nomic-embed-text
An open-source text embedding model developed by Nomic AI, featuring a context window of 8,192 tokens. The model supports local deployment and is commonly used in building vector databases and semantic retrieval systems.
Core Facts
Timeline (last 90 days)
VaultRAG's tech stack is: LLM and embedding models run locally via Ollama (using Llama 3.1, Qwen and nomic-embed-text), the backend uses Python and FastAPI, RAG orchestration uses LangChain, the frontend uses React with Tailwind CSS, and deployment is managed by Docker Compose.
nomic-embed-text is an open-source text embedding model designed specifically for retrieval scenarios, supports longer context input, can run fully locally, and is a common choice for replacing OpenAI Embeddings in private RAG solutions.
The Smart Connections plugin uses local embeddings (for example nomic-embed-text or mxbai-embed-large via Ollama) to achieve vault-level semantic search.
The experimental environment is built around a single NVIDIA RTX 3090 (24 GB VRAM, 390 W power limit), on which two shared services run: a Whisper ASR transcription service and nomic-embed-text (Ollama).
Each independent process initializing its own CUDA context consumes several hundred MB, which is one reason nomic-embed-text's 0.3 GB weights balloon to 4.6 GB.
When running Whisper ASR, a TTS voice model, and nomic-embed-text simultaneously on an RTX 3090 with 24 GB of VRAM, the three resident models occupied 17.8 GB (72%) of VRAM, peaked at 23.4 GB over the course of a week, and never spilled over into system memory.
nomic-embed-text is a text embedding model with approximately 137 million parameters; its nominal weights are about 0.3 GB, but measured as a resident Ollama service it occupies 4.5–4.7 GB.
nomic-embed-text is an open-source text embedding model released by Nomic AI, designed specifically for RAG scenarios; it encodes text chunks into 768-dimensional vectors and supports a context window of up to 8,192 tokens.
During local deployment, Ollama can be used to provide an embedding model (such as nomic-embed-text), avoiding calls to external APIs.
All Facts (10)
nomic-embed-text is an open-source embedding model that supports long-context input of up to 8,192 tokens
65%UnverifiedVaultRAG's tech stack is: LLM and embedding models run locally via Ollama (using Llama 3.1, Qwen and nomic-embed-text), the backend uses Python and FastAPI, RAG orchestration uses LangChain, the frontend uses React with Tailwind CSS, and deployment is managed by Docker Compose.
50%Unverifiednomic-embed-text is an open-source text embedding model designed specifically for retrieval scenarios, supports longer context input, can run fully locally, and is a common choice for replacing OpenAI Embeddings in private RAG solutions.
50%UnverifiedThe Smart Connections plugin uses local embeddings (for example nomic-embed-text or mxbai-embed-large via Ollama) to achieve vault-level semantic search.
50%UnverifiedThe experimental environment is built around a single NVIDIA RTX 3090 (24 GB VRAM, 390 W power limit), on which two shared services run: a Whisper ASR transcription service and nomic-embed-text (Ollama).
50%UnverifiedEach independent process initializing its own CUDA context consumes several hundred MB, which is one reason nomic-embed-text's 0.3 GB weights balloon to 4.6 GB.
50%Unverifiednomic-embed-text is a text embedding model with approximately 137 million parameters; its nominal weights are about 0.3 GB, but measured as a resident Ollama service it occupies 4.5–4.7 GB.
50%Unverifiednomic-embed-text is an open-source text embedding model released by Nomic AI, designed specifically for RAG scenarios; it encodes text chunks into 768-dimensional vectors and supports a context window of up to 8,192 tokens.
50%UnverifiedWhen running Whisper ASR, a TTS voice model, and nomic-embed-text simultaneously on an RTX 3090 with 24 GB of VRAM, the three resident models occupied 17.8 GB (72%) of VRAM, peaked at 23.4 GB over the course of a week, and never spilled over into system memory.
50%UnverifiedDuring local deployment, Ollama can be used to provide an embedding model (such as nomic-embed-text), avoiding calls to external APIs.
50%