# PrismLib – semantic LLM cache and cluster mesh that cuts token spend

> Source: <https://github.com/insightitsGit/prismlib>
> Published: 2026-06-27 00:34:04+00:00

**Tensor-native LLM cache, distributed DB driver, and cluster intelligence — one package.**

PrismLib has three layers. Use any combination:

| Layer | What it solves | Key number | Install |
|---|---|---|---|
PrismCache |
LLM API cost — semantic cache catches repeated & paraphrased queries in-process | 91–96% hit rate |
`pip install "prismlib[cache]"` |
PrismDriver |
DB read latency — WAL-streamed local index replaces network round-trips | 98.6% latency reduction (143ms → 2ms) |
`pip install "prismlib[fabric]"` |
PrismLib Micro |
Cluster token cost + HA — shares answers across containers, auto-failover, health mesh | 76% fewer tokens cluster-wide |
included in `prismlib[fabric]` |

All three run entirely in-process. No Redis. No Pinecone. No Prometheus. No Kubernetes operator.

Wraps any LLM call. Paraphrased queries return the cached answer without touching the API.
Multi-tenant math: JL projection seeded by `SHA-256(tenant_id)`

gives each tenant a mathematically
isolated address space — not a query filter, a projection matrix.

Two components on two machines:

**Server Wrapper**(DB node) — intercepts WAL/binlog, vectorizes rows, streams encrypted float32 frames via CHORUS Fabric** DLL Driver**(app node) — subscribes to the stream, keeps a local PrismResonance index warm; reads never leave the process

Built into `prismlib[fabric]`

, zero extra install:

**ClusterCache**— once any node answers a query, every peer caches it via CHORUS TOKEN_SYNC frames. BLUE and ORANGE nodes billed 0 tokens on warm queries.**AlertManager**— 12 default health rules; fires SIGNAL frame + admin email in <1s when CPU/RAM/disk thresholds are crossed. No scrape interval. No Datadog agent.**Blue/Green/Orange failover**— GREEN is active master, BLUE is warm standby (auto-promotes in ~3s if GREEN goes silent), ORANGE is syncing reserve.** ContextCompressor**— cosine-sim top-K chunk selection before every LLM call. 58–64% context token reduction, zero extra cost.

Built on two open-source InsightIts libraries:

— wave-memory similarity engine powering every cache lookup and local vector index[PrismResonance](https://github.com/insightitsGit/prismresonance)— encrypted gRPC binary streaming protocol carrying float32 tensor frames between nodes[CHORUS Fabric](https://github.com/insightitsGit/chorus_fabric)

```
# Semantic LLM cache only
pip install "prismlib[cache]"

# With OpenAI embeddings
pip install "prismlib[cache,cache-openai]"

# With Anthropic/Voyage embeddings
pip install "prismlib[cache,cache-anthropic]"

# With Ollama (local models)
pip install "prismlib[cache,cache-ollama]"

# DB driver (app node)
pip install "prismlib[fabric]"

# Server Wrapper daemon (DB node — Linux/macOS)
pip install "prismlib[wrapper]"
prism-wrapper --config /etc/prism/wrapper.toml

# Everything
pip install "prismlib[all]"
```

Save 60-80% of LLM API calls by serving semantically identical queries from cache. Paraphrases hit the cache — "How do I reset my password?" and "I forgot my password, help" return the same answer without a second LLM call.

``` python
from prism.cache import PrismCache

cache = PrismCache.build(tenant_id="my-app", llm_model="gpt-4o")

def ask(question: str) -> str:
    return cache.get_or_call(
        query=question,
        call_fn=lambda: openai_client.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": question}],
        ).choices[0].message.content,
    )
```

Each tenant gets a mathematically isolated cache space (JL projection seeded by tenant ID). One customer's cached answers never bleed into another's.

``` php
from prism.cache import PrismCache

def get_cache(tenant_id: str) -> PrismCache:
    return PrismCache.build(tenant_id=tenant_id, llm_model="gpt-4o-mini")

# Tenant A and tenant B share no cache state
cache_a = get_cache("acme-corp")
cache_b = get_cache("globex-inc")

answer = cache_a.get_or_call(query="What is my plan limit?", call_fn=llm_call)
```

Wrap your existing LLM endpoint without changing any business logic.

``` python
# FastAPI
from fastapi import FastAPI, Request
from prism.cache import PrismCache

app = FastAPI()
cache = PrismCache.build(tenant_id="api", llm_model="gpt-4o")

@app.post("/chat")
async def chat(request: Request):
    body = await request.json()
    question = body["message"]
    answer = await cache.aget_or_call(
        query=question,
        call_fn=lambda: llm_client.ask(question),
    )
    return {"answer": answer}
# Django — add to MIDDLEWARE in settings.py
# prism/middleware.py
from prism.cache import PrismCache

_cache = PrismCache.build(tenant_id="django-app", llm_model="gpt-4o")

class PrismCacheMiddleware:
    def __init__(self, get_response):
        self.get_response = get_response

    def __call__(self, request):
        return self.get_response(request)

    def process_llm_query(self, question: str, call_fn) -> str:
        return _cache.get_or_call(query=question, call_fn=call_fn)
python
import asyncio
from prism.cache import PrismCache

cache = PrismCache.build(tenant_id="batch", llm_model="gpt-4o-mini")

async def process_batch(questions: list[str]) -> list[str]:
    tasks = [
        cache.aget_or_call(query=q, call_fn=lambda q=q: llm_call(q))
        for q in questions
    ]
    return await asyncio.gather(*tasks)
python
from prism.cache import PrismCache

cache = PrismCache.build(tenant_id="finance", llm_model="gpt-4o")

# After processing queries...
metrics = cache.metrics()
print(f"Hit rate:          {metrics.hit_rate:.0%}")
print(f"Tokens saved:      {metrics.tokens_saved:,}")
print(f"Cost saved today:  ${metrics.cost_saved_usd:.2f}")
print(f"Projected monthly: ${metrics.cost_saved_usd * 30:.0f}")
```

PrismDriver has two components that work together. Install each on the right machine.

**On the DB node — Server Wrapper**

The Server Wrapper is an OS daemon that sits next to your database. It reads WAL/binlog changes, vectorizes rows using `RowVectorizer`

, encrypts them with `TensorCipher`

(via CHORUS Fabric), and streams float32 frames to every connected DLL Driver.

```
# Install on the DB node (Linux or macOS)
pip install "prismlib[wrapper]"

# Configure and start
prism-wrapper --config /etc/prism/wrapper.toml
# /etc/prism/wrapper.toml
[database]
flavor = "postgresql"
dsn = "postgresql://user:pass@localhost/mydb"

[chorus]
listen_port = 50051
tenant_id = "products-service"
```

Supported databases: PostgreSQL (WAL / wal2json), MySQL (binlog), CockroachDB (EXPERIMENTAL CHANGEFEED), TiDB (push model).

**On the app node — DLL Driver**

The DLL Driver is an in-process library that replaces your DB connection string. On startup it connects to the Server Wrapper, subscribes to the CHORUS Fabric stream, and keeps a local PrismResonance index warm. All reads hit the in-process index — no network round-trip, sub-millisecond latency.

```
# Install on the app node
pip install "prismlib[fabric]"
python
# Before
import psycopg2
conn = psycopg2.connect("postgresql://user:secret@db-host:5432/mydb")

# After — no password, no hostname in app config
from prism.ffi import PrismDriver, DriverConfig

async with PrismDriver(DriverConfig(wrapper_host="db-proxy-1")) as driver:
    results = await driver.query(
        embedding=my_embedding_vector,
        top_k=5,
        threshold=0.85,
    )
```

The driver keeps a local PrismResonance cache warm via a background WAL subscription. Reads never touch the DB — they hit the in-process float32 index.

``` python
from prism.ffi import PrismDriver, DriverConfig
import numpy as np

config = DriverConfig(
    wrapper_host="10.0.1.50",
    wrapper_port=50051,
    tenant_id="products-service",
)

async with PrismDriver(config) as driver:
    # Typical hit: < 1ms, no network round-trip
    query_vec = np.array([...], dtype=np.float32)
    matches = await driver.query(embedding=query_vec, top_k=10)
    for m in matches:
        print(f"{m.row_id}  score={m.score:.3f}  {m.text_repr}")
async with PrismDriver(config) as driver:
    ack = await driver.write(
        row_id="product-42",
        data={"name": "Widget Pro", "price": 29.99, "stock": 150},
    )
    print(f"Written: event_id={ack.event_id}")
python
// Go
import prism "github.com/insightitsGit/prismlib/go"

driver, _ := prism.Connect("db-proxy-1:50051", "my-tenant")
defer driver.Close()
results, _ := driver.Query(embedding, prism.QueryOpts{TopK: 5, Threshold: 0.85})
js
// C#
using InsightIts.Prism;

await using var driver = new PrismDriver("db-proxy-1:50051", tenantId: "my-tenant");
await driver.ConnectAsync();
var results = await driver.QueryAsync(embedding, topK: 5, threshold: 0.85f);
php
// PHP 8.0+
$driver = new PrismDriver('db-proxy-1', 50051, 'my-tenant');
$driver->connect();
$results = $driver->query($embedding, topK: 5, threshold: 0.85);
┌─ DB Node ──────────────────────────────────────────────────────┐
│  PostgreSQL / MySQL / CockroachDB / TiDB                       │
│       │ WAL / binlog / changefeed                              │
│  ┌────▼───────────────────────────────────────────────────┐    │
│  │  prism-wrapper  (pip install "prismlib[wrapper]")      │    │
│  │  RowVectorizer → TensorCipher (V_enc = V @ K)         │    │
│  │  → HMAC-SHA256 watermark → CHORUSPublisher            │    │
│  └────────────────────────┬───────────────────────────────┘    │
└───────────────────────────┼────────────────────────────────────┘
                            │  CHORUS Fabric (gRPC, encrypted float32)
┌─ App Node — GREEN ────────┼────────────────────────────────────┐
│  ┌────────────────────────▼──────────────────────────────┐     │
│  │  PrismDriver DLL  (pip install "prismlib[fabric]")    │     │
│  │  Subscribe loop → decrypt → PrismResonance index      │     │
│  └──────────────────────────┬────────────────────────────┘     │
│                             │ sub-ms query                     │
│  ┌──────────────────────────▼────────────────────────────┐     │
│  │  Your Application                                      │     │
│  │  ┌─────────────────┐   ┌──────────────────────────┐   │     │
│  │  │  PrismCache     │   │  PrismDriver             │   │     │
│  │  │  LLM cache      │   │  local PrismResonance    │   │     │
│  │  │  [cache]        │   │  (no DB round-trip)      │   │     │
│  │  └─────────────────┘   └──────────────────────────┘   │     │
│  │  ┌──────────────────────────────────────────────────┐  │     │
│  │  │  ClusterCache  ← TOKEN_SYNC frames               │  │     │
│  │  │  AlertManager  ← HEALTH / SIGNAL frames          │  │     │
│  │  └──────────────────────────────────────────────────┘  │     │
│  └────────────────────────────────────────────────────────┘     │
└──────────────────────────────┬─────────────────────────────────┘
                               │  CHORUS mesh
          ┌────────────────────┴────────────────────┐
          │  TOKEN_SYNC · HEALTH · SIGNAL · CONFIG   │
          ▼                                          ▼
┌─ App Node — BLUE ──────┐           ┌─ App Node — ORANGE ─────┐
│  ClusterCache          │           │  ClusterCache            │
│  (warm standby)        │           │  (syncing reserve)       │
│  auto-promotes if      │           │  separate network        │
│  GREEN silent >3s      │           │                          │
└────────────────────────┘           └──────────────────────────┘
```

Live results from Azure Container App (`westus2`

, 1 vCPU / 2 GiB, mock LLM baseline):

| Scenario | Users | Duration | Hit rate | Queries | Tokens saved | Monthly est. |
|---|---|---|---|---|---|---|
| Light | 20 | 60s | 91.0% |
5,936 | 1,374,464 | $594 |
| Mixed | 50 | 300s | 95.9% |
6,973 | 1,673,216 | $723 |

Numbers use a mock LLM (80ms sleep). With real GPT-4o calls (1–3s), latency speedup is 4–13×; token savings are identical.

Live two-node benchmark (Azure Container Apps `westus2`

, 30 users × 60s per phase):

| Phase | Path | Avg latency | Queries |
|---|---|---|---|
Baseline (no driver) |
App → DB node, network | 142.8 ms |
3,864 |
Driver (local index) |
App → in-process PrismResonance | 2.0 ms |
1,479 |

**70.7× faster · 98.6% latency reduction**

The 98.6% reduction is a direct result of CHORUS Fabric doing its job. The subscription loop streamed 11,000 rows at **26,000 rows/s** from the DB node into the local PrismResonance index before the load test began. By the time the first `/driver/query`

hit arrived, there were zero network hops — the answer was already in-process. This is what CHORUS Fabric was designed for: getting tensor data to where the query is, before the query arrives.

```
# Two-node benchmark (requires both container apps running)
python benchmark/load/run_driver_benchmark.py \
  --app-url https://prism-benchmark.nicestone-720c6a9b.westus2.azurecontainerapps.io \
  --db-url  https://prism-wrapper-sim.nicestone-720c6a9b.westus2.azurecontainerapps.io \
  --users 30 --duration 60

# PrismCache load test
python benchmark/load/run_benchmark.py \
  --host https://prism-benchmark.nicestone-720c6a9b.westus2.azurecontainerapps.io \
  --scenario mixed
```

See [ benchmark/](/insightitsGit/prismlib/blob/master/benchmark) for full results JSON, Locust CSV files, and the Azure deploy script.

PrismLib is built on two InsightIts open-source libraries. You can use them directly if you need lower-level access.

·[github.com/insightitsGit/prismresonance]`pip install prismresonance`

The wave-memory similarity engine. Every cache lookup and local vector index in PrismLib goes through PrismResonance.

How it works:

- Receives a float32 embedding vector
- Johnson-Lindenstrauss reduces it to 64 dimensions using a projection matrix seeded by
`SHA-256(tenant_id)`

— this is what gives each tenant mathematically isolated address space - Computes similarity as wave interference (cosine in projected space) in three lock-free phases: snapshot → ONNX MatMul → rank
- Returns ranked candidates in sub-millisecond time entirely in-process

PrismCache wraps this for LLM response caching. PrismDriver's local replica is a PrismResonance index kept warm by WAL streaming.

``` python
from prismresonance import PrismProjector, WaveIndex

projector = PrismProjector(dim=64, tenant_id="my-tenant")
index = WaveIndex(projector)

index.add(vector=my_embedding, payload={"row_id": "product-1", "text": "Widget"})
results = index.query(vector=query_embedding, top_k=5, threshold=0.85)
```

·[github.com/insightitsGit/chorus_fabric]`pip install chorus-fabric`

The secure gRPC binary streaming protocol for machine-to-machine tensor communication. PrismDriver uses CHORUS Fabric as its transport layer between the server wrapper on the DB node and the DLL driver on the app node.

How it works:

`prism-wrapper`

(DB node) vectorizes WAL row events via`RowVectorizer`

, encrypts them with`TensorCipher`

(`V_enc = V @ K`

), appends an HMAC-SHA256 watermark, and publishes batches of raw float32 frames`PrismDriver`

(app node) opens a persistent`WrapperService.Subscribe()`

gRPC stream, receives encrypted frames, decrypts, and feeds them into the local PrismResonance index- Transport is pure binary float32 over gRPC server-streaming — no JSON serialization, no REST overhead
- The
`WrapperService`

proto also exposes`Query`

,`Write`

,`Health`

, and`Hello`

RPCs for direct interaction

``` python
from chorus_fabric import CHORUSPublisher, DriverEndpoint

publisher = CHORUSPublisher(config)
publisher.add_driver(DriverEndpoint(host="10.0.1.50", port=50051, tenant_id="prod"))
await publisher.run(event_queue)  # streams WAL events to all connected drivers
```

CHORUS Fabric is the same protocol used in the CHORUS M2M system — InsightIts' 4-container gRPC topology for tensor communication between AI agents. The 98.6% latency reduction in the PrismDriver benchmark is direct proof that the protocol works at production scale: 11,000 rows streamed at 26,000 rows/s across Azure inter-container networking, then served locally at 2ms.

PrismLib Micro is the cluster layer built into `prismlib[fabric]`

. It adds three
capabilities on top of the single-node stack — no extra install, no extra infra.

| Component | What it does |
|---|---|
ClusterCache |
Shares LLM answers across all nodes via CHORUS TOKEN_SYNC frames. Once any node answers a query, every other node serves it for 0 tokens. |
AlertManager |
Broadcasts health alerts as SIGNAL frames + admin email the moment CPU/RAM/disk/latency thresholds are crossed. No Prometheus. No Datadog. |
Blue/Green/Orange failover |
Three-tier hot-standby: GREEN (active), BLUE (warm standby, auto-promotes in ~3s), ORANGE (syncing reserve). No Raft dependency. No K8s operator. |
ContextCompressor |
Ranks RAG context chunks by cosine similarity, keeps top-K. Saves 58–64% of context tokens before every LLM call. In-process, no extra model. |

| Metric | Result |
|---|---|
| Token savings — cluster avg | 76.1% |
| BLUE node (cluster cache hit) | 100% — 0 LLM calls |
| ORANGE node (cross-network cache hit) | 100% — 0 LLM calls |
| Context compression | 58–64% per query |
| Health alert propagation | <1 s (709–711 ms measured) |
| Failover — BLUE promoted to GREEN | ~3–4 s, no human step |

See [ benchmark/cluster/](/insightitsGit/prismlib/blob/master/benchmark/cluster) for the full benchmark code and

[for raw results.](/insightitsGit/prismlib/blob/master/benchmark/cluster/cluster_benchmark_results.json)

`benchmark/cluster/cluster_benchmark_results.json`

``` python
from prism.cluster.cache import ClusterCache

cache = ClusterCache(node_id="node-1", fabric=chorus_fabric)

answer = await cache.get_or_call(
    query          = user_question,
    query_vector   = embed(user_question),
    call_fn        = lambda: llm.complete(user_question),
    context_chunks = retrieved_docs,    # your RAG chunks
    chunk_vectors  = doc_embeddings,    # their vectors
)
```

Drop this in front of your existing `retrieve → generate`

step. No changes to
retrieval logic, no changes to your LLM client.

``` python
from prism.cluster.alerts import AlertManager, SMTPConfig

alerts = AlertManager(
    fabric = chorus_fabric,
    mail_config = SMTPConfig(
        host="smtp.gmail.com", port=587,
        username="you@gmail.com",
        password=os.getenv("GMAIL_APP_PASS"),
        recipients=["admin@yourcompany.com"],
    ),
)
await alerts.evaluate_health(health_snapshot)
# Fires email + SIGNAL frame to all nodes if any of the 12 default rules trigger
```

| Capability | PrismLib Micro | Prometheus + Alertmanager | Redis cluster | Raft / etcd |
|---|---|---|---|---|
| Cross-node token cache | Yes, built-in |
No | Manual (exact match) | No |
| Alert propagation | <1 s, no infra |
30–60 s, stack needed | No | No |
| Auto failover | ~3–4 s, built-in |
No | Sentinel, 2–30 s | 150–500 ms |
| Context compression | 58–64%, free |
No | No | No |
| Extra infrastructure | None |
Prometheus stack | Redis cluster | etcd cluster |

| Tier | Nodes | Price | Includes |
|---|---|---|---|
Open source |
Unlimited | Free forever |
All cluster code, Apache 2.0 |
ChorusMesh Developer (coming soon) |
Up to 3 | $29/mo after 30-day trial | ClusterCache + failover + AlertManager |
ChorusMesh Team |
Up to 10 | $149/mo | + Raft consensus, message broker adapters |
ChorusMesh Business |
Up to 50 | $499/mo | + multi-region routing, SLA 99.9% |
Enterprise |
Unlimited | Contact us | + air-gap, compliance, dedicated Slack |

For enterprise agreements: [insightits.info@gmail.com](mailto:insightits.info@gmail.com)

PrismLib is open source (Apache 2.0) and free to use. If your team needs any of the following, contact us for enterprise pricing:

**On-premises deployment support**— air-gapped installs, hardened Docker images, SOC 2 documentation** SLA-backed support**— guaranteed response times, incident escalation, dedicated Slack channel** Custom embedding model integration**— fine-tuned domain-specific embedders for higher hit rates in specialized domains (legal, medical, finance, code)** Multi-region CHORUS Fabric topology**— active-active DB node clusters, cross-region WAL fan-out, geo-aware driver routing** Audit logging and compliance exports**— per-query access logs, tenant isolation attestation reports, GDPR data lineage** Professional services**— architecture review, migration from Redis/GPTCache, custom RowVectorizer schemas

**Contact: insightits.info@gmail.com**

**GitHub:**

[github.com/insightitsGit/prismlib](https://github.com/insightitsGit/prismlib)PrismLib is free and will stay free. If it saved your team money on OpenAI bills or database infrastructure, consider sponsoring — it covers benchmark compute, maintenance time, and keeps development moving.

*Your name or logo here — become a sponsor*

**It is one package** — `prismlib`

— published once. The wrapper, driver, and cache are all extras of the same package. Users install what they need:

```
pip install "prismlib[cache]"           # PrismCache only
pip install "prismlib[wrapper]"         # Server Wrapper (DB node)
pip install "prismlib[fabric]"          # DLL Driver (App node)
pip install "prismlib[all]"             # Everything
```

**To publish a new version:**

```
# 1. Bump version in pyproject.toml (currently 0.4.0)
# 2. Build the distribution
pip install build twine
python -m build

# 3. Upload to PyPI (use your token from pypi.org/manage/account/token/)
python -m twine upload dist/* --username __token__ --password pypi-YOUR_TOKEN
```

That's it. One upload covers all three install variants — PyPI resolves the extras automatically.

Apache 2.0 — InsightIts © 2026