LangChain: Question Answering with Qdrant and GPT-3.5 Turbo
This article describes a Python script that leverages LangChain, Qdrant, and the GPT-3.5 Turbo model to perform question-answering on text data. The script combines various libraries to create an end-to-end pipeline for text retrieval and question answering. Overview The provided Python script showcases how to integrate multiple libraries to build a powerful question-answering system: Prerequisites […]
Text Embedding and Upsert with Qdrant in Python
This article describes a Python script that demonstrates how to efficiently process text data, create embeddings, and upsert the embeddings into a Qdrant database using Python. Overview In today’s era of big data and machine learning, efficient text processing and vectorization are crucial for various natural language processing (NLP) tasks. The provided Python script showcases […]
findthatbit.com – made with Qdrant
Watched a video about Bitcoin but forgotten the name of it or what part of it you liked? With findthatbit.com you can search based upon meaning. If you remember a video was about bitcoin and there was talk of “seaside” property. Just search for “seaside” and you’ll get the link to where “miami beach” was […]
Using “Upsert” with Qdrant Vector Database
Upsert additional points with the next available index – add more vectors without recreating the collections. You’ll need to specify the next index, so here’s how to do it… Check Qdrant out here : https://qdrant.tech/documentation/quick-start/ Do the imports and and create the “client” Use client.get_collection() to retrieve the current status of the collection This will […]