Elasticsearch¶

Elasticsearch 是一个支持全文检索和向量搜索的搜索型数据库。

基础示例¶

在这个基础示例中，我们将一篇保罗·格雷厄姆的文章分割成多个文本块，使用开源嵌入模型进行向量化处理，将其加载到 Elasticsearch 中，然后执行查询。如需了解采用不同检索策略的示例，请参阅 Elasticsearch 向量存储。

若在 Colab 中打开此 Notebook，您可能需要安装 LlamaIndex 🦙。

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

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

In [ ]:

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

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

In [ ]:

# set up OpenAI
import os
import getpass

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

# set up OpenAI import os import getpass os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

下载数据

In [ ]:

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

!mkdir -p 'data/paul_graham/' !wget -nv 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/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/docs/examples/data/paul_graham/paul_graham_essay.txt [75042/75042] -> "data/paul_graham/paul_graham_essay.txt" [1]

In [ ]:

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"
)

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" )

In [ ]:

# 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
)

# 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 )

In [ ]:

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

# 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.