Update Changelog, pyproject, and mintlify docs (#58)
* Update Changelog, pyproject, and mintlify docs * Update version shield in README
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CHANGELOG.md
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CHANGELOG.md
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@ -4,6 +4,23 @@ All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](http://keepachangelog.com/)
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and this project adheres to [Semantic Versioning](http://semver.org/).
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## [0.0.9] — 2024-05-16
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### Added
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* Deriver to docker compose
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* Postgres based Queue for background jobs
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### Changed
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* Deriver to use a queue instead of supabase realtime
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* Using mirascope instead of langchain
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### Removed
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* Legacy SDKs in preference for stainless SDKs
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## [0.0.8] — 2024-05-09
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### Added
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README.md
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README.md
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@ -1,5 +1,5 @@
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# 🫡 Honcho
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[](https://discord.gg/plasticlabs)
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[](https://arxiv.org/abs/2310.06983)
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@ -13,7 +13,7 @@ Honcho is a platform for making AI agents and LLM powered applications that are
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to their end users. It leverages the inherent theory-of-mind capabilities of
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LLMs to cohere to user psychology over time.
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Read about the the project [here](https://blog.plasticlabs.ai/blog/A-Simple-Honcho-Primer).
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Read about the project [here](https://blog.plasticlabs.ai/blog/A-Simple-Honcho-Primer).
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Read the user documentation [here](https://docs.honcho.dev)
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@ -92,7 +92,7 @@ isolation data between use cases.
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**Users**
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Within an `App` everything revolves around a `User`. the `User` object very
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Within an `App` everything revolves around a `User`. the `User` object
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literally represent a user of an application.
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#### Sessions
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@ -103,11 +103,11 @@ The `Session` object represents a set of interactions a `User` has with an
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**Messages**
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The `Message` represents an atomic interaction of a `User` in a `Session`.
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`Message`s are labed as either a `User` or AI message.
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`Message`s are labed as either a `User` or AI message.
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#### Metamessages
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A `Metamessage` is very similar to a `Message` with different use case. They are
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A `Metamessage` is similar to a `Message` with different use case. They are
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meant to be used to store intermediate inference from AI assistants or other
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derived information that is separate from the main `User` `App` interaction
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loop. For complicated prompting architectures like [metacognitive prompting](https://arxiv.org/abs/2310.06983)
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@ -150,7 +150,7 @@ about the `User`. This robust design let's us use this single endpoint for all
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cases where extra personalization or information about the `User` is necessary.
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A developer's application can treat Honcho as an oracle to the `User` and
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consult with it when necessary. Some examples of how to leverage the Dialectic
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consult it when necessary. Some examples of how to leverage the Dialectic
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API include:
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- Asking Honcho for a theory-of-mind insight about the `User`
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@ -60,4 +60,23 @@
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alt="pip installs"
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/>
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</a>
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</div>
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<a
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style={{
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borderStyle: "none",
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marginTop: "0.125rem",
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marginBottom: "0.125rem",
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marginLeft: "0.25rem",
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}}
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href="https://npmjs.org/package/honcho-ai"
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>
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<img
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style={{
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margin: 0,
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cursor: "pointer",
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}}
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noZoom
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src="https://img.shields.io/npm/v/honcho-ai.svg"
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alt="npm package version"
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/>
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</a>
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</div>
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@ -10,3 +10,11 @@ primitives consult [Architecture](/getting-started/Architecture)
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This part of the documentation is autogenerated for the most up to date and accurate API spec view the
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[Redoc](https://demo.honcho.dev/redoc) or [Swagger](https://demo.honcho.dev/docs) directly.
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</Warning>
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Each API endpoint is documented and includes code examples of how to interact with them via our [NodeJS](https://github.com/plastic-labs/honcho-node) and
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[Python](https://github.com/plastic-labs/honcho-python) SDKs.
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For more detailed looks on how the SDKs work and their associated types consult the api docs in each respective repository.
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- [Honcho Python SDK](https://github.com/plastic-labs/honcho-python/blob/main/api.md)
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- [Honcho NodeJS SDK](https://github.com/plastic-labs/honcho-node/blob/main/api.md)
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@ -35,17 +35,19 @@ Belows is some example code on how this works.
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```python
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from honcho import Honcho
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honcho = Honcho(app_name="demo")
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honcho.initialize()
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honcho = Honcho()
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# Create or get an existing App
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app = honcho.apps.get_or_create(name="demo-app")
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# create or get user
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user = honcho.create_or_get_user("demo-user")
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user = honcho.apps.users.get_or_create(app_id=app.id, name="demo-user")
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# create or get session
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session = honcho.create_or_get_session(user, "demo-session")
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# create a new session
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session = honcho.apps.users.session.create(app_id=app.id, user_id=user.id)
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# Talk to the dialectic agent to reason about their needs
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answer = session.chat("What is the user's favorite way of completing the task")
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answer = honcho.apps.users.session.chat(app_id=app.id, user_id=user.id, session_id=session.id, query="What is the user's favorite way of completing the task")
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```
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@ -110,4 +110,4 @@ Then we can retrieve and delete the messages associated with that metadata:
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```python
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sessions = list(user.get_sessions_generator(user_id, location_id))
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sessions[0].close() if len(sessions) > 0 else None
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```
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```
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@ -1,6 +1,6 @@
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[tool.poetry]
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name = "honcho"
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version = "0.0.8"
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version = "0.0.9"
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description = "Honcho Server"
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authors = ["Plastic Labs <hello@plasticlabs.ai>"]
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readme = "README.md"
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