Initial commit from Create Llama
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from llama_index.embeddings.openai import OpenAIEmbedding
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from llama_index.core.settings import Settings
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from typing import Dict
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import os
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DEFAULT_MODEL = "gpt-3.5-turbo"
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DEFAULT_EMBEDDING_MODEL = "text-embedding-3-large"
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class TSIEmbedding(OpenAIEmbedding):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self._query_engine = self._text_engine = self.model_name
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def llm_config_from_env() -> Dict:
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from llama_index.core.constants import DEFAULT_TEMPERATURE
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model = os.getenv("MODEL", DEFAULT_MODEL)
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temperature = os.getenv("LLM_TEMPERATURE", DEFAULT_TEMPERATURE)
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max_tokens = os.getenv("LLM_MAX_TOKENS")
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api_key = os.getenv("T_SYSTEMS_LLMHUB_API_KEY")
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api_base = os.getenv("T_SYSTEMS_LLMHUB_BASE_URL")
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config = {
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"model": model,
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"api_key": api_key,
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"api_base": api_base,
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"temperature": float(temperature),
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"max_tokens": int(max_tokens) if max_tokens is not None else None,
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}
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return config
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def embedding_config_from_env() -> Dict:
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from llama_index.core.constants import DEFAULT_EMBEDDING_DIM
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model = os.getenv("EMBEDDING_MODEL", DEFAULT_EMBEDDING_MODEL)
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dimension = os.getenv("EMBEDDING_DIM", DEFAULT_EMBEDDING_DIM)
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api_key = os.getenv("T_SYSTEMS_LLMHUB_API_KEY")
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api_base = os.getenv("T_SYSTEMS_LLMHUB_BASE_URL")
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config = {
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"model_name": model,
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"dimension": int(dimension) if dimension is not None else None,
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"api_key": api_key,
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"api_base": api_base,
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}
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return config
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def init_llmhub():
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from llama_index.llms.openai_like import OpenAILike
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llm_configs = llm_config_from_env()
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embedding_configs = embedding_config_from_env()
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Settings.embed_model = TSIEmbedding(**embedding_configs)
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Settings.llm = OpenAILike(
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**llm_configs,
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is_chat_model=True,
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is_function_calling_model=False,
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context_window=4096,
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)
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