change /knowledge commands in shell
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tsukasa
@@ -1,13 +1,9 @@
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instance_id = "Mia"
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fullname = "Mia"
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#llm_model_name = "gpt-4"
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#max_token_size = 16000
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#enable_function =["add_event"]
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#enable_kb = "true"
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#enable_timestamp = "false"
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owner_prompt = "我是你的主人{name}"
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contact_prompt = "我是你的朋友{name}"
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owner_env = "knowledge"
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owner_env = "../../knowledge_pipelines/Mia/query.py"
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[[prompt]]
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role = "system"
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@@ -0,0 +1,68 @@
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import os
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import aiofiles
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import chardet
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import logging
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import string
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from knowledge import ImageObjectBuilder, DocumentObjectBuilder, KnowledgePipelineEnvironment, KnowledgePipelineJournal
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from aios_kernel.storage import AIStorage
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class KnowledgeDirSource:
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def __init__(self, env: KnowledgePipelineEnvironment, config):
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self.env = env
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path = string.Template(config["path"]).substitute(myai_dir=AIStorage.get_instance().get_myai_dir())
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config["path"] = path
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self.config = config
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# @classmethod
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# def user_config_items(cls):
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# return [("path", "local dir path")]
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def path(self):
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return self.config["path"]
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@staticmethod
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async def read_txt_file(file_path:str)->str:
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cur_encode = "utf-8"
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async with aiofiles.open(file_path,'rb') as f:
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cur_encode = chardet.detect(await f.read())['encoding']
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async with aiofiles.open(file_path,'r',encoding=cur_encode) as f:
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return await f.read()
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async def next(self):
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while True:
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journals = self.env.journal.latest_journals(1)
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from_time = 0
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if len(journals) == 1:
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latest_journal = journals[0]
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if latest_journal.is_finish():
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yield None
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continue
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from_time = os.path.getctime(latest_journal.get_input())
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if os.path.getmtime(self.path()) <= from_time:
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yield (None, None)
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continue
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file_pathes = sorted(os.listdir(self.path()), key=lambda x: os.path.getctime(os.path.join(self.path(), x)))
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for rel_path in file_pathes:
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file_path = os.path.join(self.path(), rel_path)
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timestamp = os.path.getctime(file_path)
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if timestamp <= from_time:
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continue
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ext = os.path.splitext(file_path)[1].lower()
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if ext in ['.jpg', '.jpeg', '.png', '.gif', '.bmp']:
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logging.info(f"knowledge dir source found image file {file_path}")
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image = ImageObjectBuilder({}, {}, file_path).build(self.env.get_knowledge_store())
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await self.env.get_knowledge_store().insert_object(image)
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yield (image.calculate_id(), file_path)
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if ext in ['.txt']:
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logging.info(f"knowledge dir source found text file {file_path}")
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text = await self.read_txt_file(file_path)
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document = DocumentObjectBuilder({}, {}, text).build(self.env.get_knowledge_store())
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await self.env.get_knowledge_store().insert_object(document)
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yield (document.calculate_id(), file_path)
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yield (None, None)
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def init(env: KnowledgePipelineEnvironment, params: dict) -> KnowledgeDirSource:
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return KnowledgeDirSource(env, params)
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@@ -0,0 +1,102 @@
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# define a knowledge base class
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import json
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import string
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from aios_kernel import ComputeKernel, AIStorage
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from knowledge import *
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class EmbeddingParser:
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def __init__(self, env: KnowledgePipelineEnvironment, config: dict):
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self._default_text_model = "all-MiniLM-L6-v2"
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self._default_image_model = "clip-ViT-B-32"
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path = string.Template(config["path"]).substitute(myai_dir=AIStorage.get_instance().get_myai_dir())
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if not os.path.exists(path):
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os.makedirs(path)
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config["path"] = path
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self.env = env
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self.config = config
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def get_path(self) -> str:
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return self.config["path"]
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def __get_vector_store(self, model_name: str) -> ChromaVectorStore:
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return ChromaVectorStore(self.get_path(), model_name)
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async def __embedding_document(self, document: DocumentObject):
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for chunk_id in document.get_chunk_list():
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chunk = self.env.get_knowledge_store().get_chunk_reader().get_chunk(chunk_id)
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if chunk is None:
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raise ValueError(f"text chunk not found: {chunk_id}")
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text = chunk.read().decode("utf-8")
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vector = await ComputeKernel.get_instance().do_text_embedding(text, self._default_text_model)
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if vector:
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await self.__get_vector_store(self._default_text_model).insert(vector, chunk_id)
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async def __embedding_image(self, image: ImageObject):
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# desc = {}
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# if not not image.get_meta():
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# desc["meta"] = image.get_meta()
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# if not not image.get_exif():
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# desc["exif"] = image.get_exif()
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# if not not image.get_tags():
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# desc["tags"] = image.get_tags()
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# vector = await self.compute_kernel.do_text_embedding(json.dumps(desc), self._default_text_model)
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vector = await ComputeKernel.get_instance().do_image_embedding(image.calculate_id(), self._default_image_model)
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if vector:
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await self.__get_vector_store(self._default_image_model).insert(vector, image.calculate_id())
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async def __embedding_video(self, vedio: VideoObject):
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desc = {}
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if not not vedio.get_meta():
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desc["meta"] = vedio.get_meta()
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if not not vedio.get_info():
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desc["info"] = vedio.get_info()
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if not not vedio.get_tags():
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desc["tags"] = vedio.get_tags()
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vector = await ComputeKernel.get_instance().do_text_embedding(json.dumps(desc), self._default_text_model)
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await self.__get_vector_store(self._default_text_model).insert(vector, vedio.calculate_id())
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async def __embedding_rich_text(self, rich_text: RichTextObject):
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for document_id in rich_text.get_documents().values():
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document = DocumentObject.decode(self.env.get_knowledge_store().get_object_store().get_object(document_id))
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await self.__embedding_document(document)
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for image_id in rich_text.get_images().values():
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image = ImageObject.decode(self.env.get_knowledge_store().get_object_store().get_object(image_id))
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await self.__embedding_image(image)
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for video_id in rich_text.get_videos().values():
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video = VideoObject.decode(self.env.get_knowledge_store().get_object_store().get_object(video_id))
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await self.__embedding_video(video)
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for rich_text_id in rich_text.get_rich_texts().values():
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rich_text = RichTextObject.decode(self.env.get_knowledge_store().get_object_store().get_object(rich_text_id))
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await self.__embedding_rich_text(rich_text)
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async def __embedding_email(self, email: EmailObject):
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vector = await ComputeKernel.get_instance().do_text_embedding(json.dumps(email.get_desc()), self._default_text_model)
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await self.__get_vector_store(self._default_text_model).insert(vector, email.calculate_id())
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await self.__embedding_rich_text(email.get_rich_text())
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async def __do_embedding(self, object: KnowledgeObject):
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if object.get_object_type() == ObjectType.Document:
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await self.__embedding_document(object)
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if object.get_object_type() == ObjectType.Image:
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await self.__embedding_image(object)
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if object.get_object_type() == ObjectType.Video:
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await self.__embedding_video(object)
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if object.get_object_type() == ObjectType.RichText:
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await self.__embedding_rich_text(object)
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if object.get_object_type() == ObjectType.Email:
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await self.__embedding_email(object)
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else:
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pass
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async def parse(self, object: ObjectID) -> str:
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obj = self.env.get_knowledge_store().load_object(object)
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await self.__do_embedding(obj)
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return "insert into vector store"
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def init(env: KnowledgePipelineEnvironment, params: dict) -> EmbeddingParser:
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return EmbeddingParser(env, params)
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+3
-3
@@ -1,6 +1,6 @@
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name = "LocalEmbedding"
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input.module = "local_dir"
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name = "Mia"
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input.module = "input.py"
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input.params.path = "${myai_dir}/data"
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parser.module = "embedding"
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parser.module = "parser.py"
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parser.params.path = "${myai_dir}/knowledge/indices/embedding"
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@@ -0,0 +1,97 @@
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import os
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import logging
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import json
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from aios_kernel import *
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from knowledge import *
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class KnowledgeEnvironment(Environment):
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def __init__(self, env_id: str) -> None:
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super().__init__(env_id)
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self.path = os.path.join(AIStorage.get_instance().get_myai_dir(), "knowledge/indices/embedding")
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self._default_text_model = "all-MiniLM-L6-v2"
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self._default_image_model = "clip-ViT-B-32"
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query_param = {
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"tokens": "key words to query",
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"types": "prefered knowledge types, one or more of [text, image]",
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"index": "index of query result"
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}
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self.add_ai_function(SimpleAIFunction("query_knowledge",
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"vector query content from local knowledge base",
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self._query,
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query_param))
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def __get_vector_store(self, model_name: str) -> ChromaVectorStore:
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return ChromaVectorStore(self.path, model_name)
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async def query_objects(self, tokens: str, types: list[str], topk: int) -> [ObjectID]:
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texts = []
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if "text" in types:
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vector = await ComputeKernel.get_instance().do_text_embedding(tokens, self._default_text_model)
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texts = await self.__get_vector_store(self._default_text_model).query(vector, topk)
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images = []
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if "image" in types:
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vector = await ComputeKernel.get_instance().do_text_embedding(tokens, self._default_image_model)
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images = await self.__get_vector_store(self._default_image_model).query(vector, topk)
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return texts + images
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def tokens_from_objects(self, object_ids: [ObjectID]) -> list[str]:
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results = dict()
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for object_id in object_ids:
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parents = KnowledgeStore().get_relation_store().get_related_root_objects(object_id)
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# last parent is the root object
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root_object_id = parents[0] if parents else object_id
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logging.info(f"object_id: {str(object_id)} root_object_id: {str(root_object_id)}")
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if str(root_object_id) in results:
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results[str(root_object_id)].append(object_id)
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else:
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results[str(root_object_id)] = [root_object_id, object_id]
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content = ""
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result_desc = []
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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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root_object_id = result[0]
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if root_object_id.get_object_type() == ObjectType.Email:
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email = KnowledgeStore().load_object(root_object_id)
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desc = email.get_desc()
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desc["type"] = "email"
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desc["contents"] = []
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result_desc.append(desc)
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upper_list = desc["contents"]
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result = result[1:]
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else:
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upper_list = result_desc
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for object_id in result:
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if object_id.get_object_type() == ObjectType.Chunk:
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upper_list.append({"type": "text", "content": KnowledgeStore().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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# image = self.load_object(object_id)
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desc = dict()
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desc["id"] = str(object_id)
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desc["type"] = "image"
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upper_list.append(desc)
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if object_id.get_object_type() == ObjectType.Video:
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video = KnowledgeStore().load_object(object_id)
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desc = video.get_desc()
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desc["type"] = "video"
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upper_list.append(desc)
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else:
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pass
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content += json.dumps(result_desc)
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content += ".\n"
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return content
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async def _query(self, tokens: str, types: list[str] = ["text"], index: str=0):
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index = int(index)
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object_ids = await self.query_objects(tokens, types, 4)
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if len(object_ids) <= index:
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return "*** I have no more information for your reference.\n"
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else:
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content = "*** I have provided the following known information for your reference with json format:\n"
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return content + self.tokens_from_objects(object_ids[index:index+1])
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def init() -> KnowledgeEnvironment:
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return KnowledgeEnvironment("embedding")
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@@ -1,3 +1,3 @@
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pipelines = [
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"local_embedding"
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"Mia"
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]
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