moved files

scripts/make_rag_database.py

@@ -1,77 +0,0 @@
-import pandas as pd 
-import os.path as osp
-import os
-
-from lang_agent.rag.emb import QwenEmbeddings
-
-from langchain_text_splitters import CharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain_openai import OpenAIEmbeddings
-from langchain_core.documents import Document
-
-def main(save_path = "assets/xiaozhan_emb"):
-    cat_f = "assets/xiaozhan_data/catering_end_category.csv"
-    desc_f = "assets/xiaozhan_data/catering_end_dish.csv"
-
-    df_cat = pd.read_csv(cat_f)
-    df_desc = pd.read_csv(desc_f)
-    df_desc = df_desc[df_desc["is_available"] == 't'].reset_index(drop=True)
-
-    id_desc_dic = {}
-    for _, (id, name, desc) in df_cat[["id", "name", "description"]].iterrows():
-        id_desc_dic[id] = f"{name}-{desc}"
-
-    df_desc["cat_desc"] = df_desc["category_id"].map(id_desc_dic)
-    
-    data = []
-    for _, (name, desc, px, cat_desc) in df_desc[["name", "description", "price", "cat_desc"]].iterrows():
-        sen = f"茶名称：{name}|茶描述：{desc}|价格{px}|饮品类:{cat_desc}"
-        data.append(sen)
-    
-
-    # texts = [Document(e) for e in data]
-    texts = data
-    embeddings = QwenEmbeddings(
-        api_key=os.environ.get("ALI_API_KEY")
-    )
-
-    if not osp.exists(save_path):
-        # --- STEP 2: Create vector store ---
-        # vectorstore = FAISS.from_documents(texts, embeddings)
-
-        if os.environ.get("ALI_API_KEY") is None or os.environ.get("ALI_API_KEY") == "SOMESHIT":
-            texts = [Document(e) for e in data]
-            vectorstore = FAISS.from_documents(texts, embeddings)
-        else:
-            out_emb = embeddings.batch_embed_documents(texts)
-            vectorstore = FAISS.from_embeddings(zip(texts, out_emb), embeddings)
-
-        # --- STEP 3: SAVE the FAISS index to local files ---
-        vectorstore.save_local(save_path)
-        print(f"✅ Saved FAISS index to: {save_path}")
-
-    # --- STEP 4: LOAD later from disk in a separate session ---
-    # (You can imagine this being a new Python script.)
-    loaded_vectorstore = FAISS.load_local(
-        folder_path=save_path,
-        embeddings=embeddings,
-        allow_dangerous_deserialization=True  # Required for LangChain >= 0.1.1
-    )
-    print("✅ Loaded FAISS index successfully!")
-
-    # --- STEP 5: Use the retriever/QA chain on the loaded store ---
-    retriever = loaded_vectorstore.as_retriever(search_kwargs={
-        "k":5
-    })
-
-    u = loaded_vectorstore.similarity_search("灯与尘", k=2)
-
-    res = retriever.invoke("野心心")
-
-    for doc in res:
-        print(doc)
-        print("==============================================")
-
-
-if __name__ == "__main__":
-    main()