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@seldo
seldo committed Dec 23, 2023
1 parent afcc31e commit 858cfe0
Showing 1 changed file with 89 additions and 11 deletions.
100 changes: 89 additions & 11 deletions 5_qdrant.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,16 +3,23 @@
dotenv.load_dotenv()

# slack app deps
import os
import os, datetime
from slack_bolt import App
from slack_sdk import WebClient
from flask import Flask, request, jsonify
from slack_bolt.adapter.flask import SlackRequestHandler

# RAG app deps
import qdrant_client
from llama_index import VectorStoreIndex, Document, StorageContext
import qdrant_client, uuid
from llama_index import VectorStoreIndex, Document, StorageContext, ServiceContext, set_global_handler
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.schema import TextNode, NodeRelationship, RelatedNodeInfo
from llama_index.prompts import PromptTemplate
from llama_index.postprocessor import FixedRecencyPostprocessor

set_global_handler("simple")

PREVIOUS_NODE = None

# initialize qdrant client
client = qdrant_client.QdrantClient(
Expand Down Expand Up @@ -49,6 +56,41 @@ def get_user_name(user_id):
user_display_name = user_info['user']['profile']['display_name']
return user_name, user_display_name

# given a query and a message, answer the question and return the response
def answer_question(query, message, replies=None):
who_is_asking = get_user_name(message.get('user'))[0]
replies_stanza = ""
if (replies is not None):
replies_stanza = "In addition to the context above, the question you're about to answer has been discussed in the following chain of replies:\n"
for reply in replies['messages']:
replies_stanza += get_user_name(reply.get('user'))[0] + ": " + reply.get('text') + "\n"
template = (
"Your context is a series of chat messages. Each one is tagged with 'who:' \n"
"indicating who was speaking and 'when:' indicating when they said it, \n"
"followed by a line break and then what they said. There can be up to 20 chat messages.\n"
"The messages are sorted by recency, so the most recent one is first in the list.\n"
"The most recent messages should take precedence over older ones.\n"
"---------------------\n"
"{context_str}"
"\n---------------------\n"
"The person who is asking the question is called '" + who_is_asking + "'.\n"
+ replies_stanza + "\n"
"You are a helpful AI assistant who has been listening to everything everyone has been saying. \n"
"Given the most relevant chat messages above, please answer this question: {query_str}\n"
)
qa_template = PromptTemplate(template)
postprocessor = FixedRecencyPostprocessor(
top_k=20,
date_key="when", # the key in the metadata to find the date
service_context=ServiceContext.from_defaults()
)
query_engine = index.as_query_engine(similarity_top_k=20, node_postprocessors=[postprocessor])
query_engine.update_prompts(
{"response_synthesizer:text_qa_template": qa_template}
)
return query_engine.query(query)


# this is the challenge route required by Slack
# if it's not the challenge it's something for Bolt to handle
# (why doesn't Bolt handle the challenge? It's their framework, they should know the challenge is coming...)
Expand All @@ -66,6 +108,7 @@ def slack_challenge():
# right now it's only in one channel so it's every message in that channel
@app.message()
def reply(message, say):
global PREVIOUS_NODE
# if message contains a "blocks" key
# then look for a "block" with the type "rich text"
# if you find it
Expand All @@ -76,7 +119,7 @@ def reply(message, say):
# then look inside each "element" for one with type "user"
# if you find it
# and if that user matches the bot_user_id
# then it's a message from the bot
# then it's a message for the bot
if message.get('blocks'):
for block in message.get('blocks'):
if block.get('type') == 'rich_text':
Expand All @@ -85,17 +128,52 @@ def reply(message, say):
if element.get('type') == 'user' and element.get('user_id') == bot_user_id:
for element in rich_text_section.get('elements'):
if element.get('type') == 'text':
# the user is asking the bot a question
query = element.get('text')
print("Using query", query)
query_engine = index.as_query_engine()
response = query_engine.query(query)
print(response)
response = answer_question(query,message)
say(str(response))
return
# if it's not a question, we store it in the index along with all relevant metadata
# if it's not a question, it might be a threaded reply
# if it's a reply to the bot, we treat it as if it were a question
if message.get('thread_ts'):
if message.get('parent_user_id') == bot_user_id:
query = message.get('text')
replies = app.client.conversations_replies(
channel=message.get('channel'),
ts=message.get('thread_ts')
)
response = answer_question(query,message,replies)
#say(str(response))
app.client.chat_postMessage(
channel=message.get('channel'),
text=str(response),
thread_ts=message.get('thread_ts')
)
return
# if it's not any kind of question, we store it in the index along with all relevant metadata
user_name, user_display_name = get_user_name(message.get('user'))
text = user_display_name + " said " + message.get('text')
index.insert(Document(text=text))

# format timestamp as YYYY-MM-DD HH:MM:SS
dt_object = datetime.datetime.fromtimestamp(float(message.get('ts')))
formatted_time = dt_object.strftime('%Y-%m-%d %H:%M:%S')

# format full message
text = message.get('text')

# create a node and apply metadata
node = TextNode(
text=text,
id_=str(uuid.uuid4()),
metadata={
"who": user_name,
"when": formatted_time
}
)
if PREVIOUS_NODE is not None:
node.relationships[NodeRelationship.PREVIOUS] = RelatedNodeInfo(node_id=PREVIOUS_NODE.node_id)
PREVIOUS_NODE = node

index.insert_nodes([node])
print("Stored message:", text)

if __name__ == "__main__":
Expand Down

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