mirror of
https://github.com/hwchase17/langchain
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36 lines
1.0 KiB
Python
36 lines
1.0 KiB
Python
import os
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from pathlib import Path
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from langchain_community.vectorstores import Chroma
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from langchain_experimental.open_clip import OpenCLIPEmbeddings
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# Load images
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img_dump_path = Path(__file__).parent / "docs/"
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rel_img_dump_path = img_dump_path.relative_to(Path.cwd())
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image_uris = sorted(
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[
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os.path.join(rel_img_dump_path, image_name)
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for image_name in os.listdir(rel_img_dump_path)
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if image_name.endswith(".jpg")
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]
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)
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# Index
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vectorstore = Path(__file__).parent / "chroma_db_multi_modal"
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re_vectorstore_path = vectorstore.relative_to(Path.cwd())
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# Load embedding function
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print("Loading embedding function") # noqa: T201
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embedding = OpenCLIPEmbeddings(model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k")
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# Create chroma
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vectorstore_mmembd = Chroma(
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collection_name="multi-modal-rag",
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persist_directory=str(Path(__file__).parent / "chroma_db_multi_modal"),
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embedding_function=embedding,
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)
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# Add images
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print("Embedding images") # noqa: T201
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vectorstore_mmembd.add_images(uris=image_uris)
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