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OpenLink AI Layer
A modern solution for creating, testing, and deploying AI Agents built on loose coupling principles, ensuring platform-independent deployment and usage by design.

The OpenLink AI Layer (OPAL) provides a comprehensive, declarative framework for building AI Agents that can be deployed across webpages, chat widgets, agent stores, or exposed via APIs.

It seamlessly integrates the capabilities of Large Language Models (LLMs) with Database Management Systems, Knowledge Graphs, Document & File Systems, and Retrieval Augmented Generation (RAG) — all while supporting declarative query languages like SQL, SPARQL, and GraphQL.

Opal Architecture Overview

Benefits

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Built-in Retrieval Augment Generation
Facilitates Retrieval Augmented Generation using various declarative query languages in distributed or federated modes, ensuring high-performance access to external databases and knowledge graphs.
https://www.openlinksw.com/data/gifs/opal-built-in-rag-virtuoso-support-assistant.gif
Built-in Retrieval Augment Generation
External OpenAPI Service Support
Enhances functionality by integrating any Web Service described via an OpenAPI document. Each Web Service adds a new action to prompt response production pipelines.
https://www.openlinksw.com/data/screenshots/opal-external-function-config2.png
External OpenAPI Service Support
User-Controlled Mapping of Identity to Session Transcripts
Empowers users to control the mapping of their identity to session transcripts, ensuring privacy and security.
https://www.openlinksw.com/data/gifs/opal-user-controlled-session-mapping.gif
User-Controlled Mapping of Identity to Session Transcripts
Supports Fine-Tuning Templates
Provides integration capabilities with various structured data sources, such as databases and knowledge graphs, through fine-tuning templates.
https://www.openlinksw.com/data/screenshots/opal-predefined-query-templates.png
Supports Fine-Tuning Templates
Shareable Sessions, Prompts, and Responses
Enables the sharing of session transcripts, individual prompts, and responses through hyperlinks, facilitating future reuse and recall.
https://www.openlinksw.com/data/gifs/opal-session-promp-response-sharing.gif
Shareable Sessions, Prompts, and Responses
Multi-Protocol Authentication
Supports data access control through loosely-coupled data access policies, compatible with a variety of authentication protocols, ensuring secure access.
https://www.openlinksw.com/data/screenshots/multi-protocol-authentication-opal.png
Multi-Protocol Authentication
Exportable Session Logs
Offers the functionality to export session data to user-designated storage, allowing for easy data management and retrieval.
https://www.openlinksw.com/data/gifs/opal-exportable-session-logs.gif
Exportable Session Logs
Custom Configuration
Enables behavior control through JSON-based configuration documents, incorporating user preferences and fine-tuning templates for optimal performance.
https://www.openlinksw.com/data/screenshots/opal-llm-and-assitant-selection-config.png
Custom Configuration
Loosely Coupled Architecture
Demonstrates a powerful loosely-coupled architecture through external service integration, custom configurations, and fine-tuning templates, enhancing usage and integration options. The conversational user interface is also fully re-skinable.
https://www.openlinksw.com/data/gifs/opal-loosely-coupled-architecture-larger.gif
Loosely Coupled Architecture
Query Execution
Allows direct execution of queries using SQL, SPARQL, SPASQL, and GraphQL, including access to any query service endpoint for SPARQL and GraphQL.
http://www.openlinksw.com/data/gifs/opal-generate-sql-sparql-spasq-graphql-2.gif
Query Execution

Use Cases

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Data Space driven Retrieval Augmented Generation (RAG)
Constraining hallucinations by Loosely coupling Large Language Models (LLMs) with data managed by database managements, file systems, and knowledge graphs. .
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Open Conversational Data Connectivity
Enabling the direct and safe execution of queries against, and across, disparate data sources using declarative query languages such as SQL, SPARQL, SPASQL (SPARQL inside SQL), and GraphQL from within Large Language Models e.g., the GPT and Mistral families of language models. For instance, directly query disparate line-of-business data sources as part of a AI-driven systems of intelligence.
https://www.openlinksw.com/data/gifs/data-twingler-sql-example.gif
Enhanced Customer, Partner, and Prospect Support
Enabling the use of natural language for finding information with precision and then acting on it immediately.
https://www.openlinksw.com/data/gifs/uda-oracle-odbc-lite-driver-purchase-1.gif
Enhance Interaction with Core Functionality
Use natural language to execute actions such as integrating virtual or physical Knowledge Graphs generated from disparate data sources e.g., collection of CVS documents or ODBC- or JDBC-accessible data.
https://www.openlinksw.com/data/gifs/opal-fint-virtual-kg-test.gif

Frequently Asked Questions

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What is OPAL?
<p>OPAL (OpenLink AI Layer) is a powerful AI-oriented middleware layer that enables the creation, deployment, and use of Smart Agents. It achieves this by loosely coupling Large Language Models (LLMs), OpenAPI-compliant Web Services, and Data Spaces, which include databases, knowledgebases, knowledge graphs, and file systems.</p>
What are the main benefits of OPAL?
<ul><li>Easy development and deployment of cutting-edge Smart Agents or Assistants.</li><li>Mitigation of hallucination effects in AI models through loose coupling with data spaces.</li><li>Enhanced user interaction and experience via natural language interfaces.</li><li>Flexible deployment of Smart Agents across both public and private networks.</li></ul>
What is the OpenLink Personal Assistant (OPAL)?
<p>OPAL is a service hosted on Virtuoso, integrating ChatGPT with additional services for a comprehensive smart agent experience. It supports natural language interactions, offering an enriched Large Language Model (LLM)-based interface for users.</p>
How does OPAL enhance AI model responses?
<p>OPAL includes a built-in Retrieval Augmented Generation (RAG) feature, also known as GraphRAG. This feature enhances LLM response specificity by providing high-performance access to external databases and knowledge graphs using declarative query languages such as SQL, SPARQL, SPASQL, and GraphQL.</p>
How does OPAL handle session logs?
<p>OPAL provides exportable and importable session logs, which aids in session sharing, analysis, and troubleshooting.</p>
Can sessions, prompts, and responses be shared in OPAL?
<p>Yes, OPAL allows the sharing of session transcripts, individual prompts, and responses via hyperlinks, facilitating future reuse and recall.</p>
How do I replay a session in animated playback mode?
<p>Add the <code>&t</code> parameter to a permalink, just before the <code>#</code> fragment identifier. Example: <a href="http://netid-qa.openlinksw.com:8443/chat/?chat_id=s-2ooxbBh4WrhnXRjStCr6pgjYb2EjsUoFM4Ws7wpZZ8zC&t=30#asi-12719">http://netid-qa.openlinksw.com:8443/chat/?chat_id=s-2ooxbBh4WrhnXRjStCr6pgjYb2EjsUoFM4Ws7wpZZ8zC&t=30#asi-12719</a></p>
How do I know what identities have been used in successful OPAL connections?
<p>Execute the following SPARQL query: <code>SELECT ?s FROM urn:chat:users WHERE { ?s foaf:nick [] }</code></p>
How do I interact with OPAL using natural language?
<p>OPAL supports natural language interactions through its integration with LLMs like ChatGPT. You can ask questions, give commands, and interact with Smart Agents as if you were conversing with a human assistant.</p>
What are Smart Agents and how do they work in OPAL?
<p>Smart Agents in OPAL are advanced AI assistants that use LLMs to understand and respond to user queries. They can access and process data from various data spaces, providing relevant and accurate information or actions.</p>
Can I personalize the behavior of Smart Agents?
<p>Yes, Smart Agents can be personalized through custom configurations and fine-tuning templates, allowing them to better meet individual user needs and preferences.</p>
How do I troubleshoot issues with Smart Agents?
<p>For troubleshooting, you can export and analyze session logs, consult OPAL’s documentation, or reach out to support. Detailed logs help in identifying and resolving issues.</p>
What kind of data sources can OPAL connect to?
<p>OPAL can connect to a wide range of data sources including databases, knowledgebases, knowledge graphs, and file systems. It supports standard protocols like HTTP, ODBC, and JDBC for data access and connectivity.</p>
How can OPAL assist with customer, partner, and prospect support?
<p>OPAL offers new solutions that leverage direct conversational interactions with product support knowledge usually held captive by monolithic applications or websites. This enables direct access to relevant information with precision, the ability to perform actions like opening a support ticket, and sharing sessions via hyperlinks.</p>
How can OPAL assist with customer, partner, and prospect sales?
<p>OPAL provides solutions that leverage direct conversational interactions with product sales-related knowledge, offering direct access to relevant sales information, the ability to perform actions like purchasing offers, and sharing sessions via hyperlinks.</p>