65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
import importlib
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from mem0.configs.llms.base import BaseLlmConfig
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def load_class(class_type):
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module_path, class_name = class_type.rsplit(".", 1)
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module = importlib.import_module(module_path)
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return getattr(module, class_name)
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class LlmFactory:
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provider_to_class = {
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"ollama": "mem0.llms.ollama.py.OllamaLLM",
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"openai": "mem0.llms.openai.OpenAILLM",
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"groq": "mem0.llms.groq.GroqLLM",
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"together": "mem0.llms.together.TogetherLLM",
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"aws_bedrock": "mem0.llms.aws_bedrock.AWSBedrockLLM",
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"litellm": "mem0.llms.litellm.LiteLLM",
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"ollama": "mem0.llms.ollama.OllamaLLM",
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"azure_openai": "mem0.llms.azure_openai.AzureOpenAILLM",
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}
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@classmethod
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def create(cls, provider_name, config):
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class_type = cls.provider_to_class.get(provider_name)
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if class_type:
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llm_instance = load_class(class_type)
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base_config = BaseLlmConfig(**config)
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return llm_instance(base_config)
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else:
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raise ValueError(f"Unsupported Llm provider: {provider_name}")
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class EmbedderFactory:
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provider_to_class = {
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"openai": "mem0.embeddings.openai.OpenAIEmbedding",
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"ollama": "mem0.embeddings.ollama.OllamaEmbedding"
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}
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@classmethod
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def create(cls, provider_name):
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class_type = cls.provider_to_class.get(provider_name)
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if class_type:
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embedder_instance = load_class(class_type)()
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return embedder_instance
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else:
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raise ValueError(f"Unsupported Embedder provider: {provider_name}")
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class VectorStoreFactory:
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provider_to_class = {
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"qdrant": "mem0.vector_stores.qdrant.Qdrant",
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"chromadb": "mem0.vector_stores.chroma.ChromaDB",
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}
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@classmethod
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def create(cls, provider_name, config):
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class_type = cls.provider_to_class.get(provider_name)
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if class_type:
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if not isinstance(config, dict):
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config = config.model_dump()
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vector_store_instance = load_class(class_type)
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return vector_store_instance(**config)
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else:
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raise ValueError(f"Unsupported VectorStore provider: {provider_name}")
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