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langchain
⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain] (by hwchase17)
Now, in this follow-up article, I'll guide you through the process of building an enhanced version of the chatbot using the open-source library, LangChain.
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And check out the GitHub repo with the code here.
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The chat interface was developed using Streamlit, a versatile tool for building interactive Python web applications. This code creates a simple interface with a text input for user queries and a "Submit" button to submit the query. When the "Submit" button is clicked, the query, along with the chat history, is sent to the LLM chain, which returns a response along with the referenced documents.
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Next, I generated text embeddings for each of the pages using the OpenAI's embeddings API. Text embeddings are vectors (lists) of floating-point numbers used to measure the relatedness of text strings. They are commonly used for various tasks such as search, clustering, recommendations, anomaly detection, diversity measurement, and classification. Once the embeddings were generated, I used the vector search library Faiss to create an index, enabling rapid text searching for each user query.
