65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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import argparse
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def generate_prompt(question, prompt_file="prompt.md", metadata_file="metadata.sql"):
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with open(prompt_file, "r") as f:
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prompt = f.read()
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with open(metadata_file, "r") as f:
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table_metadata_string = f.read()
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prompt = prompt.format(
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user_question=question, table_metadata_string=table_metadata_string
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)
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return prompt
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def get_tokenizer_model(model_name):
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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trust_remote_code=True,
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torch_dtype=torch.float16,
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device_map="auto",
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use_cache=True,
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)
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return tokenizer, model
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def run_inference(question, prompt_file="prompt.md", metadata_file="metadata.sql"):
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tokenizer, model = get_tokenizer_model("defog/sqlcoder")
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prompt = generate_prompt(question, prompt_file, metadata_file)
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# make sure the model stops generating at triple ticks
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eos_token_id = tokenizer.convert_tokens_to_ids(["```"])[0]
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pipe = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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max_new_tokens=300,
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do_sample=False,
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num_beams=5, # do beam search with 5 beams for high quality results
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)
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generated_query = (
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pipe(
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prompt,
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num_return_sequences=1,
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eos_token_id=eos_token_id,
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pad_token_id=eos_token_id,
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)[0]["generated_text"]
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.split("```sql")[-1]
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.split("```")[0]
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.split(";")[0]
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.strip()
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+ ";"
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)
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return generated_query
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if __name__ == "__main__":
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# Parse arguments
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parser = argparse.ArgumentParser(description="Run inference on a question")
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parser.add_argument("-q","--question", type=str, help="Question to run inference on")
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args = parser.parse_args()
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question = args.question
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print("Loading a model and generating a SQL query for answering your question...")
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print(run_inference(question))
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