Search Results for "query" - Page 5
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Showing 165 open source projects for "query"
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Open Deep Research
An AI-powered research assistant that performs iterative research
Deep Research is a lightweight AI research agent designed to autonomously investigate complex topics through iterative web exploration and reasoning. The project combines search engines, web scraping, and large language models to progressively refine its understanding of a user’s query and dive deeper over multiple cycles. Its core goal is to provide the simplest possible implementation of a deep research workflow so developers can study and extend agent behavior without dealing with large, opaque codebases. The system exposes parameters such as breadth and depth to control how widely and how deeply the agent explores information sources. ... -
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TONL
TONL (Token-Optimized Notation Language)
TONL is a cutting-edge data platform built around a production-ready serialization format designed to be both compact and powerful, combining human readability with performance features that make it suitable for large-scale applications and AI workflows. It provides a serialization format that significantly reduces token usage compared with traditional JSON, which can result in lower costs and more efficient prompt size utilization in LLM-driven systems. TONL isn’t just a format — it... -
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Playwriter
Chrome extension to let agents control your browser
...The system enables browser automation by running Playwright commands through a persistent session managed by a background extension, allowing agents or scripts to navigate, interact with, and query browser contexts without losing state between commands. This makes it valuable for scenarios where AI agents need to perform complex web automation tasks—like multi-step navigation, form interaction, or content extraction—without reinitializing context or state every time. Playwriter’s architecture supports both extension-based control for real browser windows and CLI integration, giving developers flexibility in how they build and run browser automation workflows. -
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Qwen3-VL-Embedding
Multimodal embedding and reranking models built on Qwen3-VL
...The core embedding model maps such inputs into semantically rich vectors in a unified representation space, enabling similarity search, clustering, and cross-modal retrieval. The reranking model then precisely scores relevance between a given query and candidate documents, enhancing retrieval accuracy in complex multimodal tasks. Together, they support advanced information retrieval workflows such as image-text search, visual question answering (VQA), and video-text matching, while providing out-of-the-box support for more than 30 languages. -
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DBHub
Universal database MCP server connecting to MySQL, PostgreSQL
DBHub is a universal database gateway that implements the MCP server interface so assistants and IDEs can explore and query databases through typed tools. It supports multiple transports—stdio for desktop clients and HTTP for networked scenarios—making it flexible to embed or deploy. Configuration is environment-variable driven, with a DSN and per-engine settings covering Postgres, MySQL, MariaDB, SQL Server, and SQLite. Operational flags include read-only mode, row limits, and even SSH tunneling options for secure access into private networks. ... -
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Gorilla
Gorilla: An API store for LLMs
Gorilla is Apache 2.0 With Gorilla being fine-tuned on MPT, and Falcon, you can use Gorilla commercially with no obligations. Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla comes up with the semantically- and syntactically- correct API to invoke. With Gorilla, we are the first to demonstrate how to use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. We also release APIBench, the largest collection of APIs, curated and easy to be trained on! Join us, as we try to expand the largest API store and teach LLMs how to write them! ... -
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zvt
Modular quant framework
For practical trading, a complex algorithm is fragile, a complex algorithm building on a complex facility is more fragile, complex algorithm building on a complex facility by a complex team is more and more fragile. zvt wants to provide a simple facility for building a straightforward algorithm. Technologies come and technologies go, but market insight is forever. Your world is built by core concepts inside you, so it’s you. zvt world is built by core concepts inside the market, so it’s zvt.... -
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Text2Code for Jupyter notebook
A proof-of-concept jupyter extension which converts english queries
...When a user enters a textual command, the extension interprets the request and generates a corresponding Python code snippet that can be inserted into the notebook and executed automatically. The system uses natural language processing techniques to identify the intent of the query, extract relevant variables, and map the request to predefined code templates. Technologies such as sentence embeddings and named entity recognition are used to interpret user instructions and construct appropriate code outputs. -
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LEANN
Local RAG engine for private multimodal knowledge search on devices
LEANN is an open source system designed to enable retrieval-augmented generation (RAG) and semantic search across personal data while running entirely on local devices. It focuses on dramatically reducing the storage overhead typically required for vector search and embedding indexes, enabling efficient large-scale knowledge retrieval on consumer hardware. LEANN introduces a storage-efficient approximate nearest neighbor index combined with on-the-fly embedding recomputation to avoid storing... -
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dataline
AI data analysis and visualization on CSV, Postgres, MySQL, Snowflake
...The platform is designed with a privacy-first architecture that stores data locally on the user’s device rather than sending it to external cloud services by default. It can also hide sensitive data from language models during processing, ensuring that only necessary metadata is used for query generation. -
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trench
Open-Source Analytics Infrastructure
...The system is built on top of high-performance data technologies including Apache Kafka and ClickHouse, which allows it to ingest and process very large volumes of events while maintaining fast query performance. It was originally developed to solve scaling challenges in product analytics systems where traditional relational databases become inefficient as event tables grow. The platform enables developers to collect events such as page views, user actions, and behavioral metrics while storing them in a column-oriented analytics database optimized for time-series workloads. ... -
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AI Powered Knowledge Graph Generator
AI Powered Knowledge Graph Generator
AI-Powered Knowledge Graph is an open-source project focused on building knowledge graph systems that integrate artificial intelligence and machine learning to represent complex relationships between data entities. Knowledge graphs organize information as networks of nodes and relationships, allowing applications to analyze connections between concepts, datasets, or real-world entities. By incorporating AI techniques such as natural language processing and semantic reasoning, the project... -
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DevDocs by CyberAGI
Completely free, private, UI based Tech Documentation MCP server
...Instead of sending entire documents to a language model, DevDocs organizes documentation into sections so that only the most relevant portions are retrieved during a query. This approach reduces token usage and improves the accuracy of responses generated by AI coding assistants. The platform is designed to integrate easily with modern developer tools and AI environments such as Cursor, Cline, and Claude-based workflows. It includes a user interface that allows developers to browse documentation repositories and connect them to AI systems while keeping the data private. -
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DeepSearcher
Open Source Deep Research Alternative to Reason and Search
...It is designed around the idea that high-quality answers require more than top-k retrieval, so it orchestrates multi-step search, evidence collection, and synthesis into a comprehensive response. The project integrates with vector databases (including Milvus and related options) so organizations can index internal documents and query them with semantic retrieval. It also supports flexible embeddings, making it easier to choose different embedding models depending on domain requirements, latency targets, or accuracy goals. The overall workflow aims to minimize hallucinations by grounding outputs in retrieved material and then applying structured reasoning over that evidence before generating a final report. -
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Dash Data Agent
Self-learning data agent that grounds its answers in layers of content
Dash is a self-learning data agent built by the Agno AI community that generates grounded answers to English queries over structured data by synthesizing SQL and reasoning based on six layers of context, improving automatically with each run. It sidesteps common limitations of simple text-to-SQL agents by incorporating multiple context layers — including schema structure, human annotations, known query patterns, institutional knowledge from docs, machine-discovered error patterns, and live runtime context — to generate SQL queries that are both technically correct and semantically meaningful. The system then executes those queries against a database and interprets the results, returning human-friendly insights not just raw rows, while learning from errors and successes to reduce repeated mistakes. -
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Seamless Communication
Foundational Models for State-of-the-Art Speech and Text Translation
...The system architecture includes a real-time multimodal signal pipeline for audio, video, and sensor data, a dialog manager that can decide when to act (speak, gesture, point) or query, and a cross-modal reasoning layer that fuses perception with semantic context. The research prototype includes components for visual grounding (understanding when a user references something in view), gesture recognition and synthesis, and turn-taking mechanisms that mirror human conversational timing. Because latency and synchronization are critical, the codebase invests in asynchronous scheduling, overlap of perception and reasoning, and fast fallback responses. -
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Pal
A personal context-agent that learns how you work
Pal is an open-source AI personal agent built within the Agno ecosystem that functions as an intelligent digital assistant designed to learn from user activity over time. The system acts as an AI-powered “second brain” capable of capturing, organizing, and retrieving personal knowledge such as notes, bookmarks, research findings, people, and meeting information. Instead of acting as a simple chatbot, Pal continuously builds a structured database of a user’s knowledge and context so it can... -
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IQuest-Coder-V1 Model Family
New family of code large language models (LLMs)
IQuest-Coder-V1 is a cutting-edge family of open-source large language models specifically engineered for code generation, deep code understanding, and autonomous software engineering tasks. These models range from tens of billions to smaller footprints and are trained on a novel code-flow multi-stage paradigm that captures how real software evolves over time — not just static code snapshots — giving them a deeper semantic understanding of programming logic. They support native long contexts... -
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Controllable-RAG-Agent
This repository provides an advanced RAG
...The pipeline ingests PDFs, splits them into chapters, cleans and preprocesses text, then constructs vector stores for fine-grained chunks, chapter summaries, and book quotes to support nuanced queries. At query time, it anonymizes entities, creates a high-level plan, de-anonymizes and expands that plan into concrete retrieval or reasoning tasks, and executes them in sequence while continuously revising the plan. A key focus is hallucination control: each answer is verified against retrieved context, and responses are reworked when they are not sufficiently grounded in the source documents. -
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Data Annotator for Machine Learning
Data annotator for machine learning
...Data Annotator for Machine Learning (DAML) is an application that helps machine learning teams facilitate the creation and management of annotations. Active learning with uncertain sampling to query unlabeled data. Project tracking with real-time data aggregation and review process. User management panel with role-based access control. -
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EvoBot
EvoBot is a Discord Music Bot built with TypeScript + Discord.js
EvoBot is a Discord Music Bot built with TypeScript + Discord.js, includes Docker builds & localization in 20+ languages. After installation finishes follow the configuration instructions then run npm run start to start the bot. For those who would prefer to use our Docker container, you may provide values from config.json as environment variables. -
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uweb browser: unlimited power
minimal suckless android web browser with unlimited power
- AI bot as search engine; append file content as input for complex query. - Powerful: html5 enhancement; any urls to host a website; javascript and shell scripting for general processing; and more with Termux. - Customizable: user-defined menus, (new) buttons and gestures for user agents, bookmarklets, url services, shell commands, internal functionality links and text processing etc. - Convenient: book/dictionary/txt/command line/app can be search engine -
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MentDB Projects
Generalized Interoperability and Strong AI
MentDB is an open-source platform driving research into next-generation AI and universal data exchange. Our architecture is built around the revolutionary Mentalese Query Language (MQL). MentDB Weak (Generalized Interoperability): A unified data layer enabling seamless data exchange and application integration (SOA, ETL, Data Quality). We eliminate data silos through a single, generalized data language. MentDB Strong (Strong AI / AGI): The framework for exploring and building Machine Consciousness, free will, and advanced ethical reasoning systems. ... -
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DeepSeek LLM
DeepSeek LLM: Let there be answers
...The model is trained from scratch, reportedly on a vast multilingual + code + reasoning dataset, and competes with other open or open-weight models. The architecture mirrors established decoder-only transformer families: pre-norm structure, rotational embeddings (RoPE), grouped query attention (GQA), and mixing in languages and tasks. It supports both “Base” (foundation model) and “Chat” (instruction / conversation tuned) variants. -
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autollm
Ship RAG based LLM web apps in seconds
...The project focuses on simplifying the usual stack of model selection, document ingestion, vector storage, querying, and API deployment into a more unified developer experience. Its core idea is that a developer can create a query engine from a document set in just a few lines and then turn that same engine into a FastAPI application almost instantly. AutoLLM supports a broad range of language models and vector databases, which makes it useful for teams that want flexibility without rewriting their application architecture every time they switch providers. ...
