feat: add FalkorDB, LMDeploy, and Pogocache with configuration files and documentation

src/lmdeploy/.env.example

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+# LMDeploy Version
+# Find more tags at: https://hub.docker.com/r/openmmlab/lmdeploy/tags
+LMDEPLOY_VERSION=v0.11.1-cu12.8
+
+# Host port override
+LMDEPLOY_PORT_OVERRIDE=23333
+
+# Model path or HuggingFace model ID
+# Examples: 
+# - internlm/internlm2-chat-1_8b
+# - Qwen/Qwen2.5-7B-Instruct
+LMDEPLOY_MODEL=internlm/internlm2-chat-1_8b
+
+# HuggingFace token for private models
+HF_TOKEN=
+
+# Resource limits
+LMDEPLOY_CPU_LIMIT=4.0
+LMDEPLOY_MEMORY_LIMIT=8G
+LMDEPLOY_CPU_RESERVATION=2.0
+LMDEPLOY_MEMORY_RESERVATION=4G
+
+# Shared memory size (required for some models)
+LMDEPLOY_SHM_SIZE=4g
+
+# Timezone
+TZ=UTC

src/lmdeploy/README.md

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+# LMDeploy Docker Compose
+
+[LMDeploy](https://github.com/InternLM/lmdeploy) is a toolkit for compressing, deploying, and serving LLMs.
+
+## Quick Start
+
+1. (Optional) Configure the model and port in `.env`.
+2. Start the service:
+   ```bash
+   docker compose up -d
+   ```
+3. Access the OpenAI compatible API at `http://localhost:23333/v1`.
+
+## Configuration
+
+| Environment Variable     | Default                        | Description                          |
+| ------------------------ | ------------------------------ | ------------------------------------ |
+| `LMDEPLOY_VERSION`       | `v0.11.1-cu12.8`               | LMDeploy image version               |
+| `LMDEPLOY_PORT_OVERRIDE` | `23333`                        | Host port for the API server         |
+| `LMDEPLOY_MODEL`         | `internlm/internlm2-chat-1_8b` | HuggingFace model ID or local path   |
+| `HF_TOKEN`               |                                | HuggingFace token for private models |
+
+## Monitoring Health
+
+The service includes a health check that verifies if the OpenAI `/v1/models` endpoint is responsive.
+
+## GPU Support
+
+By default, this configuration reserves 1 NVIDIA GPU. Ensure you have the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) installed on your host.

src/lmdeploy/README.zh.md

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+# LMDeploy Docker Compose
+
+[LMDeploy](https://github.com/InternLM/lmdeploy) 是一个用于压缩、部署和服务大语言模型（LLM）的工具包。
+
+## 快速开始
+
+1. （可选）在 `.env` 中配置模型和端口。
+2. 启动服务：
+   ```bash
+   docker compose up -d
+   ```
+3. 通过 `http://localhost:23333/v1` 访问与 OpenAI 兼容的 API。
+
+## 配置项
+
+| 环境变量                 | 默认值                         | 说明                                 |
+| ------------------------ | ------------------------------ | ------------------------------------ |
+| `LMDEPLOY_VERSION`       | `v0.11.1-cu12.8`               | LMDeploy 镜像版本                    |
+| `LMDEPLOY_PORT_OVERRIDE` | `23333`                        | API 服务器的主机端口                 |
+| `LMDEPLOY_MODEL`         | `internlm/internlm2-chat-1_8b` | HuggingFace 模型 ID 或本地路径       |
+| `HF_TOKEN`               |                                | 用于访问私有模型的 HuggingFace Token |
+
+## 健康检查
+
+该配置包含健康检查，用于验证 OpenAI `/v1/models` 接口是否响应。
+
+## GPU 支持
+
+默认情况下，此配置会预留 1 个 NVIDIA GPU。请确保您的主机已安装 [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)。

src/lmdeploy/docker-compose.yaml

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+x-defaults: &defaults
+  restart: unless-stopped
+  logging:
+    driver: json-file
+    options:
+      max-size: 100m
+      max-file: "3"
+
+services:
+  lmdeploy:
+    <<: *defaults
+    image: ${GLOBAL_REGISTRY:-}openmmlab/lmdeploy:${LMDEPLOY_VERSION:-v0.11.1-cu12.8}
+    ports:
+      - "${LMDEPLOY_PORT_OVERRIDE:-23333}:23333"
+    volumes:
+      - lmdeploy_data:/root/.cache
+    environment:
+      - TZ=${TZ:-UTC}
+      - HF_TOKEN=${HF_TOKEN:-}
+    command:
+      - lmdeploy
+      - serve
+      - api_server
+      - ${LMDEPLOY_MODEL:-internlm/internlm2-chat-1_8b}
+      - --server-name
+      - "0.0.0.0"
+      - --server-port
+      - "23333"
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:23333/v1/models"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 60s
+    deploy:
+      resources:
+        limits:
+          cpus: ${LMDEPLOY_CPU_LIMIT:-4.0}
+          memory: ${LMDEPLOY_MEMORY_LIMIT:-8G}
+        reservations:
+          cpus: ${LMDEPLOY_CPU_RESERVATION:-2.0}
+          memory: ${LMDEPLOY_MEMORY_RESERVATION:-4G}
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    shm_size: ${LMDEPLOY_SHM_SIZE:-4g}
+
+volumes:
+  lmdeploy_data: