| from groq import Groq |
| client = Groq(api_key="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") |
|
|
|
|
| def ResearchDeepDive(content): |
| |
|
|
| 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 — no legal jargon. |
| |
| Keep it concise (Questions ≤ 15 words, Answers ≤ 25 words). |
| |
| Produce 3-5 pairs max. |
| |
| Format exactly like this: |
| |
| Question: … |
| Answer: … |
| |
| 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, |
| |
| stream=False, |
| stop=None, |
| tools=[] |
| ) |
|
|
| print(completion.choices[0].message) |
|
|
| return completion.choices[0].message.content |