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Add Ollama installation instructions with same-host deployment options

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Vera-AI
2026-03-26 15:24:33 -05:00
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DOCKERHUB.md
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@@ -195,13 +195,111 @@ conversations with this user and can reference them contextually.
---
## Prerequisites
## 📋 Prerequisites
| Requirement | Description |
|-------------|-------------|
| **Ollama** | LLM inference server (e.g., `http://10.0.0.10:11434`) |
| **Qdrant** | Vector database (e.g., `http://10.0.0.22:6333`) |
| **Docker** | Docker installed |
### Required Services
| Service | Version | Description |
|---------|---------|-------------|
| **Ollama** | 0.1.x+ | LLM inference server |
| **Qdrant** | 1.6.x+ | Vector database |
| **Docker** | 20.x+ | Container runtime |
### System Requirements
| Requirement | Minimum | Recommended |
|-------------|---------|-------------|
| **CPU** | 2 cores | 4+ cores |
| **RAM** | 2 GB | 4+ GB |
| **Disk** | 1 GB | 5+ GB |
---
## 🔧 Installing with Ollama
### Option A: All on Same Host (Recommended)
Install all services on a single machine:
```bash
# 1. Install Ollama
curl https://ollama.ai/install.sh | sh
# 2. Pull required models
ollama pull snowflake-arctic-embed2 # Embedding model (required)
ollama pull llama3.1 # Chat model
# 3. Run Qdrant in Docker
docker run -d --name qdrant -p 6333:6333 qdrant/qdrant
# 4. Run Vera-AI
docker run -d \
--name VeraAI \
--restart unless-stopped \
--network host \
-e APP_UID=$(id -u) \
-e APP_GID=$(id -g) \
-e TZ=America/Chicago \
-v ./config/config.toml:/app/config/config.toml:ro \
-v ./prompts:/app/prompts:rw \
-v ./logs:/app/logs:rw \
your-username/vera-ai:latest
```
**Config for same-host (config/config.toml):**
```toml
[general]
ollama_host = "http://127.0.0.1:11434"
qdrant_host = "http://127.0.0.1:6333"
qdrant_collection = "memories"
embedding_model = "snowflake-arctic-embed2"
```
### Option B: Docker Compose All-in-One
```yaml
services:
ollama:
image: ollama/ollama
ports: ["11434:11434"]
volumes: [ollama_data:/root/.ollama]
qdrant:
image: qdrant/qdrant
ports: ["6333:6333"]
volumes: [qdrant_data:/qdrant/storage]
vera-ai:
image: your-username/vera-ai:latest
network_mode: host
volumes:
- ./config/config.toml:/app/config/config.toml:ro
- ./prompts:/app/prompts:rw
volumes:
ollama_data:
qdrant_data:
```
### Option C: Different Port
If Ollama uses port 11434, run Vera on port 8080:
```bash
docker run -d --name VeraAI -p 8080:11434 ...
# Connect client to: http://localhost:8080
```
---
## ✅ Pre-Flight Checklist
- [ ] Docker installed (`docker --version`)
- [ ] Ollama running (`curl http://localhost:11434/api/tags`)
- [ ] Qdrant running (`curl http://localhost:6333/collections`)
- [ ] Embedding model (`ollama pull snowflake-arctic-embed2`)
- [ ] Chat model (`ollama pull llama3.1`)
---
---

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README.md
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@@ -67,13 +67,109 @@ Every conversation is stored in Qdrant vector database and retrieved contextuall
## 📋 Prerequisites
| Requirement | Description |
|-------------|-------------|
| **Ollama** | LLM inference server (e.g., `http://10.0.0.10:11434`) |
| **Qdrant** | Vector database (e.g., `http://10.0.0.22:6333`) |
| **Docker** | Docker and Docker Compose installed |
| **Git** | For cloning the repository |
### Required Services
| Service | Version | Description |
|---------|---------|-------------|
| **Ollama** | 0.1.x+ | LLM inference server |
| **Qdrant** | 1.6.x+ | Vector database |
| **Docker** | 20.x+ | Container runtime |
### System Requirements
| Requirement | Minimum | Recommended |
|-------------|---------|-------------|
| **CPU** | 2 cores | 4+ cores |
| **RAM** | 2 GB | 4+ GB |
| **Disk** | 1 GB | 5+ GB |
---
## 🔧 Installing with Ollama
### Option A: All on Same Host (Recommended)
Install all services on a single machine:
```bash
# 1. Install Ollama
curl https://ollama.ai/install.sh | sh
# 2. Pull required models
ollama pull snowflake-arctic-embed2 # Embedding model (required)
ollama pull llama3.1 # Chat model
# 3. Run Qdrant in Docker
docker run -d --name qdrant -p 6333:6333 qdrant/qdrant
# 4. Run Vera-AI
docker run -d \
--name VeraAI \
--restart unless-stopped \
--network host \
-e APP_UID=$(id -u) \
-e APP_GID=$(id -g) \
-e TZ=America/Chicago \
-v ./config/config.toml:/app/config/config.toml:ro \
-v ./prompts:/app/prompts:rw \
-v ./logs:/app/logs:rw \
your-username/vera-ai:latest
```
**Config for same-host (config/config.toml):**
```toml
[general]
ollama_host = "http://127.0.0.1:11434"
qdrant_host = "http://127.0.0.1:6333"
qdrant_collection = "memories"
embedding_model = "snowflake-arctic-embed2"
```
### Option B: Docker Compose All-in-One
```yaml
services:
ollama:
image: ollama/ollama
ports: ["11434:11434"]
volumes: [ollama_data:/root/.ollama]
qdrant:
image: qdrant/qdrant
ports: ["6333:6333"]
volumes: [qdrant_data:/qdrant/storage]
vera-ai:
image: your-username/vera-ai:latest
network_mode: host
volumes:
- ./config/config.toml:/app/config/config.toml:ro
- ./prompts:/app/prompts:rw
volumes:
ollama_data:
qdrant_data:
```
### Option C: Different Port
If Ollama uses port 11434, run Vera on port 8080:
```bash
docker run -d --name VeraAI -p 8080:11434 ...
# Connect client to: http://localhost:8080
```
---
## ✅ Pre-Flight Checklist
- [ ] Docker installed (`docker --version`)
- [ ] Ollama running (`curl http://localhost:11434/api/tags`)
- [ ] Qdrant running (`curl http://localhost:6333/collections`)
- [ ] Embedding model (`ollama pull snowflake-arctic-embed2`)
- [ ] Chat model (`ollama pull llama3.1`)
---
---
## 🐳 Docker Deployment