Qdrant

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

Before you start

A workspace with a balance and a spend key. See API keys.
A Qdrant server or a Qdrant Cloud cluster, with its URL 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 QDRANT_URL=http://localhost:6333
export QDRANT_API_KEY=...   # only for a cluster that needs one

2. Install the packages

# Python 3.9 or later
pip install openai qdrant-client

3. Store and search

The size of the collection must equal the vector length of the model. text-embedding-3-small returns 1,536 numbers.

import os

from openai import OpenAI
from qdrant_client import QdrantClient, models

MODEL = "text-embedding-3-small"
openai = OpenAI(
    base_url="https://router.inference.ai.exchange/v1",
    api_key=os.environ["LIQUID_API_KEY"],
)
qdrant = QdrantClient(
    url=os.environ["QDRANT_URL"],
    api_key=os.environ.get("QDRANT_API_KEY"),
)

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

qdrant.create_collection(
    collection_name="documents",
    vectors_config=models.VectorParams(size=1536, distance=models.Distance.COSINE),
)

texts = ["a cat on a bicycle", "a dog in a car"]
qdrant.upsert(
    collection_name="documents",
    points=[
        models.PointStruct(id=i, vector=vector, payload={"body": text})
        for i, (text, vector) in enumerate(zip(texts, embed(texts)))
    ],
)

hits = qdrant.query_points(
    collection_name="documents",
    query=embed(["an animal on two wheels"])[0],
    limit=3,
).points
for hit in hits:
    print(f"{hit.score:.3f}  {hit.payload['body']}")

Use the same model for the stored texts and for the query.

Notes

Send the texts in batches of up to 2,048. One request is one auction and has one cap.
Keep one model for one collection. A change of model needs a new collection and a new embed of every point. See Embeddings.
create_collection fails if the collection exists. Create it one time, or check with collection_exists.
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

Symptom	Cause and fix
`Vector dimension error`	The collection size differs from the model. Create the collection with the model's length.
`401` from the exchange	The key is wrong, revoked or expired. Export it again.
`model_not_found`	The model is not on the book. Use an exact name from `GET /v1/models`.
A request is refused for lack of funds.	The workspace balance cannot cover the cap. Add funds on the Credits page.

See Errors for the error codes.

Next steps

Embeddings: the request, the price and the limits.
LlamaIndex: build an index over your documents.
pgvector: keep the vectors in Postgres instead.