The .txt File as the Soul of a Personal AI — FileRAG Memory Architecture

Developer Dharanidharan J (JD) has built FileRAG, a memory architecture for personal AI that stores a distilled understanding of each user in a single plain-text `.txt` file. The system uses hybrid BM25 and semantic retrieval to inject relevant context from the file into conversations, and employs topic-drift detection and deduplication to maintain clean, accurate summaries. In benchmarks, FileRAG achieved 100% retrieval accuracy across 500 and 1000 conversation turns, while traditional dict and Redis-based systems failed completely at those scales.

By Dharanidharan J JD Full Stack & AI Engineer | Building Jarvix Every chatbot tutorial teaches you the same thing: history = history.append {"role": "user", "content": message} And that works — until it doesn't. After 500 turns, your dict has forgotten who the user is. After 1000 turns, you're hitting token limits. After a restart, everything is gone. Redis helps with persistence but still buries early facts under noise. Vector DBs help with retrieval but bloat storage and need infrastructure. What if the memory itself was just a file? Every conversation a user has gets distilled into a plain .txt file. That file is the brain. On every new query, a hybrid BM25 + semantic RAG retrieves the most relevant chunks from it and injects them as context. users/ └── jd.txt ← the soul file The soul file looks like this: Turns 1-5 - User's name is JD, software engineer - Building FileRAG, a novel memory architecture - Uses Pop OS with Fish shell and NVIDIA GPU Turns 6-10 - Has a cat named Pixel who distracts during coding - Paused TaskNest due to burnout - Now focused on AgenticMesh Human readable. Editable. Yours. Most memory systems store messages . FileRAG stores a relationship . | System | What it stores | |---|---| | Dict / Redis | Raw message objects | | Vector DB | Embeddings of messages | FileRAG | Distilled understanding of the user | The longer you use it, the more the AI understands you — not because it has more messages, but because it has a better summary of who you are. User message ↓ Topic drift check cosine similarity ├── Drift detected → distill current buffer immediately └── No drift → continue ↓ Hybrid retrieval BM25 + ChromaDB from soul file ↓ Inject context → LLM responds ↓ Append to turn buffer ↓ Every 5 turns → distill → append to soul file → update ChromaDB ↓ Emergency distillation on exit SIGINT/SIGTERM 1. Topic-Drift Distillation Instead of waiting every N turns blindly, the system measures semantic similarity between the current buffer and the new message. If similarity drops below 0.25, it immediately distills and starts a fresh block. This keeps topic chunks clean and isolated. 2. Deduplication Before writing any new chunk, cosine similarity is checked against all existing chunks. If 92% similar, the chunk is skipped. This prevents filler conversations from polluting the soul file. 3. Emergency Exit Handler SIGINT and SIGTERM are intercepted. On Ctrl+C, the current buffer is immediately distilled before the process exits. Nothing is lost. 4. Hybrid Retrieval BM25 catches exact keywords project names, usernames . Semantic search catches meaning preferences, personality . Together they outperform either alone. Tested on Pop OS, NVIDIA GPU, sentence-transformers all-MiniLM-L6-v2 embeddings | Metric | Dict | Redis | Vector DB | FileRAG | |---|---|---|---|---| | Write Speed ms | 0.0004 | 0.30 | 33.38 | 20.33 | | Read Speed ms | 0.002 | 0.26 | 6.64 | 9.26 | | Storage KB | 1.42 | 1.38 | 396.16 | 356.66 | | Accuracy | 100% | 100% | 67% | 100% | | Persistent | ❌ | ✅ | ✅ | ✅ | At small scale, Dict and Redis win on speed. FileRAG matches on accuracy. Fair. | Metric | Dict | Redis | Vector DB | FileRAG | |---|---|---|---|---| | Write Speed ms | 0.0002 | 0.08 | 22.23 | 18.93 | | Read Speed ms | 0.002 | 0.24 | 8.51 | 7.64 | | Storage KB | 34.75 | 33.77 | 1604.16 | 653.47 | | Accuracy | 0% ❌ | 0% ❌ | 67% | 100% | This is where it gets interesting. Dict and Redis completely fail — core facts buried under 490 turns of noise. FileRAG still retrieves perfectly. | Metric | Dict | Redis | Vector DB | FileRAG | |---|---|---|---|---| | Storage KB | 69.47 | 67.51 | 4338.36 | 938.74 | | Soul file only KB | — | — | — | 18.58 | | Accuracy | 0% ❌ | 0% ❌ | 67% | 100% | FileRAG's total storage includes ChromaDB index overhead. The soul file itself — the actual human-readable memory — is just 18 KB for 1000 turns. ---|---|---|---|---| | Storage KB | 3,478 | 3,381 | 76,159 | 29,812 | | Soul file KB | — | — | — | ~1,865 | | Accuracy | 0% | 0% | ~67% | ~100% | At 100k turns, Vector DB would consume ~74 MB just for index storage. FileRAG's soul file stays under 2 MB — human-readable, portable, private. | Category | Dict | Redis | Vector DB | FileRAG | |---|---|---|---|---| | Fastest write | ✅ | ✅ | ❌ | Medium | | Best accuracy at scale | ❌ | ❌ | Medium | ✅ | | Smallest storage at scale | ❌ | ❌ | ❌ | ✅ | | Persistent | ❌ | ✅ | ✅ | ✅ | | No infrastructure | ✅ | ❌ | ❌ | ✅ | | Local / offline | ✅ | ❌ | ⚠️ | ✅ | | Privacy on device | ✅ | ❌ | ⚠️ | ✅ | | Grows naturally with user | ❌ | ❌ | Medium | ✅ | FileRAG is not the fastest. It is not the simplest. But it is the only architecture that gets more accurate as the conversation grows , without growing infrastructure requirements. Your brain doesn't record every conversation verbatim. It compresses experiences into memory — feelings, facts, patterns. The hippocampus distills, the cortex stores. FileRAG does the same thing: Conversation → Distillation → Soul file → Retrieval → Natural response Experience → Hippocampus → Cortex → Recall → Natural behaviour The soul file is not a database. It is a diary the AI reads before speaking to you. LLM → Groq llama3-70b-8192 Distillation → Groq llama3-70b-8192 every 5 turns or on topic drift Embeddings → sentence-transformers/all-MiniLM-L6-v2 Vector store → ChromaDB persistent Retrieval → Hybrid BM25 + Cosine Semantic Memory → {user id}.txt — the soul file The full implementation — main.py , benchmark.py , and the architecture — is available on GitHub: → github.com/dharanidh75/filerag-memory This is also the memory layer being built into Jarvix — a local-first voice AI assistant for Pop OS. If you're building a local AI, a personal assistant, or just tired of your chatbot forgetting who you are after every restart — give FileRAG a try. The soul file is 18 KB. Your AI deserves better than a dict.