From b22a219cb0e90172fa61b28e50d283548843eba9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kacper=20=C5=81ukawski?= Date: Thu, 14 Mar 2024 13:09:06 +0100 Subject: [PATCH] Fix the images in the using-qdrant-and-langchain.md blog post --- .../content/blog/using-qdrant-and-langchain.md | 4 ++-- .../flow-diagram.png | Bin .../qdrant-langchain.png | Bin 3 files changed, 2 insertions(+), 2 deletions(-) rename qdrant-landing/static/blog/{qdrant-and-langchain => using-qdrant-and-langchain}/flow-diagram.png (100%) rename qdrant-landing/static/blog/{qdrant-and-langchain => using-qdrant-and-langchain}/qdrant-langchain.png (100%) diff --git a/qdrant-landing/content/blog/using-qdrant-and-langchain.md b/qdrant-landing/content/blog/using-qdrant-and-langchain.md index e9af3a82f..18fa6dfcb 100644 --- a/qdrant-landing/content/blog/using-qdrant-and-langchain.md +++ b/qdrant-landing/content/blog/using-qdrant-and-langchain.md @@ -3,7 +3,7 @@ draft: false title: "Integrating Qdrant and LangChain for Advanced Vector Similarity Search" short_description: Discover how Qdrant and LangChain can be integrated to enhance AI applications. description: Discover how Qdrant and LangChain can be integrated to enhance AI applications with advanced vector similarity search technology. -preview_image: /blog/qdrant-and-langchain/qdrant-langchain.png +preview_image: /blog/using-qdrant-and-langchain/qdrant-langchain.png date: 2024-03-12T09:00:00Z author: David Myriel featured: false @@ -44,7 +44,7 @@ Retrieval Augmented Generation is not without its challenges and limitations. On **How it Works:** LangChain receives a query and retrieves the query vector from an embedding model. Then, it dispatches the vector to a vector database, retrieving relevant documents. Finally, both the query and the retrieved documents are sent to the large language model to generate an answer. -![qdrant-langchain-rag](/blog/qdrant-and-langchain/flow-diagram.png) +![qdrant-langchain-rag](/blog/using-qdrant-and-langchain/flow-diagram.png) When supported by LangChain, Qdrant can help you set up effective question-answer systems, detection systems and chatbots that leverage RAG to its full potential. When it comes to long-term memory storage, developers can use LangChain to easily add relevant documents, chat history memory & rich user data to LLM app prompts via Qdrant. diff --git a/qdrant-landing/static/blog/qdrant-and-langchain/flow-diagram.png b/qdrant-landing/static/blog/using-qdrant-and-langchain/flow-diagram.png similarity index 100% rename from qdrant-landing/static/blog/qdrant-and-langchain/flow-diagram.png rename to qdrant-landing/static/blog/using-qdrant-and-langchain/flow-diagram.png diff --git a/qdrant-landing/static/blog/qdrant-and-langchain/qdrant-langchain.png b/qdrant-landing/static/blog/using-qdrant-and-langchain/qdrant-langchain.png similarity index 100% rename from qdrant-landing/static/blog/qdrant-and-langchain/qdrant-langchain.png rename to qdrant-landing/static/blog/using-qdrant-and-langchain/qdrant-langchain.png