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Updating Wizard to include mixture of experts
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Cox, Jordan
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Cox, Jordan
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Jun 18, 2024
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,228 @@ | ||
| """ | ||
| ELM mixture of experts | ||
| """ | ||
| import streamlit as st | ||
| import os | ||
| import openai | ||
| from glob import glob | ||
| import pandas as pd | ||
| import sys | ||
| import copy | ||
| import numpy as np | ||
|
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||
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| from elm.base import ApiBase | ||
| from elm.wizard import EnergyWizard | ||
| from elm.db_wiz import DataBaseWizard | ||
|
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| model = 'gpt-4' | ||
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| # NREL-Azure endpoint. You can also use just the openai endpoint. | ||
| # NOTE: embedding values are different between OpenAI and Azure models! | ||
| openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT") | ||
| openai.api_key = os.getenv("AZURE_OPENAI_KEY") | ||
| openai.api_type = 'azure' | ||
| openai.api_version = os.getenv('AZURE_OPENAI_VERSION') | ||
|
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||
| EnergyWizard.EMBEDDING_MODEL = 'text-embedding-ada-002-2' | ||
| EnergyWizard.EMBEDDING_URL = ('https://stratus-embeddings-south-central.' | ||
| 'openai.azure.com/openai/deployments/' | ||
| 'text-embedding-ada-002-2/embeddings?' | ||
| f'api-version={openai.api_version}') | ||
| EnergyWizard.URL = ('https://stratus-embeddings-south-central.' | ||
| 'openai.azure.com/openai/deployments/' | ||
| f'{model}/chat/completions?' | ||
| f'api-version={openai.api_version}') | ||
| EnergyWizard.HEADERS = {"Content-Type": "application/json", | ||
| "Authorization": f"Bearer {openai.api_key}", | ||
| "api-key": f"{openai.api_key}"} | ||
|
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| EnergyWizard.MODEL_ROLE = ('You are a energy research assistant. Use the ' | ||
| 'articles below to answer the question. If ' | ||
| 'articles do not provide enough information to ' | ||
| 'answer the question, say "I do not know."') | ||
| EnergyWizard.MODEL_INSTRUCTION = EnergyWizard.MODEL_ROLE | ||
|
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| DataBaseWizard.URL = (f'https://stratus-embeddings-south-central.openai.azure.com/' | ||
| f'openai/deployments/{model}/chat/' | ||
| f'completions?api-version={openai.api_version}') | ||
| DataBaseWizard.HEADERS = {"Content-Type": "application/json", | ||
| "Authorization": f"Bearer {openai.api_key}", | ||
| "api-key": f"{openai.api_key}", | ||
| } | ||
|
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| st.set_option('deprecation.showPyplotGlobalUse', False) | ||
|
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| @st.cache_data | ||
| def get_corpus(): | ||
| """Get the corpus of text data with embeddings.""" | ||
| corpus = sorted(glob('./embed/*.json')) | ||
| corpus = [pd.read_json(fp) for fp in corpus] | ||
| corpus = pd.concat(corpus, ignore_index=True) | ||
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| return corpus | ||
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| @st.cache_resource | ||
| def get_wizard(model = model): | ||
| """Get the energy wizard object. | ||
| Parameters | ||
| ---------- | ||
| model : str | ||
| State which model to use for the energy wizard. | ||
| Returns | ||
| ------- | ||
| response : str | ||
| GPT output / answer. | ||
| wizard : EnergyWizard | ||
| Returns the energy wizard object for use in chat responses. | ||
| """ | ||
|
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| # Getting Corpus of data. If no corpus throw error for user. | ||
| try: | ||
| corpus = get_corpus() | ||
| except Exception: | ||
| print("Error: Have you run 'retrieve_docs.py'?") | ||
| st.header("Error") | ||
| st.write("Error: Have you run 'retrieve_docs.py'?") | ||
| sys.exit(0) | ||
|
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| wizard = EnergyWizard(corpus, ref_col='ref', model=model) | ||
| return wizard | ||
|
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| class MixtureOfExperts(ApiBase): | ||
| """Interface to ask OpenAI LLMs about energy | ||
| research either from a database or report.""" | ||
|
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| """Parameters | ||
| ---------- | ||
| model : str | ||
| State which model to use for the energy wizard. | ||
| connection string : str | ||
| String used to connect to SQL databases. | ||
| Returns | ||
| ------- | ||
| response : str | ||
| GPT output / answer. | ||
| """ | ||
|
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| MODEL_ROLE = ("You are an expert given a query. Which of the " | ||
| "following best describes the query? Please " | ||
| "answer with just the number and nothing else." | ||
| "1. This is a query best answered by a text-based report." | ||
| "2. This is a query best answered by pulling data from " | ||
| "a database and creating a figure.") | ||
| """High level model role, somewhat redundant to MODEL_INSTRUCTION""" | ||
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| def __init__(self, connection_string, model=None, token_budget=3500, ref_col=None): | ||
| self.wizard_db = DataBaseWizard(model = model, connection_string = connection_string) | ||
| self.wizard_chat = get_wizard() | ||
| self.model = model | ||
| super().__init__(model) | ||
|
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||
| def chat(self, query, | ||
| debug=True, | ||
| stream=True, | ||
| temperature=0, | ||
| convo=False, | ||
| token_budget=None, | ||
| new_info_threshold=0.7, | ||
| print_references=False, | ||
| return_chat_obj=False): | ||
| """Answers a query by doing a semantic search of relevant text with | ||
| embeddings and then sending engineered query to the LLM. | ||
| Parameters | ||
| ---------- | ||
| query : str | ||
| Question being asked of EnergyWizard | ||
| debug : bool | ||
| Flag to return extra diagnostics on the engineered question. | ||
| stream : bool | ||
| Flag to print subsequent chunks of the response in a streaming | ||
| fashion | ||
| temperature : float | ||
| GPT model temperature, a measure of response entropy from 0 to 1. 0 | ||
| is more reliable and nearly deterministic; 1 will give the model | ||
| more creative freedom and may not return as factual of results. | ||
| convo : bool | ||
| Flag to perform semantic search with full conversation history | ||
| (True) or just the single query (False). Call EnergyWizard.clear() | ||
| to reset the chat history. | ||
| token_budget : int | ||
| Option to override the class init token budget. | ||
| new_info_threshold : float | ||
| New text added to the engineered query must contain at least this | ||
| much new information. This helps prevent (for example) the table of | ||
| contents being added multiple times. | ||
| print_references : bool | ||
| Flag to print references if EnergyWizard is initialized with a | ||
| valid ref_col. | ||
| return_chat_obj : bool | ||
| Flag to only return the ChatCompletion from OpenAI API. | ||
| Returns | ||
| ------- | ||
| response : str | ||
| GPT output / answer. | ||
| query : str | ||
| If debug is True, the engineered query asked of GPT will also be | ||
| returned here | ||
| references : list | ||
| If debug is True, the list of references (strs) used in the | ||
| engineered prompt is returned here | ||
| """ | ||
|
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| messages = [{"role": "system", "content": self.MODEL_ROLE}, | ||
| {"role": "user", "content": query}] | ||
| response_message = '' | ||
| kwargs = dict(model=self.model, | ||
| messages=messages, | ||
| temperature=temperature, | ||
| stream=stream) | ||
|
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| response = self._client.chat.completions.create(**kwargs) | ||
|
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| print(response) | ||
|
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| if stream: | ||
| for chunk in response: | ||
| chunk_msg = chunk.choices[0].delta.content or "" | ||
| response_message += chunk_msg | ||
| print(chunk_msg, end='') | ||
|
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| else: | ||
| response_message = response["choices"][0]["message"]["content"] | ||
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| message_placeholder = st.empty() | ||
| full_response = "" | ||
|
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| if '1' in response_message: | ||
| out = self.wizard_chat.chat(query, | ||
| debug=True, stream=True, token_budget=6000, | ||
| temperature=0.0, print_references=True, | ||
| convo=False, return_chat_obj=True) | ||
|
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| for response in out[0]: | ||
| full_response += response.choices[0].delta.content or "" | ||
| message_placeholder.markdown(full_response + "▌") | ||
|
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| elif '2' in response_message: | ||
| out = self.wizard_db.chat(query, | ||
| debug=True, stream=True, token_budget=6000, | ||
| temperature=0.0, print_references=True, | ||
| convo=False, return_chat_obj=True) | ||
|
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| st.pyplot(fig = out, clear_figure = False) | ||
|
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| else: | ||
| response_message = 'Error cannot find data in report or database.' | ||
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| return full_response |
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