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Upload 4 files
Browse files- app.py +114 -0
- create_chain.py +80 -0
- prompt.py +81 -0
- requirements.txt +88 -0
app.py
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import gradio as gr
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from langchain import LLMChain
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from langchain import PromptTemplate
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from langchain.llms import Cohere
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# from create_chain import chain as llm_chain
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from create_chain import create_chain_from_template
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from prompt import wikipedia_template, general_internet_template
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from langchain.retrievers import CohereRagRetriever
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from langchain.chat_models import ChatCohere
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import os
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from dotenv import load_dotenv
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load_dotenv() # take environment variables from .env.
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# https://pypi.org/project/python-dotenv/
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COHERE_API_KEY = os.getenv("COHERE_API_KEY")
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examples = [
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["What is Cellular Automata and who created it?"],
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["What is Cohere"],
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["who is Katherine Johnson"],
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]
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def create_UI(llm_chain):
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with gr.Blocks() as demo:
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# radio = gr.Radio(
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# ["wikipedia only", "any website", "none"], label="What kind of essay would you like to write?", value="wikipedia only"
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# )
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radio = gr.Radio(
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["wikipedia only", "any website", ], label="What kind of essay would you like to write?", value="wikipedia only"
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)
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chatbot = gr.Chatbot()
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msg = gr.Textbox(info="Enter your question here, press enter to submit query")
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clear = gr.Button("Clear")
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# submit_btn = gr.Button("Submit", variant="primary")
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gr.Examples(examples=examples, label="Examples", inputs=msg,)
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def user(user_message, history):
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return "", history + [[user_message, None]]
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def bot(history):
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print("Question: ", history[-1][0])
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bot_message = llm_chain.invoke(history[-1][0])
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bot_message = bot_message
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print("Response: ", bot_message)
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history[-1][1] = ""
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history[-1][1] += bot_message
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return history
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def change_textbox(choice):
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if choice == "wikipedia only":
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template = wikipedia_template
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llm_chain = create_chain_from_template(
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template,
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rag,
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llm_model
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)
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return llm_chain
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elif choice == "any website":
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template = general_internet_template
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llm_chain = create_chain_from_template(
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template,
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rag,
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llm_model
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)
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return llm_chain
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elif choice == "none":
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submit_btn = gr.Button("Submit", variant="primary")
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return gr.Textbox(lines=8, visible=True, value="Lorem ipsum dolor sit amet"), gr.Button("Submit", variant="primary")
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else:
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return gr.Textbox(visible=False), gr.Button(interactive=False)
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text = gr.Textbox(lines=2, interactive=True, show_copy_button=True)
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# radio.change(fn=change_textbox, inputs=radio, outputs=[text, submit_btn])
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radio.change(fn=change_textbox, inputs=radio, outputs=[text])
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msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, chatbot, chatbot)
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clear.click(lambda: None, None, chatbot, queue=False)
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return demo
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if __name__ == "__main__":
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template = wikipedia_template
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prompt = PromptTemplate(template=template, input_variables=["query"])
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llm_model = ChatCohere(
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cohere_api_key=COHERE_API_KEY,
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)
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rag = CohereRagRetriever(llm=llm_model,)
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llm_chain = create_chain_from_template(
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template,
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rag,
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llm_model
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)
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demo = create_UI(llm_chain)
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demo.queue()
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# demo.launch()
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demo.launch(share=True)
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# pass
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create_chain.py
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import os
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from langchain.chat_models import ChatCohere
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from langchain.schema import AIMessage, HumanMessage
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## cohere with connector
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## cohere with internet
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# https://python.langchain.com/docs/modules/data_connection/retrievers/
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# https://python.langchain.com/docs/integrations/llms/cohere
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from langchain.chat_models import ChatCohere
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from langchain.retrievers import CohereRagRetriever
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from langchain.schema.document import Document
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from langchain.chains import LLMChain
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from langchain.prompts import PromptTemplate
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate
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from langchain.schema import StrOutputParser
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from langchain.schema.runnable import RunnablePassthrough
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from langchain.prompts import (
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ChatPromptTemplate,
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MessagesPlaceholder,
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SystemMessagePromptTemplate,
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HumanMessagePromptTemplate,
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)
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from dotenv import load_dotenv
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from prompt import wikipedia_template, general_internet_template
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load_dotenv() # take environment variables from .env.
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# https://pypi.org/project/python-dotenv/
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COHERE_API_KEY = os.getenv("COHERE_API_KEY")
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def format_docs(docs):
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return "\n\n".join([d.page_content for d in docs])
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def create_chain_from_template(template, retriever, model):
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prompt = PromptTemplate(template=template, input_variables=["query"])
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chain = (
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{"context": retriever | format_docs, "query": RunnablePassthrough()}
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| prompt
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| model
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| StrOutputParser()
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)
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return chain
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if __name__ == "__main__":
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llm_model = ChatCohere(
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cohere_api_key=COHERE_API_KEY,
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)
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template = wikipedia_template
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prompt = PromptTemplate(template=template, input_variables=["query"])
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rag = CohereRagRetriever(llm=llm_model,)
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llm_chain = create_chain_from_template(
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template,
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rag,
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llm_model
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)
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sample_query = "What is Cellular Automata and who created it?"
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sample_output = llm_chain.invoke(sample_query)
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print(sample_output)
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prompt.py
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wikipedia_template = """Question: {query}
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Please only use wikipedia when searching for the answer.
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When given a query you must generate a wikipedia article based on the query given;
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You must oranization your article into sections just like in wikipedia
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The structure is open ended however you must write this article in markdown;
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Also you must have a reference section at the end with a list of all your refernces;
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If you are unsure about the exact person the user is refering to please ask questions;
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For the sake of clarity please add new lines between your inital output and the
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generated wikipedia article
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If there are many pages for a similar person or entity please as
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the user to specify which one they are talking about before geenrating the article
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Please make sure to include in-line citations
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for example:
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fact_1 [source_1]
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fact_2 [source_2, source_3]
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Answer:
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"""
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# general_internet_template = """Question: {query}
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# Please only use {website_list} when searching for the answer.
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# When given a query you must generate a wikipedia article based on the query given;
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# You must oranization your article into sections just like in wikipedia
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# The structure is open ended however you must write this article in markdown;
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# Also you must have a reference section at the end with a list of all your refernces;
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# If you are unsure about the exact person the user is refering to please ask questions;
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# For the sake of clarity please add new lines between your inital output and the
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# generated wikipedia article
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# If there are many pages for a similar person or entity please as
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# the user to specify which one they are talking about before geenrating the article
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# Please make sure to include in-line citations
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# for example:
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# fact_1 [source_1]
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# fact_2 [source_2, source_3]
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# Answer:
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# """
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general_internet_template = """Question: {query}
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Use any website so needed to help the user.
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When given a query you must generate a wikipedia article based on the query given;
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You must oranization your article into sections just like in wikipedia
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The structure is open ended however you must write this article in markdown;
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Also you must have a reference section at the end with a list of all your refernces;
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If you are unsure about the exact person the user is refering to please ask questions;
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For the sake of clarity please add new lines between your inital output and the
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generated wikipedia article
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If there are many pages for a similar person or entity please as
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the user to specify which one they are talking about before geenrating the article
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Please make sure to include in-line citations
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for example:
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fact_1 [source_1]
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fact_2 [source_2, source_3]
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Answer:
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"""
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requirements.txt
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aiofiles==23.2.1
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aiohttp==3.8.6
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aiosignal==1.3.1
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altair==5.1.2
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annotated-types==0.6.0
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anyio==3.7.1
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async-timeout==4.0.3
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attrs==23.1.0
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backoff==2.2.1
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certifi==2023.7.22
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charset-normalizer==3.3.2
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click==8.1.7
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cohere==4.34
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colorama==0.4.6
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contourpy==1.2.0
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cycler==0.12.1
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dataclasses-json==0.6.2
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exceptiongroup==1.1.3
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fastapi==0.104.1
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fastavro==1.8.2
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ffmpy==0.3.1
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filelock==3.13.1
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fonttools==4.44.3
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frozenlist==1.4.0
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fsspec==2023.10.0
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gradio==4.4.0
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gradio_client==0.7.0
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greenlet==3.0.1
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h11==0.14.0
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httpcore==1.0.2
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httpx==0.25.1
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huggingface-hub==0.19.4
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idna==3.4
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importlib-metadata==6.8.0
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importlib-resources==6.1.1
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Jinja2==3.1.2
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+
jsonpatch==1.33
|
38 |
+
jsonpointer==2.4
|
39 |
+
jsonschema==4.20.0
|
40 |
+
jsonschema-specifications==2023.11.1
|
41 |
+
kiwisolver==1.4.5
|
42 |
+
langchain==0.0.336
|
43 |
+
langsmith==0.0.64
|
44 |
+
markdown-it-py==3.0.0
|
45 |
+
MarkupSafe==2.1.3
|
46 |
+
marshmallow==3.20.1
|
47 |
+
matplotlib==3.8.1
|
48 |
+
mdurl==0.1.2
|
49 |
+
multidict==6.0.4
|
50 |
+
mypy-extensions==1.0.0
|
51 |
+
numpy==1.26.2
|
52 |
+
orjson==3.9.10
|
53 |
+
packaging==23.2
|
54 |
+
pandas==2.1.3
|
55 |
+
Pillow==10.1.0
|
56 |
+
pydantic==2.5.1
|
57 |
+
pydantic_core==2.14.3
|
58 |
+
pydub==0.25.1
|
59 |
+
Pygments==2.16.1
|
60 |
+
pyparsing==3.1.1
|
61 |
+
python-dateutil==2.8.2
|
62 |
+
python-dotenv==1.0.0
|
63 |
+
python-multipart==0.0.6
|
64 |
+
pytz==2023.3.post1
|
65 |
+
PyYAML==6.0.1
|
66 |
+
referencing==0.31.0
|
67 |
+
requests==2.31.0
|
68 |
+
rich==13.7.0
|
69 |
+
rpds-py==0.13.0
|
70 |
+
semantic-version==2.10.0
|
71 |
+
shellingham==1.5.4
|
72 |
+
six==1.16.0
|
73 |
+
sniffio==1.3.0
|
74 |
+
SQLAlchemy==2.0.23
|
75 |
+
starlette==0.27.0
|
76 |
+
tenacity==8.2.3
|
77 |
+
tomlkit==0.12.0
|
78 |
+
toolz==0.12.0
|
79 |
+
tqdm==4.66.1
|
80 |
+
typer==0.9.0
|
81 |
+
typing-inspect==0.9.0
|
82 |
+
typing_extensions==4.8.0
|
83 |
+
tzdata==2023.3
|
84 |
+
urllib3==2.1.0
|
85 |
+
uvicorn==0.24.0.post1
|
86 |
+
websockets==11.0.3
|
87 |
+
yarl==1.9.2
|
88 |
+
zipp==3.17.0
|