Building a Local AI-Powered Research Assistant with DeepSeek

Utility Function Design

🎯 Course Description:In this hands-on course, you’ll learn how to build a fully local, intelligent research assistant using LLMs like DeepSeek, and search APIs such as DuckDuckGo, SearXNG, Tavily, and Perplexity. Inspired by the agentic loop of human research, the system continuously refines its understanding of a topic by querying the web, summarizing findings, identifying knowledge gaps, and generating improved search queries—all in a fully offline or privacy-conscious environment. You'll not only build individual search wrappers but also orchestrate the entire research flow, including formatting sources, summarizing content, deduplicating URLs, fetching raw content, and generating final markdown reports with traceable sources. The final result is a reusable, extensible AI research framework that respects your local-first, data-sovereignty mindset. 🧩 Chapters & Modules:✅ Chapter 1: Introduction to Local Research AgentsOverview of the Local Deep Researcher architecture LLM + Search Loop: Thinking, Searching, Reflecting, Summarizing Real-world use cases and benefits ✅ Chapter 2: Environment Setup & Tools InstallationInstalling Python libraries (langsmith, duckduckgo_search, tavily, markdownify, etc.) Setting up LLM runners (Ollama, LM Studio) Configuring API keys and environment variables ✅ Chapter 3: Building Multi-Source Search WrappersDuckDuckGo using DDGS SearXNG using langchain_community Tavily API Perplexity API integration Fetching and structuring search results ✅ Chapter 4: Fetching & Cleaning Raw Web ContentExtracting HTML content using httpx Converting HTML to Markdown with markdownify Handling missing content and errors ✅ Chapter 5: Deduplication and Source FormattingDeduplicating search results by URL Formatting search results using title, snippet, URL, and full content Token-limit management for raw content ✅ Chapter 6: Bullet-Point & Markdown Source Report FormattingCreating minimal source summaries Generating markdown-formatted outputs for clarity Adding source citations to enhance credibility ✅ Chapter 7: LLM-Driven Reflection and Query RefinementIntegrating DeepSeek for reflection Identifying information gaps in summaries Regenerating search queries to close knowledge loops ✅ Chapter 8: Final Report GenerationStructuring full markdown reports Merging sources, summaries, and citations Saving and exporting the final result ✅ Chapter 9: Robust Error Handling and ExtensibilityRetry logic for failing APIs Handling incomplete or malformed search results Making the tool extensible via configuration flags ✅ Chapter 10: Deployment & Optional FrontendPackaging your assistant into a CLI tool Optional UI: Build with Gradio or Streamlit Local execution with no cloud dependencies