Applying Advanced Features
This section explores advanced Knowledge Bot features, allowing you to customize settings for better accuracy, optimized search results, and improved responses.
Last updated
This section explores advanced Knowledge Bot features, allowing you to customize settings for better accuracy, optimized search results, and improved responses.
Last updated
Agents are reusable, customizable prompt shortcuts that help streamline tasks, saving time and improving productivity. They allow you to automate specific queries, ensuring consistency and efficiency in responses.
Why use Agents?
Efficiency – Reduce repetitive typing and automate frequently used prompts.
Consistency – Ensure responses follow a structured and standardized format especially across scaling teams.
Customization – Tailor Agents to fit specific workflows and your needs.
Collaboration – Share Agents within your organization for uniform responses.
Faster Decision-Making – Instantly generate reports, summaries, and recommendations.
It is possible to create custom Agents or take advantage of Blockbrain’s pre-made Agents for quick and easy implementation. Simply activate the pre-made agents or create your organization's own custom agent through the bot settings.
To keep your Agents organized, you can create categories and tag each Agent accordingly. This helps improve navigation, especially when managing multiple active Agents.
Convert your commonly used prompts into Agents, allowing you to trigger complex queries with a single click. Here are some practical ways to use Agents:
Summarizing Documents – Upload long documents and use an Agent to generate concise summaries in a preferred format.
Crafting Reports – Gather multiple pieces of information and structure them into a well-organized report.
Refining Tone & Style – Improve writing clarity, simplify text, or adjust tone to match your audience.
Strategizing – Analyze company data and use an Agent to generate business insights or summaries.
Creating Sales Emails – Input client and project details, and let an Agent generate a personalized sales email.
Here are some best practices to ensure your Agents work effectively and seamlessly integrate into you and your team's workflow:
Use clear and descriptive names – Ensure each Agent is easy to identify and use.
Organize Agents into categories – Group Agents by function for better navigation.
Test and refine prompts regularly – Improve response quality over time.
Encourage team collaboration – Share and standardize Agents to improve efficiency across teams.
By following these best practices, you can maximize the effectiveness of Agents and create a more streamlined, automated workflow.
Workflows allow you to automate multi-step prompts, making complex interactions more structured and efficient. Unlike Agents, which function as single-prompt shortcuts, Workflows guide the AI through a sequence of prompts to ensure a more accurate and refined final response.When asking for too much in a single prompt, the AI may struggle to process and provide precise answers. Workflows break down complex queries into manageable steps, improving accuracy and relevance at each stage.Why use Workflows?
Improved Accuracy – The AI delivers more precise responses when given structured, step-by-step prompts.
Better Context Retention – Each step builds on previous answers, leading to a more cohesive final result.
Scalability – Automate repetitive, multi-step tasks to save time and improve efficiency.
Below are some practical ways to integrate workflows:
Crafting a Brand Analysis – A structured workflow can automate a deep dive into a company’s positioning. Start by analyzing industry trends, then gather competitive insights, and finally perform an internal assessment of services and overall performance. This approach ensures a well-researched and strategic brand analysis.
Writing a Sales Proposal – A workflow can help structure a compelling sales pitch. Begin by summarizing the client’s needs and pain points. Next, outline your company’s value proposition and how it specifically addresses those needs. Then, generate a detailed proposal with pricing and deliverables, and finally, refine the language to ensure a persuasive and engaging tones.
To maximize the effectiveness of your workflows, it's essential to design them with clarity, continuity, and precision in mind. Follow these best practices to ensure smooth execution and optimal AI performance:
Break down complex prompts – Focus on one specific query per step.
Ensure continuity – Each prompt should build on previous responses.
Be clear and specific – Define non-negotiable "need-to-know" information.
Use concise, structured prompts – Avoid vague or overly broad instructions.
Provide context – Give background information when necessary.
Customize the LLM model – Select the best model for each task to enhance output quality.
The Intent Agent is a feature that users can enable to enhance AI efficiency. By providing descriptions for folders, the Intent Agent enables the AI to sift through the database and assess which information is most relevant to users. The AI uses these folder descriptions to identify and prioritize the folders most likely to contain relevant information.
This feature transforms databases from simple file repositories into structured resources optimized for quick and accurate data retrieval.
Why use Intent Agent?
Improved Efficiency: Reduces time spent scanning irrelevant folders by narrowing the search to relevant areas.
Optimized Workflows: Simplifies the process of locating the correct folder, especially as databases grow larger and more complex.
Faster Results: Quickly identifies and retrieves the most relevant information, saving time.
Resource Optimization: Conserves computational resources by prioritizing the processing of relevant data.
Folder Descriptions: Users provide brief descriptions for each uploaded folder, enabling the AI to better understand the folder's contents.
Intelligent Scanning: The AI first analyzes these folder descriptions before processing large volumes of data.
Relevance Assessment: The AI identifies which folders are most likely to contain relevant information.
Targeted Search: The AI focuses on exploring files within the most promising folders.
Avoid Complex and Large Folders:
Organize files into clearly defined subfolders based on specific themes, topics, or categories.
Example: Divide a large department folder into smaller subfolders, such as project-specific subfolders for the entire Sales department.
Limit Folder Depth:
Keep folder structures shallow, with a maximum of four levels.
Example:
Main Folder → 1st Subfolder
Subfolder #1 → 2nd Subfolder
Subfolder #2 → 3rd Subfolder
Subfolder #3 → 4th Subfolder
Maintain a Clean Database:
Regularly remove duplicates, unused documents, and other unnecessary files to keep the database organized and efficient.
Consistent Labeling:
Create a standardized naming convention for files, including relevant keywords and dates when applicable.
Ensure Files Have Complete Information:
Make sure all documents contain the necessary details to address potential queries.
Example A: Clearly label company document templates and include the word "template" in the file name.
Example B: Include the responsibilities and scope of each team or department in documents to help the AI direct users to the correct resources.
Connect Relevant Databases:
Link related databases to your Data Room to provide consistent and accurate responses.
Start Small:
Focus on creating folder descriptions for complex folders first, while still aiming to make all folders clear and descriptive.
Manage Database Size Appropriately:
You can create up to 1,000 folders with descriptions without sacrificing accuracy. However, it’s best to gradually expand the folder structure to maintain a well-organized database.
Provide Context:
Clearly describe the folder's contents and explain how the information can be used.
Keep Descriptions Concise:
Limit your descriptions to 500–800 characters to maintain clarity and readability.
Use Keywords:
Include words commonly associated with the folder's contents. These keywords improve search accuracy and categorization.
Be Direct:
Avoid unnecessary introductions and focus on the essential details of the folder's contents.
Example:
Before: The folder '{{folder name}}' includes important files.
After: This folder contains files related to XYZ.
Case 1: Too long. Descriptions that are redundant can reduce efficiency.
sample case
bad folder description
improved description
Describing a folder filled with administrative forms.
This folder contains a variety of forms and documents used for different administrative, operational, and compliance purposes. Here is a detailed summary of its contents:
TR48-02: This form is used for documenting company property that is temporarily handed over to employees. It includes fields for item details, serial numbers, department, and signatures for both issuance and return.
TR48-03: This form is an onboarding plan for new employees. It outlines the necessary training and introductions, including safety briefings, department tours, and specific job-related training.
TR48-05: This document outlines the non-disclosure agreement terms between parties, including the return or destruction of confidential documents, rights to developments, and the governing laws.
TR48-10: This form is used for supplier information, particularly regarding environmental management systems and compliance with various environmental regulations.
Contains essential administrative and operational forms and documents for 'COMPANY A,' including forms for employee onboarding, property management, non-disclosure agreements, supplier information, access rights and device requests, preference calculations, and quality management procedures. These documents support critical aspects of company operations, ensuring compliance and facilitating effective employee management.
Case 2: Too short. Descriptions that don't give enough context.
sample case
bad folder description
improved description
Describing a folder that includes all details to an internship and trainee program in the company
Folder for trainees
Contains essential documents related to the company's apprenticeship and training programs. This folder includes information on apprenticeship positions, trainer contact details, and guidelines for apprentices. Topics covered include working hour requirements, vacation entitlements, exam policies, and training report requirements, as well as details about additional training opportunities. It serves as a vital resource for both apprentices and trainers at 'COMPANY A,' supporting effective program management and development.
Case 3: Inconsistent keywords. Use words that are commonly used in the documents and widely understood by the company.
sample case
bad folder description
improved description
Describing a folder on the equipment and tools of a sales team.
Contains documents regarding information on the items used by the sales team
Contains essential documents for 'COMPANY A's' sales team (TeamB2B), including:
An equipment list for the sales team's pilot case (Sales Kit).
An overview of product sample cases (Sales Equipment Inventory).
These documents detail the equipment, tools, and materials used for field sales activities, product demonstrations, and customer presentations, supporting the team's operational efficiency and effectiveness.
Insights are are text-based notes that you can input yourself, or allows you to store and retrieve previously saved AI interactions, making it easy to reuse knowledge from past conversations. This is useful for ensuring consistency in responses and retaining key learnings across teams.
These saved insights act as a personal knowledge base, helping users retain important AI-generated information, streamline workflows, and maintain consistency across multiple interactions. Unlike databases, Insights are stored in full context without chunking, preserving the original message structure for improved retrieval and reuse.
When to Use Insights
When past AI-generated responses need to be referenced frequently
When team members share refined prompts or key findings from Data Rooms
When specific contextual knowledge should be stored for quick access
In Depth Example Use Case of Insights:
A customer support team uses Blockbrain to handle common technical troubleshooting requests. Team members frequently ask the AI bot for solutions to repeating customer issues, but responses can vary slightly depending on how the query is phrased.
Insights can improve the workflow of that situation through the following:
A support specialist asks the AI for a troubleshooting guide on a common issue and refines the response for accuracy.
Once the response is validated, they save it as an Insight so the team can reuse the response instead of regenerating it each time.
Now, when another team member encounters the same issue, they can retrieve the saved Insight instantly instead of waiting for a new AI-generated response.
Over time, the support team builds a library of verified troubleshooting steps, ensuring consistent and accurate AI-generated answers across the entire team.
Other Use Case of Insights
Research & Development Knowledge Base – A research team compiles summaries of scientific papers, experimental findings, and competitor analyses into Insights. This allows them to quickly retrieve and reference past knowledge instead of duplicating research efforts.
Content Marketing & Copywriting – A content marketing team stores brand tone guidelines, product descriptions, and frequently used marketing messages in Insights. Writers can quickly pull pre-approved messaging to maintain brand consistency across multiple campaigns.
Create brand tone and guidelines, then save them as an Insight for easy reference when generating future marketing campaign content.
HR & Employee Training – An HR department uses Insights to store company policies, onboarding procedures, and answers to frequently asked employee questions. This ensures HR representatives provide accurate and consistent responses without searching for documents every time.
Accessing Insights: You can access all insights created across all Knowledge Bots and Data Rooms in the Knowledge Management Tab under the Insights section.
Searching Insights: Navigate to the Knowledge Management Tab and click the Insights Tab. There are two methods to search for insights:
Keyword Search – Use specific keywords to find relevant insights.
Insights AI Search – A powerful search tool that allows users to find specific insights based on context, not just keywords, enhancing retrieval efficiency by allowing users to set the number of search results to display.
This feature helps users quickly locate relevant information without manually browsing the Insights Page, which contains all insights created or shared with them.
To maximize the effectiveness of Insights, follow these best practices to ensure consistency, accuracy, and ease of access for your team.
Store Only High-Quality, Validated Information
Save accurate and well-structured content that has been reviewed or refined.
Avoid storing duplicate, outdated, or incorrect responses to maintain reliability.
Regularly audit and update Insights to ensure information remains current.
Store Only Clear and Focused Threads
Save well structured threads that focus on a desired topic
Avoid overly complicated threads that may confuse the AI when referenced in the future
Use Clear, Consistent, and Descriptive Titles
Include relevant keywords in the title to make searching faster and more intuitive.
Use consistent naming conventions across teams to improve organization.
Example: Instead of "Client Pitch", use "Sales Team: Sales Email Email - Follow up for Company X".
Keep Responses Concise and Actionable
Store only the necessary details instead of long, unstructured content.
Summarize key points clearly to make Insights quick to read and apply.
If context is needed, add links to supporting documents instead of storing long explanations.
Leverage Insights for Consistency Across Teams
Standardize customer support answers, sales scripts, company policies, and technical instructions.
Ensure that AI-generated responses align with company-approved messaging.
Regularly train team members on how to use Insights to maintain uniformity.
Regularly Review and Clean Up Insights
Schedule routine audits to remove outdated or redundant information.
Ensure Insights remain relevant and useful for evolving business needs.
Encourage team feedback on stored Insights to improve quality.
In the upper left corner of the Knowledge Bot, you'll find the gear icon, which opens the bot settings. Here, you can customize and enable various features to tailor the bot to your needs.
Capabilities & Skills: Adjust search methods and activate advanced features
LLM Models: Choose the language model that powers your bot's responses, balancing performance, accuracy, and cost
Database: Select the default database for all data rooms
Agents: Set up or activate customizable prompts for more efficient use
Automations: Set up workflows for more efficient prompting
Action Settings: Adds quick-access buttons to the Knowledge Bot navigation and enables advanced features like Intent Agent and System Agent for enhanced search and automation
Note: If you don't see any bot settings, you may not have access to them. Inquire to the admin of the bot regarding its access.
Blockbrain allows users to select from a variety of Large Language Models (LLMs) based on their specific needs. Each model has unique strengths in terms of response quality, speed, cost efficiency, and specialized capabilities. Some other factors to consider are: Context window size, Performance and Hosting location.
Below is a comprehensive guide to help you choose the best LLM for your workflows:
Model
Best For
Unique Selling Point (USP)
Claude 3.5 Sonnet (Smartest, US)
Complex problem-solving, advanced AI agents, software engineering
Best reasoning & problem-solving Claude
Claude 3.5 Sonnet (Recommended, EU)
Same as Smartest, but EU-hosted for compliance-focused users
Best EU-hosted Claude model for enterprise
Claude 3.5 Sonnet (Creative, EU)
Creative writing, brainstorming, content generation
Optimized for storytelling & marketing
Claude 3.5 Haiku (Efficient, US)
Fast responses, chatbots, lightweight AI tasks
Fastest and cheapest Claude model
GPT-4 Omni (Logical, EU)
Text structuring, logic-heavy tasks, multimodal AI
Best for structured, multimodal tasks
Mistral Large (Coding, EU)
Advanced AI-assisted coding, software development
State-of-the-art coding capabilities
Mistral Nemo (Coding, EU)
Developers, AI-assisted programming, automation
Lightweight but powerful coding model
Gemini 1.5 Pro (Large-context, EU)
Large-scale AI processing, multimodal analysis, research
1M context window for deep analysis
Gemini 1.5 Flash (Fast, EU)
High-volume AI tasks, fast processing at scale
Optimized for speed & cost-efficiency
Llama 3.2 90B (US)
General-purpose AI, knowledge-based reasoning
Latest Llama model with improved performance
Llama 3.1 405B (US)
Enterprise-scale AI, knowledge retrieval
Flagship Meta LLM with broad capabilities
Llama 3.1 70B (US)
Standard AI workloads, NLP & automation
Balanced performance vs. compute power
Llama 3.1 8B (US)
Lightweight AI tasks, cost-effective inference
Best Llama model for smaller AI projects
GPT-4 Omni (Structured, US)
Multimodal input, knowledge organization, AI structuring
Best GPT model for complex logic tasks
GPT-4 Turbo (Legacy, US)
General AI tasks, balanced intelligence & speed
More affordable GPT-4 variant
GPT-4 Vision (US)
AI-assisted image understanding, multimodal tasks
Best for visual processing in GPT models
GPT-4o Mini (Efficient, US)
Affordable AI workloads, cost-effective intelligence
Optimized for budget-conscious users
GPT-3.5 Turbo (Legacy, EU)
Simple text tasks, lightweight AI applications
Most affordable GPT model
GPT-4 Turbo (Legacy, EU)
Advanced reasoning, enterprise-grade AI
Reliable GPT-4 model for structured tasks
Claude 3 Opus (Creative, US)
Creative content, high-level AI assistance
Most powerful Claude for creative work
Claude 3 Haiku (Efficient, EU)
Fast AI interactions, real-time responsiveness
Speed-optimized Claude model
Claude 3 Sonnet (Balanced, US)
Business applications, enterprise AI use
Great balance of speed & intelligence
Gemini 1.0 Pro (Legacy, EU)
General AI use cases, multimodal processing
Balanced Gemini model for various tasks
Mistral Codestral (Coding, EU)
Code completion, AI-powered development tools
Cutting-edge model for coding workflows
Gemma 2 (Google, US)
Small AI tasks, lightweight AI workloads
Google’s compact AI model for efficiency
Take into account the Context Window – refers to the amount of text (in tokens or characters) that an LLM can process at once while maintaining coherence and context
Short context window (e.g., 16K tokens): means the model can only consider a limited portion of text at a time, making it best suited for shorter prompts and direct answers.
Large context window (e.g., 1M tokens): allows the model to process longer documents, complex discussions, and in-depth analysis without losing previous context.
Choosing based on your Region – For better accuracy and compliance, it’s recommended to select an LLM model hosted in your region
Choose the default – If you’re unsure which model to pick, you may use the default LLM Models, which offer a balanced approach, ensuring high-quality responses while maintaining cost efficiency
Claude 3.5 Sonnet v2: Well-rounded for reasoning, accuracy, and general tasks
Azure GPT-4 Omni: Ideal for structured responses, logical reasoning, and complex queries
Model modifiers allow you to fine-tune your Knowledge Bot’s behavior by adjusting key parameters that influence how it processes and generates responses. These settings help balance creativity, precision, and relevance based on the nature of your task.
Why Use Model Modifiers?
By adjusting model modifiers, you can:
Ensure responses align with specific business objectives
Optimize results for creative, technical, or research-based tasks
Improve efficiency and consistency in AI-generated outputs
It's best to stay close to the default settings when adjusting Model Modifiers to maintain balanced AI performance. For example, Creative Freedom should not be set too high, as excessive creativity may lead to unpredictable or overly abstract responses. Similarly, Search Range should remain within 5 to 8 to ensure the AI retrieves relevant information without unnecessary noise. Adjust settings gradually to fine-tune the AI’s behavior while maintaining accuracy and consistency.
To get the best results from AI-generated responses, it's important to fine-tune model modifiers based on your specific use case. These settings act as a starting point, allowing you to tailor the AI’s behavior to meet your needs.
Use Case
Recommended Settings
General Use: Balanced settings for everyday tasks.
Creative Freedom: Low → Allows for engaging but still logical responses.
Vocabulary Range: Medium → Ensures diverse yet relevant wording.
Topic Variety: Medium → Encourages AI to introduce new ideas while maintaining coherence.
Word Variety: Medium → Keeps wording fresh without sacrificing clarity.
Search Range: Medium → Provides a balance between precision and breadth of information.
Sales & Company Analysis: Slight creativity with a strong focus on structured insights.
Creative Freedom: Medium → Keeps responses logical while allowing for slight adaptability.
Vocabulary Range: Medium → Uses varied vocabulary for engaging business communication.
Topic Variety: Medium → Ensures coverage of related business topics without excessive divergence.
Word Variety: Medium → Encourages compelling, clear business writing.
Search Range: High → Retrieves a broad set of insights to support decision-making.
Technical Analysis & Reports: Prioritizes accuracy and consistency over creativity.
Creative Freedom: Lowest → Ensures predictable, fact-based responses.
Vocabulary Range: Low → Uses precise technical language with minimal variation.
Topic Variety: Low → Keeps the discussion on a single, focused subject.
Word Variety: Low → Ensures terminological consistency across technical documentation.
Search Range: Medium → Pulls reliable data while minimizing irrelevant information.
Data-Driven Insights: Uses structured retrieval to extract key information.
Creative Freedom: Lowest → Keeps AI responses structured and factual.
Vocabulary Range: Medium → Uses varied language to articulate different insights clearly.
Topic Variety: Medium → Covers related concepts while staying focused.
Word Variety: Medium → Balances consistency with fresh phrasing.
Search Range: High → Ensures that AI scans a broader dataset for useful insights.
Creative Writing: Maximizes AI’s creativity for expressive, imaginative output.
Creative Freedom: High → Encourages original, engaging, and sometimes unexpected responses.
Vocabulary Range: High→ Expands word choice for a more colorful and engaging tone.
Topic Variety: High → Allows AI to introduce fresh concepts and ideas.
Word Variety: High → Enhances writing flow and prevents repetition.
Search Range: Low → Prioritizes relevance over broad, factual accuracy.
Note: While these recommendations provide a strong foundation, it’s best to test different settings based on your specific workflow. Avoid maxing out values—going too high or too low can lead to unexpected or ineffective outputs. Use the tooltip descriptions in the settings panel to understand the limits of each modifier and experiment within the suggested range for balanced, high-quality results.
Model
Best For
Unique Selling Point (USP)
Text Embedding 3 Large (EU)
Complex, large-scale text embedding tasks
Best for high-performance, deep text understanding
Text Embedding 3 Large (US)
Same as EU-hosted version, but US-hosted
Newest OpenAI embedding model for large-scale tasks
Text Embedding Ada 002 (US)
Basic text embedding with high efficiency
Most optimized for low-cost embeddings
Text Embedding Ada 002 (EU)
General text embeddings, suitable for diverse applications
EU-hosted version for compliance-sensitive tasks
Text Embedding 3 Small (US)
Resource-sensitive tasks with complex embeddings
Best balance between efficiency and performance
BGE Embedding (EU)
Flexible embedding for various ML applications
Ideal for self-hosted, customizable deployments
English Embedding 4 (EU)
Embedding tasks for English-language content
Best embedding model for pure English text
Multilingual Embedding 2 (EU)
Embedding for multilingual content processing
Best for handling mixed-language embeddings
Agents
How do Agents differ from Workflows?
Agents handle single-use prompts, while Workflows automate multi-step processes for more structured interactions.
Can I share my Agents with a team?
Yes, you can share Agents with a team once you have given them access to the same Knowledge Bot
Why can't I see any Agents?
Simply activate your organization's agents in the Knowledge Bot settings.
Is Intent Agent different from Agents?
Yes, Intent Agents is an advanced features with different functions. While Agents are customizable prompt shortcuts designed to automate tasks, Intent Agents enhance answer quality by referencing database folder descriptions. They operate independently from regular Agents and serve different purposes in refining AI interactions.
Workflows
How do I trigger a Workflow?
You can start a Workflow manually from the Workflow tab or integrate it into other automated processes for seamless execution.
Can I share Workflows with my team?
Anyone with access to the Knowledge Bot can also access the Workflows created within that bot.
Why isn’t my Workflow giving accurate results?
Ensure each step is specific and clear, avoid overly complex prompts in a single step, and test different step sequences to optimize results.
Intent Agent
How does the Intent Agent decide which folders are most relevant?
The Intent Agent analyzes user-provided folder descriptions to identify and prioritize folders likely to contain relevant information before processing the contents.
When is it best to use the Intent Agent feature?
The feature is most useful when applied to databases containing multiple folders with distinct topic areas, allowing it to efficiently prioritize and retrieve relevant information.
Is there a minimum number of files or folders required for the Intent Agent to be effective?
There is no strict minimum number of files or folders needed to use the Intent Agent. However, the feature is most useful when applied to databases containing multiple folders with distinct topic areas, allowing it to efficiently prioritize and retrieve relevant information.
What happens if I don’t provide folder descriptions? Will the Intent Agent still work?
Without folder descriptions, the Intent Agent may not function optimally, as these descriptions are essential for relevance assessment.
Insights
Why should I use Insights?
Insights help you quickly save, organize, and retrieve useful AI responses without losing context. They are great for personal reference, collaboration, and sharing specific messages with others.
How are Insights different from Databases?
Unlike databases, which store large volumes of structured data that undergo chunking, Insights are stored in full context, preserving the original message format.
How should I name my Insights?
Use consistent and descriptive titles that include relevant keywords for easy searching. Clear titles help you quickly locate Insights when needed.
Model Modifiers
How do I know if I have optimized my Model Modifiers correctly?
Test your settings by running AI queries and checking if responses meet your expectations. If responses are too rigid or repetitive, increase Creative Freedom and Word Variety. If they’re too broad or inconsistent, lower Topic Variety and Vocabulary Range.
Should I max out any of the Model Modifiers?
No, maxing out values (e.g., setting Creative Freedom to 10) can lead to unpredictable or inaccurate outputs. It’s best to stay within the recommended range provided in the tooltips inside the settings panel.
Learning Language Models
How does the LLM affect the accuracy of AI-generated responses?
A more advanced LLM generally provides better accuracy and reasoning, but performance also depends on:
The size of the context window (larger windows retain more information).
The quality of the input prompt (clearer prompts lead to better responses).
The selected embedding model (affects how well data is retrieved).
Embedding Models
How do I choose between different embedding models?
➝ Consider the following factors:
Complexity & Scale: Large-scale tasks → Text Embedding 3 Large
Efficiency & Cost: Optimized performance → Text Embedding Ada 002
Language Support: English-only → English Embedding 4; Multilingual → Multilingual Embedding 2
Hosting Requirements: EU-based for GDPR compliance vs. US-based
What is the difference between small and large embedding models?
➝ Larger models (e.g., Text Embedding 3 Large) capture more context and nuances, making them better for complex queries.
Smaller models (e.g., Text Embedding 3 Small) prioritize efficiency and are better for lightweight applications.
