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feat: add reranker model in chroma query

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tom
2024-10-03 14:16:30 +08:00
parent 8059752437
commit 8cad8abecc
1 changed files with 57 additions and 11 deletions
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68
src/blackbox/chroma_query.py
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@ -2,6 +2,7 @@ from typing import Any, Coroutine
from fastapi import Request, Response, status
from fastapi.responses import JSONResponse
import numpy as np
from .blackbox import Blackbox
import chromadb
@ -9,6 +10,7 @@ from chromadb.utils import embedding_functions
import logging
from ..log.logging_time import logging_time
import re
from sentence_transformers import CrossEncoder
logger = logging.getLogger
DEFAULT_COLLECTION_ID = "123"
@ -24,6 +26,7 @@ class ChromaQuery(Blackbox):
self.embedding_model_2 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/home/gpu/Workspace/Models/BAAI/bge-small-en-v1.5", device = "cuda:1")
self.client_1 = chromadb.HttpClient(host='10.6.81.119', port=7000)
# self.client_2 = chromadb.HttpClient(host='10.6.82.192', port=8000)
self.reranker_model_1 = CrossEncoder("/home/gpu/Workspace/Models/bge-reranker-v2-m3", max_length=512, device = "cuda")
def __call__(self, *args, **kwargs):
return self.processing(*args, **kwargs)
@ -46,6 +49,8 @@ class ChromaQuery(Blackbox):
chroma_port = settings.get("chroma_port")
chroma_collection_id = settings.get("chroma_collection_id")
chroma_n_results = settings.get("chroma_n_results")
chroma_reranker_model = settings.get("chroma_reranker_model")
chroma_reranker_num = settings.get("chroma_reranker_num")
if usr_question is None:
return JSONResponse(content={"error": "question is required"}, status_code=status.HTTP_400_BAD_REQUEST)
@ -63,20 +68,26 @@ class ChromaQuery(Blackbox):
chroma_collection_id = "g2e"
if chroma_n_results is None or chroma_n_results == "":
chroma_n_results = 3
chroma_n_results = 10
# load client and embedding model from init
if re.search(r"10.6.81.119", chroma_host) and re.search(r"7000", chroma_port):
client = self.client_1
else:
client = chromadb.HttpClient(host=chroma_host, port=chroma_port)
try:
client = chromadb.HttpClient(host=chroma_host, port=chroma_port)
except:
return JSONResponse(content={"error": "chroma client not found"}, status_code=status.HTTP_400_BAD_REQUEST)
if re.search(r"/home/gpu/Workspace/Models/BAAI/bge-large-zh-v1.5", chroma_embedding_model):
embedding_model = self.embedding_model_1
elif re.search(r"/home/gpu/Workspace/Models/BAAI/bge-small-en-v1.5", chroma_embedding_model):
embedding_model = self.embedding_model_2
else:
embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=chroma_embedding_model, device = "cuda:1")
try:
embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=chroma_embedding_model, device = "cuda:1")
except:
return JSONResponse(content={"error": "embedding model not found"}, status_code=status.HTTP_400_BAD_REQUEST)
# load collection
collection = client.get_collection(chroma_collection_id, embedding_function=embedding_model)
@ -93,18 +104,53 @@ class ChromaQuery(Blackbox):
final_result = ''
if results is not None:
results_distances = results["distances"][0]
# if results is not None:
# results_distances = results["distances"][0]
#distance越高越不准确
top_distance = 0.8
# #distance越高越不准确
# top_distance = 0.8
for i in range(len(results_distances)):
if results_distances[i] < top_distance:
final_result += results["documents"][0][i]
# for i in range(len(results_distances)):
# if results_distances[i] < top_distance:
# final_result += results["documents"][0][i]
print("\n final_result: ", final_result)
# print("\n final_result: ", final_result)
final_result = str(results["documents"])
if chroma_reranker_model:
if re.search(r"/home/gpu/Workspace/Models/BAAI/bge-reranker-v2-m3", chroma_embedding_model):
reranker_model = self.chroma_reranker_model_1
else:
try:
reranker_model = CrossEncoder(chroma_reranker_model, max_length=512, device = "cuda")
except:
return JSONResponse(content={"error": "reranker model not found"}, status_code=status.HTTP_400_BAD_REQUEST)
if chroma_reranker_num:
if chroma_reranker_num > chroma_n_results:
chroma_reranker_num = chroma_n_results
else:
chroma_reranker_num = 5
#对每一对（查询、文档）进行评分
pairs = [[usr_question, doc] for doc in results["documents"][0]]
print('\n',pairs)
scores = reranker_model.predict(pairs)
#重新排列文件顺序：
print("New Ordering:")
i = 0
final_result = ''
for o in np.argsort(scores)[::-1]:
if i == chroma_reranker_num or scores[o] < 0.5:
break
i += 1
print(o+1)
print("Scores:", scores[o])
print(results["documents"][0][o],'\n')
final_result += results["documents"][0][o] + '\n'
return final_result