Hebbia is an advanced AI platform designed to revolutionize knowledge work by providing access to cutting-edge generative AI technologies. It empowers firms to leverage AI capabilities for achieving tangible business outcomes, optimizing workflows, and enhancing decision-making processes. Hebbia's versatile features cater to various data types and tasks while maintaining transparency and enterprise-level security.
Hebbia
AI Agents for serious work.
Introduction
Feature
Any Data Processing
Hebbia can handle and analyze vast amounts of data across different modalities, featuring an unlimited effective context window for comprehensive data processing.
Versatile Task Execution
The platform is capable of performing complex multi-step workflows, automating tasks to free up human resources for more strategic activities.
Complete Transparency
Hebbia offers full visibility into AI operations, allowing users to track and trace every action taken by the system, ensuring accountability and trust.
Enterprise-Grade Security
Built with a focus on enterprise security, Hebbia is trusted by major global institutions, including those in highly regulated industries.
Streamlined Information Synthesis
Hebbia can condense large volumes of information into clear, actionable insights, facilitating better decision-making processes.
Cross-Firm Knowledge Utilization
The platform enables leveraging knowledge across different departments or teams within an organization, accelerating client value delivery.
Workflow Automation
Hebbia automates complex workflows and tasks, increasing efficiency and productivity within organizations.
FAQ
What types of data can Hebbia process?
Hebbia is designed to handle any amount and modality of data, with an infinite effective context window, making it versatile for various data types and volumes.
How does Hebbia ensure data security?
Hebbia is built with enterprise security in mind and is trusted by large, regulated global institutions, indicating robust security measures are in place to protect sensitive information.
Can Hebbia integrate with existing workflows and tools?
Yes, Hebbia is designed to be easily integrated with existing workflows and tools, allowing for seamless incorporation into current business processes.
How can I get started with Hebbia?
To get started with Hebbia, you need to contact their sales team to schedule a demo and discuss pricing options tailored to your organization's needs.
Is Hebbia suitable for small businesses?
While Hebbia caters to large institutions, they offer a range of pricing options. It's recommended to start small and scale up as needed, making it potentially suitable for businesses of various sizes.
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That's a great idea! To make automated reviews, we need to figure out what kind of reviews you want to automate. For example: * Product reviews: Analyzing customer feedback to identify common themes and sentiment. * Code reviews: Using tools to automatically check for coding errors and style inconsistencies. * Academic paper reviews: Summarizing key findings and identifying potential areas for improvement. Tell me more about the type of automated reviews you have in mind, and I can give you more specific information and examples.
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GPT for Google Search * What is GPT? GPT stands for Generative Pre-trained Transformer. It's a type of artificial intelligence (AI) that can understand and generate human-like text. * How could GPT improve Google Search? GPT could help Google Search in several ways: * Better understanding of natural language: GPT is trained on massive amounts of text data, allowing it to understand complex and nuanced search queries. * More conversational search experience: GPT could enable users to interact with Google Search in a more natural, conversational way. * Summarizing search results: GPT could condense large amounts of information from search results into concise summaries. * Generating different creative text formats: GPT could help users find information in new and interesting ways, such as generating poems, code, scripts, musical pieces, email, letters, etc., based on their search queries. * Challenges and Considerations: While GPT offers exciting possibilities, there are also challenges to consider: * Accuracy and bias: GPT models can sometimes generate inaccurate or biased information. It's crucial to ensure that GPT-powered search results are reliable and unbiased. * Transparency and explainability: It can be difficult to understand how GPT arrives at its results. Making GPT's decision-making process more transparent is important for building trust. * Ethical implications: The use of GPT in search raises ethical questions about the potential for misuse, such as generating harmful content or manipulating search results.
GPT for Google Search * What is GPT? GPT stands for Generative Pre-trained Transformer. It's a type of artificial intelligence (AI) that can understand and generate human-like text. * How could GPT improve Google Search? GPT could help Google Search in several ways: * Better understanding of natural language: GPT is trained on massive amounts of text data, allowing it to understand complex and nuanced search queries. * More conversational search experience: GPT could enable users to interact with Google Search in a more natural, conversational way. * Summarizing search results: GPT could condense large amounts of information from search results into concise summaries. * Generating different creative text formats: GPT could help users find information in new and interesting ways, such as generating poems, code, scripts, musical pieces, email, letters, etc., based on their search queries. * Challenges and Considerations: While GPT offers exciting possibilities, there are also challenges to consider: * Accuracy and bias: GPT models can sometimes generate inaccurate or biased information. It's crucial to ensure that GPT-powered search results are reliable and unbiased. * Transparency and explainability: It can be difficult to understand how GPT arrives at its results. Making GPT's decision-making process more transparent is important for building trust. * Ethical implications: The use of GPT in search raises ethical questions about the potential for misuse, such as generating harmful content or manipulating search results.Add ChatGPT to Google Search This is a popular idea! Many people think it would be amazing to have the power of ChatGPT directly integrated into Google Search. Imagine being able to ask Google a complex question and getting a clear, concise, and human-like response generated by ChatGPT. It could revolutionize how we search for information and interact with the web.
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)
* Create a new notebook
* Install the `transformers` library by running `!pip install transformers`
* Import the `transformers` library and load the ChatGPT model using `from transformers import AutoModelForCausalLM, AutoTokenizer; model = AutoModelForCausalLM.from_pretrained('chatgpt'); tokenizer = AutoTokenizer.from_pretrained('chatgpt')`
* Use the model to generate text using `input_text = "Your input here"; inputs = tokenizer.encode_plus(input_text, return_tensors='pt', max_length=1024, padding='max_length', truncation=True); output = model(inputs['input_ids'], attention_mask=inputs['attention_mask']); print(tokenizer.decode(output.logits[0], skip_special_tokens=True))`
Method 2: Google Apps Script
* Open Google Apps Script ([script.google.com](http://script.google.com))
* Create a new project
* Install the `transformers` library by running `npm install transformers`
* Import the `transformers` library and load the ChatGPT model using `const { AutoModelForCausalLM, AutoTokenizer } = require('transformers'); const model = new AutoModelForCausalLM('chatgpt'); const tokenizer = new AutoTokenizer('chatgpt');`
* Use the model to generate text using `const inputText = "Your input here"; const inputs = tokenizer.encodePlus(inputText, { return_tensors: 'pt', max_length: 1024, padding: 'max_length', truncation: true }); const output = model(inputs.inputIds, inputs.attentionMask); console.log(tokenizer.decode(output.logits[0], { skipSpecialTokens: true }));`
Note: These methods require some technical knowledge and may have limitations compared to the original ChatGPT model.](https://static.right-ai.com/tools/2024/7/3/chromewebstore-google-com-detail-chatgpt-for-google-for-fr-pabeminldebomngnkgffiejipjjaaogi-thumbnail-1720037311.png "Get ChatGPT for Free with Google
You can now access ChatGPT, a powerful language model, for free with Google. Here's how:
Method 1: Google Colab
* Open Google Colab ([colab.research.google.com](http://colab.research.google.com))
* Create a new notebook
* Install the `transformers` library by running `!pip install transformers`
* Import the `transformers` library and load the ChatGPT model using `from transformers import AutoModelForCausalLM, AutoTokenizer; model = AutoModelForCausalLM.from_pretrained('chatgpt'); tokenizer = AutoTokenizer.from_pretrained('chatgpt')`
* Use the model to generate text using `input_text = \"Your input here\"; inputs = tokenizer.encode_plus(input_text, return_tensors='pt', max_length=1024, padding='max_length', truncation=True); output = model(inputs['input_ids'], attention_mask=inputs['attention_mask']); print(tokenizer.decode(output.logits[0], skip_special_tokens=True))`
Method 2: Google Apps Script
* Open Google Apps Script ([script.google.com](http://script.google.com))
* Create a new project
* Install the `transformers` library by running `npm install transformers`
* Import the `transformers` library and load the ChatGPT model using `const { AutoModelForCausalLM, AutoTokenizer } = require('transformers'); const model = new AutoModelForCausalLM('chatgpt'); const tokenizer = new AutoTokenizer('chatgpt');`
* Use the model to generate text using `const inputText = \"Your input here\"; const inputs = tokenizer.encodePlus(inputText, { return_tensors: 'pt', max_length: 1024, padding: 'max_length', truncation: true }); const output = model(inputs.inputIds, inputs.attentionMask); console.log(tokenizer.decode(output.logits[0], { skipSpecialTokens: true }));`
Note: These methods require some technical knowledge and may have limitations compared to the original ChatGPT model.")
Get ChatGPT for Free with Google You can now access ChatGPT, a powerful language model, for free with Google. Here's how: Method 1: Google Colab * Open Google Colab ([colab.research.google.com](http://colab.research.google.com)) * Create a new notebook * Install the `transformers` library by running `!pip install transformers` * Import the `transformers` library and load the ChatGPT model using `from transformers import AutoModelForCausalLM, AutoTokenizer; model = AutoModelForCausalLM.from_pretrained('chatgpt'); tokenizer = AutoTokenizer.from_pretrained('chatgpt')` * Use the model to generate text using `input_text = "Your input here"; inputs = tokenizer.encode_plus(input_text, return_tensors='pt', max_length=1024, padding='max_length', truncation=True); output = model(inputs['input_ids'], attention_mask=inputs['attention_mask']); print(tokenizer.decode(output.logits[0], skip_special_tokens=True))` Method 2: Google Apps Script * Open Google Apps Script ([script.google.com](http://script.google.com)) * Create a new project * Install the `transformers` library by running `npm install transformers` * Import the `transformers` library and load the ChatGPT model using `const { AutoModelForCausalLM, AutoTokenizer } = require('transformers'); const model = new AutoModelForCausalLM('chatgpt'); const tokenizer = new AutoTokenizer('chatgpt');` * Use the model to generate text using `const inputText = "Your input here"; const inputs = tokenizer.encodePlus(inputText, { return_tensors: 'pt', max_length: 1024, padding: 'max_length', truncation: true }); const output = model(inputs.inputIds, inputs.attentionMask); console.log(tokenizer.decode(output.logits[0], { skipSpecialTokens: true }));` Note: These methods require some technical knowledge and may have limitations compared to the original ChatGPT model.
Get ChatGPT for Free with Google You can now access ChatGPT, a powerful language model, for free with Google. Here's how: Method 1: Google Colab * Open Google Colab ([colab.research.google.com](http://colab.research.google.com)) * Create a new notebook * Install the `transformers` library by running `!pip install transformers` * Import the `transformers` library and load the ChatGPT model using `from transformers import AutoModelForCausalLM, AutoTokenizer; model = AutoModelForCausalLM.from_pretrained('chatgpt'); tokenizer = AutoTokenizer.from_pretrained('chatgpt')` * Use the model to generate text using `input_text = "Your input here"; inputs = tokenizer.encode_plus(input_text, return_tensors='pt', max_length=1024, padding='max_length', truncation=True); output = model(inputs['input_ids'], attention_mask=inputs['attention_mask']); print(tokenizer.decode(output.logits[0], skip_special_tokens=True))` Method 2: Google Apps Script * Open Google Apps Script ([script.google.com](http://script.google.com)) * Create a new project * Install the `transformers` library by running `npm install transformers` * Import the `transformers` library and load the ChatGPT model using `const { AutoModelForCausalLM, AutoTokenizer } = require('transformers'); const model = new AutoModelForCausalLM('chatgpt'); const tokenizer = new AutoTokenizer('chatgpt');` * Use the model to generate text using `const inputText = "Your input here"; const inputs = tokenizer.encodePlus(inputText, { return_tensors: 'pt', max_length: 1024, padding: 'max_length', truncation: true }); const output = model(inputs.inputIds, inputs.attentionMask); console.log(tokenizer.decode(output.logits[0], { skipSpecialTokens: true }));` Note: These methods require some technical knowledge and may have limitations compared to the original ChatGPT model.How to Add ChatGPT to All Google Searches ===================================================== Step 1: Create a Custom Search Engine -------------------------------------- * Go to the [Google Custom Search Engine](https://cse.google.com/) website and sign in with your Google account. * Click on the "New Search Engine" button. * Fill in the required information, such as the name and description of your search engine. * Click on the "Create" button. Step 2: Add ChatGPT to the Search Engine ----------------------------------------- * In the "Setup" tab, click on the "Add" button next to "Sites to search". * Enter the following URL: `https://chat.openai.com/` * Click on the "Add" button. Step 3: Configure the Search Engine -------------------------------------- * In the "Setup" tab, click on the "Edit" button next to "Search engine keywords". * Add the following keywords: `ChatGPT` * Click on the "Save" button. Step 4: Get the Search Engine Code ------------------------------------- * In the "Setup" tab, click on the "Get code" button. * Copy the HTML code provided. Step 5: Add the Search Engine to Your Browser ------------------------------------------------ * Open your browser and go to the "Settings" or "Options" page. * Look for the "Search engine" or "Default search engine" option. * Click on the "Add" or "Manage search engines" button. * Paste the HTML code you copied earlier. * Click on the "Add" or "Save" button. You're Done! =============== Now, whenever you search on Google, ChatGPT will be included in the search results. You can also use the custom search engine URL provided by Google to search directly.
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