\n| \u4e91\u6258\u7ba1<\/td>\n | Pinecone\u3001\u963f\u91cc\u4e91\u5411\u91cf\u670d\u52a1<\/td>\n | \u4f01\u4e1a\u7ea7\u6258\u7ba1\u65b9\u6848<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u8bfe\u7a0b\u7528Chroma\u505a\u6f14\u793a，Milvus\u505a\u6848\u4f8b\u3002<\/p>\n \u516b\u3001Chroma\u5411\u91cf\u6570\u636e\u5e93<\/p>\n python<\/p>\n from langchain_chroma import Chroma
\nfrom utils.model_factory import get_embeddings<\/p>\n embeddings = get_embeddings()<\/p>\n # \u4ece\u6587\u6863\u521b\u5efa\u5411\u91cf\u5e93
\nvector_store = Chroma.from_documents(
\n documents=chunks, # Document\u5217\u8868
\n embedding=embeddings,
\n persist_directory=".\/chroma_db" # \u6301\u4e45\u5316\u76ee\u5f55
\n)<\/p>\n # \u68c0\u7d22
\nresults = vector_store.similarity_search("\u5458\u5de5\u8fdf\u5230\u600e\u4e48\u5904\u7406", k=3)
\nfor doc in results:
\n print(doc.page_content)
\n print(doc.metadata)<\/p>\n persist_directory\u6307\u5b9a\u6301\u4e45\u5316\u76ee\u5f55，\u4e0b\u6b21\u542f\u52a8\u65f6\u53ef\u4ee5\u4ece\u78c1\u76d8\u52a0\u8f7d，\u4e0d\u7528\u91cd\u65b0\u5efa\u5e93\u3002<\/p>\n \u52a0\u8f7d\u5df2\u6709\u5411\u91cf\u5e93：<\/p>\n python<\/p>\n vector_store = Chroma(
\n persist_directory=".\/chroma_db",
\n embedding_function=embeddings
\n)<\/p>\n \u4e5d\u3001\u76f8\u4f3c\u5ea6\u5206\u6570<\/p>\n similarity_search\u53ea\u8fd4\u56de\u6587\u6863，\u4e0d\u8fd4\u56de\u5206\u6570\u3002\u7528similarity_search_with_score\u67e5\u770b\u76f8\u4f3c\u5ea6：<\/p>\n python<\/p>\n results = vector_store.similarity_search_with_score("\u5458\u5de5\u8fdf\u5230\u600e\u4e48\u5904\u7406", k=3)<\/p>\n for doc, score in results:
\n print(f"\u5206\u6570：{score}")
\n print(f"\u5185\u5bb9：{doc.page_content[:100]}")
\n print("-" * 40)<\/p>\n \u4e0d\u540c\u5411\u91cf\u5e93\u7684\u5206\u6570\u542b\u4e49\u4e0d\u540c，\u4e0d\u8981\u53ea\u51ed\u56fa\u5b9a\u6570\u5b57\u505a\u5224\u65ad\u3002\u7ed3\u5408Top K\u7ed3\u679c\u3001\u6587\u6863\u5185\u5bb9\u3001\u6d4b\u8bd5\u96c6\u6548\u679c\u7efc\u5408\u8bc4\u4f30\u3002<\/p>\n \u5341\u3001Milvus\u5411\u91cf\u6570\u636e\u5e93<\/p>\n Milvus\u662f\u5f00\u6e90\u5206\u5e03\u5f0f\u5411\u91cf\u6570\u636e\u5e93，\u9002\u5408\u5927\u89c4\u6a21\u751f\u4ea7\u73af\u5883\u3002<\/p>\n \u5b89\u88c5：<\/p>\n bash<\/p>\n pip install pymilvus langchain-milvus<\/p>\n \u786e\u4fddMilvus\u670d\u52a1\u5df2\u542f\u52a8（Docker\u6216\u672c\u5730\u90e8\u7f72）\u3002<\/p>\n python<\/p>\n from langchain_milvus import Milvus
\nfrom utils.model_factory import get_embeddings<\/p>\n embeddings = get_embeddings()<\/p>\n # \u8fde\u63a5Milvus\u5e76\u5199\u5165
\nvector_store = Milvus.from_documents(
\n documents=chunks,
\n embedding=embeddings,
\n connection_args={"host": "localhost", "port": "19530"},
\n collection_name="knowledge_base",
\n drop_old=True,
\n auto_id=True,
\n index_params={
\n "index_type": "HNSW",
\n "metric_type": "COSINE",
\n "params": {"M": 16, "efConstruction": 128}
\n }
\n)<\/p>\n # \u68c0\u7d22
\nresults = vector_store.similarity_search("\u5458\u5de5\u8fdf\u5230\u600e\u4e48\u5904\u7406", k=3)<\/p>\n index_params\u53c2\u6570\u8bf4\u660e：<\/p>\n index_type="HNSW"：\u5206\u5c42\u5bfc\u822a\u5c0f\u4e16\u754c\u56fe，\u68c0\u7d22\u901f\u5ea6\u5feb，\u9002\u5408\u767e\u4e07\u7ea7\u4ee5\u5185\u5411\u91cf\u3002
\nmetric_type="COSINE"：\u4f59\u5f26\u76f8\u4f3c\u5ea6，\u4e2d\u6587Embedding\u9996\u9009\u3002
\nM=16：\u6bcf\u4e2a\u8282\u70b9\u6700\u5927\u8fde\u63a5\u90bb\u5c45\u6570，\u8d8a\u5927\u5185\u5b58\u5360\u7528\u8d8a\u9ad8\u3002
\nefConstruction=128：\u6784\u5efa\u65f6\u5019\u9009\u90bb\u5c45\u6570，\u8d8a\u5927\u7d22\u5f15\u8d28\u91cf\u8d8a\u597d\u3002<\/p>\n \u5e26\u5206\u6570\u7684\u68c0\u7d22：<\/p>\n python<\/p>\n results = vector_store.similarity_search_with_score("\u5458\u5de5\u8fdf\u5230\u600e\u4e48\u5904\u7406", k=3)<\/p>\n \u8fd4\u56de\u7684score\u662f\u8ddd\u79bb\u503c，\u8d8a\u5c0f\u8868\u793a\u8d8a\u76f8\u4f3c\u3002<\/p>\n \u5341\u4e00\u3001\u5143\u6570\u636e\u8fc7\u6ee4<\/p>\n \u77e5\u8bc6\u5e93\u53ef\u80fd\u6709\u591a\u4e2a\u5206\u7c7b，\u68c0\u7d22\u65f6\u53ef\u4ee5\u6309\u5143\u6570\u636e\u8fc7\u6ee4\u3002<\/p>\n python<\/p>\n # \u53ea\u68c0\u7d22\u4eba\u4e8b\u5236\u5ea6
\nresults = vector_store.similarity_search(
\n "\u8fdf\u5230\u600e\u4e48\u5904\u7406",
\n k=3,
\n expr='file_name == "employee_handbook.txt"'
\n)<\/p>\n # \u53ea\u68c0\u7d22PDF\u6587\u4ef6
\nresults = vector_store.similarity_search(
\n "\u4ea7\u54c1\u53c2\u6570",
\n k=3,
\n expr='file_type == "pdf"'
\n)<\/p>\n \u6ce8\u610fMilvus\u4f7f\u7528expr\u8868\u8fbe\u5f0f\u8bed\u6cd5，\u4e0d\u662ffilter\u53c2\u6570\u3002<\/p>\n \u5143\u6570\u636e\u8fc7\u6ee4\u9002\u5408：\u53ea\u68c0\u7d22\u67d0\u4e2a\u90e8\u95e8\u7684\u8d44\u6599\u3001\u53ea\u68c0\u7d22\u67d0\u4e2a\u4ea7\u54c1\u7ebf\u7684\u624b\u518c\u3001\u53ea\u68c0\u7d22\u67d0\u4e2a\u6587\u4ef6\u6765\u6e90\u3002<\/p>\n \u5341\u4e8c\u3001\u4f01\u4e1a\u6848\u4f8b：\u77e5\u8bc6\u5e93\u68c0\u7d22\u7d22\u5f15<\/p>\n \u9879\u76ee\u7ed3\u6784：<\/p>\n text<\/p>\n knowledge_base\/
\n\u251c\u2500\u2500 hr\/
\n\u2502 \u2514\u2500\u2500 employee_handbook.txt
\n\u251c\u2500\u2500 customer_service\/
\n\u2502 \u2514\u2500\u2500 refund_policy.md
\n\u2514\u2500\u2500 product\/
\n \u2514\u2500\u2500 product_manual.pdf<\/p>\n build_index.py
\nsearch_knowledge.py
\ndocument_loader.py
\nembedding_factory.py<\/p>\n embedding_factory.py：<\/p>\n python<\/p>\n from langchain_community.embeddings import HuggingFaceEmbeddings<\/p>\n def get_embeddings():
\n return HuggingFaceEmbeddings(
\n model_name="BAAI\/bge-small-zh-v1.5",
\n model_kwargs={"device": "cpu"},
\n encode_kwargs={"normalize_embeddings": True}
\n )<\/p>\n document_loader.py：<\/p>\n python<\/p>\n from pathlib import Path
\nfrom langchain_community.document_loaders import TextLoader, PyPDFLoader
\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter<\/p>\n def load_and_split(directory_path, chunk_size=500, chunk_overlap=50):
\n docs = []
\n directory = Path(directory_path)<\/p>\n for file_path in sorted(directory.rglob("*")):
\n if not file_path.is_file():
\n continue
\n suffix = file_path.suffix.lower()
\n try:
\n if suffix == ".txt":
\n loader = TextLoader(str(file_path), encoding="utf-8")
\n elif suffix == ".md":
\n loader = TextLoader(str(file_path), encoding="utf-8")
\n elif suffix == ".pdf":
\n loader = PyPDFLoader(str(file_path))
\n else:
\n continue
\n loaded = loader.load()
\n for doc in loaded:
\n doc.metadata["file_name"] = file_path.name
\n doc.metadata["file_type"] = suffix[1:]
\n doc.metadata["category"] = file_path.parent.name
\n docs.extend(loaded)
\n except Exception as e:
\n print(f"\u52a0\u8f7d\u5931\u8d25：{file_path.name}，\u9519\u8bef：{e}")<\/p>\n splitter = RecursiveCharacterTextSplitter(
\n chunk_size=chunk_size,
\n chunk_overlap=chunk_overlap
\n )
\n return splitter.split_documents(docs)<\/p>\n build_index.py：<\/p>\n python<\/p>\n from langchain_milvus import Milvus
\nfrom embedding_factory import get_embeddings
\nfrom document_loader import load_and_split<\/p>\n print("\u52a0\u8f7d\u5e76\u5207\u5206\u6587\u6863…")
\nchunks = load_and_split("knowledge_base")
\nprint(f"\u751f\u6210{len(chunks)}\u4e2a\u6587\u6863\u5757")<\/p>\n print("\u5199\u5165Milvus…")
\nembeddings = get_embeddings()<\/p>\n vector_store = Milvus.from_documents(
\n documents=chunks,
\n embedding=embeddings,
\n connection_args={"host": "localhost", "port": "19530"},
\n collection_name="knowledge_base",
\n drop_old=True,
\n index_params={
\n "index_type": "HNSW",
\n "metric_type": "COSINE",
\n "params": {"M": 16, "efConstruction": 128}
\n }
\n)<\/p>\n print("\u7d22\u5f15\u6784\u5efa\u5b8c\u6210")<\/p>\n search_knowledge.py：<\/p>\n python<\/p>\n from langchain_milvus import Milvus
\nfrom embedding_factory import get_embeddings<\/p>\n embeddings = get_embeddings()<\/p>\n vector_store = Milvus(
\n embedding_function=embeddings,
\n connection_args={"host": "localhost", "port": "19530"},
\n collection_name="knowledge_base"
\n)<\/p>\n while True:
\n question = input("\\\\n\u8bf7\u8f93\u5165\u95ee\u9898（\u8f93\u5165q\u9000\u51fa）：")
\n if question.lower() == "q":
\n break<\/p>\n results = vector_store.similarity_search_with_score(question, k=3)<\/p>\n print(f"\\\\n\u76f8\u5173\u6587\u6863\u5757：")
\n for i, (doc, score) in enumerate(results, 1):
\n print(f"{i}. \u6765\u6e90：{doc.metadata.get('file_name')}")
\n print(f" \u5206\u7c7b：{doc.metadata.get('category')}")
\n print(f" \u76f8\u4f3c\u5ea6：{score:.4f}")
\n print(f" \u5185\u5bb9：{doc.page_content[:150]}…")
\n print()<\/p>\n \u5341\u4e09\u3001\u68c0\u7d22\u6548\u679c\u8c03\u4f18<\/p>\n \u4e0d\u51c6\u7684\u8bdd\u68c0\u67e5\u8fd9\u4e9b：<\/p>\n \u6587\u6863\u5757\u592a\u77ed\u6216\u592a\u957f：\u8c03\u6574chunk_size
\n\u4e0a\u4e0b\u6587\u88ab\u5207\u65ad：\u589e\u5927chunk_overlap
\n\u6807\u9898\u548c\u6b63\u6587\u5206\u79bb：\u8c03\u6574\u5206\u9694\u7b26
\n\u67e5\u8be2\u548c\u6587\u6863\u8868\u8fbe\u5dee\u5f02\u5927：\u589e\u52a0\u540c\u4e49\u8868\u8fbe
\nEmbedding\u6a21\u578b\u4e0d\u9002\u5408\u4e2d\u6587：\u6362\u6a21\u578b<\/p>\n \u51c6\u5907\u4e00\u7ec4\u6d4b\u8bd5\u95ee\u9898，\u9010\u4e2a\u89c2\u5bdfTop 3\u7ed3\u679c\u662f\u5426\u76f8\u5173\u3002\u8fd9\u662f\u6700\u65e9\u671f\u7684\u8bc4\u4f30\u65b9\u5f0f\u3002<\/p>\n \u5e38\u89c1\u95ee\u9898<\/p>\n DeepSeek\u80fd\u505aEmbedding\u5417<\/p>\n DeepSeek\u662f\u804a\u5929\u6a21\u578b，\u4e0d\u505aEmbedding\u3002\u672c\u8bfe\u7a0b\u7528\u672c\u5730\u4e2d\u6587\u6a21\u578b，\u4e0d\u6d88\u8017API\u989d\u5ea6\u3002<\/p>\n \u4e3a\u4ec0\u4e48\u8981\u7528\u540c\u4e00\u4e2aEmbedding\u6a21\u578b<\/p>\n \u5efa\u5e93\u548c\u67e5\u8be2\u5fc5\u987b\u7528\u540c\u4e00\u4e2a\u6a21\u578b，\u5426\u5219\u5411\u91cf\u7a7a\u95f4\u4e0d\u4e00\u81f4，\u68c0\u7d22\u6548\u679c\u5dee\u3002<\/p>\n \u76f8\u4f3c\u5ea6\u5206\u6570\u9608\u503c\u8bbe\u591a\u5c11\u5408\u9002<\/p>\n \u4e0d\u8981\u5199\u6b7b\u3002\u4e0d\u540c\u6a21\u578b\u3001\u4e0d\u540c\u5411\u91cf\u5e93\u7684\u5206\u6570\u542b\u4e49\u4e0d\u540c\u3002\u5148\u89c2\u5bdf\u771f\u5b9e\u95ee\u9898\u7ed3\u679c\u518d\u5b9a\u3002<\/p>\n \u7b2c\u4e00\u6b21\u8fd0\u884c\u6162<\/p>\n \u9996\u6b21\u8fd0\u884c\u4f1a\u4e0b\u8f7d\u6a21\u578b\u6587\u4ef6\u5230\u672c\u5730\u7f13\u5b58，\u540e\u7eed\u5c31\u5feb\u4e86\u3002<\/p>\n","protected":false},"excerpt":{"rendered":" \u6458\u8981\u6587\u6863\u5207\u597d\u4e86\u5f97\u5b58\u8d77\u6765，\u8fd8\u5f97\u80fd\u6839\u636e\u95ee\u9898\u627e\u51fa\u6765\u3002Embedding\u628a\u6587\u5b57\u8f6c\u6210\u5411\u91cf，\u5411\u91cf\u6570\u636e\u5e93\u8d1f\u8d23\u5b58\u548c\u67e5\u3002\u672c\u6587\u8bb2\u672c\u5730\u4e2d\u6587Embedding\u6a21\u578b\u7684\u4f7f\u7528\u3001Chroma\/Milvus\u7684\u57fa\u672c\u64cd\u4f5c，\u4ee5\u53ca\u600e\u4e48\u7528\u5143\u6570\u636e\u8fc7\u6ee4\u7f29\u5c0f\u68c0\u7d22\u8303\u56f4\u3002\u4e00\u3001\u4e3a\u4ec0\u4e48\u9700\u8981Embedding\u7528\u6237\u95ee\\”\u516c\u53f8\u600e\u4e48\u5904\u7406\u8fdf\u5230\\”，\u6587\u6863\u91cc\u5199\u7684\u662f\\”\u5458\u5de5\u5e94\u6309\u65f6\u51fa\u52e4，\u8fdd\u8005\u6309\u5236\u5ea6\u5904\u7406\\”\u3002\u5173\u952e\u8bcd\u4e0d\u4e00\u6837，\u610f\u601d\u4e00\u6837\u3002\u666e\u901a\u5173\u952e\u8bcd\u641c\u4e0d\u5230，\u5f97\u9760\u8bed\u4e49\u68c0\u7d22\u3002<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[346,997,81,50,100],"topic":[],"class_list":["post-87719","post","type-post","status-publish","format-standard","hentry","category-server","tag-embedding","tag-langchain","tag-python","tag-50","tag-100"],"yoast_head":"\n LangChain Embedding\u4e0e\u5411\u91cf\u6570\u636e\u5e93 - 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