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README.md
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README.md
@ -3,9 +3,20 @@ A Open Database-GPT Experiment
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DB-GPT is an experimental open-source application that builds upon the fastchat model and uses vicuna as its base model. Additionally, it looks like this application incorporates langchain and llama-index embedding knowledge to improve Database-QA capabilities.
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DB-GPT is an experimental open-source application, which based on the vicuna base model.
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Overall, it appears to be a sophisticated and innovative tool for working with databases. If you have any specific questions about how to use or implement DB-GPT in your work, please let me know and I'll do my best to assist you.
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# Install
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1. Run model server
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```
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cd pilot/server
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uvicorn icuna_server:app --host 0.0.0.0
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```
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## Featurs
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2. Run gradio webui
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Coming soon, please wait...
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# Featurs
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- SQL-Generate
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- Database-QA Based Knowledge
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- SQL-diagnosis
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@ -18,7 +18,7 @@ llm_model_config = {
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LLM_MODEL = "vicuna-13b"
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LLM_MODEL = "vicuna-13b"
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vicuna_model_server = "http://192.168.31.114:21000/"
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vicuna_model_server = "http://127.0.0.1:8000/"
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# Load model config
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# Load model config
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@ -10,7 +10,8 @@ def generate_output(model, tokenizer, params, device, context_len=2048):
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max_new_tokens = int(params.get("max_new_tokens", 256))
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max_new_tokens = int(params.get("max_new_tokens", 256))
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stop_parameter = params.get("stop", None)
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stop_parameter = params.get("stop", None)
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if stop_parameter == tokenizer.eso_token:
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print(tokenizer.__dir__())
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if stop_parameter == tokenizer.eos_token:
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stop_parameter = None
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stop_parameter = None
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stop_strings = []
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stop_strings = []
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@ -18,7 +18,7 @@ class PromptRequest(BaseModel):
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prompt: str
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prompt: str
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temperature: float
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temperature: float
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max_new_tokens: int
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max_new_tokens: int
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stop: Optional(List[str]) = None
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stop: Optional[List[str]] = None
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class EmbeddingRequest(BaseModel):
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class EmbeddingRequest(BaseModel):
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