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content/blog/A Simple Honcho Primer.md
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@ -59,6 +59,29 @@ There's [[Announcing Honcho's Private Beta#^xb6ef1|enormous potenial]] for more
## How to Use Honcho
^cd2d3c
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*.
```python
user_input = "Here's a message!"
ai_response = "I'm a helpful assistant!"
session.create_message(is_user=True, content=user_input)
session.create_message(is_user=False, content=ai_response)
```
But what about vector DBs? 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*.
```python
collection.create_document(content="The user is interested in AI")
```
Using Honcho as a storage mechanism allows you to **retrieve** rich insights via the user profiles it's building and managing on the backend. You can access them via the *dialectic API*. It's simple: just query in natural language using the `session.chat()` method!
```python
session.chat("What are the user's interests?")
```
There are a [[Introducing Honcho's Dialectic API#How It Works|ton of ways]] to use this, and we're just scratching the surface on the possibilities.
## What's Next for Honcho?
^ca46d7