File size: 6,340 Bytes
0ae3f27 | 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 | import json
import os
import time
from collections import defaultdict
import numpy as np
import tiktoken
from dotenv import load_dotenv
from jinja2 import Template
from openai import OpenAI
from tqdm import tqdm
load_dotenv()
PROMPT = """
# Question:
{{QUESTION}}
# Context:
{{CONTEXT}}
# Short answer:
"""
class RAGManager:
def __init__(self, data_path="dataset/locomo10_rag.json", chunk_size=500, k=1):
self.model = os.getenv("MODEL")
self.client = OpenAI()
self.data_path = data_path
self.chunk_size = chunk_size
self.k = k
def generate_response(self, question, context):
template = Template(PROMPT)
prompt = template.render(CONTEXT=context, QUESTION=question)
max_retries = 3
retries = 0
while retries <= max_retries:
try:
t1 = time.time()
response = self.client.chat.completions.create(
model=self.model,
messages=[
{
"role": "system",
"content": "You are a helpful assistant that can answer "
"questions based on the provided context."
"If the question involves timing, use the conversation date for reference."
"Provide the shortest possible answer."
"Use words directly from the conversation when possible."
"Avoid using subjects in your answer.",
},
{"role": "user", "content": prompt},
],
temperature=0,
)
t2 = time.time()
return response.choices[0].message.content.strip(), t2 - t1
except Exception as e:
retries += 1
if retries > max_retries:
raise e
time.sleep(1) # Wait before retrying
def clean_chat_history(self, chat_history):
cleaned_chat_history = ""
for c in chat_history:
cleaned_chat_history += f"{c['timestamp']} | {c['speaker']}: {c['text']}\n"
return cleaned_chat_history
def calculate_embedding(self, document):
response = self.client.embeddings.create(model=os.getenv("EMBEDDING_MODEL"), input=document)
return response.data[0].embedding
def calculate_similarity(self, embedding1, embedding2):
return np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
def search(self, query, chunks, embeddings, k=1):
"""
Search for the top-k most similar chunks to the query.
Args:
query: The query string
chunks: List of text chunks
embeddings: List of embeddings for each chunk
k: Number of top chunks to return (default: 1)
Returns:
combined_chunks: The combined text of the top-k chunks
search_time: Time taken for the search
"""
t1 = time.time()
query_embedding = self.calculate_embedding(query)
similarities = [self.calculate_similarity(query_embedding, embedding) for embedding in embeddings]
# Get indices of top-k most similar chunks
if k == 1:
# Original behavior - just get the most similar chunk
top_indices = [np.argmax(similarities)]
else:
# Get indices of top-k chunks
top_indices = np.argsort(similarities)[-k:][::-1]
# Combine the top-k chunks
combined_chunks = "\n<->\n".join([chunks[i] for i in top_indices])
t2 = time.time()
return combined_chunks, t2 - t1
def create_chunks(self, chat_history, chunk_size=500):
"""
Create chunks using tiktoken for more accurate token counting
"""
# Get the encoding for the model
encoding = tiktoken.encoding_for_model(os.getenv("EMBEDDING_MODEL"))
documents = self.clean_chat_history(chat_history)
if chunk_size == -1:
return [documents], []
chunks = []
# Encode the document
tokens = encoding.encode(documents)
# Split into chunks based on token count
for i in range(0, len(tokens), chunk_size):
chunk_tokens = tokens[i : i + chunk_size]
chunk = encoding.decode(chunk_tokens)
chunks.append(chunk)
embeddings = []
for chunk in chunks:
embedding = self.calculate_embedding(chunk)
embeddings.append(embedding)
return chunks, embeddings
def process_all_conversations(self, output_file_path):
with open(self.data_path, "r") as f:
data = json.load(f)
FINAL_RESULTS = defaultdict(list)
for key, value in tqdm(data.items(), desc="Processing conversations"):
chat_history = value["conversation"]
questions = value["question"]
chunks, embeddings = self.create_chunks(chat_history, self.chunk_size)
for item in tqdm(questions, desc="Answering questions", leave=False):
question = item["question"]
answer = item.get("answer", "")
category = item["category"]
if self.chunk_size == -1:
context = chunks[0]
search_time = 0
else:
context, search_time = self.search(question, chunks, embeddings, k=self.k)
response, response_time = self.generate_response(question, context)
FINAL_RESULTS[key].append(
{
"question": question,
"answer": answer,
"category": category,
"context": context,
"response": response,
"search_time": search_time,
"response_time": response_time,
}
)
with open(output_file_path, "w+") as f:
json.dump(FINAL_RESULTS, f, indent=4)
# Save results
with open(output_file_path, "w+") as f:
json.dump(FINAL_RESULTS, f, indent=4)
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