mapmeld

# this should run on a GPU CoLab notebook
# pip install langchain xformers transformers datasets bitsandbytes accelerate --quiet
# get access to the meta-llama models, accept license, and get a read token

hf_auth = '######'

from langchain.chains import ConversationChain
from langchain.llms import HuggingFacePipeline
from langchain.memory import ConversationSummaryBufferMemory
from langchain.prompts.prompt import PromptTemplate

adrienbrault

# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

# Build it
make clean
LLAMA_METAL=1 make

# Download model
export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin

kyo-takano

import gzip

def gzip_search(query: str, candidate_chunks: list[str], top_k: int=1):
    """
    文字列ベースで類似したテキストチャンクを推定するアルゴリズム.
    `query`, `chunk`, および`query + " " + chunk`をそれぞれgzipで圧縮し、編集距離のようなものをベースに評価する.

    Parameters:
        query (str): 検索クエリとして使用する文字列.
        top_k (int, optional): 返される類似チャンクの上位k個を指定する (default: 1).

JimmyWhitaker

horosin

Code for an article about LangChain structured outputs.

Article: https://horosin.com/extracting-pdf-and-generating-json-data-with-gpts-langchain-and-nodejs

In this blog post, I will share how to use LangChain, a flexible framework for building AI-driven applications, to extract and generate structured JSON data with GPTs and Node.js. I'll provide code snippets and concise instructions to help you set up and run the project.

csiebler

import os
import openai
from dotenv import load_dotenv
from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, LLMPredictor, PromptHelper
from langchain.llms import AzureOpenAI
from langchain.embeddings import OpenAIEmbeddings
from llama_index import LangchainEmbedding

# Load env variables (create .env with OPENAI_API_KEY and OPENAI_API_BASE)
load_dotenv()

gh640

"""Simple https server for development."""

import ssl
from http.server import HTTPServer, SimpleHTTPRequestHandler

CERTFILE = './localhost.pem'


def main():
    https_server(certfile=CERTFILE)

sindresorhus

Pure ESM package

The package that linked you here is now pure ESM. It cannot be require()'d from CommonJS.

This means you have the following choices:

1. Use ESM yourself. (preferred)
   Use import foo from 'foo' instead of const foo = require('foo') to import the package. You also need to put "type": "module" in your package.json and more. Follow the below guide.
2. If the package is used in an async context, you could use await import(…) from CommonJS instead of require(…).
3. Stay on the existing version of the package until you can move to ESM.

bryanbraun

Example Git Branching Diagram

You can use this diagram as a template to create your own git branching diagrams. Here's how:

1. Create a new diagram with diagrams.net (formerly draw.io)
2. Go to File > Open From > URL
3. Insert this url (it points to the xml data below): https://gist.githubusercontent.com/bryanbraun/8c93e154a93a08794291df1fcdce6918/raw/bf563eb36c3623bb9e7e1faae349c5da802f9fed/template-data.xml
4. Customize as needed for your team.

---

tusharf5

/**
 * Retries a promise n no. of times before rejecting.
 */
async function retryPromise(promise, nthTry) {
  try {
    const res = await promise;
    return res;
  } catch (e) {
    if (nthTry === 1) {
      return Promise.reject(e);