RAG:Context-Aware Chunking | Google Drive to Pinecone via OpenRouter & Gemini

For Google Drive, this automated workflow efficiently retrieves documents, splits text into manageable sections, and prepares contextual information for enhanced search retrieval. It transforms text into vectors using advanced AI models, storing them in Pinecone for quick access and improved data organization. Ideal for streamlining document processing and enhancing information retrieval capabilities.

7/8/2025
17 nodes
Complex
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Categories:
Complex WorkflowManual Triggered
Integrations:
SplitInBatchesLangChainGoogle DriveExtractFromFileSplitOutSticky Note

Target Audience

This workflow is designed for professionals and organizations that need to process and analyze large documents efficiently. Specifically, it targets:
- Data Scientists: Who require quick access to contextual information from extensive text data.
- Researchers: Who need to extract and categorize information from academic papers or reports.
- Content Managers: Who manage large volumes of documentation and require a systematic way to convert text into searchable vectors.
- Developers: Who are integrating AI and document processing capabilities into applications.
- Business Analysts: Who analyze textual data to derive insights and improve decision-making processes.

Problem Solved

This workflow effectively addresses the challenge of managing and extracting valuable insights from large text documents. It automates the process of:
- Downloading documents from Google Drive, eliminating manual retrieval.
- Extracting text data and splitting it into manageable sections.
- Creating contextual summaries for each section, enhancing searchability and retrieval.
- Converting text into vectors for use in AI applications, improving data accessibility and analysis.
This results in significant time savings and improved accuracy in information retrieval.

Workflow Steps

  • Manual Trigger: The workflow begins when the user clicks ‘Test workflow’.
    2. Download Document: The workflow retrieves a specified document from Google Drive.
    3. Extract Text Data: It extracts the textual content from the downloaded document.
    4. Split Text into Sections: The extracted text is divided into sections based on predefined delimiters.
    5. Prepare for Looping: The workflow prepares the sections for processing in batches.
    6. AI Context Preparation: For each section, an AI agent generates a succinct context to improve search retrieval.
    7. Concatenate Context and Section: The context is combined with the original section text.
    8. Convert to Vectors: The concatenated text is converted into vectors using the Google Gemini embedding model.
    9. Store in Pinecone: Finally, the vectors are stored in the Pinecone vector database for efficient search and retrieval.

    • Customization Guide

    To customize this workflow, users can:
    - Modify Document Source: Change the fileId in the ‘Get Document From Google Drive’ node to point to a different document.
    - Adjust Text Splitting Logic: Alter the splitting logic in the ‘Split Document Text Into Sections’ node to accommodate different section delimiters.
    - Change AI Agent Parameters: Update the text prompt in the ‘AI Agent - Prepare Context’ node to tailor the context generation to specific needs.
    - Select Different Models: Users can choose different language models or vectorization methods by modifying the parameters in the corresponding nodes.
    - Add Additional Processing Steps: Insert new nodes to include extra processing, such as sentiment analysis or keyword extraction, to enhance the workflow's capabilities.