从向量数据库中检索图片并集成到chat-robot

app/service/search_image_with_text/service.py

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+import chromadb
+import hashlib
+
+import pandas as pd
+from chromadb.config import Settings
+from chromadb.utils.embedding_functions.ollama_embedding_function import OllamaEmbeddingFunction
+from tqdm import tqdm
+
+# 读取 csv 文件
+csv_file_path = r'D:/Files/csv/output/output.csv'
+image_path = r'D:/images-clean'
+
+df = pd.read_csv(csv_file_path, encoding='Windows-1252')
+
+# 创建 Chroma 客户端
+client = chromadb.Client(Settings(is_persistent=True, persist_directory="/vector_db"))
+# client = chromadb.Client(Settings(is_persistent=True, persist_directory="./service/search_image_with_text/vector_db"))
+# client = chromadb.Client(Settings(is_persistent=True, persist_directory="D:/workspace/AiDLab/vector_db"))
+# 创建集合
+embedding_fn = OllamaEmbeddingFunction(url="http://localhost:11434/api/embeddings", model_name="mxbai-embed-large")
+
+
+def create_collection():
+    collection = client.get_or_create_collection("sub_sketches_description", embedding_function=embedding_fn)
+
+    # 存储数据，包括自定义属性
+    images_description = []
+    images_metadata = []
+    ids = []
+    batch_size = 41666  # 最大批量大小
+    for index, row in tqdm(df.iterrows()):
+        # 将图片的md5作为id
+        with open(image_path + row['path'], 'rb') as f:
+            image_data = f.read()
+            md5_value = hashlib.md5(image_data).hexdigest()
+            ids.append(md5_value)
+        images_description.append(row['description'])
+        images_metadata.append({
+            "gender": row['gender'],
+            "path": row['path']
+        })
+
+        # 将数据添加到集合
+        # 每达到 batch_size 就执行一次 upsert
+        if len(ids) >= batch_size:
+            collection.upsert(
+                ids=list(ids),
+                documents=images_description,
+                metadatas=images_metadata  # 添加自定义属性
+            )
+            # 清空列表以准备下一批数据
+            ids.clear()
+            images_description.clear()
+            images_metadata.clear()
+
+    if ids:
+        collection.upsert(
+            ids=list(ids),
+            documents=images_description,
+            metadatas=images_metadata  # 添加自定义属性
+        )
+
+    print("Data successfully stored in the vector database.")
+
+
+def query(gender, content):
+    collection = client.get_collection("sub_sketches_description", embedding_function=embedding_fn)
+    # 6. 查询相似内容
+    user_gender = gender  # 用户输入的性别
+    user_content = content  # 用户输入的内容
+
+    results = collection.query(
+        query_texts=user_content,
+        n_results=5,  # 返回前 5 个结果
+        where={"gender": user_gender}  # 根据性别过滤
+    )
+
+    # 输出结果
+    resp = []
+    for document, result in zip(results['documents'][0], results['metadatas'][0]):
+        # print("Path:", result['path'])
+        # print("Content:", document)
+        resp.append(result['path'])
+    return resp
+
+
+if __name__ == '__main__':
+    # create_collection()
+    query("female", "I need a long sleeve dress")