Knowledge Base Overview

Managing Information Retrieval with Pingstreams native RAG

Pingstreams offers a powerful Information Retrieval module – the Knowledge Base – purpose-built to deliver accurate, context-aware responses based on your organization’s knowledge.

With the Knowledge Base engine, based on Retrieval Augmented Generation (RAG) paradigm, your AI Agents will access to a unique platform designed to meet the real needs of companies looking for a production-ready Information Retrieval solution based on Agentic-AI.

What makes the Pingstreams solution different from our competitors is in the way the Knowledge Base is administered and delivered in production (automation).

Pingstreams is natively multi-tenant. This means that you can have one single Pingstreams instance (you can install Pingstreams using our open-source distribution) and create multiple projects. Each project is a sandbox where all the AI resources that you need live totally isolated from other projects. This means that with a single Pingstreams instance you can develop and manage multiple complex projects, saving a lot of time and computational resources. Inside a single project you can have multiple automations, multiple teammates collaborating with different roles but above all you have multiple isolated RAGs (the Knowledge Bases)

Administration, Automations and APIs

Pingstreams provides you three different tools to manage your RAG projects, each one with a specific focus.

Administration

A fully featured UI will allow you to create new Knowledge bases, upload and maintain contents indexes, create new AI Agents on the fly etc.

Find more on Administration guide

Automation

Automation flows provide the effective and fast way to use your Knowledge bases. With automation you can design automated responders for your end-users, information retrieval for your colleagues, self-learning to automatically feed your RAGs and much more.

To build your automations you must create a flow using the Ask Knowledge Base Action.

You can also feed your RAG using the Add to Knowledge Base Action.

APIs

You can use APIs to create new Knowledge Bases, index contents and query the information retrieval engine. Please refer to the official Knowledge Base APIs guide.

Technology

When an AI Assistant needs to answer a question, it uses Pingstreams’s hybrid fulltext-semantic search engine to find the most relevant information:

• Fulltext search: finds exact matches of words and phrases in your documents.
• Semantic search: understands the meaning behind the question, even if different words are used compared to the documents.
• Hybrid mode: combines both approaches to return results that are both precise and semantically relevant. (For more details, see our Hybrid search article.)

The AI then generates an answer using this content, ensuring it is consistent with your company’s information and using by default the same language the user adopted for the question.

To effectively use the Knowledge base in your automations you must use the Ask Knowledge Base Action block in you AI flows.

For more details see the How the Knowledge Base works article.

Agentic RAG

RAG is a technique where an AI model retrieves information from a knowledge base before generating its response. This retrieval augments the generation process. Traditional RAG is like a quick lookup. The AI queries a knowledge base, retrieves information, and then generates a response. Crystal clear.

Agentic RAG is more dynamic. Here, the AI agent actively manages how it gets information, integrating RAG into its reasoning process. It’s not just retrieving; it’s refining its queries using reasoning, turning RAG into a sophisticated tool, and managing information over time. This intelligent approach allows AI agents to adapt much better to changing situations.

Key Differences:

• Traditional RAG: Simple – query, retrieve, generate. Typically faster and less expensive.
• Agentic RAG: Dynamic – agent queries, refines, uses RAG as a tool, manages context over time. Works well for asynchronous tasks including research, summarization, and code correction.

Here will follow a list of super use-cases of how you can do Agenti RAG with Pingstreams, from chaining Knowledge, add analytics, use the RAG as a tool for self-learning workflows etc.

Chain of knowledge

Sequentially connect multiple knowledge bases to implement your retrieval strategy.

Pingstreams allows you to sequentially connect multiple knowledge bases: for example, you can prioritize official product documentation and, if there’s no answer from that source, automatically query other sources like KBs coming from self-training or the product website, FAQs etc. This way, the answers are always reliable and verified, while keeping the right retrieval priority and maintaining complete information coverage.

Simple and advanced guard rails

Thanks to the visual designer, you can easily add quality controls, moderation, and verification: for example, by having each response validated by a different model (perhaps with different providers), or by setting specific policies for certain topics or customers, all without code.

Dynamic labeling and analytics

Each response can be dynamically labeled by AI with custom tags that describe its quality, source, or request type, allowing you to precisely monitor the effectiveness of automations and improve the process over time.