添加PaLM支持（未完成）

modules/models/PaLM.py

@@ -0,0 +1,10 @@
+from .base_model import BaseLLMModel, CallbackToIterator, ChuanhuCallbackHandler
+from langchain.chat_models import ChatGooglePalm
+
+class PaLM_Client(BaseLLMModel):
+    def __init__(self, model_name, user="") -> None:
+        super().__init__(model_name, user)
+        self.llm = ChatGooglePalm(google_api_key="")
+
+    def get_answer_at_once(self):
+        self.llm.generate(self.history)

modules/models/base_model.py

@@ -20,6 +20,7 @@ from enum import Enum
 
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from langchain.callbacks.manager import BaseCallbackManager
+from langchain.callbacks import get_openai_callback
 
 from typing import Any, Dict, List, Optional, Union
 
@@ -108,6 +109,7 @@ class ModelType(Enum):
     MOSS = 5
     YuanAI = 6
     ChuanhuAgent = 7
+    PaLM = 8
 
     @classmethod
     def get_type(cls, model_name: str):
@@ -129,6 +131,8 @@ class ModelType(Enum):
             model_type = ModelType.YuanAI
         elif "川虎助理" in model_name_lower:
             model_type = ModelType.ChuanhuAgent
+        elif "palm" in model_name_lower:
+            model_type = ModelType.PaLM
         else:
             model_type = ModelType.Unknown
         return model_type
@@ -262,18 +266,20 @@ class BaseLLMModel:
             status = i18n("索引构建完成")
             # Summarize the document
             logging.info(i18n("生成内容总结中……"))
-            os.environ["OPENAI_API_KEY"] = self.api_key
-            from langchain.chains.summarize import load_summarize_chain
-            from langchain.prompts import PromptTemplate
-            from langchain.chat_models import ChatOpenAI
-            from langchain.callbacks import StdOutCallbackHandler
-            prompt_template = "Write a concise summary of the following:\n\n{text}\n\nCONCISE SUMMARY IN " + language + ":"
-            PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
-            llm = ChatOpenAI()
-            chain = load_summarize_chain(llm, chain_type="map_reduce", return_intermediate_steps=True, map_prompt=PROMPT, combine_prompt=PROMPT)
-            summary = chain({"input_documents": list(index.docstore.__dict__["_dict"].values())}, return_only_outputs=True)["output_text"]
-            print(i18n("总结") + f": {summary}")
-            chatbot.append([i18n("上传了")+len(files)+"个文件", summary])
+            with get_openai_callback() as cb:
+                os.environ["OPENAI_API_KEY"] = self.api_key
+                from langchain.chains.summarize import load_summarize_chain
+                from langchain.prompts import PromptTemplate
+                from langchain.chat_models import ChatOpenAI
+                from langchain.callbacks import StdOutCallbackHandler
+                prompt_template = "Write a concise summary of the following:\n\n{text}\n\nCONCISE SUMMARY IN " + language + ":"
+                PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
+                llm = ChatOpenAI()
+                chain = load_summarize_chain(llm, chain_type="map_reduce", return_intermediate_steps=True, map_prompt=PROMPT, combine_prompt=PROMPT)
+                summary = chain({"input_documents": list(index.docstore.__dict__["_dict"].values())}, return_only_outputs=True)["output_text"]
+                print(i18n("总结") + f": {summary}")
+                chatbot.append([i18n("上传了")+str(len(files))+"个文件", summary])
+            logging.info(cb)
         return gr.Files.update(), chatbot, status
 
     def prepare_inputs(self, real_inputs, use_websearch, files, reply_language, chatbot):

modules/models/models.py

@@ -606,6 +606,9 @@ def get_model(
         elif model_type == ModelType.ChuanhuAgent:
             from .ChuanhuAgent import ChuanhuAgent_Client
             model = ChuanhuAgent_Client(model_name, access_key, user_name=user_name)
+        elif model_type == ModelType.PaLM:
+            from .PaLM import PaLM_Client
+            model = PaLM_Client(model_name, user_name=user_name)
         elif model_type == ModelType.Unknown:
             raise ValueError(f"未知模型: {model_name}")
         logging.info(msg)

modules/presets.py

@@ -68,6 +68,7 @@ ONLINE_MODELS = [
     "gpt-4-32k",
     "gpt-4-32k-0314",
     "xmchat",
+    "Google PaLM",
     "yuanai-1.0-base_10B",
     "yuanai-1.0-translate",
     "yuanai-1.0-dialog",