fix: 11.24 checkpoint
This commit is contained in:
vintro
parent
90cd0b814f
commit
ace8558388
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@ -26,6 +26,7 @@
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"group": "Introduction",
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"pages": [
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"v2/documentation/introduction/overview",
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"v2/documentation/introduction/architecture",
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"v2/documentation/introduction/vibecoding"
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]
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},
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@ -37,18 +38,17 @@
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"group": "Storage",
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"expanded": true,
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"pages": [
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"v2/documentation/core-concepts/features/storing-data",
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"v2/documentation/core-concepts/features/file-uploads"
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"v2/documentation/honcho-context/storage/storing-data",
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"v2/documentation/honcho-context/storage/file-uploads"
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]
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},
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{
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"group": "Retrieval",
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"expanded": true,
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"pages": [
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"v2/documentation/honcho-context/retrieval/overview",
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"v2/documentation/core-concepts/features/using-filters",
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"v2/documentation/core-concepts/features/search",
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"v2/documentation/core-concepts/features/get-context"
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"v2/documentation/honcho-context/retrieval/using-filters",
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"v2/documentation/honcho-context/retrieval/search",
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"v2/documentation/honcho-context/retrieval/get-context"
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]
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}
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]
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@ -372,26 +372,7 @@
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}
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]
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}
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],
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"global": {
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"anchors": [
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{
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"anchor": "Dashboard",
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"href": "https://app.honcho.dev",
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"icon": "table-columns"
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},
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{
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"anchor": "Community",
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"href": "https://discord.gg/honcho",
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"icon": "discord"
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},
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{
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"anchor": "Blog",
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"href": "https://blog.plasticlabs.ai",
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"icon": "newspaper"
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}
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]
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}
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]
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},
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"logo": {
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"light": "/logo/honcho-dark.svg",
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@ -408,7 +389,8 @@
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"twitter": "https://x.com/honchodotdev",
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"github": "https://github.com/plastic-labs/honcho",
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"discord": "https://discord.gg/honcho",
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"linkedin": "https://www.linkedin.com/company/plasticlabs"
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"linkedin": "https://www.linkedin.com/company/plasticlabs",
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"youtube": "https://www.youtube.com/@plasticlabs"
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}
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},
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"integrations": {
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8201
docs/pnpm-lock.yaml
8201
docs/pnpm-lock.yaml
File diff suppressed because it is too large
Load Diff
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@ -1,10 +1,10 @@
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---
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title: 'Honcho Context Quickstart'
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title: 'Quickstart - Honcho Context'
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icon: 'bolt'
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sidebarTitle: 'Quickstart'
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---
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Implement Honcho in just a few steps. No signup required.
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Implement Honcho Context in just a few steps. No signup required.
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<Note>
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By default, the SDK uses the demo server hosted at demo.honcho.dev. The demo server is meant for quick experimentation and the data is cleared on a regular basis. Do not use for production applications.
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@ -58,7 +58,7 @@ const honcho = new Honcho({});
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## 3. Create Peers
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Peers represent individual users, AI agents, or any conversational entity in your system:
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Peers represent individual users, AI agents, or any entity in your system:
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<CodeGroup>
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```python Python
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@ -74,23 +74,25 @@ const bob = await honcho.peer("bob")
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## 4. Create a Session
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Sessions are independent conversations that can include multiple peers:
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Sessions can be used to organize messages amongst peers.
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<CodeGroup>
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```python Python
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session = honcho.session("session_1")
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session = honcho.session("session_1", config={"deriver_disabled": True})
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session.add_peers([alice, bob])
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```
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```typescript TypeScript
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const session = await honcho.session("session_1")
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const session = await honcho.session("session_1", {config:{"deriver_disabled": true}});
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await session.addPeers([alice, bob])
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```
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</CodeGroup>
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## 5. Add Messages
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<Note>
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In Honcho, memory is a reasoning task. By default it runs inference over every message. To use Honcho just for context engineering, you can toggle off this behavior.
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</Note>
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Add some conversation messages. Honcho automatically learns from these interactions:
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## 5. Add Messages
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<CodeGroup>
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```python Python
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@ -118,16 +120,16 @@ await session.addMessages([
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## 6. Get Context
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Curating your peer's context window is remarkably simple. The `get_context` method pulls in a mixture of recent messages and messages relevant to the one just sent.
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Curating your peer's context window is remarkably simple. The `get_context` method pulls recent messages for you based on a token limit.
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<CodeGroup>
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```python Python
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context = session.get_context()
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context = session.get_context() # chain with .to_openAI() or .to_anthropic() to format for APIs
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```
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```typescript TypeScript
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const context = await session.getContext();
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const context = await session.getContext(); // chain with .toOpenAI() or .toAnthropic() to format for APIs
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```
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</CodeGroup>
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@ -138,20 +140,28 @@ const context = await session.getContext();
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<CodeGroup>
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```python Python
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import os
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from openai import OpenAI
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from dotenv import load_dotenv
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from honcho import Honcho
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# Create your client
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# Load environment variables (e.g., OPENAI_API_KEY in .env file)
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load_dotenv()
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# Create OpenAI client
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openai_client = OpenAI()
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# Create your Honcho client
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honcho = Honcho()
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# Get your Peers
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# Create your peers
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alice = honcho.peer("alice")
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bob = honcho.peer("bob")
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# Make a Session and add your Peers
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session = honcho.session("session_1")
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# Make a session, add peers to the session
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session = honcho.session("session_1", config={"deriver_disabled": True})
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session.add_peers([alice, bob])
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# Add messages sent by your Peers
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# Add messages sent by your peers
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session.add_messages([
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alice.message("Hi Bob, how are you?"),
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bob.message("I'm good, thank you!"),
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@ -161,112 +171,122 @@ session.add_messages([
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bob.message("You should! I find that evening workouts help me relax."),
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])
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# Get insights about your Peers
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response = bob.chat("Tell me about Bob's interests and habits")
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print(response)
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# Get context for LLM
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messages = session.get_context(tokens=2000).to_openai(assistant=bob)
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# Returns rich context like:
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# "Bob is health-conscious and has been working on getting back in shape.
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# He regularly goes to the gym, particularly in the evenings, and finds
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# exercise helps him relax. He's encouraging about fitness and willing
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# to share advice about workout routines."
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# Add new user message and get AI response
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messages.append({
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"role": "user",
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"content": "Oh maybe I'll find them relaxing as well!"
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})
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response = openai_client.chat.completions.create(
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model="gpt-4o",
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messages=messages
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)
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# Add AI response back to session
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session.add_messages([
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user.message("Oh maybe I'll find them relaxing as well!"),
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assistant.message(response.choices[0].message.content)
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])
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print(response.choices[0].message.content)
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# Expected Output: something like "Definitely! Plus, it's a great way to end the day."
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```
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```typescript TypeScript
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import * as dotenv from 'dotenv';
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import OpenAI from 'openai';
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import { Honcho } from '@honcho-ai/sdk';
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// Create your client
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// Load environment variables (e.g., OPENAI_API_KEY in .env file)
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dotenv.config();
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// Create OpenAI client
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const openai = new OpenAI({
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apiKey: process.env.OPENAI_API_KEY,
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});
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// Create your Honcho client
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const honcho = new Honcho({});
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// Get your Peers
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const alice = await honcho.peer("alice")
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const bob = await honcho.peer("bob")
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// Main async function to handle everything
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async function main() {
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try {
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// Create peers
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const alice = await honcho.peer("alice");
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const bob = await honcho.peer("bob");
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// Make a Session and add your peers
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const session = await honcho.session("session_1")
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await session.addPeers([alice, bob])
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// Make a session, add peers to the session
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const session = await honcho.session("session_1", {config:{"deriver_disabled": true}});
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await session.addPeers([alice, bob]);
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// Add messages sent by your Peers
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await session.addMessages([
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alice.message("Hi Bob, how are you?"),
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bob.message("I'm good, thank you!"),
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alice.message("What are you doing today after work?"),
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bob.message("I'm going to the gym! I've been trying to get back in shape."),
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alice.message("That's great! I should probably start exercising too."),
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bob.message("You should! I find that evening workouts help me relax."),
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])
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// Add messages sent by your peers
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await session.addMessages([
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alice.message("Hi Bob, how are you?"),
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bob.message("I'm good, thank you!"),
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alice.message("What are you doing today after work?"),
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bob.message("I'm going to the gym! I've been trying to get back in shape."),
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alice.message("That's great! I should probably start exercising too."),
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bob.message("You should! I find that evening workouts help me relax."),
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]);
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// Get insights about your peers
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bob.chat("Tell me about Bob's interests and habits").then((response) => {
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console.log(response);
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// Returns rich context like:
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// "Bob is health-conscious and has been working on getting back in shape.
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// He regularly goes to the gym, particularly in the evenings, and finds
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// exercise helps him relax. He's encouraging about fitness and willing
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// to share advice about workout routines."
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})
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// Get context for LLM (await the context first, then call toOpenAI)
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const context = await session.getContext({ tokens: 2000 });
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const messages = context.toOpenAI(bob)
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// Add new user message
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messages.push({
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role: "user",
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content: "Oh maybe I'll find them relaxing as well!"
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});
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// Get AI response
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const response = await openai.chat.completions.create({
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model: "gpt-4o",
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messages: messages,
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});
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const aiResponse = response.choices[0].message.content;
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// Add AI response back to session (user as alice, AI as bob)
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await session.addMessages([
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alice.message("Oh maybe I'll find them relaxing as well!"),
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bob.message(aiResponse!),
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]);
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// Print the AI response
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console.log(aiResponse);
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} catch (error) {
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console.error('Error running the script:', error);
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}
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}
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// Run the main function
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main();
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// Expected Output: something like "Definitely! Plus, it's a great way to end the day."
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```
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</CodeGroup>
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## Recap
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Honcho just reasoned about a conversation between two people--Alice
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and Bob. We:
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1. We set up our connection to Honcho.
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2. Created our peers.
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3. Made a session and added our peers.
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4. Added messages from our peers to the session.
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5. Used the `get_context` method to structure a stateful request to OpenAI.
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1. Set up our connection to Honcho.
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2. Setup the participants of our conversation--these are called `Peers`.
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3. Made a `Session` and added our `Peers`.
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4. Sent messages from our `Peers`.
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5. Queried Honcho to get insights about one of the `Peers` in the conversation.
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As soon as you save a message in Honcho, it will start to reason about it to
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pull out insights and develop a profile of the user. This is the default
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behavior and can be toggled off via [the configuration](/v2/documentation/core-concepts/configuration).
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## Next Steps
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We're just scratching the surface. Choose from one of the cards below to keep building with Honcho.
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<CardGroup cols={3}>
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<Card title="Architecture" icon="rocket"
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href="/v2/documentation/core-concepts/architecture">
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Learn about the data primitives in Honcho and how they work together
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<Card title="Get Context" icon="rocket"
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href="/v2/documentation/core-concepts/features/get-context">
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Learn more about the power and flexibility of the `get_context` method
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</Card>
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<Card title="Start Building" icon="brain" href="https://app.honcho.dev">
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Sign up for Managed Honcho and get started building agents now.
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<Card title="Start Building" icon="wrench" href="https://app.honcho.dev">
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Sign up on the Honcho Platform for unlimited storage and retrieval
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</Card>
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<Card title="Guides" icon="book" href="/v2/guides/overview">
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Check out spellbooks to see different examples apps built with Honcho
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<Card title="Honcho Memory" icon="brain" href="/v2/guides/overview">
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Leverage the advanced reasoning capabilities in Honcho
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</Card>
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</CardGroup>
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---
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# SCRATCH
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### Production Environment
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<CodeGroup>
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```python Python
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import os
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from honcho import Honcho
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# Production environment with API key
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honcho = Honcho(
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api_key=os.environ["HONCHO_API_KEY"],
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environment="production",
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# Create a workspace, otherwise set to "default"
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# workspaceId="your-workspace-id"
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)
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```
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```typescript TypeScript
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import { Honcho } from '@honcho-ai/sdk';
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// Production environment with API key
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const honcho = new Honcho({
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apiKey: process.env.HONCHO_API_KEY!,
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environment: "production",
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// Create a workspace, otherwise set to "default"
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// workspace: "your-workspace-id"
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});
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```
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</CodeGroup>
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|
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@ -1,71 +0,0 @@
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---
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title: "Retrieval Overview"
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description: "Understanding how to retrieve and query data from Honcho"
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icon: "search"
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sidebarTitle: "Overview"
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---
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# Retrieval in Honcho
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Honcho provides multiple approaches for retrieving stored context, each optimized for different use cases.
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## Retrieval Methods
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### Basic Retrieval
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Direct queries for messages and session data:
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- **List Messages**: Retrieve messages from a session with pagination
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- **Get Message**: Fetch a specific message by ID
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- **List Sessions**: Query sessions with filtering options
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### Advanced Retrieval
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Context-aware retrieval methods:
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- **Search**: Hybrid search across messages using semantic and full-text search
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- **Get Context**: Intelligent context retrieval optimized for LLM consumption
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- **Filters**: Powerful filtering options to narrow down results
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## When to Use Each Method
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| Method | Use Case | Best For |
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|--------|----------|----------|
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| List Messages | Sequential access | Displaying conversation history |
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| Search | Find specific information | User searches, knowledge retrieval |
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| Get Context | Provide context to LLM | Real-time agent responses |
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| Filters | Narrow results | Time-based queries, peer-specific data |
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## Retrieval Patterns
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### Pattern 1: Conversation History
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```python
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# Get recent messages for display
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messages = session.get_messages(limit=50)
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```
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### Pattern 2: Semantic Search
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```python
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# Find relevant past discussions
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results = workspace.search(query="user preferences", limit=10)
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```
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### Pattern 3: Context for LLM
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```python
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# Get optimized context for agent
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context = session.get_context(max_tokens=1000)
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```
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## Next Steps
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<CardGroup cols={3}>
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<Card title="Filters" icon="filter" href="/v2/documentation/core-concepts/features/using-filters">
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Learn about filtering options
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</Card>
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<Card title="Search" icon="magnifying-glass" href="/v2/documentation/core-concepts/features/search">
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Explore search capabilities
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</Card>
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<Card title="Get Context" icon="brain" href="/v2/documentation/core-concepts/features/get-context">
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Understand intelligent context retrieval
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</Card>
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</CardGroup>
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|
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@ -1,9 +1,10 @@
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|||
---
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||||
title: 'Quickstart'
|
||||
title: 'Quickstart - Honcho Memory'
|
||||
icon: 'bolt'
|
||||
sidebarTitle: 'Quickstart'
|
||||
---
|
||||
|
||||
Implement Honcho in just a few steps. No signup required.
|
||||
Implement Honcho Memory in just a few steps. No signup required.
|
||||
|
||||
<Note>
|
||||
By default, the SDK uses the demo server hosted at demo.honcho.dev. The demo server is meant for quick experimentation and the data is cleared on a regular basis. Do not use for production applications.
|
||||
|
|
|
|||
|
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@ -1,25 +1,22 @@
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---
|
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title: "Architecture & Intuition"
|
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description: "Understanding Honcho's core concepts and data model."
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description: "Understanding Honcho's data model and core concepts."
|
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icon: "sitemap"
|
||||
sidebarTitle: "Architecture"
|
||||
---
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|
||||
<Note> The goal of this page is to build an intuition for the primitives in Honcho and how they fit together </Note>
|
||||
|
||||
Honcho has 2 main components that work together to manage agent identity and context.
|
||||
Honcho has 2 main components that work together to manage agent context and memory.
|
||||
|
||||
- **The Memory Layer**: The Memory layer for storing interaction history for your agents
|
||||
- **The Reasoning Layer**: The background processing layer that builds representations of users and agents
|
||||
|
||||
Below we'll deep dive into these different areas, discussing the data
|
||||
primitives, the flow of data through the system, artifacts Honcho produces, and
|
||||
how to use them.
|
||||
- **The Context Layer**: For storing and retrieving interaction history for your agents.
|
||||
- **The Memory Layer**: Background processing that builds representations of users and agents.
|
||||
|
||||
## Data Model
|
||||
|
||||
Honcho has a hierarchical data model centered around the entities below.
|
||||
|
||||
<div style={{ display: 'flex', justifyContent: 'center' }}>
|
||||
```mermaid
|
||||
graph TD
|
||||
W[Workspaces] -->|have| P[Peers]
|
||||
|
|
@ -29,21 +26,27 @@ Honcho has a hierarchical data model centered around the entities below.
|
|||
|
||||
P <-.->|many-to-many| S
|
||||
|
||||
style W fill:#FF5A7E,stroke:#333,stroke-width:2px,color:#fff
|
||||
style P fill:#e1f5fe,stroke:#0277bd,color:#000
|
||||
style S fill:#f3e5f5,stroke:#7b1fa2,color:#000
|
||||
style SM fill:#e8f5e9,stroke:#2e7d32,color:#000
|
||||
style W fill:#B6DBFF,stroke:#333,color:#000
|
||||
style P fill:#B6DBFF,stroke:#333,color:#000
|
||||
style S fill:#B6DBFF,stroke:#333,color:#000
|
||||
style SM fill:#B6DBFF,stroke:#333,color:#000
|
||||
```
|
||||
</div>
|
||||
|
||||
- A `Workspaces` has `Peers` & `Sessions`
|
||||
- A `Workspace` has `Peers` & `Sessions`
|
||||
- A `Peer` can be in multiple `Sessions` and can send `Messages` in a `Session`.
|
||||
- A `Session` can have many `Peers` and stores `Messages` sent by its `Peers`.
|
||||
|
||||
### <Icon icon="building" /> Workspaces
|
||||
---
|
||||
|
||||
<div style={{ display: 'flex', alignItems: 'center', gap: '8px' }}>
|
||||
<Icon icon="building" />
|
||||
<h3 style={{ margin: 0 }}>Workspaces</h3>
|
||||
</div>
|
||||
|
||||
Workspaces are the top-level containers that provide complete isolation between different applications or environments; they essentially serve as a namespace to isolate different workloads or environments.
|
||||
|
||||
|
||||
Workspaces are the top-level containers that provide complete isolation between
|
||||
different applications or environments; they essentially serve as a namespace
|
||||
to isolate different workloads or environments
|
||||
|
||||
**Key Features:**
|
||||
- **Isolation**: Complete data separation between workspaces
|
||||
|
|
@ -59,7 +62,10 @@ to isolate different workloads or environments
|
|||
|
||||
---
|
||||
|
||||
### <Icon icon="user" /> Peers
|
||||
<div style={{ display: 'flex', alignItems: 'center', gap: '8px' }}>
|
||||
<Icon icon="user" />
|
||||
<h3 style={{ margin: 0 }}>Peers</h3>
|
||||
</div>
|
||||
|
||||
Honcho has a Peer-Centric Architecture: Peers are the most important entity within Honcho, with everything revolving around Peers and their representations.
|
||||
|
||||
|
|
@ -83,7 +89,10 @@ multi-agent or group chat scenarios.
|
|||
|
||||
---
|
||||
|
||||
### <Icon icon="message" /> Sessions
|
||||
<div style={{ display: 'flex', alignItems: 'center', gap: '8px' }}>
|
||||
<Icon icon="message" />
|
||||
<h3 style={{ margin: 0 }}>Sessions</h3>
|
||||
</div>
|
||||
|
||||
Sessions represent individual conversation threads or interaction contexts between peers.
|
||||
|
||||
|
|
@ -102,7 +111,10 @@ Sessions represent individual conversation threads or interaction contexts betwe
|
|||
|
||||
---
|
||||
|
||||
### <Icon icon="envelope" /> Messages
|
||||
<div style={{ display: 'flex', alignItems: 'center', gap: '8px' }}>
|
||||
<Icon icon="envelope" />
|
||||
<h3 style={{ margin: 0 }}>Messages</h3>
|
||||
</div>
|
||||
|
||||
Messages are the fundamental units of interaction within sessions. They may
|
||||
also be used to ingest information of any kind that is not related to a specific interaction, but provides
|
||||
|
|
@ -6,9 +6,9 @@ sidebarTitle: "Overview"
|
|||
|
||||
Honcho gives agents state-of-the-art memory.
|
||||
|
||||
It's is a flexible memory library available through a managed platform and an open source package for self-hosting.
|
||||
It's is a flexible yet powerful library. Available through a managed platform and an open source repository for self-hosting.
|
||||
|
||||
Honcho can be thought of in two ways: Honcho Context and Honcho Memory.
|
||||
Honcho can be understood by developers through two lenses--context and memory.
|
||||
|
||||
|
||||
## Honcho Context
|
||||
|
|
@ -19,15 +19,6 @@ Honcho offers elegant, flexible primitives for initializing, storing, retrieving
|
|||
|
||||
Don't waste time redundantly building complex systems. Focus on what makes your product unique.
|
||||
|
||||
---
|
||||
|
||||
(scratch)
|
||||
|
||||
Honcho solves context engineering so you can spend more time on your product.
|
||||
|
||||
Don't waste time redundantly building complex systems. Get back to what makes your agent unique.
|
||||
|
||||
---
|
||||
|
||||
## Honcho Memory
|
||||
|
||||
|
|
@ -83,4 +74,4 @@ Memory is table stakes--it's the system that constructs context to make decision
|
|||
<CardGroup cols={2}> <Card title="Honcho Context Quickstart" icon="rocket"
|
||||
href="/v2/documentation/honcho-context/quickstart"> Get up and running with
|
||||
Honcho in minutes </Card> <Card title="Honcho Memory Quickstart" icon="brain"
|
||||
href="/v2/documentation/honcho-memory/architecture"> Leverage Honcho's Memory in minutes. </Card> </CardGroup>
|
||||
href="/v2/documentation/honcho-memory/quickstart"> Leverage Honcho for Memory in minutes. </Card> </CardGroup>
|
||||
|
|
|
|||
Loading…
Reference in New Issue