Connect Data to your Knowledge Bot
Types of Connectable Data
Blockbrain supports various types of data that you can connect and use as context in your chat rooms.
Insights: Reuse saved AI responses or personal notes for consistent, contextual outputs.
File Upload: Upload files with full structure, formatting, and images preserved (no chunking).
Database source: Connect large, chunked, and searchable document collections shared across teams.
Email Service: Import Gmail threads to make key conversations accessible and searchable.
Additional Context: Add custom details or background information to refine AI accuracy.
Insights
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 database sources, 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
File Upload
Files are uploaded as whole units without chunking, preserving the document’s full structure and original formatting. Unlike database sources, which break content into sections, files maintain context in its entirety.
When to Use Files Instead
When document structure is critical (e.g., legal contracts, research papers, reports).
When exact phrasing needs to be referenced instead of processed in chunks.
When a smaller, standalone document is used for AI retrieval rather than large-scale search queries
When images such as infographics, charts, regular images, posters, and catalogues are relevant for the prompt
Sample Use Case of Files Upload
Analyzing infographics that a graphics heavy
Analyzing all parts of legal contracts
Database Source
Database sources are ideal for storing and processing large volumes of documents, especially when shared between teams. Uploaded documents are chunked, meaning they are broken into smaller segments for AI processing.
How Chunking Affects Accuracy
Chunking allows the AI to scan and retrieve information efficiently, but it may reduce context continuity across large documents.
Smaller chunks improve precision for direct queries, while larger chunks help retain context but can dilute accuracy if too broad.
The default chunk size is 2000 characters with a 300-character overlap, ensuring a balance between accuracy and context.
When to Use Database Sources
When managing large document collections that multiple users need access to
When scalability is required for long-term data management
When high-volume AI queries need to be performed across many documents
Email Service
Email Service allows you to import Gmail threads directly into your Data Room. This makes key email conversations searchable and usable as part of the AI’s context, especially when referencing previous decisions, stakeholder instructions, or project-related discussions.
Currently, only Gmail is supported for email integration.
When to Use Email Integration
When email threads contain key information or instructions relevant to the task
When tracking client or team discussions directly from email history
When AI responses need to align with ongoing communications or past decisions
Sample Use Case of Email Integration
Referencing client instructions discussed over email
Summarizing email threads
Additional Context
Additional Context allows you to add custom notes, clarifications, or background information directly into the Data Room to guide the AI more effectively. It’s especially helpful when your workflow includes multiple context sources (e.g., database sources, files, emails) and you need to define relationships, explain terms, or provide task-specific instructions. This ensures the AI consistently considers important context without requiring you to repeat it in every prompt.
When to Use Additional Context
When the Data Room includes multiple context types and clarity is needed (e.g., files + database source + emails)
When there are special instructions, business rules, or internal nuances to be explained
When you need to control how the AI interprets or prioritizes specific content
Sample Use Case of Additional Context
Clarifying that "Doc A" should be prioritized over "Doc B" in mixed-source workflows
Defining roles or terms that appear across multiple documents (e.g., “AM” = Account Manager)
How it Works
Benefits
Comprehensive data integration
Flexible access to various knowledge sources
Seamless context enhancement
Efficient knowledge management
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