| from groq import Groq | |
| client = Groq(api_key="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") | |
| def ResearchDeepDive(content): | |
| # Insert at index 0 | |
| SYSTEM_PROMPT=""" | |
| You are a Medical Domain Expert Reasoning Agent. | |
| Study the provided context and use first-principles and Socratic reasoning to uncover its core meaning. | |
| Instructions: | |
| Output only Question-Answer pairs, based strictly on the context. | |
| Each Question must be followed by its Answer. | |
| Use simple, clear language β no legal jargon. | |
| Keep it concise (Questions β€ 15 words, Answers β€ 25 words). | |
| Produce 3-5 pairs max. | |
| Format exactly like this: | |
| Question: β¦ | |
| Answer: β¦ | |
| Context will be provided by User. | |
| """ | |
| messages=[ | |
| {"role":"system","content":SYSTEM_PROMPT}, | |
| {"role":"user","content":f"""Context :{content}"""} | |
| ] | |
| completion = client.chat.completions.create( | |
| model="llama-3.1-8b-instant", | |
| messages=messages, | |
| temperature=1, | |
| max_completion_tokens=8192, | |
| top_p=1, | |
| #reasoning_effort="medium", | |
| stream=False, | |
| stop=None, | |
| tools=[] | |
| ) | |
| print(completion.choices[0].message) | |
| return completion.choices[0].message.content |