LangChain can simplify and enhance working with a Large Language Model. I used OpenAI ChatGPT for the video as it’s currently the most widely used and easy to use when you only have a regular laptop computer. This video is split into the same chapters as the official LangChain documentation. It demonstrates the Python code […]
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 […]
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 […]
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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 […]