File size: 1,845 Bytes
b22c324 | 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 | import json
import chromadb
from agentic_workflow.config import LLM_MODEL
from langchain_ollama import ChatOllama
import re
from langchain_core.messages import HumanMessage, SystemMessage
client = chromadb.PersistentClient(path="./chroma_db")
llm = ChatOllama(model=LLM_MODEL, temperature=0)
# intent analysis
def analyse_intent(query: str) -> dict:
system = """
You are an intent classifier. Given a user query, return ONLY valid JSON with:
- "intent": one of "factual", "conversational", "follow_up", "sensitive"
- "needs_context": true if retrieval from a knowledge base is needed, false otherwise
- "needs_history": true if this looks like a follow-up to a prior turn, false otherwise
Rules:
- Greetings / chit-chat → conversational, needs_context: false
- Questions about facts / documents → factual, needs_context: true
- "you said earlier" / pronouns like "it" / "that" → follow_up, needs_history: true
- Personal data, health, finance → sensitive
Return ONLY the JSON object. No explanation.
"""
response = llm.invoke([
SystemMessage(content=system),
HumanMessage(content=query)
])
raw = response.content.strip()
# Strip markdown fences if present
raw = re.sub(r"^```(?:json)?\s*|\s*```$", "", raw).strip()
try:
parsed = json.loads(raw)
except json.JSONDecodeError:
parsed = {}
return {
"intent": "factual",
"needs_context": True,
"needs_history": False,
**parsed
}
# query= "How do I top-up the value of my card when I am abroad"
# trace = []
# intent_result = analyse_intent(query)
# trace.append({
# "step": "intent_analysis",
# "result": intent_result
# })
# print(f"[Intent] {intent_result}") |