In this series, we explore the mechanism behind the text summarization chain introduced in LangChain’s Summarization documentation.

In this post, we focus on the Refine summarization method (chain_type="refine").

• This article uses LangChain version 0.1.17.

  bash

  Read more...

   LLM, Python, LangChain

In this series, we’re diving into the mechanics of LangChain’s summarization chains as outlined in the LangChain documentation on Summarization.

This post focuses on the Map Reduce method (chain_type="map-reduce") for summarization.

• This article uses LangChain version 0.1.17.

  bash

  Read more...

   LLM, Python, LangChain

In this post, we’ll explore how the summarization chain in LangChain works, as outlined in the LangChain documentation on Summarization.

• This article uses LangChain version 0.1.17.

  bash

  Read more...

   LLM, Python, LangChain

This series dives into how to use LangChain, based on the LangChain Quickstart guide.
In this post, we’ll explore how to deploy LangChain agents as a REST API using LangServe.

Recap of the Previous Post

In our last post, we created an agent that combines tools for answering LangChain-related queries with internet search capabilities.
Here’s a recap of the code we used:

In this series, we explore the ‘Quickstart’ section of the LangChain documentation.

Previously, we developed chains that operated on predefined steps. In this article, we explore how the LLM chooses the right tools for processing based on user input, introducing the concept of an agent.

A Recap

In a previous post, we built a retriever from LangChain’s documentation.
Let’s revisit that setup:

In this post, we continue our journey through LangChain’s Quickstart guide, exploring how to enhance your chains by integrating conversation history.

Recap of Our Progress

Here’s what we’ve set up so far:

• retriever: Retrives a list of relevant documents based on the input text.
• document_chain: Generates LLM responses using the user’s questions and the list of documents.
• create_retrieval_chain: Combines retriever and document_chain to answer queries by referencing documents.