feat: universal chat in explore (#649)

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

api/core/llm/fake.py

@@ -0,0 +1,59 @@
+import time
+from typing import List, Optional, Any, Mapping
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.chat_models.base import SimpleChatModel
+from langchain.schema import BaseMessage, ChatResult, AIMessage, ChatGeneration, BaseLanguageModel
+
+
+class FakeLLM(SimpleChatModel):
+    """Fake ChatModel for testing purposes."""
+
+    streaming: bool = False
+    """Whether to stream the results or not."""
+    response: str
+    origin_llm: Optional[BaseLanguageModel] = None
+
+    @property
+    def _llm_type(self) -> str:
+        return "fake-chat-model"
+
+    def _call(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """First try to lookup in queries, else return 'foo' or 'bar'."""
+        return self.response
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        return {"response": self.response}
+
+    def get_num_tokens(self, text: str) -> int:
+        return self.origin_llm.get_num_tokens(text) if self.origin_llm else 0
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        output_str = self._call(messages, stop=stop, run_manager=run_manager, **kwargs)
+        if self.streaming:
+            for token in output_str:
+                if run_manager:
+                    run_manager.on_llm_new_token(token)
+                    time.sleep(0.01)
+
+        message = AIMessage(content=output_str)
+        generation = ChatGeneration(message=message)
+        llm_output = {"token_usage": {
+            'prompt_tokens': 0,
+            'completion_tokens': 0,
+            'total_tokens': 0,
+        }}
+        return ChatResult(generations=[generation], llm_output=llm_output)