LangChain

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• AI for Notes

  Now that my Zettelkasten has over a thousand notes, I’d like to try to quite literally create the experience of a conversation with my second brain. The AI interface should be conversational rather than search queries. It should draw from the knowledge in my notes and respond in natural language. Finally, it should be useful in helping me make connections between ideas I hadn’t thought of before.

• Turn off Chroma Telemetry in Langchain

  You can turn off sending telemetry data to ChromaDB (now a venture backed startup) when using langchain.

• Chromadb

  ChromaDB is a vector database used for similarity searches on embeddings. It’s extremely easy to use if you are using Python and works well with LangChain.

• Langchain Self Query With Dates

  Self querying by date using LangChain doesn’t work well. The default schema used for parsing natural language into the internal representation of langchain for querying a vector store does not work with dates because it uses the wrong type (it tries to use a dict but you can only filter using integers or strings).

• Using Chatgpt With Emacs

  There are a few packages and libraries that are being built to use ChatGPT along with Emacs.

• Emacs and Chromadb

  The nice part about ChromaDB is that you can read the tables in SQLite using the new Emacs 29 sqlite-mode. That means there is no other configuration for accessing the database, you can open the sqlite file directly.

• How Langchain Works

  As it turns out, combining large language models together can create powerful AI agents that can respond to and take action on complicated prompts. This is achieved by composing models and tools with an overall language model to mediate the interaction.