In modern AI applications, users should never stare at a blank screen waiting 10 seconds for a full 500-token response. Streaming tokens in real-time as they are generated by the model is the expected UX standard.
In this tutorial, we …
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 introduces irrelevant documents (false positives), the language model either hallucinates or produces diluted, unfocused answers.
In 2026, the industry standard …
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 (RAG) pipelines fail when deployed against real-world PDF documents. When a standard text splitter parses a PDF with two-column financial …
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. Streaming tokens in real-time as they are generated by the model is the expected UX standard.
In this tutorial, we …
Building an Autonomous Research Agent with Python and DuckDuckGo / Tavily Search API
When building enterprise Retrieval-Augmented Generation (RAG) systems in 2026, relying purely on dense vector embeddings (like OpenAI text-embedding-3 or BGE) introduces severe blind spots. Dense vectors excel at semantic similarity—understanding that ‘laptop’ and ‘notebook computer’ mean the same thing. However, …
Local LLM Tool Calling with Ollama and Python: Step-by-Step Implementation
Running Large Language Models locally has evolved from basic chat interfaces into sophisticated autonomous systems capable of executing deterministic software tools. With modern models like Llama 3.1, Qwen 2.5, and Mistral supporting native tool calling, developers can build private, zero-token-cost …
Hybrid Search Explained: Combining BM25 Keyword Search and Vector Embeddings in RAG
When building enterprise Retrieval-Augmented Generation (RAG) systems in 2026, relying purely on dense vector embeddings (like OpenAI text-embedding-3 or BGE) introduces severe blind spots. Dense vectors excel at semantic similarity—understanding that ‘laptop’ and ‘notebook computer’ mean the same thing. However, …
LangGraph StateGraph Tutorial: Building Multi-Agent Workflows in Python
While simple sequential chains (like those in early LangChain) work for basic question-answering, production AI applications require loops, conditional branching, human-in-the-loop approvals, and multi-agent debate. In 2026, LangGraph has become the premier framework for developing complex, cyclic agent architectures.
In …
Building Production RAG with PostgreSQL and pgvector in Python
Retrieval-Augmented Generation (RAG) is the industry standard for grounding Large Language Models in private domain data. While specialized vector databases like Pinecone, Chroma, and Qdrant are popular, managing a separate vector database creates data sync headaches, dual authentication configurations, and …
Build a Model Context Protocol (MCP) Server in Python: Complete Step-by-Step Guide
The Model Context Protocol (MCP), pioneered by Anthropic in late 2024 and adopted across the developer ecosystem in 2025 and 2026, standardizes how Large Language Models securely access local and remote data sources, developer tools, and enterprise APIs. Before MCP, …
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