: str, query: str,
                                top_k: int, score_threshold: Optional[float], reranking_model: Optional[dict],
                                all_documents: list, search_method: str, embeddings: Embeddings):
         with flask_app.app_context():
+            dataset = db.session.query(Dataset).filter(
+                Dataset.id == dataset_id
+            ).first()
 
             vector_index = VectorIndex(
                 dataset=dataset,

api/tests/integration_tests/.env.example

@@ -50,4 +50,7 @@ XINFERENCE_MODEL_UID=
 OPENLLM_SERVER_URL=
 
 # LocalAI Credentials
-LOCALAI_SERVER_URL=
+LOCALAI_SERVER_URL=
+
+# Cohere Credentials
+COHERE_API_KEY=

api/tests/integration_tests/models/reranking/test_cohere_reranking.py

@@ -0,0 +1,61 @@
+import json
+import os
+from unittest.mock import patch
+
+from langchain.schema import Document
+
+from core.model_providers.models.reranking.cohere_reranking import CohereReranking
+from core.model_providers.providers.cohere_provider import CohereProvider
+from models.provider import Provider, ProviderType
+
+
+def get_mock_provider(valid_api_key):
+    return Provider(
+        id='provider_id',
+        tenant_id='tenant_id',
+        provider_name='cohere',
+        provider_type=ProviderType.CUSTOM.value,
+        encrypted_config=json.dumps({'api_key': valid_api_key}),
+        is_valid=True,
+    )
+
+
+def get_mock_model():
+    valid_api_key = os.environ['COHERE_API_KEY']
+    provider = CohereProvider(provider=get_mock_provider(valid_api_key))
+    return CohereReranking(
+        model_provider=provider,
+        name='rerank-english-v2.0'
+    )
+
+
+def decrypt_side_effect(tenant_id, encrypted_api_key):
+    return encrypted_api_key
+
+
+@patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
+def test_run(mock_decrypt):
+    model = get_mock_model()
+
+    docs = []
+    docs.append(Document(
+        page_content='bye',
+        metadata={
+            "doc_id": 'a',
+            "doc_hash": 'doc_hash',
+            "document_id": 'document_id',
+            "dataset_id": 'dataset_id',
+        }
+    ))
+    docs.append(Document(
+        page_content='hello',
+        metadata={
+            "doc_id": 'b',
+            "doc_hash": 'doc_hash',
+            "document_id": 'document_id',
+            "dataset_id": 'dataset_id',
+        }
+    ))
+    rst = model.rerank('hello', docs, None, 2)
+
+    assert rst[0].page_content == 'hello'

api/tests/integration_tests/models/reranking/test_xinference_reranking.py

@@ -0,0 +1,78 @@
+import json
+import os
+from unittest.mock import patch, MagicMock
+
+from langchain.schema import Document
+
+from core.model_providers.models.entity.model_params import ModelType
+from core.model_providers.models.reranking.xinference_reranking import XinferenceReranking
+from core.model_providers.providers.xinference_provider import XinferenceProvider
+from models.provider import Provider, ProviderType, ProviderModel
+
+
+def get_mock_provider(valid_server_url, valid_model_uid):
+    return Provider(
+        id='provider_id',
+        tenant_id='tenant_id',
+        provider_name='xinference',
+        provider_type=ProviderType.CUSTOM.value,
+        encrypted_config=json.dumps({'server_url': valid_server_url, 'model_uid': valid_model_uid}),
+        is_valid=True,
+    )
+
+
+def get_mock_model(mocker):
+    valid_server_url = os.environ['XINFERENCE_SERVER_URL']
+    valid_model_uid = os.environ['XINFERENCE_MODEL_UID']
+    model_name = 'bge-reranker-base'
+    provider = XinferenceProvider(provider=get_mock_provider(valid_server_url, valid_model_uid))
+
+    mock_query = MagicMock()
+    mock_query.filter.return_value.first.return_value = ProviderModel(
+        provider_name='xinference',
+        model_name=model_name,
+        model_type=ModelType.RERANKING.value,
+        encrypted_config=json.dumps({
+            'server_url': valid_server_url,
+            'model_uid': valid_model_uid
+        }),
+        is_valid=True,
+    )
+    mocker.patch('extensions.ext_database.db.session.query', return_value=mock_query)
+
+    return XinferenceReranking(
+        model_provider=provider,
+        name=model_name
+    )
+
+
+def decrypt_side_effect(tenant_id, encrypted_api_key):
+    return encrypted_api_key
+
+
+@patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
+def test_run(mock_decrypt, mocker):
+    model = get_mock_model(mocker)
+
+    docs = []
+    docs.append(Document(
+        page_content='bye',
+        metadata={
+            "doc_id": 'a',
+            "doc_hash": 'doc_hash',
+            "document_id": 'document_id',
+            "dataset_id": 'dataset_id',
+        }
+    ))
+    docs.append(Document(
+        page_content='hello',
+        metadata={
+            "doc_id": 'b',
+            "doc_hash": 'doc_hash',
+            "document_id": 'document_id',
+            "dataset_id": 'dataset_id',
+        }
+    ))
+    rst = model.rerank('hello', docs, None, 2)
+
+    assert rst[0].page_content == 'hello'

api/tests/unit_tests/model_providers/test_anthropic_provider.py

@@ -31,12 +31,12 @@ def mock_chat_generate_invalid(messages: List[BaseMessage],
                                run_manager: Optional[CallbackManagerForLLMRun] = None,
                                **kwargs: Any):
     raise anthropic.APIStatusError('Invalid credentials',
-                                   request=httpx._models.Request(
-                                       method='POST',
-                                       url='https://api.anthropic.com/v1/completions',
-                                   ),
                                    response=httpx._models.Response(
                                        status_code=401,
+                                       request=httpx._models.Request(
+                                           method='POST',
+                                           url='https://api.anthropic.com/v1/completions',
+                                       )
                                    ),
                                    body=None
                                 )

api/tests/unit_tests/model_providers/test_chatglm_provider.py

@@ -2,7 +2,9 @@ import pytest
 from unittest.mock import patch
 import json
 
+import requests
 from langchain.schema import LLMResult, Generation, AIMessage, ChatResult, ChatGeneration
+from requests import Response
 
 from core.model_providers.providers.base import CredentialsValidateFailedError
 from core.model_providers.providers.chatglm_provider import ChatGLMProvider
@@ -26,8 +28,11 @@ def decrypt_side_effect(tenant_id, encrypted_key):
 
 
 def test_is_provider_credentials_valid_or_raise_valid(mocker):
-    mocker.patch('langchain.llms.chatglm.ChatGLM._call',
-                 return_value="abc")
+    mock_response = Response()
+    mock_response.status_code = 200
+    mock_response._content = json.dumps({'models': []}).encode('utf-8')
+    mocker.patch('requests.get',
+                 return_value=mock_response)
 
     MODEL_PROVIDER_CLASS.is_provider_credentials_valid_or_raise(VALIDATE_CREDENTIAL)
 

docker/docker-compose.middleware.yaml

@@ -30,7 +30,7 @@ services:
 
   # The Weaviate vector store.
   weaviate:
-    image: semitechnologies/weaviate:1.18.4
+    image: semitechnologies/weaviate:1.19.0
     restart: always
     volumes:
       # Mount the Weaviate data directory to the container.
@@ -63,4 +63,4 @@ services:
 #    environment:
 #      QDRANT__API_KEY: 'difyai123456'
 #    ports:
-#      - "6333:6333"
+#      - "6333:6333"

docker/docker-compose.yaml

@@ -2,7 +2,7 @@ version: '3.1'
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.3.31
+    image: langgenius/dify-api:0.3.32
     restart: always
     environment:
       # Startup mode, 'api' starts the API server.
@@ -128,7 +128,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.3.31
+    image: langgenius/dify-api:0.3.32
     restart: always
     environment:
       # Startup mode, 'worker' starts the Celery worker for processing the queue.
@@ -196,7 +196,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.3.31
+    image: langgenius/dify-web:0.3.32
     restart: always
     environment:
       EDITION: SELF_HOSTED
@@ -253,7 +253,7 @@ services:
 
   # The Weaviate vector store.
   weaviate:
-    image: semitechnologies/weaviate:1.18.4
+    image: semitechnologies/weaviate:1.19.0
     restart: always
     volumes:
       # Mount the Weaviate data directory to the container.
@@ -280,7 +280,7 @@ services:
 #  (if uncommented, you need to comment out the weaviate service above,
 #  and set VECTOR_STORE to qdrant in the api & worker service.)
 #  qdrant:
-#    image: qdrant/qdrant:latest
+#    image: langgenius/qdrant:latest
 #    restart: always
 #    volumes:
 #      - ./volumes/qdrant:/qdrant/storage
@@ -302,4 +302,4 @@ services:
       - api
       - web
     ports:
-      - "80:80"
+      - "80:80"

web/app/components/app/chat/answer/index.tsx

@@ -194,7 +194,7 @@ const Answer: FC<IAnswerProps> = ({
             </div>
           )
         }
-        <div className={cn(s.answerWrapWrap, 'chat-answer-container')}>
+        <div className={cn(s.answerWrapWrap, 'chat-answer-container group')}>
           <div className={`${s.answerWrap} ${showEdit ? 'w-full' : ''}`}>
             <div className={`${s.answer} relative text-sm text-gray-900`}>
               <div className={'ml-2 py-3 px-4 bg-gray-100 rounded-tr-2xl rounded-b-2xl'}>
@@ -280,7 +280,7 @@ const Answer: FC<IAnswerProps> = ({
                 {!feedbackDisabled && renderFeedbackRating(feedback?.rating, !isHideFeedbackEdit, displayScene !== 'console')}
               </div>
             </div>
-            {more && <MoreInfo more={more} isQuestion={false} />}
+            {more && <MoreInfo className='hidden group-hover:block' more={more} isQuestion={false} />}
           </div>
         </div>
       </div>

web/app/components/app/chat/more-info/index.tsx

@@ -5,15 +5,19 @@ import { useTranslation } from 'react-i18next'
 import type { MessageMore } from '../type'
 import { formatNumber } from '@/utils/format'
 
-export type IMoreInfoProps = { more: MessageMore; isQuestion: boolean }
+export type IMoreInfoProps = {
+  more: MessageMore
+  isQuestion: boolean
+  className?: string
+}
 
-const MoreInfo: FC<IMoreInfoProps> = ({ more, isQuestion }) => {
+const MoreInfo: FC<IMoreInfoProps> = ({ more, isQuestion, className }) => {
   const { t } = useTranslation()
-  return (<div className={`mt-1 w-full text-xs text-gray-400 !text-right ${isQuestion ? 'mr-2 text-right ' : 'ml-2 text-left float-right'}`}>
-    <span>{`${t('appLog.detail.timeConsuming')} ${more.latency}${t('appLog.detail.second')}`}</span>
-    <span>{`${t('appLog.detail.tokenCost')} ${formatNumber(more.tokens)}`}</span>
-    <span>· </span>
-    <span>{more.time} </span>
+  return (<div className={`mt-1 w-full text-xs text-gray-400 ${isQuestion ? 'mr-2 text-right ' : 'pl-2 text-left float-right'} ${className}`}>
+    <span className='mr-2'>{`${t('appLog.detail.timeConsuming')} ${more.latency}${t('appLog.detail.second')}`}</span>
+    <span className='mr-2'>{`${t('appLog.detail.tokenCost')} ${formatNumber(more.tokens)}`}</span>
+    <span className='mr-2'>·</span>
+    <span>{more.time}</span>
   </div>)
 }
 export default React.memo(MoreInfo)

web/app/components/datasets/hit-testing/modify-retrieval-modal.tsx

@@ -1,7 +1,6 @@
 'use client'
 import type { FC } from 'react'
 import React, { useRef, useState } from 'react'
-import { useClickAway } from 'ahooks'
 import { useTranslation } from 'react-i18next'
 import Toast from '../../base/toast'
 import { XClose } from '@/app/components/base/icons/src/vender/line/general'
@@ -31,10 +30,10 @@ const ModifyRetrievalModal: FC<Props> = ({
   const { t } = useTranslation()
   const [retrievalConfig, setRetrievalConfig] = useState(value)
 
-  useClickAway(() => {
-    if (ref)
-      onHide()
-  }, ref)
+  // useClickAway(() => {
+  //   if (ref)
+  //     onHide()
+  // }, ref)
 
   const {
     rerankDefaultModel,

web/app/components/explore/universal-chat/index.tsx

@@ -688,6 +688,7 @@ const Main: FC<IMainProps> = () => {
         onUnpin={handleUnpin}
         controlUpdateList={controlUpdateConversationList}
         onDelete={handleDelete}
+        onStartChat={() => handleConversationIdChange('-1')}
       />
     )
   }

web/app/components/header/account-setting/model-page/configs/xinference.tsx

@@ -80,6 +80,13 @@ const config: ProviderConfig = {
               'zh-Hans': 'Embeddings',
             },
           },
+          {
+            key: 'reranking',
+            label: {
+              'en': 'Rerank',
+              'zh-Hans': 'Rerank',
+            },
+          },
         ],
       },
       {

web/app/components/header/account-setting/model-page/model-modal/Form.tsx

@@ -150,10 +150,11 @@ const Form: FC<FormProps> = ({
 
     if (field.type === 'radio') {
       const options = typeof field.options === 'function' ? field.options(value) : field.options
+
       return (
         <div key={field.key} className='py-3'>
           <div className={nameClassName}>{field.label[locale]}</div>
-          <div className='grid grid-cols-2 gap-3'>
+          <div className={`grid grid-cols-${options?.length} gap-3`}>
             {
               options?.map(option => (
                 <div

web/app/components/share/chat/index.tsx

@@ -668,7 +668,7 @@ const Main: FC<IMainProps> = ({
         onUnpin={handleUnpin}
         controlUpdateList={controlUpdateConversationList}
         onDelete={handleDelete}
-        onStartChat={handleStartChat}
+        onStartChat={() => handleConversationIdChange('-1')}
       />
     )
   }

web/global.d.ts

@@ -1,5 +1,3 @@
 declare module 'lamejs';
 declare module 'react-18-input-autosize';
-declare module 'fetch-readablestream' {
-  export default function fetchReadableStream(url: string, options?: RequestInit): Promise<Response>
-}
+

web/package.json

@@ -1,6 +1,6 @@
 {
   "name": "dify-web",
-  "version": "0.3.31",
+  "version": "0.3.32",
   "private": true,
   "scripts": {
     "dev": "next dev",
@@ -36,7 +36,6 @@
     "echarts": "^5.4.1",
     "echarts-for-react": "^3.0.2",
     "emoji-mart": "^5.5.2",
-    "fetch-readablestream": "^0.2.0",
     "i18next": "^22.4.13",
     "i18next-resources-to-backend": "^1.1.3",
     "immer": "^9.0.19",

web/service/base.ts

@@ -1,11 +1,8 @@
-import fetchStream from 'fetch-readablestream'
 import { API_PREFIX, IS_CE_EDITION, PUBLIC_API_PREFIX } from '@/config'
 import Toast from '@/app/components/base/toast'
 import type { MessageEnd, MessageReplace, ThoughtItem } from '@/app/components/app/chat/type'
-import { isSupportNativeFetchStream } from '@/utils/stream'
 
 const TIME_OUT = 100000
-const supportNativeFetchStream = isSupportNativeFetchStream()
 
 const ContentType = {
   json: 'application/json',
@@ -223,9 +220,6 @@ const baseFetch = <T>(
   if (body && bodyStringify)
     options.body = JSON.stringify(body)
 
-  // for those do not support native fetch stream, we use fetch-readablestream as polyfill
-  const doFetch = supportNativeFetchStream ? globalThis.fetch : fetchStream
-
   // Handle timeout
   return Promise.race([
     new Promise((resolve, reject) => {
@@ -234,7 +228,7 @@ const baseFetch = <T>(
       }, TIME_OUT)
     }),
     new Promise((resolve, reject) => {
-      doFetch(urlWithPrefix, options as RequestInit)
+      globalThis.fetch(urlWithPrefix, options as RequestInit)
         .then((res) => {
           const resClone = res.clone()
           // Error handler

web/utils/stream.ts

@@ -1,21 +0,0 @@
-// https://developer.chrome.com/articles/fetch-streaming-requests/#feature-detection
-export const isSupportNativeFetchStream = () => {
-  const supportsRequestStreams = (() => {
-    let duplexAccessed = false
-
-    const params = {
-      body: new ReadableStream(),
-      method: 'POST',
-      get duplex() {
-        duplexAccessed = true
-        return 'half'
-      },
-    }
-
-    const hasContentType = new Request('', params).headers.has('Content-Type')
-
-    return duplexAccessed && !hasContentType
-  })()
-
-  return supportsRequestStreams
-}