Orbit Platform Documentation
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  • Welcome
  • 1. Introduction
    • 1.1 Orbit Platform Overview
    • 1.2 Key Features
    • 1.3 Target Audience
    • 1.4 Benefits of Using Orbit Platform
    • 1.5 Overview of this Documentation
  • 2. Quick Start
    • 2.1 Accessing Orbit Platform
    • 2.2 Navigating the User Interface
    • 2.3 Basic User Cases
      • 2.3.1 Conducting a Semantic Search
      • 2.3.2 Copilot Chat
      • 2.3.3 Browsing and Using Pre-Defined Bots
    • 2.4 Exploring the Bot Marketplace
    • 2.5 Understanding SaaS Features and Limitations
  • 3. Platform Overview
    • 3.1 Overview of Orbit Platform
    • 3.2 Orbit AI Studio
      • 3.2.1 Data Loaders
      • 3.2.2 Metadata Management
      • 3.2.3 PDF Pre-Processing
      • 3.2.4 LLM Integration
      • 3.2.5 Workflow Automation
    • 3.3 Custom Knowledge Base Creation
    • 3.4 Chat and Search Capabilities
    • 3.5 Bot Marketplace
      • 3.5.1 Overview of the Bot Marketplace
      • 3.5.2 Creating and Managing Bots
      • 3.5.3 Automating Manual Tasks with Bots
  • 3.6 Data Connectors
  • 4. User Guide
    • 4.1 General User Interface
      • 4.1.1 Portfolio Management
      • 4.1.2 Concept Management
      • 4.1.3 Share
    • 4.2 Semantic Search and Chat
    • 4.3 Features on Single Document
    • 4.4 Create Your Knowledge Base
  • 5. Orbit Knowledge Bases
    • 5.1 Introduction
  • 5.2 Global Exchange Filings
  • 5.3 China Earnings Transcripts
  • 5.4 Global Sustainability Reports
  • 5.5 Global Regulation Documents
  • 5.6 Global Earnings Transcripts
  • 5.7 Listed Companies Official Documents
  • 5.8 Private Companies Official Documents
  • 5.9 Google News
  • 5.10 China Bond Documents
  • 6. Off-the-Shelf Bots
    • 6.1 Data Transformer
    • 6.2 Filings Insight Extractor
    • 6.3 Portfolio News Tracker
    • 6.4 Summary Composer
    • 6.5 Financial Statement Navigator
    • 6.6 Earning Call Calendar
    • 6.7 News Flow Tracker
  • 6.8 SmartMonitor Bot
  • 7. Pricing
    • 7.1 Product Options
    • 7.2 SaaS Pricing Structure
  • 7.3 Product Selection Guide
  • 8. Enterprise Deployment
    • 8.1 Deployment Options
    • 8.2 Security and Compliance
    • 8.3 Scaling and Performance
    • 8.4 Integration with Existing Systems
  • 9. Use Cases and Examples
    • 9.1 Investment Research Use Cases
      • 9.1.1 Generate a Research Report with Copilot Chat
      • 9.1.2 Analyse Investment Themes from Annual Reports
    • 9.2 Sustainability Use Cases
      • 9.2.1 Generate an ESG Report with Copilot Chat
      • 9.2.2 Orbit vs Claude vs Perplexity
    • 9.3 Service Provider Use Cases
    • 9.4 Case Studies: Success Stories
  • 10. FAQ and Troubleshooting
    • 10.1 Common Questions
    • 10.2 Contacting Support
  • 11. Appendices
    • 11.1 Glossary of Terms
    • 11.2 Whitepapers
      • Advancing News Analytics for Financial Decision Making
    • 11.3 Release Notes
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On this page
  • 1. What Does Orbit Search Over?
  • 2. How Does the Search Work?
  • 3. How Are the Results and Relevance Determined?
  • 4. How Copilot Chat Happens
  • 5. Advanced Features
  • 6. Why Vector Search?
  1. 3. Platform Overview

3.4 Chat and Search Capabilities

Orbit Insight is equipped with advanced chat and search capabilities, allowing users to efficiently find and analyze relevant information across vast datasets. This chapter explores how these capabilities work, from the underlying technology to the advanced features that enhance the user experience.

1. What Does Orbit Search Over?

Orbit Insight searches across all the knowledge bases provided by the platform, as well as the internal knowledge bases of clients. This comprehensive search capability ensures that users can access both external data and their proprietary information in a single, unified platform, making it easier to conduct thorough and informed research.

2. How Does the Search Work?

The search functionality within Orbit Insight is powered by a sophisticated process that involves several key steps:

  • PDF Pre-Processing: The search process begins with PDF pre-processing, where clean text is extracted from documents. This step ensures that all textual information is accurately captured, regardless of the original format.

  • Embedding Models and Vectors: After text extraction, embedding models are applied to generate vectors for the text. These vectors represent the semantic meaning of the text in a numerical format.

  • Indexing: Both the clean text and the vectors are indexed into the search engine. This dual indexing ensures that the system can efficiently handle both keyword-based searches and more complex semantic queries.

  • Search Execution: When a user enters search terms—whether keywords or full sentences—those terms are also converted into vectors. The search engine then compares these vectors with the indexed vectors of the text blocks, identifying the most relevant matches based on their similarity.

3. How Are the Results and Relevance Determined?

The relevance of search results in Orbit Insight is primarily determined by the cosine similarity between the vector representation of the search terms and the vectorized text blocks within the knowledge bases.

  • Cosine Similarity: This metric measures the cosine of the angle between two vectors, with smaller angles indicating higher relevance. The closer the vectors are in direction, the more semantically similar they are, resulting in a higher relevance score for the associated documents.

  • Relevance Scoring: The search engine uses these relevance scores to rank the search results, ensuring that the most relevant blocks of text and their corresponding documents appear at the top of the results list.

4. How Copilot Chat Happens

Orbit Insight’s copilot chat functionality leverages the power of LLMs (Large Language Models) to provide users with accurate and contextually relevant answers to their questions.

  • Initial Search: When a user asks a question, the system first conducts a search within the search engine to identify relevant blocks of text along with their associated metadata.

  • LLM Processing: The system then sends both the user’s question and the relevant text blocks to the LLM. The model processes this information to generate a coherent and informative response, which is then returned to the user.

  • Integrated Response: This integration of search and LLM processing ensures that the chat responses are not only accurate but also grounded in the specific documents and data relevant to the user’s query.

5. Advanced Features

Orbit Insight’s chat and search functionalities are enhanced by several advanced features designed to improve the relevance and precision of search results.

  • Question Intention Detection: The system includes an algorithm that detects the intention behind a user’s question. This feature enriches the query by understanding its context and aligning it more closely with investment research topics, ensuring that the results are more relevant to the user’s needs.

  • Enhanced Query Understanding: By refining the user's query, the system can provide more accurate and contextually appropriate search results, improving the overall search experience.

6. Why Vector Search?

Vector search is a crucial feature of Orbit Insight, particularly for handling multilingual documents. By converting all text into vectors, the platform can perform true multilingual searches, where documents in different languages can be searched and compared on a semantic level.

  • Multilingual Capability: This approach ensures that search results are consistent across languages, maximizing the value of Orbit Insight’s global document coverage. Users can search in one language and find relevant documents in multiple languages, breaking down language barriers in investment research.

  • Semantic Consistency: Vector search allows the system to focus on the meaning of the text rather than just the exact words, making it possible to find relevant information even when the query and documents are in different languages.

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Last updated 9 months ago