sgmk

docs/tracing/faq/custom_llm_token_counting.mdx

@@ -36,7 +36,7 @@ def my_chat_model(messages: list):
 my_chat_model(
     [
         {"role": "system", "content": "You are a bot."},
-        {"role": "user", "content": "Will"},
+        {"role": "user", "content": "SolidGoldMagikarp"},
     ]
 )
 ```
@@ -60,7 +60,7 @@ def my_chat_model_with_model(messages: list, model: str):
 my_chat_model_with_model(
     [
         {"role": "system", "content": "You are a bot."},
-        {"role": "user", "content": "Will"},
+        {"role": "user", "content": "SolidGoldMagikarp"},
     ],
     model="gpt-3.5-turbo",
 )
@@ -93,7 +93,7 @@ list(
     my_streaming_chat_model(
         [
             {"role": "system", "content": "You are a bot."},
-            {"role": "user", "content": "Will but streaming"},
+            {"role": "user", "content": "SolidGoldMagikarp but streaming"},
         ],
         model="gpt-3.5-turbo",
     )
@@ -133,7 +133,7 @@ def my_chat_model_with_usage(messages: list, model: str = "gpt-3.5-turbo"):
 my_chat_model_with_usage(
     messages=[
         {"role": "system", "content": "You are a bot."},
-        {"role": "user", "content": "Will but with usage"},
+        {"role": "user", "content": "SolidGoldMagikarp but with usage"},
     ],
 )
 ```
@@ -192,7 +192,7 @@ list(
     my_streaming_chat_model_with_usage(
         messages=[
             {"role": "system", "content": "You are a bot."},
-            {"role": "user", "content": "Will but with usage"},
+            {"role": "user", "content": "SolidGoldMagikarp but with usage"},
         ]
     )
 )
@@ -207,7 +207,7 @@ For completion models, your inputs must contain a key `prompt` with a string val
 def my_llm(prompt: str):
     return {"choices": [{"text": "hello, " + prompt}]}
 
-my_llm("Will")
+my_llm("SolidGoldMagikarp")
 ```
 
 If you want to add additional "invocation params" such as the model name, you can just add those keys. The `model` key can be used to let the cost estimator know which model is being used.
@@ -217,7 +217,7 @@ If you want to add additional "invocation params" such as the model name, you ca
 def my_llm_with_model(prompt: str, model: str):
     return {"choices": [{"text": "hello, " + prompt}]}
 
-my_llm_with_model("Will", model="gpt-3.5-turbo-instruct")
+my_llm_with_model("SolidGoldMagikarp", model="gpt-3.5-turbo-instruct")
 ```
 
 For streaming, you can "reduce" the outputs into the same format as the non-streaming version:
@@ -232,5 +232,5 @@ def my_streaming_llm(prompt: str, model: str):
     for chunk in ["hello, " + prompt]:
         yield {"choices": [{"text": chunk}]}
 
-list(my_streaming_llm("Will but streaming", model="gpt-3.5-turbo-instruct"))
+list(my_streaming_llm("SolidGoldMagikarp but streaming", model="gpt-3.5-turbo-instruct"))
 ```