Gemini 2.5 Pro
Model
Google's latest multimodal
2M Tokens
Context Window
Full document analysis
PDF & DOCX
File Support
Native parsing
< 2s
Processing
Average extraction time
Upload Knowledge Base
Drag & drop a PDF, DOCX, Image, or Text file here, or click to select.
(Max 10MB)
RAGPipeline Architecture
How we process and retrieve information without vector DBs (for single doc)
Ingestion Layer
Component 1
File parsing and normalization
- pdf-parse for PDF extraction
- mammoth for DOCX conversion
- In-memory buffer processing
- Whitespace normalization
Context Injection
Component 2
Dynamic prompt engineering
- Full-text context window insertion
- System instruction priming
- Role-based history management
- Token usage optimization
Gemini 2.5 Pro
Component 3
Reasoning engine
- 2M token context window
- Multimodal capabilities
- Native reasoning on long text
- Low-latency generation
Server Actions
Component 4
Secure transport layer
- Direct client-to-cloud communication
- Type-safe interfaces
- Streaming response handling
- Error boundary management
ProductionChallenges
Optimizing RAG for real-world use
File Format Variance
Multi-library parsing strategy
Reliable extraction from dirty PDFs
Context Limits
Gemini 2.5 Pro's extended window
No need for chunking/vector DB for <2M tokens
Latency
Vertex AI streaming API
Reduced round-trip overhead
Data Privacy
Ephemeral processing
Zero data retention on server
Processing WorkflowImplementation Details
Upload & Validation
Client-side checks
Technologies:
React Dropzone
MIME type validation
Size limits (10MB)
Text Extraction
Server-side parsing
Technologies:
Buffer conversion
pdf-parse
mammoth
Context Construction
Prompt assembly
Technologies:
Template interpolation
History formatting
LLM Inference
Answer generation
Technologies:
Vertex AI SDK
Gemini 2.5 Pro
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