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Abstract

span> ]

<span class="hljs-comment"># Generate responses</span> responses = model.generate_responses(prompts) <span class="hljs-comment"># Example of deploying a ChatGPT model to Hugging Face spaces</span>

<span class="hljs-keyword">from</span> huggingface <span class="hljs-keyword">import</span> SpacesAPI

<span class="hljs-comment"># Initialize the Hugging Face Spaces API</span> api = SpacesAPI(api_key=<span class="hljs-string">"your_api_key"</span>)

<span class="hljs-keyword">import</span> requests

<span class="hljs-comment"># Define the submission data</span> submission_data = { <span class="hljs-string">"repo_url"</span>: <span class="hljs-string">"https://github.com/your_username/your_repo"</span>, <span class="hljs-string">"description"</span>: <span class="hljs-string">"Brief description of your ChatGPT over data sources"</span>, <span class="hljs-string">"contact_email"</span>: <span class="hljs-string">"[email protected]"</span> }

<span class="hljs-comment"># Submit the entry</span> response = requests.post(<span class="hljs-string">"https://forms.gle/9ckmWxQ9GAaMpcRz9"</span>, data=submission_data) <span class="hljs-built_in">print</span>(response.text) <span class="hljs-comment"># Example of cloning an example GitHub repository</span>

!git clone https://github.com/hwchase17/chat-your-data.git <span class="hljs-comment"># Example of customizing data source and prompts for ChatGPT</span>

<span class="hljs-comment"># Customize the data source and prompts as per the provided tutorial: https://blog.langchain.dev/tutorial-chatgpt-over-your-data/</span> <span class="hljs-comment"># Example of creating a frontend for ChatGPT deployment</span>

<span class="hljs-comment"># You can create a frontend using HTML, CSS, and JavaScript frameworks such as React, Vue.js, or Angular</span> <span class="hljs-comment"># After creating the frontend, you can deploy it using platforms like Netlify, Vercel, or GitHub Pages</span> <span class="hljs-comment"># Example of submitting an entry for the Chat-Your-Data Challenge</span>

Options

<span class="hljs-string">"description"</span>: <span class="hljs-string">"Brief description of your ChatGPT over data sources"</span>, <span class="hljs-string">"contact_email"</span>: <span class="hljs-string">"[email protected]"</span> }

<span class="hljs-keyword">from</span> langchain.data <span class="hljs-keyword">import</span> ChatGPTDataLoader

<span class="hljs-comment"># Create a data loader for ChatGPT</span> data_loader = ChatGPTDataLoader(processed_data) <span class="hljs-comment"># Example of creating prompts for ChatGPT</span>

<span class="hljs-keyword">from</span> langchain.models <span class="hljs-keyword">import</span> ChatGPT

<span class="hljs-comment"># Create a ChatGPT model</span> model = ChatGPT()

<span class="hljs-comment"># Define prompts</span> prompts = [ <span class="hljs-string">"Prompt 1: Your custom prompt here"</span>, <span class="hljs-string">"Prompt 2: Another custom prompt"</span> ]

<span class="hljs-comment"># Generate responses</span> responses = model.generate_responses(prompts) <span class="hljs-comment"># Example of deploying a ChatGPT model to Hugging Face spaces</span>

<span class="hljs-keyword">from</span> huggingface <span class="hljs-keyword">import</span> SpacesAPI

<span class="hljs-comment"># Initialize the Hugging Face Spaces API</span> api = SpacesAPI(api_key=<span class="hljs-string">"your_api_key"</span>)

<span class="hljs-comment"># Deploy the model to Hugging Face spaces</span> api.deploy_model(model, <span class="hljs-string">"ChatGPT over Your Data"</span>, tags=[<span class="hljs-string">"ChatGPT"</span>, <span class="hljs-string">"LangChain"</span>])</pre></div><div id="1b86" class="link-block"> <a href="https://readmedium.com/langchain-what-is-langchain-chroma-301d3240d0dd"> <div> <div> <h2>LANGCHAIN — What Is LangChain Chroma?</h2> <div><h3>The most dangerous phrase in the language is, ‘We’ve always done it this way.’ — Grace Hopper.</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></article></body>

LANGCHAIN — Chat Your Data

“Programming isn’t about what you know; it’s about what you can figure out. — Chris Pine”

LANGCHAIN — Chat Your Data Submissions?

Software and cathedrals are much the same — first we build them, then we pray. — Sam Redwine

medium.com

In the “Chat-Your-Data” Challenge, LangChain has initiated a week-long challenge to create ChatGPT models over custom data sources. To participate, users can follow the steps outlined below.

To get started, users can clone the example GitHub repository provided. Then, customize the data source and prompts according to their specific requirements, following the tutorial provided on the LangChain blog. Additionally, users can deploy a frontend to accompany their implementation, with an example deployment to Hugging Face spaces available in the tutorial.

import pandas as pd from langchain.data 
import ChatGPTDataLoader

Load data from a CSV file

data = pd.read_csv("your_data.csv")

Preprocess the data

processed_data = preprocess_function(data)

Create a data loader for ChatGPT

data_loader = ChatGPTDataLoader(processed_data)
# Example of creating prompts for ChatGPT

from langchain.models import ChatGPT

# Create a ChatGPT model
model = ChatGPT()

# Define prompts
prompts = [
    "Prompt 1: Your custom prompt here",
    "Prompt 2: Another custom prompt"
]

# Generate responses
responses = model.generate_responses(prompts)
# Example of deploying a ChatGPT model to Hugging Face spaces

from huggingface import SpacesAPI

# Initialize the Hugging Face Spaces API
api = SpacesAPI(api_key="your_api_key")

# Deploy the model to Hugging Face spaces
api.deploy_model(model, "ChatGPT over Your Data", tags=["ChatGPT", "LangChain"])
# Example of submitting an entry for the Chat-Your-Data Challenge

import requests

# Define the submission data
submission_data = {
    "repo_url": "https://github.com/your_username/your_repo",
    "description": "Brief description of your ChatGPT over data sources",
    "contact_email": "[email protected]"
}

# Submit the entry
response = requests.post("https://forms.gle/9ckmWxQ9GAaMpcRz9", data=submission_data)
print(response.text)
# Example of cloning an example GitHub repository

!git clone https://github.com/hwchase17/chat-your-data.git
# Example of customizing data source and prompts for ChatGPT

# Customize the data source and prompts as per the provided tutorial: https://blog.langchain.dev/tutorial-chatgpt-over-your-data/
# Example of creating a frontend for ChatGPT deployment

# You can create a frontend using HTML, CSS, and JavaScript frameworks such as React, Vue.js, or Angular
# After creating the frontend, you can deploy it using platforms like Netlify, Vercel, or GitHub Pages
# Example of submitting an entry for the Chat-Your-Data Challenge

# Define the submission data
submission_data = {
    "repo_url": "https://github.com/your_username/your_repo",
    "description": "Brief description of your ChatGPT over data sources",
    "contact_email": "[email protected]"
}

# Submit the entry
response = requests.post("https://forms.gle/9ckmWxQ9GAaMpcRz9", data=submission_data)
print(response.text)
# Example of creating a data loader for ChatGPT

from langchain.data import ChatGPTDataLoader

# Create a data loader for ChatGPT
data_loader = ChatGPTDataLoader(processed_data)
# Example of creating prompts for ChatGPT

from langchain.models import ChatGPT

# Create a ChatGPT model
model = ChatGPT()

# Define prompts
prompts = [
    "Prompt 1: Your custom prompt here",
    "Prompt 2: Another custom prompt"
]

# Generate responses
responses = model.generate_responses(prompts)
# Example of deploying a ChatGPT model to Hugging Face spaces

from huggingface import SpacesAPI

# Initialize the Hugging Face Spaces API
api = SpacesAPI(api_key="your_api_key")

# Deploy the model to Hugging Face spaces
api.deploy_model(model, "ChatGPT over Your Data", tags=["ChatGPT", "LangChain"])

LANGCHAIN — What Is LangChain Chroma?

The most dangerous phrase in the language is, ‘We’ve always done it this way.’ — Grace Hopper.

medium.com