Ideal multilingual model for high performance while keeping with open source. Works well on messy data. Good for short queries expected to return medium-length passages of text (1-2 paragraphs).
from pinecone import Pineconepc = Pinecone(api_key="YOUR_API_KEY")# Create a dense index with integrated inferenceindex_name = "multilingual-e5-large"pc.create_index_for_model( name=index_name, cloud="aws", region="us-east-1", embed={ "model": "multilingual-e5-large", "field_map": { "text": "text" # Map the record field to be embedded } })index = pc.Index(index_name)
data = [ {"id": "vec1", "text": "Apple is a popular fruit known for its sweetness and crisp texture."}, {"id": "vec2", "text": "The tech company Apple is known for its innovative products like the iPhone."}, {"id": "vec3", "text": "Many people enjoy eating apples as a healthy snack."}, {"id": "vec4", "text": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."}, {"id": "vec5", "text": "An apple a day keeps the doctor away, as the saying goes."}, {"id": "vec6", "text": "Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership."}]index.upsert_records( namespace="example-namespace", records=data)
query_payload = { "inputs": { "text": "Tell me about the tech company known as Apple." }, "top_k": 3}results = index.search( namespace="example-namespace", query=query_payload)print(results)
Lorem Ipsum
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