Add Python SDK docs

examples/python_sdk.md

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+# RouteLLM Python SDK
+
+You can interface with RouteLLM in Python via the `Controller` class.
+
+```python
+import os
+from routellm.controller import Controller
+
+os.environ["OPENAI_API_KEY"] = "sk-XXXXXX"
+os.environ["ANYSCALE_API_KEY"] = "esecret_XXXXXX"
+
+client = Controller(
+  # List of routers to initialize
+  routers=["mf"],
+  # The pair of strong and weak models to route to
+  strong_model="gpt-4-1106-preview",
+  weak_model="anyscale/mistralai/Mixtral-8x7B-Instruct-v0.1",
+  # The config for the router (best-performing config by default)
+  config = {
+    "mf": {
+      "checkpoint_path": "routellm/mf_gpt4_augmented"
+    }
+  },
+  # Override API base and key for LLM calls
+  api_base=None,
+  api_key=None,
+  # Display a progress bar for operations
+  progress_bar=False,
+)
+```
+
+The controller is a drop-in replacement for OpenAI's client and supports the same chat completions interface. You can call `acreate` for the async equivalent.
+
+```python
+response = client.chat.completions.create(
+  # This tells RouteLLM to use the MF router with a cost threshold of 0.11593
+  model="router-mf-0.11593",
+  messages=[
+    {"role": "user", "content": "Hello!"}
+  ]
+)
+print(response.choices[0]["message"]["content"])
+```
+You can also access these methods at `controller.completion` and `controller.acompletion`.
+
+
+In addition, the controller also supports a `route` method that returns the best model for a given prompt.
+```python
+routed_model = client.route(
+	prompt="What's the squareroot of 144?",
+	router="mf",
+	threshold=0.11593,
+)
+print(f"Prompt should be routed to {routed_model}")
+```
+
+Finally, the controller also supports a `batch_calculate_win_rate` method that takes in a `Series` of prompts and return the of win rate for the strong model on each prompt as calculated by the specified router. This is mainly used for offline evaluations and will parallelize operations wherever possible.
+```python
+import pandas as pd
+
+prompts = pd.Series(["What's the squareroot of 144?", "Who's the last president of the US?", "Is the sun a star?"])
+win_rates = client.batch_calculate_win_rate(prompts=prompts, router="mf")
+
+print(f"Calculated win rate for prompts:\n{win_rates.describe()}")
+```