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streaming_summarizer.py — Advanced streaming + parallel grant summarization
Implements:
1. True parallelization with configurable concurrency
2. OpenAI streaming (stream=True) for real-time token delivery
3. Per-grant streaming with immediate feedback
4. Graceful fallback for Gradio (not async context)
"""
import asyncio
import logging
from typing import Any, Dict, List, Optional, AsyncGenerator
from .llm_client import LLMClient
from .summarizer_optimized import SummaryCache, extract_minimal_context
logger = logging.getLogger(__name__)
async def summarize_grant_streaming(
grant: Dict[str, Any],
client: LLMClient,
cache: SummaryCache,
past_winners: Optional[List[Dict[str, Any]]] = None,
) -> AsyncGenerator[str, None]:
"""
Stream a single grant summary token-by-token.
Yields tokens as they arrive from OpenAI.
"""
grant_id = grant.get("id") or grant.get("title") or "unknown"
title = grant.get("title") or grant.get("name") or "(untitled)"
# Check cache first
cached_summary = cache.get(grant)
if cached_summary:
logger.info("📦 Cache HIT for %s", grant_id)
# Stream cached content quickly
for token in cached_summary.split():
yield token + " "
return
# Build context
context = extract_minimal_context(grant, past_winners)
# Stream from OpenAI
from .prompt_templates import build_prompt
payload = build_prompt("openai", context)
full_response = ""
try:
# Use stream=True to get token-by-token delivery
stream = client.chat(payload["messages"], max_tokens=1200, temperature=0.25, stream=True)
for token in stream:
full_response += token
yield token
# Cache the full response
cache.set(grant, full_response)
logger.info("✅ Cached summary for %s", grant_id)
except Exception as e:
logger.error("❌ Stream failed for %s: %s", grant_id, e)
error_msg = f"Error generating summary: {str(e)[:100]}"
cache.set(grant, error_msg)
yield error_msg
async def summarize_grants_parallel_streaming(
current: List[Dict[str, Any]],
past_winners: Optional[List[Dict[str, Any]]] = None,
*,
limit: Optional[int] = None,
client: Optional[LLMClient] = None,
cache: Optional[SummaryCache] = None,
batch_size: int = 5,
max_concurrent: int = 3,
) -> AsyncGenerator[Dict[str, Any], None]:
"""
Parallel + Streaming: Process multiple grants concurrently.
Each grant streams its own summary as it's being generated.
Results yielded as they complete (not in order).
Args:
max_concurrent: Max grants to process simultaneously (default: 3)
"""
client = client or LLMClient({})
cache = cache or SummaryCache(ttl_seconds=3600)
items = current[: limit or len(current)]
if not items:
return
# Create a semaphore to limit concurrent tasks
semaphore = asyncio.Semaphore(max_concurrent)
async def bounded_summarize(grant, index):
"""Summarize with concurrency limit."""
async with semaphore:
grant_id = grant.get("id") or grant.get("title") or "unknown"
title = grant.get("title") or grant.get("name") or "(untitled)"
full_summary = ""
try:
logger.info(f"[{index+1}/{len(items)}] Summarizing: {title[:50]}")
async for token in summarize_grant_streaming(grant, client, cache, past_winners):
full_summary += token
return {
"grant_id": grant_id,
"title": title,
"summary_md": full_summary,
"index": index,
}
except Exception as e:
logger.error(f"Failed to summarize {grant_id}: {e}")
return {
"grant_id": grant_id,
"title": title,
"summary_md": f"Error: {str(e)[:100]}",
"index": index,
}
# Create all tasks
tasks = [bounded_summarize(grant, i) for i, grant in enumerate(items)]
# Yield results as they complete (using as_completed)
for coro in asyncio.as_completed(tasks):
result = await coro
yield result
logger.info("✅ Completed all %d grants", len(items))
async def summarize_grants_batch_parallel_streaming(
current: List[Dict[str, Any]],
past_winners: Optional[List[Dict[str, Any]]] = None,
*,
limit: Optional[int] = None,
client: Optional[LLMClient] = None,
cache: Optional[SummaryCache] = None,
batch_size: int = 5,
max_concurrent_batches: int = 2,
) -> AsyncGenerator[Dict[str, Any], None]:
"""
Optimized: Process batches in parallel, stream batch results.
5 grants per batch → reduced API calls
Multiple batches in parallel → maximum throughput
Results stream as soon as batch completes
This is the recommended approach for 30+ grants.
"""
from .summarizer_optimized import _summarize_batch_async
client = client or LLMClient({})
cache = cache or SummaryCache(ttl_seconds=3600)
items = current[: limit or len(current)]
if not items:
return
# Build contexts
contexts = [extract_minimal_context(g, past_winners) for g in items]
# Create batches
batches = [
(items[i:i+batch_size], contexts[i:i+batch_size])
for i in range(0, len(items), batch_size)
]
# Create a semaphore for concurrent batch processing
semaphore = asyncio.Semaphore(max_concurrent_batches)
async def process_batch(batch_items, batch_contexts, batch_idx):
"""Process a batch with concurrency limit."""
async with semaphore:
try:
logger.info(f"Processing batch {batch_idx+1}/{len(batches)} ({len(batch_items)} grants)")
batch_results = await _summarize_batch_async(batch_items, batch_contexts, client, cache)
for result in batch_results:
yield result
except Exception as e:
logger.error(f"Batch {batch_idx} failed: {e}")
for grant in batch_items:
yield {
"grant_id": grant.get("id") or "unknown",
"title": grant.get("title") or "(untitled)",
"summary_md": f"Batch error: {str(e)[:100]}",
}
# Process batches concurrently
tasks = [
process_batch(batch_items, batch_contexts, i)
for i, (batch_items, batch_contexts) in enumerate(batches)
]
# Yield from all tasks as they complete
for task in asyncio.as_completed(tasks):
async for result in task:
yield result
logger.info("✅ All batches processed")
# Synchronous wrapper for use in non-async contexts (e.g., Gradio callbacks)
def summarize_grants_streaming_sync(
current: List[Dict[str, Any]],
past_winners: Optional[List[Dict[str, Any]]] = None,
*,
limit: Optional[int] = None,
client: Optional[LLMClient] = None,
cache: Optional[SummaryCache] = None,
batch_size: int = 5,
mode: str = "batch_parallel", # "grant_parallel" or "batch_parallel"
) -> List[Dict[str, Any]]:
"""
Synchronous wrapper for streaming summarization.
Returns all results as a list (blocking until complete).
Use this when you need results in order.
"""
async def run():
results = []
if mode == "batch_parallel":
async for result in summarize_grants_batch_parallel_streaming(
current,
past_winners=past_winners,
limit=limit,
client=client,
cache=cache,
batch_size=batch_size,
):
results.append(result)
else: # grant_parallel
async for result in summarize_grants_parallel_streaming(
current,
past_winners=past_winners,
limit=limit,
client=client,
cache=cache,
batch_size=batch_size,
):
results.append(result)
# Sort by original index if available
return sorted(results, key=lambda x: x.get("index", float('inf')))
try:
loop = asyncio.get_event_loop()
if loop.is_running():
# Already in async context, return async generator
raise RuntimeError("Use async version directly in async context")
except RuntimeError:
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
return loop.run_until_complete(run())
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