add completion example

README.md

@@ -27,8 +27,8 @@ Docs are available [here](https://masci.github.io/banks/).
   - [Features](#features)
   - [Cookbooks](#cookbooks)
   - [Examples](#examples)
-    - [:point\_right: Use a LLM to generate a text while rendering a prompt](#point_right-use-a-llm-to-generate-a-text-while-rendering-a-prompt)
     - [:point\_right: Render a prompt template as chat messages](#point_right-render-a-prompt-template-as-chat-messages)
+    - [:point\_right: Use a LLM to generate a text while rendering a prompt](#point_right-use-a-llm-to-generate-a-text-while-rendering-a-prompt)
     - [:point\_right: Use prompt caching from Anthropic](#point_right-use-prompt-caching-from-anthropic)
   - [Reuse templates from registries](#reuse-templates-from-registries)
   - [Async support](#async-support)
@@ -59,10 +59,40 @@ first-class citizen.
 
 For a more extensive set of code examples, [see the documentation page](https://masci.github.io/banks/examples/).
 
+### :point_right: Render a prompt template as chat messages
+
+You'll find yourself feeding an LLM a list of chat messages instead of plain text
+more often than not. Banks will help you remove the boilerplate by defining the
+messages already at the prompt level.
+
+```py
+from banks import Prompt
+
+
+prompt_template = """
+{% chat role="system" %}
+You are a {{ persona }}.
+{% endchat %}
+
+{% chat role="user" %}
+Hello, how are you?
+{% endchat %}
+"""
+
+p = Prompt(prompt_template)
+print(p.chat_messages({"persona": "helpful assistant"}))
+
+# Output:
+# [
+#   ChatMessage(role='system', content='You are a helpful assistant.\n'),
+#   ChatMessage(role='user', content='Hello, how are you?\n')
+# ]
+```
+
 ### :point_right: Use a LLM to generate a text while rendering a prompt
 
 Sometimes it might be useful to ask another LLM to generate examples for you in a
-few-shot prompt. Provided you have a valid OpenAI API key stored in an env var
+few-shots prompt. Provided you have a valid OpenAI API key stored in an env var
 called `OPENAI_API_KEY` you can ask Banks to do something like this (note we can
 annotate the prompt using comments - anything within `{# ... #}` will be removed
 from the final prompt):
@@ -72,16 +102,17 @@ from banks import Prompt
 
 
 prompt_template = """
+{% set examples %}
+{% completion model="gpt-3.5-turbo-0125" %}
+  {% chat role="system" %}You are a helpful assistant{% endchat %}
+  {% chat role="user" %}Generate a bullet list of 3 tweets with a positive sentiment.{% endchat %}
+{% endcompletion %}
+{% endset %}
+
+{# output the response content #}
 Generate a tweet about the topic {{ topic }} with a positive sentiment.
-
-{#
-    This is for illustration purposes only, there are better and cheaper ways
-    to generate examples for a few-shots prompt.
-#}
 Examples:
-{% for number in range(3) %}
-- {% generate "write a tweet with positive sentiment" "gpt-3.5-turbo" %}
-{% endfor %}
+{{ examples }}
 """
 
 p = Prompt(prompt_template)
@@ -91,60 +122,20 @@ print(p.text({"topic": "climate change"}))
 The output would be something similar to the following:
 ```txt
 Generate a tweet about the topic climate change with a positive sentiment.
-
-
 Examples:
-
-- "Feeling grateful for the amazing capabilities of #GPT3.5Turbo! It's making my work so much easier and efficient. Thank you, technology!" #positivity #innovation
-
-- "Feeling grateful for all the opportunities that come my way! With #GPT3.5Turbo, I am able to accomplish tasks faster and more efficiently. #positivity #productivity"
-
-- "Feeling grateful for all the wonderful opportunities and experiences that life has to offer! #positivity #gratitude #blessed #gpt3.5turbo"
-```
-
-If you paste Banks' output into ChatGPT you would get something like this:
-```txt
-Climate change is a pressing global issue, but together we can create positive change! Let's embrace renewable energy, protect our planet, and build a sustainable future for generations to come. 🌍💚 #ClimateAction #PositiveFuture
+- "Feeling grateful for the sunshine today! 🌞 #thankful #blessed"
+- "Just had a great workout and feeling so energized! 💪 #fitness #healthyliving"
+- "Spent the day with loved ones and my heart is so full. 💕 #familytime #grateful"
 ```
 
 > [!IMPORTANT]
-> The `generate` extension uses [LiteLLM](https://github.com/BerriAI/litellm) under the hood, and provided you have the
+> The `completion` extension uses [LiteLLM](https://github.com/BerriAI/litellm) under the hood, and provided you have the
 > proper environment variables set, you can use any model from the supported [model providers](https://docs.litellm.ai/docs/providers).
 
 > [!NOTE]
 > Banks uses a cache to avoid generating text again for the same template with the same context. By default
 > the cache is in-memory but it can be customized.
 
-### :point_right: Render a prompt template as chat messages
-
-You'll find yourself feeding an LLM a list of chat messages instead of plain text
-more often than not. Banks will help you remove the boilerplate by defining the
-messages already at the prompt level.
-
-```py
-from banks import Prompt
-
-
-prompt_template = """
-{% chat role="system" %}
-You are a {{ persona }}.
-{% endchat %}
-
-{% chat role="user" %}
-Hello, how are you?
-{% endchat %}
-"""
-
-p = Prompt(prompt_template)
-print(p.chat_messages({"persona": "helpful assistant"}))
-
-# Output:
-# [
-#   ChatMessage(role='system', content='You are a helpful assistant.\n'),
-#   ChatMessage(role='user', content='Hello, how are you?\n')
-# ]
-```
-
 ### :point_right: Use prompt caching from Anthropic
 
 Several inference providers support prompt caching to save time and costs, and Anthropic in particular offers