doc: Add guide for guided generation (#240)

docs/guides/guided_generation.md

@@ -0,0 +1,67 @@
+# Guided Generation
+
+Guided generation (also called constrained decoding) is a technique that forces LLM output to follow certain rules. For 
+example, guided generation is useful when you need generated text to parse as valid JSON or even conform to a custom [JSON schema](https://json-schema.org/).
+
+## Background: Guided Generation
+
+During each forward pass of inference, LLMs produce a probability distribution over their vocabulary of tokens. The token 
+that is actually generated is selected by sampling from this distribution. 
+
+Suppose you've tasked an LLM with generating some valid JSON, and so far the LLM has produced the text `{ "name"`. When 
+considering the next token to output, it's clear that tokens like `A` or `<` will not result in valid JSON. Guided generation
+prevents the LLM from selecting an invalid token by modifying the probability distribution and setting the probability of
+invalid tokens to something like -infinity. In this way, we can guarantee that, at each step, only tokens that will produce
+valid JSON can be selected.
+
+### Important Notes
+* Guided generation does not guarantee the _quality_ of generated text, only its _form_. Guided
+generation may force the LLM to output valid JSON, but it can't ensure that the content of the JSON is desirable or accurate.
+* Even with guided generation enabled, LLM output may not be fully valid JSON if the number of `max_new_tokens` is too low,
+    as this could result in necessary tokens (e.g., a closing `}`) being cut off.
+
+## Guided Generation with Outlines
+
+[Outlines](https://github.com/outlines-dev/outlines) is an open-source library supporting various ways of specifying and enforcing
+guided generation rules onto LLM outputs.
+
+Currently, LoRAX uses Outlines to support guided generation following a user-provided JSON schema. This JSON schema is
+converted into a regular expression, and then into a finite-state machine (FSM). For each token, LoRAX then determines the set of
+valid next tokens using this FSM and sets the probability of invalid tokens to -infinity.
+
+## Example
+
+This example follows the [JSON-guided generation example](https://outlines-dev.github.io/outlines/quickstart/#json-guided-generation) in the Outlines quickstart.
+
+We assume that you have already deployed LoRAX using a suitable base model and installed the [LoRAX Python Client](../reference/python_client.md).
+
+```python
+from lorax import Client
+
+client = Client(endpoint_url)
+
+# Specify your desired format as a JSON schema
+schema = {
+    "$defs": {
+        "Armor": {
+            "enum": ["leather", "chainmail", "plate"],
+            "title": "Armor",
+            "type": "string"
+        }
+    },
+    "properties": {
+        "name": {"maxLength": 10, "title": "Name", "type": "string"},
+        "age": {"title": "Age", "type": "integer"},
+        "armor": {"$ref": "#/$defs/Armor"},
+        "strength": {"title": "Strength", "type": "integer"}
+    },
+    "required": ["name", "age", "armor", "strength"],
+    "title": "Character",
+    "type": "object"
+}
+
+# Now simply pass this schema along when sending a generate request:
+prompt = "Generate a new character for my awesome game: name, age (between 1 and 99), armor and strength. "
+response = client.generate(prompt, schema=schema)
+print(response.generated_text)
+```

mkdocs.yml

@@ -48,6 +48,7 @@ nav:
       - Quantization: guides/quantization.md
       - CUDA Graph Compilation: guides/cuda_graphs.md
       - Merging Adapters: guides/merging_adapters.md
+      - Guided Generation: guides/guided_generation.md
       - Contributing to LoRAX:
         - Contributing to LoRAX: guides/contributing/index.md
         - Development Environment: guides/contributing/development_env.md