diff --git a/examples/python-simplechat/client.py b/examples/python-simplechat/client.py
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+++ b/examples/python-simplechat/client.py
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+import json
+import requests
+
+# NOTE: ollama must be running for this to work, start the ollama app or run `ollama serve`
+model = "llama2"  # TODO: update this for whatever model you wish to use
+
+
+def chat(messages):
+    r = requests.post(
+        "http://0.0.0.0:11434/api/chat",
+        json={"model": model, "messages": messages, "stream": True},
+    )
+    r.raise_for_status()
+    output = ""
+
+    for line in r.iter_lines():
+        body = json.loads(line)
+        if "error" in body:
+            raise Exception(body["error"])
+        if body.get("done") is False:
+            message = body.get("message", "")
+            content = message.get("content", "")
+            output += content
+            # the response streams one token at a time, print that as we receive it
+            print(content, end="", flush=True)
+
+
+        if body.get("done", False):
+            message["content"] = output
+            return message
+
+
+def main():
+    messages = []
+    
+    while True:
+        user_input = input("Enter a prompt: ")
+        print()
+        messages.append({"role": "user", "content": user_input})
+        message = chat(messages)
+        messages.append(message)
+        print("\n\n")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/python-simplechat/readme.md b/examples/python-simplechat/readme.md
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+# Simple Chat Example
+
+The **chat** endpoint is one of two ways to generate text from an LLM with Ollama. At a high level you provide the endpoint an array of objects with a role and content specified. Then with each output and prompt, you add more of those role/content objects, which builds up the history.
+
+## Review the Code
+
+You can see in the **chat** function that actually calling the endpoint is done simply with:
+
+```python
+r = requests.post(
+  "http://0.0.0.0:11434/api/chat",
+  json={"model": model, "messages": messages, "stream": True},
+)
+```
+
+With the **generate** endpoint, you need to provide a `prompt`. But with **chat**, you provide `messages`. And the resulting stream of responses includes a `message` object with a `content` field.
+
+The final JSON object doesn't provide the full content, so you will need to build the content yourself.
+
+In the **main** function, we collect `user_input` and add it as a message to our messages and that is passed to the chat function. When the LLM is done responding the output is added as another message.
+
+## Next Steps
+
+In this example, all generations are kept. You might want to experiment with summarizing everything older than 10 conversations to enable longer history with less context being used.