Ollama Embedding Model

Generate embeddings via the ollama python library. More details:

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)