通过 Milvus 内置 Sparse-BM25 算法进行全文检索并将混合检索应用于 RAG 系统

pip3 show pymilvus

pip3 install --upgrade pymilvus

pip3 install pymilvus langchain dashscope

from langchain_community.document_loaders import WebBaseLoaderfrom langchain.text_splitter import RecursiveCharacterTextSplitterfrom langchain_community.embeddings import DashScopeEmbeddingsfrom pymilvus import MilvusClient, DataType, Function, FunctionType
dashscope_api_key = "<YOUR_DASHSCOPE_API_KEY>"milvus_url = "<YOUR_MMILVUS_URL>"user_name = "root"password = "<YOUR_PASSWORD>"collection_name = "milvus_overview"dense_dim = 1536
loader = WebBaseLoader([    'https://raw.githubusercontent.com/milvus-io/milvus-docs/refs/heads/v2.5.x/site/en/about/overview.md'])
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=256)
# 使用LangChain将输入文档安照chunk_size切分all_splits = text_splitter.split_documents(docs)
embeddings = DashScopeEmbeddings(    model="text-embedding-v2", dashscope_api_key=dashscope_api_key)
text_contents = [doc.page_content for doc in all_splits]
vectors = embeddings.embed_documents(text_contents)

client = MilvusClient(    uri=f"http://{milvus_url}:19530",    token=f"{user_name}:{password}",)
schema = MilvusClient.create_schema(    enable_dynamic_field=True,)
analyzer_params = {    "type": "english"}
# Add fields to schemaschema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True, auto_id=True)schema.add_field(field_name="text", datatype=DataType.VARCHAR, max_length=65535, enable_analyzer=True, analyzer_params=analyzer_params, enable_match=True)schema.add_field(field_name="sparse_bm25", datatype=DataType.SPARSE_FLOAT_VECTOR)schema.add_field(field_name="dense", datatype=DataType.FLOAT_VECTOR, dim=dense_dim)
bm25_function = Function(   name="bm25",   function_type=FunctionType.BM25,   input_field_names=["text"],   output_field_names="sparse_bm25",)schema.add_function(bm25_function)
index_params = client.prepare_index_params()
# Add indexesindex_params.add_index(    field_name="dense",    index_name="dense_index",    index_type="IVF_FLAT",    metric_type="IP",    params={"nlist": 128},)
index_params.add_index(    field_name="sparse_bm25",    index_name="sparse_bm25_index",    index_type="SPARSE_WAND",    metric_type="BM25")
# Create collectionclient.create_collection(    collection_name=collection_name,    schema=schema,    index_params=index_params)
data = [    {"dense": vectors[idx], "text": doc}    for idx, doc in enumerate(text_contents)]
# Insert datares = client.insert(    collection_name=collection_name,    data=data)
print(f"生成 {len(vectors)} 个向量，维度：{len(vectors[0])}")

# 定义分词器参数analyzer_params = {    "type": "chinese"  # 指定分词器类型为中文}
# 添加文本字段到 Schema，并启用分词器schema.add_field(    field_name="text",                      # 字段名称    datatype=DataType.VARCHAR,              # 数据类型：字符串（VARCHAR）    max_length=65535,                       # 最大长度：65535 字符    enable_analyzer=True,                   # 启用分词器    analyzer_params=analyzer_params         # 分词器参数)

from pymilvus import MilvusClient
# 创建Milvus Client。client = MilvusClient(    uri="http://c-xxxx.milvus.aliyuncs.com:19530",  # Milvus实例的公网地址。    token="<yourUsername>:<yourPassword>",  # 登录Milvus实例的用户名和密码。    db_name="default"  # 待连接的数据库名称，本文示例为默认的default。)
search_params = {    'params': {'drop_ratio_search': 0.2},}
full_text_search_res = client.search(    collection_name='milvus_overview',    data=['what makes milvus so fast?'],    anns_field='sparse_bm25',    limit=3,    search_params=search_params,    output_fields=["text"],)
for hits in full_text_search_res:    for hit in hits:        print(hit)        print("\n")
"""{'id': 456165042536597485, 'distance': 6.128782272338867, 'entity': {'text': '## What Makes Milvus so Fast？\n\nMilvus was designed from day one to be a highly efficient vector database system. In most cases, Milvus outperforms other vector databases by 2-5x (see the VectorDBBench results). This high performance is the result of several key design decisions:\n\n**Hardware-aware Optimization**: To accommodate Milvus in various hardware environments, we have optimized its performance specifically for many hardware architectures and platforms, including AVX512, SIMD, GPUs, and NVMe SSD.\n\n**Advanced Search Algorithms**: Milvus supports a wide range of in-memory and on-disk indexing/search algorithms, including IVF, HNSW, DiskANN, and more, all of which have been deeply optimized. Compared to popular implementations like FAISS and HNSWLib, Milvus delivers 30%-70% better performance.'}}
{'id': 456165042536597487, 'distance': 4.760214805603027, 'entity': {'text': "## What Makes Milvus so Scalable\n\nIn 2022, Milvus supported billion-scale vectors, and in 2023, it scaled up to tens of billions with consistent stability, powering large-scale scenarios for over 300 major enterprises, including Salesforce, PayPal, Shopee, Airbnb, eBay, NVIDIA, IBM, AT&T, LINE, ROBLOX, Inflection, etc.\n\nMilvus's cloud-native and highly decoupled system architecture ensures that the system can continuously expand as data grows:\n\n![Highly decoupled system architecture of Milvus](../../../assets/highly-decoupled-architecture.png)"}}"""

filter = "TEXT_MATCH(text, 'query') and TEXT_MATCH(text, 'node')"
text_match_res = client.search(    collection_name="milvus_overview",    anns_field="dense",    data=query_embeddings,    filter=filter,    search_params={"params": {"nprobe": 10}},    limit=2,    output_fields=["text"])

filter = "TEXT_MATCH(text, 'scalable fast')"
text_match_res = client.query(    collection_name="milvus_overview",    filter=filter,    output_fields=["text"])

from pymilvus import MilvusClientfrom pymilvus import AnnSearchRequest, RRFRankerfrom langchain_community.embeddings import DashScopeEmbeddingsfrom dashscope import Generation
# 创建Milvus Client。client = MilvusClient(    uri="http://c-xxxx.milvus.aliyuncs.com:19530",  # Milvus实例的公网地址。    token="<yourUsername>:<yourPassword>",  # 登录Milvus实例的用户名和密码。    db_name="default"  # 待连接的数据库名称，本文示例为默认的default。)
collection_name = "milvus_overview"
# 替换为您的 DashScope API-KEYdashscope_api_key = "<YOUR_DASHSCOPE_API_KEY>"
# 初始化 Embedding 模型embeddings = DashScopeEmbeddings(    model="text-embedding-v2",  # 使用text-embedding-v2模型。    dashscope_api_key=dashscope_api_key)
# Define the queryquery = "Why does Milvus run so scalable?"
# Embed the query and generate the corresponding vector representationquery_embeddings = embeddings.embed_documents([query])
# Set the top K result counttop_k = 5  # Get the top 5 docs related to the query
# Define the parameters for the dense vector searchsearch_params_dense = {    "metric_type": "IP",    "params": {"nprobe": 2}}
# Create a dense vector search requestrequest_dense = AnnSearchRequest([query_embeddings[0]], "dense", search_params_dense, limit=top_k)
# Define the parameters for the BM25 text searchsearch_params_bm25 = {    "metric_type": "BM25"}
# Create a BM25 text search requestrequest_bm25 = AnnSearchRequest([query], "sparse_bm25", search_params_bm25, limit=top_k)
# Combine the two requestsreqs = [request_dense, request_bm25]
# Initialize the RRF ranking algorithmranker = RRFRanker(100)
# Perform the hybrid searchhybrid_search_res = client.hybrid_search(    collection_name=collection_name,    reqs=reqs,    ranker=ranker,    limit=top_k,    output_fields=["text"])
# Extract the context from hybrid search resultscontext = []print("Top K Results:")for hits in hybrid_search_res:  # Use the correct variable here    for hit in hits:        context.append(hit['entity']['text'])  # Extract text content to the context list        print(hit['entity']['text'])  # Output each retrieved document

# Define a function to get an answer based on the query and contextdef getAnswer(query, context):    prompt = f'''Please answer my question based on the content within:    ```    {context}    ```    My question is: {query}.    '''    # Call the generation module to get an answer    rsp = Generation.call(model='qwen-turbo', prompt=prompt)    return rsp.output.text
# Get the answeranswer = getAnswer(query, context)
print(answer)

# Expected output excerpt"""Milvus is highly scalable due to its cloud-native and highly decoupled system architecture. This architecture allows the system to continuously expand as data grows. Additionally, Milvus supports three deployment modes that cover a wide..."""