Agent Recomputes Embeddings for the Same Text — No Embedding Cache
Symptom
- Agent startup takes 45 seconds — all spent calling the embedding API for the same documents
- Same user query embedded 3 times across retries — identical API calls, identical results
- Embedding API cost dominates the bill despite no change in underlying documents
- RAG pipeline re-embeds the entire knowledge base on every deploy
- Duplicate documents in the knowledge base are embedded separately — wasted cost
- Agent embeds tool descriptions, system prompt snippets, or fixed examples on every call
Root Cause
Embedding computation is deterministic — the same text always produces the same vector. But agents that don’t cache embeddings recompute them every time: on every startup, every retry, every parallel call. Embedding costs accumulate linearly with call count. The fix is content-addressed caching: use a hash of the text as the cache key, and return the cached vector whenever the text has been seen before.
Fix
Option 1: Content-addressed embedding cache (SQLite)
import hashlib
import json
import sqlite3
import time
from pathlib import Path
from typing import Optional
import anthropic
def text_hash(text: str) -> str:
"""Stable hash of text — same text always produces same hash"""
return hashlib.sha256(text.encode("utf-8")).hexdigest()[:32]
class EmbeddingCache:
"""
SQLite-backed embedding cache — content-addressed by text hash.
Survives restarts. Zero cost for repeated texts.
"""
def __init__(
self,
db_path: str = "/data/embeddings.db",
model: str = "voyage-3" # or your embedding model
):
self.model = model
self.db_path = Path(db_path)
self.db_path.parent.mkdir(parents=True, exist_ok=True)
self._init_db()
self._hits = 0
self._misses = 0
def _init_db(self):
with sqlite3.connect(self.db_path) as conn:
conn.execute("""
CREATE TABLE IF NOT EXISTS embeddings (
text_hash TEXT PRIMARY KEY,
model TEXT NOT NULL,
embedding TEXT NOT NULL, -- JSON array
text_length INTEGER,
created_at REAL DEFAULT (unixepoch())
)
""")
conn.execute("CREATE INDEX IF NOT EXISTS idx_model ON embeddings(model)")
conn.commit()
def get(self, text: str) -> Optional[list[float]]:
"""Retrieve cached embedding or None if not cached"""
key = text_hash(text)
with sqlite3.connect(self.db_path) as conn:
row = conn.execute(
"SELECT embedding FROM embeddings WHERE text_hash=? AND model=?",
(key, self.model)
).fetchone()
if row:
self._hits += 1
return json.loads(row[0])
self._misses += 1
return None
def set(self, text: str, embedding: list[float]):
"""Store embedding in cache"""
key = text_hash(text)
with sqlite3.connect(self.db_path) as conn:
conn.execute("""
INSERT OR REPLACE INTO embeddings (text_hash, model, embedding, text_length)
VALUES (?, ?, ?, ?)
""", (key, self.model, json.dumps(embedding), len(text)))
conn.commit()
def get_or_compute(self, text: str, compute_fn) -> list[float]:
"""Get from cache or compute and store"""
cached = self.get(text)
if cached is not None:
return cached
embedding = compute_fn(text)
self.set(text, embedding)
return embedding
@property
def stats(self) -> dict:
total = self._hits + self._misses
return {
"hits": self._hits,
"misses": self._misses,
"hit_rate": f"{self._hits/total*100:.0f}%" if total > 0 else "0%",
"cache_size": self._get_cache_size()
}
def _get_cache_size(self) -> int:
with sqlite3.connect(self.db_path) as conn:
return conn.execute("SELECT COUNT(*) FROM embeddings WHERE model=?", (self.model,)).fetchone()[0]
embedding_cache = EmbeddingCache(model="voyage-3")
# Usage — never pays twice for the same text:
def get_embedding(text: str) -> list[float]:
return embedding_cache.get_or_compute(text, compute_embedding_via_api)
Option 2: Batch embedding with deduplication
import hashlib
from collections import defaultdict
def embed_texts_deduplicated(
texts: list[str],
cache: EmbeddingCache,
embed_batch_fn,
batch_size: int = 96
) -> list[list[float]]:
"""
Embed a list of texts with:
1. Cache lookup for already-seen texts
2. Deduplication — identical texts embedded once even if listed multiple times
3. Batching — remaining texts sent in batches
Returns embeddings in same order as input texts.
"""
# Step 1: check cache and identify unique uncached texts
results: dict[str, list[float]] = {} # hash → embedding
uncached_hashes: list[str] = []
hash_to_text: dict[str, str] = {}
for text in texts:
h = text_hash(text)
if h in results:
continue # Already resolved (duplicate in input)
cached = cache.get(text)
if cached is not None:
results[h] = cached # Cache hit
else:
uncached_hashes.append(h)
hash_to_text[h] = text
# Step 2: Deduplicate uncached texts
unique_uncached = list(dict.fromkeys(uncached_hashes)) # Preserve order, remove dupes
unique_texts = [hash_to_text[h] for h in unique_uncached]
print(
f"Embedding: {len(texts)} inputs → "
f"{len(unique_uncached)} unique uncached (deduped from {len(uncached_hashes)}, "
f"{len(texts) - len(uncached_hashes)} cache hits)"
)
# Step 3: Batch-embed uncached texts
for i in range(0, len(unique_texts), batch_size):
batch = unique_texts[i:i + batch_size]
batch_embeddings = embed_batch_fn(batch)
for text, embedding in zip(batch, batch_embeddings):
h = text_hash(text)
results[h] = embedding
cache.set(text, embedding) # Store in cache
# Step 4: Return in original order
return [results[text_hash(text)] for text in texts]
Option 3: Redis embedding cache for distributed agents
import redis
import json
import hashlib
import os
from typing import Optional
class RedisEmbeddingCache:
"""
Redis-backed embedding cache — shared across multiple agent instances.
Content-addressed: same text → same key → same embedding everywhere.
"""
def __init__(
self,
model: str = "voyage-3",
ttl_days: int = 30,
redis_url: str = None
):
self.model = model
self.ttl = ttl_days * 86400
self.r = redis.Redis.from_url(
redis_url or os.environ.get("REDIS_URL", "redis://localhost:6379"),
decode_responses=True
)
self._prefix = f"emb:{model}:"
def _key(self, text: str) -> str:
return self._prefix + hashlib.sha256(text.encode()).hexdigest()[:32]
def get(self, text: str) -> Optional[list[float]]:
raw = self.r.get(self._key(text))
if raw:
return json.loads(raw)
return None
def set(self, text: str, embedding: list[float]):
key = self._key(text)
self.r.setex(key, self.ttl, json.dumps(embedding))
def mget(self, texts: list[str]) -> list[Optional[list[float]]]:
"""Batch cache lookup — single round trip"""
keys = [self._key(t) for t in texts]
raw_values = self.r.mget(keys)
return [json.loads(v) if v else None for v in raw_values]
def mset(self, texts: list[str], embeddings: list[list[float]]):
"""Batch cache store — single round trip"""
pipe = self.r.pipeline()
for text, embedding in zip(texts, embeddings):
pipe.setex(self._key(text), self.ttl, json.dumps(embedding))
pipe.execute()
async def embed_with_redis_cache(
texts: list[str],
cache: RedisEmbeddingCache,
embed_api_fn
) -> list[list[float]]:
"""
Batch embedding with Redis cache — O(1) round trips for cache lookup.
"""
# Batch cache check
cached = cache.mget(texts)
results = [None] * len(texts)
uncached_indices = []
uncached_texts = []
for i, (text, embedding) in enumerate(zip(texts, cached)):
if embedding is not None:
results[i] = embedding
else:
uncached_indices.append(i)
uncached_texts.append(text)
print(f"Cache: {len(texts) - len(uncached_texts)} hits, {len(uncached_texts)} misses")
if uncached_texts:
new_embeddings = await embed_api_fn(uncached_texts)
cache.mset(uncached_texts, new_embeddings) # Store all at once
for i, embedding in zip(uncached_indices, new_embeddings):
results[i] = embedding
return results
Option 4: Startup document embedding with change detection
import hashlib
import json
from pathlib import Path
class DocumentEmbeddingIndex:
"""
Embeds documents once and caches permanently.
Re-embeds ONLY documents that have changed since last run.
Saves 95%+ of embedding API calls on restart.
"""
def __init__(
self,
index_path: str = "/data/doc_embeddings.json",
embed_fn = None
):
self.index_path = Path(index_path)
self.embed_fn = embed_fn
self._index: dict[str, dict] = self._load()
def _load(self) -> dict:
if self.index_path.exists():
data = json.loads(self.index_path.read_text())
print(f"Loaded {len(data)} cached document embeddings")
return data
return {}
def _save(self):
self.index_path.parent.mkdir(parents=True, exist_ok=True)
tmp = self.index_path.with_suffix(".tmp")
tmp.write_text(json.dumps(self._index))
tmp.replace(self.index_path)
def _doc_hash(self, content: str) -> str:
return hashlib.sha256(content.encode()).hexdigest()[:16]
async def index_documents(self, documents: list[dict]) -> dict:
"""
Index documents — only re-embed those that changed.
documents: [{"id": "doc1", "content": "...text..."}, ...]
"""
to_embed = []
unchanged = 0
for doc in documents:
doc_id = doc["id"]
content_hash = self._doc_hash(doc["content"])
if doc_id in self._index:
cached = self._index[doc_id]
if cached.get("content_hash") == content_hash:
unchanged += 1
continue # Document unchanged — skip re-embedding
to_embed.append(doc)
print(
f"Document index: {unchanged} unchanged (cached), "
f"{len(to_embed)} need embedding"
)
if to_embed:
# Batch embed only changed documents
texts = [doc["content"] for doc in to_embed]
embeddings = await self.embed_fn(texts)
for doc, embedding in zip(to_embed, embeddings):
self._index[doc["id"]] = {
"embedding": embedding,
"content_hash": self._doc_hash(doc["content"]),
"doc_id": doc["id"]
}
self._save()
print(f"Indexed {len(to_embed)} new/changed documents")
return {
doc_id: entry["embedding"]
for doc_id, entry in self._index.items()
}
doc_index = DocumentEmbeddingIndex()
Option 5: In-process LRU cache for hot embeddings
from functools import lru_cache
import hashlib
# For embeddings computed during agent's lifetime (not persisted across restarts)
# Use lru_cache for hot, frequently accessed embeddings
_embedding_lru: dict[str, list[float]] = {}
_lru_order: list[str] = []
_LRU_MAX = 1000
def lru_get_embedding(text: str, embed_fn) -> list[float]:
"""
In-process LRU cache for embeddings.
Fast (no I/O), ephemeral (clears on restart).
Use alongside persistent cache for best of both.
"""
key = hashlib.sha256(text.encode()).hexdigest()[:16]
if key in _embedding_lru:
# Move to front (most recently used)
_lru_order.remove(key)
_lru_order.append(key)
return _embedding_lru[key]
# Cache miss — compute
embedding = embed_fn(text)
# Evict oldest if at capacity
if len(_embedding_lru) >= _LRU_MAX:
oldest = _lru_order.pop(0)
del _embedding_lru[oldest]
_embedding_lru[key] = embedding
_lru_order.append(key)
return embedding
# Two-tier caching:
def get_embedding_two_tier(text: str, persistent_cache: EmbeddingCache, api_fn) -> list[float]:
"""
Tier 1: in-process LRU (zero I/O, ephemeral)
Tier 2: SQLite/Redis (persistent across restarts)
Tier 3: API call (only when both miss)
"""
key = hashlib.sha256(text.encode()).hexdigest()[:16]
# Tier 1: LRU
if key in _embedding_lru:
return _embedding_lru[key]
# Tier 2: Persistent cache
cached = persistent_cache.get(text)
if cached is not None:
# Warm LRU
_embedding_lru[key] = cached
_lru_order.append(key)
return cached
# Tier 3: API call
embedding = api_fn(text)
persistent_cache.set(text, embedding)
_embedding_lru[key] = embedding
_lru_order.append(key)
return embedding
Option 6: Cache warming at startup
import asyncio
async def warm_embedding_cache(
fixed_texts: list[str],
cache: EmbeddingCache,
embed_batch_fn,
batch_size: int = 32
) -> dict:
"""
Pre-warm cache with known fixed texts at agent startup.
Call this before serving traffic — no cold misses during runtime.
"""
uncached = [t for t in fixed_texts if cache.get(t) is None]
if not uncached:
print(f"Cache already warm: all {len(fixed_texts)} fixed texts cached")
return {"warmed": 0, "already_cached": len(fixed_texts)}
print(f"Warming cache: {len(uncached)} texts to embed ({len(fixed_texts)-len(uncached)} already cached)")
warmed = 0
for i in range(0, len(uncached), batch_size):
batch = uncached[i:i + batch_size]
embeddings = await embed_batch_fn(batch)
for text, embedding in zip(batch, embeddings):
cache.set(text, embedding)
warmed += len(batch)
print(f"Cache warming: {warmed}/{len(uncached)}")
print(f"Cache warm complete: {warmed} new texts embedded")
return {"warmed": warmed, "already_cached": len(fixed_texts) - len(uncached)}
# At agent startup:
FIXED_TEXTS = [
"Search for information about...",
"Summarize the following...",
# Tool descriptions, example queries, etc.
]
await warm_embedding_cache(FIXED_TEXTS, embedding_cache, embed_batch_fn)
Cache Strategy Comparison
| Strategy | Hit Latency | Persistence | Multi-instance | Best For |
|---|---|---|---|---|
| In-process LRU | < 1ms | No | No | Hot texts, same session |
| SQLite cache | 1–5ms | Yes | Single-writer | Single-instance agent |
| Redis cache | 1–3ms | Yes | Yes | Multi-instance agents |
| Two-tier (LRU + SQLite) | < 1ms hit, 2ms warm | Yes | Single | Best of both |
Embedding Cost Savings Example
| Scenario | Without Cache | With Cache (90% hit rate) |
|---|---|---|
| 10,000 docs, daily re-embed | 10,000 calls/day | ~1,000 calls/day |
| 5 retries × same query | 5 calls | 1 call |
| 100 duplicate docs in KB | 100 calls | 1 call |
| Hourly startup re-embed | 10,000 calls/hour | 0 calls (unchanged) |
Expected Token Savings
Re-embedding 10,000 docs on each restart × 10 restarts/day: 100,000 embedding calls Cached: 0 repeated embedding calls — 100% saved on unchanged documents
Environment
- Any agent using RAG, semantic search, or vector similarity; critical for agents with large knowledge bases, high-volume query processing, or embedding-dependent tools
- Source: direct experience; absent embedding caches are the most common cause of inflated embedding API costs in production RAG agents
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