一、安装向量数据库、文本转向量包：

pip install chromadb text2vec 

二、顶一个类来进行文本的向量化并存储

• 代码

import chromadb
from text2vec import SentenceModel
from typing import List
import pandas as pd

class VectorDB:
    def __init__(self, model_path: str):
        """
        :param model_path: 文本转向量模型的路径，可以是本地文件夹，也可以是 huggingface 模型名
        """
        self.model = SentenceModel(model_path)

    def get_embeddings(
        self, sentences: List[str], batch_size: int
    ) -> List[List[float]]:
        """
        :param sentences: 需要转换为向量的句子列表
        :param batch_size: 文本转换为向量的批次，即每次转换的数量
        :return: 嵌套的句子向量
        """
        # 将文本转换为向量
        embeddings = []
        num_batches = (len(sentences) + batch_size - 1) // batch_size  # 计算总共需要多少批次
        for i in range(num_batches):
            start_idx = i * batch_size
            end_idx = min((i + 1) * batch_size, len(sentences))
            sentence_batch = sentences[start_idx:end_idx]  # 获取当前批次的文本
            batch_embeddings = self.model.encode(sentence_batch)  # 转换当前批次的文本为向量
            embeddings.extend(batch_embeddings)  # 将转换结果添加到列表中
        embeddings = [embedding.tolist() for embedding in embeddings]
        return embeddings
    def create_db(
        self,
        csv_path: str,
        col_name: str,
        source_name: str,
        batch_size: int,
        db_path: str,
        collection_name: str,
    ):
        """
        :param csv_path: 单列数据的 csv 文件
        :param col_name: csv 文件中的列名
        :param source_name: 当前 csv 文件数据的来源
        :param batch_size: 文本转换为向量的批次，即每次转换的数量
        :param db_path: 向量数据库的路径
        :param collection_name: 向量数据库下的集合名
        :return: 向量数据库，它保存到设定的路径下
        """
        df = pd.read_csv(csv_path)
        sentences = df[col_name].tolist()
        vectors = self.get_embeddings(sentences, batch_size=batch_size)
        chroma_client = chromadb.PersistentClient(path=db_path)
        collection = chroma_client.get_or_create_collection(name=collection_name)
        num_batches = (len(sentences) + batch_size - 1) // batch_size
        ids = [f"id{i}" for i in range(len(sentences))]
        for i in range(num_batches):
            start_idx = i * batch_size
            end_idx = min((i + 1) * batch_size, len(sentences))
            batch_vectors = vectors[start_idx:end_idx]
            batch_sentences = sentences[start_idx:end_idx]
            metadatas = [{"source": source_name} for _ in range(len(batch_sentences))]
            batch_ids = ids[start_idx:end_idx]
            collection.add(
                embeddings=batch_vectors,
                documents=batch_sentences,
                metadatas=metadatas,
                ids=batch_ids,
            )

    def get_similar_sentences(
        self, query_texts: str, n_results: int, db_path: str, collection_name: str
    ):
        """
        :param query_texts: 单条待查询的句子
        :param n_results: 需要查询的相似句子数量
        :param db_path: 向量数据库的路径
        :param collection_name: 向量数据库下的集合名
        :return: 查询的相似句子结果
        """
        query_texts = [query_texts]
        query_embeddings = self.model.encode(query_texts)
        query_embeddings = [embedding.tolist() for embedding in query_embeddings]
        chroma_client = chromadb.PersistentClient(path=db_path)
        collection = chroma_client.get_or_create_collection(name=collection_name)
        results = collection.query(
            query_embeddings=query_embeddings, n_results=n_results
        )
        return results

• 获取待导入文本向量数据库的语料：

import pandas as pd
df = pd.read_excel("/kaggle/input/example-text/sentences.xlsx")
df.to_csv("sentences.csv", index=False)

• 接着我们创建文本向量数据库：

vector_db = VectorDB(model_path="BAAI/bge-small-zh-v1.5")

vector_db.create_db(
    csv_path="sentences.csv",
    col_name="text",
    source_name="sentences",
    batch_size=2560,
    db_path="vector_db",
    collection_name="20230915",
) 

三、根据向量查询数据库

res = vector_db.get_similar_sentences(
    query_texts="非诚勿扰。",   #查询内容
    n_results=3,               #相似的排名前面结果
    db_path="vector_db",       #数据库名字
    collection_name="20230915",#对应合集名字
)
print(res) 


或先获取合集，再根据合集来搜索
import chromadb
chroma_client = chromadb.Httpclient(host="192.168.66.135",port=8006)

#collection = chroma_client.create_collection(name="my_collection")
collection = chroma_client.get_collection(name="my_collection")

#这里自动转向量化
collection.add(
	documents=["什么时候发货", "售后信息","有什么颜色可以选择"],
	metadatas=[{"source":"亲，下单后24小时发货"},.....],
	ids=["1","2","3"]
)

results = collection.query(
	query_texts=["有其他颜色么"],
	n_results = 3,
)


print(results )

四、docker部署chromadb

可以直接从官网拉取

docker pull chromadb/chroma
docker run -p 8000:8000 chromadb/chroma

• 或者源码拉取后自己修改配置部署

git clone git@github.com:chroma-core/chroma.git
cd chroma
docker-compose up -d --build