Coming Soon
Intermediate

Solix GPT & RAG Framework Implementation

Built exclusively for AI Engineers and Developers, this technical bootcamp pulls back the curtain on the Solix GPT framework to teach you how to architect production-ready Retrieval-Augmented Generation (RAG) systems. Operating on the industry-standard Langchain framework, you will learn the exact mechanics of both real-time and offline workflows —ranging from document chunking strategies and vector embeddings via Sentence Transformers to building parallel hybrid query engines that execute on-the-fly Text-to-SQL conversions. By combining unstructured semantic similarity searches with exact, record-based database results , you will master how to feed perfectly contextualized payloads into models like Llama-2 to deliver flawless, verifiable enterprise answer.

Instructor-Led
20 hours
4 modules
Recommended Course Audience

Recommended Course Audience

AI Engineers LLM Developers Solution Architects
Pre-Required Skills

Pre-Required Skills

  • Pre-Required Skills Ckeck
    Experience: 1-2 years of software engineering or data science experience, with solid exposure to Python.
  • Pre-Required Skills Ckeck
    Core Technical Skills: Good understanding of APIs; foundational knowledge of machine learning concepts (what an LLM is, what vectors are).
  • Pre-Required Skills Ckeck
    Systems Exposure: Familiarity with the terminal, Python environments, and database queries.
Course Highlights

Course Highlights

4 modules
Deep dive into the Solix GPT RAG framework built on top of the Langchain framework. Architectural separation: Managing the differences between real-time query processing and offline data ingestion preparation workflows. Analyzing Query Reception and Dual-Path Processing mechanics to fetch both deep semantic context and exact record details. Mapping the foundational APIs that connect user interfaces straight to enterprise Large Language Models.
Preparing raw data foundations: Parsing unstructured files and executing the Chunks of Data strategy. Extracting document metadata to preserve structural boundaries within individual text chunks. Deploying an Embedding Engine using Sentence Transformers to convert text fragments into numerical arrays (Vector Embeddings). Designing, indexing, and maintaining a high-scale Vector Store optimized for near-instant similarity lookups.
Managing real-time Query Vectorization: Transforming a live user text prompt into a Query-Vector Embedding instantly. Executing fast, low-latency Vector-Based Similarity Searches against active vector stores. Constructing the Structured Data Path: Treating natural language queries as incoming SQL data inputs. Engineering Text-to-SQL conversions and running the resulting SQL query directly against relational target databases to yield exact records.
Engineering the crucial Merge Similarity Search + SQL Result orchestration layer. Algorithmic combining of semantic context from similarity lookups with precise record-based database queries. Constructing the unified context payload: Formatting text blocks and database variables for optimal token consumption. Grounding the LLM: Passing the enriched payload into Llama-2 to generate factual, accurate, and completely auditable responses.
Solix GPT & RAG Framework Implementation
$699.00

This course is coming soon. Enrollment is not yet open.

Level : Intermediate
Modules : 4
Duration : 20 hours