feat: add Ollama text vectorizer

[s-agbede]

Summary

Adds OllamaTextVectorizer support for local Ollama embedding models.

This PR includes Ollama optional dependency wiring, a sync/async OllamaTextVectorizer implementation, vectorizer registry/from-dict support for type: "ollama", mocked unit tests, and opt-in real Ollama integration tests guarded by REDISVL_TEST_OLLAMA=1.

Usage

from redisvl.utils.vectorize import OllamaTextVectorizer

vectorizer = OllamaTextVectorizer(model="nomic-embed-text")
embedding = vectorizer.embed("hello world")

To run the real Ollama integration tests locally:

ollama pull nomic-embed-text
REDISVL_TEST_OLLAMA=1 uv run pytest tests/integration/test_ollama_vectorizer_integration.py

Testing:

uv run pytest tests/unit/test_ollama_vectorizer.py tests/integration/test_ollama_vectorizer_integration.py -q
REDISVL_TEST_OLLAMA=1 uv run pytest tests/integration/test_ollama_vectorizer_integration.py -q
uv run black --check ./redisvl ./tests
uv run isort ./redisvl ./tests --check-only --profile black
uv lock --check
uv run mypy redisvl
make test

Full repo test result:

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Note

Medium Risk
Adds a new embedding provider integration that makes network calls to a locally running Ollama daemon and introduces a new optional dependency; main risk is runtime failures/misconfiguration (host/model availability) rather than changes to existing providers.

Overview
Adds Ollama embedding support via a new OllamaTextVectorizer with sync/async embed/embed_many implementations, retries, and automatic dimension detection against the local Ollama embedding API.

Wires ollama into the public vectorizer surface (Vectorizers.ollama, vectorizer_from_dict, and __all__) and introduces an ollama optional extra (also included in all) plus lockfile updates. Adds mocked unit tests and opt-in integration tests gated by REDISVL_TEST_OLLAMA=1.

^{Reviewed by Cursor Bugbot for commit 33127cd. Bugbot is set up for automated code reviews on this repo. Configure here.}

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[nkanu17]

Yess!! Let's go! Ollama finally

[cursor]

Cursor Bugbot has reviewed your changes and found 1 potential issue.

^{Reviewed by Cursor Bugbot for commit e70f8fa. Configure here.}

[Copilot]

Pull request overview

Adds an Ollama-backed text embedding provider to RedisVL’s vectorizer system, enabling local embedding generation via an Ollama daemon while fitting into the existing vectorizer registry/from-dict plumbing and test suite.

Changes:

• Introduces OllamaTextVectorizer with sync/async embedding APIs and automatic dimension discovery.
• Wires type: "ollama" into the Vectorizers enum and vectorizer_from_dict, and exports the new vectorizer from redisvl.utils.vectorize.
• Adds an ollama optional extra (also included in all), updates uv.lock, and adds mocked unit tests plus opt-in integration tests gated by REDISVL_TEST_OLLAMA=1.

Reviewed changes

Copilot reviewed 6 out of 7 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
redisvl/utils/vectorize/text/ollama.py	New Ollama vectorizer implementation (sync/async) and model-dimension initialization.
redisvl/utils/vectorize/base.py	Adds ollama to the Vectorizers enum.
redisvl/utils/vectorize/__init__.py	Exposes OllamaTextVectorizer and adds type: "ollama" support in vectorizer_from_dict.
pyproject.toml	Adds ollama optional extra and includes it in all.
uv.lock	Locks the new ollama dependency and updates extras metadata.
tests/unit/test_ollama_vectorizer.py	Mocked unit tests for init, sync/async embedding, and registry/from-dict integration.
tests/integration/test_ollama_vectorizer_integration.py	Opt-in real Ollama integration tests gated by env var.

Comments suppressed due to low confidence (1)

redisvl/utils/vectorize/text/ollama.py:298

• Same validation issue as _embed_many: the error message iterates contents to compute element types even when contents is not a list. Passing a non-iterable will raise during message formatting and obscure the actual input-type error; guard before iterating or simplify the message for non-list inputs.

        if not isinstance(contents, list) or not all(
            isinstance(c, str) for c in contents
        ):
            raise TypeError(
                f"Input contents must be a list of strings to embed, got {type(contents)} with elements of types {[type(c) for c in contents]}"

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