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https://github.com/open-webui/open-webui
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refac: embeddings function
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parent
b38e2fab32
commit
451f1bae15
@ -272,26 +272,26 @@ def get_embedding_function(
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return lambda query: embedding_function.encode(query).tolist()
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elif embedding_engine in ["ollama", "openai"]:
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if embedding_engine == "ollama":
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func = lambda query: generate_ollama_embeddings(
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func = lambda query: generate_embeddings(
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model=embedding_model,
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input=query,
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text=query,
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)
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elif embedding_engine == "openai":
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func = lambda query: generate_openai_embeddings(
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func = lambda query: generate_embeddings(
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model=embedding_model,
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text=query,
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key=openai_key,
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url=openai_url,
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)
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def generate_multiple(query, f):
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def generate_multiple(query, func):
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if isinstance(query, list):
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embeddings = []
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for i in range(0, len(query), embedding_batch_size):
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embeddings.extend(f(query[i : i + embedding_batch_size]))
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embeddings.extend(func(query[i : i + embedding_batch_size]))
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return embeddings
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else:
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return f(query)
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return func(query)
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return lambda query: generate_multiple(query, func)
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@ -438,20 +438,6 @@ def get_model_path(model: str, update_model: bool = False):
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return model
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def generate_openai_embeddings(
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model: str,
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text: Union[str, list[str]],
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key: str,
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url: str = "https://api.openai.com/v1",
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):
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if isinstance(text, list):
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embeddings = generate_openai_batch_embeddings(model, text, key, url)
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else:
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embeddings = generate_openai_batch_embeddings(model, [text], key, url)
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return embeddings[0] if isinstance(text, str) else embeddings
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def generate_openai_batch_embeddings(
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model: str, texts: list[str], key: str, url: str = "https://api.openai.com/v1"
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) -> Optional[list[list[float]]]:
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@ -475,19 +461,31 @@ def generate_openai_batch_embeddings(
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return None
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def generate_ollama_embeddings(
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model: str, input: list[str]
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) -> Optional[list[list[float]]]:
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if isinstance(input, list):
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embeddings = generate_ollama_batch_embeddings(
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GenerateEmbedForm(**{"model": model, "input": input})
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)
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else:
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embeddings = generate_ollama_batch_embeddings(
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GenerateEmbedForm(**{"model": model, "input": [input]})
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def generate_embeddings(engine: str, model: str, text: Union[str, list[str]], **kwargs):
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if engine == "ollama":
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if isinstance(text, list):
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embeddings = generate_ollama_batch_embeddings(
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GenerateEmbedForm(**{"model": model, "input": text})
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)
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else:
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embeddings = generate_ollama_batch_embeddings(
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GenerateEmbedForm(**{"model": model, "input": [text]})
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)
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return (
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embeddings["embeddings"][0]
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if isinstance(text, str)
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else embeddings["embeddings"]
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)
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elif engine == "openai":
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key = kwargs.get("key", "")
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url = kwargs.get("url", "https://api.openai.com/v1")
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return embeddings["embeddings"]
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if isinstance(text, list):
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embeddings = generate_openai_batch_embeddings(model, text, key, url)
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else:
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embeddings = generate_openai_batch_embeddings(model, [text], key, url)
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return embeddings[0] if isinstance(text, str) else embeddings
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import operator
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