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@@ -19,12 +19,6 @@ from embedchain.chunkers.qna_pair import QnaPairChunker
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from embedchain.chunkers.text import TextChunker
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from embedchain.chunkers.text import TextChunker
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from embedchain.vectordb.chroma_db import ChromaDB
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from embedchain.vectordb.chroma_db import ChromaDB
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-openai_ef = embedding_functions.OpenAIEmbeddingFunction(
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- api_key=os.getenv("OPENAI_API_KEY"),
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- organization_id=os.getenv("OPENAI_ORGANIZATION"),
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- model_name="text-embedding-ada-002"
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-)
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-sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
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gpt4all_model = None
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gpt4all_model = None
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@@ -238,7 +232,11 @@ class App(EmbedChain):
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def __int__(self, db=None, ef=None):
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def __int__(self, db=None, ef=None):
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if ef is None:
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if ef is None:
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- ef = openai_ef
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+ ef = embedding_functions.OpenAIEmbeddingFunction(
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+ api_key=os.getenv("OPENAI_API_KEY"),
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+ organization_id=os.getenv("OPENAI_ORGANIZATION"),
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+ model_name="text-embedding-ada-002"
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+ )
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super().__init__(db, ef)
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super().__init__(db, ef)
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def get_llm_model_answer(self, prompt):
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def get_llm_model_answer(self, prompt):
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@@ -270,7 +268,9 @@ class OpenSourceApp(EmbedChain):
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def __init__(self, db=None, ef=None):
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def __init__(self, db=None, ef=None):
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print("Loading open source embedding model. This may take some time...")
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print("Loading open source embedding model. This may take some time...")
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if ef is None:
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if ef is None:
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- ef = sentence_transformer_ef
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+ ef = embedding_functions.SentenceTransformerEmbeddingFunction(
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+ model_name="all-MiniLM-L6-v2"
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+ )
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print("Successfully loaded open source embedding model.")
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print("Successfully loaded open source embedding model.")
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super().__init__(db, ef)
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super().__init__(db, ef)
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