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@rudolfolah
rudolfolah committed Jul 22, 2023
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<div class="navbar fixed-top navbar-expand-lg navbar-dark bg-primary">
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<a href="/integrations/llamacpp/" class="dropdown-item">llama.cpp</a>
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<div class="navbar fixed-top navbar-expand-lg navbar-dark bg-primary">
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<a class="navbar-brand" href=".">chaincrafter</a>
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<div id="toc-collapse" class="navbar-collapse collapse card bg-secondary">
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<li class="nav-item" data-level="1"><a href="#chaincrafter" class="nav-link">chaincrafter</a>
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</div></div>
<div class="col-md-9" role="main">

<h1 id="chaincrafter">chaincrafter</h1>
<!-- BEGIN INCLUDE ../../../README.md '&lt;!--docs-index-start--&gt;' '&lt;!--docs-index-end--&gt;' -->

<p>Seamless integration and composability for large language model apps</p>
<h2 id="python">Python</h2>
<h3 id="installation">Installation</h3>
<p>Seamless integration and composability for large language model apps.</p>
<h2 id="features">Features</h2>
<ul>
<li>Composable prompts and chains<ul>
<li>Use multiple models to run one chain and then use that as input for a different chain and model</li>
</ul>
</li>
<li>Customizable prompt and response formatting<ul>
<li>Add modifiers to prompts to change the style, length, and format of the response</li>
<li>Extract data from the response to use in the next prompt</li>
<li>Add custom functions to process the response</li>
<li>Add custom functions to process the input variables</li>
</ul>
</li>
<li>Integration with OpenAI API (llama.cpp in progress)</li>
</ul>
<h1 id="python">Python</h1>
<h2 id="installation">Installation</h2>
<pre><code class="language-bash">pip install chaincrafter
</code></pre>
<h3 id="usage">Usage</h3>
<h2 id="javascript-typescript">JavaScript / TypeScript</h2>
<h3 id="installation_1">Installation</h3>
<h2 id="usage">Usage</h2>
<ol>
<li>Define your prompts and the variables that they expect</li>
<li>The input variables can be of any type, and can be processed by a function</li>
<li>The prompt message is treated as an <em>f-string</em></li>
<li>Define your chain of prompts</li>
<li>The chain is a list of tuples, where each tuple contains a prompt and the output key to store the response in</li>
<li>The output key is used to access the response in the next prompt</li>
<li>Set up the models that you want to use</li>
<li>Run the chain using the models</li>
</ol>
<pre><code class="language-python">from chaincrafter import Chain, Prompt
from chaincrafter.models import OpenAiChat

chat_model = OpenAiChat(temperature=0.65, model_name=&quot;gpt-3.5-turbo&quot;)
system_prompt = Prompt(&quot;You are a helpful assistant who responds to questions about the world&quot;)
hello_prompt = Prompt(&quot;Hello, what is the capital of France? Answer only with the city name.&quot;)
followup_prompt = Prompt(&quot;{city} sounds like a nice place to visit. What is the population of {city}?&quot;)
chain = Chain(
system_prompt,
(hello_prompt, &quot;city&quot;),
(followup_prompt, &quot;followup_response&quot;),
)
messages = chain.run(chat_model)
for message in messages:
print(f&quot;{message['role']}: {message['content']}&quot;)
</code></pre>
<h3 id="running-the-examples">Running the examples</h3>
<pre><code class="language-bash">source venv/bin/activate
export OPENAI_API_KEY=&quot;...&quot;
python -m examples.interesting_facts
</code></pre>
<h1 id="javascript-typescript">JavaScript / TypeScript</h1>
<p><strong>Work in progress</strong></p>
<h2 id="installation_1">Installation</h2>
<pre><code class="language-bash">npm install chaincrafter
</code></pre>
<h3 id="usage_1">Usage</h3>
<h2 id="usage_1">Usage</h2>
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<h1 id="llamacpp">llama.cpp</h1>
<p><strong>Status</strong></p>
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<li>JavaScript: In development</li>
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