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Knowledge Retrieval

Plug in your docs — we handle the vector search and injection. Give your AI nodes grounded, accurate answers from your own data without building a retrieval pipeline.

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What is RAG?

Retrieval-Augmented Generation (RAG) lets your AI node look up relevant information from your documents before answering a question. Instead of relying solely on the LLM's training data, the node retrieves real context from your knowledge base — producing grounded, accurate, and up-to-date responses.

Why it matters

Without RAG, LLMs can only answer from their training data, which may be stale or missing your domain knowledge entirely. RAG bridges this gap by injecting your proprietary data into every response. This means fewer hallucinations, more relevant answers, and an AI that actually knows your business.

How Interlocute helps

You upload your documents — PDFs, text files, or structured data — and Interlocute handles everything else: chunking the content, generating vector embeddings, storing them in a managed index, and retrieving the most relevant chunks at query time. There are no vector databases to provision, no embedding pipelines to build, and no retrieval logic to maintain.

Built for production

Interlocute's RAG engine is designed for real workloads. It supports incremental document updates, automatic re-indexing, configurable similarity thresholds, and per-node knowledge isolation. Every retrieval operation is metered and logged so you have full visibility into what context the LLM sees.

Frequently Asked Questions

RAG (Knowledge Retrieval)

What is RAG and how does it work with Interlocute?
RAG (Retrieval-Augmented Generation) is a technique that enhances LLM responses by retrieving relevant context from your own documents before generating an answer. In Interlocute, you upload documents to your node's knowledge base, and the platform automatically chunks, embeds, and indexes the content. When a query arrives, the most relevant chunks are retrieved and injected into the LLM prompt, producing grounded and accurate responses.
What document formats does Interlocute RAG support?
Interlocute supports common document formats including plain text, PDF, and structured data files. Documents are processed through an automated chunking and embedding pipeline. You can upload documents through the dashboard or programmatically via the API.
Do I need to set up a vector database to use RAG?
No. Interlocute manages the entire vector storage and retrieval infrastructure for you. There are no databases to provision, no connection strings to configure, and no embedding models to host. RAG works out of the box once you upload your documents.
How does Interlocute handle document updates and re-indexing?
Interlocute supports incremental document updates. When you add, replace, or remove documents, the platform automatically re-chunks and re-indexes the affected content. Your node's knowledge base stays current without manual intervention or full re-indexing.
Can I control how many chunks are retrieved per query?
Yes. You can configure the number of nearest chunks retrieved (k-nearest) and adjust similarity thresholds to fine-tune the relevance of retrieved context. These settings are configurable per node, so different use cases can have different retrieval strategies.
Is each node's knowledge base isolated from other nodes?
Yes. Every Interlocute node has its own isolated knowledge partition. Documents uploaded to one node are never visible to or retrievable by another node. This isolation is enforced at the infrastructure level, making it safe for multi-tenant and multi-use-case deployments.
How is RAG usage tracked and billed?
RAG operations are metered as part of your node's computation usage. Each retrieval operation — including the embedding of the query and the similarity search — is logged and included in your usage ledger. There are no separate charges for the vector storage; it is included in the platform's usage-based pricing.
Can I use RAG alongside other Interlocute features like memory and scheduling?
Absolutely. RAG, long-term memory, scheduling, streaming, and tool use are all composable features of an Interlocute node. You can enable RAG on a node that also uses persistent memory and scheduled tasks — the features work together without conflicts.

Documentation

RAG Quickstart API Reference

Related Features

Long-term Memory

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Tool Use & Function Calling

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Observability & Logging

Built-in logging, tracing, and token usage visibility for every node call. Understand exactly what your AI is doing and what it costs.

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