WIP

Author: yuiseki

.env.example

@@ -1,4 +1 @@
-OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxx
-CLOUDFLARE_AI_GATEWAY=https://gateway.ai.cloudflare.com/v1/xxxxxxxxxxxxxxxx/yyyyyyyy
-OLLAMA_BASE_URL=http://localhost:11434
-OLLAMA_ENABLED=false
+USE_OLLAMA=1

.node_version

@@ -1 +1 @@
-21.5.0
+22.3.0

.vscode/settings.json

@@ -30,6 +30,7 @@
     "colorraw",
     "datetime",
     "dirents",
+    "distroless",
     "ducanh",
     "duckdb",
     "exintro",
@@ -71,23 +72,27 @@
     "Mandera",
     "Masó",
     "México",
+    "myapp",
     "Nangarhar",
     "NDRRMA",
     "NEOC",
+    "nextjs",
     "nonlatin",
     "OPENAI",
     "openstreetmap",
     "OPFS",
     "pageid",
     "Panjshir",
     "Parwan",
+    "pgvector",
     "Pilon",
     "Pourriez",
     "qdrant",
     "qwen",
     "reliefweb",
     "replanner",
     "Rukum",
+    "setuptools",
     "smollm",
     "Söhne",
     "souhaitez",
@@ -107,8 +112,10 @@
     "Urayasu",
     "USGS",
     "vectorstores",
+    "vercel",
     "VFJJREVOV",
     "voir",
-    "vous"
+    "vous",
+    "wiktorn"
   ]
 }

Makefile

@@ -4,6 +4,16 @@ all: \
 	update_reliefweb \
 	voyager
 
+.PHONY: setup
+setup:
+	test ollama || $(MAKE) setup_ollama
+	ollama pull qwen2.5:1.5b
+	ollama pull snowflake-arctic-embed:22m
+
+.PHONY: setup_ollama
+setup_ollama:
+	curl -fsSL https://ollama.com/install.sh | sh
+
 .PHONY: fetch_nhk
 fetch_nhk:
 	npm run site:www3.nhk.or.jp:fetch

README.md

@@ -7,3 +7,17 @@ TRIDENT, an Interactive Smart Maps Assistant.
 - TRIDENT is an AI assistant that can interactively create Smart Maps
 - TRIDENT is based on OpenStreetMap data, specifically the Overpass API
 - TRIDENT generates Overpass API queries based on user interaction, makes requests to the Overpass API, and visualizes the results as a map
+
+## Development
+
+### Minimum Requirements
+
+- Node.js
+- npm
+- ollama
+
+```bash
+make setup
+npm ci
+npm test
+```

docker-compose.yml

@@ -1,49 +1,29 @@
-version: "3.9"
-
 services:
-  trident:
+  nextjs:
     build:
       context: .
       dockerfile: Dockerfile
+    command: npm run dev
     volumes:
       - type: bind
         source: ${LOCAL_WORKSPACE_FOLDER:-.}
         target: /app
-    command: npm run dev
     ports:
       - 3000:3000
     networks:
-      - myapp
-  llama-cpp-server:
-    image: local/llama.cpp:full
+      - trident
+  ollama:
+    image: ollama/ollama
+    volumes:
+      - ollama:/root/.ollama
     ports:
-      - 8080:8080
+      - 11434:11434
     networks:
-      - myapp
-    tty: true
-    command: >
-      --server
-      --model /models/${MODEL_NAME:-tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf}
-      --ctx-size ${CONTEXT_SIZE:-2048}
-      --threads ${THREADS:-8}
-      --mlock --no-mmap --embedding
-      --port 8080 --host 0.0.0.0
-    volumes:
-      - ~/llama.cpp/models:/models
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              capabilities: [gpu]
+      - trident
   overpass-api:
     # MEMO:
     # http://localhost:5000/api/interpreter?data=[out:json];rel(2220322);out;
     image: wiktorn/overpass-api
-    ports:
-      - 5000:80
-    volumes:
-      - overpass-db:/db
     environment:
       - OVERPASS_META=no
       - OVERPASS_MODE=init
@@ -53,10 +33,17 @@ services:
       - OVERPASS_USE_AREAS=true
       - OVERPASS_STOP_AFTER_INIT=false
       - OVERPASS_MAX_TIMEOUT=30000
+    volumes:
+      - overpass-db:/db
+    ports:
+      - 5000:80
+    networks:
+      - trident
 
 networks:
-  myapp:
-    name: myapp
+  trident:
+    name: trident
 
 volumes:
+  ollama:
   overpass-db:

docs/USE_OLLAMA.md

@@ -1,6 +1,6 @@
 # Using ollama with TRIDENT
 
-This guide explains how to set up and use ollama, particularly with the phi4:14b model, for enhanced language processing capabilities.
+This guide explains how to set up and use ollama, particularly with the qwen2.5:1.5b model, for enhanced language processing capabilities.
 
 ## Installation
 
@@ -11,65 +11,57 @@ curl -fsSL https://ollama.com/install.sh | sh
 ```
 
 This command will:
+
 1. Download the ollama installation script
 2. Install ollama on your system
 3. Set up the ollama service
 
-## Using phi4:14b Model
-
-The phi4:14b model is recommended for its improved language processing capabilities, particularly for Japanese text.
-
 ### Download and Run
 
-To use the phi4:14b model:
+To use the qwen2.5:1.5b model:
 
 ```bash
-ollama run phi4:14b
+ollama run qwen2.5:1.5b
 ```
 
 This will:
-1. Download the model if not already present (approximately 9.1GB)
+
+1. Download the model if not already present
 2. Start an interactive session with the model
 
 ### Example Usage
 
-Here's an example of using phi4:14b with Japanese text:
+Here's an example of using qwen2.5:1.5b with Japanese text:
 
 ```bash
-ollama run phi4:14b "こんにちは！簡単な自己紹介をお願いします。"
+ollama run qwen2.5:1.5b "こんにちは！簡単な自己紹介をお願いします。"
 ```
 
 Example response:
+
 ```
-こんにちは！私はOpenAIが開発した言語モデルです。質問に答えたり、情報を提供したりするのが得意です。
-さまざまなトピックについての知識を持っていますが、最新の出来事や特定の個人的な経験に関しては限界があります。
-プライバシーや安全性を尊重しながら、皆さんのお役に立てるよう努めています。何か質問や話したいことがあれば、
-遠慮なくどうぞ！
+こんにちは、私の名前はQwenです。AIの助手のような存在で、あなたが質問や要望をすると回答や情報提供を行います。私は学習データに基づいて人工知能技術を使って進化し続けますので、最新の情報を常に取り入れています。何かお困りのことであればいつでもお助けさせていただきますよ。
 ```
 
-## Features
-
-- Supports multiple languages, with good Japanese language capabilities
-- Approximately 14 billion parameters for improved performance
-- Interactive command-line interface
-- Efficient response generation
-
 ## Troubleshooting
 
 If you encounter any issues:
 
 1. Verify ollama is running:
+
 ```bash
 systemctl status ollama
 ```
 
 2. Check model status:
+
 ```bash
 ollama list
 ```
 
 3. For model-specific issues, try removing and re-pulling the model:
+
 ```bash
-ollama rm phi4:14b
-ollama pull phi4:14b
+ollama rm qwen2.5:1.5b
+ollama pull qwen2.5:1.5b
 ```

src/utils/ollamaModels.ts

@@ -38,8 +38,8 @@ export const ollamaModels: LLMModel[] = [
     modelDirName: "phi3.5/3.8b",
   },
   {
-    modelName: "phi3:3.8b",
-    modelDirName: "phi3/3.8b",
+    modelName: "phi4:14b",
+    modelDirName: "phi4/14b",
   },
   {
     modelName: "qwen2.5-coder:0.5b",