diff --git a/examples/api_request_parallel_processor.py b/examples/api_request_parallel_processor.py
index 8b206a48..cc6cd47d 100644
--- a/examples/api_request_parallel_processor.py
+++ b/examples/api_request_parallel_processor.py
@@ -98,6 +98,7 @@ import asyncio  # for running API calls concurrently
 import json  # for saving results to a jsonl file
 import logging  # for logging rate limit warnings and other messages
 import os  # for reading API key
+import re  # for matching endpoint from request URL
 import tiktoken  # for counting tokens
 import time  # for sleeping after rate limit is hit
 from dataclasses import dataclass  # for storing API inputs, outputs, and metadata
@@ -138,7 +139,7 @@ async def process_api_requests_from_file(
     available_token_capacity = max_tokens_per_minute
     last_update_time = time.time()
 
-    # intialize flags
+    # initialize flags
     file_not_finished = True  # after file is empty, we'll skip reading it
     logging.debug(f"Initialization complete.")
 
@@ -151,13 +152,13 @@ async def process_api_requests_from_file(
         while True:
             # get next request (if one is not already waiting for capacity)
             if next_request is None:
-                if queue_of_requests_to_retry.empty() is False:
+                if not queue_of_requests_to_retry.empty():
                     next_request = queue_of_requests_to_retry.get_nowait()
                     logging.debug(f"Retrying request {next_request.task_id}: {next_request}")
                 elif file_not_finished:
                     try:
                         # get new request
-                        request_json = eval(next(requests))
+                        request_json = json.loads(next(requests))
                         next_request = APIRequest(
                             task_id=next(task_id_generator),
                             request_json=request_json,
@@ -199,7 +200,7 @@ async def process_api_requests_from_file(
 
                     # call API
                     asyncio.create_task(
-                        next_request.call_API(
+                        next_request.call_api(
                             request_url=request_url,
                             request_header=request_header,
                             retry_queue=queue_of_requests_to_retry,
@@ -259,7 +260,7 @@ class APIRequest:
     attempts_left: int
     result = []
 
-    async def call_API(
+    async def call_api(
         self,
         request_url: str,
         request_header: dict,
@@ -312,7 +313,8 @@ class APIRequest:
 
 def api_endpoint_from_url(request_url):
     """Extract the API endpoint from the request URL."""
-    return request_url.split("/")[-1]
+    match = re.search('^https://[^/]+/v\\d+/(.+)$', request_url)
+    return match[1]
 
 
 def append_to_jsonl(data, filename: str) -> None:
@@ -330,21 +332,35 @@ def num_tokens_consumed_from_request(
     """Count the number of tokens in the request. Only supports completion and embedding requests."""
     encoding = tiktoken.get_encoding(token_encoding_name)
     # if completions request, tokens = prompt + n * max_tokens
-    if api_endpoint == "completions":
-        prompt = request_json["prompt"]
+    if api_endpoint.endswith("completions"):
         max_tokens = request_json.get("max_tokens", 15)
         n = request_json.get("n", 1)
         completion_tokens = n * max_tokens
-        if isinstance(prompt, str):  # single prompt
-            prompt_tokens = len(encoding.encode(prompt))
-            num_tokens = prompt_tokens + completion_tokens
-            return num_tokens
-        elif isinstance(prompt, list):  # multiple prompts
-            prompt_tokens = sum([len(encoding.encode(p)) for p in prompt])
-            num_tokens = prompt_tokens + completion_tokens * len(prompt)
-            return num_tokens
+
+        # chat completions
+        if api_endpoint.startswith("chat/"):
+            num_tokens = 0
+            for message in request_json["messages"]:
+                num_tokens += 4  # every message follows <im_start>{role/name}\n{content}<im_end>\n
+                for key, value in message.items():
+                    num_tokens += len(encoding.encode(value))
+                    if key == "name":  # if there's a name, the role is omitted
+                        num_tokens -= 1  # role is always required and always 1 token
+            num_tokens += 2  # every reply is primed with <im_start>assistant
+            return num_tokens + completion_tokens
+        # normal completions
         else:
-            raise TypeError('Expecting either string or list of strings for "prompt" field in completion request')
+            prompt = request_json["prompt"]
+            if isinstance(prompt, str):  # single prompt
+                prompt_tokens = len(encoding.encode(prompt))
+                num_tokens = prompt_tokens + completion_tokens
+                return num_tokens
+            elif isinstance(prompt, list):  # multiple prompts
+                prompt_tokens = sum([len(encoding.encode(p)) for p in prompt])
+                num_tokens = prompt_tokens + completion_tokens * len(prompt)
+                return num_tokens
+            else:
+                raise TypeError('Expecting either string or list of strings for "prompt" field in completion request')
     # if embeddings request, tokens = input tokens
     elif api_endpoint == "embeddings":
         input = request_json["input"]