Fix data transformation methods in stock LSTM prediction

Add computer vision models for image classification and object detection; update Dockerfile and requirements

Remove training script for stock LSTM model and update dependencies; add support for new ticker D05.SI

Fix upload message formatting and load environment variables for HF_TOKEN

Add LSTM training script and update requirements for model artifacts

Revert main to a344aea state

Comment out yfinance and tensorflow in requirements.txt to streamline CPU-only setup for the RNN stock-LSTM demo

Uncomment torchvision dependency in requirements.txt and re-enable yfinance and tensorflow for demo functionality

Uncomment torchvision dependency in requirements.txt to enable image processing support

Comment out torchvision dependency in requirements.txt to streamline CPU-only setup

Update requirements and improve stock LSTM demo handling; adjust cache TTL values

Update requirements.txt to specify CPU-only PyTorch and torchvision versions; add extra index URL for CPU wheels

Add image classification and object detection routes; include MobileNet and Faster R-CNN models

Refactor caching logic to improve cacheability determination based on conversation context; ensure session turns are persisted even on cache hits.

Update RAG_SYSTEM_PROMPT to restrict output format; disallow Markdown or HTML tables for tabular data presentation

Implement data-source access control for SQL agent; add caching for user access profiles and enhance query processing with access validation

Refactor multi-agent chat handling with improved caching, logging, and session management; enhance database schema for feedback logging

Enhance message routing with LLM integration, add image request override, and improve routing logic

Refactor agents to use asynchronous clients, enhance SQL execution with connection pooling, and improve rate-limiting logic

Enhance multi-agent routing with improved logging, timeout handling, and input sanitization

Add detailed Product Requirements Document for FastAPI Multi-Agent + RAG Service

Implement admin user approval flow and enhance user status verification

Update README to clarify `TOP_K`/`FETCH_K` settings and enhance explanation of history-aware query rewriting

Add history-aware query rewriting and improve follow-up detection in RAGAgent

Add caching to embedding function to reduce latency during RAG queries

Add beginner-friendly explanation of RAG pipeline with detailed steps for data ingestion, preparation, embedding, search, and answer generation

Enhance README with bold formatting for clarity in RAG techniques and evaluation metrics

Add history-aware query rewriting and groundedness scoring to RAG agent

Revise README for clarity and structure; enhance Quick Start and Environment Variables sections

Enhance README with detailed RAG techniques and ingestion sequences; update agentic flow chart for clarity

Increase expiration time for public document URLs to 3600 seconds and enhance URL handling for website-sourced documents

Refactor RAG chunking and retrieval logic; add match threshold and metadata filters for enhanced document retrieval

Add cross-encoder reranking support for improved answer accuracy and update requirements

Update CORS middleware to allow additional origins for frontend access

Refactor token decoding logic to enhance support for asymmetric and symmetric JWTs

Refactor MessageRouter to replace sales and inventory keywords with database-related keywords

Enhance README with detailed Multi-Agent Chat flow and request processing diagrams

Add Multi-Agent Chat flow diagram to README

Add image generation functionality with DeepSeek and Hugging Face integration

Remove infographic generation functionality and related imports from agents orchestration

Add Docker support with docker-compose for FastAPI and Redis setup

Refactor SQL queries to improve aliasing for clarity and consistency

Refactor SQL queries to improve readability and consistency in naming conventions

Increase max_tokens limit for SQLAgent to enhance response capacity

Refactor SQL example inputs to use consistent aliasing for clarity and readability

Revert "Clarify SQL example inputs in system prompt to enhance user understanding and ensure consistency in query structure"

Clarify SQL example inputs in system prompt to enhance user understanding and ensure consistency in query structure

Revise SQL response prompts to clarify the order of results presentation, emphasizing business-friendly summaries followed by markdown tables.

Strip LLM preamble from SQL statements by identifying the start of the SQL query

Enhance SQL response formatting by requiring markdown tables for query results in prompts