Knowledge Base
📝 Context Summary
Summary: Blueprint for an Enterprise AI Assistant
This guide outlines the architecture for building a compact, powerful, and secure Enterprise AI Assistant using open-source models. The system leverages Retrieval-Augmented Generation (RAG) to provide answers grounded in internal company documents while enforcing critical enterprise policies like data redaction and access control.
Core Components and Technologies
| Component | Technology / Model | Purpose |
|---|---|---|
| Text Generation | google/flan-t5-base |
A sequence-to-sequence model that generates human-like answers based on a given prompt. |
| Text Embedding | sentence-transformers/all-MiniLM-L6-v2 |
A model that converts text (document chunks and queries) into numerical vectors for similarity comparison. |
| Vector Search | FAISS (Facebook AI Similarity Search) |
A library for efficient similarity search. It stores the document vectors and quickly finds the most relevant ones for a user’s query. |
| Policy Guardrails | Custom Regular Expressions (Regex) | Rules designed to detect and block queries that violate security policies and to redact Personally Identifiable Information (PII). |
System Workflow: From Query to Answer
The assistant follows a multi-step process to ensure responses are accurate, relevant, and compliant.
1. Initialization and Corpus Preparation
– Load Models: The FLAN-T5 (generation) and MiniLM (embedding) models are loaded.
– Prepare Documents: A corpus of internal enterprise documents (e.g., security policies, runbooks, SOPs) is defined.
– Chunking: These documents are broken down into smaller, overlapping text chunks to improve the accuracy of the retrieval process.
2. Vector Indexing
– Embed Chunks: The MiniLM model converts each text chunk into a numerical vector (embedding).
– Build Index: All vectors are stored in a FAISS index, which allows for rapid retrieval of the most relevant chunks for any given query.
3. Query Processing and RAG Pipeline
This is the core loop that executes when a user submits a query.
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Step A: Policy Check
- The user’s query is first scanned against a set of predefined security rules (e.g., to prevent data exfiltration or tampering with encryption).
- If a violation is detected, the request is immediately rejected with an explanatory message.
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Step B: Retrieve
- The user’s query is converted into a vector embedding using the MiniLM model.
- This query vector is used to search the FAISS index, which returns the
kmost similar document chunks. This retrieved text serves as the context for the answer.
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Step C: Augment (Build Prompt)
- A structured prompt is constructed that includes:
- System Instructions: A directive for the AI (e.g., “You are an enterprise AI assistant. Answer strictly from the provided CONTEXT.”).
- Retrieved Context: The relevant document chunks fetched in the previous step.
- User Question: The original user query, with any PII automatically redacted.
- Output Instructions: Guidelines for the answer format (e.g., “Cite sources inline,” “Keep it concise”).
- A structured prompt is constructed that includes:
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Step D: Generate
- The complete, augmented prompt is passed to the FLAN-T5 model.
- The model generates a final answer that is grounded in the provided context, adheres to the system instructions, and maintains any redactions.
4. Evaluation
– The system’s performance is tested with sample queries to evaluate the relevance of the retrieved context and the accuracy of the final answer.
This architecture provides a complete, self-contained blueprint for creating a scalable, auditable, and secure AI assistant for enterprise use.
