Upload llm.py

modules/llm.py

@@ -50,25 +50,4 @@ class GroqCompletion:
             result += chunk.choices[0].delta.content or ""
 
         return result
-    
-
-
-# # Example usage
-# api_key = os.environ.get("GROQ_API_KEY")
-# groq_client = GroqClient(api_key)
-
-# model = "gemma2-9b-it"
-# domain = "LLM"
-# prompt_template = "Summarize me this content in just one line"
-# user_content = """1. **Domain Adaptation and Inference**: He developed a novel semantic encoding and decoding (SEDO) algorithm that uses knowledge graphs to generate semantic labels for unlabeled data. He applied this algorithm to detect suicide risk on social media.
-# 2. **Weighted Constraints Conditioned on Time-Evolving Events**: He developed a semi-deep infusion-based framework that integrates real-world knowledge as weighted constraints conditioned upon time-evolving events. He applied this framework to estimate the rise in infection rate during a crisis event.      
-# 3. **Matching and Ranking**: He developed a semi-deep K-IL system that models a patient's trust of GPs using knowledge of consultation history and ICD-10 graphs. He also applied this system to recommend patients to GPs."""
-# temperature = 0
-# max_tokens = 8192
-# top_p = 1
-# stream = True
-# stop = None
-
-# groq_completion = GroqCompletion(groq_client, model, domain, prompt_template, user_content, temperature, max_tokens, top_p, stream, stop)
-# result = groq_completion.create_completion()
-# print(result)
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