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GenAI

Comprehensive developer guides for Generative AI systems, foundation models, and autonomous intelligence. Learn how to deploy local LLMs with vLLM and Docker, run DeepSeek-R1 and Llama 3.3 with PagedAttention and continuous batching, and integrate bleeding-edge open-weights models into enterprise microservices.

Migrating from OpenAI Assistants API to LangGraph in Python: Cutting Enterprise AI Costs by 80%

During the initial generative AI wave, many enterprise engineering teams in the US and UK built conversational workflows and internal…

Human-in-the-Loop AI Agents: Implementing Approval Breakpoints in LangGraph

Fully autonomous AI agents are powerful, but deploying them in production without safety boundaries is dangerous. An autonomous agent equipped…

Fixing Ollama Connection Refused (HTTP 11434) in Python and Docker

When calling a local Ollama instance from Python scripts, AI agents, or Docker containers, you may encounter:…

Fixing CUDA Out of Memory (OOM) When Serving DeepSeek-R1 with vLLM

When deploying DeepSeek-R1 (such as DeepSeek-R1-Distill-Qwen-14B or 32B) using vLLM, encountering a sudden torch.cuda.OutOfMemoryError: CUDA out of memory during startup…

Quantizing and Running DeepSeek-R1 with Ollama, llama.cpp, and GGUF in Python

The release of DeepSeek-R1 has transformed open-source artificial intelligence. By matching or exceeding OpenAI o1 on complex reasoning, mathematics, and…

Building a Multimodal RAG Pipeline in Python with Qdrant and CLIP Embeddings

Standard Retrieval-Augmented Generation (RAG) pipelines are built exclusively for textual data. They parse markdown files, split paragraphs into token chunks,…

Building an Agentic SQL Data Analyst in Python with DuckDB and LangChain

In enterprise data analytics, querying relational warehouses has traditionally required specialized SQL knowledge or static business intelligence dashboards. With modern…

Handling Tool Calling Errors, Retries, and Fallbacks in Autonomous AI Agents with Python

In autonomous artificial intelligence systems, Large Language Models (LLMs) do not operate in a vacuum. To perform real-world work, agents…

Connecting Claude & AI Agents to Custom Local MCP Tools in Python

The release of the open-source Model Context Protocol (MCP) by Anthropic has fundamentally solved the fragmented landscape of AI tool…

Reranking in RAG Pipelines: When and How to Use Cross-Encoders vs Bi-Encoders

Retrieval-Augmented Generation (RAG) performance is bounded by the quality of the context supplied to the LLM. If your retrieval step…