File size: 6,766 Bytes
bac90e2 4a6ffa9 bac90e2 4a6ffa9 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 |
import os
from typing import Dict, List, Union
import numpy as np
import openai
import pandas as pd
import streamlit as st
from langchain.document_loaders import TextLoader
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from scipy.spatial.distance import cosine
openai.api_key = os.environ["OPENAI_API_KEY"]
def merge_dataframes(dataframes: List[pd.DataFrame]) -> pd.DataFrame:
"""Merges a list of DataFrames, keeping only specific columns."""
# Concatenate the list of dataframes
combined_dataframe = pd.concat(
dataframes, ignore_index=True
) # Combine all dataframes into one
# Ensure that the resulting dataframe only contains the columns "context", "questions", "answers"
combined_dataframe = combined_dataframe[
["context", "questions", "answers"]
] # Filter for specific columns
return combined_dataframe # Return the merged and filtered DataFrame
def call_chatgpt(prompt: str) -> str:
"""
Uses the OpenAI API to generate an AI response to a prompt.
Args:
prompt: A string representing the prompt to send to the OpenAI API.
Returns:
A string representing the AI's generated response.
"""
# Use the OpenAI API to generate a response based on the input prompt.
response = openai.Completion.create(
model="gpt-3.5-turbo-instruct",
prompt=prompt,
temperature=0.5,
max_tokens=500,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
)
# Extract the text from the first (and only) choice in the response output.
ans = response.choices[0]["text"]
# Return the generated AI response.
return ans
def openai_text_embedding(prompt: str) -> str:
return openai.Embedding.create(input=prompt, model="text-embedding-ada-002")[
"data"
][0]["embedding"]
def calculate_sts_openai_score(sentence1: str, sentence2: str) -> float:
# Compute sentence embeddings
embedding1 = openai_text_embedding(sentence1) # Flatten the embedding array
embedding2 = openai_text_embedding(sentence2) # Flatten the embedding array
# Convert to array
embedding1 = np.asarray(embedding1)
embedding2 = np.asarray(embedding2)
# Calculate cosine similarity between the embeddings
similarity_score = 1 - cosine(embedding1, embedding2)
return similarity_score
def add_dist_score_column(
dataframe: pd.DataFrame,
sentence: str,
) -> pd.DataFrame:
dataframe["stsopenai"] = dataframe["questions"].apply(
lambda x: calculate_sts_openai_score(str(x), sentence)
)
sorted_dataframe = dataframe.sort_values(by="stsopenai", ascending=False)
return sorted_dataframe.iloc[:5, :]
def convert_to_list_of_dict(df: pd.DataFrame) -> List[Dict[str, str]]:
"""
Reads in a pandas DataFrame and produces a list of dictionaries with two keys each, 'question' and 'answer.'
Args:
df: A pandas DataFrame with columns named 'questions' and 'answers'.
Returns:
A list of dictionaries, with each dictionary containing a 'question' and 'answer' key-value pair.
"""
# Initialize an empty list to store the dictionaries
result = []
# Loop through each row of the DataFrame
for index, row in df.iterrows():
# Create a dictionary with the current question and answer
qa_dict_quest = {"role": "user", "content": row["questions"]}
qa_dict_ans = {"role": "assistant", "content": row["answers"]}
# Add the dictionary to the result list
result.append(qa_dict_quest)
result.append(qa_dict_ans)
# Return the list of dictionaries
return result
# file_names = [f"output_files/file_{i}.txt" for i in range(131)]
file_names = [f"output_files_large/file_{i}.txt" for i in range(1310)]
# Initialize an empty list to hold all documents
all_documents = [] # this is just a copy, you don't have to use this
# Iterate over each file and load its contents
for file_name in file_names:
loader = TextLoader(file_name)
documents = loader.load()
all_documents.extend(documents)
# Split the loaded documents into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(all_documents)
# Create the open-source embedding function
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
# embedding_function = SentenceTransformer("all-MiniLM-L6-v2")
# embedding_function = openai_text_embedding
# Load the documents into Chroma
db = Chroma.from_documents(docs, embedding_function)
st.title("Youth Homelessness Chatbot")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
st.sidebar.markdown("""This is an app to help you navigate the website of YSA""")
clear_button = st.sidebar.button("Clear Conversation", key="clear")
if clear_button:
st.session_state.messages = []
# React to user input
if prompt := st.chat_input("Tell me about YSA"):
# Display user message in chat message container
st.chat_message("user").markdown(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
question = prompt
docs = db.similarity_search(question)
docs_2 = db.similarity_search_with_score(question)
docs_2_table = pd.DataFrame(
{
"source": [docs_2[i][0].metadata["source"] for i in range(len(docs))],
"content": [docs_2[i][0].page_content for i in range(len(docs))],
"distances": [docs_2[i][1] for i in range(len(docs))],
}
)
ref_from_db_search = docs_2_table["content"]
engineered_prompt = f"""
Based on the context: {ref_from_db_search},
answer the user question: {question}.
Answer the question directly (don't say "based on the context, ...")
"""
answer = call_chatgpt(engineered_prompt)
response = answer
# Display assistant response in chat message container
with st.chat_message("assistant"):
with st.spinner("Wait for it..."):
st.markdown(response)
with st.expander("See reference:"):
st.table(docs_2_table)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})
st.session_state.messages.append(
{"role": "assistant", "content": docs_2_table.to_json()}
)
|