Ankit Singhal

Principal Architect

[Ankit Singhal @singhalankit]

@myntra @MyntraSupport wrong product delivered for order #132508860792340290403
.instee of da Milano handbag ,i got an wrong items .this is careless and unacceptable.i want an immediate full refund.fix this https://t.co/CepuQnHVMh @goyalanki18

[Ankit Singhal @singhalankit]

@Myntra @MyntraCare Wrong product delivered for Order #132037579519899291003. Instead of House of Fett Satin Maxi Dress, I got an untagged random item. This is careless and unacceptable. I want an immediate replacement or full refund. Fix this now.
Cc @goyalanki18

[Daniel (dB.) Doubrovkine (parody of myself) @dblockdotorg]

@sidbid Nice. Was this inspired by @pauloarruda ‘s claude-swarm? Are you going to make a UI next?

https://t.co/bZZhIbxJhG
https://t.co/fdmVIUuh9b

[Ankit Singhal @singhalankit]

@DTDCIndia Utterly disappointed! Raised complaint, agent closed ticket without discussion. Another promised to reopen 15 days ago—still closed. Assurances mean nothing. Pathetic, useless service! #DTDC #CustomerServiceFail

[Ankit Singhal @singhalankit]

@ckbirlarbh I think you should work on your services. Your services related to billing and admin staff are pathetic. Nobody cares about the patient condition. #noforckbirla

[Ankit Singhal @singhalankit]

@ANI I never had good experience with SpiceJet. They always reschedule their flights without reasons. So I do not book tickets with SpiceJet even they are cheaper compared to other flights nowadays.

[Aviator Anil Chopra @Chopsyturvey]

She means what she says

[Ankit Singhal @singhalankit]

@mr_prateekjain I think it helps you

[Aurimas Griciūnas @Aurimas_Gr]

How do you build a 𝗟𝗟𝗠 𝗯𝗮𝘀𝗲𝗱 𝗖𝗵𝗮𝘁𝗯𝗼𝘁 𝘁𝗼 𝗾𝘂𝗲𝗿𝘆 𝘆𝗼𝘂𝗿 𝗣𝗿𝗶𝘃𝗮𝘁𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲?

Let’s find out.

First step is to store the knowledge of your internal documents in a format that is suitable for querying. We do so by embedding it using an embedding model:

𝟭: Split text corpus of the entire knowledge base into chunks - a chunk will represent a single piece of context available to be queried. Data of interest can be from multiple sources, e.g. Documentation in Confluence supplemented by PDF reports.
𝟮: Use the Embedding Model to transform each of the chunks into a vector embedding.
𝟯: Store all vector embeddings in a Vector Database.
𝟰: Save text that represents each of the embeddings separately together with the pointer to the embedding (we will need this later).

Next we can start constructing the answer to a question/query of interest:

𝟱: Embed a question/query you want to ask using the same Embedding Model that was used to embed the knowledge base itself.
𝟲: Use the resulting Vector Embedding to run a query against the index in the Vector Database. Choose how many vectors you want to retrieve from the Vector Database - it will equal the amount of context you will be retrieving and eventually using for answering the query question.
𝟳: Vector DB performs an Approximate Nearest Neighbour (ANN) search for the provided vector embedding against the index and returns previously chosen amount of context vectors. The procedure returns vectors that are most similar in a given Embedding/Latent space. 
𝟴: Map the returned Vector Embeddings to the text chunks that represent them.
𝟵: Pass a question together with the retrieved context text chunks to the LLM via prompt. Instruct the LLM to only use the provided context to answer the given question. This does not mean that no Prompt Engineering will be needed - you will want to ensure that the answers returned by LLM fall into expected boundaries, e.g. if there is no data in the retrieved context that could be used make sure that no made up answer is provided.

To make it a real Chatbot - face the entire application with a Web UI that exposes a text input box to act as a chat interface. After running the provided question through steps 1. to 9. - return and display the generated answer. This is how most of the chatbots that are based on a single or multiple internal knowledge base sources are actually built nowadays.

We will build such a chatbot as an upcoming hands on SwirlAI Newsletter series so stay tuned in!

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𝗗𝗼𝗻’𝘁 𝗳𝗼𝗿𝗴𝗲𝘁 𝘁𝗼 𝗹𝗶𝗸𝗲 💙, 𝘀𝗵𝗮𝗿𝗲 𝗮𝗻𝗱 𝗰𝗼𝗺𝗺𝗲𝗻𝘁!

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[Johns. @CricCrazyJohns]

Indian team schedule for World Cup 2023:

IND vs AUS, Oct 8, Chennai
IND vs AFG, Oct 11, Delhi
IND vs PAK, Oct 15, Ahmedabad
IND vs BAN, Oct 19, Pune
IND vs NZ, Oct 22, Dharamsala
IND vs ENG, Oct 29, Lucknow
IND vs Qualifier, Nov 2, Mumbai
IND vs SA, Nov 5, Kolkata
IND vs Qualifier, Nov 11, Bengaluru

[Awanish Sharan 🇮🇳 @AwanishSharan]

“अरे, यूपीएससी का अटेम्प्ट खतम हुआ है; ज़िंदगी का नहीं.”
एक बार फिर से संदीप भैया.

[Mariusz Michalowski @MariuszMichaow3]

Software Architecture Books by @mhadidg

https://t.co/bEGJuArQ1A

#devops #devsecops #kubernetes #cicd #k8s #linux #docker #sysadmin #automation #Azure #infrastructureascode #cloudcomputing #serverless #terraform #ansible #yaml #sre

[Steve (Builder.io) @Steve8708]

amazing resource to visually learn the node.js event loop: https://t.co/1ppLLtznES

highlights in 🧵:

[Ankit Singhal @singhalankit]

Now you guys are delaying flight again and no support from your staff. Pathetic service and pathetic flight @flyspicejet #boycottspicejet

[Ankit Singhal @singhalankit]

@flyspicejet your flight SG8264/SG9265 are continuously delaying due to your operational issue. I have a booking till 9AM in Port Blair. And you guys are not providing any support. Your services and flights both are pathetic and disgusting

[Khawaja Shams @ksshams]

A customer was considering leaving #dynamodb due to costs.

@pj_naylor and I helped them save half a million dollars per year with one simple, magical, optimization.

Want to save DynamoDB costs like a pro? Let’s start with some background…

[Architecture Notes @arcnotes]

Redis Explained https://t.co/yd92b1upPX

[sudhanshu @whoshud]

scenes inside chetan sharma house

[Hacker News Bot @newsycombinator]

Path to a free, self-taught education in Computer Science https://t.co/g4H59uja4m

[Gavin Ray @GavinRayDev]

Mind-blowing: just found this tool to visualize Postgres B-Tree indexes, and a blogpost explaining them from @louisemeta

How have I never heard of this tool before?
Where has it been all my life? 😍

https://t.co/v4Rt3oGd25

https://t.co/FXD68Oq64q