39 lines
1.1 KiB
Markdown
39 lines
1.1 KiB
Markdown
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### Start services locally
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This is a manual task you need to do in the terminal.
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This spins up Neo4j and Weaviate containers and is configuring Weaviate to use embeddings from Hugging Face's Sentence Transformers using the "all-MiniLM-L6-v2" model, which has 384 dimensions.
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```bash
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docker compose -f tests/e2e/docker-compose.yml up
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```
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### Write data (once)
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Run this from the project root to write data to both dbs.
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```
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uv run python -m tests/e2e/weaviate_e2e/populate_dbs.py
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```
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### Install Weaviate client
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You need to install the `weaviate-client` package to use this retriever.
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```bash
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pip install weaviate-client
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```
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### Search
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```
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# search by vector
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uv run python -m examples.customize.retrievers.external.weaviate.vector_search
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# search by text, with embeddings generated locally (via embedder argument)
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uv run python -m examples.customize.retrievers.external.weaviate.text_search_local_embedder
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# search by text, with embeddings generated on the Weaviate side, via configured vectorizer
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uv run python -m examples.customize.retrievers.external.weaviate.text_search_remote_embedder
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```
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