feat: add Ollama text vectorizer

docs/user_guide/04_vectorizers.ipynb

@@ -6,19 +6,19 @@
       "source": [
         "# Create Embeddings with Vectorizers\n",
         "\n",
-        "This guide demonstrates how to create embeddings using RedisVL's built-in text vectorizers. RedisVL supports multiple embedding providers: OpenAI, HuggingFace, Vertex AI, Cohere, Mistral AI, Amazon Bedrock, VoyageAI, and custom vectorizers.\n",
+        "This guide demonstrates how to create embeddings using RedisVL's built-in text vectorizers. RedisVL supports multiple embedding providers: OpenAI, HuggingFace, Ollama, Vertex AI, Cohere, Mistral AI, Amazon Bedrock, VoyageAI, and custom vectorizers.\n",
         "\n",
         "## Prerequisites\n",
         "\n",
         "Before you begin, ensure you have:\n",
         "- Installed RedisVL: `pip install redisvl`\n",
         "- A running Redis instance ([Redis 8+](https://redis.io/downloads/) or [Redis Cloud](https://redis.io/cloud))\n",
-        "- API keys for the embedding providers you plan to use\n",
+        "- API keys or local model servers for the embedding providers you plan to use\n",
         "\n",
         "## What You'll Learn\n",
         "\n",
         "By the end of this guide, you will be able to:\n",
-        "- Create embeddings using multiple providers (OpenAI, HuggingFace, Cohere, etc.)\n",
+        "- Create embeddings using multiple providers (OpenAI, HuggingFace, Ollama, Cohere, etc.)\n",
         "- Use synchronous and asynchronous embedding methods\n",
         "- Batch embed multiple texts efficiently\n",
         "- Build custom vectorizers for your own embedding functions\n",
@@ -290,6 +290,72 @@
         "embeddings = hf.embed_many(sentences, as_buffer=True)\n"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Ollama\n",
+        "\n",
+        "[Ollama](https://ollama.com/) lets you run embedding models locally. RedisVL supports Ollama through the `OllamaTextVectorizer`.\n",
+        "\n",
+        "Install the Python client and pull an embedding model before running this example:\n",
+        "\n",
+        "```bash\n",
+        "pip install 'redisvl[ollama]'\n",
+        "ollama pull nomic-embed-text\n",
+        "```\n",
+        "\n",
+        "Make sure the Ollama daemon is running with `ollama serve`. By default, the Ollama client uses `OLLAMA_HOST` if set, otherwise it connects to `http://localhost:11434`. To connect to a custom Ollama server explicitly, pass `host=\"http://your-host:11434\"` when creating the vectorizer."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from redisvl.utils.vectorize import OllamaTextVectorizer\n",
+        "\n",
+        "ollama_model = os.environ.get(\"OLLAMA_MODEL\", \"nomic-embed-text\")\n",
+        "\n",
+        "try:\n",
+        "    ollama = OllamaTextVectorizer(model=ollama_model)\n",
+        "\n",
+        "    test = ollama.embed(\"This is a test sentence.\")\n",
+        "    print(\"Vector dimensions:\", len(test))\n",
+        "    print(test[:10])\n",
+        "except (ImportError, ConnectionError, ValueError) as exc:\n",
+        "    print(\"Skipping Ollama example:\", exc)\n",
+        "    ollama = None\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "if ollama is not None:\n",
+        "    embeddings = ollama.embed_many(sentences, batch_size=2)\n",
+        "    print(\"Number of embeddings:\", len(embeddings))\n",
+        "    print(\"Vector dimensions:\", len(embeddings[0]))\n",
+        "else:\n",
+        "    print(\"Skipping: run the Ollama cell above with a running Ollama server and pulled model.\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "if ollama is not None:\n",
+        "    embeddings = await ollama.aembed_many(sentences, batch_size=2)\n",
+        "    print(\"Number of async embeddings:\", len(embeddings))\n",
+        "else:\n",
+        "    print(\"Skipping: run the Ollama cell above with a running Ollama server and pulled model.\")\n"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

pyproject.toml

@@ -73,6 +73,10 @@ all = [
     "urllib3<2.2.0",
     "pillow>=11.3.0",
     "sql-redis>=0.5.0",
+    "ollama>=0.5.4",
+]
+ollama = [
+    "ollama>=0.5.4",
 ]
 
 [project.urls]

redisvl/utils/vectorize/__init__.py

@@ -8,6 +8,7 @@
 from redisvl.utils.vectorize.text.custom import CustomTextVectorizer
 from redisvl.utils.vectorize.text.huggingface import HFTextVectorizer
 from redisvl.utils.vectorize.text.mistral import MistralAITextVectorizer
+from redisvl.utils.vectorize.text.ollama import OllamaTextVectorizer
 from redisvl.utils.vectorize.text.openai import OpenAITextVectorizer
 from redisvl.utils.vectorize.text.vertexai import VertexAITextVectorizer
 from redisvl.utils.vectorize.text.voyageai import VoyageAITextVectorizer
@@ -23,6 +24,7 @@
     "VertexAITextVectorizer",
     "AzureOpenAITextVectorizer",
     "MistralAITextVectorizer",
+    "OllamaTextVectorizer",
     "CustomVectorizer",
     "CustomTextVectorizer",
     "BedrockVectorizer",
@@ -55,6 +57,8 @@ def vectorizer_from_dict(
         return HFTextVectorizer(**args)
     elif vectorizer_type == Vectorizers.mistral:
         return MistralAITextVectorizer(**args)
+    elif vectorizer_type == Vectorizers.ollama:
+        return OllamaTextVectorizer(**args)
     elif vectorizer_type == Vectorizers.vertexai:
         return VertexAIVectorizer(**args)
     elif vectorizer_type == Vectorizers.voyageai:

redisvl/utils/vectorize/base.py

@@ -26,6 +26,7 @@ class Vectorizers(Enum):
     openai = "openai"
     cohere = "cohere"
     mistral = "mistral"
+    ollama = "ollama"
     vertexai = "vertexai"
     hf = "hf"
     voyageai = "voyageai"