surveilr for AI RAGs and orchestration of LLMs

Surveilr RSSDs for AI RAGs and AI Data Orchestration

The evolution of AI and Large Language Models (LLMs) has brought about the necessity for Retrieval-Augmented Generation (RAG) engines to enhance contextual understanding and real-time information retrieval. Surveilr, with its SQL-centric architecture, can function as a powerful RAG engine, enabling seamless data orchestration for EOH, Opsfolio, AI Workforce agents, and beyond.

Surveilr as a RAG Engine

Surveilr’s RSSD (Resource-Semantic Storage & Distribution) model allows for efficient ingestion, transformation, and retrieval of structured and unstructured data. Given its deep integration with SQL, Surveilr ensures that any ingested content can be effectively queried and served as context for LLMs.

1. Ingesting Data into Surveilr

Surveilr supports ingestion from multiple sources, including:

  • Files & File Systems – Tracked via uniform_resource and ur_ingest_session_fs_path_entry tables.
  • Emails & Communications – Via ur_ingest_session_imap_acct_folder_message.
  • Project Management Systems – Using ur_ingest_session_plm_acct_project_issue.
  • Databases & Structured Documents – Through ur_ingest_session_udi_pgp_sql.

Each ingested resource benefits from:

  • Content Digest – Ensuring uniqueness and integrity.
  • Metadata & Frontmatter – Structured attributes for better retrieval.
  • Transformations – Supporting multiple versions and format adjustments.

2. Retrieval for LLMs

To act as a RAG engine, Surveilr retrieves relevant content dynamically by:

  • Querying uniform_resource based on semantic similarity (vectorized search on content and frontmatter).
  • Fetching documents using keywords, timestamps, file types, and governance metadata.
  • Using orchestration_session to track and refine retrieval operations.

Example SQL Query for retrieval:

SELECT uri, content, frontmatter
FROM uniform_resource
WHERE content LIKE '%relevant keyword%'
ORDER BY last_modified_at DESC
LIMIT 5;

This ensures that LLMs receive the most recent and relevant information.

3. Augmenting LLM Responses

Once relevant resources are fetched, they can be utilized in multiple ways:

  • Injected into the model’s context using Model Communication Protocol (MCP).
  • Formatted as structured prompts for improved interpretation.
  • Cached for optimized future retrievals.

Example MCP payload:

{
  "query": "What are the latest compliance updates?",
  "retrieved_docs": [
    { "uri": "doc1", "content": "Latest compliance change XYZ..." },
    { "uri": "doc2", "content": "Regulatory updates on ABC..." }
  ]
}

This structured data enhances the LLM’s response generation accuracy.

4. SQL-Based Augmentation & Tracking

Instead of MCP, raw SQL queries can be used for LLM context retrieval. The orchestration_session_exec table allows for execution tracking and retrieval reproducibility.

Example SQL Logging Query:

INSERT INTO orchestration_session_exec
(exec_nature, session_id, exec_code, output_text)
VALUES ('retrieval', 'session_123', 'SELECT * FROM uniform_resource WHERE content LIKE "%AI%"', 'Retrieved 5 documents');

This logs retrieval operations, ensuring transparency and auditability.

Conclusion

Surveilr can be configured as a fully SQL-based RAG engine by:

  1. Ingesting data from diverse sources into RSSDs (uniform_resource, ingest_sessions, etc.).
  2. Retrieving relevant context via SQL queries or orchestration_session_exec.
  3. Augmenting LLM responses using MCP or direct SQL-based augmentation.
  4. Tracking retrieval processes for evidence-based AI decision-making.

By leveraging its structured data management capabilities, Surveilr positions itself as a powerful tool for AI RAGs and advanced data orchestration.