Architecture
How Graffold Works
A multi-source ingestion pipeline transforms unstructured documents into a consolidated knowledge graph — queryable via hybrid vector + graph retrieval with full provenance.
Ingestion Pipeline
Query & Retrieval Architecture
Integrations
Graph Databases
- Neo4j
- Amazon Neptune
- Kuzu (embedded)
- DuckDB (analytics)
LLM Providers
- AWS Bedrock
- AWS SageMaker
- Ollama (local / air-gapped)
- OpenAI-compatible APIs
Data Sources
- Any source with an API
- PDF files (vision + OCR)
- CSV / Excel / Parquet
- PubMed / bioRxiv (built-in)
Infrastructure
- Redis (distributed cache)
- Grafana + Prometheus
- Docker Compose / AWS CDK
- HuggingFace embeddings
Performance Stack
Rust-accelerated JSON
3-10× faster serialization
Rust ASGI Server
2-4× request throughput
Native Graph Driver
Up to 10× for large result sets
Rust-native DataFrames
2-5× faster for data processing
Token-aware Chunking
BPE tokenizer with sentence boundaries
Hybrid Retrieval
Vector + fulltext + graph traversal
Performance Benchmarks
Measured speedups from Rust- and C-backed drop-in replacements across the stack. Zero application-code rewrites required.
3–10×
JSON Serialization
Rust-backed encoder vs stdlib
2–4×
HTTP Throughput
Rust ASGI server vs Python default
Up to 10×
Graph Query Results
Native driver extensions for large result sets
2–5×
DataFrame Operations
Rust-native DataFrames vs legacy libraries
~10×
Cache Parsing
C-backed parser vs pure-Python
250 ms
P50 Query Latency
Hybrid vector + graph retrieval
Processing Throughput
1,000+
Documents per pipeline run
Multi-source
Parallel ingestion across data feeds
47 → 1
Duplicate edges consolidated per entity pair
< 15 min
Incremental update for ~100 new documents