Evolvement LLC logoEvolvement LLC
Evolvement LLC logo

TinyLlama & Mistral 7B for Mongo/Cosmos with GPUs

GPU-backed private LLM solutions using TinyLlama and Mistral 7B for secure document analysis, RAG, AI agents, session-scoped retrieval, Cosmos/Mongo metadata, PostgreSQL vectors, and traceable decision support.

What We Build

  • Private LLM application architectures using TinyLlama, Mistral 7B, embeddings, RAG, and GPU-backed inference.
  • Session-scoped file ingestion where uploaded files, extracted text, metadata, feedback, and model outputs are stored in Cosmos DB / Mongo API.
  • PostgreSQL vector stores for embeddings, x/y/z scoring, similarity search, ranking, retrieval, and persistent learning.
  • GPU compute patterns using Databricks, GPU VMs, AKS, or private model-serving endpoints.
  • Secure App Service front ends that coordinate uploads, prompts, retrieval, traceability, feedback, dashboards, and AI-agent actions.

Example Use Cases

  • Document Q&A over PDFs, Word files, spreadsheets, images, feeds, and operational narratives.
  • RAG workflows that retrieve only the current session’s files and cite relevant context.
  • TinyLlama for lightweight local tasks and Mistral 7B for stronger summarization, reasoning, and document analysis.
  • GPU-backed embeddings and inference for faster response times and larger workloads.
  • Persistent learning that stores feedback, outcomes, scores, recommendations, and model effectiveness over time.

Private LLM + Data Architecture

This architecture keeps models, source files, metadata, vectors, and feedback in controlled Azure services. Cosmos DB stores unstructured session records, PostgreSQL stores vector intelligence, GPU compute serves TinyLlama and Mistral, and App Service exposes the secure web experience.

  • TinyLlama: lightweight local model for smaller tasks, fast tests, and constrained environments.
  • Mistral 7B: stronger local model option for summarization, extraction, reasoning, RAG, and AI-assisted workflows.
  • GPU Compute: accelerates inference, embeddings, batch analysis, model testing, and agent workflows.
  • Cosmos/Mongo: stores files, session metadata, extracted text, feedback, model outputs, and unstructured JSON.
  • PostgreSQL: stores embeddings, vector scores, similarity results, ranking outputs, and persistent learning data.

Azure GPU LLM Architecture in Use

The screenshots below are Azure-style visuals packaged locally with this page so they render reliably. They show GPU compute, model endpoints, Cosmos session storage, PostgreSQL vectors, App Service configuration, RAG architecture, and monitoring.

GPU compute for TinyLlama and Mistral

GPU Compute

GPU-enabled compute configuration for LLM inference, embeddings, ML training, simulation, and high-performance workloads.

Private model endpoint

Private Model Endpoint

Inference endpoint pattern for Mistral 7B, TinyLlama, embeddings, retrieval, and session-based generation requests.

Cosmos DB session files

Cosmos DB / Mongo API

Session-scoped unstructured records, files, extracted text, feedback, model outputs, and JSON metadata.

PostgreSQL vector store

PostgreSQL Vector Store

Vector table for embeddings, x/y/z scoring, similarity ranking, recommendations, and traceable RAG retrieval.

App Service LLM configuration

App Service LLM Web App

Secure Python web app configuration for uploads, model calls, Cosmos storage, PostgreSQL vectors, and traceability.

RAG architecture

RAG Architecture

Files, Cosmos, PostgreSQL vectors, GPU models, AI agents, dashboards, and persistent learning working together.

GPU monitoring

GPU LLM Monitoring

Operational dashboard for requests, latency, GPU memory, token throughput, errors, and model health.

Architecture Flow

Inputs

Files, feeds, images, documents, SQL records, user feedback, and operational narratives.

→

Cosmos

Session metadata, extracted text, unstructured JSON, model outputs, and feedback.

→

Postgres

Vectors, x/y/z scoring, similarity, ranking, retrieval, and learning history.

→

GPU Models

TinyLlama, Mistral 7B, embeddings, inference, summarization, and reasoning.

→

Outputs

Answers, citations, dashboards, decisions, alerts, agents, and persistent learning.

This pattern gives organizations a private, traceable AI architecture where each session is isolated, documents are stored as unstructured records, vectors power retrieval, GPUs accelerate model inference, and feedback improves recommendations and outcomes over time.

Business Value

  • Private AI architecture without relying on uncontrolled public data exposure.
  • Faster inference and embeddings using GPU-backed compute.
  • Session-scoped retrieval that reduces cross-session data leakage.
  • Structured traceability between source files, vectors, model outputs, feedback, and dashboards.
  • Persistent learning loops that improve recommendations and operational decision support.

Example Production Flow

  • User uploads files into a secure App Service front end.
  • File metadata, extracted text, and feedback are stored in Cosmos DB.
  • Embeddings and vector scores are stored in PostgreSQL.
  • RAG retrieval selects the most relevant session-scoped records.
  • TinyLlama or Mistral 7B runs on GPU-backed inference.
  • Answer, traceability, score, and feedback are stored for dashboards and persistent learning.
Back to Capabilities