163 lines
6.5 KiB
Markdown
163 lines
6.5 KiB
Markdown
# 🫡 Honcho
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[](https://discord.gg/plasticlabs)
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[](https://arxiv.org/abs/2310.06983)
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[](https://twitter.com/plastic_labs)
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[](https://pypi.org/project/honcho-ai/)
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[](https://npmjs.org/package/honcho-ai)
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Honcho is a platform for making AI agents and LLM powered applications that are personalized
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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 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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## Table of Contents
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- [Project Structure](#project-structure)
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- [Usage](#usage)
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- [Architecture](#architecture)
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- [Storage](#storage)
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- [Insights](#insights)
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- [License](#license)
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## Project Structure
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The Honcho project is split between several repositories with this one hosting
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the core service logic. This is implemented as a FastAPI server/API to store
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data about an application's state.
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There are also client-sdks that are created using
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[Stainless](https://www.stainlessapi.com/). Currently, there is a [Python](https://github.com/plastic-labs/honcho-python) and
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[TypeScript/JavaScript](https://github.com/plastic-labs/honcho-node) SDK available.
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Examples on how to use the SDK are located within each SDK repository. There is
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also SDK example usage available in the [API Reference](https://docs.honcho.dev/api-reference/introduction)
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along with various guides.
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## Usage
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Currently, there is a demo server of Honcho running at https://demo.honcho.dev.
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This server is not production ready and does not have an reliability guarantees.
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It is purely there for evaluation purposes.
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A private beta for a tenant isolated production ready version of Honcho is
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currently underway. If interested fill out this
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[typeform](https://plasticlabs.typeform.com/honchobeta) and the Plastic Labs
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team will reach out to onboard users.
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Additionally, Honcho can be self-hosted for testing and evaluation purposes. See
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[Contributing](./CONTRIBUTING.md) for more details on how to setup a local
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version of Honcho.
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## Architecture
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The functionality of Honcho can be split into two different services: Storage
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and Insights.
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### Storage
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Honcho contains several different primitives used for storing application and
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user data. This data is used for managing conversations, modeling user
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psychology, building RAG applications, and more.
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The philosophy behind Honcho is to provide a platform that is user-centric and
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easily scalable from a single user to a million.
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Below is a mapping of the different primitives.
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```
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Apps
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└── Users
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├── Sessions
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│ └── Messages
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├── Collections
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│ └── Documents
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└── Metamessages
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```
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Users familiar with APIs such as the OpenAI Assistants API will be familiar with
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much of the mapping here.
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#### Apps
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This is the top level construct of Honcho. Developers can register different
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`Apps` for different assistants, agents, AI enabled features, etc. It is a way to
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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
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literally represent a user of an application.
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#### Sessions
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The `Session` object represents a set of interactions a `User` has with an
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`App`. Other application may refer to this as a thread or conversation.
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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 labeled as either a `User` or AI message.
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#### Collections
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At a high level a `Collection` is a named group of `Documents`. Developers
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familiar with RAG based applications will be familiar with these. `Collection`s
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store vector embedded data that developers and agents can retrieve against using
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functions like cosine similarity.
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Developers can create multiple `Collection`s for a user for different purposes
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such as modeling different personas, adding third-party data such as emails and
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PDF files, and more.
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#### Documents
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As stated before a `Document` is vector embedded data stored in a `Collection`.
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#### Metamessages
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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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metamessages can store thought and reflection steps along with having developer
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information such as logs.
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Each `Metamessage` is associated with a `User` with the ability to optionally
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tie to a `Session` and a `Message`.
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### Insights
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The Insight functionality of Honcho is built on top of the Storage service. As
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`Messages` and `Sessions` are created for a `User`, Honcho will asynchronously
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reason about the `User`'s psychology to derive facts about them and store them
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in a reserved `Collection`.
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To read more about how this works read our [Research Paper](https://arxiv.org/abs/2310.06983)
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Developers can then leverage these insights in their application to better
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server `User` needs. The primary interface for using these insights is through
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the [Dialectic Endpoint](https://blog.plasticlabs.ai/blog/Introducing-Honcho's-Dialectic-API).
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This is a regular API endpoint that takes natural language requests to get data
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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 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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- Asking Honcho to hydrate a prompt with data about the `User`s behavior
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- Asking Honcho for a 2nd opinion or approach about how to respond to the User
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## License
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Honcho is licensed under the AGPL-3.0 License. Learn more at the [License file](./LICENSE)
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