RAG System Visualizer

Current document type (text) is not recommended for this workflow. Consider using: markdown, code
Chunk:
Overlap:%
Document
chunk1
chunk2
chunk3
chunk4
Embedding
Embedding Model Parameters
Modeltext-embedding-ada-002
ProviderOpenAI
Dimensions1536
Max Length8191
Document Embedding

Neural networks are...

Tokenization & Encoding
1536d
Vector Store
High-Dimensional Vector Space
Embedded document chunks
📊 View DB Leaderboard
Vector Dimension Groups
Semantic
384d
25%
Captures meaning and concepts
Syntactic
256d
17%
Captures structure and grammar
Contextual
256d
17%
Captures context and relationships
Entity
256d
17%
Named entities and references
Temporal
128d
8%
Time-related features
Relational
256d
17%
Cross-document connections
x
y
z
Total Dimensions
1536
Stored Vectors
0
Vector Store
1
Query
2
Embedding
Embedding Model Parameters
Modeltext-embedding-ada-002
ProviderOpenAI
Dimensions1536
Max Length8191
Query Embedding

How do neural networks work?

Tokenization & Encoding
1536d
Cosine Similarity
Active Instruction

Ground responses in document context

Cosine Similarity Calculation
Query Vector
[0.80, 0.60, 0.40]
Document Vector
[0.70, 0.50, 0.50]
cos(θ) =0.980
98.00
Most Similar Chunks
The neural network architecture consists of multiple layers of interconnected nodes...
0.95
Deep learning models can process and analyze large amounts of unstructured data...
0.85
Machine learning algorithms learn patterns from training data to make predictions...
0.75
5
LLM
LLM Parameters📊 View Models Leaderboard
Temperature0.70
Top P0.90
Top K50.00
Max Tokens2000.00

AI Response

Based on the context from your documents, I can help answer your question...

3
4

© 2025 NexGen Technologies. All rights reserved.

Built with ❤ by NexGen Technologies