Anthropic Migration (#72)

* ready to test deriver

* prompts ready for testing

* compare entire list, don't loop through facts one by one

* parse xml, batch process ai query docs

* timing works

* session id is writing to csv now

* ignore timing logs

* infinite backrooms-inpsired prompts

* yousim/ib inspired prompts v0

* accept incoming changes on agent.py

* dialectic to sonnet for now

* remove timing, use py-spy

* cleanup from timing removal

* remove extra timing stuff

* Route for User LeveL Metamessages

* functional state

* chore: Clean up artifacts

* fix(deriver) update database calls for new schema and payload

* chore(docs) Update Honcho Version

---------

Co-authored-by: Vineeth Voruganti <[email protected]>

.gitignore

@@ -175,3 +175,5 @@ cython_debug/
 supabase/
 
 docs/node_modules
+
+timing_logs.csv

README.md

@@ -1,5 +1,6 @@
 # 🫡 Honcho
-![Static Badge](https://img.shields.io/badge/Version-0.0.11-blue)
+
+![Static Badge](https://img.shields.io/badge/Version-0.0.12-blue)
 [![Discord](https://img.shields.io/discord/1016845111637839922?style=flat&logo=discord&logoColor=23ffffff&label=Plastic%20Labs&labelColor=235865F2)](https://discord.gg/plasticlabs)
 [![arXiv](https://img.shields.io/badge/arXiv-2310.06983-b31b1b.svg)](https://arxiv.org/abs/2310.06983)
 ![GitHub License](https://img.shields.io/github/license/plastic-labs/honcho)
@@ -8,7 +9,6 @@
 [![PyPI version](https://img.shields.io/pypi/v/honcho-ai.svg)](https://pypi.org/project/honcho-ai/)
 [![NPM version](https://img.shields.io/npm/v/honcho-ai.svg)](https://npmjs.org/package/honcho-ai)
 
-
 Honcho is a platform for making AI agents and LLM powered applications that are personalized
 to their end users. It leverages the inherent theory-of-mind capabilities of
 LLMs to cohere to user psychology over time.
@@ -22,34 +22,34 @@ Read the user documentation [here](https://docs.honcho.dev)
 - [Project Structure](#project-structure)
 - [Usage](#usage)
 - [Architecture](#architecture)
-    - [Storage](#storage)
-    - [Insights](#insights)
+  - [Storage](#storage)
+  - [Insights](#insights)
 - [License](#license)
 
 ## Project Structure
 
 The Honcho project is split between several repositories with this one hosting
 the core service logic. This is implemented as a FastAPI server/API to store
-data about an application's state. 
+data about an application's state.
 
 There are also client-sdks that are created using
-[Stainless](https://www.stainlessapi.com/). Currently, there is a [Python](https://github.com/plastic-labs/honcho-python) and 
-[TypeScript/JavaScript](https://github.com/plastic-labs/honcho-node) SDK available. 
+[Stainless](https://www.stainlessapi.com/). Currently, there is a [Python](https://github.com/plastic-labs/honcho-python) and
+[TypeScript/JavaScript](https://github.com/plastic-labs/honcho-node) SDK available.
 
 Examples on how to use the SDK are located within each SDK repository. There is
 also SDK example usage available in the [API Reference](https://docs.honcho.dev/api-reference/introduction)
-along with various guides. 
+along with various guides.
 
 ## Usage
 
 Currently, there is a demo server of Honcho running at https://demo.honcho.dev.
 This server is not production ready and does not have an reliability guarantees.
-It is purely there for evaluation purposes. 
+It is purely there for evaluation purposes.
 
 A private beta for a tenant isolated production ready version of Honcho is
 currently underway. If interested fill out this
 [typeform](https://plasticlabs.typeform.com/honchobeta) and the Plastic Labs
-team will reach out to onboard users. 
+team will reach out to onboard users.
 
 Additionally, Honcho can be self-hosted for testing and evaluation purposes. See
 [Contributing](./CONTRIBUTING.md) for more details on how to setup a local
@@ -58,18 +58,18 @@ version of Honcho.
 ## Architecture
 
 The functionality of Honcho can be split into two different services: Storage
-and Insights. 
+and Insights.
 
-### Storage 
+### Storage
 
 Honcho contains several different primitives used for storing application and
 user data. This data is used for managing conversations, modeling user
-psychology, building RAG applications, and more. 
+psychology, building RAG applications, and more.
 
 The philosophy behind Honcho is to provide a platform that is user-centric and
-easily scalable from a single user to a million. 
+easily scalable from a single user to a million.
 
-Below is a mapping of the different primitives. 
+Below is a mapping of the different primitives.
 
 ```
 Apps
@@ -82,7 +82,7 @@ Apps
 ```
 
 Users familiar with APIs such as the OpenAI Assistants API will be familiar with
-much of the mapping here. 
+much of the mapping here.
 
 #### Apps
 
@@ -93,17 +93,17 @@ isolation data between use cases.
 **Users**
 
 Within an `App` everything revolves around a `User`. the `User` object
-literally represent a user of an application.  
+literally represent a user of an application.
 
 #### Sessions
 
 The `Session` object represents a set of interactions a `User` has with an
-`App`. Other application may refer to this as a thread or conversation.  
+`App`. Other application may refer to this as a thread or conversation.
 
 **Messages**
 
 The `Message` represents an atomic interaction of a `User` in a `Session`.
-`Message`s are labed as either a `User` or AI message. 
+`Message`s are labed as either a `User` or AI message.
 
 #### Metamessages
 
@@ -112,32 +112,32 @@ meant to be used to store intermediate inference from AI assistants or other
 derived information that is separate from the main `User` `App` interaction
 loop. For complicated prompting architectures like [metacognitive prompting](https://arxiv.org/abs/2310.06983)
 metamessages can store thought and reflection steps along with having developer
-information such as logs. 
+information such as logs.
 
 Each `Metamessage` is associated with a `Message`. The convention we recommend
-is to attach a `Metamessage` to the `Message` it was derived from or based on. 
+is to attach a `Metamessage` to the `Message` it was derived from or based on.
 
 #### Collections
 
 At a high level a `Collection` is a named group of `Documents`. Developers
 familiar with RAG based applications will be familar with these. `Collection`s
 store vector embedded data that developers and agents can retrieve against using
-functions like cosine similarity. 
+functions like cosine similarity.
 
 Developers can create multiple `Collection`s for a user for different purposes
 such as modeling different personas, adding third-party data such as emails and
-PDF files, and more. 
+PDF files, and more.
 
 #### Documents
 
-As stated before a `Document` is vector embedded data stored in a `Collection`. 
+As stated before a `Document` is vector embedded data stored in a `Collection`.
 
 ### Insights
 
 The Insight functionality of Honcho is built on top of the Storage service. As
 `Messages` and `Sessions` are created for a `User`, Honcho will asynchronously
 reason about the `User`'s psychology to derive facts about them and store them
-in a reserved `Collection`. 
+in a reserved `Collection`.
 
 To read more about how this works read our [Research Paper](https://arxiv.org/abs/2310.06983)