put example

README.md

@@ -14,9 +14,11 @@ pip install paramount
 
 ```py
 @paramount.record()
-def my_ai_function(message_history, question,  ...): #  Inputs
-    # LLM invocations happen here
-    return llm_answer, llm_references, llm_message_history,  ...  # Outputs
+def my_ai_function(message_history, new_question): # Inputs
+    # <LLM invocations happen here>
+    new_message = {'role': 'user', 'content': new_question}
+    updated_history = message_history + [new_message]
+    return updated_history  # Outputs.
 ```
 
 3. After `my_ai_function(...)` has run several times, launch the Paramount UI to evaluate results:
@@ -27,20 +29,24 @@ paramount
 
 Your SMEs can now evaluate recordings and track accuracy improvements over time.
 
-Paramount runs completely offline in your private environment.
+Paramount can run completely offline in your private environment.
+
+### Example
+
+After installation, run `python example.py` for a minimal working example.
 
 ### Configuration
 
 In order to set up successfully, define which input and output parameters represent the chat list used in the LLM.
 
 This is done via the `paramount.toml` configuration file that you add in your project root dir.
 
-It will be autogenerated for you with defaults if this file doesn't already exist on first run.
+It will be autogenerated for you with defaults if it doesn't already exist on first run.
 
 ```toml
 [record]
 enabled = true
-function_url = "http://localhost:9000"  # The url to the flask app, for replay
+function_url = "http://localhost:9000"  # The url to your LLM API flask app, for replay
 
 [db]
 type = "csv" # postgres also available
@@ -57,11 +63,11 @@ identifier_colname = ""
 
 # For the table display - define which columns should be shown
 meta_cols = ['recorded_at']
-input_cols = ['args__message_history', 'args__question']  # Matches my_ai_function() example
+input_cols = ['args__message_history', 'args__new_question']  # Matches my_ai_function() example
 output_cols = ['1', '2']  # 1 and 2 are indexes for llm_answer and llm_references in example above
 
 # For the chat display - describe how your chat structure is set up. This example uses OpenAI format.
-chat_list = "output__3"  # Matches llm_message_history. Must be a list of dicts to display chat format
+chat_list = "output__1"  # Matches output updated_history. Must be a list of dicts to display chat format
 chat_list_role_param = "role"  # Key in list of dicts describing the role in the chat
 chat_list_content_param = "content"  # Key in list of dicts describing the content
 ```

example.py

@@ -0,0 +1,19 @@
+import paramount
+
+messages = [
+    {'role': 'system', 'content': 'You help users with their cake recipes.'},
+    {'role': 'user', 'content': 'Hello!'},
+    {'role': 'assistant', 'content': 'Hello, how can I help you?'},
+]
+
+
+@paramount.record()
+def my_ai_function(message_history, new_question):
+    # The above inputs become "input_args__message_history", "input_args__new_question" in the recordings csv/table
+    # LLM invocations happen here
+    new_message = {'role': 'user', 'content': new_question}
+    updated_history = message_history + [new_message]
+    return updated_history  # Outputs. This becomes "output__1". Can be used as the chat_list param in paramount.toml
+
+
+my_ai_function(messages, "How do I bake a cake?")

paramount/server/record.py

@@ -141,6 +141,8 @@ def wrapper(*args, **kwargs):
                                                                                 (str, bytes, bytearray, memoryview)):
                         # In order to not iterate over strings, bytes, or other uniterable objects.
                         serialized_result = [serialized_result]
+                    if isinstance(serialized_result, list):  # In case of a singular list, treat it as one item
+                        serialized_result = [serialized_result]
                     for i, output in enumerate(serialized_result, start=1):
                         if isinstance(output, dict):
                             for key, value in output.items():

setup.py

@@ -8,7 +8,7 @@
 
 setup(
    name='paramount',
-   version='0.3.7',
+   version='0.3.8',
    description='Business Evals for LLMs',
    long_description=long_description,
    long_description_content_type='text/markdown',