56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
import subprocess
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import sys
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from typing import Optional
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from mem0.configs.embeddings.base import BaseEmbedderConfig
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from mem0.embeddings.base import EmbeddingBase
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try:
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from ollama import Client
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except ImportError:
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user_input = input("The 'ollama' library is required. Install it now? [y/N]: ")
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if user_input.lower() == "y":
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try:
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subprocess.check_call([sys.executable, "-m", "pip", "install", "ollama"])
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from ollama import Client
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except subprocess.CalledProcessError:
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print("Failed to install 'ollama'. Please install it manually using 'pip install ollama'.")
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sys.exit(1)
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else:
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print("The required 'ollama' library is not installed.")
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sys.exit(1)
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class OllamaEmbedding(EmbeddingBase):
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def __init__(self, config: Optional[BaseEmbedderConfig] = None):
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super().__init__(config)
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if not self.config.model:
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self.config.model = "nomic-embed-text"
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if not self.config.embedding_dims:
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self.config.embedding_dims = 512
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self.client = Client(host=self.config.ollama_base_url)
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self._ensure_model_exists()
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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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local_models = self.client.list()["models"]
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if not any(model.get("name") == self.config.model for model in local_models):
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self.client.pull(self.config.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 = self.client.embeddings(model=self.config.model, prompt=text)
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return response["embedding"]
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