move document_loaders & text_splitter under server

server/document_loaders/myimgloader.py

@@ -1,6 +1,6 @@
 from typing import List
 from langchain.document_loaders.unstructured import UnstructuredFileLoader
-from document_loaders.ocr import get_ocr
+from server.document_loaders.ocr import get_ocr
 
 
 class RapidOCRLoader(UnstructuredFileLoader):

server/document_loaders/mypdfloader.py

@@ -4,7 +4,7 @@ import cv2
 from PIL import Image
 import numpy as np
 from configs import PDF_OCR_THRESHOLD
-from document_loaders.ocr import get_ocr
+from server.document_loaders.ocr import get_ocr
 import tqdm
 
 

server/knowledge_base/kb_service/base.py

@@ -1,5 +1,6 @@
 from abc import ABC, abstractmethod
 
+import operator
 import os
 from pathlib import Path
 from langchain.docstore.document import Document

server/knowledge_base/utils.py

@@ -11,7 +11,7 @@ from configs import (
     TEXT_SPLITTER_NAME,
 )
 import importlib
-from text_splitter import zh_title_enhance as func_zh_title_enhance
+from server.text_splitter import zh_title_enhance as func_zh_title_enhance
 import langchain.document_loaders
 from langchain.docstore.document import Document
 from langchain.text_splitter import TextSplitter
@@ -153,15 +153,15 @@ def get_loader(loader_name: str, file_path: str, loader_kwargs: Dict = None):
     try:
         if loader_name in ["RapidOCRPDFLoader", "RapidOCRLoader", "FilteredCSVLoader",
                            "RapidOCRDocLoader", "RapidOCRPPTLoader"]:
-            document_loaders_module = importlib.import_module('document_loaders')
+            document_loaders_module = importlib.import_module("server.document_loaders")
         else:
-            document_loaders_module = importlib.import_module('langchain.document_loaders')
+            document_loaders_module = importlib.import_module("langchain.document_loaders")
         DocumentLoader = getattr(document_loaders_module, loader_name)
     except Exception as e:
         msg = f"为文件{file_path}查找加载器{loader_name}时出错：{e}"
         logger.error(f'{e.__class__.__name__}: {msg}',
                      exc_info=e if log_verbose else None)
-        document_loaders_module = importlib.import_module('langchain.document_loaders')
+        document_loaders_module = importlib.import_module("langchain.document_loaders")
         DocumentLoader = getattr(document_loaders_module, "UnstructuredFileLoader")
 
     if loader_name == "UnstructuredFileLoader":
@@ -204,10 +204,10 @@ def make_text_splitter(
         else:
 
             try:  ## 优先使用用户自定义的text_splitter
-                text_splitter_module = importlib.import_module('text_splitter')
+                text_splitter_module = importlib.import_module("server.text_splitter")
                 TextSplitter = getattr(text_splitter_module, splitter_name)
             except:  ## 否则使用langchain的text_splitter
-                text_splitter_module = importlib.import_module('langchain.text_splitter')
+                text_splitter_module = importlib.import_module("langchain.text_splitter")
                 TextSplitter = getattr(text_splitter_module, splitter_name)
 
             if text_splitter_dict[splitter_name]["source"] == "tiktoken":  ## 从tiktoken加载

server/model_workers/gemini.py

@@ -1,123 +0,0 @@
-import sys
-from fastchat.conversation import Conversation
-from server.model_workers.base import *
-from server.utils import get_httpx_client
-from fastchat import conversation as conv
-import json, httpx
-from typing import List, Dict
-from configs import logger, log_verbose
-
-
-class GeminiWorker(ApiModelWorker):
-    def __init__(
-            self,
-            *,
-            controller_addr: str = None,
-            worker_addr: str = None,
-            model_names: List[str] = ["gemini-api"],
-            **kwargs,
-    ):
-        kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr)
-        kwargs.setdefault("context_len", 4096)
-        super().__init__(**kwargs)
-
-    def create_gemini_messages(self, messages) -> json:
-        has_history = any(msg['role'] == 'assistant' for msg in messages)
-        gemini_msg = []
-
-        for msg in messages:
-            role = msg['role']
-            content = msg['content']
-            if role == 'system':
-                continue
-            if has_history:
-                if role == 'assistant':
-                    role = "model"
-                transformed_msg = {"role": role, "parts": [{"text": content}]}
-            else:
-                if role == 'user':
-                    transformed_msg = {"parts": [{"text": content}]}
-
-            gemini_msg.append(transformed_msg)
-
-        msg = dict(contents=gemini_msg)
-        return msg
-
-    def do_chat(self, params: ApiChatParams) -> Dict:
-        params.load_config(self.model_names[0])
-        data = self.create_gemini_messages(messages=params.messages)
-        generationConfig = dict(
-            temperature=params.temperature,
-            topK=1,
-            topP=1,
-            maxOutputTokens=4096,
-            stopSequences=[]
-        )
-
-        data['generationConfig'] = generationConfig
-        url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent" + '?key=' + params.api_key
-        headers = {
-            'Content-Type': 'application/json',
-        }
-        if log_verbose:
-            logger.info(f'{self.__class__.__name__}:url: {url}')
-            logger.info(f'{self.__class__.__name__}:headers: {headers}')
-            logger.info(f'{self.__class__.__name__}:data: {data}')
-
-        text = ""
-        json_string = ""
-        timeout = httpx.Timeout(60.0)
-        client = get_httpx_client(timeout=timeout)
-        with client.stream("POST", url, headers=headers, json=data) as response:
-            for line in response.iter_lines():
-                line = line.strip()
-                if not line or "[DONE]" in line:
-                    continue
-
-                json_string += line
-
-            try:
-                resp = json.loads(json_string)
-                if 'candidates' in resp:
-                    for candidate in resp['candidates']:
-                        content = candidate.get('content', {})
-                        parts = content.get('parts', [])
-                        for part in parts:
-                            if 'text' in part:
-                                text += part['text']
-                                yield {
-                                    "error_code": 0,
-                                    "text": text
-                                }
-                        print(text)
-            except json.JSONDecodeError as e:
-                print("Failed to decode JSON:", e)
-                print("Invalid JSON string:", json_string)
-
-    def get_embeddings(self, params):
-        print("embedding")
-        print(params)
-
-    def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
-        return conv.Conversation(
-            name=self.model_names[0],
-            system_message="You are a helpful, respectful and honest assistant.",
-            messages=[],
-            roles=["user", "assistant"],
-            sep="\n### ",
-            stop_str="###",
-        )
-
-
-if __name__ == "__main__":
-    import uvicorn
-    from server.utils import MakeFastAPIOffline
-    from fastchat.serve.base_model_worker import app
-
-    worker = GeminiWorker(
-        controller_addr="http://127.0.0.1:20001",
-        worker_addr="http://127.0.0.1:21012",
-    )
-    sys.modules["fastchat.serve.model_worker"].worker = worker
-    MakeFastAPIOffline(app)
-    uvicorn.run(app, port=21012)