From 5fd1a09c869de9e713fcc4c36432b8dfb63e13de Mon Sep 17 00:00:00 2001 From: valentimarco Date: Tue, 7 May 2024 19:02:56 +0200 Subject: [PATCH] Add docs regarding vscode debugger, c# client, FAQ, Architecture and forms (#146) * Vscode Debugger Document * C# client page * Documentations regarding FAQ, Architecture and forms * Update mkdocs/administrators/architecture.md Co-authored-by: Adelina * Update mkdocs/administrators/architecture.md Co-authored-by: Adelina * Update mkdocs/administrators/architecture.md Co-authored-by: Adelina * Update mkdocs/administrators/architecture.md Co-authored-by: Adelina * Update mkdocs/administrators/architecture.md Co-authored-by: Adelina * Update mkdocs/administrators/architecture.md Co-authored-by: Adelina * Update mkdocs/administrators/architecture.md Co-authored-by: Adelina * Update mkdocs/administrators/architecture.md Co-authored-by: Adelina * Update mkdocs/faq.md Co-authored-by: Adelina * Update mkdocs/technical/debugging/vscode.md Co-authored-by: Adelina * Update mkdocs/technical/debugging/vscode.md Co-authored-by: Adelina * Update mkdocs/technical/debugging/vscode.md Co-authored-by: Adelina * Update mkdocs/technical/debugging/vscode.md Co-authored-by: Adelina * Update mkdocs/technical/debugging/vscode.md Co-authored-by: Adelina * Update mkdocs/faq.md Co-authored-by: Adelina * Update mkdocs/faq.md Co-authored-by: Adelina * Update mkdocs/faq.md Co-authored-by: Adelina * Update mkdocs/faq.md Co-authored-by: Adelina * Update mkdocs/faq.md Co-authored-by: Adelina * Update mkdocs/faq.md Co-authored-by: Adelina * Update mkdocs/faq.md Co-authored-by: Adelina * Update mkdocs/technical/debugging/vscode.md Co-authored-by: Adelina * Update mkdocs/technical/debugging/vscode.md Co-authored-by: Adelina * Update mkdocs/technical/debugging/vscode.md Co-authored-by: Adelina * Update mkdocs/technical/debugging/vscode.md Co-authored-by: Adelina * English fixes * Validators link --------- Co-authored-by: Adelina --- mkdocs.yml | 8 +- mkdocs/administrators/architecture.md | 117 +++++++++-- .../forms/how_invalid_is_display.png | Bin 0 -> 25438 bytes .../forms/how_is_confirm_display.png | Bin 0 -> 37042 bytes .../img/technical/forms/how_is_display.png | Bin 0 -> 18228 bytes mkdocs/assets/img/vscode-debugger/image.png | Bin 0 -> 148228 bytes .../img/vscode-debugger/run_debugger.png | Bin 0 -> 16049 bytes mkdocs/faq.md | 37 ++-- mkdocs/plugins-registry/installing-plugin.md | 3 +- .../technical/clientlib/clientlib-csharp.md | 6 + mkdocs/technical/debugging/vscode.md | 121 +++++++++++- mkdocs/technical/plugins/forms.md | 184 ++++++++++++++++++ 12 files changed, 439 insertions(+), 37 deletions(-) create mode 100644 mkdocs/assets/img/technical/forms/how_invalid_is_display.png create mode 100644 mkdocs/assets/img/technical/forms/how_is_confirm_display.png create mode 100644 mkdocs/assets/img/technical/forms/how_is_display.png create mode 100644 mkdocs/assets/img/vscode-debugger/image.png create mode 100644 mkdocs/assets/img/vscode-debugger/run_debugger.png create mode 100644 mkdocs/technical/clientlib/clientlib-csharp.md create mode 100644 mkdocs/technical/plugins/forms.md diff --git a/mkdocs.yml b/mkdocs.yml index f097b63d7..941d77512 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -46,6 +46,7 @@ nav: - Plugin: technical/plugins/plugins.md - Tools: technical/plugins/tools.md - Hooks: technical/plugins/hooks.md + - Forms: technical/plugins/forms.md - Logging: technical/plugins/logging.md - Settings: technical/plugins/settings.md - Dependencies: technical/plugins/dependencies.md @@ -53,8 +54,8 @@ nav: - Registry: - Using the Plugin Template: plugins-registry/plugin-from-template.md - Publishing a Plugin: plugins-registry/publishing-plugin.md - # - Debugging: - # - Visual Studio Code: technical/debugging/vscode.md + - Debugging: + - Visual Studio Code: technical/debugging/vscode.md - Framework: - LLM Concepts: @@ -93,6 +94,7 @@ nav: - TypeScript: technical/clientlib/clientlib-typescript.md - PHP: technical/clientlib/clientlib-php.md - Ruby: technical/clientlib/clientlib-ruby.md + - C#: technical/clientlib/clientlib-csharp.md - Administrations: - Architecture: administrators/architecture.md - Environment Variables: administrators/env-variables.md @@ -185,4 +187,4 @@ markdown_extensions: custom_fences: - name: mermaid class: mermaid - format: !!python/name:pymdownx.superfences.fence_code_format \ No newline at end of file + format: !!python/name:pymdownx.superfences.fence_code_format diff --git a/mkdocs/administrators/architecture.md b/mkdocs/administrators/architecture.md index 42b2cf48f..7a9a97534 100644 --- a/mkdocs/administrators/architecture.md +++ b/mkdocs/administrators/architecture.md @@ -1,27 +1,122 @@ # Architecture -The Cheshire Cat framework consists of four components: the Core, the Vector Database, the LLM and the Admin Portal. +The Cheshire Cat framework consists of four components: **the Core**, **the Vector Database**, **the LLM** and **the embedder**. -The Core and the Admin Portal are implemented within the framework, while the Vector Database and the LLM are external dependencies. +The Core and the Admin Portal are implemented within the framework, while the Vector Database, the LLM and the embedder are external dependencies. -The Core communicates with both the Vector Database and the LLM, while The Admin Portal communicates with the Core. +The Core communicates with the Vector Database, the LLM and the embedder, while The Admin Portal communicates with the Core. -The Core is implemented in Python, Qdrant is utilized as Vector Database, the Core support different LLMs (see the [complete list](#compatible-llms) below), the Admin Portal is implemented using the Vue framework. +The Core is implemented in Python, Qdrant is utilized as Vector Database, the Core support different LLMs and Embbeders (see the [complete list](#compatible-models) below), the Admin Portal is implemented using the Vue framework. -## Docker Compose Images -To facilitate, speed up, and standardize the Cat's user experience, the Cat contains configuration for use inside Docker. +# Core -When executing `docker compose build` two Docker images are generated. The first one contains the Cat Core and Admin Portal, while the second one contains the Vector Database. The container name of the core is `cheshire_cat_core` and the name of the Vector Database image is `cheshire_vector_memory`. +## Docker Images -The local Cat core path `./core` is mounted to the image `cheshire_cat_core`, by default changes to files in this folder force a restart of the Core, this behavior can be disable using the [`DEBUG`](env-variables.md/#debug) environment variables. +To facilitate, speed up, and standardize the Cat's user experience, the Cat contains configuration for use inside Docker. -As default the Admin Portal connect to the core using `localhost` and port `1678`, these values can be customized using [environment variables](env-variables.md#core_host). This port is the only one exposed by the `cheshire_cat_core` image. +You can use the pre-compiled images present in the Repo's Docker Registry or build it from scratch: + +1. To use the pre-compiled image, add `ghcr.io/cheshire-cat-ai/core:` as value of `image` under the name of the service in the docker-compose: + + ```yml + cheshire-cat-core: + image: ghcr.io/cheshire-cat-ai/core:1.5.1 + ``` + +2. To build it from scratch execute `docker compose build` in the repo folder just cloned. + + This will generate two Docker images. The first one contains the Cat Core and Admin Portal. + The container name of the core is `cheshire_cat_core`. + +The Cat core path `./core` is mounted into the image `cheshire_cat_core`, by default changes to files in this folder force a restart of the Core, this behavior can be disabled using the [`DEBUG`](env-variables.md/#debug) environment variable. + +## Admin Portal + +The Admin Portal connects to the core using `localhost` through the port exposed when the container was created, this value can be customized using [environment variables](env-variables.md#core_host). This port is the only one exposed by the `cheshire_cat_core` image. ## Logging + All the log messages are printed on the standard output and log level can be configured with [`LOG_LEVEL`](env-variables.md#log_level) environment variables. You can check logging system documentation [here](../technical/plugins/logging.md). ## Configuration + Some options of the Core can be customized using [environment variables](env-variables.md). -## Compatible LLMs -TODO \ No newline at end of file +## Compatible Models + +The cat is agnostic, meaning You can attach your preferred llm and embedder model/provider. The Cat supports the most used ones, but you can increase the number of models/providers by [plugins](/technical/plugins/hooks/#__tabbed_1_5), here is a list of the main ones: + +1. OpenAI and Azure OpenAI +2. Cohere +3. Ollama (LLM model only) +4. HuggingFace TextInference API (LLM model only) +5. Google Gemini +6. Qdrant FastEmbed (Embedder model only) + +# Vector Memory + +## What we use as vector memory? + +The Cat provides a connection to [qdrant](https://qdrant.tech/) through his Python client. +By default the Core tries to connect to a Qdrant database, if the connection fails then it switches to the `local Qdrant database`. +It is highly recommended to connect the Cat to a **Qdrant database** to increase performance and capacity! + +## Qdrant Cloud or Self Hosting + +**Qdrant** provides to 2 paths: + +1. Self-host Qdrant by using docker, follows an example docker-compose: + + ```yml + version: '3.7' + + services: + cheshire-cat-core: + image: ghcr.io/cheshire-cat-ai/core:1.5.1 + container_name: cheshire_cat_core + depends_on: + - cheshire-cat-vector-memory + environment: + - PYTHONUNBUFFERED=1 + - WATCHFILES_FORCE_POLLING=true + - CORE_HOST=${CORE_HOST:-localhost} + - CORE_PORT=${CORE_PORT:-1865} + - QDRANT_HOST=${QDRANT_HOST:-cheshire_cat_vector_memory} + - QDRANT_PORT=${QDRANT_PORT:-6333} + - CORE_USE_SECURE_PROTOCOLS=${CORE_USE_SECURE_PROTOCOLS:-} + - API_KEY=${API_KEY:-} + - LOG_LEVEL=${LOG_LEVEL:-WARNING} + - DEBUG=${DEBUG:-true} + - SAVE_MEMORY_SNAPSHOTS=${SAVE_MEMORY_SNAPSHOTS:-false} + ports: + - ${CORE_PORT:-1865}:80 + volumes: + - ./cat/static:/app/cat/static + - ./cat/plugins:/app/cat/plugins + - ./cat/data:/app/cat/data + restart: unless-stopped + + cheshire-cat-vector-memory: + image: qdrant/qdrant:v1.7.1 + container_name: cheshire_cat_vector_memory + expose: + - 6333 + volumes: + - ./cat/long_term_memory/vector:/qdrant/storage + restart: unless-stopped + ``` + +2. Use Qdrant Cloud, by setting `QDRANT_HOST`, `QDRANT_PORT` and `QDRANT_API_KEY` Enviroment Variables. 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zGA!J1N2xAjZL=_Z;Sj)y9u^OzNUu4`T;$V*E6Gg>!}p-37THncgXC(u(qcccDAee&&DqRRN}rm(rl1I3FspYz%EpoFg& zO$N(`{>WTxv&0A;yrsR|K08qTp8gZZbf<)&6y=_FMYVo~0xU`Ua3%zip9(-|wYsvz z-Wf{JL`E?8TJ*{*U#w@+5a*4H?+)|6M&hDxjaRy-mvZb0NKbfA!>Z_$s zBP_5h_}L&OVhd>2PzIs%&+eo$`$NvSzLFBqd*-q*Qnri@ZO_cC#heH):` argument in the command like so: + + ```bash + docker run --rm -it \ + -v ./data:/app/cat/data \ + -v ./plugins:/app/cat/plugins \ + -p 1865:80 \ + -p 5678:5678 \ < --- add this line + ghcr.io/cheshire-cat-ai/core:latest + ``` + +## Configure vscode + +Once you have exposed the port, you will need to create a `launch.json` file having two different options: + + 1. Use the `Run and Debug` tab to create it, selecting `Python Debugger` and then `Remote Attach` (Follow the prompts by answering with the default proposed). + 2. Create a folder in the root directory called `.vscode` and add the `launch.json` file into it. + + β”œβ”€β”€ + β”‚ β”œβ”€β”€ core + β”‚ β”œβ”€β”€ .vscode + β”‚ β”‚ β”œβ”€β”€launch.json + +After the creation of the launch.json, Copy-Paste this config: + +1. If you run using `docker-compose`: + + ```json + { + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Remote Attach to Cat", + "type": "python", + "request": "attach", + "connect": { + "host": "localhost", + "port": 5678 + }, + "pathMappings": [ + { + "localRoot": "${workspaceFolder}/core", + "remoteRoot": "/app" + } + ], + "justMyCode": true + } + ] + } + ``` + +2. If you run using `docker run`: + + ```json + { + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Remote Attach to Cat", + "type": "python", + "request": "attach", + "connect": { + "host": "localhost", + "port": 5678 + }, + "pathMappings": [ + { + "localRoot": "${workspaceFolder}/", + "remoteRoot": "/app/cat" + } + ], + "justMyCode": true + } + ] + } + ``` + +## Connect vscode to the cat + +To Connect the vscode debugger, ask the cat to help you on debugging ![ask help to debug](../../assets/img/vscode-debugger/image.png) and in the `Run and Debug` tab start the debugging by clicking the `Play button` ▢️ or Use the shortcut `F5`. + +![Enable The debugger](../../assets/img/vscode-debugger/run_debugger.png) + +You are ready to debug your plugin! + +If you are new in VS code debugging, check the [official docs](https://code.visualstudio.com/Docs/editor/debugging). + +## Troubleshooting + +#### I click the button but then I don't see the debugging bar / the breakpoints are not respected + +This usually means that the debugger is not active, make sure to activate the debugger by asking the Cat. + +#### I cannot explore the code outside of my plugin + +By default, you can only explore your "own" code but you can disable this by setting the param `justMyCode` to false in the `launch.json` file. + +#### My Cat is installed in a remote server, can I debug it? + +Of course you can! Just set the correct `host` and `port` in the `connect` param of the `launch.json` file. diff --git a/mkdocs/technical/plugins/forms.md b/mkdocs/technical/plugins/forms.md new file mode 100644 index 000000000..913f1f7af --- /dev/null +++ b/mkdocs/technical/plugins/forms.md @@ -0,0 +1,184 @@ +# πŸ“‹ Forms + +Forms are Particular Tools useful to collect users' information during the given conversation! + + + +## How the Forms work + +Imagine a scenario where you need to create an Order system for a pizzeria, using only the conversation of the user. The user must provide 3 information: + +1. Type of pizza, must be a string in a restrictive set +2. Phone number, must be 10 numbers long and restrictive to a specific dialling code +3. Address, must be a valid address of "Milano" + +How can I resolve this problem? Well, these type of information are **very specific**: + +- need [**validators**](https://docs.pydantic.dev/latest/concepts/validators/#field-validators) (Phone numbers can be of different structure based on the country, the pizzeria has a menu of pizzas, you can deliver only in a certain area of the city) +- **can be provided in different orders** (A user can give the address before the type of pizza!). + +This is where the forms comes handy! + +## Implementation + +```python + +class PizzaOrder(BaseModel): #(1) + pizza_type: str + phone: str + address: str + + +@form #(2) +class PizzaForm(CatForm): #(3) + description = "Pizza Order" #(4) + model_class = PizzaOrder #(5) + start_examples = [ #(6) + "order a pizza!", + "I want pizza" + ] + stop_examples = [ #(7) + "stop pizza order", + "not hungry anymore", + ] + ask_confirm = True #(8) + + def submit(self, form_data): #(9) + + # do the actual order here! + # Fake call + out = request.post("https://fakecallpizza/order",{ + "pizza_type": form_data.pizza_type, + "phone": form_data.phone, + "address": form_data.address + }) + + if(out.status_code != 201): + raise Exception() + + time = out.json()["estimatedTime"] + + # return to conversation + return { + "output": f"Pizza order on its way: {form_data}. Estimated time: {time}" + } + +``` + +1. Pydantic class representing the information you need to retrive. +2. Every class decorated with `@forms` is a Form. +3. Every Form must inherit from `CatForm`. +4. Description of the Form. +5. Pydantic class name. +6. Every Form must have a list of *start examples*, so the LLM can select the form properly. It's the same principal of tool's docstring. +7. Every Form must have a list of *stop examples*, so the LLM can stop properly the form during the conversation. +8. Every Form can ask the user confirmation of the data provided. +9. Every Form must overload this method and the fuctionality is the same as tools: call database to collect the information, call the Order API, call another agent or LLM etc.. + +## Changing the "moves" of the Form + +Forms are implemented as [FSM](https://en.wikipedia.org/wiki/Finite-state_machine) and you can change any move of the FSM by overloading the methods. + +Here the diagram of the FSM: +TODO + +### State-transition function + +Each FSM has a State-Transition function that describes what is the next move to perform based on the given input. In the case of Form's implementation the input is the **User prompt** and the `def next(self)` method is State-Transition function. + +The form has 4 states to be evaluated: + +1. INCOMPLETE +2. WAIT_CONFIRM +3. CLOSED +4. COMPLETE + +each state executes one or more Phases: + +- User Stop Confirmation Phase +- User Confirmation Phase +- Updating Phase +- Visualization Phase +- Submit Phase + +You can change this state-transition by overloading the method `def next(self)` and accessing the state by `self._state`. The states are values from the `CatFormState` enum. + +### User Stop Confirmation Phase + +The User Stop Confirmation phase is where the Form produces a prompt to ask the user his willingness to continue. You can change this phase by overloading the method `def check_exit_intent(self)`. + +### User Confirmation Phase + +The User Confirmation Phase is where the Form produces a prompt to ask confirmation of the information provided by the user, if `ask_confirm` is true. You can change this phase by overloading the method `def confirm(self)`. + +### Updating Phase + +The Updating Phase is where the Form executes the Extraction Phase, Sanitization Phase and Validation Phase. You can change this phase by overloading the method `def update(self)`. + +#### Extraction Phase + +The Extraction Phase is where the Form extracts all possibile information from the user's prompt. You can change this phase by overloading the method `def extract(self)`. + +#### Sanitization Phase + +The Sanitization Phase is where the information is sinitized from unwanted values (null, None, '', ' ', etc...). You can change this phase by overloading the method `def sanitize(self, model)` + +#### Validation Phase + +The Validation Phase is where the Form attempts to construct the model, so pydantic can use the validators implemented and check each field. You can change this phase by overloading the method `def valdiate(self, model)` + +### Visualization Phase + +The Visualization Phase is where the Form displays a message to the user, showing the model's status. + +By default the cat shows the forms like so ![display form](../../assets/img/technical/forms/how_is_display.png) + +When there is invalid info that was retrived from the conversation, the cat tells you exacly which and what issue ![display invalid info](../../assets/img/technical/forms/how_invalid_is_display.png) + +You can change this phase by overloading the method `def message(self)`: + +```python + #in the form you define + def message(self): #(1) + if self._state == CatFormState.CLOSED: #(2) + return { + "output": f"Form {type(self).__name__} closed" + } + missing_fields: List[str] = self._missing_fields #(3) + errors: List[str] = self._errors #(4) + out: str = f""" + the missing information are: {missing_fields}. + this are the invalid ones: {errors} + """ + if self._state == CatFormState.WAIT_CONFIRM: + out += "\n --> Confirm? Yes or no?" + + return { + "output": out + } + +``` + +1. This method is useful to change the Form visualization. +2. Forms have states that can be checked. +3. Forms can access the list of the missing fields. +4. Forms can access the list of the invalid fields and the correlated errors. + +### Final Phase: Submit + +The Submit Phase is where the Form conclude the journey by executing all defined instructions with the information gathered from the user's conversation. +The method have two params: + +- **self** (You can access information about the form and the `StrayCat` instance) +- **form_data** (The defined pydantic model) +And must return a Dict where the value of key `output` is a string that will be displayed in the chat. + +If you need to use the Form in future conversations, you can retrive the model from the working memory by accessing the key `form`. + +Below an example: + +```python + @hook + def before_cat_sends_message(message, cat): + form_data = cat.working_memory["form"] +```