One integration surface
Connect your preferred AI and data infrastructure behind a consistent SDK and API.
What is Retrivora?•Plug-and-Play AI Engine for RAG Chat & Document Vector SearchThe Universal RAG Platform &
The Universal RAG Platform &
Embeddable SDK Libraryfor
Connect document ingestion, vector search, embedding models, and grounded LLM citations in a single typed pipeline. Drop into any site or app.
Add SDK Library to Your Product
<script src="https://cdn.retrivora.com/v1/retrivora.min.js" async></script>
Paste this single tag into your HTML or website template to launch the instant Retrivora AI RAG widget.
Interactive Pipeline
From Documents to Intelligent Responses
Click any step to watch it execute in real time.
Ingest — Live Trace
Chunking
Embedding
Done
Smart Document ChunkerchunkSize: 1000 · overlap: 200
Parsing annual_report.pdf…
Under the Hood
When you upload a document, Retrivora's smart chunker breaks it into overlapping segments — carefully splitting at natural boundaries like headings, paragraphs, and sentences rather than cutting mid-thought. Each segment is converted into a dense mathematical vector (an embedding) that captures its meaning. Before sending to the embedding model, Retrivora checks an in-memory cache — if the same text has been embedded recently, it reuses the result instantly, saving both time and API cost. New vectors are batched and written to your vector database in parallel, with automatic retries if anything fails transiently. The result: your entire document library is searchable by meaning, not just keywords.
How it works
Upload documents and build your knowledge base
Context-Aware Chunking
Splits documents at natural boundaries — headers, paragraphs, sentences — preserving meaning across every segment.
LRU Embedding Cache
Avoids re-embedding identical text. The cache holds up to 500 entries and automatically evicts the oldest on overflow.
Parallel Batch Processing
Sends chunks to the embedding API in parallel batches of 50, with exponential backoff retries for transient failures.
Vector Index Upsert
Writes vectors and metadata to your index in batches of 100, isolated to your project namespace for multi-tenancy.
Works with your stack
OpenAILLM
AnthropicLLM
GeminiLLM
GroqLocal LLM
OllamaLocal LLM
QwenLocal LLM
PineconeVector DB
MySQLVector DB
WeaviateVector DB
PostgresVector DB
MongoDBVector DB
ChromaVector DB
QdrantVector DB
RedisVector DB
MilvusVector DB
SupabaseVector DB Platform Demo
See Retrivora AI SDK in Action
Watch how Retrivora orchestrates document parsing, vector indexing, and grounded response streaming in real time.
retrivora.com/demo
Core Infrastructure
The Ultimate Universal Bridge
Retrivora is engineered to be the ultimate universal bridge for your AI stack. It seamlessly integrates generative AI services, vector databases, and knowledge graphs into high-performance, developer-friendly RAG pipelines.
Generative AI Integration
Retrivora AI acts as a universal bridge connecting the latest generative AI models to your custom infrastructure. By unifying diverse SDKs from OpenAI, Anthropic, and Google Gemini into a single, standardized API layer, developers can switch providers with zero code modifications. The bridge handles prompt construction, dynamic history loading, safety guardrails, and token stream transformations.
Vector Databases Adapter
A key challenge in building retrieval pipelines is managing connection schemas and query semantics across multiple search indexes. Retrivora simplifies this by natively interfacing with leading vector databases like Pinecone, pgvector (PostgreSQL), MongoDB Atlas, Milvus, and Qdrant. Whether you require a managed serverless index or a local, self-hosted vector database, Retrivora abstracts the underlying protocols.
Structured Knowledge Graphs
While simple semantic search is powerful, production-grade applications require contextual relationships to answer complex user queries. Retrivora embeds support for hybrid retrieval by connecting text chunks to structured knowledge graphs. By mapping entity relations alongside vector proximity, the query engine can traverse relational nodes and retrieve secondary context that traditional vector searches miss.
Engineered RAG Pipelines
Retrivora handles the entire lifecycle of enterprise RAG pipelines, from document parsing and chunking to real-time prompt generation. Designed for maximum throughput and low latency, it implements context-aware chunking, batch uploads, and an LRU embedding cache that prevents redundant API calls. Secure API proxies ensure client-side widgets can run queries without exposing private database credentials.
Platform Value
Build features, not infrastructure
Stop building the same integration layers repeatedly. Retrivora provides the complete toolkit to orchestrate intelligent search experiences.
The full RAG path
Move from raw documents to a streamed, cited answer without stitching together five products.
Answers you can inspect
Expose sources, latency, tokens, retrieval data, and response-shaping decisions when you need them.
Designed for your data
Keep vector data in the provider that fits your architecture and organize it by project namespace.
Why teams choose Retrivora
Build less infrastructure. Deliver more AI value.
See what a team typically has to build itself, and what Retrivora gives you from day one.
Capability
Traditional way
Retrivora
Provider choice
Select, integrate, authenticate, and maintain a separate code path for every model and vector provider.
Provider adapters for LLMs, embeddings, vector stores, and optional graph retrieval behind one typed configuration.
RAG assembly
Build document parsing, chunking, embeddings, retrieval, retries, reranking, and response streaming as separate services.
A single RAG pipeline that coordinates ingestion, batched upserts, retrieval, reranking, citations, and streamed responses.
Application delivery
Build the API routes, streaming protocol, chat interface, uploads, and source display independently.
Ready-to-use server handlers, React components, document upload, and a drop-in web component from one codebase.
You still choose the AI and data providers. Retrivora removes the repeated engineering work between them and your customers.
Explore the platformKeep your AI stack adaptable as the market changes.
Start with the hosted workspace or embed Retrivora in your own application.
Architecture
Built for every layer of the stack
Swap providers without rewriting a single line of business logic.
Vector DB
Vector Store
Universal support for Pinecone, PGVector, MongoDB, Milvus, Qdrant, and more.
Learn more
Models
Embeddings
Seamlessly switch between OpenAI, Ollama, or custom embedding providers.
Learn more
Inference
LLM Orchestration
Optimized inference across OpenAI, Anthropic, Gemini, and local LLMs.
Learn more
Start Building Today
Ready to supercharge
your AI pipelines?
Join thousands of developers building production-ready RAG applications with Retrivora AI SDK. Free forever tier, no credit card required.