--- layout: single title: "FastEmbed" date: 2024-07-06 08:00:00 +0800 categories: [编程开发, 框架与库] tags: [FastEmbed, Qdrant] --- ## [FastEmbed](https://github.com/qdrant/fastembed) FastEmbed 是一个轻量级、快速的 Python 库，专为嵌入生成而构建。 ### 安装 ```bash pip install -Uqq fastembed ``` ### 支持的嵌入模型 ```python import pandas as pd from fastembed import TextEmbedding supported_models = ( pd.DataFrame(TextEmbedding.list_supported_models()) .sort_values("size_in_GB") .drop(columns=["sources", "model_file", "additional_files"]) .reset_index(drop=True) ) print(supported_models) ``` | | model | dim | description | size_in_GB | |---:|--------------------------------------------------:|----:|--------------------------------------------------:|-----------:| | 1 | BAAI/bge-small-en-v1.5 | 384 | Fast and Default English model | 0.067 | | 2 | sentence-transformers/all-MiniLM-L6-v2 | 384 | Sentence Transformer model, MiniLM-L6-v2 | 0.090 | | 3 | BAAI/bge-small-zh-v1.5 | 512 | Fast and recommended Chinese model | 0.090 | | 4 | snowflake/snowflake-arctic-embed-xs | 384 | Based on all-MiniLM-L6-v2 model with only 22m ... | 0.090 | | 5 | jinaai/jina-embeddings-v2-small-en | 512 | English embedding model supporting 8192 sequen... | 0.120 | | 6 | BAAI/bge-small-en | 384 | Fast English model | 0.130 | | 7 | snowflake/snowflake-arctic-embed-s | 384 | Based on infloat/e5-small-unsupervised, does n... | 0.130 | | 8 | nomic-ai/nomic-embed-text-v1.5-Q | 768 | Quantized 8192 context length english model | 0.130 | | 9 | BAAI/bge-base-en-v1.5 | 768 | Base English model, v1.5 | 0.210 | | 10 | sentence-transformers/paraphrase-multilingual-... | 384 | Sentence Transformer model, paraphrase-multili... | 0.220 | | 11 | Qdrant/clip-ViT-B-32-text | 512 | CLIP text encoder | 0.250 | | 12 | jinaai/jina-embeddings-v2-base-de | 768 | German embedding model supporting 8192 sequenc... | 0.320 | | 13 | BAAI/bge-base-en | 768 | Base English model | 0.420 | | 14 | snowflake/snowflake-arctic-embed-m | 768 | Based on intfloat/e5-base-unsupervised model, ... | 0.430 | | 15 | nomic-ai/nomic-embed-text-v1.5 | 768 | 8192 context length english model | 0.520 | | 16 | jinaai/jina-embeddings-v2-base-en | 768 | English embedding model supporting 8192 sequen... | 0.520 | | 17 | nomic-ai/nomic-embed-text-v1 | 768 | 8192 context length english model | 0.520 | | 18 | snowflake/snowflake-arctic-embed-m-long | 768 | Based on nomic-ai/nomic-embed-text-v1-unsuperv... | 0.540 | | 19 | mixedbread-ai/mxbai-embed-large-v1 |1024 | MixedBread Base sentence embedding model, does... | 0.640 | | 20 | sentence-transformers/paraphrase-multilingual-... | 768 | Sentence-transformers model for tasks like clu... | 1.000 | | 21 | snowflake/snowflake-arctic-embed-l |1024 | Based on intfloat/e5-large-unsupervised, large... | 1.020 | | 22 | BAAI/bge-large-en-v1.5 |1024 | Large English model, v1.5 | 1.200 | | 23 | thenlper/gte-large |1024 | Large general text embeddings model | 1.200 | | 24 | intfloat/multilingual-e5-large |1024 | Multilingual model, e5-large. Recommend using ... | 2.240 | - [Supported Models](https://qdrant.github.io/fastembed/examples/Supported_Models/) ## FastEmbed 检索 ```python import numpy as np from typing import List from fastembed import TextEmbedding documents: List[str] = [ "玛哈拉纳·普拉塔普是来自梅瓦尔的拉其普特战士国王", "他与阿克巴领导的莫卧儿帝国作战", "1576 年的哈尔迪加蒂战役是他最著名的战役", "他拒绝向阿克巴屈服,继续进行游击战", "他的首都是奇托加尔,后来被莫卧儿人占领", "他于 1597 年去世,享年 57 岁", "玛哈拉纳·普拉塔普被认为是拉其普特反抗外国统治的象征", "他的遗产在拉贾斯坦邦通过节日和纪念碑来庆祝", "他有 11 个妻子和 17 个儿子,包括继任他成为梅瓦尔统治者的阿玛尔·辛格一世", "他的一生出现在各种电影、电视节目和书籍中", ] embedding_model = TextEmbedding(model_name="BAAI/bge-small-zh-v1.5") embeddings: List[np.ndarray] = list( embedding_model.passage_embed(documents) ) def print_top_k(query_embedding, embeddings, documents, k=5): # 使用 numpy 计算查询和文档之间的余弦相似度 scores = np.dot(embeddings, query_embedding) # 降序排列分数 sorted_scores = np.argsort(scores)[::-1] # print the top 5 for i in range(k): print(f"{i+1}: {documents[sorted_scores[i]]}") query = "玛哈拉纳·普拉塔普是哪个国家的国王？" query_embedding = list(embedding_model.query_embed(query))[0] print_top_k(query_embedding, embeddings, documents) ``` ``` 1: 玛哈拉纳·普拉塔普是来自梅瓦尔的拉其普特战士国王 2: 玛哈拉纳·普拉塔普被认为是拉其普特反抗外国统治的象征 3: 他的遗产在拉贾斯坦邦通过节日和纪念碑来庆祝 4: 1576 年的哈尔迪加蒂战役是他最著名的战役 5: 他与阿克巴领导的莫卧儿帝国作战 ``` `query_embed` 专为查询而设计，可带来更相关、更符合上下文的结果。检索到的文档往往与查询意图紧密相关。 - [Retrieval with FastEmbed](https://qdrant.github.io/fastembed/qdrant/Retrieval_with_FastEmbed/) ## FastEmbed & Qdrant Qdrant 是一个开源向量相似性搜索引擎，用于存储、组织和查询高维向量集合。 ### 安装 ```bash pip install 'qdrant-client[fastembed]' --quiet --upgrade ``` ```python from typing import List from qdrant_client import QdrantClient documents: List[str] = [ "玛哈拉纳·普拉塔普是来自梅瓦尔的拉其普特战士国王", "他与阿克巴领导的莫卧儿帝国作战", "1576 年的哈尔迪加蒂战役是他最著名的战役", "他拒绝向阿克巴屈服,继续进行游击战", "他的首都是奇托加尔,后来被莫卧儿人占领", "他于 1597 年去世,享年 57 岁", "玛哈拉纳·普拉塔普被认为是拉其普特反抗外国统治的象征", "他的遗产在拉贾斯坦邦通过节日和纪念碑来庆祝", "他有 11 个妻子和 17 个儿子,包括继任他成为梅瓦尔统治者的阿玛尔·辛格一世", "他的一生出现在各种电影、电视节目和书籍中", ] # client = QdrantClient("localhost", port=6333) # For production client = QdrantClient(":memory:") # List of supported models: https://qdrant.github.io/fastembed/examples/Supported_Models client.set_model(embedding_model_name="BAAI/bge-small-zh-v1.5") client.add(collection_name="test_collection", documents=documents) query = "玛哈拉纳·普拉塔普是哪个国家的国王？" search_result = client.query(collection_name="test_collection", query_text=query, limit=5) for result in search_result: print(f'score: {result.score:.2f}, doc: {result.document}') ``` ``` score: 0.83, doc: 玛哈拉纳·普拉塔普是来自梅瓦尔的拉其普特战士国王 score: 0.76, doc: 玛哈拉纳·普拉塔普被认为是拉其普特反抗外国统治的象征 score: 0.44, doc: 他的遗产在拉贾斯坦邦通过节日和纪念碑来庆祝 score: 0.40, doc: 1576 年的哈尔迪加蒂战役是他最著名的战役 score: 0.39, doc: 他与阿克巴领导的莫卧儿帝国作战 ``` `set_model` 方法用于设置嵌入模型，`add` 方法用于添加文档，`query` 方法用于查询文档。 插入带有 `metadata` 的文档： ```python # Prepare your documents, metadata, and IDs docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integrations"] metadata = [ {"source": "Langchain-docs"}, {"source": "Linkedin-docs"}, ] ids = [42, 2] # Use the new add method client.add(collection_name="demo_collection", documents=docs, metadata=metadata, ids=ids) ``` - [Usage With Qdrant](https://qdrant.github.io/fastembed/qdrant/Usage_With_Qdrant/)