- What is this?
- Results
- Features
- Installation
- Code Usage
- Examples (@ samples/)
- GNBF Grammar
- Remarks!
- Citation
GrammarFlow abstracts the LLM constraining process for complex-response tasks. It helps you define your grammar rules using Pydantic and Typing in a pythonic way, and inherently embeds metadata from these dataclasses into the prompt. Parsing is enabled in JSON, TOML and XML formats, with custom parsers that avoid the issues faced by json.loads (..etc) while parsing direct outputs.
Importantly, the package supports the generation of GNBF grammar, which integrates seamlessly with the llama.cpp package. This integration allows for more intelligent sampling of logits, optimizing the response quality from models.
The goal of this package was to overcome the issues faced when using LangChain's output parsers with instruct language models locally. While GPT-4 produces consistent results in returning the correct formats, local models from families like Llama and Mistral would cause parsing errors in my testing chains when I need more than just a single string response. Since then, GrammarFlow was extended to cover more features to help anyone trying to work with LLMs for complex use-cases: multi-grammar generation, regex patterns, etc.
Moreover, GrammarFlow is meant for use-cases with (any kind of) AI Agents, as well as extracting content from text or question-answering problems. This allows it to have an edge over batched LLM generation and schema recomposing. These methods, although foolproof, would require many more calls to an inference function, which will increase the total cost of a use-case if using a paid service like GPT or Gemini. Kindly go through Remarks! section to get a complete understanding of what this means.
Please reach out to
araviki[at]purdue[dot]eduor open an issue on Github if you have any questions or inquiry related to GrammarFlow and its usage.
GrammarFlow was tested against popular LLM datasets, with a focus on constraining model outputs. The goal was to ensure that the final parsed output matched both the structure and data types of the ground truth.
Some basic tests using Google's BIG-bench:
- Logic Grid Puzzle - Nested Objects with lists.
- StrategyQA - Nested Objects with str/int.
- ReasoningAboutColors - Single object with list.
|------------------------------------------------------------------------------------------------------|
| Model Name |Parameters|Logic Grid Puzzle (n=200)|StrategyQA (n=200)|ReasoningAboutColors (n=200)|
|-----------------+----------+-------------------------+------------------+----------------------------|
| Mistral-7B | 7B | 100.0 | 100.0 | 100.0 |
|-----------------+----------+-------------------------+------------------+----------------------------|
| CodeLlama2-13B | 13B | 100.0 | 100.0 | 100.0 |
|-----------------+----------+-------------------------+------------------+----------------------------|
| Llama2-70B | 70B | 100.0 | 100.0 | 100.0 |
+------------------------------------------------------------------------------------------------------|
More complex tests:
- PrOntoQA - Chain of Thought reasoning, with randomly-scattered supporting facts in prompt. Taken from llm-reasoners. Tests the ability to place specific reasoning statements in the right place.
- HotPotQA - Multi-hop questions, with strong supervision for supporting facts. Integrated within the first ReAct prompting paper's code. Incremental steps, leading to large prompts.
|------------------------------------------------------------------------------+-------------------------|
| Model Name |Parameters|PrOntoQA Parsing (n=200)|PrOntoQA Accuracy (n=200)|HotPotQA Parsing (n=200)|
|-----------------+----------+------------------------+-------------------------+------------------------|
| Mistral-7B | 7B | 99 | 88.5 | 99.0 |
|-----------------+----------+------------------------+-------------------------+------------------------|
| CodeLlama2-13B | 13B | 98.5 | 55.5 | 100.0 |
|-------------------------------------------------------------------------------+------------------------|
| Llama2-70B | 70B | 97.5 | 81.9 | 99.0 |
|-------------------------------------------------------------------------------+------------------------|
GrammarFlow is mainly meant to be an add-on to your existing LLM applications. It works on the input to and output from your llm() call, treating everything in between as a black box. It contains pre-made template prompts for local GGUF models like Llama2 (70B, 13B, 7B), Mistral, Mixtral and has template grammars for common tasks like Chain-of-Thought and Iterative Agents. Making these prompts and grammars are trivial and require minimal effort, as long as you know the format of what you're building.
- GBNF Support: Converts any Pydantic model to GNBF grammar for using with llama.cpp's token-based sampling. Enables adding regex patterns directly through Pydantic's
Field(..., pattern=""). - Easy Integration: Integrates with any package or stack by just manipulating the prompt and decoding the result into a pythonic data abstractor. Treats everything in between as a black box.
- Handles Complex Grammars: Can handle typing objects ('List', 'Dict', etc.) and nested Pydantic logic with complex data-types.
- Experiments with different 'formats': Defines grammar rules in XML, JSON and TOML formats. JSON is the standard, while XML is best for nested parsing and TOML is best when you want to get multiple models parsed simulatenously. Each has it's own usecase as described in the guide.
- Reduces hallucinations or garbage results during sampling: GBNF grammars allow for controlled whitespacing/identation and model ordering, while parsing logic allows for ignoring incorrect terminal symbols.
pip install grammarflow
conda create --name grammarflow python=3.9 -y
conda activate grammarflow
git clone https://github.com/e-lab/SyntaxShaper
cd grammarflow
pip install .
The guide contains an in-depth explanation of all the classes and functions.
Map out what your agent chain is doing. Understand what it's goals are and what data needs to be carried forward from one step to the next. For example, consider the ReAct prompting framework. In every call, we want to pass in the Action and subsequent Observation to the next call.
First, some basic imports:
from grammarflow import *
from grammarflow.prompt.template import Agent # Prompt
from grammarflow.grammars.template import AgentStep # Structured Grammar
from grammarflow.tools.llm import LocalLlama # Barebones inference call; interfaces with llama.cpp
llm = LocalLlama()
prompt = Agent()
# prompt.placeholders lists out what you can pass into the prompt.
system_context = """Your goal is to think and plan out how to solve questions using agent tools provided to you. Think about all aspects of your thought process."""
user_message = """Who is Vladmir Putin?"""Now, to see GrammarFlow's magic:
with Constrain('xml') as manager:
# Makes the changes to the prompt
prompt = manager.format(
prompt,
placeholders={'prompt': user_message, 'instructions': system_context},
grammars=[{'model': AgentStep}]
)
llm_response = llm(prompt, temperature=0.01)
# Parse the response into a custom dataclass for holding values
response = manager.parse(llm_response)Finally, extracting the data:
observation = PerformSomeAction(
action = response.AgentStep.action,
action_input = response.AgentStep.action_input
) - For a general overview of what GrammarFlow can do, look at guide.ipynb.
- For my modification to ReAct's evaluation code on HotPotQA, look at hotpotqa_modified.
- I've also added an implementation of a data annotator for this BERT fine-tuning guide.
"GBNF (GGML BNF) is a format for defining formal grammars to constrain model outputs in llama.cpp. For example, you can use it to force the model to generate valid JSON, or speak only in emojis." Read more about it here: https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md
# Define your model
class TeamMember(BaseModel):
name: str
role: str
class TaskUpdate(BaseModel):
update_time: float
comment: Optional[str] = None
status: bool
class Task(BaseModel):
title: str
description: str
assigned_to: str = Field(..., pattern='("Akshath"|"Raghav"|"Ravikiran")')
due_date: List[str]
updates: List[TaskUpdate]
class Project(BaseModel):
name: str
description: str
project_url: Optional[str] = Field(None, pattern='"https://"[0-9a-fA-F]*')
team_members: List[TeamMember]
task: Task
# Convert to grammar
from grammarflow import GNBF
grammar = GNBF(Project).generate_grammar()
# Verify with LlamaGrammar from llama-cpp-python
GNBF.verify_grammar(grammar, format_='json')Results:
root ::= ws Project
Project ::= nl "{" "\"Project\":" ws "{" ws "\"name\":" ws string "," nl "\"description\":" ws string "," nl "\"project-url\":" ws project-url "," nl "\"team-members\":" ws TeamMember "," nl "\"task\":" ws Task "}" ws "}"
project-url ::= "https://"[0-9a-fA-F]*
assigned-to ::= ("Akshath"|"Raghav"|"Ravikiran")
ws ::= [ \t\n]
nl ::= [\n]
string ::= "\"" (
[^"\\] |
"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
)* "\""
TeamMember ::= nl "{" ws "\"name\":" ws string "," nl "\"role\":" ws string "}"
number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)?
boolean ::= ("True" | "False")
TaskUpdate ::= nl "{" ws "\"update-time\":" ws number "," nl "\"comment\":" ws string "," nl "\"status\":" ws boolean "}"
array ::= "[" ws (
due-date-value
("," ws due-date-value)*
)? "]" ws
due-date-value ::= string
Task ::= nl "{" ws "\"title\":" ws string "," nl "\"description\":" ws string "," nl "\"assigned-to\":" ws assigned-to "," nl "\"due-date\":" ws array "," nl "\"updates\":" ws TaskUpdate "}"
You can use this grammar to pass into llama.cpp through a barebones LLM class that is provided.
from grammarflow import LocalLlama
llm = LocalLlama()
with Constrain('xml') as manager:
prompt = manager.format(...)
response = llm(prompt, grammar=manager.get_grammar(CoT), stop_at=prompt.stop_at)Please keep in mind that this package is purely software driven and aims to make developers lives simpler. It can work across model families and parameter counts with great success in parsing.
However, with an increase in complexity of the prompt, the accuracy and 'performance' of the model's thinking capability will degrade. This is attributed to the context-window problem that a lot of researchers are working to improve. LLMs are autoregressive models which track previously seen tokens in order to iteratively predict the next one, and thus provide (a lot) of token probabilities in every generation. Different decoding startegies like nucleus sampling (used in GPT) and beam search are expensive and need to be used in combination with other methods to prune bad thinking patterns at generation time.
In language models, a larger prompt provides more context, leading to a wider range of plausible continuations and increasing the uncertainty in the next token's prediction. Mathematically, this manifests as a higher entropy in the distribution over possible next tokens, reflecting a greater number of likely sequences or "divergent trees" during decoding. Incorporating grammar-based constraining in language models forces the parsing of outputs to adhere to predefined syntactic rules, increasing the computational complexity and reducing flexibility in generation. This constriction narrows the search space of possible outputs, complicating the task of finding optimal sequences that satisfy both grammatical and contextual criteria.
This is why people have come up with great workarounds like prompting strategies, prompt pruning, batch processing prompts (like in JSONFormer and super-json-mode), etc. Using those practices along with this library boosts the efficiency of whatever you're building!
Batch-processing techniques entail generating simple strings in batches and subsequently formatting them into JSON structures manually. This approach, while straightforward, encounters significant limitations when the generated content requires internal consistency or interdependence among fields.
For instance, take the generation of responses for a Chain of Thought (CoT) prompt. Traditional batch processing might yield a series of isolated responses, each reflecting distinct, possibly unrelated thought processes. When these responses need to be structured into a JSON format that adheres to a list, manual entry is not sufficient. This method lacks the capability to ensure that subsequent entries are contextually aligned with previous ones.
This is where GrammarFlow steps in -- leveraging context-free grammars (CFGs) combined with carefully engineered prompts to guide the generation process.
We appreciate it if you would please cite this repo if you found the library useful for your work:
@software{GrammarFlow,
author = {Ravikiran, Akshath Raghav and Culurciello, Eugenio},
title = {GrammarFlow: Powering Agent Chains by Constraining LLM Outputs},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/e-lab/GrammarFlow}},
version = {0.1.0}
}