Once you have a collection of documents stored in a Chroma database, you can effectively retrieve relevant chunks of text based on user queries. This process involves generating embeddings for both the query and the documents, performing similarity search, and extracting the most relevant chunks.<\/p>\n\n\n\n
Prerequisites<\/strong><\/p>\n\n\n\n Example<\/strong><\/p>\n\n\n\n Python<\/p>\n\n\n\n By following these steps, you can effectively retrieve relevant chunks of text from your Chroma database based on user queries. This technique can be used for tasks such as question answering, summarization, and information retrieval.<\/p>\n","protected":false},"excerpt":{"rendered":" Once you have a collection of documents stored in a Chroma database, you can effectively retrieve relevant chunks of text based on user queries. This process involves generating embeddings for both the query and the documents, performing similarity search, and extracting the most relevant chunks. Prerequisites Query Processing Extracting Relevant Chunks Example Python By following…<\/p>\n","protected":false},"author":16,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-136186","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\n\n
Query Processing<\/strong><\/h2>\n\n\n\n
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Extracting Relevant Chunks<\/strong><\/h2>\n\n\n\n
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import chromadb\nfrom sentence_transformers import SentenceTransformer\n\n# ... (code to create a Chroma collection and add documents)\n\nquery_text = \"What is the capital of France?\"\nquery_embedding = embedding_model.encode([query_text])\nresults = collection.query(\n query_embeddings=query_embedding,\n n_results=5\n)\n\nfor result in results[\"matches\"]:\n document = result[\"document\"]\n # Tokenize the document into chunks\n chunks = document.split(\".\")\n for chunk in chunks:\n # Generate embedding for the chunk\n chunk_embedding = embedding_model.encode([chunk])\n # Calculate similarity\n similarity = 1 - cosine(query_embedding[0], chunk_embedding[0])\n if similarity > 0.8:\n print(chunk)\n<\/code><\/pre>\n\n\n\n