65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
import numpy as np
|
|
from abc import ABC
|
|
from ollama import Client
|
|
|
|
|
|
class Base(ABC):
|
|
def __init__(self, key, model_name):
|
|
pass
|
|
|
|
def encode(self, texts: list):
|
|
raise NotImplementedError("Please implement encode method!")
|
|
|
|
def encode_queries(self, text: str):
|
|
raise NotImplementedError("Please implement encode method!")
|
|
|
|
def total_token_count(self, resp):
|
|
try:
|
|
return resp.usage.total_tokens
|
|
except Exception:
|
|
pass
|
|
try:
|
|
return resp["usage"]["total_tokens"]
|
|
except Exception:
|
|
pass
|
|
return 0
|
|
|
|
|
|
class OllamaEmbed(Base):
|
|
def __init__(self, model_name, **kwargs):
|
|
self.client = Client(host="http://localhost:11434", **kwargs)
|
|
self.model_name = model_name
|
|
|
|
def encode(self, texts: list):
|
|
arr = []
|
|
tks_num = 0
|
|
for txt in texts:
|
|
res = self.client.embeddings(prompt=txt, model=self.model_name)
|
|
arr.append(res["embedding"])
|
|
tks_num += 128
|
|
return np.array(arr), tks_num
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# 初始化嵌入模型
|
|
embedder = OllamaEmbed(model_name="bge-m3")
|
|
|
|
# 测试文本
|
|
test_texts = ["测试文本"]
|
|
|
|
# 获取嵌入向量和token计数
|
|
embeddings, total_tokens = embedder.encode(test_texts)
|
|
|
|
# 打印结果
|
|
print(f"Total tokens used: {total_tokens}")
|
|
print("\nEmbedding vectors:")
|
|
for i, (text, embedding) in enumerate(zip(test_texts, embeddings)):
|
|
print(f"\nText {i + 1}: '{text}'")
|
|
print(f"Embedding shape: {embedding.shape}")
|
|
print(f"First 5 values: {embedding[:5]}")
|
|
print(f"Embedding dtype: {embedding.dtype}")
|
|
|
|
# 打印完整的第一个embedding向量
|
|
print("\nComplete first embedding vector:")
|
|
print(embeddings[0])
|