Search Results for "structured text"
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Showing 38 open source projects for "structured text"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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text-extract-api
Document (PDF, Word, PPTX ...) extraction and parse API
text-extract-api is an open-source service designed to extract readable text from a wide variety of document formats through a simple API interface. The project focuses on converting complex files such as PDFs, images, scanned documents, and office files into structured plain text that can be processed by downstream applications or language models. -
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MarkPDFDown
A high-quality PDF to Markdown tool based on large language model
...By producing Markdown rather than raw text, the tool makes it easier to integrate documents into knowledge bases, documentation systems, or language model pipelines that rely on structured input. The software is particularly useful for developers working with technical documents, academic papers, or reports that need to be indexed, summarized, or processed by downstream AI systems. -
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kg-gen
Knowledge Graph Generation from Any Text
kg-gen is an open-source framework developed by the STAIR Lab that automatically generates knowledge graphs from unstructured text using large language models. The system is designed to transform plain text sources such as documents, articles, or conversation transcripts into structured graphs composed of entities and relationships. Instead of relying on traditional rule-based extraction techniques, KG-Gen uses language models to identify entities and their relationships, producing higher-quality graph structures from raw text. -
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Sparrow
Structured data extraction and instruction calling with ML, LLM
Sparrow is an open-source platform designed to extract structured information from documents, images, and other unstructured data sources using machine learning and large language models. The system focuses on transforming complex documents such as invoices, receipts, forms, and scanned pages into structured formats like JSON that can be processed by downstream applications. It combines several components, including OCR pipelines, vision-language models, and LLM-based reasoning modules to... -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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BAML
The AI framework that adds the engineering to prompt engineering
BAML is an open-source framework and domain-specific language designed to bring structured engineering practices to prompt development for large language model applications. Instead of treating prompts as unstructured text, BAML introduces a schema-driven approach where prompts are defined as typed functions with explicit inputs and outputs. This design allows developers to treat language model interactions as predictable software components rather than ad-hoc prompt strings. ... -
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node-llama-cpp
Run AI models locally on your machine with node.js bindings for llama
...The system automatically detects the available hardware on a machine and selects the most appropriate compute backend, including CPU or GPU acceleration. Developers can use the library to perform tasks such as text generation, conversational chat, embedding generation, and structured output generation. Because it runs models locally, the platform is particularly useful for privacy-sensitive environments or offline AI deployments. -
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NarratoAI
Using AI models to automatically provide commentary and edit videos
NarratoAI is an open-source platform designed to automate the generation of narrative content using artificial intelligence. The system combines large language models with media processing capabilities to create scripts, stories, and structured narrative outputs from user inputs. NarratoAI supports workflows where users provide prompts, themes, or source materials, and the software organizes them into coherent narrative structures suitable for articles, scripts, or multimedia storytelling. The project integrates multiple AI components such as text generation models, content structuring pipelines, and automated editing tools to streamline content creation. ... -
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repo2txt
Web-based tool converts GitHub repository contents
repo2txt is an open-source developer tool that converts the contents of a code repository into a single structured text file that can be easily consumed by large language models. The tool is designed to address the challenge of analyzing entire codebases with AI assistants, where code is normally distributed across many files and directories. By collecting repository contents and formatting them into a single text document, repo2txt allows developers to feed complete projects into AI systems for analysis, documentation, or code explanation tasks. ... -
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Engram
A New Axis of Sparsity for Large Language Models
Engram is a high-performance embedding and similarity search library focused on making retrieval-augmented workflows efficient, scalable, and easy to adopt by developers building search, recommendation, or semantic matching systems. It provides utilities to generate embeddings from text or other structured data, index them using efficient approximate nearest neighbor algorithms, and perform real-time similarity queries even on large corpora. Engineered with speed and memory efficiency in mind, Engram supports batched indexing, incremental updates, and custom distance metrics so developers can tailor search behaviors to their domain’s needs. ... -
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POML
Prompt Orchestration Markup Language
POML, or Prompt Orchestration Markup Language, is a structured markup language created to improve the organization and maintainability of prompts used in large language model applications. Traditional prompt engineering often relies on unstructured text, which can become difficult to manage as prompts grow more complex and incorporate dynamic data sources. POML addresses this issue by introducing an HTML-like syntax that allows developers to organize prompts into structured components such as roles, tasks, and examples. ... -
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Kor
LLM
This is a half-baked prototype that “helps” you extract structured data from text using LLMs. Specify the schema of what should be extracted and provide some examples. Kor will generate a prompt, send it to the specified LLM and parse out the output. You might even get results back. -
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GraphRAG
A modular graph-based Retrieval-Augmented Generation (RAG) system
The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs. -
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Hollama
A minimal LLM chat app that runs entirely in your browser
...Because the application runs as a static web interface, it does not require complex backend infrastructure and can be easily deployed or self-hosted. Hollama supports both text-based and multimodal interactions, allowing users to work with models that process images as well as text. The interface includes features for editing prompts, retrying responses, copying generated code snippets, and storing conversation history locally within the browser. Mathematical expressions can be rendered using KaTeX, and Markdown formatting allows code blocks and structured outputs to appear clearly within conversations. -
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Mirascope
LLM abstractions that aren't obstructions
Mirascope is a powerful, flexible, and user-friendly library that simplifies the process of working with LLMs through a unified interface that works across various supported providers, including OpenAI, Anthropic, Mistral, Gemini, Groq, Cohere, LiteLLM, Azure AI, Vertex AI, and Bedrock. Whether you're generating text, extracting structured information, or developing complex AI-driven agent systems, Mirascope provides the tools you need to streamline your development process and create powerful, robust applications. -
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files-to-prompt
Concatenate a directory full of files into a single prompt
...It includes rich filtering controls, letting you limit by extension, include or skip hidden files, and ignore paths that match glob patterns or .gitignore rules. The output format is flexible: you can emit plain text, Markdown with fenced code blocks, or a Claude-XML style format designed for structured multi-file prompts. It can read file paths from stdin (including NUL-separated paths), which makes it easy to combine with find, rg, or other shell tools. -
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DocETL
A system for agentic LLM-powered data processing and ETL
DocETL is an open-source system designed to build and execute data processing pipelines powered by large language models, particularly for analyzing complex collections of documents and unstructured datasets. The platform allows developers and researchers to construct structured workflows that extract, transform, and organize information from sources such as reports, transcripts, legal documents, and other text-heavy data. Instead of relying on single prompts or ad-hoc scripts, DocETL provides a declarative pipeline framework that breaks complex document analysis tasks into manageable operations that can be optimized and orchestrated automatically. ... -
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LlamaIndex
Central interface to connect your LLM's with external data
LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. LlamaIndex is a simple, flexible interface between your external data and LLMs. It provides the following tools in an easy-to-use fashion. Provides indices over your unstructured and structured data for use with LLM's. These indices help to abstract away common boilerplate and pain points for in-context learning. Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when... -
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RAPTOR
The official implementation of RAPTOR
RAPTOR is a retrieval architecture designed to improve retrieval-augmented generation systems by organizing documents into hierarchical structures that enable more effective context retrieval. Traditional RAG systems typically retrieve small text chunks independently, which can limit a model’s ability to understand broader document context. RAPTOR addresses this limitation by recursively embedding, clustering, and summarizing documents to create a tree-structured hierarchy of information. Each level of the tree represents summaries at different levels of abstraction, allowing retrieval to operate at both detailed and high-level conceptual layers. ... -
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LandPPT
An LLM-based presentation generation platform
...The system allows users to create complete PowerPoint presentations simply by entering a topic or uploading source documents such as PDFs, Word files, or Markdown notes. Using natural language processing and structured content generation, the platform produces presentation outlines and converts them into fully formatted slide decks. The application integrates multiple AI models from providers such as OpenAI, Anthropic, Google, and locally hosted models to generate text, images, and structured presentation layouts. It also includes template systems and style options that allow presentations to be customized for different industries, visual themes, or storytelling formats. -
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LOTUS
AI-Powered Data Processing: Use LOTUS to process all of your datasets
LOTUS is an open-source framework and query engine designed to enable efficient processing of structured and unstructured datasets using large language models. The system provides a declarative programming model that allows developers to express complex AI data operations using high-level commands rather than manually orchestrating model calls. It offers a Python interface with a Pandas-like API, making it familiar for data scientists and engineers already working with data analysis libraries. ... -
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StarVector
StarVector is a foundation model for SVG generation
...Its architecture combines computer vision techniques with language modeling capabilities so it can understand visual inputs and textual prompts simultaneously. The model converts raster images or text instructions into structured vector representations, enabling high-quality vectorization and design generation. This approach allows StarVector to create scalable graphics that maintain visual quality regardless of resolution, which is especially useful for design tools and illustration workflows. Because the model produces SVG code rather than pixel images, the output can be edited programmatically or integrated directly into web and design environments. -
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Qwen-2.5-VL
Qwen2.5-VL is the multimodal large language model series
...The models are available in various sizes, including 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B parameters, catering to diverse computational requirements. Trained on a comprehensive dataset of up to 18 trillion tokens, Qwen2.5 models exhibit significant improvements in instruction following, long-text generation (exceeding 8,000 tokens), and structured data comprehension, such as tables and JSON formats. They support context lengths up to 128,000 tokens and offer multilingual capabilities in over 29 languages, including Chinese, English, French, Spanish, and more. The models are open-source under the Apache 2.0 license, with resources and documentation available on platforms like Hugging Face and ModelScope. -
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MyScaleDB
A @ClickHouse fork that supports high-performance vector search
...The system is built on top of the ClickHouse database engine and extends it with specialized indexing and search capabilities optimized for vector embeddings. This design allows developers to store structured data, unstructured text, and high-dimensional vector embeddings within a single database platform. MyScaleDB enables developers to perform vector similarity searches using standard SQL syntax, eliminating the need to learn specialized vector database query languages. The database is optimized for high performance and scalability, allowing it to handle extremely large datasets and high query loads typical of production AI applications. -
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Aix-DB
Based on the LangChain/LangGraph framework
Aix-DB is an open-source intelligent data analysis platform that combines large language models with database technologies to enable conversational data exploration. The system is designed as a ChatBI solution that allows users to query datasets using natural language and receive structured insights, charts, and visualizations automatically. Built on frameworks such as LangChain and LangGraph, Aix-DB integrates retrieval-augmented generation and Text-to-SQL capabilities to convert user questions into executable database queries. The platform supports multiple types of data sources and provides an end-to-end pipeline that includes intent recognition, SQL generation, database execution, and visual presentation of results. ... -
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PaperBanana
Extension of Google Research’s PaperBanana
PaperBanana is an open-source agentic framework designed to automatically generate publication-quality academic diagrams and statistical plots directly from text descriptions. The project focuses on helping researchers, educators, and data scientists transform conceptual descriptions of figures into structured visual outputs suitable for research papers, presentations, and technical reports. Instead of manually designing charts or diagrams using traditional visualization tools, users can describe the desired figure in natural language and allow the system to generate the visual representation automatically. ...
