Spaces:
Build error
Build error
File size: 4,267 Bytes
7550f47 78476ba 7550f47 |
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 json
import openai
import tiktoken
import numpy as np
import pandas as pd
import gradio as gr
import pickle
COMPLETIONS_MODEL = "text-davinci-003"
EMBEDDING_MODEL = "text-embedding-ada-002"
with open("document_embeddings_clean_code.pickle", "rb") as document_embeddings_clean_code:
document_embeddings = pickle.load(document_embeddings_clean_code)
df = pd.read_csv('./clean_code_processed.csv')
df = df.set_index(["title", "section"])
df = df[df.tokens > 40]
df_for_embeddings = df
def get_embedding(text, model=EMBEDDING_MODEL):
result = openai.Embedding.create(
model=model,
input=text
)
return result["data"][0]["embedding"]
def vector_similarity(x, y):
"""
Returns the similarity between two vectors.
Because OpenAI Embeddings are normalized to length 1, the cosine similarity is the same as the dot product.
"""
return np.dot(np.array(x), np.array(y))
def order_document_sections_by_query_similarity(query, contexts):
"""
Find the query embedding for the supplied query, and compare it against all of the pre-calculated document embeddings
to find the most relevant sections.
Return the list of document sections, sorted by relevance in descending order.
"""
query_embedding = get_embedding(query)
document_similarities = sorted([
(vector_similarity(query_embedding, doc_embedding), doc_index) for doc_index, doc_embedding in contexts.items()
], reverse=True)
return document_similarities
def construct_prompt(question, context_embeddings, df):
"""
Fetch relevant
"""
most_relevant_document_sections = order_document_sections_by_query_similarity(question, context_embeddings)
chosen_sections = []
chosen_sections_len = 0
chosen_sections_indexes = []
for _, section_index in most_relevant_document_sections:
# Add contexts until we run out of space.
tokens = df._get_value(section_index, "tokens")
if type(tokens) != np.int64:
continue
chosen_sections_len += df._get_value(section_index, "tokens") + separator_len
if chosen_sections_len > MAX_SECTION_LEN:
break
chosen_sections.append(SEPARATOR + df._get_value(section_index, "content").replace("\n", " "))
chosen_sections_indexes.append(section_index)
# Useful diagnostic information
print(f"Selected {len(chosen_sections)} document sections:")
print("\n".join(str(index) for index in chosen_sections_indexes))
header = """Answer the question as truthfully as possible using the provided context, and if the answer is not contained within the text below, say "I don't know."\n\nContext:\n"""
return (header + "".join(chosen_sections) + "\n\n Q: " + question + "\n A:", chosen_sections_indexes)
MAX_SECTION_LEN = 2000
SEPARATOR = "\n* "
ENCODING = "gpt2" # encoding for text-davinci-003
encoding = tiktoken.get_encoding(ENCODING)
separator_len = len(encoding.encode(SEPARATOR))
COMPLETIONS_API_PARAMS = {
# We use temperature of 0.0 because it gives the most predictable, factual answer.
"temperature": 0.0,
"max_tokens": 1500,
"model": COMPLETIONS_MODEL,
}
def answer_query_with_context(
query,
df,
document_embeddings
):
prompt, chosen_sections_indexes = construct_prompt(
query,
document_embeddings,
df
)
for i in range(len(chosen_sections_indexes)):
chosen_sections_indexes[i] = chosen_sections_indexes[i][0]
response = openai.Completion.create(
prompt=prompt,
**COMPLETIONS_API_PARAMS
)
return (response["choices"][0]["text"].strip(" \n"), chosen_sections_indexes)
def handle(question):
answer, related_documents = answer_query_with_context(question, df_for_embeddings, document_embeddings)
return answer + "\n\nRelated chapters:\n" + "\n".join(related_documents)
demo = gr.Interface(
fn=handle,
inputs="text",
outputs="text",
cache_examples=False,
examples=[
"How to properly name a variable?",
"How to write a good comment?",
"What are best practices of unit testing?",
]
)
demo.launch() |