add sample/chroma_rerank.py

sample/chroma_rerank.py

@@ -0,0 +1,73 @@
+from sentence_transformers import CrossEncoder
+import chromadb
+from chromadb.utils import embedding_functions
+import numpy as np
+from langchain_community.document_loaders import TextLoader
+from langchain_community.vectorstores import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+import time
+
+# chroma run --path chroma_db/ --port 8000 --host 0.0.0.0
+# loader = TextLoader("/home/administrator/Workspace/chroma_data/粤语语料.txt",encoding="utf-8")
+loader = TextLoader("/home/administrator/Workspace/jarvis-models/sample/RAG_boss.txt")
+documents = loader.load()
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=10, chunk_overlap=0, length_function=len, is_separator_regex=True,separators=['\n', '\n\n'])
+docs = text_splitter.split_documents(documents)
+print("len(docs)", len(docs))
+ids = ["粤语语料"+str(i) for i in range(len(docs))]
+
+embedding_model = SentenceTransformerEmbeddings(model_name='/home/administrator/Workspace/Models/BAAI/bge-m3', model_kwargs={"device": "cuda:1"})
+client = chromadb.HttpClient(host='172.16.4.7', port=7000)
+
+id = "boss"
+client.delete_collection(id)
+# 插入向量(如果ids已存在，则会更新向量)
+db = Chroma.from_documents(documents=docs, embedding=embedding_model, ids=ids, collection_name=id, client=client)
+
+
+
+
+embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/home/administrator/Workspace/Models/BAAI/bge-m3", device = "cuda:1")
+
+client = chromadb.HttpClient(host='172.16.4.7', port=7000)
+
+collection = client.get_collection(id, embedding_function=embedding_model)
+
+reranker_model = CrossEncoder("/home/administrator/Workspace/Models/BAAI/bge-reranker-v2-m3", max_length=512, device = "cuda:1")
+
+while True:
+    usr_question = input("\n 请输入问题: ")
+    # query it
+    time1 = time.time()
+    results = collection.query(
+        query_texts=[usr_question],
+        n_results=10,    
+    )
+    time2 = time.time()
+    print("query time: ", time2 - time1)
+
+    # print("query: ",usr_question)
+    # print("results: ",print(results["documents"][0]))
+
+
+    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 == 3 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'
+
+    print("\n final_result: ", final_result)
+    time3 = time.time()
+    print("rerank time: ", time3 - time2)