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959c484 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | import os
from dotenv import load_dotenv
from groq import Groq
load_dotenv()
client = Groq(api_key=os.getenv("GROQ_API_KEY"))
MODEL = "llama-3.3-70b-versatile"
def ask_llm(retrieved_chunks, question, history=None):
if history is None:
history = []
if isinstance(retrieved_chunks, list):
context = "\n\n".join(
[r["chunk"] for r in retrieved_chunks if isinstance(r, dict) and "chunk" in r]
)
else:
context = str(retrieved_chunks)
system_prompt = f"""You are an Enterprise AI Knowledge Assistant.
Answer ONLY using the provided context.
If the answer is not found in the context, reply exactly:
"I couldn't find that information in the uploaded document."
Context:
{context}
"""
messages = [
{"role": "system", "content": system_prompt}
]
# Append conversation history (sanitizes messages to only include role and content for Groq API)
for msg in history:
if isinstance(msg, dict) and "role" in msg and "content" in msg:
messages.append({"role": str(msg["role"]), "content": str(msg["content"])})
elif isinstance(msg, (list, tuple)) and len(msg) == 2:
messages.append({"role": "user", "content": str(msg[0])})
messages.append({"role": "assistant", "content": str(msg[1])})
elif hasattr(msg, "role") and hasattr(msg, "content"):
messages.append({"role": str(getattr(msg, "role")), "content": str(getattr(msg, "content"))})
# Append current user question
messages.append({"role": "user", "content": question})
response = client.chat.completions.create(
model=MODEL,
messages=messages,
temperature=0.2,
max_tokens=1024,
)
return response.choices[0].message.content
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