Практика Shadowing: AI Agent Memory Architecture: Structured SQL, Semantic Vector Search & Memory Bank - Изучайте разговорный английский по видео

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Hi, I'm Annie.

If your agent can hear, see, and take action, the next question is, what should I remember after the session ends?

In this episode, I will show you the architecture for long -term memory in a multi -model agent,

facts in structure memory, understanding in semantic memory, and raw audio or video outside of memory.

Hi there.

Hey there.

Good for that customer dinner tomorrow

or just browsing oh i want

you suggest something for customer dinner tomorrow based on my wish list for sure you have those leather ankle boots

and a classic black pump on your list either would go great with

that red cocktail dress you bought but

if you want something a bit more relaxed for dinner maybe mix it up with the plaid button -up

and classic denim jeans all right let's start with the two types of the memory when you're talking to a multimodal agent.

It feels like the agent remembers everything.

It remembers what you said five minutes ago.

It can use what it just heard.

But most of that is short -term memory.

It is like RAM.

It is useful while the session is running, but it's gone when the session ends.

So the real design question is not, can we save everything?

The real question is, what is worth carrying into the next session?

And And that memory comes in two kinds.

The first kind is facts.

Facts are exact.

You have a dinner on Thursday.

You bought the blue jacket.

Those are things you can put in rows and columns.

They have IDs.

They have dates.

They can be created, updated, and deleted.

That is structured memory.

The second kind is understanding.

Understanding is not one clean role in a table.

It is an age in learning what something means about you.

You prefer simple over flashy, you run warm, you reach for dark colors when you want to feel confident.

That is not just a fact to look up.

That is a pattern.

It is a difference between knowing your calendar and knowing you.

So, here is the first takeaway.

Facts are exact.

You look them up.

Understanding is meaning.

You recall it.

Now let's talk about the decision that makes or breaks your memory system.

Those two kinds of memory should not live in the same place.

Facts belong in a structured database.

That usually means SQL, Postgres, MySQL, Cloud SQL, and Google Cloud whatever fits your stack.

Why SQL?

Because facts need exact answers.

If I ask what is on my calendar Thursday, I do not want a similar memory.

I want an exact event.

The database should return the actual roles.

That is what structured databases are good at.

Now, understanding belongs somewhere else.

Understanding stays in a vector store.

A vector store lets you search by meaning.

So instead of asking, find a role where preference equals dark colors,

the agent can ask, what do I know about any style preference for this kind of event?

And the memory system can bring back memories that are semantically related.

And that is the job of embeddings and vector search.

Here is an important detail.

VectorStore is a category.

It is not one specific product.

You could use PG Vector inside Postgres.

You could use AlloyDB.

On Google Cloud, one managed option is agent platform memory bank.

Memory bank is useful because it does more than store vectors.

You can read past sessions, extract useful memories, and update those memories over time so

if you use a plain vector store you usually build

that extraction logic yourself if you use memory bank

that part is managed for you

but the lesson is not you must use memory bank the lesson is understanding goes in a system

that can search by meaning now here's a mistake many people make they hear semantic memory

and they put everything into the vector store.

Facts, preferences, calendar events, everything.

That sounds simple, but it creates a real problem.

A vector store is designed for similarity.

Similarity is great when the question is fuzzy, but similarity is dangerous.

when the answer must be exact.

If the user asks, what did I buy?

The agent should not guess from a similar memory.

It should curate the database.

If the user asks, what do I usually like for dinners?

Then semantic memory makes sense.

That is a meaning -based question.

So the rule is, structured facts goes in database like Cloud SQL.

Unstructured understanding goes in a vector store.

And there is one more category people often forget, raw perception.

The camera frames, the audio waveform, the live stream itself, that does not usually belong in memory.

Your agent does not need to permanently remember every pixel or every sound wave.

It needs the meaning you extract from them.

For example, she is wearing that black hat again.

Or that you just said this dinner is formal.

Those meanings can become memory.

The raw video and audio are separate storage decisions.

You might keep them for replay, audit, debugging, or training data.

But that is not the same thing as agent memory.

Agent memory should keep what matters, not every raw signal that pass through the app.

So here is a second takeaway.

100

Facts go in structured memory, like classical.

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Understanding goes in a vector store, like agent, platform, memory, bank.

102

Role perception usually stays out of memory, unless you have a separate reason to store the media itself.

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Alright, let's make this concrete.

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How does the agent actually remember?

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There are two important moments, the start of a session and the end of a session.

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At the start, the agent loads memory before the conversation really begins.

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It reads structured facts from the database, for example, upcoming occasions, wishlist items, profile details.

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Then it searches the vector store for a relevant understanding, for example, style preference, comfort constraints, favorite colors from past conversations.

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Then the agent gets both kinds of context, so when the user starts talking, the agent is not starting from zero,

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it already has the facts and it already has a meaning.

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That is why the code open works.

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The agent can say, you have that dinner on Thursday from the structure database

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and I know you like to keep it simple from semantic memory.

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One answer, two memory systems.

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Now let's talk about saving memory.

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Facts should usually be saved immediately.

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If the user says I Add that Blazor to my wish list, the agent should write that to the database right away.

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That is a tool call.

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It is structured, it is exact, and the user expects it to be saved now.

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Understanding is different.

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You usually do not want to update someone's long -term identity from one random sentence.

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If I say, maybe I like yellow today, that does not mean yellow is now my whole style.

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Understanding needs judgment.

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It should come from stronger signals.

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Maybe the user explains why something matters.

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Maybe the same pattern repeats across sessions.

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That is how you keep memory from getting noisy.

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So instead of treating every sentence as a permanent preference, you let the memory series review the conversation and extract what is actually worth keeping.

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Now, when does the extraction happen?

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You have a choice.

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You can extract memory during the conversation, after each turn, or at interverse.

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Agent Platform Memory Bank supports this directly.

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You can trigger memory generation mid -session, not only at the end

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and extracting as you go has real advantages shorter exchanges often produce cleaner memories

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and if the user drops off early you have already saved what they said

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but there's a catch in a live multi -modal agent extraction

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is heavier than a database write a model has to read the conversation

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and infer meaning that takes time and a live voice agent is latency -sensitive,

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you cannot let a slow extraction step block the audio.

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So the rule is not always wait until the session ends.

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The rule is keep extraction off the live path.

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Run it asynchronously, after the turn or after the session, wherever it will not stall the conversation.

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And in this demo, the structured facts are written immediately during the conversation.

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The understanding is extracted off the hot path after the session and stored as semantic memory.

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That gives you two tracks, fast synchronous writes for facts, slower asynchronous extraction for understanding,

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and the next time the user comes back and the agent loads both again.

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That is the loop.

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Read at the start, write what changes, bring it forward next time.

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Here's the third takeaway.

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So both memory systems, when the session starts, save facts as soon as it happens,

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save understanding after you have enough context to know what is actually worth remembering.

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All right, let's recap.

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If you want your AI agent to remember users across sessions, do not start by dumping everything into one type of database.

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Start by asking, what kind of memory is this?

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There are two main kinds.

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Facts are exact.

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It looks them up.

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Understanding is meaning.

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You recall it.

160

Facts belong in a structural database like classical.

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Understanding belongs in a vector store.

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Agent Platform Memory Bank is one managed option because it can help extract and maintain that semantic memory for you.

163

But the architecture works beyond one product and raw camera frames or raw audio do not automatically become memory.

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You usually store the meaning you extract from them, not the raw signal itself.

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That is what turns a forgetful agent into an agent that can actually pick up where it left off.

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It knows the facts of your life and it understands what patterns that makes those facts useful.

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That's it.

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So what is the first thing you would want your agent to remember about you?

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Don't forget to leave that in the comments.

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In the next episode, we will give the agent eyes with real -time vision.

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All right, I will see you in the next episode.

172

Bye.

173

Thank you.

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Лексика и советы по произношению к этому уроку

Этот урок разговорной практики построен на видео «AI Agent Memory Architecture: Structured SQL, Semantic Vector Search & Memory Bank». Чаще всего повторяются слова: memory, agent, understanding, session, vector. В этом видео 173 предложений и 1681 слов для шедоуинга. Речь длится 12:17. Говорящий держит ровный темп — около 137 слов в минуту, удобный для шедоуинга. 84% слов входят в 3000 самых частых слов английского языка; остальные стоит разобрать перед занятием.

Ключевая лексика этого видео

15 слов из видео, которые стоит выучить, с произношением и значением:

Слово	Произношение	Значение
vector существительное	/ˈvɛktɚ/	ве́ктор
database существительное	/ˈdeɪtəˌbeɪs/	ба́за да́нных
extract существительное	/ˈɛkstɹækt/	экстра́кт, извлече́ние
audio прилагательное	/ˈɔ.di.oʊ/	а́удио-, звуково́й
belong глагол	/bɪˈlɒŋ/	быть «свои́м»
preference существительное	/ˈpɹɛf(ə)ɹ(ə)ns/	предпочте́ние, префере́нция
calendar существительное	/ˈkæl.ən.də/	календа́рь
similarity существительное	/ˌsɪm.ɪˈlæɹ.ɪ.ti/	схо́дство, подо́бие
lesson существительное	/ˈlɛs.ən/	уро́к, ле́кция
architecture существительное	/ˈɑː.kɪˌtɛk.t͡ʃə/	архитекту́ра, зо́дчество
recall глагол	/ɹɪˈkɔl/	отзыва́ть, отозва́ть
perception существительное	/pɚˈsɛpʃ(ə)n/	восприя́тие, перце́пция
sensitive прилагательное	/ˈsɛn.sɪt.ɪv/	чувстви́тельный, чу́ткий
formal прилагательное	/ˈfɔɹməl/	форма́льный
concrete прилагательное	/ˌkɑnˈkɹit/	конкре́тный, определённый

Фразовые глаголы, которые вы услышите

Слово	Произношение	Значение
come back глагол		возвраща́ться, возврати́ться
drop off глагол		па́дать, упа́сть
look up глагол	/ˌlʊk ˈʌp/	улучша́ться
pick up глагол		подбира́ть, подобра́ть

Фразы, которые стоит повторять

Короткие законченные фразы из видео, которые пригодятся в повседневном разговоре:

If the user asks, what did I buy?
If the user asks, what do I usually like for dinners?
Alright, let's make this concrete.

Произношение, на которое стоит обратить внимание

Говорящий использует 3 сокращённых и редуцированных форм, например don't, I'm, you're. Произносите их коротко, так, как слышите.

Звуки «sh» и «zh»: extraction /ɪkˈstɹækʃən/, perception /pɚˈsɛpʃ(ə)n/, occasion /əˈkeɪ̯ʒən/, structural /ˈstɹʌk(t͡)ʃəɹəl/, flashy /ˈflæʃi/
Длинные слова — следите за ударением: similarity /ˌsɪm.ɪˈlæɹ.ɪ.ti/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, permanently /ˈpɜː.mə.nənt.li/, asynchronous /eɪˈsɪŋ.kɹə.nəs/

Звуки, трудные для русскоязычных:

/w/ — губы округлены, это не /v/: wherever /(h)wɛɹˈɛvɚ/, takeaway /ˈteɪkəweɪ/
Звонкие согласные в конце слова — не оглушайте их: sensitive /ˈsɛn.sɪt.ɪv/, jeans /d͡ʒiːnz/, columns /ˈkɑləmz/, browse /bɹaʊz/, debug /diːˈbʌɡ/
/ŋ/ в конце слова — носовой звук без «г»: belong /bɪˈlɒŋ/, embedding /ɪmˈbɛdɪŋ/

Как заниматься с этим видео

Прослушайте всё видео один раз молча и выпишите незнакомые слова.
Повторяйте предложение за предложением на обычной скорости, пока ваш ритм не совпадёт с ритмом говорящего.
Запишите себя и сравните с оригиналом, обращая внимание на такие слова, как vector, database, extract.

Что такое техника Shadowing?

Shadowing — это научно обоснованная техника изучения языка, изначально разработанная для подготовки профессиональных переводчиков и популяризированная полиглотом доктором Александром Аргуэльесом. Метод прост, но эффективен: вы слушаете аудио на английском от носителей языка и немедленно повторяете вслух — как тень, следующая за говорящим с задержкой в 1–2 секунды. В отличие от пассивного прослушивания или грамматических упражнений, Shadowing заставляет мозг и мышцы рта одновременно обрабатывать и воспроизводить реальные речевые паттерны. Исследования показывают, что это значительно улучшает точность произношения, интонацию, ритм, связную речь, понимание на слух и беглость речи — что делает его одним из самых эффективных методов для подготовки к IELTS Speaking и реального общения на английском.

Техника шедоуинга: читать полное пошаговое руководство →

Практика Shadowing: AI Agent Memory Architecture: Structured SQL, Semantic Vector Search & Memory Bank - Изучайте разговорный английский по видео

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Лексика и советы по произношению к этому уроку

Ключевая лексика этого видео

Фразовые глаголы, которые вы услышите

Фразы, которые стоит повторять

Произношение, на которое стоит обратить внимание

Как заниматься с этим видео

Что такое техника Shadowing?