Initial commit

2026-09-05 07:36:53 +08:00 · 2025-08-24 18:49:00 +08:00
parent 99866befe1
commit 6b0f2bd4fa
129 changed files with 11587 additions and 0 deletions
@@ -0,0 +1,90 @@
+from typing import Dict, Any
+from fastapi.responses import Response
+import base64
+from services.ai import describe_image_base64, get_text_embedding
+from services.vector_db import VectorDBService
+from services.logging import LogService
+
+
+class VectorIndexProcessor:
+    name = "向量索引"
+    supported_exts = ["jpg", "jpeg", "png", "bmp", "txt", "md"]
+    config_schema = [
+        {
+            "key": "action", "label": "操作", "type": "select", "required": True, "default": "create",
+            "options": [
+                {"value": "create", "label": "创建索引"},
+                {"value": "destroy", "label": "销毁索引"},
+            ]
+        },
+        {
+            "key": "index_type", "label": "索引类型", "type": "select", "required": True, "default": "vector",
+            "options": [
+                {"value": "vector", "label": "向量索引"},
+                {"value": "simple", "label": "普通索引"},
+            ]
+        }
+    ]
+    produces_file = False
+
+    async def process(self, input_bytes: bytes, path: str, config: Dict[str, Any]) -> Response:
+        action = config.get("action", "create")
+        index_type = config.get("index_type", "vector")
+        vector_db = VectorDBService()
+        collection_name = "vector_collection"
+        if action == "destroy":
+            vector_db.delete_vector(collection_name, path)
+            await LogService.info(
+                "processor:vector_index",
+                f"Destroyed {index_type} index for {path}",
+                details={"path": path, "action": "destroy", "index_type": index_type},
+            )
+            return Response(content=f"文件 {path} 的 {index_type} 索引已销毁", media_type="text/plain")
+
+        if index_type == 'simple':
+            vector_db.ensure_collection(collection_name, vector=False)
+            vector_db.upsert_vector(collection_name, {'path': path})
+            await LogService.info(
+                "processor:vector_index",
+                f"Created simple index for {path}",
+                details={"path": path, "action": "create", "index_type": "simple"},
+            )
+            return Response(content=f"文件 {path} 的普通索引已创建", media_type="text/plain")
+
+        file_ext = path.split('.')[-1].lower()
+        description = ""
+        embedding = None
+
+        if file_ext in ["jpg", "jpeg", "png", "bmp"]:
+            base64_image = base64.b64encode(input_bytes).decode("utf-8")
+            description = await describe_image_base64(base64_image)
+            embedding = await get_text_embedding(description)
+            log_message = f"Indexed image {path}"
+            response_message = f"图片已索引，描述：{description}"
+        elif file_ext in ["txt", "md"]:
+            text = input_bytes.decode("utf-8")
+            embedding = await get_text_embedding(text)
+            description = text[:100] + "..." if len(text) > 100 else text
+            log_message = f"Indexed text file {path}"
+            response_message = f"文本文件已索引"
+        
+        if embedding is None:
+            return Response(content="不支持的文件类型", status_code=400)
+
+        vector_db.ensure_collection(collection_name, vector=True)
+        vector_db.upsert_vector(
+            collection_name, {'path': path, 'embedding': embedding})
+        
+        await LogService.info(
+            "processor:vector_index",
+            log_message,
+            details={"path": path, "description": description, "action": "create", "index_type": "vector"},
+        )
+        return Response(content=response_message, media_type="text/plain")
+
+
+PROCESSOR_TYPE = "vector_index"
+PROCESSOR_NAME = VectorIndexProcessor.name
+SUPPORTED_EXTS = VectorIndexProcessor.supported_exts
+CONFIG_SCHEMA = VectorIndexProcessor.config_schema
+def PROCESSOR_FACTORY(): return VectorIndexProcessor()