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Embedding models convert text into numerical representations that enable search by meaning rather than exact keywords. Social impact organizations can use these open weight models to build searchable knowledge bases over their own documents, program records, and transcripts, all running on infrastructure they control. Retrieval augmented generation (RAG) connects this search capability to a language model, producing answers grounded in your organization's own data rather than general training knowledge.
Last updated: February 20, 2026 | Tech To The Rescue | Open Source AI series, Part 2 of 4
How embedding models let you build a searchable knowledge base from your organization's own documents
Embedding models are AI systems that convert text into arrays of numbers (called vectors) that capture the meaning of words, sentences, or documents. Two pieces of text about the same topic produce similar vectors even when they use completely different words. This enables semantic search: finding relevant information based on meaning rather than requiring exact keyword matches.
For social impact organizations, this solves a common and concrete problem. A program manager searching for 'community health outcomes' should find documents about 'patient wellness metrics' or 'beneficiary health indicators' even when those exact phrases aren't in the query. Traditional keyword search misses these connections. Embedding models close that gap.
Many embedding models are small enough to run on consumer hardware. Organizations working with sensitive program data can keep all search processing on their own infrastructure. No data leaves the building to get results.
Selecting the right embedding model means comparing performance on standardized benchmarks. The MTEB Leaderboard (Massive Text Embedding Benchmark) ranks models across retrieval, classification, clustering, and other tasks. Scores are independently calculated by the research community, not by the model authors themselves, which gives organizations a reliable comparison point.
Three factors guide model selection for social impact organizations. Language coverage determines whether the model handles the languages present in your documents. Model size affects whether it can run locally or requires cloud infrastructure. Task performance on the specific MTEB retrieval subtask indicates how well the model will function as a search engine for your knowledge base.
Open source platforms let you filter models by task type, language, and size, then test candidates through API access before committing to a deployment. This means you can evaluate several options on your actual data before making a decision.
For multilingual search, which covers the needs of many social impact organizations, Qwen3-Embedding-0.6B supports 100+ languages, runs efficiently on modest hardware, and is licensed under Apache 2.0. For organizations that want something even more lightweight, EmbeddingGemma (308M parameters) runs entirely in a browser with no local installation.
Retrieval augmented generation (RAG) is a technique that connects a search system to a language model. When someone asks a question, the system first searches for relevant documents using an embedding model, then passes those documents to a language model that generates an answer grounded in the retrieved sources.
RAG addresses a key limitation of language models: they can only work with information from their training data or from content explicitly provided in the prompt. By connecting a language model to your organization's own document collection through embedding-based search, RAG produces answers that cite specific internal sources rather than generating responses from general training knowledge.
A practical example: a social impact organization could build a RAG system over its program evaluation reports, enabling staff to ask questions like 'What were the outcomes of our 2023 community health pilot?' and receive answers drawn directly from the relevant report, with source attribution. The same system works over meeting transcripts, field notes, intake records, or any document collection the organization has digitized.
Organizations that use open weight transcription tools can feed those transcripts directly into a RAG system, making spoken content from meetings and interviews searchable by meaning. This creates a unified, searchable archive across both written and spoken records.
An embedding model converts text into numerical vectors that represent meaning. Two sentences about the same concept produce similar vectors even when they use different words. Social impact organizations use embedding models to build search systems that find relevant documents based on meaning, not just exact keyword matches.
The MTEB (Massive Text Embedding Benchmark) Leaderboard ranks embedding models across standardized tasks including retrieval, classification, and clustering. Scores are independently calculated by the research community, not by model authors, giving organizations a reliable comparison tool. The leaderboard is updated as new models are published.
RAG (retrieval augmented generation) connects a search system to a language model. When a user asks a question, the system retrieves relevant documents first, then generates an answer grounded in those sources. RAG matters because it produces answers from your organization's own data, reducing the risk of inaccurate responses that can occur when language models rely only on general training knowledge.
Basic semantic search over documents can be set up using pre-built open source tools without deep coding knowledge. Production RAG systems typically require developer support for document processing, database setup, and API integration. Tech To The Rescue's AI Impact Lab pairs organizations with pro bono technology teams who build these systems.
Organizations interested in building search and knowledge retrieval tools can apply to the AI Impact Lab with a specific AI implementation idea. Pro bono technology partners in the TTTR ecosystem have built custom AI solutions for social impact organizations across health, education, climate, and economic opportunity.
Register to explore the AI Impact Lab and AI Impact Scaling Program: techtotherescue.org/social-impact-organizations
Free open source FAQ guide: github.com/huggingface/faq
