47 lines
1.9 KiB
Python
47 lines
1.9 KiB
Python
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from app.core.config import settings
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from langchain_openai import OpenAIEmbeddings
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from langchain_ollama import OllamaEmbeddings
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# If you plan on adding other embeddings, import them here
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# from some_other_module import AnotherEmbeddingClass
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class EmbeddingsFactory:
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@staticmethod
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def create():
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"""
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Factory method to create an embeddings instance based on .env config.
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"""
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# Suppose your .env has a value like EMBEDDINGS_PROVIDER=openai
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embeddings_provider = settings.EMBEDDINGS_PROVIDER.lower()
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if embeddings_provider == "openai":
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return OpenAIEmbeddings(
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openai_api_key=settings.OPENAI_API_KEY,
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openai_api_base=settings.OPENAI_API_BASE,
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model=settings.OPENAI_EMBEDDINGS_MODEL
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)
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elif embeddings_provider == "dashscope":
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return OpenAIEmbeddings(
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openai_api_key=settings.DASH_SCOPE_API_KEY,
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openai_api_base=settings.DASH_SCOPE_API_BASE,
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model=settings.DASH_SCOPE_EMBEDDINGS_MODEL,
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# DashScope OpenAI-compatible embedding expects string input,
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# while LangChain's len-safe path may send token ids.
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check_embedding_ctx_length=False,
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tiktoken_enabled=False,
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skip_empty=True,
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# DashScope embedding API supports at most 10 inputs per batch.
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chunk_size=10,
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)
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elif embeddings_provider == "ollama":
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return OllamaEmbeddings(
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model=settings.OLLAMA_EMBEDDINGS_MODEL,
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base_url=settings.OLLAMA_API_BASE
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)
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# Extend with other providers:
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# elif embeddings_provider == "another_provider":
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# return AnotherEmbeddingClass(...)
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else:
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raise ValueError(f"Unsupported embeddings provider: {embeddings_provider}")
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