6 projects tagged with "llm"
- Python FastAPI with JWT Auth serving RAG by Hugging Face embeddings (v2)
21-August-2026Python FastAPI with JWT Auth serving a RAG Application using Groq + Hugging Face embeddings (v2) - hosted at Vercel Cloud using Serverless Functions
There is a Vue 3 SPA for the FastAPI
A Starter FastAPI + JWT Auth + Retrieval-Augmented Generation (RAG) by Groq LLM + Real embeddings by Hugging Face model + Vue 3 SPA + OpenAPI / Swagger - secured by HTTPS
A PostgreSQL database was used with the pgvector extension
During the development process, I used ChatGPT for assisting with code generation and Github Copilot for code inline suggestion
DevOps by VS Code + GitHub + Vercel Cloud
The Vue SPA at GitHub - Python FastAPI with JWT Auth serving a Tool-Calling AI Agent using LangChain
21-August-2026Python FastAPI with JWT Auth serving a Tool-Calling AI Agent using LangChain - hosted at Vercel Cloud using Serverless Functions
There is a Vue 3 SPA for the FastAPI
A production-style FastAPI backend built with LangChain, Groq LLMs, and JWT authentication. The system implements a modern tool-calling AI agent architecture that automatically decides when to use external tools such as Wikipedia for factual retrieval
A Starter FastAPI + JWT Auth + AI Agent + LangChain + Vue 3 + OpenAPI / Swagger - secured by HTTPS
During the development process, I used ChatGPT for assisting with code generation and Github Copilot for code inline suggestion
DevOps by VS Code + GitHub + Vercel Cloud
The Vue SPA at GitHub - Python FastAPI with JWT Auth serving a Tool-Calling AI Agent
25-July-2026Python FastAPI with JWT Auth serving a Tool-Calling AI Agent - hosted at Render Cloud by Webservice
The AI agent implements a modern 3-phase Tool Agent Pipeline (Plan → Execute → Synthesize) where tools are safely selected, executed, and used to generate grounded responses using a minimal of Langchain
There is a Vue 3 SPA for the FastAPI
A Starter FastAPI + JWT Auth + AI Agent + Vue 3 + OpenAPI / Swagger - secured by HTTPS
During the development process, I used ChatGPT for assisting with code generation and Github Copilot for code inline suggestion
DevOps by VS Code + GitHub + Render Cloud
The Vue SPA at GitHub - Python FastAPI with JWT Auth serving RAG by fake Embeddings (v1)
08-July-2026Python FastAPI with JWT Auth serving a RAG Application using Groq + fake embeddings (v1) - hosted at Vercel Cloud using Serverless Functions
A Starter FastAPI + JWT Auth + Retrieval-Augmented Generation (RAG) by Groq LLM + fake embeddings + OpenAPI / Swagger - secured by HTTPS
A PostgreSQL database was used with the pgvector extension
During the development process, I used ChatGPT for assisting with code generation and Github Copilot for code inline suggestion
DevOps by VS Code + GitHub + Vercel Cloud
The Web API at GitHub - Python FastAPI with JWT Auth serving a ReAct-inspired AI agent system
03-July-2026Python FastAPI with JWT Auth serving a ReAct-inspired AI agent system - hosted at Vercel Cloud using Serverless Functions
The AI agent system follows a lightweight ReAct-inspired flow. A simple router determines when to use a Wikipedia tool, and the retrieved context is passed to the model to generate the final answer using a minimal of Langchain
A Starter FastAPI + JWT Auth + AI Agent system + OpenAPI / Swagger - secured by HTTPS
During the development process, I used ChatGPT for assisting with code generation and Github Copilot for code inline suggestion
DevOps by VS Code + GitHub + Vercel Cloud
The Web API at GitHub - AI Agent Design Patterns
07-June-2026ReAct (Reason + Act)
Concept: Alternate between reasoning steps and taking actions (tool calls, API calls, etc.)
Flow:
- LLM generates reasoning / thought
- Take action (call a tool or external system)
- Observe output and feed it back into reasoning
Strengths: Multi-step problem solving, tool orchestration, grounded responses
Use case: Complex question answering, multi-tool agents, decision-making systems
Tool-Calling / Tool-Augmented Agents
Concept: The LLM acts as a controller that decides whether to call external tools
Flow:
- LLM receives input
- LLM selects which tool(s) to call
- Execute tool(s)
- Synthesize final answer from tool output
Strengths: Reduces hallucinations, improves grounding, enables use of APIs and external systems
Use case: Wikipedia-style assistants, math solvers, structured data retrieval
Reflex / Reactive Agents
Concept: Immediate response without planning or multi-step reasoning
Flow: Input → Response
Strengths: Very fast, low complexity, low cost
Use case: Chatbots, simple Q&A systems
Plan-and-Execute / Hierarchical Planning Agents
Concept: The agent first creates a plan, then executes steps sequentially
Flow:
- LLM generates a plan
- Execute each step using tools or computations
- Return final result
Strengths: Strong for complex workflows and multi-step tasks
Use case: Automation systems, workflow orchestration, research agents
Debate / Self-Reflection Agents
Concept: Multiple candidate outputs are generated and evaluated before final selection
Flow:
- Generate multiple candidate answers
- Critique or evaluate each candidate
- Select the best final output
Strengths: Reduces errors, improves reliability, reduces hallucinations
Use case: Code review, summarization, high-accuracy Q&A
Memory-Augmented Agents
Concept: The agent stores and retrieves long-term memory to maintain context
Flow:
- Retrieve relevant past memory
- Reason using current input + memory
- Update memory with new information
Strengths: Personalization, continuity, long-term context awareness
Use case: Personal assistants, long-running agents, adaptive systems
Summary- ReAct: reasoning + acting with tools in loops
- Tool-Calling: LLM chooses external tools
- Reflex: direct single-step response
- Plan-and-Execute: plan first, then execute steps
- Debate: multiple outputs + self-evaluation
- Memory-Augmented: persistent context over time