@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
@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
@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
@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
@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.
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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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
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@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
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…
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