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Move langauge model abstractions and implementation into their own module; create a new README; update dependencies

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graph_of_thoughts/controller/README.md
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# Controller
The Controller class is responsible for traversing the Graph of Operations (GoO), which is a static structure that is constructed once, before the execution starts.
The Controller class is responsible for traversing the Graph of Operations (GoO), which is a static structure that is constructed once, before the execution starts.
GoO prescribes the execution plan of thought operations and the Controller invokes their execution, generating the Graph Reasoning State (GRS).
In order for a GoO to be executed, an instance of Large Language Model (LLM) must be supplied to the controller.
Currently, the framework supports the following LLMs:
- GPT-4 / GPT-3.5 (Remote - OpenAI API)
- Llama-2 (Local - HuggingFace Transformers)
In order for a GoO to be executed, an instance of Large Language Model (LLM) must be supplied to the controller (along with other required objects).
Please refer to the [Language Models](../language_models/README.md) section for more information about LLMs.
The following section describes how to instantiate individual LLMs and the Controller to run a defined GoO.
Furthermore, the process of adding new LLMs into the framework is outlined at the end.
## LLM Instantiation
- Create a copy of `config_template.json` named `config.json`.
- Fill configuration details based on the used model (below).
### GPT-4 / GPT-3.5
- Adjust predefined `chatgpt`, `chatgpt4` or create new configuration with an unique key.
| Key | Value |
|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| model_id | Model name based on [OpenAI model overview](https://platform.openai.com/docs/models/overview). |
| prompt_token_cost | Price per 1000 prompt tokens based on [OpenAI pricing](https://openai.com/pricing), used for calculating cumulative price per LLM instance. |
| response_token_cost | Price per 1000 response tokens based on [OpenAI pricing](https://openai.com/pricing), used for calculating cumulative price per LLM instance. |
| temperature | Parameter of OpenAI models that controls randomness and the creativity of the responses (higher temperature = more diverse and unexpected responses). Value between 0.0 and 2.0, default is 1.0. More information can be found in the [OpenAI API reference](https://platform.openai.com/docs/api-reference/completions/create#completions/create-temperature). |
| max_tokens | The maximum number of tokens to generate in the chat completion. Value depends on the maximum context size of the model specified in the [OpenAI model overview](https://platform.openai.com/docs/models/overview). More information can be found in the [OpenAI API reference](https://platform.openai.com/docs/api-reference/chat/create#chat/create-max_tokens). |
| stop | String or array of strings specifying sequence of characters which if detected, stops further generation of tokens. More information can be found in the [OpenAI API reference](https://platform.openai.com/docs/api-reference/chat/create#chat/create-stop). |
| organization | Organization to use for the API requests (may be empty). |
| api_key | Personal API key that will be used to access OpenAI API. |
- Instantiate the language model based on the selected configuration key (predefined / custom).
```
lm = controller.ChatGPT(
"path/to/config.json",
model_name=<configuration key>
)
```
### Llama-2
- Requires local hardware to run inference and a HuggingFace account.
- Adjust predefined `llama7b-hf`, `llama13b-hf`, `llama70b-hf` or create a new configuration with an unique key.
| Key | Value |
|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| model_id | Specifies HuggingFace Llama 2 model identifier (`meta-llama/<model_id>`). |
| cache_dir | Local directory where model will be downloaded and accessed. |
| prompt_token_cost | Price per 1000 prompt tokens (currently not used - local model = no cost). |
| response_token_cost | Price per 1000 response tokens (currently not used - local model = no cost). |
| temperature | Parameter that controls randomness and the creativity of the responses (higher temperature = more diverse and unexpected responses). Value between 0.0 and 1.0, default is 0.6. |
| top_k | Top-K sampling method described in [Transformers tutorial](https://huggingface.co/blog/how-to-generate). Default value is set to 10. |
| max_tokens | The maximum number of tokens to generate in the chat completion. More tokens require more memory. |
- Instantiate the language model based on the selected configuration key (predefined / custom).
```
lm = controller.Llama2HF(
"path/to/config.json",
model_name=<configuration key>
)
```
- Request access to Llama-2 via the [Meta form](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) using the same email address as for the HuggingFace account.
- After the access is granted, go to [HuggingFace Llama-2 model card](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), log in and accept the license (_"You have been granted access to this model"_ message should appear).
- Generate HuggingFace access token.
- Log in from CLI with: `huggingface-cli login --token <your token>`.
Note: 4-bit quantization is used to reduce the model size for inference. During instantiation, the model is downloaded from HuggingFace into the cache directory specified in the `config.json`. Running queries using larger models will require multiple GPUs (splitting across many GPUs is done automatically by the Transformers library).
The following section describes how to instantiate the Controller to run a defined GoO.
## Controller Instantiation
- Requires custom `Prompter`, `Parser` and instantiated `GraphOfOperations` - creation of these is described separately.
- Use instantiated `lm` from above.
- Requires custom `Prompter`, `Parser`, as well as instantiated `GraphOfOperations` and `AbstractLanguageModel` - creation of these is described separately.
- Prepare initial state (thought) as dictionary - this can be used in the initial prompts by the operations.
```
lm = ...create
graph_of_operations = ...create
executor = controller.Controller(
@ -83,35 +26,3 @@ executor.run()
executor.output_graph("path/to/output.json")
```
- After the run the graph is written to an output file, which contains individual operations, their thoughts, information about scores and validity and total amount of used tokens / cost.
## Adding LLMs
More LLMs can be added by following these steps:
- Create new class as a subclass of `AbstractLanguageModel`.
- Use the constructor for loading configuration and instantiating the language model (if needed).
```
class CustomLanguageModel(AbstractLanguageModel):
def __init__(
self,
config_path: str = "",
model_name: str = "llama7b-hf",
cache: bool = False
) -> None:
super().__init__(config_path, model_name, cache)
self.config: Dict = self.config[model_name]
# Load data from configuration into variables if needed
# Instantiate LLM if needed
```
- Implement `query` abstract method that is used to get a list of responses from the LLM (call to remote API or local model inference).
```
def query(self, query: str, num_responses: int = 1) -> Any:
# Support caching
# Call LLM and retrieve list of responses - based on num_responses
# Return LLM response structure (not only raw strings)
```
- Implement `get_response_texts` abstract method that is used to get a list of raw texts from the LLM response structure produced by `query`.
```
def get_response_texts(self, query_response: Union[List[Dict], Dict]) -> List[str]:
# Retrieve list of raw strings from the LLM response structure
```

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graph_of_thoughts/controller/__init__.py
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from .chatgpt import ChatGPT
from .llamachat_hf import Llama2HF
from .abstract_language_model import AbstractLanguageModel
from .controller import Controller

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graph_of_thoughts/controller/controller.py
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import json
import logging
from typing import List
from .abstract_language_model import AbstractLanguageModel
from graph_of_thoughts.language_models import AbstractLanguageModel
from graph_of_thoughts.operations import GraphOfOperations, Thought
from graph_of_thoughts.prompter import Prompter
from graph_of_thoughts.parser import Parser

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graph_of_thoughts/language_models/README.md Normal file
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# Language Models
The Language Models module is responsible for managing the large language models (LLMs) used by the Controller.
Currently, the framework supports the following LLMs:
- GPT-4 / GPT-3.5 (Remote - OpenAI API)
- Llama-2 (Local - HuggingFace Transformers)
The following sections describe how to instantiate individual LLMs and how to add new LLMs to the framework.
## LLM Instantiation
- Create a copy of `config_template.json` named `config.json`.
- Fill configuration details based on the used model (below).
### GPT-4 / GPT-3.5
- Adjust predefined `chatgpt`, `chatgpt4` or create new configuration with an unique key.
| Key | Value |
|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| model_id | Model name based on [OpenAI model overview](https://platform.openai.com/docs/models/overview). |
| prompt_token_cost | Price per 1000 prompt tokens based on [OpenAI pricing](https://openai.com/pricing), used for calculating cumulative price per LLM instance. |
| response_token_cost | Price per 1000 response tokens based on [OpenAI pricing](https://openai.com/pricing), used for calculating cumulative price per LLM instance. |
| temperature | Parameter of OpenAI models that controls randomness and the creativity of the responses (higher temperature = more diverse and unexpected responses). Value between 0.0 and 2.0, default is 1.0. More information can be found in the [OpenAI API reference](https://platform.openai.com/docs/api-reference/completions/create#completions/create-temperature). |
| max_tokens | The maximum number of tokens to generate in the chat completion. Value depends on the maximum context size of the model specified in the [OpenAI model overview](https://platform.openai.com/docs/models/overview). More information can be found in the [OpenAI API reference](https://platform.openai.com/docs/api-reference/chat/create#chat/create-max_tokens). |
| stop | String or array of strings specifying sequence of characters which if detected, stops further generation of tokens. More information can be found in the [OpenAI API reference](https://platform.openai.com/docs/api-reference/chat/create#chat/create-stop). |
| organization | Organization to use for the API requests (may be empty). |
| api_key | Personal API key that will be used to access OpenAI API. |
- Instantiate the language model based on the selected configuration key (predefined / custom).
```
lm = controller.ChatGPT(
"path/to/config.json",
model_name=<configuration key>
)
```
### Llama-2
- Requires local hardware to run inference and a HuggingFace account.
- Adjust predefined `llama7b-hf`, `llama13b-hf`, `llama70b-hf` or create a new configuration with an unique key.
| Key | Value |
|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| model_id | Specifies HuggingFace Llama 2 model identifier (`meta-llama/<model_id>`). |
| cache_dir | Local directory where model will be downloaded and accessed. |
| prompt_token_cost | Price per 1000 prompt tokens (currently not used - local model = no cost). |
| response_token_cost | Price per 1000 response tokens (currently not used - local model = no cost). |
| temperature | Parameter that controls randomness and the creativity of the responses (higher temperature = more diverse and unexpected responses). Value between 0.0 and 1.0, default is 0.6. |
| top_k | Top-K sampling method described in [Transformers tutorial](https://huggingface.co/blog/how-to-generate). Default value is set to 10. |
| max_tokens | The maximum number of tokens to generate in the chat completion. More tokens require more memory. |
- Instantiate the language model based on the selected configuration key (predefined / custom).
```
lm = controller.Llama2HF(
"path/to/config.json",
model_name=<configuration key>
)
```
- Request access to Llama-2 via the [Meta form](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) using the same email address as for the HuggingFace account.
- After the access is granted, go to [HuggingFace Llama-2 model card](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), log in and accept the license (_"You have been granted access to this model"_ message should appear).
- Generate HuggingFace access token.
- Log in from CLI with: `huggingface-cli login --token <your token>`.
Note: 4-bit quantization is used to reduce the model size for inference. During instantiation, the model is downloaded from HuggingFace into the cache directory specified in the `config.json`. Running queries using larger models will require multiple GPUs (splitting across many GPUs is done automatically by the Transformers library).
## Adding LLMs
More LLMs can be added by following these steps:
- Create new class as a subclass of `AbstractLanguageModel`.
- Use the constructor for loading configuration and instantiating the language model (if needed).
```
class CustomLanguageModel(AbstractLanguageModel):
def __init__(
self,
config_path: str = "",
model_name: str = "llama7b-hf",
cache: bool = False
) -> None:
super().__init__(config_path, model_name, cache)
self.config: Dict = self.config[model_name]
# Load data from configuration into variables if needed
# Instantiate LLM if needed
```
- Implement `query` abstract method that is used to get a list of responses from the LLM (call to remote API or local model inference).
```
def query(self, query: str, num_responses: int = 1) -> Any:
# Support caching
# Call LLM and retrieve list of responses - based on num_responses
# Return LLM response structure (not only raw strings)
```
- Implement `get_response_texts` abstract method that is used to get a list of raw texts from the LLM response structure produced by `query`.
```
def get_response_texts(self, query_response: Union[List[Dict], Dict]) -> List[str]:
# Retrieve list of raw strings from the LLM response structure
```

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from .abstract_language_model import AbstractLanguageModel
from .chatgpt import ChatGPT
from .llamachat_hf import Llama2HF

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graph_of_thoughts/controller/abstract_language_model.py → graph_of_thoughts/language_models/abstract_language_model.py
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graph_of_thoughts/controller/chatgpt.py → graph_of_thoughts/language_models/chatgpt.py
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graph_of_thoughts/controller/config_template.json → graph_of_thoughts/language_models/config_template.json
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graph_of_thoughts/controller/llamachat_hf.py → graph_of_thoughts/language_models/llamachat_hf.py
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graph_of_thoughts/operations/operations.py
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@ -14,7 +14,7 @@ from abc import ABC, abstractmethod
import itertools
from graph_of_thoughts.operations.thought import Thought
from graph_of_thoughts.controller.abstract_language_model import AbstractLanguageModel
from graph_of_thoughts.language_models import AbstractLanguageModel
from graph_of_thoughts.prompter import Prompter
from graph_of_thoughts.parser import Parser