Add multi-turn documentation to readme

Author: sjmonson

README.md

@@ -126,7 +126,8 @@ The `guidellm benchmark` command is used to run benchmarks against a generative
 
   - `prompt_tokens`: Average number of tokens for prompts.
   - `output_tokens`: Average number of tokens for outputs.
-  - `TYPE_stdev`, `TYPE_min`, `TYPE_max`: Standard deviation, minimum, and maximum values for the specified type (e.g., `prompt_tokens`, `output_tokens`). If not provided, will use the provided tokens value only.
+  - `turns`: Average number of request-response pairs per sample. Values above `1` result in a multi-turn[^1] benchmark.
+  - `TYPE_stdev`, `TYPE_min`, `TYPE_max`: Standard deviation, minimum, and maximum values for the specified type (e.g., `prompt_tokens`, `output_tokens`, `turns`). If not provided, will use the provided tokens value only.
   - `samples`: Number of samples to generate, defaults to 1000.
   - `source`: Source text data for generation, defaults to a local copy of Pride and Prejudice.
 
@@ -208,3 +209,7 @@ If you find GuideLLM helpful in your research or projects, please consider citin
   howpublished={\url{https://github.com/vllm-project/guidellm}},
 }
 ```
+
+- - -
+
+[^1]: Multi-turn refers to a benchmark where each dataset row represents a series of sequential requests, with each subsequent request building upon the context of the previous ones.

src/guidellm/benchmark/entrypoints.py

@@ -90,11 +90,11 @@ async def benchmark_generative_text(
         ),
         random_seed=random_seed,
     )
-    unique_requests = request_loader.num_unique_items(raise_err=False)
+    unique_samples = request_loader.num_unique_items(raise_err=False)
     console.print_line(
-        f"Created loader with {unique_requests} unique requests from {data}.\n\n"
-        if unique_requests > 0
-        else f"Created loader with unknown number unique requests from {data}.\n\n"
+        f"Created loader with {unique_samples} unique samples from {data}.\n\n"
+        if unique_samples > 0
+        else f"Created loader with unknown number unique samples from {data}.\n\n"
     )
 
     profile = create_profile(rate_type=rate_type, rate=rate)