Remove BAML rules

.bamlrules

@@ -1,147 +0,0 @@
-<Overview>
-  BAML (Basically, A Made-Up Language) is a domain-specific language for building LLM prompts as functions.
-  You can build an agentic workflow with BAML.
-</Overview>
-
-  <Schema>
-    // Define output schemas using classes
-    class MyObject {
-      // Optional string fields use ?
-      // @description is optional, but if you include it, it goes after the field.
-      name string? @description("The name of the object")
-      
-      // Arrays of primitives
-      // arrays cannot be optional.
-      tags string[]
-      
-      // Enums must be declared separately and are optional
-      status MyEnum?
-      
-      // Union types
-      type "success" | "error"
-      
-      // Primitive types
-      count int
-      enabled bool
-      score float
-
-      // nested objects
-      nested MyObject2
-
-      // image type
-      myImg image
-
-      {#// checks and assertions. Uses jinja syntax inside the parentheses.
-      // For a single property use one @
-      bar int @assert(between_0_and_10, {{ "{{ this > 0 and this < 10 }}" }}) //this = MyObject.bar value
-      quux string
-      // assertions for multiple fields use @@ and go at the bottom of the class. Uses jinja syntax inside the parentheses.
-      // Do NOT add descriptions after the assertion.
-      @@assert(length_limit, {{ "{{ this.quux|length < this.baz }}" }})#}
-    }
-
-    // Enums are declared separately
-    enum MyEnum {
-      PENDING
-      ACTIVE @description("Item is currently active")
-      COMPLETE
-    }
-
-    // Comments use double slashes
-    // Recursive types and inline definitions are not supported
-
-  </Schema>
-
-  <Functions>
-    // Functions define inputs, outputs and prompts
-    // function name is always PascalCase
-    function MyFunction(input: MyObject) -> string {
-      client "openai/gpt-4o"
-      // prompt with jinja syntax inside here. with double curly braces for variables.
-      // make sure to include: \{\{ ctx.output_format \}\} in the prompt, which prints the output schema instructions so the LLM returns the output in the correct format (json or string, etc.). DO NOT write the output schema manually.
-      prompt #"
-        
-      "#
-    }
-
-    <LLMClients>
-      You can use any of the following:
-      - openai/gpt-4o
-      - openai/gpt-4o-mini
-      - anthropic/claude-3-5-sonnet-latest (note the "3-5")
-      - anthropic/claude-3-5-haiku-latest
-    </LLMClients>
-
-    <Prompt>
-      When writing the prompt:
-      1. Make sure to include the input in the prompt (even if it's an image) using {{ "{{ input }}" }}
-      2. Make sure to include {{ "{{ ctx.output_format }}" }} in the prompt so the LLM knows how to format the output.
-      3. You do not need to specify to "answer in JSON format". Only write in the prompt brief instruction, and any other task-specific things to keep in mind for the task.
-      4. Write a {{ "{{ _.role(\"user\") }}" }} tag to indicate where the user's inputs start. So if there's a convo you can write
-      #"{{ "{{ _.role(\"user\") }}" }} {{ "{{ some-variable }}" }}#
-
-      DO NOT REPEAT output schema fields in the prompt. They are included with {{ "{{ ctx.output_format }}" }}.
-      ```baml
-      class TweetAnalysis {
-        mainTopic string @description("The primary topic or subject matter of the tweet")
-        isSpam bool @description("Whether the tweet appears to be spam")
-      }
-
-      function ClassifyTweets(tweets: string[]) -> TweetAnalysis[] {
-        client "openai/gpt-4o-mini"
-        prompt #"
-          Analyze each of the following tweets and classify them:
-          {{ "{{ _.role(\"user\") }}" }} {{ "{{ tweets }}" }}
-
-          {{ "{{ ctx.output_format }}" }}
-        "#
-      }
-      ```
-    </Prompt>
-
-  </Functions>
-
-  <Usage in other languages>
-    You can use BAML in python, typescript, and other languages.
-
-    ```python
-    import asyncio
-    from baml_client import b // this client is autogenerated
-    from baml_client.types import WeatherAPI
-
-    def main():
-        # In python, BAML functions are synchronous.
-        weather_info = b.UseTool("What's the weather like in San Francisco?")
-        print(weather_info)
-        assert isinstance(weather_info, WeatherAPI)
-        print(f"City: {weather_info.city}")
-        print(f"Time of Day: {weather_info.timeOfDay}")
-
-    if __name__ == '__main__':
-        main()
-    ```
-
-    ```typescript
-    import { b } from './baml_client' // this client is autogenerated
-    import { WeatherAPI } from './baml_client/types'
-    import assert from 'assert'
-
-    const main = async () => {
-      const weatherInfo = await b.UseTool("What's the weather like in San Francisco?")
-      console.log(weatherInfo)
-      assert(weatherInfo instanceof WeatherAPI)
-      console.log(`City: ${weatherInfo.city}`)
-      console.log(`Time of Day: ${weatherInfo.timeOfDay}`)
-        }
-    ```
-
-  </Usage>
-
-Do NOT use numbers as confidence intervals if you need to use them. Prefer an enum with descriptions or literals like "high", "medium", "low".
-Don't add confidence levels to extraction schemas.
-
-Don't use LLM functions to "validate" any other output. {#You should use @assert for that on each field in the output type. Search the docs for "assert" to see how to use it.#}
-
-Dedent all declarations.
-
-Note that the types exported by BAML are pydantic classes in python, and interfaces in Tyepscript, except for primitive types.