# Explore memory in vector space

A visual tour of text, image, video, audio, geospatial, and time-series representations.

Author: Murali Sid (https://linkedin.com/in/muralisid)
Source: https://www.agenticarchitectureskills.com/memory/vector-spaces
Published: 2026-09-18
Updated: 2026-10-15
Licence: CC BY-SA 4.0, https://creativecommons.org/licenses/by-sa/4.0/
Cite as: Murali Sid. Explore memory in vector space. Agentic Architecture Skills. https://www.agenticarchitectureskills.com/memory/vector-spaces

---


You have chosen what the agent should remember. Now choose what a search should treat as similar. These pictures explain the intuition; they are optional deeper reading after the memory foundations.

## Different information needs different representations [#different-information-needs-different-representations]

An enterprise holds words, photographs, recordings, maps, and readings over time. Turning all of them into short text summaries is convenient. But a summary can lose what a later question needs. That may be a visual feature, a sequence of events, a surface pattern, or a changing signal.

The illustrations are **conceptual maps, not measured embeddings**. Start with text, then look at what changes for other forms of information.

## Text: find related meaning [#text-find-related-meaning]

<TeachingIllustration src="/images/memory/text-space-v1.webp" title="Text: find related meaning" alt="Text encoder maps two differently worded pump-pressure passages near each other and a leave-policy passage elsewhere. A query retrieves nearby passages while retaining the original source, version, and permissions." caption="Text similarity helps locate a passage. It does not establish whether that passage is current or applicable." />

An embedding represents selected features as numbers so a search can find useful similarities. Picture a space in which related examples lie near each other. What counts as related depends on the model and its training. Real spaces use many numbers for each item. A flat picture cannot show every relationship.

A representation is not the whole memory. Keep the original sources, exact records, dates, and access rules alongside it. Start with the kind of question the agent will ask.

"Pump pressure is falling" and "low discharge pressure" may belong near each other even though their words differ. This helps find relevant procedures or earlier incidents. An exact asset number, policy version, or quantity still needs an exact check.

[Explore this memory type](/memory/text-embeddings).

## Images: find visual resemblance [#images-find-visual-resemblance]

<TeachingIllustration src="/images/memory/image-space-v1.webp" title="Images: find visual resemblance" alt="Two inspection photographs of corroded pipe joints map to nearby points while clean-valve images form a different group. An aligned text-image model can connect a phrase to visual results. Each result links back to the original image." caption="Inspect the original image, date, and asset. Visual resemblance is not a diagnosis." />

A model can place visually related inspection images near each other. Then you can look for earlier examples of corrosion or damage. With a suitably aligned text-image model, a written query can also find images. What counts as similar depends on training.

[Explore this memory type](/memory/image-embeddings).

## Video: find a similar event [#video-find-a-similar-event]

<TeachingIllustration src="/images/memory/video-space-v1.webp" title="Video: find a similar event" alt="A timestamped CCTV clip becomes a representation of an event interval. Similar filmstrips are grouped together; a retrieved result opens the original clip with its camera, start and end time, and surrounding context." caption="Keep timestamps and original clips. A vector is not a directly readable event chronology." />

A video contains change: a person approaches a loading zone, a vehicle moves, or an inspection unfolds. A suitable representation can help search for similar clips or events. Finding a related video is different from locating the exact interval that supports a claim.

[Explore this memory type](/memory/video-embeddings).

## Audio: find a similar sound [#audio-find-a-similar-sound]

<TeachingIllustration src="/images/memory/audio-space-v1.webp" title="Audio: find a similar sound" alt="Pump recordings with rattling patterns are grouped separately from a steady hum. Each match opens a recording with equipment and interval metadata. A speech transcript is shown as a separate representation." caption="Listen to the original and check operating conditions. Similar sounds need not have the same cause." />

A pump rattle and a spoken maintenance instruction need different kinds of information. Sound representations can help find acoustic resemblance; a transcript can help find words. Choose the form that preserves what the question asks about.

[Explore this memory type](/memory/audio-embeddings).

## Geospatial data: find similar places or change [#geospatial-data-find-similar-places-or-change]

<TeachingIllustration src="/images/memory/geospatial-space-v1.webp" title="Geospatial data: find similar places or change" alt="Earth patches containing forest, water, fields, and mining ground map into groups by learned surface characteristics rather than physical distance. Yearly representations can be compared, while coordinates, observation intervals, and source observations remain attached." caption="Nearby in the representation does not mean nearby on Earth. Annual patterns do not identify an exact event date." />

A place can be represented by learned surface characteristics and, in some models, behavior over a period. Distant areas may be similar in that space. Comparing the same location across years can guide a change investigation, while exact coordinates and dates remain explicit.

[Explore this memory type](/memory/location-and-satellite-data).

## Time-series data: find similar behavior [#time-series-data-find-similar-behavior]

<TeachingIllustration src="/images/memory/time-series-space-v1.webp" title="Time-series data: find similar behavior" alt="Operating windows with growing oscillations and a spike form a different group from steady signals. A query finds comparable historical windows and returns original readings and confirmed outcomes, with units, duration, and operating state." caption="A similar operating pattern suggests an investigation. It does not establish the same underlying cause." />

The useful resemblance may be a developing oscillation, a spike, or a sequence of changes across pressure and vibration. This lets an agent ask, "What behaved like this before?" Decide whether shape, magnitude, duration, or operating state should matter.

[Explore this memory type](/memory/time-series).

## These are different spaces, not one universal memory map [#these-are-different-spaces-not-one-universal-memory-map]

Two models can produce vectors of the same length without giving those coordinates the same meaning. Search within a suitable space and combine the resulting evidence by asset, place, time, and source. A jointly trained or explicitly aligned model can support some cross-modal comparisons; do not assume every combination is aligned.

These six types describe the **form of information**. A different question is how long an agent should retain it and how it should be stored. Task context, past events, approved procedures, source records, summaries, and relationships can each use different representations. The [six memory designs](/memory/six-architectures) examine those storage choices.

**Continue the story:** [Search text by meaning](/memory/text-embeddings).