Local AI memory can mean saved facts about you, a searchable index of your files, or both. In document retrieval, software turns text into embeddings, searches for relevant passages, and places those passages in the language model’s prompt. Whether that whole process stays on your computer depends on where the files, databases, embedding service, model, logs, and backups actually run—not simply on a “local” label.
What “memory” means in a local AI assistant
Two different features are often called memory:
- Saved user memories are selected facts or preferences, such as how you like answers formatted. An assistant may add these to later conversations as context.
- Document retrieval, often called retrieval-augmented generation (RAG), searches indexed files or conversation material for passages relevant to a question.
They can work together, but they are not the same system. A saved preference is a piece of user information; a document index is a way to find source material. Open WebUI describes persistent memories as manageable snippets and says they are stored in its local database and scoped to the user account by default. Its settings allow users to manage memories and control whether they are injected into the system context. Open WebUI’s Memory & Personalization documentation explains these controls.
How document memory retrieves an answer
RAG is a sequence of steps: prepare source text, represent it for search, retrieve likely matches, then give the selected text to the language model. The model generates the response; the search system supplies context rather than an answer guarantee.
- Extract and split text. The application reads a document and divides its text into smaller units, or chunks, that can be searched and later supplied as context.
- Create embeddings. An embedding model converts each chunk into a numeric vector designed to represent semantic meaning. Ollama describes embeddings as “long arrays of numbers that represent semantic meaning for a given sequence of text.” Ollama’s embedding models article explains how vectors can be compared to find semantically similar text.
- Store searchable data. The system keeps vectors alongside source text or references and related metadata. The text matters: a vector is a representation for comparison, not a substitute for the passage that the assistant must read.
- Embed the question and search. When you ask something, the configured embedding model represents the query in the same searchable space. Retrieval finds candidate chunks by similarity, and may also use full-text matching or metadata filters.
- Add retrieved passages to the prompt. The application places selected text in the language model’s prompt. The model uses that context to compose a response, but it can still misunderstand, omit, or misstate what the passages say.
Open WebUI’s RAG documentation describes the query-embedding, vector-search, and prompt-context path. The embedding model used to build an index should be compatible with the one used to embed queries; changing that configuration may require re-indexing, depending on the application.
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What embeddings do—and what they do not do
An embedding is a numerical representation used to compare text by semantic similarity. This is why a search can sometimes find a passage that uses different wording from the question. For example, a query asking how to “remove a saved user fact” might surface a passage about deleting a memory, even when the wording does not match exactly.
Embeddings are not the original document, a memory policy, or a database on their own. Nor do they prove that a passage is relevant or that a generated answer is correct. A usable retrieval system needs to connect search results back to the associated text, document identity, and any metadata needed to interpret them.
Where memories, documents, and models are stored
There is no single universal “local AI memory” folder or database. Depending on the application, separate components may store chat history, saved user facts, vector representations, source text, uploaded originals, metadata, and model files in different places.
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For example, Chroma supports storing documents and metadata with embeddings, along with dense and sparse vector search, full-text and regular-expression search, metadata filtering, and multimodal retrieval. Its usage guide also explains that adding documents can trigger embedding and that the supplied documents are stored. Chroma’s introduction and usage guide describe these capabilities.
Open WebUI’s storage depends on configuration and deployment. Its scaling documentation says uploaded files are stored by default on the local filesystem under DATA_DIR, typically /app/backend/data. That default is specific to Open WebUI; another installation may use another path or storage service. Open WebUI’s scaling guide covers storage and deployment choices.
Which search methods matter?
Search methods solve different matching problems, and an application may combine them:
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- Semantic or vector search can find related meaning when a question and a passage use different words.
- Full-text or lexical search can help when the exact wording, identifier, or phrase matters.
- Metadata filters can narrow results by properties such as document attributes or collection.
Chroma documents support for vector and full-text search and metadata filtering. These options do not establish that one method is always more accurate: results depend on the corpus, query, configuration, and what the user is trying to find.
Does local AI store your chats, and does memory leave your computer?
Those are separate questions. A chat application may save conversation history even if it does not turn every message into a persistent user memory. A RAG system may index uploaded documents without treating them as profile facts. Check the application’s own storage and memory settings to see which kinds of data it retains and how they can be removed.
“Local” is a description of the configured data path, not proof that every step happens on the same device. To assess where information goes, check:
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- Where chat records, saved memories, uploaded originals, indexes, and logs are stored.
- Whether the language model runs on the device or is reached through a remote endpoint.
- Whether embeddings are generated locally or sent to an external embedding service.
- Whether backups, shared storage, or other deployment services copy data elsewhere.
Open WebUI supports local embedding as well as external embedding engines, and its storage arrangements vary by deployment. A locally run language model paired with a remote embedding endpoint still sends text outside the device for embedding. Open WebUI’s RAG documentation and scaling guide are relevant to checking those choices.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What affects reliability and resource use?
Saved memories are conveniences, not guaranteed records. Open WebUI warns that model quality affects memory behavior and that small local models may store or retrieve information inconsistently. Review saved facts, correct errors, and delete information you no longer want retained. Its documentation also describes optional background review and controls for context injection. Open WebUI’s memory documentation details those features.
Embedding workloads also use resources. Open WebUI’s Essentials documentation says its default local SentenceTransformers embedding engine runs on CPU and uses roughly 500 MB of RAM per worker. That is a configuration-specific figure, not a general hardware requirement for every local AI application. Open WebUI’s Essentials guide provides the configuration context.
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Storage choices also affect deployment. An embedded database may suit a simple, single-user installation, while concurrent workers, network storage, or a larger deployment make concurrency behavior and supported integrations more important. Open WebUI documents limitations for its SQLite-backed Chroma default in multi-worker settings and describes alternatives including PGVector and Chroma HTTP mode. The appropriate choice depends on the deployment’s concurrency, storage, backup, recovery, and maintenance needs; no one database is best for every setup. See the scaling guide.
How to evaluate a local memory setup
Before relying on an assistant for persistent facts or private files, trace what happens to one representative question and one representative document. Check which memory feature is involved, what gets indexed, which service creates embeddings, what is sent to the language model, and where each component persists data. Test retrieval with both paraphrased questions and exact terms from your own files, then inspect whether the returned passages support the answer.
For privacy, verify the model and embedding endpoints as well as storage, logs, and backups. For reliability, inspect and correct saved facts and confirm that retrieved passages are relevant. For deployment, confirm that the database and storage backend suit the number of users and workers you actually run.
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