31 lines
879 B
Python
31 lines
879 B
Python
import ollama
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from mem0.embeddings.base import EmbeddingBase
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class OllamaEmbedding(EmbeddingBase):
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def __init__(self, model="nomic-embed-text"):
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self.model = model
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self._ensure_model_exists()
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self.dims = 512
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def _ensure_model_exists(self):
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"""
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Ensure the specified model exists locally. If not, pull it from Ollama.
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"""
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model_list = [m["name"] for m in ollama.list()["models"]]
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if not any(m.startswith(self.model) for m in model_list):
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ollama.pull(self.model)
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def embed(self, text):
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"""
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Get the embedding for the given text using Ollama.
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Args:
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text (str): The text to embed.
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Returns:
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list: The embedding vector.
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"""
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response = ollama.embeddings(model=self.model, prompt=text)
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return response["embedding"]
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