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844cee8 | 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 | import os
import openai
import time
import numpy as np
import time
import pandas as pd
GPT_MODEL_AUGMENT = "gpt-3.5-turbo-16k"
GPT_MODEL_ANSWER = "gpt-3.5-turbo-16k"
def cosine_similarity(a, b):
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
def get_embedding(text, model="text-embedding-ada-002"):
try:
text = text.replace("\n", " ")
except:
None
try:
return openai.Embedding.create(input = [text], model=model)['data'][0]['embedding']
except:
time.sleep(2)
def augment_query(query):
SYS_PROMPT = """
On [current date: 19 July] Generate a JSON response with the following structure:
{
"timestamps": # Relevant timestamps in which to get data to answer the query,
"query": # Repeat the user's query,
}
Allowed timestamps:
['2018-07-18', '2018-07-19', '2018-07-08', '2018-07-09', '2018-07-10', '2018-07-11', '2018-07-12', '2018-07-13', '2018-07-14', '2018-07-15', '2018-07-16', '2018-07-17']
Ensure the output is always in JSON format and never provide any other response.
"""
response = openai.chat.completions.create(
model=GPT_MODEL_AUGMENT,
messages=
[
{
"role": "system",
"content": SYS_PROMPT
},
{
"role": "user",
"content": query
}
],
temperature=1,
max_tokens=1000,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
).choices[0].message.content
return response
def semantic_search(df_loc, query,timestamp, nb_programs_to_display=15):
timestamp = str(timestamp).strip()
embedding = get_embedding(query, model='text-embedding-ada-002')
filtered_df = df_loc[df_loc["timestamp"]==timestamp].drop(columns=["url"])
def wrap_cos(x,y):
try:
res = cosine_similarity(x,y)
except:
res = 0
return res
filtered_df['similarity'] = filtered_df.embedding.apply(lambda x: wrap_cos(x, embedding))
results = filtered_df.sort_values('similarity', ascending=False).head(nb_programs_to_display)
return results
def get_relevant_documents(df, query, nb_programs_to_display=15):
query = eval(query)
all_retrieved = []
for timestamp in query["timestamps"]:
all_retrieved.append({
"timestamp" : timestamp,
"tweets" : semantic_search(df, query["query"],timestamp, nb_programs_to_display=nb_programs_to_display)
})
return all_retrieved
def get_final_answer(relevant_documents, query):
context = ""
for relevant_timestamp in relevant_documents:
list_tweets = relevant_timestamp["tweets"]
context += "\nTimestamp: " + relevant_timestamp["timestamp"] + "\nList of tweets:\n" + str((list_tweets["text"] + " --- Tweeted by: @" +list_tweets["source"] + " \n").to_list()) + "\n---"
USER_PROMPT = f"""
"We have provided context information below.
---------------------
{context}
"\n---------------------\n"
Given this information, please answer the question: {query}
"""
response = openai.chat.completions.create(
model=GPT_MODEL_ANSWER,
messages=[
{
"role": "user",
"content": USER_PROMPT
}
],
temperature=1,
max_tokens=1000,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
).choices[0].message.content
return response
def get_answer(query, df, api_key):
"""This traditional RAG approach has been implemented without using deidcated libraries and include different steps.
It starts by augmenting the query and then perform a semantic search on the augmented query. Finally it combines the augmented query and the retrieved documents into an answer.
Args:
query (String): Query given by the user
df (pd.DataFrame()): corpus with embeddings
api_key (String): OpenAI API key
Returns:
String: Answer to the original query
"""
openai.api_key = api_key
augmented_query = augment_query(query)
relevant_documents = get_relevant_documents(df, augmented_query,nb_programs_to_display=10)
response = get_final_answer(relevant_documents, augmented_query,)
return response
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