open-source-mirrors
/
GPT3Discord
mirror of https://github.com/Kav-K/GPT3Discord
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Pagination for gpt responses, max tokens 4096

Browse Source
Kaveen Kumarasinghe 1 year ago
parent aeb947b817
commit 01fb514a9b
3 changed files with 175 additions and 23 deletions
Show Stats Download Patch File Download Diff File

55
cogs/search_service_cog.py
Unescape View File

@ -3,6 +3,7 @@ import traceback
import aiohttp
import re
import discord
from discord.ext import pages
from models.deepl_model import TranslationModel
from models.search_model import Search
@ -27,9 +28,36 @@ class SearchService(discord.Cog, name="SearchService"):
self.bot = bot
self.usage_service = usage_service
self.model = Search(gpt_model, usage_service)
self.EMBED_CUTOFF = 2000
# Make a mapping of all the country codes and their full country names:
async def search_command(self, ctx, query, search_scope, nodes):
async def paginate_embed(self, response_text):
"""Given a response text make embed pages and return a list of the pages. Codex makes it a codeblock in the embed"""
response_text = [
response_text[i : i + self.EMBED_CUTOFF]
for i in range(0, len(response_text), self.EMBED_CUTOFF)
]
pages = []
first = False
# Send each chunk as a message
for count, chunk in enumerate(response_text, start=1):
if not first:
page = discord.Embed(
title=f"Page {count}",
description=chunk,
)
first = True
else:
page = discord.Embed(
title=f"Page {count}",
description=chunk,
)
pages.append(page)
return pages
async def search_command(self, ctx: discord.ApplicationContext, query, search_scope, nodes):
"""Command handler for the translation command"""
user_api_key = None
if USER_INPUT_API_KEYS:
@ -48,7 +76,15 @@ class SearchService(discord.Cog, name="SearchService"):
await ctx.defer()
response = await self.model.search(query, user_api_key, search_scope, nodes)
try:
response = await self.model.search(query, user_api_key, search_scope, nodes)
except ValueError:
await ctx.respond("The Google Search API returned an error. Check the console for more details.", ephemeral=True)
return
except Exception:
await ctx.respond("An error occurred. Check the console for more details.", ephemeral=True)
traceback.print_exc()
return
url_extract_pattern = "https?:\\/\\/(?:www\\.)?[-a-zA-Z0-9@:%._\\+~#=]{1,256}\\.[a-zA-Z0-9()]{1,6}\\b(?:[-a-zA-Z0-9()@:%_\\+.~#?&\\/=]*)"
urls = re.findall(
@ -58,6 +94,17 @@ class SearchService(discord.Cog, name="SearchService"):
)
urls = "\n".join(f"<{url}>" for url in urls)
await ctx.respond(
f"**Query:**\n\n{query.strip()}\n\n**Query response:**\n\n{response.response.strip()}\n\n**Sources:**\n{urls}"
query_response_message = f"**Query:**`\n\n{query.strip()}`\n\n**Query response:**\n\n{response.response.strip()}\n\n**Sources:**\n{urls}"
query_response_message = query_response_message.replace("<|endofstatement|>", "")
# If the response is too long, lets paginate using the discord pagination
# helper
embed_pages = await self.paginate_embed(query_response_message)
paginator = pages.Paginator(
pages=embed_pages,
timeout=None,
author_check=False,
)
await paginator.respond(ctx.interaction)

84
models/index_model.py
Unescape View File

@ -1,14 +1,18 @@
import os
import tempfile
import traceback
import asyncio
from collections import defaultdict
import aiohttp
import discord
import aiofiles
from functools import partial
from typing import List, Optional
from pathlib import Path
from datetime import date
from discord.ext import pages
from langchain import OpenAI
from gpt_index.readers import YoutubeTranscriptReader
@ -45,7 +49,6 @@ def get_and_query(
index: [GPTSimpleVectorIndex, ComposableGraph] = index_storage[
user_id
].get_index_or_throw()
prompthelper = PromptHelper(4096, 500, 20)
if isinstance(index, GPTTreeIndex):
response = index.query(
query,
@ -53,7 +56,6 @@ def get_and_query(
child_branch_factor=2,
llm_predictor=llm_predictor,
embed_model=embed_model,
prompt_helper=prompthelper,
)
else:
response = index.query(
@ -63,7 +65,6 @@ def get_and_query(
llm_predictor=llm_predictor,
embed_model=embed_model,
similarity_top_k=nodes,
prompt_helper=prompthelper,
)
return response
@ -134,6 +135,33 @@ class Index_handler:
"Given the context information and not prior knowledge, "
"answer the question: {query_str}\n"
)
self.EMBED_CUTOFF = 2000
async def paginate_embed(self, response_text):
"""Given a response text make embed pages and return a list of the pages. Codex makes it a codeblock in the embed"""
response_text = [
response_text[i : i + self.EMBED_CUTOFF]
for i in range(0, len(response_text), self.EMBED_CUTOFF)
]
pages = []
first = False
# Send each chunk as a message
for count, chunk in enumerate(response_text, start=1):
if not first:
page = discord.Embed(
title=f"Index Query Results",
description=chunk,
)
first = True
else:
page = discord.Embed(
title=f"Page {count}",
description=chunk,
)
pages.append(page)
return pages
# TODO We need to do predictions below for token usage.
def index_file(self, file_path, embed_model) -> GPTSimpleVectorIndex:
@ -141,6 +169,23 @@ class Index_handler:
index = GPTSimpleVectorIndex(document, embed_model=embed_model)
return index
async def index_web_pdf(self, url, embed_model) -> GPTSimpleVectorIndex:
print("Indexing a WEB PDF")
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
if response.status == 200:
data = await response.read()
f = tempfile.NamedTemporaryFile(delete=False)
f.write(data)
f.close()
else:
return "An error occurred while downloading the PDF."
document = SimpleDirectoryReader(input_files=[f.name]).load_data()
index = GPTSimpleVectorIndex(document, embed_model=embed_model)
return index
def index_gdoc(self, doc_id, embed_model) -> GPTSimpleVectorIndex:
document = GoogleDocsReader().load_data(doc_id)
index = GPTSimpleVectorIndex(document, embed_model=embed_model)
@ -243,11 +288,32 @@ class Index_handler:
# TODO Link validation
try:
embedding_model = OpenAIEmbedding()
# Pre-emptively connect and get the content-type of the response
try:
async with aiohttp.ClientSession() as session:
async with session.get(link, timeout=2) as response:
print(response.status)
if response.status == 200:
content_type = response.headers.get("content-type")
else:
await ctx.respond("Failed to get link", ephemeral=True)
return
except Exception:
traceback.print_exc()
await ctx.respond("Failed to get link", ephemeral=True)
return
# Check if the link contains youtube in it
if "youtube" in link:
index = await self.loop.run_in_executor(
None, partial(self.index_youtube_transcript, link, embedding_model)
)
elif "pdf" in content_type:
index = await self.index_web_pdf(link, embedding_model)
else:
index = await self.loop.run_in_executor(
None, partial(self.index_webpage, link, embedding_model)
@ -349,7 +415,7 @@ class Index_handler:
for doc_id in [docmeta for docmeta in _index.docstore.docs.keys()]
if isinstance(_index.docstore.get_document(doc_id), Document)
]
llm_predictor = LLMPredictor(llm=OpenAI(model_name="text-davinci-003"))
llm_predictor = LLMPredictor(llm=OpenAI(model_name="text-davinci-003", max_tokens=-1))
embedding_model = OpenAIEmbedding()
tree_index = GPTTreeIndex(
documents=documents,
@ -466,9 +532,15 @@ class Index_handler:
await self.usage_service.update_usage(
embedding_model.last_token_usage, embeddings=True
)
await ctx.respond(
f"**Query:**\n\n{query.strip()}\n\n**Query response:**\n\n{response.response.strip()}"
query_response_message=f"**Query:**\n\n`{query.strip()}`\n\n**Query response:**\n\n{response.response.strip()}"
query_response_message = query_response_message.replace("<|endofstatement|>", "")
embed_pages = await self.paginate_embed(query_response_message)
paginator = pages.Paginator(
pages=embed_pages,
timeout=None,
author_check=False,
)
await paginator.respond(ctx.interaction)
except Exception:
traceback.print_exc()
await ctx.respond(

59
models/search_model.py
Unescape View File

@ -2,9 +2,11 @@ import asyncio
import os
import random
import re
import tempfile
import traceback
from functools import partial
import discord
from bs4 import BeautifulSoup
import aiohttp
from gpt_index import (
@ -14,12 +16,12 @@ from gpt_index import (
Document,
PromptHelper,
LLMPredictor,
OpenAIEmbedding,
OpenAIEmbedding, SimpleDirectoryReader,
)
from gpt_index.readers.web import DEFAULT_WEBSITE_EXTRACTOR
from langchain import OpenAI
from services.environment_service import EnvService
from services.environment_service import EnvService, app_root_path
from services.usage_service import UsageService
@ -40,6 +42,7 @@ class Search:
"answer the question, say that you were unable to answer the question if there is not sufficient context to formulate a decisive answer. The search query was: {query_str}\n"
)
self.openai_key = os.getenv("OPENAI_TOKEN")
self.EMBED_CUTOFF = 2000
def index_webpage(self, url) -> list[Document]:
documents = BeautifulSoupWebReader(
@ -47,6 +50,26 @@ class Search:
).load_data(urls=[url])
return documents
async def index_pdf(self, url) -> list[Document]:
# Download the PDF at the url and save it to a tempfile
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
if response.status == 200:
data = await response.read()
f = tempfile.NamedTemporaryFile(delete=False)
f.write(data)
f.close()
else:
return "An error occurred while downloading the PDF."
# Get the file path of this tempfile.NamedTemporaryFile
# Save this temp file to an actual file that we can put into something else to read it
documents = SimpleDirectoryReader(input_files=[f.name]).load_data()
print("Loaded the PDF document data")
# Delete the temporary file
return documents
async def get_links(self, query, search_scope=3):
"""Search the web for a query"""
async with aiohttp.ClientSession() as session:
@ -56,11 +79,12 @@ class Search:
if response.status == 200:
data = await response.json()
# Return a list of the top 5 links
return [item["link"] for item in data["items"][:search_scope]], [
return ([item["link"] for item in data["items"][:search_scope]], [
item["link"] for item in data["items"]
]
])
else:
return "An error occurred while searching."
print("The Google Search API returned an error: " + str(response.status))
return ["An error occurred while searching.", None]
async def search(self, query, user_api_key, search_scope, nodes):
DEFAULT_SEARCH_NODES = 1
@ -71,6 +95,8 @@ class Search:
# Get the links for the query
links, all_links = await self.get_links(query, search_scope=search_scope)
if all_links is None:
raise ValueError("The Google Search API returned an error.")
# For each link, crawl the page and get all the text that's not HTML garbage.
# Concatenate all the text for a given website into one string and save it into an array:
@ -78,6 +104,7 @@ class Search:
for link in links:
# First, attempt a connection with a timeout of 3 seconds to the link, if the timeout occurs, don't
# continue to the document loading.
pdf = False
try:
async with aiohttp.ClientSession() as session:
async with session.get(link, timeout=2) as response:
@ -94,8 +121,14 @@ class Search:
try:
print("Adding redirect")
links.append(response.url)
continue
except:
pass
continue
else:
# Detect if the link is a PDF, if it is, we load it differently
if response.headers["Content-Type"] == "application/pdf":
print("Found a PDF at the link " + link)
pdf = True
except:
traceback.print_exc()
@ -112,22 +145,23 @@ class Search:
continue
try:
document = await self.loop.run_in_executor(
None, partial(self.index_webpage, link)
)
if not pdf:
document = await self.loop.run_in_executor(
None, partial(self.index_webpage, link)
)
else:
document = await self.index_pdf(link)
[documents.append(doc) for doc in document]
except Exception as e:
traceback.print_exc()
prompthelper = PromptHelper(4096, 1024, 20)
embedding_model = OpenAIEmbedding()
index = GPTSimpleVectorIndex(documents, embed_model=embedding_model)
await self.usage_service.update_usage(
embedding_model.last_token_usage, embeddings=True
)
llm_predictor = LLMPredictor(llm=OpenAI(model_name="text-davinci-003"))
llm_predictor = LLMPredictor(llm=OpenAI(model_name="text-davinci-003", max_tokens=-1))
# Now we can search the index for a query:
embedding_model.last_token_usage = 0
response = index.query(
@ -135,7 +169,6 @@ class Search:
verbose=True,
embed_model=embedding_model,
llm_predictor=llm_predictor,
prompt_helper=prompthelper,
similarity_top_k=nodes or DEFAULT_SEARCH_NODES,
text_qa_template=self.qaprompt,
)

Write Preview
Loading…
Cancel
Save