Documentation Index
Fetch the complete documentation index at: https://docs.pinecone.io/llms.txt
Use this file to discover all available pages before exploring further.
Overview
nvidia/llama-text-embed-v2 is a state-of-the-art embedding model available natively in Pinecone Inference. Developed by NVIDIA Research, it is built on the Llama 3.2 1B architecture and optimized for high retrieval quality with low-latency inference. Also known as llama-3_2-nv-embedqa-1b-v2, the model distills techniques from NVIDIA’s industry-leading NV-2 (7B parameters) into an efficient, production-ready solution.
- Retrieval quality: The model surpasses OpenAI’s text-embedding-3-large across multiple benchmarks, in some cases improving accuracy by more than 20%
- Real-time queries: Predictable and consistent query speeds for responsive search with p99 latencies 12x faster than OpenAI Large
- Multilingual: Supports 26 languages, including English, Spanish, Chinese, Hindi, Japanese, Korean, French, and German
You can call the embed operation through Pinecone Inference to turn text into vectors without writing to an index. That differs from upsert_records on an index with integrated embedding, where each request embeds and stores records in one step. To see how embedding consumption appears in billing and usage reports, see Embedding tokens. Installation
pip install --upgrade pinecone
Create index
from pinecone import Pinecone
pc = Pinecone(api_key="YOUR_API_KEY")
# Create an index for dense vectors with integrated inference
index_name = "llama-text-embed-v2"
pc.create_index_for_model(
name=index_name,
cloud="aws",
region="us-east-1",
embed={
"model": "llama-text-embed-v2",
"field_map": {
"text": "text" # Map the record field to be embedded
}
}
)
index = pc.Index(index_name)
Embed & upsert
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
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
