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Graph rag optimisations (#527)
* Tech spec for GraphRAG optimisation * Implement GraphRAG optimisation and update tests
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cybermaggedon
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4 changed files with 881 additions and 104 deletions
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@ -1,12 +1,56 @@
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import asyncio
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import logging
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import time
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from collections import OrderedDict
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# Module logger
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logger = logging.getLogger(__name__)
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LABEL="http://www.w3.org/2000/01/rdf-schema#label"
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class LRUCacheWithTTL:
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"""LRU cache with TTL for label caching
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CRITICAL SECURITY WARNING:
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This cache is shared within a GraphRag instance but GraphRag instances
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are created per-request. Cache keys MUST include user:collection prefix
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to ensure data isolation between different security contexts.
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"""
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def __init__(self, max_size=5000, ttl=300):
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self.cache = OrderedDict()
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self.access_times = {}
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self.max_size = max_size
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self.ttl = ttl
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def get(self, key):
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if key not in self.cache:
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return None
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# Check TTL expiration
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if time.time() - self.access_times[key] > self.ttl:
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del self.cache[key]
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del self.access_times[key]
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return None
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# Move to end (most recently used)
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self.cache.move_to_end(key)
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return self.cache[key]
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def put(self, key, value):
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if key in self.cache:
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self.cache.move_to_end(key)
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else:
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if len(self.cache) >= self.max_size:
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# Remove least recently used
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oldest_key = next(iter(self.cache))
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del self.cache[oldest_key]
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del self.access_times[oldest_key]
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self.cache[key] = value
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self.access_times[key] = time.time()
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class Query:
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def __init__(
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@ -61,8 +105,14 @@ class Query:
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async def maybe_label(self, e):
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if e in self.rag.label_cache:
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return self.rag.label_cache[e]
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# CRITICAL SECURITY: Cache key MUST include user and collection
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# to prevent data leakage between different contexts
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cache_key = f"{self.user}:{self.collection}:{e}"
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# Check LRU cache first with isolated key
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cached_label = self.rag.label_cache.get(cache_key)
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if cached_label is not None:
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return cached_label
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res = await self.rag.triples_client.query(
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s=e, p=LABEL, o=None, limit=1,
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@ -70,60 +120,104 @@ class Query:
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)
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if len(res) == 0:
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self.rag.label_cache[e] = e
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self.rag.label_cache.put(cache_key, e)
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return e
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self.rag.label_cache[e] = str(res[0].o)
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return self.rag.label_cache[e]
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label = str(res[0].o)
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self.rag.label_cache.put(cache_key, label)
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return label
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async def execute_batch_triple_queries(self, entities, limit_per_entity):
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"""Execute triple queries for multiple entities concurrently"""
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tasks = []
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for entity in entities:
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# Create concurrent tasks for all 3 query types per entity
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tasks.extend([
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self.rag.triples_client.query(
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s=entity, p=None, o=None,
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limit=limit_per_entity,
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user=self.user, collection=self.collection
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),
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self.rag.triples_client.query(
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s=None, p=entity, o=None,
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limit=limit_per_entity,
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user=self.user, collection=self.collection
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),
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self.rag.triples_client.query(
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s=None, p=None, o=entity,
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limit=limit_per_entity,
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user=self.user, collection=self.collection
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)
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])
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# Execute all queries concurrently
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results = await asyncio.gather(*tasks, return_exceptions=True)
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# Combine all results
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all_triples = []
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for result in results:
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if not isinstance(result, Exception):
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all_triples.extend(result)
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return all_triples
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async def follow_edges_batch(self, entities, max_depth):
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"""Optimized iterative graph traversal with batching"""
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visited = set()
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current_level = set(entities)
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subgraph = set()
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for depth in range(max_depth):
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if not current_level or len(subgraph) >= self.max_subgraph_size:
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break
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# Filter out already visited entities
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unvisited_entities = [e for e in current_level if e not in visited]
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if not unvisited_entities:
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break
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# Batch query all unvisited entities at current level
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triples = await self.execute_batch_triple_queries(
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unvisited_entities, self.triple_limit
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)
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# Process results and collect next level entities
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next_level = set()
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for triple in triples:
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triple_tuple = (str(triple.s), str(triple.p), str(triple.o))
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subgraph.add(triple_tuple)
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# Collect entities for next level (only from s and o positions)
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if depth < max_depth - 1: # Don't collect for final depth
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s, p, o = triple_tuple
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if s not in visited:
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next_level.add(s)
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if o not in visited:
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next_level.add(o)
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# Stop if subgraph size limit reached
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if len(subgraph) >= self.max_subgraph_size:
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return subgraph
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# Update for next iteration
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visited.update(current_level)
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current_level = next_level
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return subgraph
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async def follow_edges(self, ent, subgraph, path_length):
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# Not needed?
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"""Legacy method - replaced by follow_edges_batch"""
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# Maintain backward compatibility with early termination checks
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if path_length <= 0:
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return
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# Stop spanning around if the subgraph is already maxed out
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if len(subgraph) >= self.max_subgraph_size:
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return
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res = await self.rag.triples_client.query(
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s=ent, p=None, o=None,
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limit=self.triple_limit,
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user=self.user, collection=self.collection,
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)
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for triple in res:
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subgraph.add(
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(str(triple.s), str(triple.p), str(triple.o))
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)
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if path_length > 1:
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await self.follow_edges(str(triple.o), subgraph, path_length-1)
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res = await self.rag.triples_client.query(
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s=None, p=ent, o=None,
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limit=self.triple_limit,
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user=self.user, collection=self.collection,
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)
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for triple in res:
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subgraph.add(
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(str(triple.s), str(triple.p), str(triple.o))
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)
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res = await self.rag.triples_client.query(
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s=None, p=None, o=ent,
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limit=self.triple_limit,
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user=self.user, collection=self.collection,
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)
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for triple in res:
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subgraph.add(
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(str(triple.s), str(triple.p), str(triple.o))
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)
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if path_length > 1:
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await self.follow_edges(
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str(triple.s), subgraph, path_length-1
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)
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# For backward compatibility, convert to new approach
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batch_result = await self.follow_edges_batch([ent], path_length)
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subgraph.update(batch_result)
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async def get_subgraph(self, query):
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@ -132,31 +226,52 @@ class Query:
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if self.verbose:
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logger.debug("Getting subgraph...")
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subgraph = set()
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# Use optimized batch traversal instead of sequential processing
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subgraph = await self.follow_edges_batch(entities, self.max_path_length)
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for ent in entities:
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await self.follow_edges(ent, subgraph, self.max_path_length)
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return list(subgraph)
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subgraph = list(subgraph)
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async def resolve_labels_batch(self, entities):
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"""Resolve labels for multiple entities in parallel"""
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tasks = []
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for entity in entities:
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tasks.append(self.maybe_label(entity))
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return subgraph
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return await asyncio.gather(*tasks, return_exceptions=True)
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async def get_labelgraph(self, query):
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subgraph = await self.get_subgraph(query)
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# Filter out label triples
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filtered_subgraph = [edge for edge in subgraph if edge[1] != LABEL]
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# Collect all unique entities that need label resolution
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entities_to_resolve = set()
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for s, p, o in filtered_subgraph:
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entities_to_resolve.update([s, p, o])
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# Batch resolve labels for all entities in parallel
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entity_list = list(entities_to_resolve)
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resolved_labels = await self.resolve_labels_batch(entity_list)
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# Create entity-to-label mapping
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label_map = {}
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for entity, label in zip(entity_list, resolved_labels):
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if not isinstance(label, Exception):
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label_map[entity] = label
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else:
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label_map[entity] = entity # Fallback to entity itself
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# Apply labels to subgraph
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sg2 = []
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for edge in subgraph:
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if edge[1] == LABEL:
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continue
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s = await self.maybe_label(edge[0])
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p = await self.maybe_label(edge[1])
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o = await self.maybe_label(edge[2])
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sg2.append((s, p, o))
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for s, p, o in filtered_subgraph:
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labeled_triple = (
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label_map.get(s, s),
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label_map.get(p, p),
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label_map.get(o, o)
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)
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sg2.append(labeled_triple)
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sg2 = sg2[0:self.max_subgraph_size]
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@ -171,6 +286,13 @@ class Query:
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return sg2
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class GraphRag:
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"""
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CRITICAL SECURITY:
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This class MUST be instantiated per-request to ensure proper isolation
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between users and collections. The cache within this instance will only
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live for the duration of a single request, preventing cross-contamination
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of data between different security contexts.
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"""
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def __init__(
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self, prompt_client, embeddings_client, graph_embeddings_client,
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@ -184,7 +306,9 @@ class GraphRag:
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self.graph_embeddings_client = graph_embeddings_client
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self.triples_client = triples_client
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self.label_cache = {}
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# Replace simple dict with LRU cache with TTL
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# CRITICAL: This cache only lives for one request due to per-request instantiation
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self.label_cache = LRUCacheWithTTL(max_size=5000, ttl=300)
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if self.verbose:
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logger.debug("GraphRag initialized")
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@ -45,6 +45,10 @@ class Processor(FlowProcessor):
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self.default_max_subgraph_size = max_subgraph_size
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self.default_max_path_length = max_path_length
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# CRITICAL SECURITY: NEVER share data between users or collections
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# Each user/collection combination MUST have isolated data access
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# Caching must NEVER allow information leakage across these boundaries
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self.register_specification(
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ConsumerSpec(
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name = "request",
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@ -93,11 +97,14 @@ class Processor(FlowProcessor):
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try:
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self.rag = GraphRag(
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embeddings_client = flow("embeddings-request"),
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graph_embeddings_client = flow("graph-embeddings-request"),
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triples_client = flow("triples-request"),
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prompt_client = flow("prompt-request"),
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# CRITICAL SECURITY: Create new GraphRag instance per request
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# This ensures proper isolation between users and collections
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# Flow clients are request-scoped and must not be shared
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rag = GraphRag(
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embeddings_client=flow("embeddings-request"),
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graph_embeddings_client=flow("graph-embeddings-request"),
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triples_client=flow("triples-request"),
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prompt_client=flow("prompt-request"),
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verbose=True,
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)
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@ -128,7 +135,7 @@ class Processor(FlowProcessor):
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else:
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max_path_length = self.default_max_path_length
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response = await self.rag.query(
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response = await rag.query(
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query = v.query, user = v.user, collection = v.collection,
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entity_limit = entity_limit, triple_limit = triple_limit,
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max_subgraph_size = max_subgraph_size,
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