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057c21e | 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 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 | """WebSocket endpoint for real-time grant query streaming."""
import asyncio
import json
import logging
import time
from typing import Dict, Any
from fastapi import APIRouter, WebSocket, WebSocketDisconnect
from uuid import uuid4
from src.logging.logger import get_logger
from src.analyzer.data_loader import load_current_grants, load_past_winners
from src.analyzer.chat.chat_tools import ChatTools
from src.analyzer.chat.query_router import route
from src.analyzer.llm_client import LLMClient
from src.analyzer.config import load_config
logger = get_logger()
router = APIRouter(prefix="/ws", tags=["websocket"])
# Global cache for initialized tools
_chat_tools: ChatTools | None = None
_llm_client: LLMClient | None = None
def get_chat_tools() -> ChatTools:
"""Lazy load chat tools on first use."""
global _chat_tools
if _chat_tools is None:
try:
from pathlib import Path
snapshots_dir = Path("data/snapshots")
history_xlsx = Path("data/IUK-141025-InnovateUKFundedProjects-FY2015-16topresent.xlsx")
current = load_current_grants(snapshots_dir, limit=100)
past = load_past_winners(history_xlsx=history_xlsx)
_chat_tools = ChatTools(current, past)
logging.info(f"Loaded ChatTools with {len(current)} current and {len(past)} past grants")
except Exception as e:
logging.error(f"Failed to initialize ChatTools: {e}")
raise
return _chat_tools
def get_llm_client() -> LLMClient:
"""Lazy load LLM client on first use."""
global _llm_client
if _llm_client is None:
try:
config = load_config()
_llm_client = LLMClient(config)
logging.info("Initialized LLM client for WebSocket streaming")
except Exception as e:
logging.error(f"Failed to initialize LLM client: {e}")
raise
return _llm_client
@router.websocket("/query")
async def websocket_query_endpoint(websocket: WebSocket):
"""
WebSocket endpoint for real-time grant query streaming.
Protocol:
Client sends: {"query": "user question", "session_id": "optional-id"}
Server streams:
- {"type": "metadata", "session_id": "...", "query": "..."}
- {"type": "intent", "intent": "search"}
- {"type": "token", "token": "word"}
- {"type": "citations", "citations": [...]}
- {"type": "done", "latency_ms": 1234}
- {"type": "error", "error": "message"}
"""
await websocket.accept()
session_id = str(uuid4())
try:
# Initialize tools and client
tools = get_chat_tools()
llm_client = get_llm_client()
logging.info(f"WebSocket connection established: {session_id}")
while True:
# Receive query from client
try:
data = await websocket.receive_json()
except WebSocketDisconnect:
logging.info(f"WebSocket disconnected: {session_id}")
break
query = data.get("query", "")
session_id = data.get("session_id", session_id)
if not query:
await websocket.send_json({
"type": "error",
"error": "Query is required"
})
continue
start_time = time.time()
try:
# Send metadata
await websocket.send_json({
"type": "metadata",
"session_id": session_id,
"query": query
})
# Route the query
routed = route(query, use_llm=True)
intent = str(routed.get("intent") or "general")
args = routed.get("args") or {}
logging.info(f"WebSocket {session_id}: Intent={intent}, Query={query}")
# Send intent
await websocket.send_json({
"type": "intent",
"intent": intent
})
citations_list = []
# Handle different intents
if intent in {"search", "list", "list_grants"}:
# Search for grants - non-streaming response
keyword = args.get("keyword") or args.get("query") or ""
results = tools.list_grants(keyword=keyword, limit=5)
if results:
answer_text = f"Found {len(results)} grants matching your query:\n"
for grant in results:
title = grant.get("title", "Unknown")
grant_id = grant.get("id") or grant.get("grant_id", "")
answer_text += f"\n- **{title}** (ID: {grant_id})"
if grant_id:
citations_list.append({
"grant_id": grant_id,
"title": title,
"url": grant.get("url")
})
else:
answer_text = f"No grants found matching '{keyword}'."
# Stream the complete answer token by token
words = answer_text.split()
for word in words:
await websocket.send_json({
"type": "token",
"token": word + " "
})
await asyncio.sleep(0.01) # Small delay for visual effect
elif intent == "summarize" and args.get("grant_id"):
# Summarize a specific grant
grant_id = args.get("grant_id")
result = tools.summarize_grant(grant_id)
summary = result.get("summary_md", "No summary available")
# Stream summary token by token
words = summary.split()
for word in words:
await websocket.send_json({
"type": "token",
"token": word + " "
})
await asyncio.sleep(0.01)
citations_list.append({
"grant_id": grant_id,
"title": result.get("title", grant_id)
})
elif intent == "compare" and args.get("grant_id_a") and args.get("grant_id_b"):
# Compare two grants
result = tools.compare_grants(args["grant_id_a"], args["grant_id_b"])
comparison = result.get("comparison_md", "Comparison unavailable")
# Stream comparison token by token
words = comparison.split()
for word in words:
await websocket.send_json({
"type": "token",
"token": word + " "
})
await asyncio.sleep(0.01)
citations_list.extend([
{"grant_id": args["grant_id_a"], "title": f"Grant {args['grant_id_a']}"},
{"grant_id": args["grant_id_b"], "title": f"Grant {args['grant_id_b']}"}
])
else:
# Default: Use LLM streaming for general queries
results = tools.list_grants(keyword=query, limit=5)
context = f"User query: {query}\n\n"
if results:
context += "Relevant grants:\n"
for grant in results:
title = grant.get("title", "Unknown")
grant_id = grant.get("id") or grant.get("grant_id", "")
context += f"- {title} (ID: {grant_id})\n"
if grant_id:
citations_list.append({
"grant_id": grant_id,
"title": title,
"url": grant.get("url")
})
# Stream LLM response token by token
messages = [
{"role": "system", "content": "You are a UK grant analyst. Answer questions about grants concisely and accurately."},
{"role": "user", "content": context}
]
# Get streaming response from LLM
stream_generator = llm_client.chat(
messages,
stream=True,
max_tokens=1200,
temperature=0.3
)
# Stream each token via WebSocket
for token in stream_generator:
await websocket.send_json({
"type": "token",
"token": token
})
await asyncio.sleep(0) # Allow other tasks to run
# Send citations
if citations_list:
await websocket.send_json({
"type": "citations",
"citations": citations_list
})
# Send completion
latency_ms = int((time.time() - start_time) * 1000)
await websocket.send_json({
"type": "done",
"latency_ms": latency_ms
})
except Exception as e:
logging.error(f"Error processing WebSocket query: {e}", exc_info=True)
await websocket.send_json({
"type": "error",
"error": str(e)
})
except Exception as e:
logging.error(f"WebSocket connection error: {e}", exc_info=True)
finally:
logging.info(f"WebSocket connection closed: {session_id}")
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