fastapi-semcache 0.2.12

Drop in FastAPI middleware/reverse proxy with semantic caching for APIs & LLMs

fastapi-semcache

Drop-in semantic caching for FastAPI APIs and LLM endpoints.

fastapi-semcache adds semantic response caching with minimal refactoring, using pgvector for similarity search and optional Redis for faster response lookups. It works as FastAPI middleware today and can also run as a reverse proxy in front of an upstream API or LLM service.

The PyPI distribution and GitHub repository are fastapi-semcache. The import package remains semanticcache.

Why fastapi-semcache?

fastapi-semcache is built for Python teams who want semantic caching without rewriting their app around a larger framework.

It is designed to plug into FastAPI with minimal refactoring, while still giving you direct control over embeddings, similarity thresholds, vector storage, and cache behavior. The default setup keeps things simple: find the highest-similarity match, apply a threshold, and return a cached response only when it is safe to do so.

It supports FastAPI middleware as a first-class integration path and can also run as a reverse proxy in front of an upstream API or LLM service. Planned support for Django and Flask will extend the same integration model to other Python web stacks.

Install

pip install fastapi-semcache

Custom embedders: subclass BaseEmbedder from semanticcache.embedders and pass it to SemanticCache(embedder=...) to skip the optional embedding extras. See docs/embedders.md.

Optional extras:

• embed-huggingface / embed-huggingface-cpu: Sentence Transformers with CPU PyTorch.
• embed-huggingface-gpu: Sentence Transformers with a CUDA-enabled PyTorch install.
• embed-openai: OpenAI embeddings (openai, tiktoken).

CPU

pip install "fastapi-semcache[embed-huggingface-cpu]"
# or: pip install "fastapi-semcache[embed-huggingface]"

GPU

Pick a CUDA version that matches your system from PyTorch Get Started, then install with that index so pip selects CUDA wheels.

pip install "fastapi-semcache[embed-huggingface-gpu]" \
  --extra-index-url https://download.pytorch.org/whl/cu124

OpenAI embeddings

Install the OpenAI extra so embedder_type="openai" works (pulls openai and tiktoken). Set OPENAI_API_KEY in your environment.

pip install "fastapi-semcache[embed-openai]"

FastAPI middleware

Add SemanticCacheMiddleware to your app and reuse one SemanticCache instance for all requests. Configure Postgres, Redis, and the embedder with SEMANTIC_CACHE_* environment variables (see .env.example). By default only POST requests are intercepted; the middleware derives cache-key text from JSON bodies using query, prompt, input, or chat-style messages (see default_extract_query in semanticcache.middleware). Successful responses whose body parses as a JSON object are candidates for storage.

Redis is optional. If SEMANTIC_CACHE_REDIS_URI is empty (or whitespace), the cache runs in Postgres-only mode: semantic lookup and response storage still work via pgvector, but Redis TTL-based payload caching is disabled.

from typing import Any

from fastapi import FastAPI

from semanticcache import SemanticCache, SemanticCacheMiddleware

app = FastAPI()
cache = SemanticCache()
app.add_middleware(SemanticCacheMiddleware, cache=cache)


@app.post("/v1/chat/completions")
async def chat_completions(body: dict[str, Any]) -> dict[str, Any]:
    # Clients should send JSON with prompt, query, input, or chat messages so the
    # middleware can build the cache key (see default_extract_query). Misses run your
    # handler; hits short-circuit with a cached JSON body.
    return {"choices": [{"message": {"role": "assistant", "content": "Hello"}}]}

Run with uvicorn mymodule:app --host 0.0.0.0 --port 8000.

Custom cache key text (extract_query)

If your JSON body does not follow the usual query / prompt / messages patterns, pass an async callable as extract_query. It receives the Starlette Request and the raw body bytes (already buffered by the middleware). Return a non-empty string to embed and look up; return None to skip semantic caching for that request (the route still runs).

You can wrap default_extract_query and add fallbacks for your own fields, or replace it entirely.

from fastapi import FastAPI, Request

from semanticcache import SemanticCache
from semanticcache.middleware import SemanticCacheMiddleware, default_extract_query

async def extract_query(request: Request, body: bytes) -> str | None:
    base = await default_extract_query(request, body)
    if base is not None:
        return base
    # Parse ``body`` for your schema; return None to bypass the cache.
    return None

app = FastAPI()
cache = SemanticCache()
app.add_middleware(
    SemanticCacheMiddleware,
    cache=cache,
    extract_query=extract_query,
)

Use extract_model when the cache key should also vary by model id from headers or JSON (same async (request, body) -> str | None idea). That model id is passed through to SemanticCache.get / put, which scope Postgres rows and Redis payload keys per model bucket as described in docs/cache-tuning.md. For create_semantic_cache_proxy_app, pass extract_query=... (and other middleware options) as keyword arguments; they are forwarded to SemanticCacheMiddleware.

Other advanced options (path_prefix, HTTP 429 circuit breaker via cache_settings, enabled=False) are documented on SemanticCacheMiddleware in semanticcache.middleware.fastapi. On shutdown, call await cache.close() from a lifespan handler if you want pools closed cleanly.

Cache behavior and tuning

SemanticCache uses a two-stage retrieval pipeline:

• A primary similarity threshold (SEMANTIC_CACHE_THRESHOLD) and top-k candidate limit (SEMANTIC_CACHE_TOP_K_CANDIDATES) control which nearest neighbors are fetched from pgvector.
• An optional rejection threshold (SEMANTIC_CACHE_REJECTION_THRESHOLD) can then filter out borderline matches; if no candidate passes this second stage, the middleware returns a cache miss.
• Dependency timeouts let you fail fast when providers or storage are slow: SEMANTIC_CACHE_EMBED_TIMEOUT_SECONDS applies to embedder calls, and SEMANTIC_CACHE_STORE_TIMEOUT_SECONDS applies to Postgres/Redis operations. On timeout, SemanticCache raises a timeout error, middleware logs it, and request handling continues in fail-open mode.
• In-flight lock registry cap bounds middleware memory used for concurrent miss coordination: SEMANTIC_CACHE_MIDDLEWARE_FLIGHT_LOCK_MAX_ENTRIES limits retained (query, model) lock keys and evicts least-recently-used unlocked entries when needed.

See docs/cache-tuning.md for concrete tuning tips and examples.

Reverse proxy

The reverse proxy mode is optional: it forwards traffic to an upstream base URL while using the same semantic cache middleware. Use it when you want a standalone hop in front of another service rather than importing routes into your FastAPI app.

Minimal programmatic setup:

from semanticcache import SemanticCache, create_semantic_cache_proxy_app

cache = SemanticCache()
app = create_semantic_cache_proxy_app(
    upstream="http://127.0.0.1:11434",
    cache=cache,
)

Run with uvicorn mymodule:app --host 0.0.0.0 --port 8080.

This repository includes a small ASGI app at app/main.py (import app for uvicorn). Set SEMANTIC_CACHE_PROXY_UPSTREAM to the backend base URL; the default is http://127.0.0.1:11434.

uv run uvicorn app.main:app --host 0.0.0.0 --port 8080

See create_semantic_cache_proxy_app in semanticcache.proxy for timeout, TLS verification, httpx_client_kwargs, and middleware options such as path_prefix and extract_query.

Streaming and chunked responses

Today the middleware buffers the full downstream response before sending it to the client. That applies even when your route returns a streaming-style response (for example token streaming); the bytes are collected first, then returned as one response. Cached hits are served as ordinary JSON bodies. The reverse proxy uses httpx’s full response body, not a streamed upstream read.

Chunked pass-through and streaming-friendly caching are planned so SSE and similar flows can deliver early bytes while still integrating with semantic caching where feasible.

Current features

• Huggingface embeddings via Sentence Transformers (embedder_type="huggingface").

• OpenAI embeddings via the official async client (embedder_type="openai"; install embed-openai and set OPENAI_API_KEY). Use OpenAIEmbedder(..., send_dimensions_to_api=False) when the model has a fixed output size and the API must not get a dimensions field.

• PostgreSQL + pgvector for semantic similarity lookup. The library creates a dedicated cache table per embedder configuration (derived from model id and vector dimension) on first use, so you are not tied to a single hard-coded vector width.

• Optional Redis for response caching (keys include an embedder-specific prefix so separate models do not collide). If Redis is not configured, responses are read from Postgres only.

• FastAPI middleware for in-app semantic caching.

• Reverse proxy mode via create_semantic_cache_proxy_app().

Future support

• Chunked / streaming responses for the middleware (and related proxy behavior): pass-through streaming instead of full buffering; see Streaming and chunked responses.
• Django and Flask middleware for in-app semantic caching (not yet shipped; same role as the FastAPI middleware).

Embeddings from the following providers are planned:

• Ollama (HTTP embedding API against a configurable base URL, so the server can run locally or on another host).
• Cohere
• Voyage

Requirements

Python 3.12+.

Links

• Repository: fastapi-semcache

License

Apache-2.0. See LICENSE.