Title here
Summary here
文件处理：打造知识库4
当用户向 LLM 提问为例，流程如下：
原始文本 → Token → 嵌入向量 → 模型处理 → 输出 Token → 解码为文本
其中 Token 的意思是将文本转成数字，然后这些数字通过嵌入向量模型, 得了一个连续的向量维度，用来描述Token,
在把这些丢给 LLM, LLL 通过上下文关联, 输出Token, 然后再转成文本，展示给用户。
Tokenization 分词
那什么是 token 化呢？ 或者说，如何将文本转成数字？其实就是分词，切割，将文本分割为模型认识的单元。
例如：句子 "Hello, world!" → Tokens ["Hello", ",", "world", "!"]（具体取决于分词器）。
接着进行数字化映射：每个 Token 被转换为整数 ID。 例如："Hello" → ID 12345，"world" → ID 67890。
一般来说，分词器是配合嵌入向量合在一起用的，如果我们要单独使用分词器，可以运行额外的分词模型。这里举个简单的例子，看看 分词器是如何分词的。
先安装分词器：
pip install transformers sentencepiece # 安装依赖接着运行，第一次运行可能比较慢，需要耐心等待：
from transformers import AutoTokenizer
# 加载 Qwen 分词器（例如 qwen-7b）
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-7B", trust_remote_code=True)
text = "Hello"
tokens = tokenizer.tokenize(text)
token_ids = tokenizer.encode(text)
print("Tokens:", tokens) # 输出 Token 列表
print("Token IDs:", token_ids) # 输出对应的数值 ID输出结果：
/usr/local/bin/python3.11 /data/workspace/token_demo.py
Tokens: [b'Hello']
Token IDs: [9707]
Process finished with exit code 0结果是 [9707]。
但是知道这个对我们来说没什么用，不同的分词器，结果 Token 数量，值都是不一样的，此外，得到了分词后的映射 ID，我们也不知道它的具体意义。
所以一般需要配合 嵌入向量 来共同作用。
Token 是嵌入向量的基础，嵌入向量根据分词器得到的 Token ID，转成高维度向量，是 Token 的语义升级.
Embedding 嵌入向量
嵌入向量是语义的连续表示，关注上下文和关系，所以嵌入向量也可以叫语义理解。举个例子：这个多少钱? 与 这个价格是多少？, 正常情况下，我们认为这两句话的语义是一样的，
在高维度的向量中，他们的值应该也是趋近的，我们可以用嵌入向量的模型来验证这一点。
嵌入向量有很多模型，我们用的比较多的是：SentenceTransformers，OpenAI Embeddings，以及 HuggingFace 上的BAAI/bge-small-en
- SentenceTransformers： https://sbert.net/
- OpenAI Embeddings：https://platform.openai.com/docs/models
- BAAI/bge-small-en：https://huggingface.co/BAAI/bge-m3
一般来说，嵌入向量模型的维度越多，则它对语义的理解就越强，因为维度越多表示它对语义的描述的细节越多。
举个例子，假设：妈妈 这个词最终经过嵌入向量的转换后，生成的结果如下：
# 模型 1
[9708] # 假设 9708 表示妈妈的意思
# 模型 2
[9708,9709] # 假设 9708 表示妈妈的意思，假设 9709 表示母亲的意思我们可以看到，模型 1 的维度只有 1，所以它只能表示一种意思，但是模型 2 的维度有 2 个，所以它能表示两种意思。
而用户在搜索的时候，搜:爱迪生的妈妈叫什么名字?，如果 AI 仅仅学了到 爱迪生的母亲叫艾玛，这时候，用模型 1，就无法回答用户，但是用了模型 2，就能回答
用户，可见，嵌入模型对用户的提问过程中，起非常关键的作用，好的嵌入模型能极大的提高 LLM 的能力。
目前公认的最好的模型应该还是 OpenAI 的那几个模型，维度高达 1500+， 而 bge 的维度只有 1024， 但目前OpenAI 的嵌入模型是收费的，而且没有开源，所以
我们要根据自己的实际业务需求，选择合适的嵌入模型：
下图是 OpenAI 的三个嵌入模型，其中 large 对语义的理解是最强的，但是它最贵，比较也比较慢，我们在实际的生产中，一般都是用 small ，速度快，价格比低：
模型对比
| 特性 | SentenceTransformers | OpenAI Embeddings | HuggingFace BAAI/bge-small-en |
|---|---|---|---|
| 模型类型 | 开源（本地部署） | 闭源（API 调用） | 开源（本地部署） |
| 典型模型 | all-mpnet-base-v2 | text-embedding-3-small | BAAI/bge-small-en |
| 训练数据 | 通用英文语料 | 大规模多语言混合数据 | 中英文混合优化 |
| 向量维度 | 768 | 1536 | 512 |
| 语义理解能力 | 高 | 极高 | 中高（中文优化） |
| 多语言支持 | 需特定多语言模型（如 paraphrase-multilingual-mpnet-base-v2） | 支持（100+ 语言） | 中英文为主 |
| 速度 | 较快（本地 GPU 加速） | 依赖网络延迟（约 100-300ms/请求） | 极快（轻量级设计） |
| 成本 | 免费 | 按调用量付费（约 $0.00002/千 token） | 免费 |
| 隐私与合规 | 完全本地处理，数据无外传 | 需上传数据至云端 | 完全本地处理 |
| 可定制性 | 支持微调 | 不支持 | 支持微调 |
测试嵌入模型
Ollama 本身支持测试向量模型，官方用例：
curl http://localhost:11434/api/embed -d '{
"model": "all-minilm",
"input": ["Why is the sky blue?", "Why is the grass green?"]
}'转成 python 后：
import json
import requests
def aaa():
# 设置请求的 URL 和数据
url = "http://localhost:11434/api/embed"
data = {
"model": "deepseek-r1:7b",
# 这个多少钱?` 与 `这个价格是多少？
"input": ["这个多少钱?", "这个价格是多少?"],
'stream': False,
}
# 将数据转换为 JSON 格式
headers = {"Content-Type": "application/json"}
# 发送 POST 请求并启用流式处理
with requests.post(url, data=json.dumps(data), stream=False, headers=headers) as response:
for line in response.iter_lines():
if line:
print(json.loads(line).get("response"))
if __name__ == '__main__':
aaa()结果如下：
{'model': 'deepseek-r1:7b', 'embeddings': [[-0.0025235526, -0.01901969, -0.018454643, -0.0039711054, 0.0077189836, -0.0023009726, -0.018039182, -0.011503955, -0.032006823, 0.02485924, -0.0048791533, 0.006145271, 0.0025916332, -0.006168392, 0.001041633, -0.008324594, 0.0017008941, 0.0017069805, -0.013247196, -0.0057647377, 0.007684458, 0.014061387, -0.013574904, 0.008071824, 0.013953541, -0.007168137, 0.0050165923, -0.019988252, -0.013895274, -0.003986475, -0.0019780595, -0.0099051045, 0.008129556, -0.0025644102, -0.008892117, 0.0005316697, -0.0012943088, 0.021532621, 0.00987024, -0.01167172, -0.013097491, 0.0066685835, -0.0055979337, -0.0028298995, 0.021996, -0.016528023, 0.0019344726, -0.005843465, -0.016344013, 0.019525118, 0.014070451, -0.0055901115, 0.005651729, 0.011355905, 0.0057323687, 0.00030432467, -0.0018968856, -0.016419103, 0.014209726, 0.011349289, -0.00026507775, -0.00081920205, 0.0034591882, -0.0050430144, -0.010326, -0.014720899, -0.010900608, -0.013193159, -0.00541685, 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-0.0020375478, 0.006594074, 0.0068648886, 0.019029431, 0.0005373346, 0.001388145, -0.009981596, 0.00200032, -0.010300199, -0.0051790094, -0.011748022, 0.015533977, -0.014319158, -0.021856343, -0.02121978, -0.018391354, 0.017556563, -0.004123229, -0.0010605493, 0.0024601503, 0.013003913, 0.0078045256, 0.020415982, 0.004985642, -0.03291264, -0.0028053594, 0.004540199, -0.0150122, 0.0030741035, 0.00032548752, -0.00038586286, -0.0039725597, -0.010675716, 0.002931213, 0.00789498, 0.008458235, -0.02649478, -0.006393663, -0.012558877, 0.0009845953, -0.0034246536, 0.017722381, -0.083795436, -0.014760826, 0.039034784, 0.0034072292, -0.008515433, 0.024774121, -0.0074137757]], 'total_duration': 164069888, 'load_duration': 941073, 'prompt_eval_count': 7}结果返回了两个向量：
光看着两个向量，是没有办法知道他们的相似度的，我们可以通过前文中，提到的余弦相似度算法来计算着两个向量的相似度：
import json
import requests
import numpy as np
def cosine_similarity(a, b):
dot_product = np.dot(a, b) # 计算点积
norm_a = np.linalg.norm(a) # 计算向量a的模长
norm_b = np.linalg.norm(b) # 计算向量b的模长
return dot_product / (norm_a * norm_b) # 返回余弦相似度
def aaa():
# 设置请求的 URL 和数据
url = "http://localhost:11434/api/embed"
data = {
"model": "deepseek-r1:7b",
# 这个多少钱?` 与 `这个价格是多少？
"input": ["这个多少钱?", "这个价格是多少?"],
'stream': False,
}
# 将数据转换为 JSON 格式
headers = {"Content-Type": "application/json"}
# 发送 POST 请求并启用流式处理
with requests.post(url, data=json.dumps(data), stream=False, headers=headers) as response:
embeddings = json.loads(response.text)['embeddings']
embed1= embeddings[0]
embed2= embeddings[1]
similarity = cosine_similarity(embed1, embed2)
print(f"余弦相似度: {similarity:.4f}") # 输出: 0.9746
if __name__ == '__main__':
aaa()最终输出结果：
余弦相似度: 0.5863这个相似度不高，有几个原因，可能是模型对语义理解不够，或者对中文支持不够，或者是模型量化参数太小了，我们换成一样的文本，相似度就是 1 了：
经过多次测试，发现deepseek-r1:7b这个模型对中文理解并不是很好，如果我们用 qwen2.5:7b 模型：
可以看到，qwen 模型,同样的参数，对中文的理解就稍微好一点，之前前面介绍的 OpenAI 的语义理解模型，它是一个通用的，混合语言理解大模型，目前对语义的理解是最强的
，但是收费也是最贵的。
而不同的模型，量化参数大小，对语义的理解程度都是不一样的，所以我们要根据自己业务的实际生产中，选择合适的模型。
存储到向量数据库
前文我们介绍了 Chroma 向量数据库，它支持直接将文本转成向量，如果我们在插入数据的时候，没有填写向量，那它就会自动生成向量。
它本身支持我们添加外部的嵌入模型，详细文档看这里, 这个是 Ollama 的嵌入模型：
https://docs.trychroma.com/integrations/embedding-models/ollama
它提供了一个封装好的 SDK ，其实际上也是调用 Ollama 的 /api/embeddings, 但是这个 API 一次只能转换一个文本，
OLlama 现在已经提供批量调用的嵌入文本了，就是我们上面用到的 /api/embed，我们直接使用新的 API 来调用。
直接嵌入向量
Chroma 支持之前插入文本向量，如下：
import json
import chromadb
import requests
from chromadb import EmbeddingFunction, Documents, Embeddings
def change2Enbeddings(texts):
# 在这里实现嵌入逻辑，返回嵌入向量
url = "http://localhost:11434/api/embed"
data = {
"model": "qwen2.5:7b",
"input": texts,
'stream': False,
}
# 将数据转换为 JSON 格式
headers = {"Content-Type": "application/json"}
print('\ntext 2 embeddings...\n')
# 发送 POST 请求并启用流式处理
with requests.post(url, data=json.dumps(data), stream=False, headers=headers) as response:
embeddings = json.loads(response.text)['embeddings']
return embeddings
def test_chromadb():
text = ["This is document1", "This is document2", "This is a video"]
embeddings = change2Enbeddings(text)
client = chromadb.HttpClient()
client.delete_collection("sample_collection")
collection = client.create_collection("sample_collection")
collection.add(
# 增加一条新的文档 video
embeddings=embeddings,
# documents=text,
metadatas=[{"source": "notion"}, {"source": "google-docs"}, {"source": "test-dir"}],
ids=["doc1", "doc2", 'videoDir'],
)
print('\nquery...\n')
results = collection.query(
query_embeddings=change2Enbeddings(["This is a query document"]),
# 查询结果，最多返回 10 条
n_results=10,
)
print("\n")
print(json.dumps(results))
if __name__ == '__main__':
test_chromadb()其中注意参数，add 的时候，传入的是：embeddings， 而不是 documents， 查询的时候，查询的是向量query_embeddings，而不是 query_texts,
这两个参数要成双成对出现，不然可能会出现向量大小不匹配的问题。
原因是，我们使用的是 OLlama 里激活的模型转的向量，但是 Chroma 的默认向量大小跟我们激活的未必是一致的，所以必须保持一致，否则就会报错。
输出结果：
text 2 embeddings...
query...
text 2 embeddings...
{"ids": [["doc1", "doc2", "videoDir"]], "distances": [[0.5738595648611112, 0.6490618497792824, 0.6717018194735118]], "embeddings": null, "metadatas": [[{"source": "notion"}, {"source": "google-docs"}, {"source": "test-dir"}]], "documents": [["This is document1", "This is document2", "This is a video"]], "uris": null, "data": null, "included": ["distances", "documents", "metadatas"]}可以看到一共调用了两次转换，一次是输入，用来将当前输入的文本转换成向量，一次是查询，用来查询当前问题的向量
全局设置嵌入向量
除了直接嵌入向量，我们还可以全局嵌入向量，在创建 create_collection 的时候，添加参数 embedding_function 来转换，
这样，我们在添加数据的时候可以直接添加文本，会自动调用转换：
import json
import chromadb
import requests
from chromadb import EmbeddingFunction, Documents, Embeddings
class MyEmbeddingFunction(EmbeddingFunction):
def __call__(self, texts: Documents) -> Embeddings:
# 在这里实现嵌入逻辑，返回嵌入向量
url = "http://localhost:11434/api/embed"
data = {
"model": "qwen2.5:7b",
# 这个多少钱?` 与 `这个价格是多少？
"input": texts,
'stream': False,
}
# 将数据转换为 JSON 格式
headers = {"Content-Type": "application/json"}
print('\ntext 2 embeddings...\n')
# 发送 POST 请求并启用流式处理
with requests.post(url, data=json.dumps(data), stream=False, headers=headers) as response:
embeddings = json.loads(response.text)['embeddings']
return embeddings
def test_chromadb():
text = ["This is document1", "This is document2", "This is a video"]
client = chromadb.HttpClient()
client.delete_collection("sample_collection")
collection = client.create_collection("sample_collection", embedding_function=MyEmbeddingFunction())
collection.add(
# 增加一条新的文档 video
documents=text,
metadatas=[{"source": "notion"}, {"source": "google-docs"}, {"source": "test-dir"}],
ids=["doc1", "doc2", 'videoDir'],
)
print('\nquery...\n')
results = collection.query(
query_texts=["This is a query document"],
# 查询结果，最多返回 10 条
n_results=10,
)
print("\n")
print(json.dumps(results))
if __name__ == '__main__':
test_chromadb()输出结果与上面是一致的，只是在添加与查询的时候，设置的参数不一致。
总结
本节课主要是讲解文本如何转成向量，以及将向量存储输入库，并且查询出来。
下节课将结合 OLlama，对文本，Pdf 等文件，处理成向量，然后打造 RAG 的雏形。
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