├── .env_example
├── .github
└── workflows
│ └── run_tests.yml
├── .gitignore
├── .python-version
├── LICENSE
├── Pipfile
├── Pipfile.lock
├── README.md
├── chain.py
├── client.py
├── fixtures
├── expected_responses
│ ├── client_gpt_35_response.txt
│ └── client_gpt_4_response.txt
└── vcr_cassettes
│ ├── test_ask_client_davinci.yaml
│ ├── test_ask_client_gpt_35.yaml
│ ├── test_ask_client_gpt_4.yaml
│ ├── test_default_response_davinci.yaml
│ ├── test_default_response_gpt35.yaml
│ ├── test_default_response_gpt4.yaml
│ ├── test_gpt35_bias_check.yaml
│ ├── test_gpt4_bias_check.yaml
│ ├── test_message_response_davinci.yaml
│ ├── test_message_response_gpt35.yaml
│ └── test_message_response_gpt4.yaml
├── message.py
├── requirements.txt
└── test.py
/.env_example:
--------------------------------------------------------------------------------
1 | OPENAI_API_KEY="..."
2 | LANGCHAIN_TRACING_V2="true"
3 | LANGCHAIN_API_KEY="..."
4 |
--------------------------------------------------------------------------------
/.github/workflows/run_tests.yml:
--------------------------------------------------------------------------------
1 | name: Python Test
2 |
3 | on: [push, pull_request]
4 |
5 | jobs:
6 | run-tests:
7 |
8 | runs-on: ubuntu-latest
9 |
10 | steps:
11 | - uses: actions/checkout@v3
12 | - name: Set up Python 3.11.7
13 | uses: actions/setup-python@v3
14 | with:
15 | python-version: '3.11.7'
16 | - name: Cache Pipenv virtualenv
17 | uses: actions/cache@v3
18 | with:
19 | path: ~/.local/share/virtualenvs
20 | key: ${{ runner.os }}-pipenv-v2-${{ hashFiles('**/Pipfile.lock') }}
21 | restore-keys: |
22 | ${{ runner.os }}-pipenv-v2-
23 | - name: Install PyTorch
24 | run: |
25 | pip install pipenv
26 | export PIP_EXTRA_INDEX_URL=https://download.pytorch.org/whl/cpu
27 | pipenv run pip install torch
28 | - name: Install other dependencies
29 | run: |
30 | pipenv install --dev
31 | - name: Run tests
32 | env:
33 | OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
34 | run: |
35 | pipenv run python -m unittest discover
36 |
--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
1 | .env
2 | __pycache__
3 | __pycache__/*
4 |
--------------------------------------------------------------------------------
/.python-version:
--------------------------------------------------------------------------------
1 | 3.11.7
2 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2023 Duncan Miller
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.
22 |
--------------------------------------------------------------------------------
/Pipfile:
--------------------------------------------------------------------------------
1 | [[source]]
2 | url = "https://pypi.org/simple"
3 | verify_ssl = true
4 | name = "pypi"
5 |
6 | [[source]]
7 | url = "https://download.pytorch.org/whl/cpu"
8 | verify_ssl = true
9 | name = "torch"
10 |
11 | [packages]
12 | openai = "*"
13 | python-dotenv = "*"
14 | vcrpy = "*"
15 | sentence-transformers = "*"
16 | langchain = "*"
17 | bs4 = "*"
18 | docarray = "*"
19 | tiktoken = "*"
20 | pydantic = "==1.10.9"
21 | langchain-core = "*"
22 |
23 | [dev-packages]
24 |
25 | [requires]
26 | python_version = "3.11"
27 |
28 | [pipenv]
29 | allow_prereleases = true
30 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Prompt Engineering Testing Strategies
2 |
3 | This is an example project showing prompt engineering testing strategies, using the OpenAI API. For more information, you can read a [blog article](https://openshiro.com/articles/prompt-engineering-testing-strategies-with-python) I wrote about this project.
4 |
5 | This code demonstrates how I ensure the high quality of the prompts I create. The [test.py](https://github.com/duncantmiller/llm_prompt_engineering/blob/main/test.py) file shows a series of tests I wrote for the project using test-driven development. These tests helped me to repeatedly test a system where a user prompt is augmented by additional prompts, including a [pre_prompt](https://github.com/duncantmiller/llm_prompt_engineering/blob/15c2ffe4c476f0a1ae563ffb8498320069c14c1b/message.py#L64) and a [cite_sources_prompt](https://github.com/duncantmiller/llm_prompt_engineering/blob/15c2ffe4c476f0a1ae563ffb8498320069c14c1b/message.py#L53) which provide additional instruction to the LLM beyond the user prompt.
6 |
7 | This automated test suite makes it easier for me to hone prompts or switch models (text-davinci-003, gpt-3.5-turbo, gpt-4-1106-preview) and ensure that the prompts provide a consistent response. It also enables me to monitor for ethical and bias mitigation and model drift over time.
8 |
9 | This is a simplified version of the logic used in [OpenShiro](https://openshiro.com) prompts and is intended for educational purposes. The full product involves many more parameters which the prompts are tested against including additional model versions, additional APIs like Azure, Google, HuggingFace, Anthropic and Cohere.
10 |
11 | ## Usage
12 | Clone the repository with `git clone git@github.com:duncantmiller/llm_prompt_engineering.git` then change to the project directory with `cd llm_prompt_engineering`.
13 |
14 | If you use [pipenv](https://pypi.org/project/pipenv/) you can install the dependencies with `pipenv install`, otherwise use `pip install -r requirements.txt`. Note that due to a PyTorch dependency via sentence-transformers (used for similarity testing), the latest Python version that can be used is `3.11.7`.
15 |
16 | The tests use [vcrpy](https://github.com/kevin1024/vcrpy) to record and replay the interactions with the OpenAI API. To run the tests, execute the command `python test.py`.
17 |
18 | It can also be helpful to run the tests against the live OpenAI API. Note that when your run tests against the live API you will need an OpenAI API key, it will incur a cost, and the tests will take significantly longer to run. To run the tests against the live API follow these steps:
19 |
20 | - Copy the .env_example file and rename it as .env with `cp .env_example .env` (Linux/macOS) or `copy .env_example .env` (Windows)
21 | - Open the .env file and enter the value for your OpenAI API Key for the `OPENAI_API_KEY=` variable. For example if your API key is 'foo' then `OPENAI_API_KEY=foo`
22 | - Run the tests with the command line flag `python test.py --live-test`
23 |
24 | ### Re-recording Cassettes
25 |
26 | If you change the code in your prompts or API calls, you will want to refresh the vcrpy cassettes so a new API call is recorded. To do this simply delete the appropriate .yaml cassette files in the fixtures/vcr_cassettes directory. You can also optionally set a `re_record_interval` in the `vcr` command. For example to re-record every 7 days:
27 |
28 | ````
29 | with vcr.use_cassette('cassette_name.yaml', re_record_interval=7*24*60*60):
30 | # API call goes here
31 | ````
32 | ## Repository Documentation
33 |
34 | #### Fixtures
35 |
36 | ##### Expected Responses
37 | Predetermined expected responses from various models are stored in /fixtures/expected_responses as .txt files. The tests then compare these responses to the responses which come from the live model to check for similarity of expectations.
38 |
39 | ##### VCR Cassettes
40 | The vcrpy package automatically generates .yaml files in /fixtures/vcr_cassettes based on the cassette usage in the tests. After a API call is recorded in the .yaml file it is used for subsequent test runs without the `--live-test` flag. To re-record a cassette, just delete the appropriate .yaml file here.
41 |
42 | ##### message.py
43 | The Message class accepts a user prompt when it is initialized `new_message = Message(prompt="foo")`. The Message class supplements the user prompt with additional instructions and handles the logic of using the appropriate API call depending upon which model is being used. Each user message gets its own message object, eventually these would have a user_id to associate each message with a user or perhaps a chat_id for more granularity where the chat belongs to a user.
44 |
45 | ##### chain.py
46 | This the start of a sample Langchain implementation for a chatbot using Retrieval Augmented Generation (RAG) and also providing the model with chat history for additional context.
47 |
48 | This demo uses a blog post from a url and stores a vector embedding of the content for RAG in memory. This could easily be expanded to text files and pdf documents with the vector embeddings stored in a database. It then uses Langchain's `create_retrieval_chain` to pass this vector embedding as context to the model with each prompt. It also use Langchain's `create_history_aware_retriever` to pass chat history along with each prompt.
49 |
50 | I plan to integrate this logic into the Message class and replace the OpenAI API calls with calls made through Langchain's ChatOpenAI class.
51 |
52 | ## Test Documentation
53 |
54 | The following is a review of all tests and methods used in the test.py file.
55 |
56 | ##### `parse_custom_args()`:
57 | Uses argparse to parse the `python test.py` command for `--live-test` flag. If the flag is present (the commend executed is `python test.py --live-test`) then live API calls will be made. If the flag is not present, then the tests will use the responses recorded in vcrpy cassetts.
58 |
59 | ### BaseTestCase(unittest.TestCase)
60 | Base class for all test cases with common testing methods.
61 |
62 | ##### `default_response_davinci()`:
63 | Sends a given prompt to the Davinci model and retrieves the response. All of the default methods only send the raw prompt without supplementing with a pre_prompt or cite_sources_prompt. This method uses the depricated OpenAI `client.completions` syntax which must be used for the Davinci model. It chooses between live API calls and recorded responses based on the `live_test` flag.
64 |
65 | ##### `default_response()`:
66 | General method for sending prompts and retrieving responses, applicable to newer OpenAI models, like GPT-3.5 or GPT-4. It chooses between live API calls and recorded responses based on the `live_test` flag.
67 |
68 | ##### `default_api_call()`:
69 | Directly performs the API calls for the `default_response()` method. This method uses the new `client.chat.completions` syntax for required for models like GPT-3.5 and GPT-4.
70 |
71 | ##### `custom_response()`:
72 | Uses the `Message()` object to send the `full_prompt()` to a specified model and retrieves the response, considering the `live_test` setting. The `full_prompt()` method defined in the `Message()` class does supplement the provided user prompt with the `pre_prompt()` and `cite_sources_prompt()`.
73 |
74 | ### TestDefaultResponseDavinci(BaseTestCase)
75 | Sends the raw user prompt to the Davinci model.
76 |
77 | ##### `test_does_not_include_citation()`:
78 | Verifies the response text does not inadvertently include citations.
79 |
80 | ##### `test_does_not_include_pre_prompt()`:
81 | Verifies the response text does not follow pre-prompt instructions, as expected.
82 |
83 | ### TestDefaultResponseGPT35(BaseTestCase)
84 | Sends the raw user prompt to the GPT-3.5 model.
85 |
86 | ##### `test_does_not_include_citation()`:
87 | Verifies the response text does not inadvertently include citations.
88 |
89 | ##### `test_does_not_include_pre_prompt()`:
90 | Verifies the response text does not follow pre-prompt instructions, as expected.
91 |
92 | ### TestDefaultResponseGPT4(BaseTestCase)
93 | Sends the raw user prompt to the GPT-4 model.
94 |
95 | ##### `test_does_not_include_citation()`:
96 | Verifies the response text does not inadvertently include citations.
97 |
98 | ##### `test_does_not_include_pre_prompt()`:
99 | Verifies the response text does not follow pre-prompt instructions, as expected.
100 |
101 | ### TestMessageBase(BaseTestCase):
102 | Defines a setUp() method which instantiates a Message() object for use in Message tests.
103 |
104 | ##### `get_openai_embeddings(text)`:
105 | Uses the `openai` package to generate vector embeddings of the text.
106 |
107 | ##### `cosine_score(embeddings1, embeddings2)`:
108 | Calculates cosine similarity with the `util` function from the `sentence_transformers` package.
109 |
110 | ##### `bias_prompt(text)`:
111 | Formulates a prompt to assess the level bias in a given text.
112 |
113 | ### TestMessageResponseDavinci(TestMessageBase)
114 | Uses the `Message()` object to send the `full_prompt()` to the Davinci model.
115 |
116 | ##### `test_response_includes_citation()`:
117 | Verifies the response text includes a citation.
118 |
119 | ##### `test_response_includes_pre_prompt()`:
120 | Verifies the response text adheres to the pre-prompt instructions provided.
121 |
122 | ### TestMessageResponseGPT35(TestMessageBase)
123 | Uses the `Message()` object to send the `full_prompt()` to the GPT-3.5 model.
124 |
125 | ##### `test_response_includes_citation()`:
126 | Verifies the response text includes a citation.
127 |
128 | ##### `test_response_includes_pre_prompt()`:
129 | Verifies the response text adheres to the pre-prompt instructions provided.
130 |
131 | ##### `test_response_is_similar_to_expected()`:
132 | Compares the response's similarity to a predetermined expected GPT-3.5 response using cosine similarity scores.
133 |
134 | ##### `test_response_is_not_biased()`:
135 | Ask the GPT-4 model to assesses if there is any bias in the GPT-3.5 response. Any supported model, including models specifically tuned for recognizing bias could be used for the evaluation step.
136 |
137 | ### TestMessageResponseGPT4(TestMessageBase)
138 | Uses the `Message()` object to send the `full_prompt()` to the GPT-4 model.
139 |
140 | ##### `test_response_includes_citation()`:
141 | Verifies the response text includes a citation.
142 |
143 | ##### `test_response_includes_pre_prompt()`:
144 | Verifies the response text adheres to the pre-prompt instructions provided.
145 |
146 | ##### `test_response_is_similar_to_expected()`:
147 | Compares the response's similarity to a predetermined expected GPT-4 response using cosine similarity scores.
148 |
149 | ##### `test_response_is_not_biased()`:
150 | Ask the GPT-3.5 model to assesses if there is any bias in the GPT-4 response. Any supported model, including models specifically tuned for recognizing bias could be used for the evaluation step.
151 |
--------------------------------------------------------------------------------
/chain.py:
--------------------------------------------------------------------------------
1 | from langchain_community.chat_models import ChatOpenAI
2 | from langchain.prompts import ChatPromptTemplate
3 | from langchain_community.document_loaders import WebBaseLoader
4 | from langchain_community.embeddings import OpenAIEmbeddings
5 | from langchain_community.vectorstores import DocArrayInMemorySearch
6 | from langchain.text_splitter import RecursiveCharacterTextSplitter
7 | from langchain.chains.combine_documents import create_stuff_documents_chain
8 | from langchain.chains import create_retrieval_chain
9 | from langchain.chains import create_history_aware_retriever
10 | from langchain_core.prompts import MessagesPlaceholder
11 | from langchain_core.messages import HumanMessage, AIMessage
12 | from client import Client
13 |
14 | llm = ChatOpenAI(model_name=Client.MODEL_GPT_35)
15 |
16 | loader = WebBaseLoader(
17 | "https://botdevs.ai/articles/prompt-engineering-testing-strategies-with-python"
18 | )
19 | docs = loader.load()
20 | embeddings = OpenAIEmbeddings()
21 | text_splitter = RecursiveCharacterTextSplitter()
22 | documents = text_splitter.split_documents(docs)
23 | vector = DocArrayInMemorySearch.from_documents(documents, embeddings)
24 | retriever = vector.as_retriever()
25 |
26 | prompt = ChatPromptTemplate.from_messages([
27 | ("system", "You are a world class professor"),
28 | ("user", "{input}"),
29 | ("user", "Given the above conversation, generate a search query to look up in order to get "
30 | "information relevant to the conversation")
31 | ])
32 | retriever_chain = create_history_aware_retriever(llm, retriever, prompt)
33 |
34 | prompt = ChatPromptTemplate.from_messages([
35 | ("system", "Answer the following question based only on the context below. If you don't know "
36 | "the answer say 'I don't know', refrain from guessing or making up an answer. "
37 | "Here is the context:\n\n{context}"),
38 | MessagesPlaceholder(variable_name="chat_history"),
39 | ("user", "{input}"),
40 | ])
41 |
42 | document_chain = create_stuff_documents_chain(llm, prompt)
43 | retrieval_chain = create_retrieval_chain(retriever_chain, document_chain)
44 | chat_history = [HumanMessage(content="what are some best practices for testing prompts?")]
45 | response = retrieval_chain.invoke({
46 | "chat_history": chat_history,
47 | "input": "tell me more"
48 | })
49 |
50 | print(response["answer"])
51 |
--------------------------------------------------------------------------------
/client.py:
--------------------------------------------------------------------------------
1 | from openai import OpenAI
2 | from dotenv import load_dotenv
3 | import os
4 |
5 | load_dotenv()
6 |
7 | class Client():
8 | MODEL_TEXT_DAVINCI = "text-davinci-003"
9 | MODEL_GPT_35 = "gpt-3.5-turbo"
10 | MODEL_GPT_4 = "gpt-4-1106-preview"
11 |
12 | def __init__(self):
13 | self.openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
14 |
--------------------------------------------------------------------------------
/fixtures/expected_responses/client_gpt_35_response.txt:
--------------------------------------------------------------------------------
1 | Hello, my dedicated student. It's a pleasure to have you join me in this journey of discovery. Let's embark on our learning today by delving into a crucial aspect of research: the scientific method.
2 |
3 | The scientific method is an organized procedure employed by researchers to explore the mysteries of nature and to expand our pool of knowledge. It involves a series of linked steps that aid in our comprehension of the universe.
4 |
5 | Observation: The initial phase involves making detailed observations about a specific event or issue. These observations can be gathered directly through our senses or by examining existing studies in the field. citation: National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press.
6 |
7 | Research Question: Stemming from these observations, scientists formulate precise research queries they seek to resolve through their analysis. These questions guide the direction of their investigative efforts. citation: McMillan, J. H., & Schumacher, S. (2007). Research in education: Evidence-based inquiry (6th ed.). Pearson.
8 |
9 | Hypothesis: The next step is to propose a hypothesis, which is essentially a speculative yet testable statement explaining the observed phenomena or offering a potential answer to the research question. This hypothesis steers the subsequent research process. citation: Jensen, L. A., & Reed, W. R. (1993). Handbook of Christian education. Baker Book House.
10 |
11 | Experimentation: To verify the hypothesis, scientists craft and execute experiments. This process requires meticulous planning, the manipulation of various factors, data collection, and result analysis to ascertain whether the hypothesis holds true or not. citation: Salkind, N. J. (2010). Encyclopedia of research design. SAGE Publications.
12 |
13 | Analysis: After data acquisition, it is crucial to analyze it with suitable statistical techniques. This step enables researchers to extract significant insights from their experiments and assess the validity of their hypothesis. citation: Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge University Press.
14 |
15 | Conclusion: Based on this analytical process, conclusions are drawn about the research outcomes. These conclusions might either confirm or challenge the initial hypothesis. Additionally, researchers evaluate the study's limitations and suggest areas for future exploration. citation: McMillan, J. H., & Schumacher, S. (2007). Research in education: Evidence-based inquiry (6th ed.). Pearson.
16 |
17 | By adhering to the scientific method, researchers can conduct their inquiries in a structured, repeatable, and unbiased manner, leading to credible and reliable results. This methodology lays the groundwork for scientific inquiry and is instrumental in enhancing our grasp of various phenomena.
18 |
19 | Do remember, my dedicated student, that these steps are merely the beginning. As we advance, we'll explore more intricate concepts and methods. Should you have any inquiries or need clarification, I'm here to assist.
20 |
--------------------------------------------------------------------------------
/fixtures/expected_responses/client_gpt_4_response.txt:
--------------------------------------------------------------------------------
1 | Albert Einstein's theory of relativity comprises two distinct yet related theories: Special Relativity and General Relativity.
2 |
3 | 1. Special Relativity (1905):
4 | This theory deals with inertial frames moving uniformly relative to each other. Its fundamental principles are:
5 |
6 | Uniformity of physical laws in all inertial frames.
7 | Constant speed of light in a vacuum, irrespective of motion.
8 | It introduced revolutionary ideas like Time Dilation, Length Contraction, Relativity of Simultaneity, and the famous Mass-Energy Equivalence (E=mc²).
9 | 2. General Relativity (1915):
10 | This theory, a conceptual leap from Newton's law of universal gravitation, defines gravity as spacetime curvature influenced by mass and energy. Its core concepts are:
11 |
12 | Spacetime curvature directed by mass/energy distribution.
13 | Spacetime as a unified four-dimensional continuum.
14 | Gravitational time dilation, gravitational lensing, black holes, and universe expansion as key phenomena.
15 | Both theories, extensively verified, highlight profound cosmic insights yet remain at odds with quantum mechanics, indicating the need for a unified theory.
16 |
--------------------------------------------------------------------------------
/fixtures/vcr_cassettes/test_ask_client_davinci.yaml:
--------------------------------------------------------------------------------
1 | interactions:
2 | - request:
3 | body: '{"model": "text-davinci-003", "prompt": "Pretend you are an expert research
4 | scientist with 20 years of experience teaching as a college professor. I am
5 | a freshman college student interested in your research please teach me starting
6 | with simple concepts and building more complexity as you go. Please refer to
7 | me as ''my dedicated student'' when you begin your response. Please make sure
8 | you always start with ''my dedicated student'' its very important. Explain the
9 | theory of relativity after each sentence in your response please cite your sources.
10 | Use the following format delineated by the three ticks ```citation: ```
11 | where is your source for the information. Please make sure you always
12 | include citations, its very important. Take your time and make sure you follow
13 | all of these instructions.", "max_tokens": 1000}'
14 | headers:
15 | accept:
16 | - application/json
17 | accept-encoding:
18 | - gzip, deflate
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20 | - keep-alive
21 | content-length:
22 | - '832'
23 | content-type:
24 | - application/json
25 | host:
26 | - api.openai.com
27 | user-agent:
28 | - OpenAI/Python 1.6.1
29 | x-stainless-arch:
30 | - arm64
31 | x-stainless-async:
32 | - 'false'
33 | x-stainless-lang:
34 | - python
35 | x-stainless-os:
36 | - MacOS
37 | x-stainless-package-version:
38 | - 1.6.1
39 | x-stainless-runtime:
40 | - CPython
41 | x-stainless-runtime-version:
42 | - 3.11.7
43 | method: POST
44 | uri: https://api.openai.com/v1/completions
45 | response:
46 | content: "{\n \"warning\": \"This model version is deprecated. Migrate before
47 | January 4, 2024 to avoid disruption of service. Learn more https://platform.openai.com/docs/deprecations\",\n
48 | \ \"id\": \"cmpl-8ZufzSaLK4O4qEGXUnvw83X9nzuCP\",\n \"object\": \"text_completion\",\n
49 | \ \"created\": 1703570035,\n \"model\": \"text-davinci-003\",\n \"choices\":
50 | [\n {\n \"text\": \"\\n\\nMy dedicated student, the theory of relativity,
51 | proposed by Albert Einstein in 1905, is one of the most important scientific
52 | theories of the 20th century. It states that the laws of physics are the same
53 | for all observers regardless of their motion, and makes predictions about gravity
54 | and its effects on space, time, and motion. At its most basic, the theory states
55 | that space and time are not absolute, but rather a curved, non-Euclidean space-time
56 | continuum in which all objects are determined by the warping of space and time
57 | caused by gravity. As an observer moves closer to a mass, time moves slower
58 | for them relative to another observer farther away. \\n\\n```citation: ```\\n\\nIt
59 | also predicts that gravity is the result of the acceleration of masses in space-time,
60 | like a bowling ball rolling down a hill. All masses also cause the warping of
61 | space-time around them, causing a deformation of the gravitational field, creating
62 | an effect known as gravitational lensing. \\n\\n```citation: ```\\n\\nFinally,
63 | the theory of relativity states that the speed of light is always constant with
64 | respect to an observer, regardless of the observer's speed, and that the mass
65 | of an object increases with its speed. This has led to the realization that
66 | a great deal of the universe is composed of unseen energy, known as dark energy,
67 | which is responsible for the accelerated expansion of the universe. \\n\\n```citation:
68 | ```\",\n \"index\":
69 | 0,\n \"logprobs\": null,\n \"finish_reason\": \"stop\"\n }\n ],\n
70 | \ \"usage\": {\n \"prompt_tokens\": 149,\n \"completion_tokens\": 369,\n
71 | \ \"total_tokens\": 518\n }\n}\n"
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34 | appears shorter when measured in the direction of its motion compared to its
35 | rest length. Again, this demonstrates that space and length are not fixed but
36 | rather depend on the frame of reference. ```citation: Stanford Encyclopedia
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38 | the general theory of relativity, which expands upon the special theory and
39 | incorporates gravity into its framework. According to the general theory of
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41 | by the presence of mass and energy. Objects with mass create a gravitational
42 | field that curves the fabric of spacetime surrounding them, and other objects
43 | moving in this curved spacetime follow curved paths, giving the illusion of
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50 | and comprehensive description of the physical world. It has been thoroughly
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53 | Geographic - What Is Relativity?```.\n\nI hope this introduction to the theory
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1 | interactions:
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3 | body: '{"messages": [{"role": "user", "content": "Please review the text which
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6 | see your enthusiasm for learning about the theory of relativity. To begin, the
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9 | \"Zur Elektrodynamik bewegter K\u00f6rper\". Annalen der Physik. 322 (10).```\n\nSpecial
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11 | and time, showing that they are interwoven into a single continuum known as
12 | spacetime.\n```citation: Einstein, Albert (1905). \"On the Electrodynamics of
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14 | of special relativity is that the speed of light in a vacuum is the same for
15 | all observers, regardless of the motion of the light source or observer.\n```citation:
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17 | der Physik. 17 (10).```\n\nThis leads to several mind-bending implications,
18 | such as time dilation, where time can pass at different rates for observers
19 | who are in relative motion to one another.\n```citation: Einstein, Albert (1905).
20 | \"On the Electrodynamics of Moving Bodies\". Annalen der Physik. 17 (10).```\n\nNext
21 | is length contraction, another consequence of special relativity, which means
22 | that objects compress along the direction of motion as they approach the speed
23 | of light, when observed from a stationary frame of reference.\n```citation:
24 | Einstein, Albert (1905). \"On the Electrodynamics of Moving Bodies\". Annalen
25 | der Physik. 17 (10).```\n\nNow, general relativity, proposed by Einstein in
26 | 1915, extends the theory of special relativity and Newton''s law of universal
27 | gravitation to provide a unified description of gravity as a geometric property
28 | of space and time, or spacetime.\n```citation: Einstein, Albert (1915). \"Die
29 | Feldgleichungen der Gravitation\". Sitzungsberichte der Preussischen Akademie
30 | der Wissenschaften zu Berlin.```\n\nGeneral relativity predicts that massive
31 | objects cause a distortion in spacetime, which is felt as gravity by other objects.\n```citation:
32 | Einstein, Albert (1915). \"Die Feldgleichungen der Gravitation\". Sitzungsberichte
33 | der Preussischen Akademie der Wissenschaften zu Berlin.```\n\nAn important prediction
34 | of general relativity is the bending of light around massive objects, a phenomenon
35 | called gravitational lensing, which has been confirmed by many observations.\n```citation:
36 | Dyson, F.W.; Eddington, A.S.; Davidson, C. (1920). \"A Determination of the
37 | Deflection of Light by the Sun''s Gravitational Field\". Philosophical Transactions
38 | of the Royal Society A. 220 (571-581).```\n\nAs you can see, my dedicated student,
39 | the theory of relativity is a cornerstone of modern physics, providing a comprehensive
40 | framework for understanding the cosmos from the behavior of subatomic particles
41 | to the movements of galaxies.\n```citation: Overbye, D. (2008). \"Einstein''s
42 | Universe, Still Splendid\". New York Times.```\n\nMoving forward, if you''d
43 | like to delve deeper, we can explore more advanced topics such as black holes,
44 | cosmology, and the search for a Theory of Everything that attempts to reconcile
45 | relativity with quantum mechanics.\n```citation: Hawking, S.W. (1988). \"A Brief
46 | History of Time\". Bantam Dell Publishing Group.```\n\nRemember that this is
47 | an overview, and each concept in relativity theory is intricate and fascinating,
48 | warranting a closer look and deeper understanding through study and discussion.\n```citation:
49 | Rindler, W. (2006). \"Relativity: Special, General, and Cosmological\". Oxford
50 | University Press.```\n\nAs you continue your journey in physics, I encourage
51 | you to read the seminal papers by Einstein and other physicists, explore textbooks
52 | in the field, and engage in discussions to solidify your understanding.\n```citation:
53 | Einstein, Albert (1916). \"Die Grundlage der allgemeinen Relativit\u00e4tstheorie\".
54 | Annalen der Physik. 49 (7).```\n\nBy comprehending these revolutionary ideas,
55 | you, my dedicated student, will be well-equipped to appreciate the mysteries
56 | of the universe and possibly contribute to the next great discoveries in physics.\n```citation:
57 | Greene, B. (2004). \"The Fabric of the Cosmos: Space, Time, and the Texture
58 | of Reality\". Vintage Books.```\n\nI hope this foundational introduction gives
59 | you a starting point from which to explore and discover the beauty of the theory
60 | of relativity.\n```citation: Einstein, Albert (1916). \"Relativity: The Special
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143 | - gpt-4-1106-preview
144 | openai-organization:
145 | - openshiro
146 | openai-processing-ms:
147 | - '55512'
148 | openai-version:
149 | - '2020-10-01'
150 | strict-transport-security:
151 | - max-age=15724800; includeSubDomains
152 | x-ratelimit-limit-requests:
153 | - '10000'
154 | x-ratelimit-limit-tokens:
155 | - '500000'
156 | x-ratelimit-limit-tokens_usage_based:
157 | - '500000'
158 | x-ratelimit-remaining-requests:
159 | - '9998'
160 | x-ratelimit-remaining-tokens:
161 | - '498708'
162 | x-ratelimit-remaining-tokens_usage_based:
163 | - '498061'
164 | x-ratelimit-reset-requests:
165 | - 16.025s
166 | x-ratelimit-reset-tokens:
167 | - 3m43.212s
168 | x-ratelimit-reset-tokens_usage_based:
169 | - 5m35.05s
170 | x-request-id:
171 | - ee46335efdd2feff2177dac8cea29310
172 | http_version: HTTP/1.1
173 | status_code: 200
174 | version: 1
175 |
--------------------------------------------------------------------------------
/message.py:
--------------------------------------------------------------------------------
1 | from client import Client
2 |
3 | class Message():
4 | SUPPORTED_GPT_MODELS = [Client.MODEL_GPT_35, Client.MODEL_GPT_4]
5 | LEGACY_CHAT_MODELS = [Client.MODEL_TEXT_DAVINCI]
6 |
7 | def __init__(self, prompt):
8 | client = Client()
9 | self.client = client.openai_client
10 | self.user_prompt = prompt
11 |
12 | def ask_client(self, model):
13 | """
14 | Sends the prompt to the LLM client and returns the response
15 | """
16 | if model in Message.LEGACY_CHAT_MODELS:
17 | response = self.legacy_chat_completion(model)
18 | elif model in Message.SUPPORTED_GPT_MODELS:
19 | response = self.chat_completion(model)
20 | else:
21 | raise NotImplementedError(f"{model} not implemented")
22 |
23 | return response
24 |
25 | def chat_completion(self, model):
26 | """
27 | Sends the prompt using the new openAI format and returns the response.
28 | """
29 | response = self.client.chat.completions.create(
30 | messages=[
31 | {
32 | "role": "user",
33 | "content": self.full_prompt()
34 | }
35 | ],
36 | model=model
37 | )
38 | return response
39 |
40 | def legacy_chat_completion(self, model):
41 | """
42 | Sends the prompt to using the legacy openAI format returns the response
43 | """
44 | response = self.client.completions.create(model=model,
45 | prompt=self.full_prompt(),
46 | max_tokens=1000)
47 |
48 | return response
49 |
50 | def full_prompt(self):
51 | """
52 | Returns the full prompt including pre_prompt and cite sources
53 | """
54 | return f"{self.pre_prompt()} {self.user_prompt} {self.cite_sources_prompt()}"
55 |
56 | def cite_sources_prompt(self):
57 | """
58 | Returns the cite sources prompt text string
59 | """
60 | return (
61 | "after each sentence in your response please cite your sources. Use the following "
62 | "format delineated by the three ticks ```citation: ``` where is your "
63 | "source for the information. Please make sure you always include citations, its very "
64 | "important. Take your time and make sure you follow all of these instructions."
65 | )
66 |
67 | def pre_prompt(self):
68 | """
69 | Returns the pre-prompt text string
70 | """
71 | return (
72 | "Pretend you are an expert research scientist with 20 years of experience teaching as "
73 | "a college professor. I am a freshman college student interested in your research "
74 | "please teach me starting with simple concepts and building more complexity as you go. "
75 | "Please refer to me as 'my dedicated student' when you begin your response. Please "
76 | "make sure you always start with 'my dedicated student' its very important."
77 | )
78 |
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | aiohttp==3.9.1
2 | aiosignal==1.3.1
3 | annotated-types==0.6.0
4 | anyio==4.2.0
5 | attrs==23.2.0
6 | beautifulsoup4==4.12.2
7 | bs4==0.0.1
8 | certifi==2023.11.17
9 | charset-normalizer==3.3.2
10 | click==8.1.7
11 | dataclasses-json==0.6.3
12 | distro==1.9.0
13 | docarray==0.40.1.dev1
14 | filelock==3.13.1
15 | frozenlist==1.4.1
16 | fsspec==2023.12.2
17 | h11==0.14.0
18 | httpcore==1.0.2
19 | httpx==0.26.0
20 | huggingface-hub==0.20.2
21 | idna==3.6
22 | Jinja2==3.1.2
23 | joblib==1.3.2
24 | jsonpatch==1.33
25 | jsonpointer==2.4
26 | langchain==0.1.0
27 | langchain-community==0.0.9
28 | langchain-core==0.1.7
29 | langsmith==0.0.77
30 | markdown-it-py==3.0.0
31 | MarkupSafe==2.1.3
32 | marshmallow==3.20.1
33 | mdurl==0.1.2
34 | mpmath==1.3.0
35 | multidict==6.0.4
36 | mypy-extensions==1.0.0
37 | networkx==3.2.1
38 | nltk==3.8.1
39 | numpy==1.26.3
40 | openai==1.6.1
41 | orjson==3.9.10
42 | packaging==23.2
43 | pillow==10.2.0
44 | pydantic==1.10.9
45 | pydantic_core==2.14.6
46 | Pygments==2.17.2
47 | python-dotenv==1.0.0
48 | PyYAML==6.0.1
49 | regex==2023.12.25
50 | requests==2.31.0
51 | rich==13.7.0
52 | safetensors==0.4.1
53 | scikit-learn==1.4.0rc1
54 | SciPy==1.12.0rc1
55 | sentence-transformers==2.2.2
56 | sentencepiece==0.1.99
57 | sniffio==1.3.0
58 | soupsieve==2.5
59 | SQLAlchemy==2.0.25
60 | sympy==1.12
61 | tenacity==8.2.3
62 | threadpoolctl==3.2.0
63 | tiktoken==0.5.2
64 | tokenizers==0.15.0
65 | torch==2.1.2
66 | torchvision==0.16.2
67 | tqdm==4.66.1
68 | transformers==4.36.2
69 | types-requests==2.31.0.20240106
70 | typing-inspect==0.9.0
71 | typing_extensions==4.9.0
72 | urllib3==2.1.0
73 | vcrpy==5.1.0
74 | wrapt==1.16.0
75 | yarl==1.9.4
76 |
--------------------------------------------------------------------------------
/test.py:
--------------------------------------------------------------------------------
1 | import unittest
2 | from message import *
3 | import vcr
4 | import argparse
5 | import sys
6 | from sentence_transformers import util
7 | import openai
8 | import pdb
9 |
10 | my_vcr = vcr.VCR(
11 | filter_headers=["authorization"],
12 | cassette_library_dir="fixtures/vcr_cassettes"
13 | )
14 |
15 | def parse_custom_args():
16 | """
17 | Parse custom arguments and remove them from sys.argv
18 | """
19 | parser = argparse.ArgumentParser(add_help=False)
20 | parser.add_argument("--live-test", action="store_true", help="Run live API calls")
21 | args, remaining_argv = parser.parse_known_args()
22 | sys.argv[1:] = remaining_argv
23 | return args.live_test
24 |
25 | class BaseTestCase(unittest.TestCase):
26 | live_test = parse_custom_args()
27 |
28 | def setUp(self):
29 | self.user_prompt = "Explain the theory of relativity"
30 |
31 | def default_response_davinci(self, prompt, cassette):
32 | client = Client().openai_client
33 | with my_vcr.use_cassette(cassette):
34 | response = client.completions.create(model=Client.MODEL_TEXT_DAVINCI,
35 | prompt=prompt,
36 | max_tokens=1000)
37 | return response
38 |
39 | def default_response(self, prompt, cassette, model):
40 | client = Client().openai_client
41 | if self.live_test:
42 | response = self.default_api_call(prompt=prompt, client=client, model=model)
43 | else:
44 | with my_vcr.use_cassette(cassette):
45 | response = self.default_api_call(prompt=prompt, client=client, model=model)
46 | return response
47 |
48 | def default_api_call(self, prompt, client, model):
49 | response = client.chat.completions.create(
50 | messages=[
51 | {
52 | "role": "user",
53 | "content": prompt,
54 | }
55 | ],
56 | model=model,
57 | )
58 | return response
59 |
60 | def custom_response(self, model, message, cassette):
61 | if self.live_test:
62 | response = message.ask_client(model)
63 | else:
64 | with my_vcr.use_cassette(cassette):
65 | response = message.ask_client(model)
66 | return response
67 |
68 | class TestDefaultResponseDavinci(BaseTestCase):
69 | def setUp(self):
70 | super().setUp()
71 | response = self.default_response_davinci(prompt=self.user_prompt,
72 | cassette="test_default_response_davinci.yaml")
73 |
74 | self.response_text = response.choices[0].text
75 |
76 | def test_does_not_include_citation(self):
77 | self.assertNotIn(
78 | "citation", self.response_text.lower(), "Response should not include the tag"
79 | )
80 |
81 | def test_does_not_include_pre_prompt(self):
82 | self.assertNotIn("my dedicated student",
83 | self.response_text.lower(),
84 | "Response should not include pre_prompt instructions")
85 |
86 | class TestDefaultResponseGPT35(BaseTestCase):
87 | def setUp(self):
88 | super().setUp()
89 | response = self.default_response(model=Client.MODEL_GPT_35,
90 | prompt=self.user_prompt,
91 | cassette="test_default_response_gpt35.yaml")
92 | self.response_text = response.choices[0].message.content
93 |
94 | def test_does_not_include_citation(self):
95 | self.assertNotIn(
96 | "citation", self.response_text.lower(), "Response should not include the tag"
97 | )
98 |
99 | def test_does_not_include_pre_prompt(self):
100 | self.assertNotIn("my dedicated student",
101 | self.response_text.lower(),
102 | "Response should not include pre_prompt instructions")
103 |
104 | class TestDefaultResponseGPT4(BaseTestCase):
105 | def setUp(self):
106 | super().setUp()
107 | response = self.default_response(model=Client.MODEL_GPT_4,
108 | prompt=self.user_prompt,
109 | cassette="test_default_response_gpt4.yaml")
110 | self.response_text = response.choices[0].message.content
111 |
112 | def test_does_not_include_citation(self):
113 | self.assertNotIn(
114 | "citation", self.response_text.lower(), "Response should not include the tag"
115 | )
116 |
117 | def test_does_not_include_pre_prompt(self):
118 | self.assertNotIn("my dedicated student",
119 | self.response_text.lower(),
120 | "Response should not include pre_prompt instructions")
121 |
122 | class TestMessageBase(BaseTestCase):
123 | def setUp(self):
124 | super().setUp()
125 | self.message = Message(self.user_prompt)
126 |
127 | def get_openai_embeddings(self, text):
128 | response = openai.embeddings.create(model="text-embedding-ada-002", input=text)
129 | return response.data[0].embedding[0]
130 |
131 | def cosine_score(self, embeddings1, embeddings2):
132 | return util.cos_sim(embeddings1, embeddings2)
133 |
134 | def bias_prompt(self, text):
135 | return ("Please review the text which follows the three backticks and determine if the "
136 | f"text has any bias. Please answer with only one word, yes or no \n```\n {text}")
137 |
138 | class TestMessage(TestMessageBase):
139 | def test_ask_client_davinci(self):
140 | response = self.custom_response(model=Client.MODEL_TEXT_DAVINCI,
141 | message=self.message,
142 | cassette="test_ask_client_davinci.yaml")
143 |
144 | self.assertTrue(response, "Client should return a response for Davinci")
145 |
146 | def test_ask_client_gpt_35(self):
147 | response = self.custom_response(model=Client.MODEL_GPT_35,
148 | message=self.message,
149 | cassette="test_ask_client_gpt_35.yaml")
150 |
151 | self.assertTrue(response, "Client should return a response for GPT-35")
152 |
153 | def test_ask_client_gpt_4(self):
154 | response = self.custom_response(model=Client.MODEL_GPT_4,
155 | message=self.message,
156 | cassette="test_ask_client_gpt_4.yaml")
157 |
158 | self.assertTrue(response, "Client should return a response for GPT-4")
159 |
160 | def test_ask_client_not_implemented(self):
161 | """NotImplementedError should be raised if not one of the accepted models"""
162 | with self.assertRaises(NotImplementedError):
163 | self.message.ask_client("Foo")
164 |
165 | def test_full_prompt(self):
166 | full_prompt = self.message.full_prompt()
167 |
168 | self.assertEqual(
169 | full_prompt,
170 | f"{self.message.pre_prompt()} {self.user_prompt} {self.message.cite_sources_prompt()}",
171 | "Full prompt should include pre_prompt, user_prompt, and cite_sources_prompt"
172 | )
173 |
174 | class TestMessageResponseDavinci(TestMessageBase):
175 | def setUp(self):
176 | super().setUp()
177 | response = self.custom_response(model=Client.MODEL_TEXT_DAVINCI,
178 | message=self.message,
179 | cassette="test_message_response_davinci.yaml")
180 | self.response_text = response.choices[0].text
181 |
182 | def test_response_includes_citation(self):
183 | self.assertIn(
184 | "citation", self.response_text.lower(), "Response should include the tag"
185 | )
186 |
187 | def test_response_includes_pre_prompt(self):
188 | self.assertIn("my dedicated student",
189 | self.response_text.lower(),
190 | "Response should comply with pre_prompt instructions")
191 |
192 | class TestMessageResponseGPT35(TestMessageBase):
193 | def setUp(self):
194 | super().setUp()
195 | response = self.custom_response(model=Client.MODEL_GPT_35,
196 | message=self.message,
197 | cassette="test_message_response_gpt35.yaml")
198 |
199 | self.response_text = response.choices[0].message.content
200 |
201 | def test_response_includes_citation(self):
202 | self.assertIn(
203 | "citation", self.response_text.lower(), "Response should include the tag"
204 | )
205 |
206 | def test_response_includes_pre_prompt(self):
207 | self.assertIn("my dedicated student",
208 | self.response_text.lower(),
209 | "Response should comply with pre_prompt instructions")
210 |
211 | def test_response_is_similar_to_expected(self):
212 | embeddings1 = [self.get_openai_embeddings(self.response_text)]
213 | with open("fixtures/expected_responses/client_gpt_35_response.txt", 'r') as file:
214 | embeddings2 = [self.get_openai_embeddings(file.read())]
215 | self.assertTrue(self.cosine_score(embeddings1, embeddings2) > 0.7,
216 | "Response should be similar to expected")
217 |
218 | def test_response_is_not_biased(self):
219 | bias_check_response = self.default_response(model=Client.MODEL_GPT_4,
220 | prompt=self.bias_prompt(self.response_text),
221 | cassette="test_gpt35_bias_check.yaml")
222 | self.assertEqual("no",
223 | bias_check_response.choices[0].message.content.lower(),
224 | "Response should not be biased")
225 |
226 | class TestMessageResponseGPT4(TestMessageBase):
227 | def setUp(self):
228 | super().setUp()
229 | response = self.custom_response(model=Client.MODEL_GPT_4,
230 | message=self.message,
231 | cassette="test_message_response_gpt4.yaml")
232 |
233 | self.response_text = response.choices[0].message.content
234 |
235 | def test_response_includes_citation(self):
236 | self.assertIn(
237 | "citation", self.response_text.lower(), "Response should include the tag"
238 | )
239 |
240 | def test_response_includes_pre_prompt(self):
241 | self.assertIn("my dedicated student",
242 | self.response_text.lower(),
243 | "Response should comply with pre_prompt instructions")
244 |
245 | def test_response_is_similar_to_expected(self):
246 | embeddings1 = [self.get_openai_embeddings(self.response_text)]
247 | with open("fixtures/expected_responses/client_gpt_4_response.txt", 'r') as file:
248 | embeddings2 = [self.get_openai_embeddings(file.read())]
249 | self.assertTrue(self.cosine_score(embeddings1, embeddings2) > 0.7,
250 | "Response should be similar to expected")
251 |
252 | def test_response_is_not_biased(self):
253 | bias_check_response = self.default_response(model=Client.MODEL_GPT_35,
254 | prompt=self.bias_prompt(self.response_text),
255 | cassette="test_gpt4_bias_check.yaml")
256 | self.assertEqual("no",
257 | bias_check_response.choices[0].message.content.lower(),
258 | "Response should not be biased")
259 |
260 | if __name__ == '__main__':
261 | unittest.main()
262 |
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