├── .gitignore ├── LICENSE ├── README.md ├── poetry.lock ├── pyproject.toml ├── src └── semantic_deduplicator │ ├── __init__.py │ ├── main.py │ └── utils.py └── tests └── test_main.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | *.ipynb 81 | 82 | # IPython 83 | profile_default/ 84 | ipython_config.py 85 | 86 | # pyenv 87 | # For a library or package, you might want to ignore these files since the code is 88 | # intended to run in multiple environments; otherwise, check them in: 89 | # .python-version 90 | 91 | # pipenv 92 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 93 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 94 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 95 | # install all needed dependencies. 96 | #Pipfile.lock 97 | 98 | # poetry 99 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 100 | # This is especially recommended for binary packages to ensure reproducibility, and is more 101 | # commonly ignored for libraries. 102 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 103 | #poetry.lock 104 | 105 | # pdm 106 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 107 | #pdm.lock 108 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 109 | # in version control. 110 | # https://pdm.fming.dev/#use-with-ide 111 | .pdm.toml 112 | 113 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 114 | __pypackages__/ 115 | 116 | # Celery stuff 117 | celerybeat-schedule 118 | celerybeat.pid 119 | 120 | # SageMath parsed files 121 | *.sage.py 122 | 123 | # Environments 124 | .env 125 | .venv 126 | env/ 127 | venv/ 128 | ENV/ 129 | env.bak/ 130 | venv.bak/ 131 | 132 | # Spyder project settings 133 | .spyderproject 134 | .spyproject 135 | 136 | # Rope project settings 137 | .ropeproject 138 | 139 | # mkdocs documentation 140 | /site 141 | 142 | # mypy 143 | .mypy_cache/ 144 | .dmypy.json 145 | dmypy.json 146 | 147 | # Pyre type checker 148 | .pyre/ 149 | 150 | # pytype static type analyzer 151 | .pytype/ 152 | 153 | # Cython debug symbols 154 | cython_debug/ 155 | 156 | # PyCharm 157 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 158 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 159 | # and can be added to the global gitignore or merged into this file. For a more nuclear 160 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 161 | #.idea/ 162 | 163 | # Other 164 | clips/ 165 | .DS_Store 166 | .pypirc -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Semantic Deduplicator 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

2 | 3 |

4 | 5 | # Semantic Deduplicator [alpha] 6 | ### Consolidate your list of items based on their *meaning*, not just keywords 7 | 8 | ```bash 9 | pip install semantic_deduplicator 10 | ``` 11 | 12 | The Semantic Deduplicator is a lightweight LLM driven package that takes a list of items and consolidates them based off of their semantic meaning 13 | 14 | One of the most annoy parts about list management is overlapping items. It can be a pain to manually go through a list and combine items. This isn't so hard when the items match exactly, but when you have unstructured data this can be a big pain. 15 | 16 | Areas of application: 17 | * Parsing Customer Feedback 18 | * Joining Grocery Lists 19 | * Parsing Employee Surveys 20 | * Combining social media comments 21 | 22 | SemanticDeduplicators's goal is to ease the pain of list management. 23 | 24 | This package currently only works with OpenAI models. There are more on the roadmap 25 | 26 | ## Quick Start 27 | ```python 28 | from semantic_deduplicator import SemanticDeduplicator 29 | 30 | sd = SemanticDeduplicator( 31 | background_context=""" 32 | You are helping me consolidate my to do list 33 | """, 34 | openai_api_key = '...' # Or set env variable 35 | ) 36 | 37 | sd.add_single_item("Go to the grocery store") 38 | sd.add_single_item("Pick up laundry") 39 | sd.add_single_item("Head over to the grocery store to get food") 40 | 41 | sd.deduplicated_items_list 42 | 43 | >> [DeduplicatedItem("Purchase groceries"), DeduplicatedItem("Collect laundry")] 44 | 45 | sd.get_formatted_deduplicated_list(type='string_list') 46 | 47 | >> "Purchase groceries, Collect laundry" 48 | ``` 49 | 50 | ## Community 51 | To ask questions, share ideas, or just chat with like-minded developers, join me on [Twitter](https://twitter.com/gregkamradt)! 52 | 53 | ## Core Components 54 | 55 | There are 3 key concepts to consider: 56 | 57 | * 🧩 Add, delete 58 | * 🤝 Similarity Scores 59 | * 🪄 deduplicated_items_list 60 | * 📚 Background Context 61 | 62 | ### 🧩 Add, Delete 63 | 64 | You have a list, you want to add items, to it, we get it! 65 | 66 | There are 3 ways to do this 67 | 1. ```sd.add_single_item()``` (recommended) : This will add a single item to your deduplicated list. It assumes there is only 1 point of interest in your data point. For example: "I want dark mode" only has one interesting data point but "I want dark mode and your app is slow" has two. If you have multiple items in your submission, use ```sd.add_item``` 68 | ```python 69 | sd.add_single_item("I want dark mode") 70 | ``` 71 | 2. ```sd.add_item()```: This will first parse your submission for points of interest and then add those to your deduplicated list. There is a bit of information loss w/ the first parsing step so only use this if necessary 72 | ```python 73 | sd.add_item("I want dark mode and your app is too slow") 74 | ``` 75 | 3. ```sd.add_single_items()```: This takes a list and iteratively ```sd.add_single_item``` 76 | ```python 77 | sd.add_single_items(["My original input from the user", "My 2nd input from a user"]) 78 | ``` 79 | 80 | similarly, you can semantically delete an item by passing in user feedback once more. 81 | 82 | ```python 83 | sd.delete_item_from_string(["I would love to have this feature in your app!"]) 84 | ``` 85 | 86 | If you would like to delete from your ```deduplicated_items_list``` by index, pop the item. 87 | ```python 88 | sd.deduplicated_items_list.pop(1) 89 | ``` 90 | 91 | ### 🤝 Similarity Scores 92 | 93 | At the core of thie package is the ability to compare items on your list semantically rather than by matching strings or keywords. 94 | 95 | This is done in a hybrid approach (not to be confused with hybrid search in retrieval). First a Cosine Similarity Check followed by a LLM similarity check 96 | 97 | Similarity Check Steps: 98 | 1. **Cosine Similiary Check** 99 | 100 | This package first checks the cosine similarity a new item has to the rest of the items already present in the deduplicated_items_list. It does this to reduce the number of items that need to be manually checked by the next step. The default value for minimum cosine similarity is .75. You can adjust this score by editing the ```cosine_similarity_threshold``` on your parent object. The lower the score, the more items which will be included in Step #2 101 | 102 | ```python 103 | sd.cosine_similarity_threshold = .6 104 | ``` 105 | 2. LLM Similarity Check 106 | 107 | With the similar candidates that are returned from Step 1, we move onto a Language Model Similarity check. This is to more accurately determine whether or not two items should be combined. We ask the language model how similar two items are based on their names with regards to the ```background_context```. The default value is .8 (0-1 scale). The higher the number, the more strict you'll be with matching items. 108 | 109 | ```python 110 | sd.llm_similarity_threshold = .9 111 | ``` 112 | 113 | The existing item with the highest similarity score will be combined with your new item. 114 | 115 | ### 📚 Deduplicated Items List 116 | 117 | Finally, the end result is held within ```deduplicated_items_list``` 118 | 119 | ```python 120 | sd.deduplicated_items_list 121 | 122 | >> ['Your 1st item', 'Your 2nd item'] 123 | ``` 124 | 125 | ### 📚 Background Context 126 | 127 | Without context it is difficult for your model to know how items should be combined. Adding ```background_context``` will let your model know more about your expected output. When debugging, start by adding more details here first. 128 | 129 | Good background context descriptions include 130 | * What you want in your output 131 | * What you don't want in your output 132 | * Your goal 133 | * Formatting points 134 | 135 | See the *Product Feedback Consolidation* below for an example 136 | 137 | # Examples 138 | 139 | ### Product Feedback Consolidation 140 | 141 | ```python 142 | from semantic_deduplicator import SemanticDeduplicator 143 | 144 | sd = SemanticDeduplicator( 145 | background_context=""" 146 | You are helping me deduplicate feature requests for a product. 147 | Please make sure to stay concise. 148 | Remove the first-person pronouns and focusing on the specific functionalities or improvements. 149 | Stripping away the "I" or "my" references to make the requests more general and applicable to a broader audience. 150 | Create clear and direct feature requests that can be easily understood and implemented by developers or relevant parties. 151 | Do not use puncutation 152 | """ 153 | ) 154 | 155 | sd.add_single_item("Please speed up your app, it is very slow") 156 | sd.add_single_item("It's also tough to find my friends on the home page") 157 | sd.add_single_item("I want dark mode") 158 | sd.add_single_item("I wish there was a darker version of your app") 159 | sd.add_single_item("I wish there was a button to change my settings") 160 | sd.add_single_item("How do I change my profile picture?") 161 | sd.add_single_item("Your app is awesome! But I wish I could invite my friends easily") 162 | sd.add_single_items(["I don't see a spot to put my credit card", "I can't figure out how to invite my friends"]) 163 | sd.delete_item_from_string("users have been requesting dark mode") 164 | 165 | sd.deduplicated_items_list 166 | 167 | >> [DeduplicatedItem("Improve application speed"), 168 | >> DeduplicatedItem("Improve visibility of friends on the home page"), 169 | >> DeduplicatedItem("Addition of a button to alter settings"), 170 | >> DeduplicatedItem("Change profile picture feature"), 171 | >> DeduplicatedItem("Enhance friend invitation functionality on app"), 172 | >> DeduplicatedItem("Add space for credit card input")] 173 | 174 | ``` 175 | 176 | ### Grocery Lists 177 | 178 | ```python 179 | from semantic_deduplicator import SemanticDeduplicator 180 | 181 | sd = SemanticDeduplicator( 182 | background_context=""" 183 | You are a helpful bot that consolidates grocery items for me as I'm about to go to the store. 184 | Combine like items 185 | Do not combine items based on their use case. Your focus is to combine them based on the item. 186 | """ 187 | ) 188 | 189 | sd.add_single_item("Berries") 190 | sd.add_single_item("Milk for cereal") 191 | sd.add_single_item("Milk for drinking") 192 | 193 | sd.deduplicated_items_list 194 | 195 | >> [DeduplicatedItem("Berries"), DeduplicatedItem("Milk")] 196 | ``` 197 | 198 | # Notes 199 | 200 | ### Disclaimer 201 | This package is in alpha and not claiming to be fast, cheap, or 100% accurate. 202 | 203 | Please be mindful of api costs -------------------------------------------------------------------------------- /poetry.lock: -------------------------------------------------------------------------------- 1 | # This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 2 | 3 | [[package]] 4 | name = "aiohttp" 5 | version = "3.8.6" 6 | description = "Async http client/server framework (asyncio)" 7 | optional = false 8 | python-versions = ">=3.6" 9 | files = [ 10 | {file = 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12 | homepage = "https://github.com/gkamradt/SemanticDeduplicator" 13 | repository = "https://github.com/gkamradt/SemanticDeduplicator" 14 | documentation = "" 15 | classifiers = [ 16 | "Programming Language :: Python :: 3", 17 | "License :: OSI Approved :: MIT License", 18 | "Operating System :: OS Independent", 19 | ] 20 | 21 | [tool.poetry.dependencies] 22 | python = "^3.9.13" 23 | numpy = "^1.21.6" 24 | openai = "^0.27.9" 25 | python-dotenv = "^1.0.0" 26 | 27 | [tool.poetry.dev-dependencies] 28 | pytest = "^7.1.2" 29 | [tool.poetry.group.dev.dependencies] 30 | pytest = "^7.4.2" -------------------------------------------------------------------------------- /src/semantic_deduplicator/__init__.py: -------------------------------------------------------------------------------- 1 | from .main import SemanticDeduplicator, DeduplicatedItem -------------------------------------------------------------------------------- /src/semantic_deduplicator/main.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import openai 3 | from typing import List 4 | import os 5 | from dotenv import load_dotenv 6 | import warnings 7 | import json 8 | from .utils import call_llm, get_embedding 9 | 10 | load_dotenv() 11 | 12 | class DeduplicatedItem: 13 | def __init__(self, item_name, original_input=None, background_context=None): 14 | """ 15 | This class represents a deduplicated item in the Semantic Deduplicator. 16 | 17 | Attributes: 18 | item_name (str): The name of the item. 19 | original_input (str): The original input string from which the item was extracted. 20 | background_context (str): The context in which the item is being consolidated. 21 | original_input_list (list): A list of original inputs. Initialized with the original input. 22 | formatted_name (str): The formatted name of the item, obtained by transforming the item name. 23 | item_embedding (np.array): The embedding of the formatted item name. 24 | """ 25 | 26 | self.original_input_list = [original_input] 27 | self.name = self.transform_item_name(background_context, item_name=item_name) 28 | self.item_embedding = get_embedding(self.name) 29 | 30 | def update_item_name(self, background_context, new_item_name): 31 | """ 32 | Updates the name of the item and its corresponding embedding. 33 | 34 | Args: 35 | background_context (str): The context in which the item is being consolidated. 36 | new_item_name (str): The new name for the item. 37 | """ 38 | 39 | self.name = self.transform_item_name(background_context, new_item_name) 40 | self.update_item_embedding() 41 | 42 | def update_item_embedding(self): 43 | """ 44 | Updates the embedding given a name string. 45 | 46 | Args: None 47 | """ 48 | new_item_embedding = get_embedding(self.name) 49 | self.item_embedding = new_item_embedding 50 | 51 | def transform_item_name(self, background_context, item_name=None): 52 | """ 53 | Takes the raw user input and outputs a clean name given the background context 54 | 55 | Args: 56 | background_context (str): The context in which the item is being consolidated. 57 | item_name (str): Optional, a string to transform. This defaults to the first item on the original_input_list 58 | """ 59 | 60 | # We can upgrade this to function calling 61 | """A string that comes in might not be in a clear format. This function helps standardize names and extract requests""" 62 | system_prompt = f""" 63 | Your goal is to reword a user input according to their instructions. 64 | Their instructions will describe a desired goal or output and you should transform the phrase or item to the best of your ability 65 | 66 | % Start of user's background 67 | {background_context} 68 | % End of user's background 69 | 70 | Respond with nothing else besides the new item name. 71 | No not include any labels, or double-quotes 72 | Capitalize the first letter of your response 73 | """ 74 | 75 | # Use the provided item_name if it's not None, otherwise use the first item from original_input_list 76 | item_to_transform = item_name if item_name is not None else self.original_input_list[0] 77 | 78 | human_prompt = f""" 79 | Here is my item: {item_to_transform} 80 | """ 81 | 82 | new_item_name = call_llm(system_prompt=system_prompt, human_prompt=human_prompt) 83 | 84 | return new_item_name 85 | 86 | def __repr__(self): 87 | return f'DeduplicatedItem("{self.name}")' 88 | 89 | class SemanticDeduplicator: 90 | def __init__(self, background_context="", llm_similarity_threshold=0.8, cosine_similarity_threshold=.75, openai_api_key='', similarity_model='gpt-4'): 91 | """ 92 | Initializes the SemanticDeduplicator class. 93 | 94 | Args: 95 | background_context (str): The context in which the items are being consolidated. Defaults to an empty string. 96 | llm_similarity_threshold (float): The final threshold for similarity. Defaults to 0.8. 97 | cosine_similarity_threshold (float): The threshold for cosine similarity. Defaults to 0.75. 98 | openai_api_key (str): The API key for OpenAI. Defaults to an empty string. 99 | similarity_model (str): The name of the similarity model to be used. Defaults to 'gpt-4'. 100 | """ 101 | 102 | self.deduplicated_items_list = [] 103 | self.cosine_similarity_threshold = cosine_similarity_threshold 104 | self.llm_similarity_threshold = llm_similarity_threshold 105 | self.background_context = background_context 106 | self.similarity_model = similarity_model # Use the provided similarity_model 107 | 108 | # If the API key is provided during initialization, use it. Else, get it from the environment variable. 109 | openai.api_key = openai_api_key or os.getenv('OPENAI_API_KEY') 110 | if not openai.api_key: 111 | raise ValueError("OpenAI API key must be provided or set in the environment variable 'OPENAI_API_KEY'") 112 | 113 | if background_context == "": 114 | warnings.warn("The 'background_context' variable is empty. This is used to inform the language model what type of items it's parsing and extracting. It's recommended to provide context on your data for better results. See https://github.com/gkamradt/SemanticDeduplicator for more information") 115 | 116 | def add_item(self, item): 117 | """ 118 | This method takes an item as input and adds it to the 'deduplicated_items_list'. If the item is similar to an existing item in the list, it merges the two items. 119 | """ 120 | 121 | items = self.parse_items_from_raw_item(item) 122 | 123 | for extracted_item in items: 124 | # Create a DeduplicatedItem object for the extracted item 125 | potential_item = DeduplicatedItem(item_name=extracted_item, 126 | original_input=item, 127 | background_context=self.background_context) 128 | 129 | self._add_item_to_list(potential_item) 130 | 131 | def add_single_item(self, item): 132 | """ 133 | This method takes a single item as input and adds it to the 'deduplicated_items_list'. 134 | If the item is similar to an existing item in the list, it merges the two items. 135 | 136 | This function assumes there is only one 'interesting' point within your item 137 | Args: 138 | item (str): The item to be added to the 'deduplicated_items_list'. 139 | 140 | """ 141 | 142 | # Create a DeduplicatedItem object for the item 143 | potential_item = DeduplicatedItem(item_name=item, 144 | original_input=item, 145 | background_context=self.background_context) 146 | 147 | self._add_item_to_list(potential_item) 148 | 149 | def _add_item_to_list(self, item): 150 | """ 151 | This method takes a DeduplicatedItem and adds it to the 'deduplicated_items_list'. 152 | If the list is empty, it adds the item directly. 153 | If the list is not empty, it checks if the item is similar to an existing item in the list and merges them if necessary. 154 | """ 155 | 156 | # Check to see if your items list has any data 157 | if len(self.deduplicated_items_list) == 0: 158 | self.add_item_to_empty_list(item) 159 | else: 160 | self.add_item_to_existing_list(item) 161 | 162 | def parse_items_from_raw_item(self, item): 163 | """ 164 | There may be multiple requests in a single submission from the user. 165 | This splits them into distinct requests to be processed individually 166 | """ 167 | 168 | system_prompt = f""" 169 | You are a bot that is part of a semantic item deduplicator. 170 | You will be given a submission from a user which may or may not contain multiple items. 171 | Your goal is to return a list of item(s) that you find in a users submission. 172 | 173 | Here is background on the items they are submitting 174 | % Start of background 175 | {self.background_context} 176 | % End of background 177 | 178 | Keep your responses to as close to what the user said as possible. 179 | If there is only one item present, only return exactly what the user said. 180 | """ 181 | 182 | function_schema = [ 183 | { 184 | "name": "extract_items_from_submission", 185 | "description": "Extract the items from the users submission", 186 | "parameters": { 187 | "type": "object", 188 | "properties": { 189 | "items": { 190 | "type": "array", 191 | "description": "Items listed within a users request", 192 | "items": { 193 | "type" : "string" 194 | } 195 | } 196 | } 197 | } 198 | } 199 | ] 200 | 201 | list_of_items = call_llm(system_prompt=system_prompt, 202 | human_prompt=item, 203 | function_schema=function_schema, 204 | model="gpt-4-0613") 205 | 206 | return list_of_items 207 | 208 | def add_item_to_empty_list(self, item_to_add): 209 | self.add_item_to_deduplicated_list(item_to_add) 210 | 211 | def add_item_to_existing_list(self, item_to_add): 212 | # If your deduplist has data, then check to see if the item is similar to any of the existing items 213 | similar_items = self.get_similar_items(item_to_add) 214 | 215 | if len(similar_items) == 0: 216 | self.add_new_item_to_list(item_to_add) 217 | else: 218 | self.combine_item_with_existing_item(item_to_add, similar_items) 219 | 220 | def add_new_item_to_list(self, item_to_add): 221 | self.add_item_to_deduplicated_list(item_to_add) 222 | 223 | def combine_item_with_existing_item(self, item_to_add, similar_items): 224 | """ 225 | This method combines a new item with an existing similar item in the 'deduplicated_items_list'. 226 | It first selects the most similar item from the list of similar items. 227 | Then it updates the name of the existing item with a combined name of the new item and the existing item. 228 | It also extends the original input list of the existing item with the original input list of the new item. 229 | 230 | Args: 231 | item_to_add (DeduplicatedItem): The new item to be added. 232 | similar_items (list): A list of items that are similar to the new item. 233 | """ 234 | 235 | # Just taking the top item to make it easy for now. 236 | # Will edit this later if it becomes an issue 237 | top_item = similar_items[0][0] 238 | 239 | # Get the index of the item you found similar in the existing list 240 | index_of_item_being_edited = next(i for i, item in enumerate(self.deduplicated_items_list) if item.name == top_item.name) 241 | 242 | new_item_name = self.get_combined_items_name(item_to_add=item_to_add, existing_item=top_item) 243 | 244 | top_item.update_item_name(background_context=self.background_context, new_item_name=new_item_name) 245 | top_item.original_input_list.extend(item_to_add.original_input_list) 246 | 247 | def add_item_to_deduplicated_list(self, item_to_add): 248 | self.deduplicated_items_list.append(item_to_add) 249 | 250 | def add_single_items(self, items: List[str]): 251 | """ 252 | This method takes a list of items as input and adds each item to the 'deduplicated_items_list'. 253 | If an item is similar to an existing item in the list, it merges the two items. 254 | 255 | Args: 256 | items (List[str]): The list of new items to be added. 257 | """ 258 | 259 | for item in items: 260 | self.add_single_item(item) 261 | 262 | def get_combined_items_name(self, item_to_add, existing_item): 263 | """ 264 | This method combines the names of a new item and an existing similar item. 265 | It uses the Language Model to generate a new name that incorporates information from both items. 266 | 267 | Args: 268 | item_to_add (DeduplicatedItem): The new item to be added. 269 | existing_item (DeduplicatedItem): The existing item that is similar to the new item. 270 | 271 | Returns: 272 | new_item_name (str): The combined name of the new item and the existing item. 273 | """ 274 | 275 | system_prompt = f""" 276 | Your goal is to combine two different similar items together. They have been deemed similar and should be comebined. 277 | Example: "I went to the park" & "I went to outside" > "I went outside" 278 | Example: "I want dark mode" & "I want two modes, light and dark" > "I want dark mode" 279 | 280 | Make sure you lose minimal information about the two items. 281 | 282 | Here is background information from the user about the items. 283 | Make sure to listen to the user and take their context into account. 284 | 285 | % Start of background 286 | {self.background_context} 287 | % End of background 288 | 289 | Respond with nothing else besides the new item name. 290 | No not include any labels, or double-quotes 291 | """ 292 | 293 | human_prompt = f""" 294 | New Item: {item_to_add.name} 295 | Existing Item: {existing_item.name} 296 | """ 297 | 298 | new_item_name = call_llm(system_prompt=system_prompt, human_prompt=human_prompt) 299 | 300 | return new_item_name 301 | 302 | def delete_item_from_string(self, item_string): 303 | item = DeduplicatedItem(item_name=item_string, background_context=self.background_context) 304 | 305 | similarities = self.get_similar_items(item) 306 | 307 | if len(similarities) == 0: 308 | pass 309 | 310 | else: 311 | top_item = similarities[0][0] 312 | 313 | index_of_item_being_deleted = next(i for i, item in enumerate(self.deduplicated_items_list) if item.name == top_item.name) 314 | 315 | self.deduplicated_items_list.pop(index_of_item_being_deleted) 316 | 317 | def cosine_similarity(self, item, existing_item): 318 | # Calculate the dot product of the two vectors 319 | dot_product = np.dot(item.item_embedding, existing_item.item_embedding) 320 | 321 | # Calculate the norms of each vector 322 | norm_1 = np.linalg.norm(item.item_embedding) 323 | norm_2 = np.linalg.norm(existing_item.item_embedding) 324 | 325 | # Calculate the cosine similarity 326 | cosine_similarity = dot_product / (norm_1 * norm_2) 327 | 328 | return cosine_similarity 329 | 330 | def get_cosine_similarity(self, item, existing_item): 331 | cosine_similarity_score = self.cosine_similarity(item, existing_item) 332 | 333 | return cosine_similarity_score 334 | 335 | def get_llm_similarity(self, item_1, item_2): 336 | """ 337 | This method calculates the semantic similarity between two items using a Language Model. 338 | It generates a system prompt and a human prompt based on the names of the two items, 339 | and then calls the Language Model to get a similarity score. 340 | 341 | Args: 342 | item_1 (DeduplicatedItem): The first item. 343 | item_2 (DeduplicatedItem): The second item. 344 | 345 | Returns: 346 | llm_similarity (int): The semantic similarity score between the two items, as determined by the Language Model. 347 | """ 348 | 349 | system_prompt = f""" 350 | Your goal is to give a rating as to how semantically similar to items or phrases are together. 351 | You will be given two phrases. 352 | Words which are interchangable should be considered similar. Ex: Awesome, cool, great, wonderful are all similar 353 | 354 | Here is background information from the user about the items. 355 | Make sure to listen to the user and take their context into account. 356 | 357 | % Start of background 358 | {self.background_context} 359 | % End of background 360 | 361 | Respond with only the number 0-100 362 | 363 | 100=Exact same phrase 364 | 0=Opposite phrase 365 | 366 | Examples: 367 | "I want to go to the park" > "Let's go to the park" = 95 368 | "We went to the tall building" > "Skyscrapers are awesome" = 60 369 | "I want ice cream" > "the horses name is bob" = 0 370 | """ 371 | 372 | human_prompt = f""" 373 | Item #1: {item_1.name} 374 | Item #2: {item_2.name} 375 | """ 376 | 377 | llm_similarity = call_llm(system_prompt=system_prompt, 378 | human_prompt=human_prompt, 379 | model=self.similarity_model) 380 | 381 | llm_similarity = int(llm_similarity) 382 | 383 | return llm_similarity 384 | 385 | def get_similar_items(self, item: DeduplicatedItem) -> List[DeduplicatedItem]: 386 | """ 387 | The goal is to return items which are semantically similar to the one that is provided 388 | We'll first do a rough pass of cosine similarity to get candidates. 389 | Then do a more thorough check with the LLM 390 | """ 391 | similarities = [] 392 | 393 | # iterate through all existing items and check similarity 394 | 395 | # First, get the cosine similarities for all the items in the list 396 | cosine_similarities = [self.get_cosine_similarity(item, existing_item) for existing_item in self.deduplicated_items_list] 397 | 398 | # Then find the ones that are above your first pass threshold 399 | above_threshold_indexes = [i for i, sim in enumerate(cosine_similarities) if sim >= self.cosine_similarity_threshold] 400 | 401 | # Then run through each item that was deemed similar via the cosine similarity and ask the LLM what it thinks 402 | for index in above_threshold_indexes: 403 | similar_item = self.deduplicated_items_list[index] 404 | similar_item_name = similar_item.name 405 | llm_sim = int(self.get_llm_similarity(item, similar_item)) / 100 406 | if llm_sim >= self.llm_similarity_threshold: 407 | # Append a tuple with your similar item and it's similarity score 408 | similarities.append((similar_item, llm_sim)) 409 | 410 | # Return the similar items in descending order of similarity (most similar at the top) 411 | return sorted(similarities, key=lambda x: x[1], reverse=True) 412 | 413 | def get_formatted_deduplicated_list(self, get_type="string_list"): 414 | """ 415 | Pretty print the list contents 416 | Args: 417 | get_type (str): The type of print format. Defaults to "string_list". 418 | "string_list": Joins the items together in a comma separated string 419 | "dict_list": Prints the list of deduplicated items as a list of dictionaries, with each dictionary representing an item and the original values as a list of strings 420 | "json": Prints the list of deduplicated items in JSON format. 421 | """ 422 | if get_type == "string_list": 423 | return ', '.join([item.name for item in self.deduplicated_items_list]) 424 | 425 | elif get_type == "dict_list": 426 | return [{'Formatted Name': item.name, 'Original Names': item.original_input_list} for item in self.deduplicated_items_list] 427 | 428 | elif get_type == "json": 429 | return json.dumps([{'Formatted Name': item.name, 'Original Names': item.original_input_list} for item in self.deduplicated_items_list]) 430 | 431 | else: 432 | raise ValueError(f"Invalid get_type: {get_type}. Expected one of: 'string_list', 'dict_list', 'json'") 433 | -------------------------------------------------------------------------------- /src/semantic_deduplicator/utils.py: -------------------------------------------------------------------------------- 1 | # utils.py 2 | import openai 3 | import json 4 | import time 5 | 6 | def call_llm(system_prompt="You are a helpful assistant.", human_prompt="Hello!", function_schema=[], model="gpt-4-0613"): 7 | params = { 8 | "model": model, 9 | "messages": [ 10 | {"role": "system", "content": system_prompt}, 11 | {"role": "user", "content": human_prompt} 12 | ] 13 | } 14 | 15 | max_attempts = 3 16 | backoff_factor = 1.5 17 | 18 | for attempt in range(max_attempts): 19 | try: 20 | if function_schema: 21 | params["functions"] = function_schema 22 | params["function_call"] = {"name": function_schema[0]['name']} 23 | completion = openai.ChatCompletion.create(**params) 24 | 25 | return json.loads(completion.choices[0]['message']['function_call']['arguments'])['items'] 26 | else: 27 | completion = openai.ChatCompletion.create(**params) 28 | return completion.choices[0].message['content'] 29 | except openai.error.ServiceUnavailableError: 30 | if attempt < max_attempts - 1: # no need to sleep on the last attempt 31 | sleep_time = backoff_factor * (2 ** attempt) 32 | time.sleep(sleep_time) 33 | else: 34 | raise 35 | 36 | def get_embedding(string): 37 | embedding = openai.Embedding.create( 38 | model="text-embedding-ada-002", 39 | input=string 40 | ) 41 | return embedding['data'][0]['embedding'] -------------------------------------------------------------------------------- /tests/test_main.py: -------------------------------------------------------------------------------- 1 | from semantic_deduplicator import SemanticDeduplicator, DeduplicatedItem 2 | import json 3 | 4 | def test_simple_deduplication_product_feedback(): 5 | sd = SemanticDeduplicator( 6 | background_context=""" 7 | You are helping me deduplicate feature requests for a product. 8 | Please make sure to stay concise. 9 | Remove the first-person pronouns and focusing on the specific functionalities or improvements. 10 | Stripping away the "I" or "my" references to make the requests more general and applicable to a broader audience. 11 | Create clear and direct feature requests that can be easily understood and implemented by developers or relevant parties. 12 | Do not use puncutation 13 | """ 14 | ) 15 | 16 | sd.add_single_item("I want dark mode") 17 | sd.add_single_item("I wish there was a darker version of your app") 18 | 19 | # testing to make sure these items were actually consolidated 20 | assert len(sd.deduplicated_items_list)==1 21 | 22 | def test_simple_delete_product_feedback(): 23 | sd = SemanticDeduplicator( 24 | background_context=""" 25 | You are helping me deduplicate feature requests for a product. 26 | Please make sure to stay concise. 27 | Remove the first-person pronouns and focusing on the specific functionalities or improvements. 28 | Stripping away the "I" or "my" references to make the requests more general and applicable to a broader audience. 29 | Create clear and direct feature requests that can be easily understood and implemented by developers or relevant parties. 30 | Do not use puncutation 31 | """ 32 | ) 33 | 34 | sd.add_single_item("I want dark mode") 35 | sd.delete_item_from_string("I wish there was a darker version of your app") 36 | 37 | # Making sure the item was removed successfully 38 | assert len(sd.deduplicated_items_list)==0 39 | 40 | def test_simple_deduplication_groceries(): 41 | 42 | sd = SemanticDeduplicator( 43 | background_context=""" 44 | You are a helpful bot that consolidates grocery items for me as I'm about to go to the store. 45 | Combine like items 46 | Do not combine items based on their use case. Your focus is to combine them based on the item. 47 | """ 48 | ) 49 | 50 | sd.add_single_item("Berries") 51 | sd.add_single_item("Milk for cereal") 52 | sd.add_single_item("Milk for drinking") 53 | 54 | assert len(sd.deduplicated_items_list)==2 55 | 56 | def test_add_multitem_deduplication_item(): 57 | 58 | sd = SemanticDeduplicator( 59 | background_context=""" 60 | You are a helpful bot that consolidates grocery items for me as I'm about to go to the store. 61 | Combine like items 62 | Do not combine items based on their use case. Your focus is to combine them based on the item. 63 | """ 64 | ) 65 | 66 | sd.add_item("Berries, milk and meat") 67 | 68 | assert len(sd.deduplicated_items_list)==3 69 | 70 | def test_add_deduplication_items(): 71 | 72 | sd = SemanticDeduplicator( 73 | background_context=""" 74 | You are a helpful bot that consolidates grocery items for me as I'm about to go to the store. 75 | Combine like items 76 | Do not combine items based on their use case. Your focus is to combine them based on the item. 77 | """ 78 | ) 79 | 80 | sd.add_single_items(["Berries", "milk", "meat"]) 81 | 82 | assert len(sd.deduplicated_items_list)==3 83 | 84 | def test_simple_duplicated_item_create(): 85 | di = DeduplicatedItem("My test item") 86 | assert len(di.item_embedding) > 0 87 | 88 | def test_get_formatted_deduplicated_list(): 89 | sd = SemanticDeduplicator( 90 | background_context=""" 91 | You are helping me consolidate my to do list 92 | """ 93 | ) 94 | 95 | sd.add_single_item("Go to the grocery store") 96 | sd.add_single_item("Pick up laundry") 97 | sd.add_single_item("Head over to the grocery store to get food") 98 | 99 | string_output = sd.get_formatted_deduplicated_list(get_type="string_list") 100 | dict_output = sd.get_formatted_deduplicated_list(get_type="dict_list") 101 | json_output = sd.get_formatted_deduplicated_list(get_type="json") 102 | 103 | assert string_output.count(',') == 1 104 | assert len(dict_output) == 2 105 | assert len(json.loads(json_output)) == 2 --------------------------------------------------------------------------------