fix typos

configs/model_config.py.example

@@ -28,7 +28,7 @@ EMBEDDING_MODEL_OUTPUT_PATH = "output"
 
 # chatglm3-6b输出角色标签<|user|>及自问自答的问题详见项目wiki->常见问题->Q20.
 
-LLM_MODELS = ["chatglm3-6b", "zhipu-api", "openai-api"] # "Qwen-1_8B-Chat",
+LLM_MODELS = ["chatglm3-6b", "zhipu-api", "openai-api"]  # "Qwen-1_8B-Chat",
 
 # AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是LLM_MODELS[0])
 Agent_MODEL = None

requirements.txt

@@ -25,7 +25,7 @@ faiss-cpu~=1.7.4 # `conda install faiss-gpu -c conda-forge` if you want to accel
 accelerate==0.24.1
 spacy~=3.7.2
 PyMuPDF~=1.23.8
-rapidocr_onnxruntime~=1.3.8
+rapidocr_onnxruntime==1.3.8
 requests>=2.31.0
 pathlib>=1.0.1
 pytest>=7.4.3
@@ -35,24 +35,25 @@ markdownify>=0.11.6
 tiktoken~=0.5.2
 tqdm>=4.66.1
 websockets>=12.0
-numpy~=1.26.2
-pandas~=2.1.4
+numpy~=1.24.4
+pandas~=2.0.3
 einops>=0.7.0
 transformers_stream_generator==0.0.4
 vllm==0.2.6; sys_platform == "linux"
-httpx[brotli,http2,socks]~=0.25.2
+httpx[brotli,http2,socks]==0.25.2
+llama-index
 
 # optional document loaders
 
-rapidocr_paddle[gpu]>=1.3.0.post5 # gpu accelleration for ocr of pdf and image files
+# rapidocr_paddle[gpu]>=1.3.0.post5 # gpu accelleration for ocr of pdf and image files
 jq>=1.6.0 # for .json and .jsonl files. suggest `conda install jq` on windows
 # html2text # for .enex files
 beautifulsoup4~=4.12.2 # for .mhtml files
 pysrt~=1.1.2
 
 # Online api libs dependencies
 
-zhipuai>=1.0.7，<=2.0.0 # zhipu
+zhipuai>=1.0.7, <=2.0.0 # zhipu
 dashscope>=1.13.6 # qwen
 # volcengine>=1.0.119 # fangzhou
 
@@ -75,5 +76,4 @@ streamlit-option-menu>=0.3.6
 streamlit-chatbox==1.1.11
 streamlit-modal>=0.1.0
 streamlit-aggrid>=0.3.4.post3
-httpx[brotli,http2,socks]>=0.25.2
 watchdog>=3.0.0

server/reranker/reranker.py

@@ -1,38 +1,41 @@
 import os
 import sys
+
 sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
 from typing import Any, List, Optional
 from sentence_transformers import CrossEncoder
 from typing import Optional, Sequence
 from langchain_core.documents import Document
 from langchain.callbacks.manager import Callbacks
 from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
-from llama_index.bridge.pydantic import Field,PrivateAttr
+from llama_index.bridge.pydantic import Field, PrivateAttr
+
 
 class LangchainReranker(BaseDocumentCompressor):
     """Document compressor that uses `Cohere Rerank API`."""
-    model_name_or_path:str = Field()
+    model_name_or_path: str = Field()
     _model: Any = PrivateAttr()
-    top_n:int= Field()
-    device:str=Field()
-    max_length:int=Field()           
+    top_n: int = Field()
+    device: str = Field()
+    max_length: int = Field()
     batch_size: int = Field()
     # show_progress_bar: bool = None
     num_workers: int = Field()
+
     # activation_fct = None
     # apply_softmax = False
-       
+
     def __init__(self,
-                model_name_or_path:str,
-                top_n:int=3, 
-                device:str="cuda", 
-                max_length:int=1024,               
-                batch_size: int = 32,
-                # show_progress_bar: bool = None,
-                num_workers: int = 0,
-                # activation_fct = None,
-                # apply_softmax = False,     
-                ):
+                 model_name_or_path: str,
+                 top_n: int = 3,
+                 device: str = "cuda",
+                 max_length: int = 1024,
+                 batch_size: int = 32,
+                 # show_progress_bar: bool = None,
+                 num_workers: int = 0,
+                 # activation_fct = None,
+                 # apply_softmax = False,
+                 ):
         # self.top_n=top_n
         # self.model_name_or_path=model_name_or_path
         # self.device=device
@@ -43,24 +46,24 @@ def __init__(self,
         # self.activation_fct=activation_fct
         # self.apply_softmax=apply_softmax
 
-        self._model = CrossEncoder(model_name=model_name_or_path,max_length=1024,device=device)
+        self._model = CrossEncoder(model_name=model_name_or_path, max_length=1024, device=device)
         super().__init__(
-                        top_n=top_n,
-                        model_name_or_path=model_name_or_path,
-                        device=device,
-                        max_length=max_length,
-                        batch_size=batch_size,
-                        # show_progress_bar=show_progress_bar,
-                        num_workers=num_workers,
-                        # activation_fct=activation_fct,
-                        # apply_softmax=apply_softmax
-                        )
+            top_n=top_n,
+            model_name_or_path=model_name_or_path,
+            device=device,
+            max_length=max_length,
+            batch_size=batch_size,
+            # show_progress_bar=show_progress_bar,
+            num_workers=num_workers,
+            # activation_fct=activation_fct,
+            # apply_softmax=apply_softmax
+        )
 
     def compress_documents(
-        self,
-        documents: Sequence[Document],
-        query: str,
-        callbacks: Optional[Callbacks] = None,
+            self,
+            documents: Sequence[Document],
+            query: str,
+            callbacks: Optional[Callbacks] = None,
     ) -> Sequence[Document]:
         """
         Compress documents using Cohere's rerank API.
@@ -77,40 +80,43 @@ def compress_documents(
             return []
         doc_list = list(documents)
         _docs = [d.page_content for d in doc_list]
-        sentence_pairs = [[query,_doc] for _doc in _docs]
+        sentence_pairs = [[query, _doc] for _doc in _docs]
         results = self._model.predict(sentences=sentence_pairs,
-                                     batch_size=self.batch_size,
-                                    #  show_progress_bar=self.show_progress_bar,
-                                     num_workers=self.num_workers,
-                                    #  activation_fct=self.activation_fct,
-                                    #  apply_softmax=self.apply_softmax,
-                                     convert_to_tensor=True
-                                     )
+                                      batch_size=self.batch_size,
+                                      #  show_progress_bar=self.show_progress_bar,
+                                      num_workers=self.num_workers,
+                                      #  activation_fct=self.activation_fct,
+                                      #  apply_softmax=self.apply_softmax,
+                                      convert_to_tensor=True
+                                      )
         top_k = self.top_n if self.top_n < len(results) else len(results)
-        
+
         values, indices = results.topk(top_k)
         final_results = []
-        for value, index in zip(values,indices):
+        for value, index in zip(values, indices):
             doc = doc_list[index]
             doc.metadata["relevance_score"] = value
             final_results.append(doc)
         return final_results
-if __name__ == "__main__":  
-    from configs import (LLM_MODELS, 
-                     VECTOR_SEARCH_TOP_K, 
-                     SCORE_THRESHOLD, 
-                     TEMPERATURE,
-                     USE_RERANKER,
-                     RERANKER_MODEL,
-                     RERANKER_MAX_LENGTH,
-                     MODEL_PATH) 
+
+
+if __name__ == "__main__":
+    from configs import (LLM_MODELS,
+                         VECTOR_SEARCH_TOP_K,
+                         SCORE_THRESHOLD,
+                         TEMPERATURE,
+                         USE_RERANKER,
+                         RERANKER_MODEL,
+                         RERANKER_MAX_LENGTH,
+                         MODEL_PATH)
     from server.utils import embedding_device
+
     if USE_RERANKER:
-        reranker_model_path = MODEL_PATH["reranker"].get(RERANKER_MODEL,"BAAI/bge-reranker-large")
+        reranker_model_path = MODEL_PATH["reranker"].get(RERANKER_MODEL, "BAAI/bge-reranker-large")
         print("-----------------model path------------------")
         print(reranker_model_path)
         reranker_model = LangchainReranker(top_n=3,
-                                        device=embedding_device(),
-                                        max_length=RERANKER_MAX_LENGTH,
-                                        model_name_or_path=reranker_model_path
-                                        )
+                                           device=embedding_device(),
+                                           max_length=RERANKER_MAX_LENGTH,
+                                           model_name_or_path=reranker_model_path
+                                           )