引言

前面我们已经做好了必要的准备工作，包括对相关知识点的了解以及环境的安装。今天我们将重点关注代码方面的内容。如果你已经具备了Java编程基础，那么理解Python语法应该不会成为问题，毕竟只是语法的差异而已。随着时间的推移，你自然会逐渐熟悉和掌握这门语言。现在让我们开始吧！

环境安装命令

在使用之前，我们需要先进行一些必要的准备工作，其中包括执行一些命令。如果你已经仔细阅读了Milvus的官方文档，你应该已经了解到了这一点。下面是需要执行的一些命令示例：

pip3 install langchain

pip3 install openai

pip3 install protobuf==3.20.0

pip3 install grpcio-tools

python3 -m pip install pymilvus==2.3.2

python3 -c "from pymilvus import Collection"

快速入门

现在，我们来尝试使用官方示例，看看在没有集成LangChain的情况下，我们需要编写多少代码才能完成插入、查询等操作。官方示例已经在前面的注释中详细讲解了所有的流程。总体流程如下：

1. 连接到数据库
2. 创建集合（这里还有分区的概念，我们不深入讨论）
3. 插入向量数据（我看官方文档就简单插入了一些数字…）
4. 创建索引（根据官方文档的说法，通常在一定数据量下是不会经常创建索引的）
5. 查询数据
6. 删除数据
7. 断开与数据库的连接

通过以上步骤，你会发现与连接MySQL数据库的操作非常相似。

# hello_milvus.py demonstrates the basic operations of PyMilvus, a Python SDK of Milvus.
# 1. connect to Milvus
# 2. create collection
# 3. insert data
# 4. create index
# 5. search, query, and hybrid search on entities
# 6. delete entities by PK
# 7. drop collection
import time

import numpy as np
from pymilvus import (
    connections,
    utility,
    FieldSchema, CollectionSchema, DataType,
    Collection,
)

fmt = "\n=== {:30} ===\n"
search_latency_fmt = "search latency = {:.4f}s"
num_entities, dim = 3000, 8

#################################################################################
# 1. connect to Milvus
# Add a new connection alias `default` for Milvus server in `localhost:19530`
# Actually the "default" alias is a buildin in PyMilvus.
# If the address of Milvus is the same as `localhost:19530`, you can omit all
# parameters and call the method as: `connections.connect()`.
#
# Note: the `using` parameter of the following methods is default to "default".
print(fmt.format("start connecting to Milvus"))
connections.connect("default", host="localhost", port="19530")

has = utility.has_collection("hello_milvus")
print(f"Does collection hello_milvus exist in Milvus: {has}")

#################################################################################
# 2. create collection
# We're going to create a collection with 3 fields.
# +-+------------+------------+------------------+------------------------------+
# | | field name | field type | other attributes |       field description      |
# +-+------------+------------+------------------+------------------------------+
# |1|    "pk"    |   VarChar  |  is_primary=True |      "primary field"         |
# | |            |            |   auto_id=False  |                              |
# +-+------------+------------+------------------+------------------------------+
# |2|  "random"  |    Double  |                  |      "a double field"        |
# +-+------------+------------+------------------+------------------------------+
# |3|"embeddings"| FloatVector|     dim=8        |  "float vector with dim 8"   |
# +-+------------+------------+------------------+------------------------------+
fields = [
    FieldSchema(name="pk", dtype=DataType.VARCHAR, is_primary=True, auto_id=False, max_length=100),
    FieldSchema(name="random", dtype=DataType.DOUBLE),
    FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=dim)
]

schema = CollectionSchema(fields, "hello_milvus is the simplest demo to introduce the APIs")

print(fmt.format("Create collection `hello_milvus`"))
hello_milvus = Collection("hello_milvus", schema, consistency_level="Strong")

################################################################################
# 3. insert data
# We are going to insert 3000 rows of data into `hello_milvus`
# Data to be inserted must be organized in fields.
#
# The insert() method returns:
# - either automatically generated primary keys by Milvus if auto_id=True in the schema;
# - or the existing primary key field from the entities if auto_id=False in the schema.

print(fmt.format("Start inserting entities"))
rng = np.random.default_rng(seed=19530)
entities = [
    # provide the pk field because `auto_id` is set to False
    [str(i) for i in range(num_entities)],
    rng.random(num_entities).tolist(),  # field random, only supports list
    rng.random((num_entities, dim)),  # field embeddings, supports numpy.ndarray and list
]

insert_result = hello_milvus.insert(entities)

hello_milvus.flush()
print(f"Number of entities in Milvus: {hello_milvus.num_entities}")  # check the num_entities

################################################################################
# 4. create index
# We are going to create an IVF_FLAT index for hello_milvus collection.
# create_index() can only be applied to `FloatVector` and `BinaryVector` fields.
print(fmt.format("Start Creating index IVF_FLAT"))
index = {
    "index_type": "IVF_FLAT",
    "metric_type": "L2",
    "params": {"nlist": 128},
}

hello_milvus.create_index("embeddings", index)

################################################################################
# 5. search, query, and hybrid search
# After data were inserted into Milvus and indexed, you can perform:
# - search based on vector similarity
# - query based on scalar filtering(boolean, int, etc.)
# - hybrid search based on vector similarity and scalar filtering.
#

# Before conducting a search or a query, you need to load the data in `hello_milvus` into memory.
print(fmt.format("Start loading"))
hello_milvus.load()

# -----------------------------------------------------------------------------
# search based on vector similarity
print(fmt.format("Start searching based on vector similarity"))
vectors_to_search = entities[-1][-2:]
search_params = {
    "metric_type": "L2",
    "params": {"nprobe": 10},
}

start_time = time.time()
result = hello_milvus.search(vectors_to_search, "embeddings", search_params, limit=3, output_fields=["random"])
end_time = time.time()

for hits in result:
    for hit in hits:
        print(f"hit: {hit}, random field: {hit.entity.get('random')}")
print(search_latency_fmt.format(end_time - start_time))

# -----------------------------------------------------------------------------
# query based on scalar filtering(boolean, int, etc.)
print(fmt.format("Start querying with `random > 0.5`"))

start_time = time.time()
result = hello_milvus.query(expr="random > 0.5", output_fields=["random", "embeddings"])
end_time = time.time()

print(f"query result:\n-{result[0]}")
print(search_latency_fmt.format(end_time - start_time))

# -----------------------------------------------------------------------------
# pagination
r1 = hello_milvus.query(expr="random > 0.5", limit=4, output_fields=["random"])
r2 = hello_milvus.query(expr="random > 0.5", offset=1, limit=3, output_fields=["random"])
print(f"query pagination(limit=4):\n\t{r1}")
print(f"query pagination(offset=1, limit=3):\n\t{r2}")

# -----------------------------------------------------------------------------
# hybrid search
print(fmt.format("Start hybrid searching with `random > 0.5`"))

start_time = time.time()
result = hello_milvus.search(vectors_to_search, "embeddings", search_params, limit=3, expr="random > 0.5",
                             output_fields=["random"])
end_time = time.time()

for hits in result:
    for hit in hits:
        print(f"hit: {hit}, random field: {hit.entity.get('random')}")
print(search_latency_fmt.format(end_time - start_time))

###############################################################################
# 6. delete entities by PK
# You can delete entities by their PK values using boolean expressions.
ids = insert_result.primary_keys

expr = f'pk in ["{ids[0]}" , "{ids[1]}"]'
print(fmt.format(f"Start deleting with expr `{expr}`"))

result = hello_milvus.query(expr=expr, output_fields=["random", "embeddings"])
print(f"query before delete by expr=`{expr}` -> result: \n-{result[0]}\n-{result[1]}\n")

hello_milvus.delete(expr)

result = hello_milvus.query(expr=expr, output_fields=["random", "embeddings"])
print(f"query after delete by expr=`{expr}` -> result: {result}\n")

###############################################################################
# 7. drop collection
# Finally, drop the hello_milvus collection
print(fmt.format("Drop collection `hello_milvus`"))
utility.drop_collection("hello_milvus")

升级版

现在，让我们来看一下使用LangChain版本的代码。由于我们使用的是封装好的Milvus，所以我们需要一个嵌入模型。在这里，我们选择了HuggingFaceEmbeddings中的sensenova/piccolo-base-zh模型作为示例，当然你也可以选择其他模型，这里没有限制。只要能将其作为一个变量传递给LangChain定义的函数调用即可。

下面是一个简单的示例，包括数据库连接、插入数据、查询以及得分情况的定义：

from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Milvus 


model_name = "sensenova/piccolo-base-zh"
embeddings = HuggingFaceEmbeddings(model_name=model_name) 

print("链接数据库")
vector_db = Milvus(
    embeddings,
    connection_args={"host": "localhost", "port": "19530"},
    collection_name="hello_milvus",
) 
print("简单传入几个值")
vector_db.add_texts(["12345678","789","努力的小雨是一个知名博主，其名下有公众号【灵墨AI探索室】，博客：稀土掘金、博客园、51CTO及腾讯云等","你好啊","我不好"])

print("查询前3个最相似的结果")
docs = vector_db.similarity_search_with_score("你好啊",3)

print("查看其得分情况，分值越低越接近")
for text in docs:
    print('文本:%s,得分:%s'%(text[0].page_content,text[1]))

注意，以上代码只是一个简单示例，具体的实现可能会根据你的具体需求进行调整和优化。

在langchain版本的代码中，如果你想要执行除了自己需要开启docker中的milvus容器之外的操作，还需要确保你拥有网络代理。这里不多赘述，因为HuggingFace社区并不在国内。

个人定制版

接下来，我们将详细了解如何调用openai模型来回答问题！

from dotenv import load_dotenv
from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate; 
from langchain import PromptTemplate
from langchain.chains import LLMChain 
from langchain.chat_models.openai import ChatOpenAI 
from langchain.schema import BaseOutputParser

# 加载env环境变量里的key值
load_dotenv()
# 格式化输出
class CommaSeparatedListOutputParser(BaseOutputParser):
    """Parse the output of an LLM call to a comma-separated list."""

    def parse(self, text: str):
        """Parse the output of an LLM call."""
        return text.strip().split(", ")
# 先从数据库查询问题解
docs = vector_db.similarity_search("努力的小雨是谁？")
doc = docs[0].page_content

chat = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0)
template = "请根据我提供的资料回答问题，资料： {input_docs}"
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)

chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])

# chat_prompt.format_messages(input_docs=doc, text="努力的小雨是谁？")
chain = LLMChain(
    llm=chat,
    prompt=chat_prompt,
    output_parser=CommaSeparatedListOutputParser()
)
chain.run(input_docs=doc, text="努力的小雨是谁？") 

当你成功运行完代码后，你将会得到你所期望的答案。如下图所示，这些答案将会展示在你的屏幕上。不然，如果系统不知道这些问题的答案，那它又如何能够给出正确的回答呢？

总结

通过本系列文章的学习，我们已经对个人或企业知识库有了一定的了解。尽管OpenAI已经提供了私有知识库的部署选项，但是其高昂的成本对于一些企业来说可能是难以承受的。无论将来国内企业是否会提供个人或企业知识库的解决方案，我们都需要对其原理有一些了解。无论我们的预算多少，都可以找到适合自己的玩法，因为不同预算的玩法也会有所不同。