Ollama embeddings

Generate embeddings via the ollama python library. More details:

• Ollama docs on embeddings
• Ollama blog on embeddings

Parameter	Type	Default Value	Description
name	str	nomic-embed-text	The name of the model.
host	str	http://localhost:11434	The Ollama host to connect to.
options	ollama.Options or dict	None	Additional model parameters listed in the documentation for the Modelfile such as temperature.
keep_alive	float or str	"5m"	Controls how long the model will stay loaded into memory following the request.
ollama_client_kwargs	dict	{}	kwargs that can be past to the ollama.Client.

import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry

db = lancedb.connect("/tmp/db")
func = get_registry().get("ollama").create(name="nomic-embed-text")

class Words(LanceModel):
    text: str = func.SourceField()
    vector: Vector(func.ndims()) = func.VectorField()

table = db.create_table("words", schema=Words, mode="overwrite")
table.add([
    {"text": "hello world"},
    {"text": "goodbye world"}
])

query = "greetings"
actual = table.search(query).limit(1).to_pydantic(Words)[0]
print(actual.text)