Guardrails AI’s Post

An interesting tutorial regarding how to implement a GraphRAG system with LangChain. https://lnkd.in/dCMAu7cA

❓What would LangChain x DSPy look like? My favorite part of our recent webinar with Omar K. was towards the end when we talked about how to combine LangChain and DSPy TLDR: possible that the easiest way forward would be to have the optimizers in DSPy expose a standard API that LangChain/Graph can interact with. Would make it so that any LCEL/LangGraph program could be optimized with DSPy!

Optimization of Prompts: A big step from Prompt Engineering to Prompt Optimisation. #langchain #dspy #prompts

Really interesting webinar I attended this week from LangChain 🦜that introduced me to #DSPy and has me rethinking how to optimize some of the chains that I am experimenting with for different purposes using #Langchain #Langsmith & DSPy. ❇️ What is DSPy? ➡️ Imagine you want a computer to be able to understand and carry out tasks based on natural language instructions, like "summarize this news article" or "translate this document to Spanish". Traditionally, this required complex programming and a deep understanding of natural language processing techniques, but DSPy provides a new approach called Natural Language Signatures that makes this much easier. With Natural Language Signatures, you can define a task for the AI using plain English descriptions of what the task does, what inputs it needs, and what outputs it produces. For example: ➕Task: Question Answerer 💬Description: Answer a question based on the given context 🔢 Inputs: - question (The question to answer) - context (Background information to answer the question) 🔣 Outputs: - answer (The answer to the question) Under the hood, the AI system uses this signature to figure out the optimal way to accomplish the task, without the developer needing to specify all the details. This is especially useful when you are switching between models for different use-cases, because the way you prompt one language model to respond to a query can vary significantly between models. Here’s how DSPy works: 1️⃣ Separates program flow from LM parameters. 2️⃣ Introduces LM-driven optimizers to tune prompts and weights based on desired metrics. So what peaked my interest? This: 💡 DSPy could act as the core framework, LangChain provides pluggable components, and LangSmith enables development, testing and monitoring of the end-to-end application💡 🔎How? 1. Use DSPy as the core orchestrator and compiler for LLM programs. 2. Integrate LangChain components like prompts, chains, agents into the DSPy framework. 3. Leverage LangSmith for observability, debugging, testing of the application built with DSPy and LangChain. 4. Use LangSmith to manage prompts and datasets used in the application. LangSmith allows creating datasets for fine-tuning, few-shot prompting, evaluation from user production data. Prompts can be hosted in LangSmith and reused in LangChain chains/agents. 5. Deploy the final DSPy orchestrated application and monitor it in production using LangSmith. LangSmith can capture production analytics for insights and continuous improvement of the live application and actual user generated queries and outputs can be fed back to DSPy for contextual learning for the abstractions! Thoughts? Anyone using DSPy currently? #legaltech #ownyourai #codeandcounsel

😍Optimization of LLM Systems with DSPy and LangChain Recording from webinar: Introduction to DSPy Similarities to LangChain and most excitingly... ? How LangChain <> DSPy can collaborate https://lnkd.in/gse_sPjr

😍Optimization of LLM Systems with DSPy and LangChain Recording from our webinar with @hwchase17 and @lateinteraction is up! Covers: 👨🏫Introduction to DSPy 🦜Similarities to LangChain and most excitingly... ❓How LangChain <> DSPy can collaborate! https://lnkd.in/g_a32aHk

https://lnkd.in/eqP_rfCs LangChain vs LangGraph: A Tale of Two Frameworks

If you are looking for a great intro into LangChain, I can recommend you this article by Thirunavukkarasu. Agents are going to be the next big topic in 2025, so get ready to deep dive into libraries like LangChain, LangGraph & Co. https://lnkd.in/ec85KihC

Today, I delved into the world of Vector Embeddings and Embedding models, mastering the utilization of a Vector store for storing and querying data in the DB. The Langchain loaders proved to be incredibly convenient, simplifying the process of loading YouTube video transcripts directly from the URL. Exciting times ahead! #VectorEmbeddings #LangChain #DataQuerying LangChain Meghdip Karmakar Souradip Pal

😍Optimization of LLM Systems with DSPy and LangChain Recording from webinar: 👨🏫Introduction to DSPy 🦜Similarities to LangChain and most excitingly... ❓How LangChain <> DSPy can collaborate! https://lnkd.in/gse_sPjr