Added LMFormatEnforcer integration (#65)

* Added LMFormatEnforcer integration

* Editing based on PR review

* Adding frontmatter tag

* Added logo

* More README work

* Adding introduction paragraph

integrations/lmformatenforcer.md

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+---
+layout: integration
+name: LM Format Enforcer
+description: Use the LM Format Enforcer to enforce JSON Schema / Regex output of your Local Models.
+authors:
+  - name: noamgat
+    socials:
+      github: noamgat
+      twitter: noamgat
+pypi: https://pypi.org/project/lm-format-enforcer/
+repo: https://github.com/noamgat/lm-format-enforcer
+type: Model Provider
+report_issue: https://github.com/noamgat/lm-format-enforcer/issues
+logo: /logos/lmformatenforcer.png
+version: Haystack 2.0
+---
+
+# LM Format Enforcer Haystack Integration Layer
+
+![LM Format enforcer](../logos/lmformatenforcer.png)
+
+Use the [LM Format Enforcer](https://github.com/noamgat/lm-format-enforcer)  to enforce JSON Schema / Regex output of your local models in your haystack pipelines.
+
+Language models are able to generate text, but when requiring a precise output format, they do not always perform as instructed. Various prompt engineering techniques have been introduced to improve the robustness of the generated text, but they are not always sufficient. [LM Format Enforcer](https://github.com/noamgat/lm-format-enforcer) solves the issues by filtering the tokens that the language model is allowed to generate at every timestep, thus ensuring that the output format is respected, while minimizing the limitations on the language model.
+
+### What is the LM Format enforcer?
+![Solution at a glance](https://raw.githubusercontent.com/noamgat/lm-format-enforcer/main/docs/Intro.webp)
+
+
+
+## Installation
+Install the format enforcer via pip:  `pip install lm-format-enforcer`
+
+## Usage
+This integration supports both Haystack 1.x and Haystack 2.0:
+- `LMFormatEnforcerPromptNode`: A Haystack 1.x `PromptNode` that activates the format enforcer.
+- `LMFormatEnforcerLocalGenerator`: A Haystack 2.0 Generator component that activates the format enforcer.
+
+Important note: LM Format Enforcer requires a LOCAL generator - currently only Local HuggingFace transformers are supported, vLLM suport is coming soon.
+
+### Creating a CharacterLevelParser
+The `CharacterLevelParser` is the class that connects the output parsing to the format enforcing. Two main parsers are available : `JsonSchemaParser` for JSON Schemas, and `RegexParser` for regular expressions.
+
+We will start off by defining the format we want to decode, regardless of Haystack.
+
+```python
+
+from pydantic import BaseModel
+from lmformatenforcer import JsonSchemaParser
+
+class AnswerFormat(BaseModel):
+    first_name: str
+    last_name: str
+    year_of_birth: int
+    num_seasons_in_nba: int
+
+parser = JsonSchemaParser(AnswerFormat.schema())
+```
+### Haystack 1.x Integration
+<a target="_blank" href="https://colab.research.google.com/github/noamgat/lm-format-enforcer/blob/main/samples/colab_haystackv1_integration.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+
+To activate the the enforcer with Haystack V1, a `LMFormatEnforcerPromptNode` has to be used. 
+
+Here is a simple example:
+```python
+from haystack.nodes import PromptModel
+from lmformatenforcer.integrations.haystackv1 import LMFormatEnforcerPromptNode
+
+question = 'Please give me information about {query}. You MUST answer using the following json schema: '
+schema_json_str = AnswerFormat.schema_json().replace("{", "{{").replace("}", "}}")
+question_with_schema = f'{question}{schema_json_str}'
+prompt = get_prompt(question_with_schema)
+
+
+model = PromptModel(model_name_or_path="meta-llama/Llama-2-7b-chat-hf")
+prompt_node = LMFormatEnforcerPromptNode(model, prompt, character_level_parser=parser)
+
+result = prompt_node(query='Michael Jordan')
+print(result[0])
+
+```
+The model will be inferred with the format enforcer, and the output will look like this:
+
+```
+{
+"first_name": "Michael",
+"last_name": "Jordan",
+"year_of_birth": 1963,
+"num_seasons_in_nba": 15
+}
+```
+For a full example, see the [example notebook](https://github.com/noamgat/lm-format-enforcer/blob/main/samples/colab_haystackv1_integration.ipynb)
+
+### Haystack 2.0 Integration
+<a target="_blank" href="https://colab.research.google.com/github/noamgat/lm-format-enforcer/blob/main/samples/colab_haystackv2_integration.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+
+To activate the the enforcer with Haystack V2, a `LMFormatEnforcerLocalGenerator` has to be used. 
+
+Here is a simple example:
+```python
+from haystack.preview.components.generators.hugging_face.hugging_face_local import HuggingFaceLocalGenerator
+from lmformatenforcer.integrations.haystackv2 import LMFormatEnforcerLocalGenerator
+
+
+question = 'Please give me information about Michael Jordan. You MUST answer using the following json schema: '
+schema_json_str = AnswerFormat.schema_json()
+prompt = f'{question}{schema_json_str}'
+
+
+model = HuggingFaceLocalGenerator(model_name_or_path="meta-llama/Llama-2-7b-chat-hf")
+format_enforcer = LMFormatEnforcerLocalGenerator(model, character_level_parser)
+pipeline = Pipeline()
+pipeline.add_component(instance=format_enforcer, name='model')
+
+
+result = pipeline.run({
+    "model": {"prompt": prompt}
+})
+print(result['model']['replies'][0])
+
+```
+The model will be inferred with the format enforcer, and the output will look like this:
+
+```
+{
+"first_name": "Michael",
+"last_name": "Jordan",
+"year_of_birth": 1963,
+"num_seasons_in_nba": 15
+}
+```
+For a full example, see the [example notebook](https://github.com/noamgat/lm-format-enforcer/blob/main/samples/colab_haystackv2_integration.ipynb)
+