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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…

Deploying DeepSeek-R1 and Llama 3.3 Locally with vLLM and Docker

The release of open-weights frontier reasoning models like DeepSeek-R1 and high-parameter foundational models like Llama 3.3 70B has triggered a…

FastAPI Streaming LLM Responses with Server-Sent Events (SSE) in Python

In modern AI applications, users should never stare at a blank screen waiting 10 seconds for a full 500-token response.…

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…

Chunking Strategies for Complex PDFs: Dealing with Tables and Multi-Column Layouts in RAG

Naive chunking algorithms (like fixed-length character splitters with 500 characters and 50 overlap) are the primary reason enterprise Retrieval-Augmented Generation…

FastAPI Streaming LLM Responses with Server-Sent Events (SSE) in Python

In modern AI applications, users should never stare at a blank screen waiting 10 seconds for a full 500-token response.…