feat: universal chat in explore (#649)

Co-authored-by: StyleZhang <jasonapring2015@outlook.com>

api/core/llm/streamable_chat_anthropic.py

@@ -1,8 +1,10 @@
 from typing import List, Optional, Any, Dict
 
+from httpx import Timeout
 from langchain.callbacks.manager import Callbacks
 from langchain.chat_models import ChatAnthropic
-from langchain.schema import BaseMessage, LLMResult
+from langchain.schema import BaseMessage, LLMResult, SystemMessage, AIMessage, HumanMessage, ChatMessage
+from pydantic import root_validator
 
 from core.llm.wrappers.anthropic_wrapper import handle_anthropic_exceptions
 
@@ -12,6 +14,14 @@ class StreamableChatAnthropic(ChatAnthropic):
     Wrapper around Anthropic's large language model.
     """
 
+    default_request_timeout: Optional[float] = Timeout(timeout=300.0, connect=5.0)
+
+    @root_validator()
+    def prepare_params(cls, values: Dict) -> Dict:
+        values['model_name'] = values.get('model')
+        values['max_tokens'] = values.get('max_tokens_to_sample')
+        return values
+
     @handle_anthropic_exceptions
     def generate(
             self,
@@ -37,3 +47,16 @@ class StreamableChatAnthropic(ChatAnthropic):
         del params['presence_penalty']
 
         return params
+
+    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"<admin>{message.content}</admin>"
+        else:
+            raise ValueError(f"Got unknown type {message}")
+        return message_text