chromadb默认使用sentence-transformers/all-MiniLM-L6-v2的词嵌入(词向量)模型,如果在程序首次运行时,collection的add或query操作时如果没有指定embeddings或query_embeddings,程序会自动下载相关嵌入向量模型,但是由于默认hugging face后端网络下载速度常常非常慢,所以需要指定镜像网站以加快模型下载速度。
windows系统下具体操作步骤如下:
1、安装huggingface_hub:
            
            
              bash
              
              
            
          
          pip install huggingface_hub2、设置huggingface后端镜像网址系统变量:
            
            
              bash
              
              
            
          
          set HF_ENDPOINT=https://hf-mirror.com3、检查系统变量是否设置成功:
            
            
              bash
              
              
            
          
          hf env4、x下载指定模型(如all-MiniLM-L6-v2模型)到本地指定文件夹中:
            
            
              bash
              
              
            
          
          huggingface-cli download sentence-transformers/all-MiniLM-L6-v2 --local-dir ./models/all-MiniLM-L6-v2 --resume-download --local-dir-use-symlinks False5、在程序中使用本地模型(如all-MiniLM-L6-v2模型)示例:
            
            
              python
              
              
            
          
          from sentence_transformers import SentenceTransformer
# 指定本地模型路径(注意替换为实际路径)
model_path = r".\models\all-MiniLM-L6-v2"  # Windows路径建议用r""避免转义问题
model = SentenceTransformer(model_path)  # 从本地加载模型
# 输入句子列表
sentences = ["This is an example sentence.", "Each sentence is converted."]
embeddings = model.encode(sentences)  # 生成384维向量
# 打印结果(示例)
print("向量维度:", embeddings.shape)
for i, emb in enumerate(embeddings):
    print(f"句子 '{sentences[i]}' 的前5维向量: {emb[:5]}")6、在chromadb中使用本地词嵌入向量模型示例:
            
            
              python
              
              
            
          
          import chromadb
from sentence_transformers import SentenceTransformer
# 指定本地模型路径(注意替换为实际路径)
model_path = r".\models\all-MiniLM-L6-v2"  # Windows路径建议用r""避免转义问题
model = SentenceTransformer(model_path)  # 从本地加载模型
chroma_client = chromadb.Client()
collection = chroma_client.create_collection(
    name="my_collection"
)
#文本
documents=[
    "This is a document about pineapple",
    "This is an island of the USA",
    "This is a location where there are many tourists",
    "This is a document about oranges"
    
]
#文本通过模型转换为向量
embeddings = model.encode(documents) 
#像集合中添加记录
collection.add(
    embeddings=embeddings,
    ids=["id1", "id2","id3","id4"],
    documents=documents
)
#查询语句
query_texts=["This is a query document about hawaii"]
#查询语句通过模型转换为向量
query_embeddings = model.encode(query_texts)
#查询数据
results = collection.query(
    query_embeddings=query_embeddings,
    query_texts=query_texts, # Chroma will embed this for you
    n_results=2 # how many results to return
)
print(results)