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vector_load2.py

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+import os
+from langchain_community.vectorstores import FAISS
+# from langchain_huggingface import HuggingFaceEmbeddings
+
+# embedding_path = "/data/Z/Z_llm_dm/vector_data/bge-m3"
+# embeddings = HuggingFaceEmbeddings(model_name=embedding_path)
+
+
+from typing import List
+import requests
+from langchain.embeddings.base import Embeddings
+
+
+class SiliconFlowEmbeddings(Embeddings):
+    def __init__(self, api_key: str, model: str = "bge-m3"):
+        self.api_key = api_key
+        self.model = model
+        self.url = "http://10.1.16.39:9995/v1/embeddings"
+        self.headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json"
+        }
+
+    def _embed(self, input: List[str]) -> List[List[float]]:
+        payload = {
+            "model": self.model,
+            "input": input,
+            "encoding_format": "float"
+        }
+        response = requests.post(self.url, json=payload, headers=self.headers)
+        response.raise_for_status()
+        data = response.json()
+        return [item["embedding"] for item in data["data"]]
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        return self._embed(texts)
+
+    def embed_query(self, text: str) -> List[float]:
+        return self._embed([text])[0]
+
+embeddings = SiliconFlowEmbeddings(api_key="sk-ftnofbucchwnscojohyxwmfzgaykdxihafnlphohsinftkbr")
+
+def Mixed_retrieval(input_path):
+    file_name = os.path.splitext(os.path.basename(input_path))[0]
+    faiss_archived = f"./faiss_data/{file_name}"
+
+    txt_list = []
+    with open(input_path, 'r', encoding='utf-8') as file:
+        txt_list = [line.strip() for line in file]
+    vectorstore_txt_faiss = FAISS.from_texts(txt_list, embeddings)
+    vectorstore_txt_faiss.save_local(faiss_archived)
+
+    # vectorstore_txt_faiss = FAISS.load_local(vectorstore_txt_faiss,
+    #                                          embeddings=embeddings,
+    #                                          allow_dangerous_deserialization=True)
+
+    retriever_txt_faiss1 = vectorstore_txt_faiss.as_retriever(search_kwargs={"k": 5})
+    retriever_txt_faiss2 = vectorstore_txt_faiss.as_retriever(
+        search_type="mmr",
+        search_kwargs={"k": 5,  # 检索结果
+                       "fetch_k": 2,  # 候选结果数量
+                       "lambda_mult": 0.1}  # 平衡指数，1为相关性；0为多样性
+    )
+    retriever_txt_faiss3 = vectorstore_txt_faiss.as_retriever(
+        search_type="similarity_score_threshold",
+        search_kwargs={"score_threshold": 0.3}
+    )
+
+    return retriever_txt_faiss1, retriever_txt_faiss2, retriever_txt_faiss3
+
+
+
+def interface_search(input_str, retriever_txt_faiss1, retriever_txt_faiss2, retriever_txt_faiss3):
+    index_keyword1 = []
+    for i in retriever_txt_faiss1.invoke(input_str):
+        index_keyword1.append(i.page_content)
+    index_keyword2 = []
+    for i in retriever_txt_faiss2.invoke(input_str):
+        index_keyword2.append(i.page_content)
+    index_keyword3 = []
+    for i in retriever_txt_faiss3.invoke(input_str):
+        index_keyword3.append(i.page_content)
+
+    return list(set(index_keyword1) & set(index_keyword2) & set(index_keyword3))
+
+
+def Building_search_dictionary(input_csv_path1, input_csv_path2, index_keyword):
+    import pandas as pd
+
+    df1 = pd.read_csv(input_csv_path1, encoding='utf-8')
+    df2 = pd.read_csv(input_csv_path2, encoding='utf-8', names=['path', 'id'])
+    #df2 = pd.read_csv(input_csv_path2, encoding='utf-8')
+
+    matching_path = df1.loc[df1['name'] == index_keyword, 'index']
+
+    # print(matching_path)
+
+    # print(matching_path.tolist()[0] )
+
+    # todo: bug修改: 避免matching_path和matching_ids没有映射
+    if matching_path.empty:
+        return(None, None)
+    else:
+        matching_ids = df2.loc[df2['path'] == matching_path.tolist()[0], 'id']
+
+    # print(matching_ids)
+    if matching_ids.empty:
+        return (matching_path.tolist()[0], None)
+    else:
+        return (matching_path.tolist()[0], int(matching_ids.values[0]))
+
+
+def Official_website_kg_search(input_id):
+    # info = WikijsTool.get_all_documents()
+
+    import re
+    from bs4 import BeautifulSoup
+    from booway_kg_api.WikijsTool import WikijsTool
+
+    html_text = WikijsTool.query_doc_info(input_id)['content']
+    cleaned_img_text = re.sub(r'<img\s+[^>]*>', '', html_text)
+
+    soup = BeautifulSoup(cleaned_img_text, "html.parser")
+    plain_text = soup.get_text()
+
+    return plain_text