Create research_deepdive_agent.py

components/research_deepdive_agent.py

@@ -0,0 +1,54 @@
+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 — 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,
+        #reasoning_effort="medium",
+        stream=False,
+        stop=None,
+        tools=[]
+    )
+
+    print(completion.choices[0].message)
+
+    return completion.choices[0].message.content