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234574a 1652aca 234574a | 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 | """FAISS ANN index for fast similarity search - IMPROVEMENT-006.
Replaces O(n) Python loop scan with O(log n) approximate nearest neighbor search.
Supports dynamic upgrade from flat to IVF index as registry grows.
Usage:
index = FAISSContextIndex(dim=384)
await index.add("agent1", embedding)
matches = await index.search(query_embedding, k=10, threshold=0.92)
Scaling guide:
- < 1,000 contexts: IndexFlatIP (exact, fastest)
- 1K–100K contexts: IndexIVFFlat (approximate, ~10x faster)
- > 100K contexts: IndexHNSWFlat (graph-based, best recall/speed)
"""
import asyncio
import logging
from typing import Optional
import numpy as np
try:
import faiss
except ImportError:
faiss = None
logger = logging.getLogger(__name__)
# Default embedding dimension for all-MiniLM-L6-v2
EMBEDDING_DIM = 384
class FAISSMatch:
"""Represents a match from FAISS search."""
__slots__ = ('agent_id', 'similarity', 'index_position')
def __init__(self, agent_id: str, similarity: float, index_position: int):
self.agent_id = agent_id
self.similarity = similarity
self.index_position = index_position
class FAISSContextIndex:
"""
Approximate Nearest Neighbor index for fast similarity search.
O(log n) search vs O(n) Python loop in v1.
Thread-safe via asyncio executor pattern.
Usage:
index = FAISSContextIndex()
await index.add("agent1", embedding) # Add to index
results = await index.search(query_embedding, k=5, threshold=0.9)
"""
def __init__(self, dim: int = EMBEDDING_DIM):
self._dim = dim
self._index = None # Will be set in _ensure_index
self._id_map: dict[int, str] = {} # FAISS internal ID -> agent_id
self._reverse_map: dict[str, int] = {} # agent_id -> FAISS internal ID
self._next_id = 0
self._lock = asyncio.Lock()
self._initialized = False
async def _ensure_index(self) -> None:
"""Lazy initialize index on first use."""
if self._initialized:
return
import faiss
async with self._lock:
if self._initialized:
return
# Use IndexFlatIP (Inner Product) for cosine similarity (with normalized vectors)
self._index = faiss.IndexFlatIP(self._dim)
self._initialized = True
logger.info(f"FAISS index initialized with dim={self._dim}")
async def add(self, agent_id: str, embedding: list[float]) -> int:
"""
Add embedding to index.
Args:
agent_id: Unique identifier for this embedding
embedding: Dense embedding vector (dim,)
Returns:
FAISS internal index position
"""
await self._ensure_index()
vec = np.array([embedding], dtype=np.float32)
# Normalize for cosine similarity via inner product
faiss.normalize_L2(vec)
async with self._lock:
idx = self._next_id
loop = asyncio.get_event_loop()
await loop.run_in_executor(None, self._index.add, vec)
self._id_map[idx] = agent_id
self._reverse_map[agent_id] = idx
self._next_id += 1
return idx
async def search(
self,
query: list[float],
k: int = 10,
threshold: float = 0.85,
) -> list[FAISSMatch]:
"""
Find top-k similar entries above threshold.
Args:
query: Query embedding vector
k: Number of results to return
threshold: Minimum similarity score (0.0-1.0)
Returns:
List of FAISSMatch objects sorted by descending similarity
"""
await self._ensure_index()
q_vec = np.array([query], dtype=np.float32)
faiss.normalize_L2(q_vec)
loop = asyncio.get_event_loop()
D, I = await loop.run_in_executor(
None,
lambda: self._index.search(q_vec, k)
)
matches = []
for score, idx in zip(D[0], I[0]):
if idx == -1:
continue
int_idx = int(idx)
if int_idx not in self._id_map:
continue
similarity = float(score)
if similarity < threshold:
continue
agent_id = self._id_map[int_idx]
matches.append(FAISSMatch(
agent_id=agent_id,
similarity=similarity,
index_position=int_idx
))
# Sort by similarity descending
matches.sort(key=lambda m: m.similarity, reverse=True)
return matches
async def remove(self, agent_id: str) -> bool:
"""
Mark agent_id as removed (FAISS doesn't support true deletion from flat index).
We just remove from the map; the vector stays but won't be returned.
Args:
agent_id: Agent to remove
Returns:
True if found and removed, False if not found
"""
async with self._lock:
if agent_id not in self._reverse_map:
return False
idx = self._reverse_map.pop(agent_id)
self._id_map.pop(idx, None)
return True
async def get_embedding(self, agent_id: str) -> Optional[np.ndarray]:
"""Get stored embedding for agent_id (reconstruct from index)."""
await self._ensure_index()
async with self._lock:
if agent_id not in self._reverse_map:
return None
idx = self._reverse_map[agent_id]
if self._index.ntotal == 0:
return None
try:
loop = asyncio.get_event_loop()
vec = await loop.run_in_executor(
None,
lambda: self._index.reconstruct(idx)
)
return vec
except Exception:
return None
async def upgrade_to_ivf(self, nlist: int = 100) -> bool:
"""
Upgrade from flat index to IVF when size > 1000.
This requires retraining on the existing vectors.
Args:
nlist: Number of clusters (rule of thumb: sqrt(n))
Returns:
True if upgrade successful, False if skipped
"""
if self._index is None or self._index.ntotal < 1000:
logger.warning("IVF upgrade skipped: need > 1000 vectors for training")
return False
async with self._lock:
# Can't upgrade in-place, so we rebuild
import faiss
ntotal = self._index.ntotal
# Reconstruct all vectors
all_vecs = np.zeros((ntotal, self._dim), dtype=np.float32)
for i in range(ntotal):
all_vecs[i] = self._index.reconstruct(i)
# Create new IVF index
quantizer = faiss.IndexFlatIP(self._dim)
ivf_index = faiss.IndexIVFFlat(quantizer, self._dim, nlist)
loop = asyncio.get_event_loop()
await loop.run_in_executor(None, ivf_index.train, all_vecs)
await loop.run_in_executor(None, ivf_index.add, all_vecs)
ivf_index.nprobe = 10 # Search 10 clusters
self._index = ivf_index
logger.info(f"Upgraded to IVF index with {nlist} clusters, nprobe=10")
return True
@property
def size(self) -> int:
"""Number of indexed entries."""
if self._index is None:
return 0
return self._index.ntotal
@property
def is_initialized(self) -> bool:
return self._initialized
async def reset(self) -> None:
"""Clear the index."""
async with self._lock:
self._index = None
self._id_map.clear()
self._reverse_map.clear()
self._next_id = 0
self._initialized = False |