> ## 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. # text-embedding-ada-002 > Use the text-embedding-ada-002 embedding or reranking model with Pinecone: specs and index setup. Legacy embedding model from OpenAI, great for general. export const ModelPageTabs = ({inferenceEnabled}) => { const showGuides = () => { document.querySelector('.model-page-guides').style.display = 'flex'; document.querySelector('.model-page-playground').style.display = 'none'; document.querySelector('.guides').classList.add('active'); document.querySelector('.playground').classList.remove('active'); }; const showPlayground = () => { document.querySelector('.model-page-guides').style.display = 'none'; document.querySelector('.model-page-playground').style.display = 'block'; document.querySelector('.guides').classList.remove('active'); document.querySelector('.playground').classList.add('active'); }; return
{false && }
; }; export const ModelPageCard = ({image, title, company, inferenceEnabled, metric, dimension, seq, task, price}) => { return
{image && image !== "null" &&
}

{title} {company !== "null" && "| " + company}

{inferenceEnabled === "true" &&
Hosted
}
{!!metric && metric.length > 0 &&
METRIC

{metric.join(", ")}

} {!!dimension && dimension.length > 0 &&
DIMENSION

{dimension.join(", ")}

} {!!seq && seq.length > 0 &&
MAX INPUT TOKENS

{seq}

} {!!task && task.length > 0 &&
TASK

{task}

} {price && price.length > 0 &&
PRICE

${price}

}
; }; export const ModelPageHeader = () => { return
Back to all models
; };
### Overview Legacy embedding model from OpenAI, great for general purpose applications. ### Using the model #### Installation: ```python theme={null} !pip install -qU openai==1.2.2 ``` ### Create Index ```python theme={null} from pinecone import Pinecone, ServerlessSpec pc = Pinecone(api_key="API_KEY") # Create Index index_name = "text-embedding-ada-002" if not pc.has_index(index_name): pc.create_index( name=index_name, dimension=1536, metric="cosine", spec=ServerlessSpec( cloud='aws', region='us-east-1' ) ) index = pc.Index(index_name) ``` ### Embed & Upsert ```python theme={null} # Embed data 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."}, ] import openai openai.api_key = "OPENAI_API_KEY" def embed(docs: list[str]) -> list[list[float]]: res = openai.embeddings.create( input=docs, model="text-embedding-ada-002" ) doc_embeds = [r.embedding for r in res.data] return doc_embeds doc_embeds = embed([d["text"] for d in data]) vectors = [] for d, e in zip(data, doc_embeds): vectors.append({ "id": d['id'], "values": e, "metadata": {'text': d['text']} }) index.upsert( vectors=vectors, namespace="ns1" ) ### Query query = "Tell me about the tech company known as Apple" x = embed([query]) results = index.query( namespace="ns1", vector=x[0], top_k=3, include_values=False, include_metadata=True ) print(results) ```