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from langchain_community.embeddings.sentence_transformer import ( |
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SentenceTransformerEmbeddings, |
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) |
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from langchain_community.vectorstores import Chroma |
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embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") |
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db = Chroma(embedding_function=embedding_function, persist_directory="./chroma_db") |
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print("There are", db._collection.count(), " docs in the collection") |
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queries = [ |
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"Where is the Nowhere event?", |
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"Give me some information about the toilets.", |
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"What is consent?", |
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] |
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for query in queries: |
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docs = db.similarity_search(query) |
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print(f"\n\nQuery: {query}") |
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print(f"Results: {len(docs)}") |
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print(f"First result: {docs[0].page_content}") |
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print(f"Second result: {docs[1].page_content}") |
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