The guide to text-embedding-ada-002 model

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OpenAI / text-embedding-ada-002

OpenAI / text-embedding-ada-002

AI Model Milvus Integrated

Task: Embedding

Modality: Text

Similarity Metric: Any (Normalized)

License: Proprietary

Dimensions: 1536

Max Input Tokens: 8191

Price: $0.10 / 1M tokens

Introduction to text-embedding-ada-002

text-embedding-ada-002 is OpenAI's legacy text embedding model.

Comparing text-embedding-ada-002 with the other two newly released embedding models:

Model	Dimensions	Max Tokens	Model MIRACL avg	METB avg	Price
text-embedding-3-large	3072	8191	54.9	64.6	$0.13 / 1M tokens
text-embedding-ada-002	1536	8191	31.4	61.0	$0.10 / 1M tokens
text-embedding-3-small	1536	8191	44.0	62.3	$0.02 / 1M tokens

How to generate vector embeddings with text-embedding-ada-002

There are two primary ways to create vector embeddings:

PyMilvus: the Python SDK for Milvus that seamlessly integrates with the text-embedding-ada-002 model.
OpenAI Library: the Python SDK offered by OpenAI.

Once the vector embeddings are generated, they can be stored in Zilliz Cloud (a fully managed vector database service powered by Milvus) and used for semantic similarity search. Here are four key steps:

Sign up for a Zilliz Cloud account for free.
Set up a serverless cluster and obtain the Public Endpoint and API Key.
Create a vector collection and insert your vector embeddings.
Run a semantic search on the stored embeddings.

Generate vector embeddings via PyMilvus and insert them into Zilliz Cloud for semantic search

from pymilvus.model.dense import OpenAIEmbeddingFunction
from pymilvus import MilvusClient

OPENAI_API_KEY = "your-openai-api-key"
ef = OpenAIEmbeddingFunction("text-embedding-ada-002", api_key=OPENAI_API_KEY)

docs = [
   "Artificial intelligence was founded as an academic discipline in 1956.",
   "Alan Turing was the first person to conduct substantial research in AI.",
   "Born in Maida Vale, London, Turing was raised in southern England."
]

# Generate embeddings for documents
docs_embeddings = ef(docs)

queries = ["When was artificial intelligence founded",
          "Where was Alan Turing born?"]

# Generate embeddings for queries
query_embeddings = ef(queries)

# Connect to Zilliz Cloud with Public Endpoint and API Key
client = MilvusClient(
    uri=ZILLIZ_PUBLIC_ENDPOINT,
    token=ZILLIZ_API_KEY)

COLLECTION = "documents"
if client.has_collection(collection_name=COLLECTION):
    client.drop_collection(collection_name=COLLECTION)
client.create_collection(
    collection_name=COLLECTION,
    dimension=ef.dim,
    auto_id=True)

for doc, embedding in zip(docs, docs_embeddings):
    client.insert(COLLECTION, {"text": doc, "vector": embedding})
    
results = client.search(
    collection_name=COLLECTION,
    data=query_embeddings,
    consistency_level="Strong",
    output_fields=["text"])

For more information, refer to our PyMilvus Embedding Model documentation.

Generate vector embeddings via OpenAI’s Python SDK and insert them into Zilliz Cloud for semantic search

from openai import OpenAI
from pymilvus import MilvusClient

OPENAI_API_KEY = "your-openai-api-key"
client = OpenAI(api_key=OPENAI_API_KEY)
docs = [
   "Artificial intelligence was founded as an academic discipline in 1956.",
   "Alan Turing was the first person to conduct substantial research in AI.",
   "Born in Maida Vale, London, Turing was raised in southern England."
]

# Generate embeddings for documents
results = client.embeddings.create(input=docs, model="text-embedding-ada-002")
docs_embeddings = [data.embedding for data in results.data]

queries = ["When was artificial intelligence founded",
          "Where was Alan Turing born?"]

# Generate embeddings for queries
response = client.embeddings.create(input=queries, model="text-embedding-ada-002")
query_embeddings = [data.embedding for data in response.data]

# Connect to Zilliz Cloud with Public Endpoint and API Key
client = MilvusClient(
    uri=ZILLIZ_PUBLIC_ENDPOINT,
    token=ZILLIZ_API_KEY)

COLLECTION = "documents"
if client.has_collection(collection_name=COLLECTION):
    client.drop_collection(collection_name=COLLECTION)
client.create_collection(
    collection_name=COLLECTION,
    dimension=1536,
    auto_id=True)

for doc, embedding in zip(docs, docs_embeddings):
    client.insert(COLLECTION, {"text": doc, "vector": embedding})
    
results = client.search(
    collection_name=COLLECTION,
    data=query_embeddings,
    consistency_level="Strong",
    output_fields=["text"])