feat: add function call feature & embedding batch size of azure models

api/core/llm/provider/azure_provider.py

@@ -9,7 +9,7 @@ from core.llm.provider.errors import ValidateFailedError
 from models.provider import ProviderName
 
 
-AZURE_OPENAI_API_VERSION = '2023-06-01-preview'
+AZURE_OPENAI_API_VERSION = '2023-07-01-preview'
 
 
 class AzureProvider(BaseProvider):
@@ -45,9 +45,10 @@ class AzureProvider(BaseProvider):
         """
         config = self.get_provider_api_key(model_id=model_id)
         config['openai_api_type'] = 'azure'
+        config['openai_api_version'] = AZURE_OPENAI_API_VERSION
         if model_id == 'text-embedding-ada-002':
             config['deployment'] = model_id.replace('.', '') if model_id else None
-            config['chunk_size'] = 1
+            config['chunk_size'] = 16
         else:
             config['deployment_name'] = model_id.replace('.', '') if model_id else None
         return config

api/core/llm/streamable_azure_chat_open_ai.py

-from langchain.callbacks.manager import Callbacks
-from langchain.schema import BaseMessage, LLMResult
+from langchain.callbacks.manager import Callbacks, CallbackManagerForLLMRun
+from langchain.chat_models.openai import _convert_dict_to_message
+from langchain.schema import BaseMessage, LLMResult, ChatResult, ChatGeneration
 from langchain.chat_models import AzureChatOpenAI
 from typing import Optional, List, Dict, Any
 
@@ -71,3 +72,43 @@ class StreamableAzureChatOpenAI(AzureChatOpenAI):
         params['model_kwargs'] = model_kwargs
 
         return params
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        message_dicts, params = self._create_message_dicts(messages, stop)
+        params = {**params, **kwargs}
+        if self.streaming:
+            inner_completion = ""
+            role = "assistant"
+            params["stream"] = True
+            function_call: Optional[dict] = None
+            for stream_resp in self.completion_with_retry(
+                messages=message_dicts, **params
+            ):
+                if len(stream_resp["choices"]) > 0:
+                    role = stream_resp["choices"][0]["delta"].get("role", role)
+                    token = stream_resp["choices"][0]["delta"].get("content") or ""
+                    inner_completion += token
+                    _function_call = stream_resp["choices"][0]["delta"].get("function_call")
+                    if _function_call:
+                        if function_call is None:
+                            function_call = _function_call
+                        else:
+                            function_call["arguments"] += _function_call["arguments"]
+                    if run_manager:
+                        run_manager.on_llm_new_token(token)
+            message = _convert_dict_to_message(
+                {
+                    "content": inner_completion,
+                    "role": role,
+                    "function_call": function_call,
+                }
+            )
+            return ChatResult(generations=[ChatGeneration(message=message)])
+        response = self.completion_with_retry(messages=message_dicts, **params)
+        return self._create_chat_result(response)

api/core/orchestrator_rule_parser.py

@@ -3,6 +3,7 @@ from typing import Optional
 
 from langchain import WikipediaAPIWrapper
 from langchain.callbacks.manager import Callbacks
+from langchain.chat_models import ChatOpenAI
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.tools import BaseTool, Tool, WikipediaQueryRun
 from pydantic import BaseModel, Field
@@ -15,7 +16,6 @@ from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHan
 from core.chain.sensitive_word_avoidance_chain import SensitiveWordAvoidanceChain
 from core.conversation_message_task import ConversationMessageTask
 from core.llm.llm_builder import LLMBuilder
-from core.llm.streamable_chat_open_ai import StreamableChatOpenAI
 from core.tool.dataset_retriever_tool import DatasetRetrieverTool
 from core.tool.provider.serpapi_provider import SerpAPIToolProvider
 from core.tool.serpapi_wrapper import OptimizedSerpAPIWrapper, OptimizedSerpAPIInput
@@ -64,8 +64,8 @@ class OrchestratorRuleParser:
 
             planning_strategy = PlanningStrategy(agent_mode_config.get('strategy', 'router'))
 
-            # only OpenAI chat model support function call, use ReACT instead
-            if not isinstance(agent_llm, StreamableChatOpenAI) \
+            # only OpenAI chat model (include Azure) support function call, use ReACT instead
+            if not isinstance(agent_llm, ChatOpenAI) \
                     and planning_strategy in [PlanningStrategy.FUNCTION_CALL, PlanningStrategy.MULTI_FUNCTION_CALL]:
                 planning_strategy = PlanningStrategy.REACT
 

api/requirements.txt

@@ -10,7 +10,7 @@ flask-session2==1.3.1
 flask-cors==3.0.10
 gunicorn~=20.1.0
 gevent~=22.10.2
-langchain==0.0.230
+langchain==0.0.239
 openai~=0.27.8
 psycopg2-binary~=2.9.6
 pycryptodome==3.17