Harrison Chase’s Post

😍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

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 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

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

This is all about real time implimentation of previous post LLM'S are stateless but inclusion of previous conversation give the llm ability to remember and react according to previous conversation. The feature of cache memory through langchain makes llm remember previous conversation and react accordingly. Go through previous post to know the working of the LLM this is a basic version of my idea need to work on it alot.

pub.towardsai.net: The article discusses the practical application of LangChain for LLM development. It provides insights into the hands-on approach for developing LLM applications.

😍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

New feature in Portchain Connect: 𝐁𝐞𝐫𝐭𝐡 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐅𝐢𝐥𝐭𝐞𝐫 This tool enables users to view aligned or unaligned port calls with ease. Here's how it works: 🔍 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐅𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠: The filter considers both the ETA Berth and ETD Berth values. 📅 𝐃𝐢𝐬𝐜𝐫𝐞𝐩𝐚𝐧𝐜𝐲 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: It displays a port call as unaligned when there is a discrepancy between carrier and terminal timestamps. With this new filter, you can quickly identify and address misalignments, ensuring smoother operations and better planning.

🍕🍕 Learn about Semantic Cache and how you can use it to speed up your LLM applications in this short video by Denis Rosa👇 + read more from our friends at LangChain here https://bit.ly/4exdn4Z

🔍 Fresh ZND Analysis - Jan 10, 2025 Latest technical breakdown of the ZND/EUR pair on zondacrypto Watch the detailed analysis: 👉 https://lnkd.in/dfvrGcSB Stay informed on your $ZND trades! 📈 #Zondacrypto #Trading #CryptoTrading #TechnicalAnalysis