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REALTIME GROQ SERVICE MODULE
=============================
Extents GroqService to add Tavily web search before calling the LLM. Used by
ChatService for POST /chat/realtime. Same session and history as general chat;
the only difference is we run a Tavily search for the user's question and add
the results to the system message, them call Groq.
ROUND-ROBIN API KEYS:
- Shares the same round-robin counter as GroqService (class-level _shared_key_index)
- This means /chat and /chat/realtime requests use the same rotation sequence
- Example: If /chat uses key 1, the next /chat/realtime request will use key 2
- All API key usage is logged wih masked keys for security and debugging
FLOW:
1. search_tavily(question): call Tavily API, format results as text (or "" on failure).
2. get_response(question, chat_history): add search results to system message,
then same as parent: retrieve context from vector store, build prompt , call Groq.
If TAVILY_API_KEY is not set, tavily_clinet is None and search_tavily returns "";
the user still gets an answer from Groq with no search results.
"""
from typing import List, Optional, Iterator, Any
from tavily import TavilyClient
import logging
import os
import time
from app.services.groq_service import GroqService, escape_curly_braces, AllGroqApisFailedError
from app.services.vector_store import VectorStoreService
from app.utils.retry import with_retry
from config import REALTIME_CHAT_ADDENDUM, GROQ_API_KEYS, GROQ_MODEL
logger = logging.getLogger("J.A.R.V.I.S")
GROQ_REQUEST_TIMEOUT_FAST = 15
_QUERY_EXTRACTION_PROMPT = (
"You are a search query optimizer. Given the user's message and recent conversation, "
"produce a single, focused web search query (max 12 word) that will find the "
"information the user needs. Resolve any references (like 'that website ', 'him', 'it) "
"using the conversation history. Output ONLY the search query, nothing else."
)
# ==============================================================================
# REALTIME GROQ SERVICE CLASS (extends GroqService)
# ==============================================================================
class RealtimeGroqService(GroqService):
"""
Same as GroqService but runs a Tavily web search first and adds the results
to the system message. If Tavily is missing or fails, we still call Groq with
no search results (user gets and answer without real-time data).
"""
def __init__(self, vector_store_service: VectorStoreService):
"""Call parent init (Groq LLM + vector store); then create Tavily client if key is set."""
super().__init__(vector_store_service)
tavily_api_key = os.getenv("TAVILY_API_KEY", "")
if tavily_api_key:
self.tavily_client = TavilyClient(api_key=tavily_api_key)
logger.info("Tavily search client initialized successfully")
else:
self.tavily_client = None
logger.warning("TAVILY_API_KEY not set. Realtime search will be unavailable.")
if GROQ_API_KEYS:
from langchain_groq import ChatGroq
self._fast_llm = ChatGroq(
groq_api_key=GROQ_API_KEYS[0],
model_name=GROQ_MODEL,
temperature=0.0,
request_timeout=GROQ_REQUEST_TIMEOUT_FAST,
max_tokens=50,
)
else:
self._fast_llm = None
def _extract_search_query(
self, question:str, chat_history: Optional[List[tuple]] = None
) -> str:
if not self._fast_llm:
return question
try:
t0 = time.perf_counter()
history_context = ""
if chat_history:
recent = chat_history[-3:]
parts = []
for h, a in recent:
parts.append(f"User: {h[:200]}")
parts.append(f"Assistant: {a[:200]}")
history_context = "\n".join(parts)
if history_context:
full_prompt = (
f"{_QUERY_EXTRACTION_PROMPT}\n\n"
f"Recent conversation:\n{history_context}\n\n"
f"User's latest message: {question}\n\n"
f"Search query:"
)
else:
full_prompt = (
f"{_QUERY_EXTRACTION_PROMPT}\n\n"
f"User's message: {question}\n\n"
f"Search query:"
)
response = self._fast_llm.invoke(full_prompt)
extracted = response.content.strip().strip('"').strip("'")
if extracted and 3<= len(extracted) <= 200:
logger.info(
"[REALTIME] Query extraction: '%s' -> '%s' (%.3fs)",
question[:80], extracted[:80], time.perf_counter() - t0,
)
return extracted
logger.warning("[REALTIME] Query extraction returned unusable result, using raw question")
return question
except Exception as e:
logger.error("[REALTIME] Error extracting search query: %s", e)
return question
def search_tavily(self, query: str, num_results: int = 7) -> str:
"""
Call Tavily API with the given query and return formatted result text for the prompt.
On any failure (no key, rate limit, network) we return "" so the LLM still gets a reply.
"""
if not self.tavily_client:
logger.warning("Tavily client not initialized. TAVILY_API_KEY not set.")
return ("",None)
t0 = time.perf_counter()
try:
# Perform Tavily search with retries for rate limits and transient errors.
response = with_retry(
lambda: self.tavily_client.search(
query=query,
search_depth="advanced",
max_results=num_results,
include_answer=False,
include_raw_content=False,
),
max_retries=3,
initial_delay=1.0,
)
results = response.get('results', [])
ai_answer = response.get("answer", "")
if not results and not ai_answer:
logger.warning(f"No Tavily search results found for query: {query}")
return ("",None)
payload: Optional[dict] = {
"query": query,
"answer": ai_answer,
"results": [
{
"title": r.get("title", "No title"),
"content": (r.get("content") or "")[:500],
"url": r.get("url", ""),
"score": round(float(r.get("score", 0)), 2),
}
for r in results[:num_results]
],
}
parts = [f"=== WEB SEARCH RESULTS FOR: {query} ===\n"]
if ai_answer:
parts.append(f"AI-SYNTHESIZED ANSWER (use this as your primary source):\n{ai_answer}\n")
if results:
parts.append("INDIVIDUAL SOURCES:")
for i, results in enumerate(results[:num_results], 1):
title = results.get("title", "No title")
content = results.get("content", "")
url = results.get("url", "")
score = results.get("score", 0)
parts.append(f"\n[Source {i}] relevance: {score:.2f}")
parts.append(f"Title: {title}")
if content:
parts.append(f"Content: {content}")
if url:
parts.append(f"URL: {url}")
parts.append("\n=== END SEARCH RESULTS ===")
formatted = "\n".join(parts)
logger.info(
"[TAVILY] %d results, AI answer: %s, formatted: %d chars (%.3fs)",
len(results), "yes" if ai_answer else "no",
len(formatted), time.perf_counter() - t0,
)
return (formatted, payload)
except Exception as e:
logger.error("Error performing Tavily search: %s", e)
return ("", None)
def get_response(self, question: str, chat_history: Optional[List[tuple]] = None) -> str:
"""
Run Tavily search for the question, add results to system message, then call the Groq
via the parent's _invoke_llm (same multi-key round-robin and fallback as general chat).
"""
try:
search_query = self._extract_search_query(question, chat_history)
logger.info("[REALTIME] Searching Tavily for: %s", search_query)
formatted_results, _ = self.search_tavily(search_query, num_results=7)
if formatted_results:
logger.info("[REALTIME] Tavily returned results (length: %d chars)", len(formatted_results))
else:
logger.warning("[REALTIME] Tavily returned no results for: %s", search_query)
extra_parts = [escape_curly_braces(formatted_results)] if formatted_results else None
prompt, messages = self._build_prompt_and_messages(
question, chat_history,
extra_system_parts=extra_parts,
mode_addendum=REALTIME_CHAT_ADDENDUM,
)
t0 = time.perf_counter()
response_content = self._invoke_llm(prompt, messages, question)
logger.info("[TIMING] groq_api: %.3fs", time.perf_counter() - t0)
logger.info(
"[RESPONSE] Realtime chat | Length: %d chars | Preview: %.120s",
len(response_content), response_content,
)
return response_content
except AllGroqApisFailedError:
raise
except Exception as e:
logger.error("Error in realtime get_response: %s", e, exc_info=True)
raise
def stream_response(self, question: str, chat_history: Optional[List[tuple]] = None) -> Iterator[Any]:
try:
search_query = self._extract_search_query(question, chat_history)
logger.info("[REALTIME] Searching Tavily for: %s", search_query)
formatted_results, payload = self.search_tavily(search_query, num_results=7)
if formatted_results:
logger.info("[REALTIME] Tavily returned results (length: %d chars)", len(formatted_results))
else:
logger.warning("[REALTIME] Tavily returned no results for: %s", search_query)
if payload:
yield {"_search_results": payload}
extra_parts = [escape_curly_braces(formatted_results)] if formatted_results else None
prompt, messages = self._build_prompt_and_messages(
question, chat_history,
extra_system_parts=extra_parts,
mode_addendum=REALTIME_CHAT_ADDENDUM,
)
yield from self._stream_llm(prompt, messages, question)
logger.info("[REALTIME] stream completed for %s", search_query)
except AllGroqApisFailedError:
raise
except Exception as e:
logger.error("Error in realtime stream_response: %s", e, exc_info=True)
raise
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