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ba5532a ae86b65 0e4ec89 ba5532a 62c16a7 ba5532a 44f30ad ba5532a 44f30ad ba5532a 44f30ad ba5532a 44f30ad ba5532a 1b72b3a ba5532a ae86b65 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | import streamlit as st
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
from langchain.agents import create_csv_agent
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
class Chatbot_txt:
_template = """Given the following conversation and a follow-up question, rephrase the follow-up question to be a standalone question.
Chat History:
{chat_history}
Follow-up entry: {question}
Standalone question:"""
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
qa_template = """"You are an AI conversational assistant to answer questions based on a context.
You are given data from a txt file and a question, you must help the user find the information they need.
Your answers should be friendly, in the same language.
question: {question}
=========
context: {context}
=======
"""
QA_PROMPT = PromptTemplate(template=qa_template, input_variables=["question", "context"])
def __init__(self, model_name, temperature, vectors):
self.model_name = model_name
self.temperature = temperature
self.vectors = vectors
def conversational_chat(self, query):
"""
Starts a conversational chat with a model via Langchain
"""
chain = ConversationalRetrievalChain.from_llm(
llm=ChatOpenAI(model_name=self.model_name, temperature=self.temperature),
condense_question_prompt=self.CONDENSE_QUESTION_PROMPT,
qa_prompt=self.QA_PROMPT,
retriever=self.vectors.as_retriever(),
)
result = chain({"question": query, "chat_history": st.session_state["history"]})
st.session_state["history"].append((query, result["answer"]))
return result["answer"]
class Chatbot:
_template = """Given the following conversation and a follow-up question, rephrase the follow-up question to be a standalone question.
Chat History:
{chat_history}
Follow-up entry: {question}
Standalone question:"""
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
qa_template = """"You are an AI conversational assistant to answer questions based on a context.
You are given data from a csv file and a question, you must help the user find the information they need.
Your answers should be friendly, in the same language.
question: {question}
=========
context: {context}
=======
"""
QA_PROMPT = PromptTemplate(template=qa_template, input_variables=["question", "context"])
def __init__(self, model_name, temperature, vectors):
self.model_name = model_name
self.temperature = temperature
self.vectors = vectors
def conversational_chat(self, query):
"""
Starts a conversational chat with a model via Langchain
"""
chain = ConversationalRetrievalChain.from_llm(
llm=ChatOpenAI(model_name=self.model_name, temperature=self.temperature),
condense_question_prompt=self.CONDENSE_QUESTION_PROMPT,
qa_prompt=self.QA_PROMPT,
retriever=self.vectors.as_retriever(),
)
result = chain({"question": query, "chat_history": st.session_state["history"]})
st.session_state["history"].append((query, result["answer"]))
return result["answer"]
class Chatbot_ledger:
def __init__(self, model_name, temperature, csv):
self.model_name = model_name
self.temperature = temperature
self.csv = csv
def csv_agent(self, query):
agent = create_csv_agent(OpenAI(temperature=self.temperature, model_name=self.model_name),
self.csv,
verbose=True,
index_col=0)
result = agent.run(query)
st.session_state['history'].append((query, result))
return result
def conversational_chat(self, query):
"""
Starts a conversational chat with a model via Langchain
"""
chain = ConversationalRetrievalChain.from_llm(
llm=ChatOpenAI(model_name=self.model_name, temperature=self.temperature),
condense_question_prompt=self.CONDENSE_QUESTION_PROMPT,
qa_prompt=self.QA_PROMPT,
retriever=self.vectors.as_retriever(),
)
result = chain({"question": query, "chat_history": st.session_state["history"]})
st.session_state["history"].append((query, result["answer"]))
return result["answer"]
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