Open Source Semantic Search Tools - Page 2
Sort By:
Semantic Search Tools
View 42 business solutions-
Inventory and Order Management Software for Multichannel SellersWe are the most powerful inventory and order management platform for Amazon, Walmart, and multichannel product sellers. Centralize orders, product information, and fulfillment operations to run more efficiently, sell more products, and stay compliant with marketplace requirements so you can grow profitably.
-
Failed Payment Recovery for Subscription BusinessesFlexPay’s innovative platform uses multiple technologies to achieve the highest number of retained customers, resulting in reduced involuntary churn, longer life span after recovery, and higher revenue. Leading brands like LegalZoom, Hooked on Phonics, and ClinicSense trust FlexPay to recover failed payments, reduce churn, and increase customer lifetime value.
-
1
txtai
Build AI-powered semantic search applications
txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. Traditional search systems use keywords to find data. Semantic search applications have an understanding of natural language and identify results that have the same meaning, not necessarily the same keywords. Backed by state-of-the-art machine learning models, data is transformed into vector representations for search (also known as embeddings). Innovation is happening at a rapid pace, models can understand concepts in documents, audio, images and more. Machine-learning pipelines to run extractive question-answering, zero-shot labeling, transcription, translation, summarization and text extraction. Cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes). Applications range from similarity search to complex NLP-driven data extractions to generate structured databases. The following applications are powered by txtai. -
2
PaperAI
Semantic search and workflows for medical/scientific papers
PaperAI is an open-source framework for searching and analyzing scientific papers, particularly useful for researchers looking to extract insights from large-scale document collections. -
3
SentenceTransformers
Multilingual sentence & image embeddings with BERT
SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. You can use this framework to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning. This can be useful for semantic textual similar, semantic search, or paraphrase mining. The framework is based on PyTorch and Transformers and offers a large collection of pre-trained models tuned for various tasks. Further, it is easy to fine-tune your own models. Our models are evaluated extensively and achieve state-of-the-art performance on various tasks. Further, the code is tuned to provide the highest possible speed. -
4
pgai
A suite of tools to develop RAG, semantic search, and other AI apps
pgai is a suite of PostgreSQL extensions developed by Timescale to empower developers in building AI applications directly within their databases. It integrates tools for vector storage, advanced indexing, and AI model interactions, facilitating the development of applications like semantic search and Retrieval-Augmented Generation (RAG) without leaving the SQL environment. -
Premier Construction SoftwareRated #1 Construction Accounting Software by Forbes Advisor in 2022 & 2023. Our modern SAAS solution is designed to meet the needs of General Contractors, Developers/Owners, Homebuilders & Specialty Contractors.
-
5
Microsoft Learn MCP Server
Official Microsoft Learn MCP Server, powering LLMs and AI agents
Microsoft Learn MCP Server is the official GitHub repository for the Microsoft Learn MCP (Model Context Protocol) Server, a service that implements the Model Context Protocol to provide AI assistants and tools with reliable, real-time access to Microsoft’s official documentation. Rather than relying on training data that may be outdated or incomplete, MCP servers let agents like GitHub Copilot, Claude, or other LLM-based tools search and pull context directly from up-to-date Microsoft Learn content, including Azure, .NET, and other tech docs. By connecting to the MCP endpoint, coding agents can answer questions, retrieve code examples, and offer best practices grounded in authoritative sources without requiring API keys or manual browser searches. This capability helps eliminate hallucinations, improve accuracy, and streamline developer workflows by keeping relevant tech guidance close at hand. -
6
Open Semantic Search
Open source semantic search and text analytics for large document sets
Open Semantic Search is an open source research and analytics platform designed for searching, analyzing, and exploring large collections of documents using semantic search technologies. It provides an integrated search server combined with a document processing pipeline that supports crawling, text extraction, and automated analysis of content from many different sources. Open Semantic Search includes an ETL framework that can ingest documents, process them through analysis steps, and enrich the data with extracted information such as named entities and metadata. It also supports optical character recognition to extract text from images and scanned documents, including images embedded inside PDF files. It integrates text mining and analytics capabilities that allow users to examine relationships, topics, and structured data within document collections. -
7
Python Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, source code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API. You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models. -
8
SolidGPT
Developer AI Persona Search Agent
SolidGPT is a AI searching assistant for developers that helps code and workspace semantic search. -
9
OpenAI Cookbook
Examples and guides for using the OpenAI API
openai-cookbook is a repository containing example code, tutorials, and guidance for how to build real applications on top of the OpenAI API. It covers a wide range of use cases: prompt engineering, embeddings and semantic search, fine-tuning, agent architectures, function calling, working with images, chat workflows, and more. The content is primarily in Python (notebooks, scripts), but the conceptual guidance is applicable across languages. The repository is kept up to date and often expanded, and its examples are intended to serve both beginners and intermediate users of the API. It also includes deployment recipes, integration snippets (e.g. with GitHub Actions), and production considerations. Because OpenAI’s API evolves rapidly, the Cookbook acts as a living, community-curated reference to show “how to do X with the API” rather than only reprinting documentation. -
AestheticsPro Medical Spa SoftwareAestheticsPro is the most complete Aesthetics Software on the market today. HIPAA Cloud Compliant with electronic charting, integrated POS, targeted marketing and results driven reporting; AestheticsPro delivers the tools you need to manage your medical spa business. It is our mission To Provide an All-in-One Cutting Edge Software to the Aesthetics Industry.
-
10
PHP Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API. You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models. Pass the model you want to use and the NLP Cloud token to the client during initialization. If you are making asynchronous requests, you will always receive a quick response containing a URL. -
11
RAG API
ID-based RAG FastAPI: Integration with Langchain and PostgreSQL
rag_api is an open-source REST API for building Retrieval-Augmented Generation (RAG) systems using LLMs like GPT. It lets users index documents, search semantically, and retrieve relevant content for use in generative AI workflows. Designed for rapid prototyping, it is ideal for chatbot development, document assistants, and knowledge-based LLM apps. -
12
Supermemory
Memory engine and app that is extremely fast, scalable
Supermemory is an ambitious and extensible AI-powered personal knowledge management system that aims to help users capture, organize, retrieve, and reason over information in a manner that mimics human memory structures. The platform allows individuals to ingest text, documents, and other content forms, then uses advanced retrieval and embedding techniques to index and relate information intelligently so that users can recall relevant knowledge in context rather than just by keyword match. It often incorporates clustering, semantic search, and summarization modules to reduce cognitive load and surface key ideas, which makes it useful for research, study, writing, and long-term project tracking. Users can interact with the system via conversational queries or traditional search interfaces, and the system leverages vector embeddings and memory scoring to prioritize the most relevant results. -
13
Vector AI
A platform for building vector based applications
Vector AI is a framework designed to make the process of building production-grade vector-based applications as quick and easily as possible. Create, store, manipulate, search and analyze vectors alongside json documents to power applications such as neural search, semantic search, personalized recommendations etc. Image2Vec, Audio2Vec, etc (Any data can be turned into vectors through machine learning). Store your vectors alongside documents without having to do a db lookup for metadata about the vectors. Enable searching of vectors and rich multimedia with vector similarity search. The backbone of many popular A.I use cases like reverse image search, recommendations, personalization, etc. There are scenarios where vector search is not as effective as traditional search, e.g. searching for skus. Vector AI lets you combine vector search with all the features of traditional search such as filtering, fuzzy search, and keyword matching to create an even more powerful search. -
14
ChatGPT Retrieval Plugin
The ChatGPT Retrieval Plugin lets you easily find personal documents
The chatgpt-retrieval-plugin repository implements a semantic retrieval backend that lets ChatGPT (or GPT-powered tools) access private or organizational documents in natural language by combining vector search, embedding models, and plugin infrastructure. It can serve as a custom GPT plugin or function-calling backend so that a chat session can “look up” relevant documents based on user queries, inject those results into context, and respond more knowledgeably about a private knowledge base. The repo provides code for ingestion pipelines (embedding documents), APIs for querying, local server components, and privacy / PII detection modules. It also contains plugin manifest files (OpenAPI spec, plugin JSON) so that the retrieval backend can be registered in a plugin ecosystem. Because retrieval is often needed to make LLMs “know what’s in your docs” without leaking everything, this plugin aims to be a secure, flexible building block for retrieval-augmented generation (RAG) systems. -
15
FlagEmbedding
Retrieval and Retrieval-augmented LLMs
FlagEmbedding is an open-source toolkit for building and deploying high-performance text embedding models used in information retrieval and retrieval-augmented generation systems. The project is part of the BAAI FlagOpen ecosystem and focuses on creating embedding models that transform text into dense vector representations suitable for semantic search and large language model pipelines. FlagEmbedding includes a family of models known as BGE (BAAI General Embedding), which are designed to achieve strong performance across multilingual and cross-lingual retrieval benchmarks. The toolkit provides infrastructure for inference, fine-tuning, evaluation, and dataset preparation, enabling developers to train custom embedding models for specific domains or applications. It also includes reranker models that refine search results by re-evaluating candidate documents using cross-encoder architectures, improving retrieval accuracy in complex queries. -
16
Hugging Face Transformer
CPU/GPU inference server for Hugging Face transformer models
Optimize and deploy in production Hugging Face Transformer models in a single command line. At Lefebvre Dalloz we run in-production semantic search engines in the legal domain, in the non-marketing language it's a re-ranker, and we based ours on Transformer. In that setup, latency is key to providing a good user experience, and relevancy inference is done online for hundreds of snippets per user query. Most tutorials on Transformer deployment in production are built over Pytorch and FastAPI. Both are great tools but not very performant in inference. Then, if you spend some time, you can build something over ONNX Runtime and Triton inference server. You will usually get from 2X to 4X faster inference compared to vanilla Pytorch. It's cool! However, if you want the best in class performances on GPU, there is only a single possible combination: Nvidia TensorRT and Triton. You will usually get 5X faster inference compared to vanilla Pytorch. -
17
ModernBERT
Bringing BERT into modernity via both architecture changes and scaling
ModernBERT is an open-source research project that modernizes the classic BERT encoder architecture by incorporating recent advances in transformer design, training techniques, and efficiency improvements. The goal of the project is to bring BERT-style models up to date with the capabilities of modern large language models while preserving the strengths of bidirectional encoder architectures used for tasks such as classification, retrieval, and semantic search. ModernBERT introduces architectural improvements that enhance both training efficiency and inference performance, making the model more suitable for modern large-scale machine learning pipelines. The repository also includes FlexBERT, a modular framework that allows developers to experiment with different encoder building blocks and configurations when constructing new models. -
18
Semantra
Multi-tool for semantic search
Semantra is an open-source semantic search tool designed to help users explore large collections of documents by meaning rather than simple keyword matching. The software analyzes text and PDF documents stored locally and creates embeddings that allow queries to retrieve results based on conceptual similarity. It is primarily intended for individuals who need to extract insights from large document collections, including researchers, journalists, students, and historians. The system runs from the command line and automatically launches a local web interface where users can perform interactive searches and examine document passages related to a query. By relying on semantic embeddings and contextual analysis, the tool can identify passages that are relevant even when the query uses different wording than the source documents. -
19
SimpleMem
SimpleMem: Efficient Lifelong Memory for LLM Agents
SimpleMem is a lightweight memory-augmented model framework that helps developers build AI applications that retain long-term context and recall relevant information without overloading model context windows. It provides easy-to-use APIs for storing structured memory entries, querying those memories using semantic search, and retrieving context to augment prompt inputs for downstream processing. Unlike monolithic systems where memory management is ad-hoc, SimpleMem formalizes a memory lifecycle—write, index, retrieve, refine—so applications can handle user history, document collections, or dynamic contextual state systematically. It supports customizable embedding models, efficient vector indexes, and relevance weighting, making it practical for building assistants, personal agents, or domain-specific retrieval systems that need persistent knowledge. -
20
ViMax
Director, Screenwriter, Producer, and Video Generator All-in-One
ViMax is an open-source framework for performing large-scale multi-modal vision-language modeling and reasoning by combining powerful image encoders with advanced language models to solve complex visual tasks. It integrates components like visual encoders, cross-modal fusion techniques, and reasoning modules so that users can go beyond simple captioning or classification to perform tasks such as visual question answering, multi-image inference, and structured scene understanding. ViMax’s design accommodates large image sets and supports retrieval augmentation, enabling it to work with external image databases, supplementary metadata, and semantic search to enhance context awareness. The system aims to bridge foundational vision backbones and generative language models through adapters and fusion layers that maximize both signal integration and reasoning depth, and includes utility pipelines for training, evaluation, and deployment. -
21
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. The framework addresses common problems in automatic knowledge graph construction, particularly sparsity and duplication of entities, by applying a clustering and entity-resolution process that merges semantically similar nodes. This allows the generated graphs to be denser, more coherent, and easier to use for downstream tasks such as retrieval-augmented generation, semantic search, and reasoning systems. -
22
A SOA framework for web content classification, clustering and automated interlinking of terms between documents. Will provide an expandable set of services such as semantic search, ranking, retrieval and classification of large scale web resources.
-
23
Catalogo is a system for cataloguing resources on a web site. It allows semantic search of information on an intranet using metadata, RDF and ontology concepts. It provides a Catalog server (Java web applications) and a Catalog client (Firefox plug-in).
-
24
QTE Technologies-Industrial-Scientific
1M+ Industrial & Scientific MRO Metadata for AI and Research
This is the official open-data repository for QTE Technologies, providing a comprehensive archive of over 1,000,000 industrial and scientific MRO (Maintenance, Repair, and Operations) records. Optimized for Industrial AI training, RAG applications, and semantic search, this dataset includes technical specifications, global standards, and manufacturer metadata. Verification & Authority: Managed via DVC on DagsHub. Archived on Zenodo, Harvard Dataverse, and Figshare. Linked Data via Wikidata (Q138411149). Built for engineers, data scientists, and procurement professionals worldwide. -
25
Controllable-RAG-Agent
This repository provides an advanced RAG
Controllable-RAG-Agent is an advanced Retrieval-Augmented Generation (RAG) system designed specifically for complex, multi-step question answering over your own documents. Instead of relying solely on simple semantic search, it builds a deterministic control graph that acts as the “brain” of the agent, orchestrating planning, retrieval, reasoning, and verification across many steps. 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.
