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