mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
import os
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from langchain_community.document_loaders import JSONLoader
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain_community.vectorstores.elasticsearch import ElasticsearchStore
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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ELASTIC_CLOUD_ID = os.getenv("ELASTIC_CLOUD_ID")
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ELASTIC_USERNAME = os.getenv("ELASTIC_USERNAME", "elastic")
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ELASTIC_PASSWORD = os.getenv("ELASTIC_PASSWORD")
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ES_URL = os.getenv("ES_URL", "http://localhost:9200")
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if ELASTIC_CLOUD_ID and ELASTIC_USERNAME and ELASTIC_PASSWORD:
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es_connection_details = {
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"es_cloud_id": ELASTIC_CLOUD_ID,
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"es_user": ELASTIC_USERNAME,
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"es_password": ELASTIC_PASSWORD,
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}
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else:
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es_connection_details = {"es_url": ES_URL}
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# Metadata extraction function
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def metadata_func(record: dict, metadata: dict) -> dict:
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metadata["name"] = record.get("name")
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metadata["summary"] = record.get("summary")
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metadata["url"] = record.get("url")
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metadata["category"] = record.get("category")
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metadata["updated_at"] = record.get("updated_at")
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return metadata
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## Load Data
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loader = JSONLoader(
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file_path="./data/documents.json",
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jq_schema=".[]",
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content_key="content",
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metadata_func=metadata_func,
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)
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=250)
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all_splits = text_splitter.split_documents(loader.load())
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# Add to vectorDB
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vectorstore = ElasticsearchStore.from_documents(
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documents=all_splits,
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embedding=HuggingFaceEmbeddings(
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model_name="all-MiniLM-L6-v2", model_kwargs={"device": "cpu"}
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),
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**es_connection_details,
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index_name="workplace-search-example",
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)
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