pinecone_serverless_index_upsert_and_similarity_search_quickstart.py

1import os
2from pinecone import Pinecone, ServerlessSpec
3
4# Initialize Pinecone client
5pc = Pinecone(api_key="YOUR_API_KEY")
6
7# Create a serverless index
8index_name = "docs-quickstart-index"
9
10if index_name not in pc.list_indexes().names():
11    pc.create_index(
12        name=index_name,
13        dimension=2, # Replace with your model dimensions
14        metric="cosine", # Replace with your model metric
15        spec=ServerlessSpec(
16            cloud="aws",
17            region="us-east-1"
18        ) 
19    )
20
21# Target the index
22index = pc.Index(index_name)
23
24# Prepare sample data (id, vector, metadata)
25vectors = [
26    {"id": "vec1", "values": [0.1, 0.1], "metadata": {"genre": "drama"}},
27    {"id": "vec2", "values": [0.2, 0.2], "metadata": {"genre": "action"}},
28    {"id": "vec3", "values": [0.3, 0.3], "metadata": {"genre": "drama"}},
29    {"id": "vec4", "values": [0.4, 0.4], "metadata": {"genre": "action"}}
30]
31
32# Upsert vectors
33index.upsert(vectors=vectors, namespace="ns1")
34
35# Check index statistics
36print(index.describe_index_stats())
37
38# Perform a similarity search
39query_results = index.query(
40    namespace="ns1",
41    vector=[0.3, 0.3],
42    top_k=2,
43    include_values=True,
44    include_metadata=True
45)
46
47print(query_results)