Local Deep Research is a self-hosted, privacy-focused AI research assistant that performs deep, agentic research using multiple LLMs and web searches. It provides a full-stack web application (FastAPI + JavaScript), a programmatic API, and a Model Context Protocol (MCP) server. The system ingests user queries, decomposes them, executes parallel searches across web and academic sources (SearXNG, Google, arXiv, PubMed, Semantic Scholar, etc.), fetches and extracts content, evaluates evidence, and synthesizes reports with citations. It features per-user encrypted SQLCipher databases, a settings system with environment variable overrides, a RAG library with FAISS vector store, journal quality scoring, news subscriptions, and extensive security controls (egress policy, SSRF protection, credential scrubbing). The codebase is heavily tested with unit, integration, UI, accessibility, and security tests. It is a hybrid of a web application and a research data pipeline, but the dominant character is a production-grade application.
fakesnow is a Python library that provides a local, in-memory fake implementation of the Snowflake database connector. It uses DuckDB as the underlying SQL engine and sqlglot to transpile Snowflake SQL dialect into DuckDB-compatible queries. The library monkey-patches `snowflake.connector.connect` and `snowflake.connector.pandas_tools.write_pandas`, enabling developers to run Snowflake-dependent code entirely locally without a real Snowflake account. It also includes an optional HTTP server that mimics Snowflake's REST API, allowing non-Python clients (JDBC, Go, .NET) to connect. The project is primarily a test double and development tool, with a strong emphasis on behavior equivalence to real Snowflake. It supports a wide range of Snowflake SQL features, including DDL, DML, COPY INTO, MERGE, semi-structured data (JSON), sequences, stages, file formats, and session variables. The codebase is well-structured with a modular transform pipeline, extensive tests, and CI/CD pipelines for release. However, some features are partially implemented or marked as not yet supported, and the project is tightly coupled to evolving dependencies (DuckDB, sqlglot) which may cause drift.