change /knowledge commands in shell

2023-10-20 14:19:41 +08:00
parent 030077e4d3
commit d0b74545eb
13 changed files with 279 additions and 74 deletions
@@ -0,0 +1,102 @@
+# define a knowledge base class
+import json
+import string
+from aios_kernel import ComputeKernel, AIStorage
+from knowledge import *
+
+
+class EmbeddingParser:
+    def __init__(self, env: KnowledgePipelineEnvironment, config: dict):
+        self._default_text_model = "all-MiniLM-L6-v2"
+        self._default_image_model = "clip-ViT-B-32"
+
+        path = string.Template(config["path"]).substitute(myai_dir=AIStorage.get_instance().get_myai_dir())
+        if not os.path.exists(path):
+            os.makedirs(path)
+        config["path"] = path
+        
+        self.env = env
+        self.config = config
+
+    def get_path(self) -> str:
+        return self.config["path"]
+
+    def __get_vector_store(self, model_name: str) -> ChromaVectorStore:
+        return ChromaVectorStore(self.get_path(), model_name)
+
+    async def __embedding_document(self, document: DocumentObject):
+        for chunk_id in document.get_chunk_list():
+            chunk = self.env.get_knowledge_store().get_chunk_reader().get_chunk(chunk_id)
+            if chunk is None:
+                raise ValueError(f"text chunk not found: {chunk_id}")
+        
+            text = chunk.read().decode("utf-8")
+            vector = await ComputeKernel.get_instance().do_text_embedding(text, self._default_text_model)
+            if vector:
+                await self.__get_vector_store(self._default_text_model).insert(vector, chunk_id)
+
+    async def __embedding_image(self, image: ImageObject):
+        # desc = {}
+        # if not not image.get_meta():
+        #     desc["meta"] = image.get_meta()
+        # if not not image.get_exif():
+        #     desc["exif"] = image.get_exif()
+        # if not not image.get_tags():
+        #     desc["tags"] = image.get_tags()
+        # vector = await self.compute_kernel.do_text_embedding(json.dumps(desc), self._default_text_model)
+        vector = await ComputeKernel.get_instance().do_image_embedding(image.calculate_id(), self._default_image_model)
+        if vector:
+            await self.__get_vector_store(self._default_image_model).insert(vector, image.calculate_id())
+
+    async def __embedding_video(self, vedio: VideoObject):
+        desc = {}
+        if not not vedio.get_meta():
+            desc["meta"] = vedio.get_meta()
+        if not not vedio.get_info():
+            desc["info"] = vedio.get_info()
+        if not not vedio.get_tags():
+            desc["tags"] = vedio.get_tags()
+        vector = await ComputeKernel.get_instance().do_text_embedding(json.dumps(desc), self._default_text_model)
+        await self.__get_vector_store(self._default_text_model).insert(vector, vedio.calculate_id())
+
+    async def __embedding_rich_text(self, rich_text: RichTextObject):
+        for document_id in rich_text.get_documents().values():
+            document = DocumentObject.decode(self.env.get_knowledge_store().get_object_store().get_object(document_id))
+            await self.__embedding_document(document)
+        for image_id in rich_text.get_images().values():
+            image = ImageObject.decode(self.env.get_knowledge_store().get_object_store().get_object(image_id))
+            await self.__embedding_image(image)
+        for video_id in rich_text.get_videos().values():
+            video = VideoObject.decode(self.env.get_knowledge_store().get_object_store().get_object(video_id))
+            await self.__embedding_video(video)
+        for rich_text_id in rich_text.get_rich_texts().values():
+            rich_text = RichTextObject.decode(self.env.get_knowledge_store().get_object_store().get_object(rich_text_id))
+            await self.__embedding_rich_text(rich_text)
+
+    async def __embedding_email(self, email: EmailObject):
+        vector = await ComputeKernel.get_instance().do_text_embedding(json.dumps(email.get_desc()), self._default_text_model)
+        await self.__get_vector_store(self._default_text_model).insert(vector, email.calculate_id())
+        await self.__embedding_rich_text(email.get_rich_text())
+
+
+    async def __do_embedding(self, object: KnowledgeObject):
+        if object.get_object_type() == ObjectType.Document:
+            await self.__embedding_document(object)
+        if object.get_object_type() == ObjectType.Image:
+            await self.__embedding_image(object)
+        if object.get_object_type() == ObjectType.Video:
+            await self.__embedding_video(object)
+        if object.get_object_type() == ObjectType.RichText:
+            await self.__embedding_rich_text(object)
+        if object.get_object_type() == ObjectType.Email:
+            await self.__embedding_email(object)
+        else:
+            pass
+    
+    async def parse(self, object: ObjectID) -> str:
+        obj = self.env.get_knowledge_store().load_object(object)
+        await self.__do_embedding(obj)
+        return "insert into vector store"
+
+def init(env: KnowledgePipelineEnvironment, params: dict) -> EmbeddingParser:
+    return EmbeddingParser(env, params)