Chroma is a powerful vector database that can be used to store and query large-scale datasets represented as numerical vectors. In this comprehensive guide, we will explore how to create a Chroma vector database and query it using documents as input.<\/p>\n\n\n\n
Prerequisites<\/strong><\/p>\n\n\n\n Import Necessary Libraries:<\/strong><\/p>\n\n\n\n Python<\/p>\n\n\n\n import chromadb Create a Chroma Client:<\/strong><\/p>\n\n\n\n Python<\/p>\n\n\n\n client = chromadb.Client()<\/p>\n\n\n\n Create a Collection:<\/strong><\/p>\n\n\n\n Python<\/p>\n\n\n\n collection = client.create_collection( Prepare Documents:<\/strong> Load your documents into a list of strings.<\/p>\n\n\n\n Generate Embeddings:<\/strong> Use the embedding model to generate embeddings for each document.<\/p>\n\n\n\n Add Documents to the Collection:<\/strong><\/p>\n\n\n\n Python<\/p>\n\n\n\n collection.add( Create a Query:<\/strong><\/p>\n\n\n\n Python<\/p>\n\n\n\n query_text = “What is the capital of France?”<\/p>\n\n\n\n Generate Query Embedding:<\/strong><\/p>\n\n\n\n Python<\/p>\n\n\n\n query_embedding = embedding_model.encode([query_text])<\/p>\n\n\n\n Perform Query:<\/strong><\/p>\n\n\n\n Python<\/p>\n\n\n\n results = collection.query( Python<\/p>\n\n\n\n Chroma is a powerful vector database that can be used to store and query large-scale datasets represented as numerical vectors. In this comprehensive guide, we will explore how to create a Chroma vector database and query it using documents as input. Prerequisites Creating a Chroma Collection Import Necessary Libraries: Python import chromadbfrom sentence_transformers import SentenceTransformer…<\/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-136158","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\n\n
pip install chromadb<\/code><\/li>\n\n\n\nCreating a Chroma Collection<\/strong><\/h2>\n\n\n\n
from sentence_transformers import SentenceTransformer<\/p>\n\n\n\n
name=”my_collection”,
embedding_function=SentenceTransformer(“all-MiniLM-L6-v2”)
)<\/p>\n\n\n\nIngesting Documents<\/strong><\/h2>\n\n\n\n
documents=documents,
embeddings=embeddings
)<\/p>\n\n\n\nQuerying the Collection<\/strong><\/h2>\n\n\n\n
query_embeddings=query_embedding,
n_results=5
)<\/p>\n\n\n\nAccessing Results<\/strong><\/h2>\n\n\n\n
for result in results[\"matches\"]:\n print(result[\"document\"])\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"