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Langchain-Chatchat
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fix typos

This commit is contained in:
imClumsyPanda 2023-12-31 20:13:14 +08:00
parent 349de9b955
commit 719e2713ed
3 changed files with 69 additions and 63 deletions
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2
configs/model_config.py.example
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@ -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

14
requirements.txt
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@ -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

108
server/reranker/reranker.py
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@ -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
@ -7,32 +8,34 @@ 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 @@ 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,
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 @@ 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,
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)
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
)