Elasticsearch 是一个搜索数据库，支持全文搜索和向量搜索。

基本示例

在这个基本示例中，我们取一篇 Paul Graham 的文章，将其分块，使用开源嵌入模型进行嵌入，加载到 Elasticsearch 中，然后对其进行查询。有关使用不同检索策略的示例，请参见 Elasticsearch Vector Store。

如果你在 Colab 中打开这个 Notebook，可能需要先安装 LlamaIndex 🦙。

%pip install -qU llama-index-vector-stores-elasticsearch llama-index-embeddings-huggingface llama-index

# import
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.elasticsearch import ElasticsearchStore
from llama_index.core import StorageContext

# set up OpenAI
import os
import getpass

os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

下载数据

!mkdir -p 'data/paul_graham/'
!wget -nv 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

2024-05-13 15:10:43 URL:https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt [75042/75042] -> "data/paul_graham/paul_graham_essay.txt" [1]

from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings

# define embedding function
Settings.embed_model = HuggingFaceEmbedding(
    model_name="BAAI/bge-small-en-v1.5"
)

# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()

# define index
vector_store = ElasticsearchStore(
    es_url="http://localhost:9200",  # see Elasticsearch Vector Store for more authentication options
    index_name="paul_graham_essay",
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

# Query Data
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(response)

The author worked on writing and programming outside of school. They wrote short stories and tried writing programs on an IBM 1401 computer. They also built a microcomputer kit and started programming on it, writing simple games and a word processor.