Clear our non log & detect (#96)

* Clear our non log & detect

* Remove imports

* Fix readme

Author: freddiev4

README.md

@@ -3,7 +3,7 @@
 
 ## Overview
 
-`quotientai` is an SDK and CLI built to manage artifacts (prompts, datasets), and run evaluations on [Quotient](https://quotientai.co).
+`quotientai` is an SDK and CLI for logging data to [Quotient](https://quotientai.co), and running hallucination and document attribution detections for retrieval and search-augmented AI systems.
 
 ## Installation
 
@@ -17,41 +17,6 @@ Create an API key on Quotient and set it as an environment variable called `QUOT
 
 ### Examples
 
-**Create a prompt:**
-
-```python
-from quotientai import QuotientAI
-
-quotient = QuotientAI()
-
-new_prompt = quotient.prompts.create(
-    name="customer-support-inquiry",
-    system_prompt="You are a helpful assistant.",
-    user_prompt="How can I assist you today?"
-)
-
-print(new_prompt)
-```
-
-**Create a dataset:**
-
-```python
-from quotientai import QuotientAI
-
-quotient = QuotientAI()
-
-new_dataset = quotient.datasets.create(
-    name="my-sample-dataset"
-    description="My first dataset",
-    rows=[
-        {"input": "Sample input", "expected": "Sample output"},
-        {"input": "Another input", "expected": "Another output"}
-    ]
-)
-
-print(new_dataset)
-```
-
 **Create a log with hallucination detection:**
 Log an event with hallucination detection. This will create a log event in Quotient and perform hallucination detection on the model output, input, and documents. This is a fire and forget operation, so it will not block the execution of your code.
 
@@ -74,7 +39,7 @@ quotient_logger = quotient.logger.init(
 # Mock retrieved documents
 retrieved_documents = [{"page_content": "Sample document"}]
 
-response = quotient_logger.log(
+log_id = quotient_logger.log(
     user_query="Sample input",
     model_output="Sample output",
     # Page content from Documents from your retriever used to generate the model output
@@ -94,7 +59,7 @@ response = quotient_logger.log(
     tags={"model": "gpt-4o-mini", "feature": "customer-support"},
 )
 
-print(response)
+print(log_id)
 ```
 
 You can also use the async client if you need to create logs asynchronously.
@@ -120,7 +85,7 @@ async def main():
     # Mock retrieved documents
     retrieved_documents = [{"page_content": "Sample document"}]
 
-    response = await quotient_logger.log(
+    log_id = await quotient_logger.log(
         user_query="Sample input",
         model_output="Sample output",
         # Page content from Documents from your retriever used to generate the model output
@@ -140,7 +105,7 @@ async def main():
         tags={"model": "gpt-4o-mini", "feature": "customer-support"},
     )
 
-    print(response)
+    print(log_id)
 
 
 # Run the async function