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LLM Client for generating customer support responses using Google Gemini API.
This module provides the LLMClient class that handles:
- Google Gemini API integration
- Prompt construction with customer support instructions
- Response generation with uncertainty detection
- Error handling for API timeouts and rate limits
"""
import os
import time
from typing import List, Optional
from dataclasses import dataclass
import google.generativeai as genai
@dataclass
class LLMResponse:
"""Response from LLM generation."""
answer: str
uncertain: bool # True if LLM signals uncertainty
class LLMClient:
"""Client for interacting with Google Gemini API to generate support responses."""
# Uncertainty signals that indicate the LLM is not confident
UNCERTAINTY_SIGNALS = [
"i'm not certain",
"i'm not sure",
"i don't have enough information",
"let me connect you with",
"human agent",
"i cannot answer",
"i'm unable to",
"not enough information",
"outside my knowledge",
"i don't know"
]
def __init__(
self,
api_key: Optional[str] = None,
model_name: str = "gemini-1.5-flash",
temperature: float = 0.3,
max_retries: int = 2,
timeout: int = 30
):
"""
Initialize LLM client with Google Gemini API.
Args:
api_key: Google API key (defaults to GOOGLE_API_KEY env var)
model_name: Gemini model to use
temperature: Sampling temperature (0.0-1.0, lower = more deterministic)
max_retries: Maximum number of retry attempts for failed requests
timeout: Request timeout in seconds
"""
self.api_key = api_key or os.getenv("GOOGLE_API_KEY")
if not self.api_key:
raise ValueError("Google API key not provided. Set GOOGLE_API_KEY environment variable.")
self.model_name = model_name
self.temperature = temperature
self.max_retries = max_retries
self.timeout = timeout
# Configure the API
genai.configure(api_key=self.api_key)
# Initialize the model
self.model = genai.GenerativeModel(
model_name=self.model_name,
generation_config={
"temperature": self.temperature,
"top_p": 0.95,
"top_k": 40,
"max_output_tokens": 1024,
}
)
def build_prompt(self, question: str, context_chunks: List[str]) -> str:
"""
Build prompt with customer support instructions and context.
Args:
question: Customer question
context_chunks: Retrieved help article chunks
Returns:
Formatted prompt string
"""
# Format context chunks
context_text = "\n\n".join([
f"[Context {i+1}]\n{chunk}"
for i, chunk in enumerate(context_chunks)
])
prompt = f"""You are a friendly customer support agent for TaskFlow, a project management SaaS.
Answer the customer's question using ONLY the information provided in the help articles below.
IMPORTANT RULES:
- Use a friendly, helpful tone
- Ground your answer in the provided context
- If the context doesn't contain enough information to answer confidently, respond with: "I'm not certain about this. Let me connect you with a human agent."
- Do not make up or infer information not present in the context
- Be concise and direct in your response
HELP ARTICLE CONTEXT:
{context_text}
CUSTOMER QUESTION:
{question}
YOUR ANSWER:"""
return prompt
def _detect_uncertainty(self, response_text: str) -> bool:
"""
Detect if LLM response contains uncertainty signals.
Args:
response_text: Generated response text
Returns:
True if uncertainty detected, False otherwise
"""
response_lower = response_text.lower()
return any(signal in response_lower for signal in self.UNCERTAINTY_SIGNALS)
def generate_answer(
self,
question: str,
context_chunks: List[str]
) -> LLMResponse:
"""
Generate answer using LLM with retrieved context.
Args:
question: Customer question
context_chunks: Retrieved help article chunks
Returns:
LLMResponse with answer text and uncertainty flag
Raises:
ValueError: If question or context is empty
RuntimeError: If API call fails after retries
"""
if not question or not question.strip():
raise ValueError("Question cannot be empty")
if not context_chunks:
raise ValueError("Context chunks cannot be empty")
# Build the prompt
prompt = self.build_prompt(question, context_chunks)
# Attempt generation with retries
last_error = None
for attempt in range(self.max_retries + 1):
try:
# Generate response
response = self.model.generate_content(
prompt,
request_options={"timeout": self.timeout}
)
# Extract text from response
if not response.text:
# Empty response indicates uncertainty
return LLMResponse(
answer="I'm not certain about this. Let me connect you with a human agent.",
uncertain=True
)
answer_text = response.text.strip()
# Detect uncertainty in response
uncertain = self._detect_uncertainty(answer_text)
return LLMResponse(
answer=answer_text,
uncertain=uncertain
)
except Exception as e:
last_error = e
error_msg = str(e).lower()
# Check for rate limiting
if "429" in error_msg or "quota" in error_msg or "rate limit" in error_msg:
if attempt < self.max_retries:
# Exponential backoff for rate limits
wait_time = 2 ** attempt
time.sleep(wait_time)
continue
else:
raise RuntimeError(
"Rate limit exceeded. Please try again later."
) from e
# Check for timeout
if "timeout" in error_msg:
if attempt < self.max_retries:
continue
else:
raise RuntimeError(
"Request timed out. Please try again."
) from e
# Check for invalid API key
if "api key" in error_msg or "authentication" in error_msg or "401" in error_msg:
raise RuntimeError(
"Invalid API key. Please check your configuration."
) from e
# For other errors, retry if attempts remain
if attempt < self.max_retries:
time.sleep(1)
continue
else:
raise RuntimeError(
f"Failed to generate response: {str(e)}"
) from e
# Should not reach here, but handle gracefully
raise RuntimeError(
f"Failed to generate response after {self.max_retries + 1} attempts: {str(last_error)}"
)
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