It adds a local deployment approach for Qdrant and Neo4j.

code/chapter8/docker/docker-compse.yml

@@ -0,0 +1,55 @@
+version: '3.8'
+
+services:
+  # Qdrant 向量数据库
+  qdrant:
+    image: qdrant/qdrant:latest
+    container_name: qdrant
+    ports:
+      - "6333:6333"  # REST API
+      - "6334:6334"  # gRPC API
+    volumes:
+      - qdrant_storage:/qdrant/storage
+    environment:
+      - QDRANT__SERVICE__GRPC_PORT=6334
+    restart: unless-stopped
+    networks:
+      - hello_agents_network
+
+  # Neo4j 图数据库
+  neo4j:
+    image: neo4j:latest
+    container_name: neo4j
+    ports:
+      - "7474:7474"  # HTTP
+      - "7687:7687"  # Bolt
+    volumes:
+      - neo4j_data:/data
+      - neo4j_logs:/logs
+      - neo4j_import:/var/lib/neo4j/import
+      - neo4j_plugins:/plugins
+    environment:
+      - NEO4J_AUTH=neo4j/hello-agents-password
+      - NEO4J_ACCEPT_LICENSE_AGREEMENT=yes
+      - NEO4J_dbms_memory_pagecache_size=512M
+      - NEO4J_dbms_memory_heap_initial__size=512M
+      - NEO4J_dbms_memory_heap_max__size=1G
+    restart: unless-stopped
+    networks:
+      - hello_agents_network
+
+volumes:
+  qdrant_storage:
+    driver: local
+  neo4j_data:
+    driver: local
+  neo4j_logs:
+    driver: local
+  neo4j_import:
+    driver: local
+  neo4j_plugins:
+    driver: local
+
+networks:
+  hello_agents_network:
+    driver: bridge

docs/chapter8/Chapter8-Memory-and-Retrieval.md

@@ -208,6 +208,28 @@ EMBED_API_KEY=
 EMBED_BASE_URL=
 ```
 
+The local deployment approach is as follows:
+
+```bash
+# First, check if Docker is installed
+>> docker -v
+   Docker version 27.0.3, build 7d4bcd8
+
+# Start qdrant and neo4j services simultaneously using docker-compose
+>> cd project_to_path/code/chapter8
+>> docker-compose -f docker/docker-compse.yml 
+
+# Configure .env for your local deployment of qdrant and neo4j
+# Comment out cloud configuration and use local Qdrant (requires Docker)
+QDRANT_URL=http://localhost:6333
+QDRANT_API_KEY=
+
+# Comment out cloud configuration and use local Neo4j (requires Docker)
+NEO4J_URI=bolt://localhost:7687
+NEO4J_USERNAME=neo4j
+NEO4J_PASSWORD=hello-agents-password
+```
+
 Learning in this chapter can be done in two ways:
 
 1. **Experiential Learning**: Directly install the framework using `pip`, run example code, and quickly experience various functions

docs/chapter8/第八章 记忆与检索.md

@@ -197,14 +197,40 @@ NEO4J_MAX_CONNECTION_LIFETIME=3600
 NEO4J_MAX_CONNECTION_POOL_SIZE=50
 NEO4J_CONNECTION_TIMEOUT=60
 
+# ==========================
 # ==========================
 # 嵌入（Embedding）配置示例 - 可从阿里云控制台获取：https://dashscope.aliyun.com/
 # ==========================
 # - 若为空，dashscope 默认 text-embedding-v3；local 默认 sentence-transformers/all-MiniLM-L6-v2
 EMBED_MODEL_TYPE=dashscope
-EMBED_MODEL_NAME=
-EMBED_API_KEY=
-EMBED_BASE_URL=
+EMBED_MODEL_NAME=text-embedding-v4
+# - 方案 1：如提供 base_url，则优先使用 OpenAI 兼容的 REST 接口（POST {base_url}/embeddings）。
+EMBED_API_KEY=sk-xxx
+EMBED_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
+# - 方案 2：否则使用官方 dashscope SDK 的 TextEmbedding.call。EMBED_BASE_URL为空，配置DASHSCOPE_API_KEY
+# DASHSCOPE_API_KEY=sk-xx
+# EMBED_BASE_URL=
+```
+
+本地部署方案如下：
+```bash
+#先检查 docker 是否已安装。
+>> docker -v
+   Docker version 27.0.3, build 7d4bcd8
+
+# 用docker-compose同时启动 qdrant 和 neo4j服务。
+>> cd project_to_path/code/chapter8
+>> docker-compose -f docker/docker-compse.yml 
+
+# 在.env中配置本地部署的 qdrant 和 neo4j
+# 注释掉云端配置，使用本地Qdrant (需要Docker)
+QDRANT_URL=http://localhost:6333
+QDRANT_API_KEY=
+
+#注释掉云端配置，使用本地Neo4j (需要Docker)
+NEO4J_URI=bolt://localhost:7687
+NEO4J_USERNAME=neo4j
+NEO4J_PASSWORD=hello-agents-password
 ```
 
 本章的学习可以采用两种方式：