feat(app): ml | cyberver | redesign | + more (#1182)

Author: dasein108

.env

@@ -0,0 +1 @@
+# CHAIN_ID = space-pussy

.eslintrc

@@ -37,6 +37,7 @@
     "FileReader": true,
     "cyblog": "readonly"
   },
+
   "rules": {
     "valid-jsdoc": "off",
     "no-shadow": "off",

.gitignore

@@ -29,7 +29,7 @@ yarn-error.log*
 src/.DS_Store
 
 storybook-static
-.env
+.env.*.local
 
 # Local Netlify folder
 .netlify

.storybook/main.ts

@@ -56,6 +56,10 @@ const config: StorybookConfig = {
             test: /\.cozo$/,
             use: 'raw-loader',
           },
+          {
+            test: /\.rn$/,
+            type: 'asset/source',
+          },
         ],
       },
     };

.vscode/settings.json

@@ -5,6 +5,7 @@
     "cybercongress",
     "cyberlink",
     "cyberlinks",
+    "cybernet",
     "denoms",
     "helia",
     "investmint",
@@ -13,6 +14,8 @@
     "negentropy",
     "stylelint",
     "superintelligence",
+    "websockets",
+    "cyberver",
     "websockets"
   ],
   "eslint.enable": true,
@@ -23,7 +26,7 @@
     "typescriptreact"
   ],
   "editor.codeActionsOnSave": {
-    "source.fixAll.eslint": true
+    "source.fixAll.eslint": "explicit"
   },
   "css.validate": false,
   "less.validate": false,

docs/backend.md

@@ -1,51 +1,194 @@
-# Backend Architecture
+# CYB local backend(in-browser)
+
+Cyb plays singinficat role in cyber infrastructure. The app reconstruct self-sufficient backend+frontend pattern inside the browser.
+In big view app consist from 3 parts:
 
 ```mermaid
 graph TD;
+        App["frontend\n(main thread)"]-.proxy.->Backend["backend\n(shared worker)"];
+        App-.proxy.->Db["graph db\n(shared worker)"];
+        Backend-.proxy.->Db;
+        App<-.message\nchannels.->Backend;
+```
 
-    subgraph frontend["frontend(main thread)"]
-        App["Frontend"]-->Hook["useBackend()"];
-        Hook-->methods("startSync()\nloadIpfs()\n...\nisReady\nipfsError\n...");
-        Hook-.broadcast channel\n(any worker).->reducer["redux(state)"]
-        Hook-.save history from app.->defferedDbApiFront[/"DefferedDbApi(proxy)"/]
-        Hook--osenseApi["senseApi"];
-        Hook--oipfsApiFront[/"ipfsApi(proxy)"/];
-        senseApi--odbApi[/"dbApi(proxy)"/];
-    end
+To reduce overload of main thread we have created 2 separate shared workers, where all the stuff is hosted. Bi-interraction between all layers occurs using proxy(comlink abstraction) or directly using broadcast channels.
 
-    dbApi<-.message channel.->dbWorker["dbApi"];
-    subgraph dbWorkerGraph["cyb~db(worker)"]
-        dbWorker<-.bindings(webApi).->cozodb{{"CozoDb(wasm)"}}
-    end
+## Db layer
+
+Db worker is pretty simple it it's host only local relational-graph-vector database - [[cozo]]. It's represented with DbApi in frontend and backend layers.
+Cozo provide bazing fast access to brain and ipfs data in relational form and also in vector format, processing by [ml]embedder.
 
-    defferedDbApiFront-.->defferedDbApi;
-    ipfsApiFront<-.->ipfsApi;
-    subgraph backgroundWorker["cyb~backend(worker)"]
-        subgraph sync["sync service"]
-            ipfsNode["ipfs node"];
-            links;
-            transactions;
+```mermaid
+graph TD;
+        dbApi["dbApi"]--odb_meta_orm;
+        subgraph rune["cozo db"]
+            db_meta_orm[["meta orm"]]-.->db;
         end
-        sync--oparticleResolver[["Particle resolver"]]
-        particleResolver--oqueue;
-        particleResolver--odbProxyWorker;
-        sync--oipfsApi;
-        sync--odbProxyWorker[/"dbApi(proxy)"/];
-        defferedDbApi[["defferedDbApi"]]-->dbProxyWorker;
-        queue-->defferedDbApi;
-        ipfsApi--oqueue[["queue"]];
-        ipfsApi--onode["node"];
-        queue--balancer-->node;
-        node--embedded-->helia;
-        node--rpc-->kubo;
-        node--embedded-->js-ipfs;
+```
+
+### Db entities
+
+- brain:
+  - particles
+    - embeddings
+  - links
+  - transactions
+  - community
+- sense:
+
+  - sync items + update status
+
+- system:
+  - config
+  - queue messages
+
+## Backend layer
+
+Backend worker is more complicated it contains significant elements of cyb architecture:
+
+```mermaid
+graph TD;
+    subgraph Backend["backend(shared worker)"]
+
         subgraph ipfs["ipfs implementations"]
             helia;
             kubo;
             js-ipfs;
         end
 
-        dbProxyWorker<-.message channel.->dbWorker
+        subgraph queues["message brokers"]
+            ipfs_queue["ipfs load balancer"];
+            queue["data processing queue aka bus"];
+        end
+
+        subgraph rune["rune"]
+            vm["virtual machine"]--ovm_bingen{{"cyb bindings"}};
+        end
+
+        subgraph sense["sense"]
+            link_sync["link sync"];
+            msg_sync["message sync"];
+            swarm_sync["swarm sync"];
+        end
+
+        subgraph ml["ML transformers"]
+            feature_extractor["embedder"];
+        end
+
+    end
+```
+
+### Ipfs module
+
+Represented with IpfsApi at frontend layer, but also have direct access for some edge cases
+
+- Uses module that encapsulate different Ipfs implementations(kubo, helia, js-ipfs(obsolete))
+  - cache content(local storage & cozo)
+  - preserve redundancy
+- Ipfs queue, process all requests to ipfs, prioritize, cancel non-actual requests and organize content pipeline
+  - responsible for:
+    - ipfs load balancing(limit of requests)
+    - request prioritizing(actual requests first)
+    - fault processing(switch fetch policy)
+    - post processing(**inline rune vm** into pipeline)
+
+```mermaid
+graph LR
+user(ipfsApi\nenqueue particle) --> q[["queue\n(balancer)"]] --> node[/"ipfs"/] -- found --> rune[rune vm] -- mutation | content --> cache["cache"] --> app(app\ncontent)
+node -. not found\n(retry | error) .-> q
+```
+
+## Bus
+
+Represented with some helpers and used for cases when blaancer is needed, some services not initialized yet(deffered actions), or long calculations is requered(ml inference, ipfs requests):
+
+- particle, request ipfs, save; calc embedding
+- link, deffered save
+- message persistence is protected by db store
+
+```mermaid
+graph TD;
+        sender{{"enqueue(...)"}} -.message bus.-> bus
+        subgraph task["task manager"]
+            bus[["queue listener"]];
+
+            bus-.task.->db("store\ndata")--odb1["dbApi"];
+            bus-.task.->ml("calculate\nembedding")--oml1["mlApi"];
+            bus-.task.->ipfs("request ipfs\nlow-priority")--oi["ipfsApi"]
+        end
+```
+
+## Sense
+
+Represented by SenseApi + subscription to broadcast channel at fronted layer. Provide continious update of cyberlinks related to my brain and my swarm, recieving on chain messages etc.:
+
+- Particles service (pooling)
+- Transactions service (pooling + websocket)
+- My friends service (pooling)
+- Ipfs service(pooling)
+
+All data and update status is stored into db, when some new data is recieved that triggers notification for frontendю
+
+```mermaid
+graph TD;
+    db[["dbApi"]];
+    bus[["particle queue"]];
+
+    subgraph sense["sync service"]
+        notification("notification service")
+
+        particles[["particle service"]]--onotification;
+        transactions[["transaction service"]]--onotification;
+        myfriend[["my friends service"]]--onotification;
+
+        particles -.loop.-> particles;
+        transactions -.loop.-> transactions;
+        myfriend -.loop.-> myfriend;
+    end
+
+
+    subgraph blockchain["blockchain"]
+        lcd[["lcd"]]
+        websockets("websockets")
+        indexer[["indexer"]]
+    end
+
+    subgraph app["frontend"]
+        redux["redux"]
+        sender{{"senseApi"}};
+    end
+
+    notification -.message.-> redux;
+    sender -.proxy.-> db;
+    sense -.proxy.-> db;
+    sense -.message.-> bus;
+    bus -.proxy.-> db;
+
+    sense <-.request\nsubscriptin.->blockchain;
+
+```
+
+## Rune
+
+Rune VM execution is pipelined thru special abstraction called entrypoints. VM have bindings to all app parts: DB, transformers, signer, blockchain api, ipfs and also includes context of the entrypoint.(see. [[scripting]] for detailed description).
+
+## ML transformers
+
+Represented my mlApi. Uses inference from local ML models hosted inside browser.
+
+- future extractor. BERT-like model to trnsform text-to-embeddings.
+
+```mermaid
+graph TD;
+    subgraph ml["transformers"]
+        embedder["embedder"];
+    end
+
+    subgraph dbApi["dbApi"]
+        db[["DB"]];
     end
+    mlApi["mlApi"];
 
+    mlApi--odb;
+    mlApi--oembedder;
 ```