Improve the documentation for structured generation

docs/api/guide.md

@@ -0,0 +1 @@
+::: outlines.fsm.guide

docs/reference/cfg.md

@@ -1 +1,63 @@
 # Grammar-structured generation
+
+You can pass any context-free grammar in the EBNF format and Outlines will generate an output that is valid to this grammar:
+
+```python
+from outlines import models, generate
+
+arithmetic_grammar = """
+    ?start: expression
+
+    ?expression: term (("+" | "-") term)*
+
+    ?term: factor (("*" | "/") factor)*
+
+    ?factor: NUMBER
+           | "-" factor
+           | "(" expression ")"
+
+    %import common.NUMBER
+"""
+
+model = models.transformers("WizardLM/WizardMath-7B-V1.1")
+generator = generate.cfg(model, arithmetic_grammar)
+sequence = generator(
+  "Alice had 4 apples and Bob ate 2. "
+  + "Write an expression for Alice's apples:"
+)
+
+print(sequence)
+# (8-2)
+```
+
+!!! Note "Performance"
+
+    The implementation of grammar-structured generation in Outlines is very naive. This does not reflect the performance of [.txt](https://dottxt.co)'s product, where we made grammar-structured generation as fast as regex-structured generation.
+
+
+## Ready-to-use grammars
+
+Outlines contains a (small) library of grammars that can be imported and use directly. We can rewrite the previous example as:
+
+```python
+from outlines import models, generate
+
+arithmetic_grammar = outlines.grammars.arithmetic
+
+model = models.transformers("WizardLM/WizardMath-7B-V1.1")
+generator = generate.cfg(model, arithmetic_grammar)
+sequence = generator(
+  "Alice had 4 apples and Bob ate 2. "
+  + "Write an expression for Alice's apples:"
+)
+
+print(sequence)
+# (8-2)
+```
+
+The following grammars are currently available:
+
+- Arithmetic grammar via `outlines.grammars.arithmetic`
+- JSON grammar via `outlines.grammars.json`
+
+If you would like more grammars to be added to the repository, please open an [issue](https://github.com/outlines-dev/outlines/issues) or a [pull request](https://github.com/outlines-dev/outlines/pulls).

docs/reference/choices.md

@@ -1,14 +1,16 @@
 # Multiple choices
 
-Choice between different options
-In some cases we know the output is to be chosen between different options. We can restrict the completion’s output to these choices using the is_in keyword argument:
+Oultines allows you to make sure the generated text is chosen between different options:
 
 ```python
-import outlines.models as models
+from outlines import models, generate
+
+model = models.transformers("mistralai/Mistral-7B-v0.1")
+generator = generate.choice(model, ["skirt", "dress", "pen", "jacket"])
+answer = generator("Pick the odd word out: skirt, dress, pen, jacket")
 
-complete = models.openai("gpt-3.5-turbo")
-answer = complete(
-    "Pick the odd word out: skirt, dress, pen, jacket",
-    is_in=["skirt", "dress", "pen", "jacket"]
-)
 ```
+
+!!! Note "Performance"
+
+    `generation.choice` computes an index that helps Outlines guide generation. This can take some time, but only needs to be done once. If you want to generate from the same list of choices several times make sure that you only call `generate.choice` once.

docs/reference/custom_fsm_ops.md

@@ -1,83 +1,37 @@
 # Custom FSM Operations
 
-```RegexFSM.from_interegular_fsm``` leverages the flexibility of ```interegular.FSM``` to use the available operations in ```interegular```.
+Outlines is fast because it compiles regular expressions into an index ahead of inference. To do so we use the equivalence between regular expressions and Finite State Machines (FSMs), and the library [interegular](https://github.com/MegaIng/interegular) to perform the translation.
 
-## Examples
+Alternatively, one can pass a FSM built using `integular` directly to structure the generation.
 
-### ```difference```
+## Example
 
-Returns an FSM which recognises only the strings recognised by the first FSM in the list, but none of the others.
+### Using the `difference` operation
+
+In the following example we build a fsm which recognizes only the strings valid to the first regular expression but not the second. In particular, it will prevent the words "pink" and "elephant" from being generated:
 
 ```python
+import interegular
+from outlines import models, generate
+
+
 list_of_strings_pattern = """\["[^"\s]*"(?:,"[^"\s]*")*\]"""
 pink_elephant_pattern = """.*(pink|elephant).*"""
 
 list_of_strings_fsm = interegular.parse_pattern(list_of_strings_pattern).to_fsm()
 pink_elephant_fsm = interegular.parse_pattern(pink_elephant_pattern).to_fsm()
 
-list_of_strings_fsm.accepts('["a","pink","elephant"]')
-# True
-
 difference_fsm = list_of_strings_fsm - pink_elephant_fsm
 
 difference_fsm_fsm.accepts('["a","pink","elephant"]')
 # False
 difference_fsm_fsm.accepts('["a","blue","donkey"]')
 # True
-```
-
-### ```union```
-
-Returns a finite state machine which accepts any sequence of symbols that is accepted by either self or other.
-
-```python
-list_of_strings_pattern = """\["[^"\s]*"(?:,"[^"\s]*")*\]"""
-tuple_of_strings_pattern = """\("[^"\s]*"(?:,"[^"\s]*")*\)"""
-
-list_of_strings_fsm = interegular.parse_pattern(list_of_strings_pattern).to_fsm()
-tuple_of_strings_fsm = interegular.parse_pattern(tuple_of_strings_pattern).to_fsm()
-
-list_of_strings_fsm.accepts('("a","pink","elephant")')
-# False
 
-union_fsm = list_of_strings_fsm|tuple_of_strings_fsm
 
-union_fsm.accepts('["a","pink","elephant"]')
-# True
-union_fsm.accepts('("a","blue","donkey")')
-# True
+model = models.transformers("mistralai/Mistral-7B-Instruct-v0.2")
+generator = generate.fsm(model, difference_fsm)
+response = generator("Don't talk about pink elephants")
 ```
 
-### ```intersection```
-
-Returns an FSM which accepts any sequence of symbols that is accepted by both of the original FSMs.
-
-```python
-list_of_strings_pattern = """\["[^"\s]*"(?:,"[^"\s]*")*\]"""
-pink_elephant_pattern = """.*(pink|elephant).*"""
-
-list_of_strings_fsm = interegular.parse_pattern(list_of_strings_pattern).to_fsm()
-pink_elephant_fsm = interegular.parse_pattern(pink_elephant_pattern).to_fsm()
-
-list_of_strings_fsm.accepts('["a","blue","donkey"]')
-# True
-
-intersection_fsm = list_of_strings_fsm & pink_elephant_fsm
-
-intersection_fsm.accepts('["a","pink","elephant"]')
-# True
-intersection_fsm.accepts('["a","blue","donkey"]')
-# False
-```
-
-_There are more operations available, we refer to https://github.com/MegaIng/interegular/blob/master/interegular/fsm.py._
-
-# Loading Custom FSM
-
-```python
-import outlines
-
-generator = outlines.generate.fsm(model, custom_fsm)
-
-response = generator(prompt)
-```
+To see the other operations available, consult [interegular's documentation](https://github.com/MegaIng/interegular/blob/master/interegular/fsm.py).

docs/reference/json.md

@@ -1,4 +1,4 @@
-# Make the LLM follow a JSON Schema
+# JSON structured generation
 
 Outlines can make any open source model return a JSON object that follows a structure that is specified by the user. This is useful whenever we want the output of the model to be processed by code downstream: code does not understand natural language but rather the structured language it has been programmed to understand.
 
@@ -16,8 +16,7 @@ Outlines can infer the structure of the output from a Pydantic model. The result
 ```python
 from pydantic import BaseModel
 
-from outlines import models
-from outlines import text
+from outlines import models, generate
 
 
 class User(BaseModel):
@@ -27,20 +26,59 @@ class User(BaseModel):
 
 
 model = models.transformers("mistralai/Mistral-7B-v0.1")
-generator = text.generate.json(model, User)
-result = generator("Create a user profile with the fields name, last_name and id")
+generator = generate.json(model, User)
+result = generator(
+    "Create a user profile with the fields name, last_name and id"
+)
 print(result)
 # User(name="John", last_name="Doe", id=11)
 ```
 
-!!! warning "JSON and whitespaces"
+!!! Note "JSON and whitespaces"
 
     By default Outlines lets model choose the number of linebreaks and white spaces used to structure the JSON. Small models tend to struggle with this, in which case we recommend to set the value of the parameter `whitespace_pattern` to the empty string:
 
     ```python
-    generator = text.generate.json(model, User, whitespace_pattern="")
+    generator = generate.json(model, User, whitespace_pattern="")
     ```
 
+!!! Note "Performance"
+
+    `generation.json` computes an index that helps Outlines guide generation. This can take some time, but only needs to be done once. If you want to generate several times with the same schema make sure that you only call `generate.json` once.
+
+
+## Using a JSON Schema
+
+Instead of a Pydantic model you can pass a string that represents a [JSON Schema](https://json-schema.org/) specification to `generate.json`:
+
+```python
+from pydantic import BaseModel
+
+from outlines import models
+from outlines import text
+
+model = models.transformers("mistralai/Mistral-7B-v0.1")
+
+schema = """
+{
+  "title": "User",
+  "type": "object",
+  "properties": {
+    "name": {"type": "string"},
+    "last_name": {"type": "string"},
+    "id": {"type": "integer"}
+  }
+}
+"""
+
+generator = generate.json(model, schema)
+result = generator(
+    "Create a user profile with the fields name, last_name and id"
+)
+print(result)
+# User(name="John", last_name="Doe", id=11)
+```
+
 ## From a function's signature
 
 Outlines can infer the structure of the output from the signature of a function. The result is a dictionary, and can be passed directly to the function using the usual dictionary expansion syntax `**`:

docs/reference/json_mode.md

@@ -0,0 +1,17 @@
+# JSON mode
+
+Outlines can guarantee that the LLM will generate valid JSON, using [Grammar-structured generation](cfg.md):
+
+```python
+from outlines import models, generate
+
+json_grammar = outlines.grammars.json
+
+model = models.transformers("mistralai/Mistral-7b-v0.1")
+generator = generate.cfg(model, json_grammar)
+sequence = generator("Generate valid JSON")
+```
+
+!!! Note "JSON that follows a schema"
+
+    If you want to guarantee that the generated JSON follows a given schema, consult [this section](json.md) instead.

docs/reference/regex.md

@@ -1 +1,27 @@
 # Regular expressions
+
+Outlines can guarantee that the text generated by the LLM will be valid to a regular expression:
+
+```python
+from outlines import models, generate
+
+model = models.transformers("mistralai/Mistral-7B-Instruct-v0.2")
+
+generator = generate.regex(
+    model,
+    r"((25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(25[0-5]|2[0-4]\d|[01]?\d\d?)",
+)
+
+prompt = "What is the IP address of the Google DNS servers? "
+answer = generator(prompt, max_tokens=30)
+
+print(answer)
+# What is the IP address of the Google DNS servers?
+# 2.2.6.1
+```
+
+If you find yourself using `generate.regex` to restrict the answers' type you can take a look at [type-structured generation](types.md) instead.
+
+!!! Note "Performance"
+
+    `generate.regex` computes an index that helps Outlines guide generation. This can take some time, but only needs to be done once. If you want to generate several times using the same regular expression make sure that you only call `generate.regex` once.

docs/reference/samplers.md

@@ -108,3 +108,8 @@ answer = generator("What is 2+2?")
 print(answer)
 # 4
 ```
+
+
+!!! Warning "Compatibility"
+
+    Only models from the `transformers`  and `exllamav2 ` libraries are compatible with Beam Search.