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https://github.com/hwchase17/langchain
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fa5d49f2c1
ran ```bash g grep -l "langchain.vectorstores" | xargs -L 1 sed -i '' "s/langchain\.vectorstores/langchain_community.vectorstores/g" g grep -l "langchain.document_loaders" | xargs -L 1 sed -i '' "s/langchain\.document_loaders/langchain_community.document_loaders/g" g grep -l "langchain.chat_loaders" | xargs -L 1 sed -i '' "s/langchain\.chat_loaders/langchain_community.chat_loaders/g" g grep -l "langchain.document_transformers" | xargs -L 1 sed -i '' "s/langchain\.document_transformers/langchain_community.document_transformers/g" g grep -l "langchain\.graphs" | xargs -L 1 sed -i '' "s/langchain\.graphs/langchain_community.graphs/g" g grep -l "langchain\.memory\.chat_message_histories" | xargs -L 1 sed -i '' "s/langchain\.memory\.chat_message_histories/langchain_community.chat_message_histories/g" gco master libs/langchain/tests/unit_tests/*/test_imports.py gco master libs/langchain/tests/unit_tests/**/test_public_api.py ```
24 lines
691 B
Python
24 lines
691 B
Python
from pathlib import Path
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from langchain.text_splitter import TokenTextSplitter
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from langchain_community.document_loaders import TextLoader
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from langchain_community.embeddings.openai import OpenAIEmbeddings
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from langchain_community.vectorstores import Neo4jVector
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txt_path = Path(__file__).parent / "dune.txt"
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# Load the text file
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loader = TextLoader(str(txt_path))
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raw_documents = loader.load()
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# Define chunking strategy
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splitter = TokenTextSplitter(chunk_size=512, chunk_overlap=24)
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documents = splitter.split_documents(raw_documents)
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# Calculate embedding values and store them in the graph
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Neo4jVector.from_documents(
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documents,
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OpenAIEmbeddings(),
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index_name="dune",
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)
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