Neon x LangChain: HNSW in Postgres with pg_embedding Editor’s Note: This blog post was written in collaboration with the Neon team (Raouf Chebri in particular). The vectorstore space is on fire, and
🎉 Prem Challenge🎉 We're excited to announce a challenge hosted by Prem in collaboration with LangChain. At LangChain we try to make it easy as possible
LangChain <> MongoDB Atlas Today we’re announcing LangChain’s integration with MongoDB Atlas, adding support for one of the most popular developer data platforms in the world. This
Data-Driven Characters Data-driven-characters is a repo for creating, debugging, and interacting your own chatbots conditioned on your own story corpora.
LangChain + Vectara: better together Introduction One of the main use cases of LangChain is connecting LLMs to user data, allowing users to build personalized LLM applications. A key part
GPTeam: A multi-agent simulation Editor's Note: This is another edition in our series of guest posts highlighting novel applications of LangChain. After the Generative Agents paper was
Going Beyond Chatbots: How to Make GPT-4 Output Structured Data Using LangChain By Jacob Lee Over the past few months, I had the opportunity to do some cool exploratory work for a client that integrated LLMs like
Auto-Evaluation of Anthropic 100k Context Window Lance Martin Retrieval Architectures LLM question answering (Q+A) typically involves retrieval of documents relevant to the question followed by synthesis of the retrieved chunks
Rebuff: Detecting Prompt Injection Attacks [Editor's Note]: we're excited to highlight a guest blog by Willem Pienaar. As more and more LangChains make their way into
Plan-and-Execute Agents TL;DR: We’re introducing a new type of agent executor, which we’re calling “Plan-and-Execute”. This is to contrast against the previous types of
Feature Stores and LLMs Editor's note: big thank you to Willem Pienaar (Feast), Mike Del Balso (Tecton), and Simba Khadder (FeatureForm) for their comments and help on
