mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
import os
|
|
|
|
from langchain.document_loaders import JSONLoader
|
|
from langchain.embeddings import HuggingFaceEmbeddings
|
|
from langchain.text_splitter import RecursiveCharacterTextSplitter
|
|
from langchain.vectorstores.elasticsearch import ElasticsearchStore
|
|
|
|
ELASTIC_CLOUD_ID = os.getenv("ELASTIC_CLOUD_ID")
|
|
ELASTIC_USERNAME = os.getenv("ELASTIC_USERNAME", "elastic")
|
|
ELASTIC_PASSWORD = os.getenv("ELASTIC_PASSWORD")
|
|
ES_URL = os.getenv("ES_URL", "http://localhost:9200")
|
|
|
|
if ELASTIC_CLOUD_ID and ELASTIC_USERNAME and ELASTIC_PASSWORD:
|
|
es_connection_details = {
|
|
"es_cloud_id": ELASTIC_CLOUD_ID,
|
|
"es_user": ELASTIC_USERNAME,
|
|
"es_password": ELASTIC_PASSWORD,
|
|
}
|
|
else:
|
|
es_connection_details = {"es_url": ES_URL}
|
|
|
|
|
|
# Metadata extraction function
|
|
def metadata_func(record: dict, metadata: dict) -> dict:
|
|
metadata["name"] = record.get("name")
|
|
metadata["summary"] = record.get("summary")
|
|
metadata["url"] = record.get("url")
|
|
metadata["category"] = record.get("category")
|
|
metadata["updated_at"] = record.get("updated_at")
|
|
|
|
return metadata
|
|
|
|
|
|
## Load Data
|
|
loader = JSONLoader(
|
|
file_path="./data/documents.json",
|
|
jq_schema=".[]",
|
|
content_key="content",
|
|
metadata_func=metadata_func,
|
|
)
|
|
|
|
text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=250)
|
|
all_splits = text_splitter.split_documents(loader.load())
|
|
|
|
# Add to vectorDB
|
|
vectorstore = ElasticsearchStore.from_documents(
|
|
documents=all_splits,
|
|
embedding=HuggingFaceEmbeddings(
|
|
model_name="all-MiniLM-L6-v2", model_kwargs={"device": "cpu"}
|
|
),
|
|
**es_connection_details,
|
|
index_name="workplace-search-example",
|
|
)
|