test prompt from knowledge object
This commit is contained in:
@@ -121,7 +121,7 @@ class ComputeKernel:
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def text_embedding(self,input:str,model_name:Optional[str] = None):
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def text_embedding(self,input:str,model_name:Optional[str] = None):
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task_req = ComputeTask()
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task_req = ComputeTask()
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task_req.set_text_embeding_params(input,model_name)
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task_req.set_text_embedding_params(input,model_name)
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self.run(task_req)
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self.run(task_req)
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return task_req
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return task_req
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@@ -38,7 +38,7 @@ class ComputeTask:
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self.params["model_name"] = "gpt-4-0613"
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self.params["model_name"] = "gpt-4-0613"
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self.params["max_token_size"] = max_token_size
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self.params["max_token_size"] = max_token_size
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def set_text_embeding_params(self, input, model_name=None, callchain_id = None):
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def set_text_embedding_params(self, input, model_name=None, callchain_id = None):
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self.task_type = "text_embedding"
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self.task_type = "text_embedding"
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self.create_time = time.time()
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self.create_time = time.time()
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self.task_id = uuid.uuid4().hex
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self.task_id = uuid.uuid4().hex
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@@ -1,7 +1,7 @@
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# define a knowledge base class
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# define a knowledge base class
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import json
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import json
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from . import AgentPrompt, ComputeKernel
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from . import AgentPrompt, ComputeKernel
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from ..knowledge import *
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from knowledge import *
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class KnowledgeBase:
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class KnowledgeBase:
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@@ -62,7 +62,7 @@ class KnowledgeBase:
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async def __embedding_email(self, email: EmailObject):
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async def __embedding_email(self, email: EmailObject):
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vector = await self.compute_kernel.do_text_embedding(json.dumps(email.get_desc()))
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vector = await self.compute_kernel.do_text_embedding(json.dumps(email.get_desc()))
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self.store.get_vector_store("default").insert(vector, email.calculate_id())
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await self.store.get_vector_store("default").insert(vector, email.calculate_id())
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await self.__embedding_rich_text(email.get_rich_text())
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await self.__embedding_rich_text(email.get_rich_text())
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@@ -80,89 +80,94 @@ class KnowledgeBase:
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else:
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else:
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pass
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pass
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def __save_document(self, document: DocumentObject):
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# def __save_document(self, document: DocumentObject):
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doc_id = document.calculate_id()
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# doc_id = document.calculate_id()
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self.store.get_object_store().put_object(doc_id, document.encode())
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# self.store.get_object_store().put_object(doc_id, document.encode())
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for chunk_id in document.get_chunk_list():
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# for chunk_id in document.get_chunk_list():
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self.store.get_relation_store().add_relation(chunk_id, doc_id)
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# self.store.get_relation_store().add_relation(chunk_id, doc_id)
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def __save_image(self, image: ImageObject):
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# def __save_image(self, image: ImageObject):
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image_id = image.calculate_id()
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# image_id = image.calculate_id()
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self.store.get_object_store().put_object(image_id, image.encode())
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# self.store.get_object_store().put_object(image_id, image.encode())
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def __save_video(self, video: VideoObject):
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# def __save_video(self, video: VideoObject):
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video_id = video.calculate_id()
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# video_id = video.calculate_id()
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self.store.get_object_store().put_object(video_id, video.encode())
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# self.store.get_object_store().put_object(video_id, video.encode())
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def __save_rich_text(self, rich_text: RichTextObject):
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# def __save_rich_text(self, rich_text: RichTextObject):
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rich_text_id = rich_text.calculate_id()
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# rich_text_id = rich_text.calculate_id()
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# rich_text_enc = dict()
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# # rich_text_enc = dict()
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# rich_text_enc["desc"] = rich_text.desc
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# # rich_text_enc["desc"] = rich_text.desc
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# rich_text_enc["body"] = {"documents": {}, "images": {}, "videos": {}, "rich_texts": {}}
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# # rich_text_enc["body"] = {"documents": {}, "images": {}, "videos": {}, "rich_texts": {}}
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for key, document in rich_text.get_documents().items():
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# for key, document in rich_text.get_documents().items():
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self.__save_document(document)
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# self.__save_document(document)
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doc_id = document.calculate_id()
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# doc_id = document.calculate_id()
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self.store.get_relation_store().add_relation(doc_id, rich_text_id)
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# self.store.get_relation_store().add_relation(doc_id, rich_text_id)
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# rich_text_enc["body"]["documents"][key] = doc_id
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# # rich_text_enc["body"]["documents"][key] = doc_id
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for key, image in rich_text.get_images().items():
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# for key, image in rich_text.get_images().items():
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self.__save_image(image)
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# self.__save_image(image)
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image_id = image.calculate_id()
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# image_id = image.calculate_id()
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self.store.get_relation_store().add_relation(image_id, rich_text_id)
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# self.store.get_relation_store().add_relation(image_id, rich_text_id)
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# rich_text_enc["body"]["images"][key] = image_id
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# # rich_text_enc["body"]["images"][key] = image_id
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for key, video in rich_text.get_videos().items():
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# for key, video in rich_text.get_videos().items():
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self.__save_video(video)
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# self.__save_video(video)
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video_id = video.calculate_id()
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# video_id = video.calculate_id()
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self.store.get_relation_store().add_relation(video_id, rich_text_id)
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# self.store.get_relation_store().add_relation(video_id, rich_text_id)
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# rich_text_enc["body"]["videos"][key] = video_id
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# # rich_text_enc["body"]["videos"][key] = video_id
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for key, rich_text in rich_text.get_rich_texts().items():
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# for key, rich_text in rich_text.get_rich_texts().items():
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self.__save_rich_text(rich_text)
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# self.__save_rich_text(rich_text)
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rich_text_id = rich_text.calculate_id()
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# rich_text_id = rich_text.calculate_id()
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self.store.get_relation_store().add_relation(rich_text_id, rich_text_id)
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# self.store.get_relation_store().add_relation(rich_text_id, rich_text_id)
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# rich_text_enc["body"]["rich_texts"][key] = rich_text_id
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# # rich_text_enc["body"]["rich_texts"][key] = rich_text_id
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self.store.get_object_store().put_object(rich_text_id, rich_text.encode())
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# self.store.get_object_store().put_object(rich_text_id, rich_text.encode())
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def __save_email(self, email: EmailObject):
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# def __save_email(self, email: EmailObject):
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email_id = email.calculate_id()
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# email_id = email.calculate_id()
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# email_enc = dict()
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# # email_enc = dict()
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# email_enc["desc"] = email.desc
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# # email_enc["desc"] = email.desc
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# email_enc["body"] = {"content": None}
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# # email_enc["body"] = {"content": None}
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self.__save_rich_text(email.get_rich_text())
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# self.__save_rich_text(email.get_rich_text())
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rich_text_id = email.get_rich_text().calculate_id()
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# rich_text_id = email.get_rich_text().calculate_id()
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self.store.get_relation_store().add_relation(rich_text_id, email_id)
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# self.store.get_relation_store().add_relation(rich_text_id, email_id)
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# email_enc["body"]["content"] = rich_text_id
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# # email_enc["body"]["content"] = rich_text_id
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self.store.get_object_store().put_object(email_id, email.encode())
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# self.store.get_object_store().put_object(email_id, email.encode())
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def __save_object(self, object: KnowledgeObject):
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# def __save_object(self, object: KnowledgeObject):
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if object.get_object_type() == ObjectType.Document:
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# if object.get_object_type() == ObjectType.Document:
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self.__save_document(object)
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# self.__save_document(object)
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if object.get_object_type() == ObjectType.Image:
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# if object.get_object_type() == ObjectType.Image:
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self.__save_image(object)
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# self.__save_image(object)
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if object.get_object_type() == ObjectType.Video:
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# if object.get_object_type() == ObjectType.Video:
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self.__save_video(object)
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# self.__save_video(object)
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if object.get_object_type() == ObjectType.RichText:
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# if object.get_object_type() == ObjectType.RichText:
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self.__save_rich_text(object)
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# self.__save_rich_text(object)
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if object.get_object_type() == ObjectType.Email:
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# if object.get_object_type() == ObjectType.Email:
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self.__save_email(object)
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# self.__save_email(object)
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else:
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# else:
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pass
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# pass
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async def insert_object(self, object: KnowledgeObject):
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async def insert_object(self, object: KnowledgeObject):
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self.__save_object(object)
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# self.__save_object(object)
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self.__do_embedding(object)
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await self.__do_embedding(object)
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async def query_prompt(self, prompt: AgentPrompt):
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objects = await self.query_objects(prompt)
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knowledge_prompt = self.prompt_from_objects(objects)
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prompt.append(knowledge_prompt)
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async def query_objects(self, prompt: AgentPrompt) -> [ObjectID]:
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async def query_objects(self, prompt: AgentPrompt) -> [ObjectID]:
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results = []
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results = []
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for msg in prompt.messages:
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for msg in prompt.messages:
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if msg.role == "user":
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if msg.role == "user":
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vector = await self.compute_kernel.do_text_embedding(msg.content)
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vector = await self.compute_kernel.do_text_embedding(msg.content)
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object_ids = self.store.get_vector_store("default").query(vector, 10)
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object_ids = await self.store.get_vector_store("default").query(vector, 10)
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results.append(object_ids)
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results.append(object_ids)
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return results
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return results
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async def __load_object(self, object_id: ObjectID) -> KnowledgeObject:
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def __load_object(self, object_id: ObjectID) -> KnowledgeObject:
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if object_id.get_object_type() == ObjectType.Document:
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if object_id.get_object_type() == ObjectType.Document:
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return DocumentObject.decode(self.store.get_object_store().get_object(object_id))
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return DocumentObject.decode(self.store.get_object_store().get_object(object_id))
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if object_id.get_object_type() == ObjectType.Image:
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if object_id.get_object_type() == ObjectType.Image:
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@@ -177,10 +182,10 @@ class KnowledgeBase:
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pass
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pass
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async def prompt_from_objects(self, object_ids: [ObjectID]) -> AgentPrompt:
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def prompt_from_objects(self, object_ids: [ObjectID]) -> AgentPrompt:
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results = dict()
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results = dict()
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for object_id in object_ids:
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for object_id in object_ids:
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parents = self.store.get_relation_store().get_related_objects(object_id)
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parents = self.store.get_relation_store().get_related_root_objects(object_id)
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# last parent is the root object
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# last parent is the root object
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root_object_id = parents[-1]
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root_object_id = parents[-1]
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if results[root_object_id] is None:
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if results[root_object_id] is None:
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@@ -192,9 +197,9 @@ class KnowledgeBase:
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result_desc = []
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result_desc = []
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for result in results.values():
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for result in results.values():
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# first element in result is the root object
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# first element in result is the root object
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root_object = await self.__load_object(result[0])
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root_object_id = result[0]
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if root_object.get_object_type() == ObjectType.Email:
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if root_object_id.get_object_type() == ObjectType.Email:
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email = await self.__load_object(object_id)
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email = self.__load_object(root_object_id)
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desc = email.get_desc()
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desc = email.get_desc()
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desc["type"] = "email"
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desc["type"] = "email"
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desc["contents"] = []
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desc["contents"] = []
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@@ -208,12 +213,12 @@ class KnowledgeBase:
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if object_id.get_object_type() == ObjectType.Chunk:
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if object_id.get_object_type() == ObjectType.Chunk:
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upper_list.append({"type": "text", "content": self.store.get_chunk_reader().get_chunk(object_id).read().decode("utf-8")})
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upper_list.append({"type": "text", "content": self.store.get_chunk_reader().get_chunk(object_id).read().decode("utf-8")})
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if object_id.get_object_type() == ObjectType.Image:
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if object_id.get_object_type() == ObjectType.Image:
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image = await self.__load_object(object_id)
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image = self.__load_object(object_id)
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desc = image.get_desc()
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desc = image.get_desc()
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desc["type"] = "image"
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desc["type"] = "image"
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upper_list.append(desc)
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upper_list.append(desc)
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if object_id.get_object_type() == ObjectType.Video:
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if object_id.get_object_type() == ObjectType.Video:
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video = await self.__load_object(object_id)
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video = self.__load_object(object_id)
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desc = video.get_desc()
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desc = video.get_desc()
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desc["type"] = "video"
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desc["type"] = "video"
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upper_list.append(desc)
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upper_list.append(desc)
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@@ -82,7 +82,7 @@ class OpenAI_ComputeNode(ComputeNode):
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input = task.params["input"]
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input = task.params["input"]
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logger.info(f"call openai {model_name} input: {input}")
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logger.info(f"call openai {model_name} input: {input}")
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resp = openai.Embeding.create(model=model_name,
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resp = openai.Embedding.create(model=model_name,
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input=input)
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input=input)
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logger.info(f"openai response: {resp}")
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logger.info(f"openai response: {resp}")
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@@ -53,6 +53,7 @@ class DocumentObjectBuilder:
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chunk_list = KnowledgeStore().get_chunk_list_writer().create_chunk_list_from_text(
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chunk_list = KnowledgeStore().get_chunk_list_writer().create_chunk_list_from_text(
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self.text,
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self.text,
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1024 * 4,
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1024 * 4,
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"."
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)
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)
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doc = DocumentObject(self.meta, self.tags, chunk_list)
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doc = DocumentObject(self.meta, self.tags, chunk_list)
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doc_id = doc.calculate_id()
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doc_id = doc.calculate_id()
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+16
-23
@@ -18,39 +18,32 @@ root.addHandler(handler)
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from knowledge import ObjectID, HashValue, EmailObjectBuilder
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from knowledge import ObjectID, HashValue, EmailObjectBuilder
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from aios_kernel import KnowledgeBase, AgentPrompt
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from aios_kernel import KnowledgeBase, AgentPrompt, OpenAI_ComputeNode, ComputeKernel
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import asyncio
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import asyncio
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import unittest
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import unittest
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async def test_embedding_email():
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async def test_embedding_email(test):
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data = HashValue.hash_data("1233".encode("utf-8"));
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open_ai_node = OpenAI_ComputeNode()
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print(data.to_base58())
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open_ai_node.start()
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print(data.to_base36())
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ComputeKernel().add_compute_node(open_ai_node)
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data2 = HashValue.from_base58(data.to_base58())
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email_folder = os.path.join(dir_path, "../rootfs/data/email/")
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self.assertEqual(data.to_base36(), data2.to_base36())
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print("explore emails in folder ", email_folder)
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for root, dirs, files in os.walk(email_folder):
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data2 = HashValue.from_base36(data.to_base36())
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for dir in dirs:
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self.assertEqual(data.to_base58(), data2.to_base58())
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email_object = EmailObjectBuilder({}, os.path.join(root, dir)).build()
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await KnowledgeBase().insert_object(email_object)
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email_folder = "F:\\system\\Downloads\\8081ffdb80925f5bff9c6ab9c4756c7d"
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email_object = EmailObjectBuilder({}, email_folder).build()
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await KnowledgeBase().do_embedding(email_object)
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async def test_query_email():
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msg_prompt = AgentPrompt()
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msg_prompt = AgentPrompt()
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msg_prompt.messages = [{"role":"user","content":"abcdef"}]
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msg_prompt.messages = [{"role":"user","content":"abcdef"}]
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KnowledgeBase().query(msg_prompt)
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await KnowledgeBase().query_prompt(msg_prompt)
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class TestKnowledgeBase(unittest.TestCase):
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class TestKnowledgeBase(unittest.TestCase):
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def test_embedding(self):
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def test_embedding(self):
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asyncio.run(test_embedding_email())
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asyncio.run(test_embedding_email(self))
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def test_query(self):
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asyncio.run(test_query_email())
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if __name__ == "__main__":
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if __name__ == "__main__":
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Reference in New Issue
Block a user