🧠 Neo4j RAG + BitNet Chat (local developer mode)
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💬 Chat History
👤
You 2:45 PM
What is BitNet and how does it achieve memory efficiency?
🤖
Assistant 2:45 PM
BitNet is an ultra-efficient 1.58-bit ternary quantized language model that achieves 87% memory reduction through innovative ternary quantization (-1, 0, +1) instead of traditional 16-bit floating point representations. This breakthrough allows the model to run efficiently on consumer hardware while maintaining competitive performance.
📚 Sources (3) ▼
1. bitnet_paper.pdf
Score: 0.92
BitNet b1.58 achieves 87% memory reduction compared to traditional LLMs through ternary quantization, representing weights as -1, 0, or +1. This approach enables efficient inference on consumer hardware...
2. efficiency_guide.pdf
Score: 0.89
Memory efficiency in BitNet is achieved through several optimizations: ternary weight representation, optimized matrix multiplication kernels, and reduced memory bandwidth...
3. architecture.md
Score: 0.84
The architecture leverages ARM TL1 optimized kernels and custom SIMD instructions to accelerate ternary operations, resulting in 2-5x speedup...
⚡ 29ms 💾 0.4GB
👤
You 2:46 PM
How does Neo4j improve RAG performance compared to traditional vector databases?
🤖
Assistant 2:46 PM
🗄️ Neo4j Healthy
Avg: 45ms
Port: 7687
⚡ RAG Service Online
Avg: 1.2s
Port: 8000
🤖 BitNet LLM Loaded
Avg: 3.5s
Port: 8001
📄 Documents 32
🧩 Chunks 29,129 ↑ 910/doc
⚡ Response 29ms ↓ 95%
💾 Memory 0.4GB ↓ 87%
🎯 Cache 85% ↑ 15%
⚙️ RAG Configuration
Max Results 5
Similarity Threshold 0.7
🤖 LLM Configuration
Use BitNet LLM
Temperature 0.7
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📊 Full System Statistics

📄 Total Documents
32
Documents loaded and indexed in Neo4j database
🧩 Total Chunks
29,129
↑ 910 avg per document
Text chunks processed with 300 char size and 50 char overlap
📐 Vector Dimensions
384
Embedding dimensions using all-MiniLM-L6-v2 model
⚡ Avg Response Time
29ms
↓ 95% improvement
From 46s baseline to 29ms with Neo4j optimizations
🎯 Cache Hit Rate
85%
↑ 15% increase
FIFO cache with 100 entry capacity, thread-safe
🔄 Queries Processed
1,247
Total queries since system initialization
💾 Memory Usage
0.4GB
↓ 87% reduction
BitNet ternary quantization vs traditional 16-bit models
💻 RAM Available
7.2GB
System memory available for operations
🗄️ Database Size
342MB
Neo4j database storage including indexes
🟢 Neo4j Status
Healthy
Connected to Neo4j 5.11 on localhost:7687
🤖 BitNet Status
Active
BitNet.cpp inference engine running on port 8001
⏱️ Uptime
12h 34m
System running since startup
📈 Query Response Time Trend (Last Hour)
12:00 PM
1:00 PM
4s
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0s
🔍 Query Analytics (Recent Queries)
2:46 PM How does Neo4j improve RAG performance? 29ms
2:45 PM What is BitNet and how does it achieve memory efficiency? 32ms
2:42 PM Explain vector search in Neo4j 28ms
2:38 PM What are the benefits of ternary quantization? 45ms
2:35 PM How to optimize RAG retrieval? 31ms