Pinecone

Pinecone is a hosted vector database. It does not call the exchange. Your code gets the vectors from the exchange and writes them to a Pinecone index.

Before you start

• A workspace with a balance and a spend key. See API keys.
• A Pinecone account and API key.
• A model name from GET /v1/models?kind=embedding. See Embeddings.

1. Export the keys

export LIQUID_API_KEY=sk-liquid-...
export PINECONE_API_KEY=...

2. Install the packages

# Python 3.9 or later
pip install openai pinecone

3. Store and search

Create the index with the vector length of the model. text-embedding-3-small returns 1,536 numbers. Do not use a Pinecone integrated model: that index embeds the text itself and does not take your vectors.

import os

from openai import OpenAI
from pinecone import Pinecone, ServerlessSpec

MODEL = "text-embedding-3-small"
openai = OpenAI(
    base_url="https://router.inference.ai.exchange/v1",
    api_key=os.environ["LIQUID_API_KEY"],
)
pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])

def embed(texts):
    result = openai.embeddings.create(model=MODEL, input=texts)
    return [item.embedding for item in result.data]

if not pc.has_index("documents"):
    pc.create_index(
        name="documents",
        dimension=1536,
        metric="cosine",
        spec=ServerlessSpec(cloud="aws", region="us-east-1"),
    )
index = pc.Index("documents")

texts = ["a cat on a bicycle", "a dog in a car"]
index.upsert(
    vectors=[
        {"id": f"doc-{i}", "values": vector, "metadata": {"body": text}}
        for i, (text, vector) in enumerate(zip(texts, embed(texts)))
    ]
)

result = index.query(
    vector=embed(["an animal on two wheels"])[0],
    top_k=3,
    include_metadata=True,
)
for match in result.matches:
    print(f"{match.score:.3f}  {match.metadata['body']}")

A new write can take a few seconds to appear in a query. Use the same model for the stored texts and for the query.

Notes

• Send the texts in batches of up to 2,048 to the exchange. Pinecone limits the size of an upsert request, so write the vectors in smaller batches, for example 100 at a time.
• Keep one model for one index. A change of model needs a new index and a new embed of every record. See Embeddings.
• The exchange sets a cap on each request. It is in the x-liquid-cap-usd response header. To limit total spend, set a spending limit on the key. See API keys.

Troubleshooting

See Errors for the error codes.

Next steps

• Embeddings: the request, the price and the limits.
• Qdrant and pgvector: other places to keep the vectors.