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Abstract

with LLMs, we’ve got you covered with resources to help you get started and ideas for apps we’d love to see. Plus, we’ll be sponsoring a prize for the best LLM application. Let’s dive in!</p><h2 id="448d">Getting Started with LangChain</h2><div id="ee21"><pre><span class="hljs-comment"># Install LangChain</span> !pip <span class="hljs-keyword">install</span> langchain</pre></div><div id="014e"><pre><span class="hljs-meta"># Quickstart guide</span> <span class="hljs-title">from</span> langchain <span class="hljs-keyword">import</span> LangChain

<span class="hljs-title">lc</span> = <span class="hljs-type">LangChain</span>() <span class="hljs-title">lc</span>.train_model(<span class="hljs-class"><span class="hljs-keyword">data</span>)</span> <span class="hljs-title">lc</span>.generate_output()</pre></div><div id="10ab"><pre># Use-<span class="hljs-keyword">case</span> specific walkthroughs <span class="hljs-keyword">from</span> langchain.use_cases <span class="hljs-keyword">import</span> question_answering, chatbots, web_scraping

response = question_answering(input_question) response = chatbots(input_message) response = web_scraping(url)</pre></div><h2 id="6cd9">LangChain Prompt Hub</h2><div id="95e4"><pre><span class="hljs-comment"># Submit, discover, and remix prompts</span> <span clas

Options

s="hljs-keyword">from</span> langchain.hub <span class="hljs-keyword">import</span> prompt_hub

prompt_hub.submit_prompt(prompt) prompt_hub.discover_prompt() prompt_hub.remix_prompt(original_prompt)</pre></div><h2 id="1628">Projects we’d love to see</h2><ol><li>Applications that use open source models</li><li>Innovative retrieval tactics</li><li>Practical agents</li><li>Adventurous agents</li><li>Apps that connect unique data sources/formats</li></ol><p id="0e42">Get ready to showcase your project by tagging us on Twitter @langchainai or sending us a note at [email protected]. We’re eager to witness your creations!</p><div id="6cf4" class="link-block"> <a href="https://readmedium.com/langchain-langchain-chat-6f4fce14a106"> <div> <div> <h2>LANGCHAIN — Langchain Chat?</h2> <div><h3>Digital design is like painting, except the paint never dries. — Neville Brody</h3></div> <div><p>medium.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*nu7ZXSdSXeo6aCLEJYoZpg.jpeg)"></div> </div> </div> </a> </div><p id="aab7">That’s it for now. Happy hacking!</p></article></body>

LANGCHAIN — Hackathon Kickoff for Ted AI

The computer was born to solve problems that did not exist before. — Bill Gates

Are you ready for the TED AI Hackathon? We’re thrilled to partner with TED for this event and can’t wait to see the innovative projects that will emerge. If you’re new to building with LLMs, we’ve got you covered with resources to help you get started and ideas for apps we’d love to see. Plus, we’ll be sponsoring a prize for the best LLM application. Let’s dive in!

Getting Started with LangChain

# Install LangChain
!pip install langchain
# Quickstart guide
from langchain import LangChain

lc = LangChain()
lc.train_model(data)
lc.generate_output()
# Use-case specific walkthroughs
from langchain.use_cases import question_answering, chatbots, web_scraping

response = question_answering(input_question)
response = chatbots(input_message)
response = web_scraping(url)

LangChain Prompt Hub

# Submit, discover, and remix prompts
from langchain.hub import prompt_hub

prompt_hub.submit_prompt(prompt)
prompt_hub.discover_prompt()
prompt_hub.remix_prompt(original_prompt)

Projects we’d love to see

  1. Applications that use open source models
  2. Innovative retrieval tactics
  3. Practical agents
  4. Adventurous agents
  5. Apps that connect unique data sources/formats

Get ready to showcase your project by tagging us on Twitter @langchainai or sending us a note at [email protected]. We’re eager to witness your creations!

That’s it for now. Happy hacking!

Kickoff
Langchain
ChatGPT
Hackathon
Ted
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