diff --git a/README.md b/README.md index 9ecf652..852d5f4 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,7 @@ PyWhy-LLM seamlessly integrates into your existing causal inference process. Imp from pywhyllm.suggesters.model_suggester import ModelSuggester from pywhyllm.suggesters.identification_suggester import IdentificationSuggester from pywhyllm.suggesters.validation_suggester import ValidationSuggester +from pywhyllm.suggesters.augmented_model_suggester import AugmentedModelSuggester from pywhyllm import RelationshipStrategy ``` @@ -49,11 +50,22 @@ domain_expertises = modeler.suggest_domain_expertises(all_factors) # Suggest a set of potential confounders suggested_confounders = modeler.suggest_confounders(treatment, outcome, all_factors, domain_expertises) -# Suggest pair-wise relationship between variables +# Suggest pair-wise relationships between variables suggested_dag = modeler.suggest_relationships(treatment, outcome, all_factors, domain_expertises, RelationshipStrategy.Pairwise) ``` +### Retrieval Augmented Generation (RAG)-based Modeler +```python +# Create instance of Modeler +modeler = AugmentedModelSuggester('gpt-4') + +treatment = "smoking" +outcome = "lung cancer" + +# Suggest pair-wise relationship between two given variables, utilizing CauseNet for RAGing the LLM +suggested_relationship = modeler.suggest_relationships(treatment, outcome) +``` ### Identifier diff --git a/docs/notebooks/augmented_model_suggester_examples.ipynb b/docs/notebooks/augmented_model_suggester_examples.ipynb new file mode 100644 index 0000000..e75fcce --- /dev/null +++ b/docs/notebooks/augmented_model_suggester_examples.ipynb @@ -0,0 +1,156 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "source": [ + "pip install dotenv" + ], + "metadata": { + "id": "cmZerbMu6Uk4" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EulKv3Km4nMa" + }, + "outputs": [], + "source": [ + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "load_dotenv()\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = '' # specify your key here" + ] + }, + { + "cell_type": "code", + "source": [ + "pip install pywhyllm" + ], + "metadata": { + "collapsed": true, + "id": "83sxVcP97xlH" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Here we introduce the AugmentedModelSuggester class. Creating an instance of it enables the chosen LLM to utilize Retrieval Augmented Generation (RAG) to determine causality. It currently does this by searching the CauseNet dataset for a relevant causal pair and augmenting the LLM with the corresponding evidence/information stored in the dataset.\n", + "\n", + "CauseNet is a large-scale knowledge base of causal relations extracted from the web, created by Heindorf et al. (2020). CauseNet is available at [causenet.org](https://causenet.org) and is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)." + ], + "metadata": { + "id": "DjYECuX84vbN" + } + }, + { + "cell_type": "code", + "source": [ + "from pywhyllm.suggesters.augmented_model_suggester import AugmentedModelSuggester\n", + "\n", + "model = AugmentedModelSuggester('gpt-4')" + ], + "metadata": { + "id": "VdfEKuDLEYcU" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "AugmentedModelSuggester can suggest the pairwise relationship given two variables. If a relevant causal pair is found in CauseNet, the LLM is augmented with the aforementioned information in CauseNet. If not found, by default, the LLM will rely on its own knowledge." + ], + "metadata": { + "id": "dES0LwHV57eX" + } + }, + { + "cell_type": "code", + "source": [ + "result = model.suggest_pairwise_relationship(\"smoking\", \"lung cancer\")" + ], + "metadata": { + "id": "D85ec6Pk5JzA" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "result" + ], + "metadata": { + "id": "W3bFehXh5SQl" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "result = model.suggest_pairwise_relationship(\"income\", \"exercise level\")" + ], + "metadata": { + "id": "odFkp921hQsX" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "result" + ], + "metadata": { + "id": "ZIeStj9OwIPe" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "result = model.suggest_pairwise_relationship(\"flooding\", \"rain\")" + ], + "metadata": { + "id": 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"pytest-mypy (>=0.9.1) ; platform_python_implementation != \"PyPy\"", "pytest-ruff"] +cffi = ["cffi (>=1.11)"] [metadata] lock-version = "2.1" python-versions = ">=3.9,<3.13" -content-hash = "8017cc8ee476462c09e6ede2bb26b9a11c6e0564b12a8c809c2aabebad55fd19" +content-hash = "6240737c89e0178464a3a83c9123160e79be6c2984019f63ab17b1daf30d851d" diff --git a/pyproject.toml b/pyproject.toml index 1fe6858..90a2f35 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,6 +55,13 @@ networkx = "<=3.2.1" guidance = ">=0.2" openai = ">=1.70" pydantic = ">=2.11" +langchain = ">=0.3.25" +langchain-chroma = ">=0.2.4" +langchain-community = ">=0.3.24" +langchain-core = ">=0.3.60" +langchain-huggingface = ">=0.2.0" +rank-bm25 = ">=0.2.2" +sentence-transformers = ">=4.1.0" [tool.poetry.group.dev.dependencies] poethepoet = "^0.33.0" @@ -110,7 +117,7 @@ _isort_check = 'isort --check .' # testing tasks test = "pytest -v -m 'not advanced' --durations=0 --durations-min=60.0" -test_no_notebooks= "pytest -v -m 'not advanced and not notebook' --durations=0 --durations-min=60.0" +test_no_notebooks = "pytest -v -m 'not advanced and not notebook' --durations=0 --durations-min=60.0" test_durations = "poetry run poe test --store-durations" test_advanced = "pytest -v" test_focused = "pytest -v -m 'focused'" diff --git a/pywhyllm/suggesters/augmented_model_suggester.py b/pywhyllm/suggesters/augmented_model_suggester.py new file mode 100644 index 0000000..c356329 --- /dev/null +++ b/pywhyllm/suggesters/augmented_model_suggester.py @@ -0,0 +1,75 @@ +import logging +import re + +from .simple_model_suggester import SimpleModelSuggester +from pywhyllm.utils.data_loader import download_causenet, load_causenet_json, create_causenet_dict +from pywhyllm.utils.augmented_model_suggester_utils import * + + +class AugmentedModelSuggester(SimpleModelSuggester): + """ + A class that extends SimpleModelSuggester and currently provides methods for suggesting causal relationships between variables by leveraging the CauseNet dataset for Retrieval Augmented Generation (RAG). + + Methods: + - suggest_pairwise_relationship(variable1: str, variable2: str) -> List[str]: + Suggests the causal relationship between two variables and returns a list containing the cause, effect, and a description of the relationship. + """ + + def __init__(self, llm, file_path: str = 'data/causenet-precision.jsonl.bz2'): + """ + Initialize the AugmentedModelSuggester with a language model and download CauseNet data. + + Args: + llm: The language model instance to be used for querying. + file_path (str, optional): Path to save the downloaded CauseNet JSONL file. + Defaults to 'data/causenet-precision.jsonl.bz2'. + """ + + super().__init__(llm) + self.file_path = file_path + + logging.basicConfig(level=logging.INFO) + url = "https://groups.uni-paderborn.de/wdqa/causenet/causality-graphs/causenet-precision.jsonl.bz2" + success = download_causenet(url, file_path) + + if success: + print(f"File downloaded to {file_path}") + json_data = load_causenet_json(file_path) + self.causenet_dict = create_causenet_dict(json_data) + else: + print("Download failed") + + def suggest_pairwise_relationship(self, variable1: str, variable2: str): + """ + Suggests a cause-and-effect relationship between two variables, leveraging the CauseNet dataset for Retrieval Augmented Generation (RAG). + If a relevant causal pair is found in CauseNet, the LLM is augmented with corresponding information regarding the relationship stored + in CauseNet. If not found, by default, the LLM will rely on its own knowledge. + + Args: + variable1 (str): The name of the first variable. + variable2 (str): The name of the second variable. + + Returns: + list: A list containing the suggested cause variable, the suggested effect variable, and a description of the reasoning behind the suggestion. If there is no relationship between the two variables, the first two elements will be None. + """ + + result = find_top_match_in_causenet(self.causenet_dict, variable1, variable2) + if result: + source_text = get_source_text(result) + retriever = split_data_and_create_vectorstore_retriever(source_text) + response = query_llm(self.llm, variable1, variable2, source_text, retriever) + else: + response = query_llm(self.llm, variable1, variable2) + + answer = re.findall(r'(.*?)', response) + answer = [ans.strip() for ans in answer] + answer_str = "".join(answer) + + if answer_str == "A": + return [variable1, variable2, response] + elif answer_str == "B": + return [variable2, variable1, response] + elif answer_str == "C": + return [None, None, response] + else: + assert False, "Invalid answer from LLM: " + answer_str diff --git a/pywhyllm/suggesters/simple_model_suggester.py b/pywhyllm/suggesters/simple_model_suggester.py index 597b62a..d2b130a 100644 --- a/pywhyllm/suggesters/simple_model_suggester.py +++ b/pywhyllm/suggesters/simple_model_suggester.py @@ -55,11 +55,11 @@ def suggest_pairwise_relationship(self, variable1: str, variable2: str): answer = [ans.strip() for ans in answer] answer_str = "".join(answer) - if (answer_str == "A"): + if answer_str == "A": return [variable1, variable2, description] - elif (answer_str == "B"): + elif answer_str == "B": return [variable2, variable1, description] - elif (answer_str == "C"): + elif answer_str == "C": return [None, None, description] # maybe we want to save the description in this case too else: assert False, "Invalid answer from LLM: " + answer_str diff --git a/pywhyllm/utils/__init__.py b/pywhyllm/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pywhyllm/utils/augmented_model_suggester_utils.py b/pywhyllm/utils/augmented_model_suggester_utils.py new file mode 100644 index 0000000..6f6025f --- /dev/null +++ b/pywhyllm/utils/augmented_model_suggester_utils.py @@ -0,0 +1,120 @@ +import os +from langchain_text_splitters import RecursiveCharacterTextSplitter +from langchain_core.documents import Document +from langchain_chroma import Chroma +from langchain_huggingface import HuggingFaceEmbeddings +from langchain_core.prompts import ChatPromptTemplate +from langchain.chains import create_retrieval_chain +from langchain.chains.combine_documents import create_stuff_documents_chain +import numpy as np +from rank_bm25 import BM25Okapi +from sentence_transformers import SentenceTransformer, util + + +def find_top_match_in_causenet(causenet_dict, variable1, variable2, threshold=0.7): + pair_strings = [ + f"{causenet_dict[key]['causal_relation']['cause']}-{causenet_dict[key]['causal_relation']['effect']}" + for key in causenet_dict] + + tokenized_pairs = [text.split() for text in pair_strings] + bm25 = BM25Okapi(tokenized_pairs) + + query = variable1 + "-" + variable2 + reverse_query = variable2 + "-" + variable1 + tokenized_query = query.split() + tokenized_reverse_query = reverse_query.split() + + combined_query = list(set(tokenized_query + tokenized_reverse_query)) + + k = 5 + scores = bm25.get_scores(combined_query) + top_k_indices = np.argsort(scores)[::-1][:k] + candidate_pairs = [pair_strings[i] for i in top_k_indices] + + model = SentenceTransformer('all-MiniLM-L6-v2') + query_embedding = model.encode(query, convert_to_tensor=True) + reverse_query_embedding = model.encode(reverse_query, convert_to_tensor=True) + candidate_embeddings = model.encode(candidate_pairs, convert_to_tensor=True) + + similarities = util.cos_sim(query_embedding, candidate_embeddings).flatten() + reverse_similarities = util.cos_sim(reverse_query_embedding, candidate_embeddings).flatten() + + max_similarities = np.maximum(similarities, reverse_similarities) + + top_idx = np.argmax(max_similarities) + top_similarity = max_similarities[top_idx] + top_pair = candidate_pairs[top_idx] + + if top_similarity >= threshold: + print(f"Best match: {top_pair} (Similarity: {top_similarity:.4f})") + return causenet_dict[top_pair] + else: + print(f"No match found with similarity above {threshold} (Best similarity: {top_similarity:.4f})") + return None + + +def get_source_text(causenet_query_result): + source_text = "" + if causenet_query_result: + for item in causenet_query_result["sources"]: + if item["type"] == 'wikipedia_sentence' or item["type"] == 'clueweb12_sentence': + source_text += item["payload"]["sentence"] + " " + + return source_text + + +def split_data_and_create_vectorstore_retriever(source_text): + document = Document(page_content=source_text) + + text_splitter = RecursiveCharacterTextSplitter( + chunk_size=100, + chunk_overlap=20 + ) + splits = text_splitter.split_documents([document]) + + embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") + + vectorstore = Chroma.from_documents( + documents=splits, + embedding=embeddings, + persist_directory="./chroma_db" # Optional: Save to disk for reuse + ) + + retriever = vectorstore.as_retriever( + search_type="similarity", + search_kwargs={"k": 5} + ) + + return retriever + + +def query_llm(llm, variable1, variable2, source_text=None, retriever=None): + if source_text: + system_prompt = """You are a helpful assistant for causal reasoning. + + Context: {context} + """ + else: + system_prompt = """You are a helpful assistant for causal reasoning. + """ + + prompt = ChatPromptTemplate.from_messages([ + ("system", system_prompt), + ("human", "{input}") + ]) + + query = f"""Which cause-and-effect-relationship is more likely? Provide reasoning and you must give your final answer (A, B, or C) in tags with the letter only. + A. {variable1} causes {variable2} B. {variable2} causes {variable1} C. neither {variable1} nor {variable2} cause each other.""" + + if source_text: + question_answer_chain = create_stuff_documents_chain(llm, prompt) + rag_chain = create_retrieval_chain(retriever, question_answer_chain) + + response = rag_chain.invoke({"input": query}) + return response['answer'] + + + else: + default_chain = prompt | llm + response = default_chain.invoke({"input": query}) + return response.content diff --git a/pywhyllm/utils/data_loader.py b/pywhyllm/utils/data_loader.py new file mode 100644 index 0000000..13f2a94 --- /dev/null +++ b/pywhyllm/utils/data_loader.py @@ -0,0 +1,98 @@ +import bz2 +import json +import os +import requests +from tqdm import tqdm +import logging + + +def download_causenet(url: str, file_path: str) -> bool: + """ + Download the CauseNet-Precision dataset from causenet.org and save it to the specified path. + + The CauseNet-Precision dataset is a subset of the CauseNet + knowledge base containing high-precision causal relations extracted from web sources. + The dataset is described in the following paper: + + Citation: + Stefan Heindorf, Yan Scholten, Henning Wachsmuth, Axel-Cyrille Ngonga Ngomo, and Martin Potthast. + 2020. CauseNet: Towards a Causality Graph Extracted from the Web. In Proceedings of the 29th ACM + International Conference on Information & Knowledge Management (CIKM '20). Association for + Computing Machinery, New York, NY, USA, 3023–3030. https://doi.org/10.1145/3340531.3412763 + + License: + CauseNet data is licensed under the Creative Commons Attribution (CC BY) license. + For full license details, see: https://creativecommons.org/licenses/by/4.0/ + + Args: + url (str): The URL of the file to download. + file_path (str): The local path where the file will be saved. + + Returns: + bool: True if the download was successful, False otherwise. + """ + try: + os.makedirs(os.path.dirname(file_path), exist_ok=True) + + response = requests.get(url, stream=True) + + if response.status_code != 200: + logging.error(f"Failed to download file from {url}. Status code: {response.status_code}") + return False + + total_size = int(response.headers.get("content-length", 0)) + + with open(file_path, "wb") as file, tqdm( + desc="Downloading", + total=total_size, + unit="B", + unit_scale=True, + unit_divisor=1024, + ) as progress_bar: + for chunk in response.iter_content(chunk_size=8192): + if chunk: + file.write(chunk) + progress_bar.update(len(chunk)) + + logging.info(f"File downloaded successfully to {file_path}") + return True + + except requests.exceptions.RequestException as e: + logging.error(f"Error downloading file from {url}: {e}") + return False + except OSError as e: + logging.error(f"Error saving file to {file_path}: {e}") + return False + + +def load_causenet_json(file_path): + json_data = [] + print("Loading CauseNet using json") + with bz2.open(file_path, 'rt', + encoding='utf-8') as file: + for line in file: + line = line.strip() + if line: + json_obj = json.loads(line) + json_data.append(json_obj) + print("Done loading CauseNet using json") + return json_data + + +def create_causenet_dict(json_data): + causenet_dict = {} + print("Creating dictionary from CauseNet json data") + for item in json_data: + cause = item['causal_relation']['cause']['concept'] + effect = item['causal_relation']['effect']['concept'] + key = cause + "-" + effect + + if key not in causenet_dict: + causenet_dict[key] = { + 'causal_relation': {'cause': cause, 'effect': effect}, + 'sources': item['sources'] + } + else: + causenet_dict[key]['sources'].extend(item['sources']) + print("Done creating dictionary from CauseNet json data") + return causenet_dict diff --git a/tests/test_notebooks.py b/tests/test_notebooks.py index dfa56a4..3aca7c5 100644 --- a/tests/test_notebooks.py +++ b/tests/test_notebooks.py @@ -16,6 +16,7 @@ # notebooks that require openai key to run "examples.ipynb", "walkthrough.ipynb" + "augmented_model_suggester_examples.ipynb" ] # Adding the root folder to the python path so that jupyter notebooks