Area Of Work:
Chat bot
,
Generative AI
Industry:
IT/Software
Artificial Intelligence - Chat bot
Don't let delayed responses and operational bottlenecks hold your business back from reaching its full potential. Oodles’ AI Chatbots powered by ChatGPT, Llama, and Azure, help you slash response time by hours to seconds, automate complex data processing, and accelerate sales cycles across all major platforms, turning every customer touchpoint into an opportunity for growth.
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Building a Chatbot with Mistral AI Step by Step Guide
Building a Chatbot with Mistral AI : Step by Step GuidePrerequisitesBefore we get started, you'll need to have the following:1. Basic understanding of JavaScript and Node.js.2. Node.js installed on your machine.3. Understand RESTful APIs.4. Access to Mistral AI API credentials.Step 1: Setting Up Your Node.js ProjectTo begin, create a new Node.js project. Navigate to your project directory in your terminal and run the following command:mkdir mistral-chatbot
cd mistral-chatbot
npm init -y
This will create a new directory named mistral-chatbot and initialize a new Node.js project with the default settings.Step 2: Installing DependenciesNext, install the necessary dependencies that we'll need for our project. In this tutorial, we'll use Axios to make HTTP requests. Run the following command to install Axios:npm install axiosStep 3: Environment Setupa. Create a file named .env in the root of your project to store environment variables.b. Add the following variables to the .env file, replacing "MISTRAL_API_URL" and "YOUR_MISTRAL_API_KEY" with your actual Mistral API URL and API key, respectively MISTRAL_API_URL = MISTRAL_API_URL
MISTRAL_API_KEY = YOUR_MISTRAL_API_KEY Step 4: Writing the Chatbot LogicNow, let's write the logic for our chatbot. Create a new file named chatbot.js in your project directory and add the following code:const MistralChatbot = async (req,res) => {
try {
let {message, mistralChatHistory, conversationId} = req.body;
const history = JSON.parse(mistralChatHistory);
const format = TEXT_RESPONSE_FORMAT; // if you want any type of formating in response , provide here
// Append format to the last message in chat history
history[history.length - 1].content += format;
// Prepare data for Mistral API request
const requestData = {
model: 'mistral-tiny',
messages: history,
temperature: 0,
};
// Send request to Mistral API
const response = await axios.post(process.env.MISTRAL_API_URL, requestData, {
headers: {
'Content-Type': 'application/json',
'Accept': 'application/json',
'Authorization': `Bearer ${process.env.MISTRAL_API_KEY}`
}
});
// Extract response from Mistral AI
const data = response.data.choices[0].message.content;
return res.status(200).json({success:true,data}); // Return response to the user
} catch (error) {
console.log(error);
throw new Error('Failed to process message');
}
};
module.exports = MistralChatbot;Step 5: Testing Your ChatbotYou can now test your chatbot by sending messages to your application and observing the responses generated by Mistral AI. Ensure that your Mistral API credentials are valid and that your application can communicate with the Mistral API endpoint.
Mohd Ubaish
24 Feb 2024
ConvoSense: Interactive Chatbot with Document Insights
Introduction:In today's digital age, chatbots have become an integral part of various online platforms, ranging from customer service portals to virtual assistants. However, creating a chatbot that can engage in meaningful conversations requires more than just predefined responses. In this article, we delve into the development of an interactive chatbot that leverages document retrieval techniques and contextual response generation to provide users with relevant and informative answers. Today, we have implemented such an interactive chatbot - ConvoSense, that promises to revolutionize the way users engage with information and assistance.The Vision:At the heart of our endeavor lies a simple yet profound vision: to create a chatbot that not only understands user queries but also provides contextually relevant responses backed by a wealth of knowledge. We envisioned a chatbot that could seamlessly retrieve information from a vast repository of documents, distill it into meaningful insights, and communicate with users in a natural and engaging manner.The Journey:Our journey began with meticulous planning and strategizing, as we mapped out the various components and functionalities of ConvoSense:Document Acquisition: We painstakingly curated a diverse collection of documents spanning a wide range of topics, ensuring that ConvoSense had access to a wealth of information to draw upon.Document Embeddings: Leveraging cutting-edge natural language processing techniques, we transformed each document into high-dimensional embeddings, capturing the semantic essence of the text.Vector Database: ConvoSense's brainpower resides in the vector database, where document embeddings are stored and indexed for lightning-fast retrieval.User Interface: We designed an intuitive and user-friendly interface using Streamlit, allowing users to interact with our chatbot effortlessly.Language Model Integration: To imbue ConvoSense with conversational prowess, we integrated a state-of-the-art language model trained on vast amounts of text data.The Implementation:With our blueprint in hand, we set to work bringing our vision to life. Through a combination of coding wizardry and relentless iteration, we fine-tuned each component of ConvoSense to perfection:Data Preparation: We meticulously preprocessed our document collection, cleaning and tokenizing the text to prepare it for embedding generation.Embedding Generation: Using the latest advancements in natural language processing, we generated dense embeddings for each document, encoding its semantic meaning into a compact numerical representation.Vector Database Management: The document embeddings were seamlessly integrated into the vector database, enabling rapid and efficient retrieval based on user queries.Streamlit Interface Development: Our Streamlit interface emerged as the crown jewel of our project, providing users with a sleek and intuitive platform to interact with our chatbot.Contextual Response Generation: When users posed queries to our chatbot, it sprung into action, retrieving relevant documents from the vector database and generating contextually appropriate responses using the integrated language model.The Impact:As ConvoSense takes its first steps into the world, we anticipate a profound impact on how users access information and seek assistance. From customer support portals to educational platforms, the applications of our chatbot are limitless, offering a glimpse into a future where conversational AI powers seamless interactions across diverse domains.Conclusion:In conclusion, our journey to develop an interactive chatbot represents a testament to the transformative potential of conversational AI. By combining cutting-edge technology with a clear vision and unwavering determination, we have created a tool that promises to redefine the way humans and machines communicate. As we look ahead to the future, we remain committed to pushing the boundaries of AI innovation, unlocking new possibilities and shaping a world where intelligent chatbots are at the forefront of human-machine interaction.
Area Of Work:
Chat bot
Industry:
IT/Software
Technology:
Chatgpt
Rozi Ali
19 Mar 2024
Chat with your own Document using PrivateGPT
Chat with your own Document Using PrivateGPTPrivateGPT is a programme that allows you to ask questions about your documents without requiring an online connection, thanks to the power of Language Models. It is completely private, with no data leaving your execution environment at any stage. You can ingest documents and ask inquiries even without an internet connection!How privateGPT worksThis project is Divided intotwo phases1.)Ingest Phase (ingest.py file) , 2.)Query Phase(privateGPT.py file)Workings of Ingest Phase(ingest.py file)In our privateGPT project it takes the data from the source_document folder, split it into chunks and chunk overlaps by usingRecursiveCharacterTextSplitterclass.with the help of HuggingFaceEmbeddings(SentenceTransformers) and all-MiniLM-L6-v2 Model, it embedthe text.Embeddings are refers to the process of converting text or words into numerical representation (vectors) that can be understand and processed by machine learning algorithm. these text or words embeddings are essential for many Natural Language Processing tasks as they capture semantic relationships and contextual information between words, enabling machine to understand the meaning of the words and sentence in a numerical format.words with similar meanings or contexts are mapped closer together in the vector space. this allows machine learning models to leverage the similarities between words and make more informed decisions based on the context in which words appear.overall, text or word embeddings play a crucial role in bridging the gap between human language and machine learning algorithm, enabling computers to process and understand Natural language text more effectively.after creating the Embeddings we store into our Chroma DBChroma is a powerful toolset that allows developers to work with embeddings, store, index, and query these embeddings effectively and efficiently, and perform similarity searches on large-scale datasets.Working of Query Phase(privateGPT.py file)In this phase the user input serves as a query that is passed to the local Language Model (LLM) for processing and generating an answer. The LLM processes the query and produces an answer based on the context retrieved from the local vector database.User Input: The user provides a natural language text query, which serves as the input question.LLM Processing: The privateGPT.py script uses a local LLM (GPT4All-J or LlamaCpp) to process the user's question. The LLM has been pre-trained on a vast corpus of text and is capable of understanding Natural language and generating relevant responses.Context Retrieval: The script performs a similarity search using the local vector database (Chroma) to retrieve relevant context from the ingested documents by using RetrievalQA. The context consists of a selection of documents that are most similar to the user's query.Answer Generation: The LLM generates an answer based on the user's question and the context retrieved from the vector database. The LLM uses its language modeling capabilities to produce a response that fits the given context.Displaying the Answer: The script displays the answer generated by the LLM and presents the four sources (documents) used as context for generating that answer. This information helps the user understand how the LLM arrived at the response.SummaryIn summary, the user input is used as a query for the local LLM, and the LLM leverages the vector embeddings of the ingested documents (retrieved from the local vector database) to generate a relevant answer. The process does not directly create embeddings for the user input but rather uses embeddings of the documents to contextualize the LLM's response to the user's query.
Area Of Work:
Chat bot
,
Generative AI
Ishandeep Singh
30 Jul 2023
Building a Chatbot with Gemini AI Step by Step Guide
Building a Chatbot with Gemini AI Step by Step GuidePrerequisitesBefore we start, you'll need the following:1. Basic understanding of JavaScript and Node.js.2. Node.js installed on your machine.3. Understand RESTful APIs.4. Access to Gemini AI API credentials.Step 1: Setting Up Your Node.js ProjectTo start, build a new Node.js project.Navigate to your project directory in your terminal and run the following command:mkdir gemini-chatbot
cd gemini-chatbot
npm init -yStep 2: Installing DependenciesInstall the necessary dependencies for your project. In addition to Axios, you'll need the @openai/gemini package for interacting with Gemini AI:npm install axios @openai/gemini express dotenvStep 3: Environment ConfigurationCreate a .env file in your project directory and add your Gemini API credentials:GEMINI_API_KEY=YOUR_GEMINI_API_KEYReplace YOUR_GEMINI_API_KEY with your actual Gemini API key.Step 4: Writing the Chatbot LogicCreate a new file named geminiChatbot.js in your project directory and add the following code:const { GoogleGenerativeAI } = require("@google/generative-ai");
const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);
const GeminiChatbot = async (req,res) => {
try {
let { message, geminiChatHistory, conversationId } = req.body;
// const history = chatHistory;
const history = JSON.parse(geminiChatHistory);
history.pop();
const model = genAI.getGenerativeModel({ model: "gemini-pro" });
const chat = model.startChat({
history: history,
settings: { temperature: 0 }
});
const format = TEXT_REPONSE_FORMAT;
const result = await chat.sendMessage(message + format);
const response = await result.response;
const text = response.text();
const data = SimplifyGeminiData(text);
return res.status(200).json({
success: true,
data,
conversationId
})
} catch (error) {
console.log(error);
return res.status(500).json({ success: false, message: error.message });
}
};
Step 6: Testing Your ChatbotYou can now test your chatbot by sending messages to your application and observing the responses generated by Gemini AI. Make sure your Gemini API credentials are valid and that your application can communicate with the Gemini API endpoint.ConclusionCongratulations! You've successfully integrated Gemini AI into your Node.js application to create a chatbot. Happy Coding!
Area Of Work:
Chat bot
Industry:
IT/Software
Mohd Ubaish
26 Feb 2024
Unleashing the Power of OpenAIs GPT 4 for Web Development
How DemoGPT WorksPrompt-based Web Development:Users interact with DemoGPT using prompts to specify the desired website structure and content.Example Prompt: "Create a simple blog website with a homepage, about us page, and a blog section."GPT-4 Vision Integration:For handling image references, GPT-4 Vision Preview is employed. Users can include images in their prompts, and DemoGPT intelligently incorporates them into the generated HTML code.Example Prompt: "Add an image of a scenic landscape to the homepage."Web Deployment with Vercel APIAfter generating the HTML code, the next step is deploying the website. DemoGPT seamlessly integrates with the Vercel API for swift and efficient deployment.Vercel API Integration:The generated HTML code is passed to the Vercel API, initiating the deployment process.Example: Using Vercel CLI - vercel deployUser Deployment Control:Users have full control over the deployment process. They can specify deployment configurations, domains, and other relevant settings.Consider the following scenario: "Deploy the website with the domain 'my-demo-website.vercel.app'."Secure Storage with MongoDBTo ensure the security and persistence of user details, including IDs, passwords, and deployment configurations, DemoGPT utilizes MongoDB as a reliable database solution.MongoDB Integration:User details, HTML code, and deployment configurations are securely stored in MongoDB.Example: Storing user details - { username: 'user123', password: 'securepass', deploymentConfig: {...} }Data Retrieval and Update:Users can retrieve their details and update deployment configurations as needed.Example: Retrieving user details - db.users.findOne({ username: 'user123' })ConclusionDemoGPT, powered by OpenAI's GPT-4, redefines the landscape of web development by offering an intuitive and efficient platform. With prompt-based interactions, GPT-4 Vision for image handling, Vercel for deployment, and MongoDB for secure data storage, DemoGPT provides a comprehensive solution for users to bring their web ideas to life. As we embrace the future of AI, projects like DemoGPT showcase the transformative potential of natural language processing and computer vision in simplifying complex tasks. Try it out and witness the magic of GPT-4 in action!
Area Of Work:
Chat bot
Sanjeev Kumar
30 Jan 2024
