fix typos

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
@@ -35,16 +35,17 @@ 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
@@ -52,7 +53,7 @@ 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

@@ -1,5 +1,6 @@
 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
@@ -7,26 +8,28 @@ 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,               
+                 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,
@@ -43,7 +46,7 @@ class LangchainReranker(BaseDocumentCompressor):
         # 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,
@@ -77,7 +80,7 @@ class LangchainReranker(BaseDocumentCompressor):
             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,
@@ -90,11 +93,13 @@ class LangchainReranker(BaseDocumentCompressor):
 
         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,
@@ -105,8 +110,9 @@ if __name__ == "__main__":
                          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,