Production RAG as a service
Build a production RAG pipeline without managing vector databases, embedding APIs, or ETL scripts. One managed API for your entire RAG stack.
What is retrieval-augmented generation
A RAG pipeline lets your AI answer questions using your company's knowledge — products, policies, documentation, FAQs.
Instead of relying only on what the model learned during training, you retrieve relevant context at query time and include it in the prompt. The result: accurate, grounded answers without fine-tuning.
Teams use RAG for customer support bots, internal documentation search, product recommendations, and research assistants. FoxNose gives you RAG as a service — a managed RAG backend so you can focus on your product, not on stitching infrastructure together.
The typical RAG stack — and why it breaks
To build RAG in production, you typically need a vector database for semantic search, an embedding service to convert text to vectors, a search engine for keyword matching, and a database for structured data. Plus ETL pipelines and sync scripts to keep everything consistent when content changes.
Instead of managing Pinecone or Weaviate separately, wiring up OpenAI embeddings, and writing sync scripts — you can build RAG without a vector database entirely. That's why teams choose a managed LLM database with RAG as a service.
Sync scripts, cron jobs, webhooks, ETL pipelines
Cron jobs, webhooks, ETL pipelines
Pinecone, Weaviate, Qdrant...
OpenAI, Cohere, Voyage...
Elasticsearch, OpenSearch...
PostgreSQL, MongoDB...
Managed RAG backend with auto-embeddings
One managed API replaces the entire RAG stack. No vector database to run, no ETL pipelines to maintain, no embedding service to call.
No pipeline to maintain
No batching logic. No retry handlers. No "is this already embedded?" checks. Auto-embeddings on every write — just save your document.
Before: Your embedding pipeline
After: FoxNose SDK
Indexed in milliseconds
Save a document — it's searchable immediately. No cron jobs. No "wait for reindex". No stale data.
RRF ranking built-in
No custom ranking logic. No magic weight coefficients. Reciprocal Rank Fusion merges vector and keyword results automatically.
Full audit trail
Every change has an AI audit trail. See exactly what was in the index when. Debug retrieval issues in minutes, not hours.
Updated refund policy to 30 days
Added holiday exception clause
Initial refund policy
Know exactly which version was in the index at any point in time
Build a RAG knowledge base in three steps
Define your content schema, save documents, query with hybrid search. Auto-embeddings and indexing happen behind the scenes.
Define schema
Create a folder, add fields, mark which ones need vector embeddings. Schema changes are versioned — no reindexing required.
Knowledge base versioning →Save content
POST your documents via the knowledge base API. Auto-embeddings generate on every write — no external embedding API, no sync scripts.
Search with hybrid RAG
Query with vector similarity, keyword matching, and metadata filters in one request. The hybrid search API merges results with RRF ranking automatically.
RAG pipeline in 20 lines
A full RAG flow with FoxNose SDK: hybrid search retrieval, context assembly, and LLM response — in Python or TypeScript.
# pip install foxnose-sdk openai
from foxnose_sdk.flux import FluxClient
from foxnose_sdk.auth import SimpleKeyAuth
from openai import OpenAI
# 1. Retrieve context with hybrid search
auth = SimpleKeyAuth("public_key", "secret_key")
flux = FluxClient(base_url="https://your-env.fxns.io", api_prefix="my-api", auth=auth)
results = flux.search("kb-articles", body={
"vector_search": {"query": "how to process refunds"},
"where": {"$": {"all_of": [{"status__eq": "published"}]}},
"limit": 3
})
# 2. Build prompt with retrieved context
context = "\n".join([r["data"]["content"] for r in results["results"]])
# 3. Get LLM response
response = OpenAI().chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": f"Answer based on:\n{context}"},
{"role": "user", "content": "How do refunds work?"}
]
)Prefer LangChain? Use the native FoxNoseRetriever for an even simpler setup. Building AI agents? See AI agent memory for read-write workflows.
Start building your RAG backend
Create your first production RAG application in minutes. Python SDK included.
Explore the platform
Hybrid Search API
Semantic search, full-text, and pre-filter vector search in one query.
Learn more →Knowledge Base API
Schema-first, auto-generated REST API with built-in search.
Learn more →LLM Database
AI-native database for RAG with built-in search, auto-embeddings, and structured storage.
Learn more →AI Agent Memory
Persistent, structured knowledge base for AI agents. Read-write API included.
Learn more →