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113 lines
7.4 KiB
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113 lines
7.4 KiB
Markdown
---
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title: A Simple Honcho Primer
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date: 04.16.24
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tags:
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- blog
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- honcho
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---
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![[bot reading primer.png]]
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> [!NOTE] Welcome to our quick, "explain it like I'm 5" guide to [Honcho](https://honcho.dev)!
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> We'll keep it simple, covering [[A Simple Honcho Primer#^ef795f|what Honcho is]], [[A Simple Honcho Primer#^x125da|why we built it]], [[A Simple Honcho Primer#^cd2d3c|how to use it]], and [[A Simple Honcho Primer#^ca46d7|where the product is going]]. But throughout, we'll link to places you can dive deeper.
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## What Is Honcho?
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^ef795f
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Honcho is a personalization platform for large language model (LLM) applications built by [Plastic Labs](https://plasticlabs.ai).
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It's software infrastructure that lets AI apps "get to know" their users, resulting in delightful experiences and optimized time to value.
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We'll have direct consumer experiences in the future, but today, the product is for application developers. It allows them to [[Introducing Honcho's Dialectic API#^a14c2f|reduce overhead]] and [[Introducing Honcho's Dialectic API#^x7f7f8|enhance their machine learning pipeline]].
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Right now, Honcho is in private beta, that means integrating our hosted version requires permission and onboarding[^1]. [You can sign-up here](https://plasticlabs.typeform.com/honchobeta).
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In its current form, Honcho has three core components:
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1. [[Announcing Honcho's Private Beta#^x15f37|Storage]] - managing each user's data & inference about each user
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2. [[Announcing Honcho's Private Beta#^x53717|Insights]] - processing user data with our proprietary AI models
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3. [[Announcing Honcho's Private Beta#^ee4516|Retrieval]] - surfacing user data to personalize user experience (UX)
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If you've heard of [Retrieval Augmented Generation](https://en.wikipedia.org/wiki/Prompt_engineering#Retrieval-augmented_generation) (RAG), this might sound familiar. But Honcho is doing *much* more than simple RAG.
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Behind the scenes, Honcho learns about users as people--[[User State is State of the Art|richly modeling identity]]. It seeks to understand their beliefs, hopes, dreams, history, interests, and preferences.
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It then acts as [[Introducing Honcho's Dialectic API|an oracle to each user]], allowing apps to ask for any personal context they need to improve UX and giving them access to a social cognition layer.
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## Why We Built Honcho
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^x125da
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Plastic Labs was founded as an edtech company. The original mission was to build an AI tutor that [[Open Sourcing Tutor-GPT#^x527dc|could reason like]] the best human instructors. We quickly found the key limitation was data not on the subject matter, but on the student. To overcome it, the tutor needed [[Theory of Mind Is All You Need|a way to]] get to know *each* of its students deeply.
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Honcho was born by running up against this challenge, building technology to solve it, and realizing all AI applications are going to need the same solutions. The promise of *generative* AI isn't one-size-fits-all products, but bespoke experiences in each moment for each user. The same limitation emerges--how well do you know your user?
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So we believe Honcho will be a critical, table-stakes part of the AI app development stack.
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Why? Because [[Humans like personalization|users will want]] their AI experiences to be personalized and app developers shouldn't be redundantly solving that problem.
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But it's not intuitive for a few reasons:
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- AI app builders are [[Machine learning is fixated on task performance|still focused on]] just getting general tasks to work
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- LLMs' [[LLMs excel at theory of mind because they read|potential to personalize]] is still under-appreciated
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- Historic examples of personalized apps usually just leverage our activity & engagement data
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- Those examples tend to target only base user desire, lead to addictive behavior, & have poor privacy records
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Still, when interacting with an AI app, there's a sense that it *should* be getting to know us. In fact, we're often surprised when we realize it's not learning about us over time. And probably annoyed at having to start over.
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Think about personalization here as more like the experience of close human companionship or white glove services than the attention hacking mechanisms of TikTok. There's [[Announcing Honcho's Private Beta#^xb6ef1|enormous potenial]] for more positive-sum use of user data and for aligning AI applications more closely with user needs and preferences[^2].
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## How to Use Honcho
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^cd2d3c
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Honcho is first and foremost a **storage** framework. Think of it like an open source version of the OpenAI Assistants API. User `sessions` store both user and AI generated `messages` as well as any intermediate inferences you might want to store as `metamessages`:
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```python
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user_input = "Here's a message!"
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ai_response = "I'm a helpful AI assistant!"
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session.create_message(is_user=True, content=user_input)
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session.create_message(is_user=False, content=ai_response)
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```
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But what about vectorDBs? Don't worry, Honcho has you covered there too. You can embed data and store them as `documents` in per-user vector DBs called `collections`:
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```python
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collection.create_document(content="The user is interested in AI")
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```
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Using Honcho as a storage mechanism allows you to **retrieve** rich insights via the user profiles it's building and managing on the backend. Your application's LLM can access [[Loose theory of mind imputations are superior to verbatim response predictions|theory-of-mind]] inference over those profiles via the *[[Introducing Honcho's Dialectic API|dialectic]]* API.
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It's simple: just query in natural language using the `session.chat()` method:
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```python
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session.chat("What are the user's interests?")
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```
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There are a [[Introducing Honcho's Dialectic API#How It Works|ton of ways]] to use Honcho, this primer only scratches the surface[^3].
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## What's Next for Honcho?
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^ca46d7
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Beyond improving our internal AI models so they can get to know users as richly as possible, we see three natural extensions in [[Announcing Honcho's Private Beta#^eb15f3|Honcho's future]]:
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1. [[Announcing Honcho's Private Beta#^x2dd3b|Monitoring & Evaluation]] - developer tools to understand & assess the impact of personalization + machine learning tools to build personalized datasets
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2. [[Announcing Honcho's Private Beta#^a84f44|User-Facing Controls]] - chat with *your* Honcho to direct how it manages & shares data + authenticate with Honcho to sign-in to AI apps
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3. [[Announcing Honcho's Private Beta#^ebf071|Honcho Application Ecosystem]] - a network of apps contributing to & sharing Honcho data, user-owned & stored in confidential environments
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And in just a few weeks, we'll be launching a demo platform where anyone can interact with (& eventually build) Honcho powered apps.
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## Join the Beta
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[Sign-up for the private beta](https://plasticlabs.typeform.com/honchobeta) and start building personalized experiences.
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[Join Discord](https://discord.gg/plasticlabs), introduce yourself, and tell us what you're working on.
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[Visit our open-source repo](https://github.com/plastic-labs/honcho) and get your hands dirty.
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🫡
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[^1]: There's also [an open source repo for Honcho](https://github.com/plastic-labs/honcho), so you can self-host a basic version--[join our Discord](https://discord.gg/plasticlabs) for support.
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[^2]: If you want to go deeper on the philosophical or machine learning side, take some time to explore the [rest of the blog](https://blog.plasticlabs.ai).
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[^3]: To get further into the technical weeds, head over to [our docs](https://docs.honcho.dev).
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