Creating Powerful Applications with Weaviate and LangChain
Learn how Weaviate and LangChain enable powerful applications using large language models, including fact-checking and search result summarization.
00:00:00 Discover how Weaviate and LangChain can be used to create powerful applications using large language models in this presentation by Erika Cardenas.
📚 Large language models are transformative and can enable developers to build powerful applications.
🔗 Combining large language models with other sources of computation or knowledge can enhance their power.
💡 Using LangChain enables the combination of multiple inferences, solving the problem of limited token length.
00:01:38 A demonstration of using Weaviate and LangChain for LLM apps by Erika Cardenas. Sequential chains execute steps in order, and an example conversation between a bot and Erika is shown.
🔗 Sequential chains in Weaviate execute links in a sequential order, using one output as the input for the next step.
🤖 Example conversation between a bot and the speaker, demonstrating the use of sequential chains.
🔍 The bot acts as a fact checker, improving its answer based on a given statement and making a list of assumptions and assertions.
00:03:17 This video discusses the use of Weaviate and LangChain for LLM apps. It explores fact-checking and various techniques for handling input token length limitations.
🐘 Elephants are mammals and do not lay eggs.
🥚 Eggs come in different sizes.
⚙️ Weaviate + LangChain uses techniques like stuffing, map reduce, refine, and map rebrink to overcome limitations of input token length.
00:04:58 Using Weaviate and LangChain, Erika Cardenas presents a method for summarizing and refining search results to generate a concise description of a golden doodle.
📚 Weaviate and LangChain are used to process and analyze chunks of data.
🧩 Multiple prompts are combined to create a comprehensive answer.
📝 The large language model refines its output using local memory and previous summaries.
🔍 Mapri rank assigns scores to the answers based on certainty.
00:06:38 Weaviate + LangChain enable language models to execute code and generate responses accurately. Chat Vector DB enhances prompt-based conversation generation by utilizing context and Standalone questions.
🔧 Equipping language models with tools like Weaviate and LangChain to execute code and generate code.
💬 Using Chat Vector DB to implement a Vector database and answering questions by rephrasing them as standalone questions.
🧠 Addressing the hallucination problem by stating 'don't know' if the answer is not known.
00:08:18 Connecting Weaviate with LangChain for LLM apps. Demo of using chat history and query to vector database. Simple and cool, only 20 lines of code.
🔍 The video demonstrates how to use Weaviate and LangChain together to search for specific content.
🔌 The speaker explains how to connect to the Weaviate instance and specify the class and property for LangChain to search.
💻 The demonstration showcases the simplicity of the implementation with just a few lines of code.
00:09:58 Weaviate + LangChain for LLM apps demo by Erika Cardenas, showcasing how the large language model connected to a vector database can provide relevant context and explain hybrid search in Weaviate 117.
💡 Weaviate and LangChain were released in version 1.17.
🔍 The combination of a large language model and a vector database allows Weaviate to provide contextually relevant information.
📚 Hybrid search, introduced in version 1.17, is explained and demonstrated.
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