disable vector-base knowledge base.

2024-08-19 12:20:56 -07:00
parent c1f3ae4fea
commit dfcc5efaa0
27 changed files with 297 additions and 1024 deletions
@@ -1,8 +0,0 @@
-name = "JarvisPlus"
-input.module = "scan_local"
-input.params.workspace = "${myai_dir}/workspace/JarvisPlus"
-input.params.path = "${myai_dir}/data"
-parser.module = "parse_local"
-parser.params.workspace = "${myai_dir}/workspace/JarvisPlus"
-parser.params.assign_to = "JarvisPlus"
-
@@ -1,9 +0,0 @@
-import sys
-import os
-from aios import KnowledgePipelineEnvironment
-directory = os.path.dirname(__file__)
-sys.path.append(directory + '/../../../../src/component/')
-
-from mail_environment import LocalEmail 
-def init(env: KnowledgePipelineEnvironment, params: dict):
-    return LocalEmail(env, params)
@@ -1,10 +0,0 @@
-import sys
-import os
-from aios import *
-directory = os.path.dirname(__file__)
-
-sys.path.append(directory + '/../../../../src/component/')
-from mail_environment import IssueParser
-
-def init(env: KnowledgePipelineEnvironment, params: dict):
-    return IssueParser(env, params)
@@ -1,13 +0,0 @@
-name = "Mail.Issue"
-input.module = "local.py"
-input.params.path = "${myai_dir}/mail"
-input.params.watch = true
-parser.module = "parser.py"
-parser.params.mail_path = "${myai_dir}/mail"
-parser.params.issue_path = "${myai_dir}/mail/issue.json"
-[parser.params.root_issue]
-summary = "巴克云公司推进中的项目"
-[[parser.params.root_issue.children]]
-summary = "去中心存储项目DMC"
-
-
@@ -1,10 +0,0 @@
-import sys
-import os
-from knowledge import KnowledgePipelineEnvironment
-directory = os.path.dirname(__file__)
-sys.path.append(directory + '/../../../../src/component/')
-
-from mail_environment import EmailSpider
-
-def init(env: KnowledgePipelineEnvironment, params: dict):
-    return EmailSpider(env, params)
@@ -1,14 +0,0 @@
-name = "Mail.Sync"
-input.module = "input.py"
-[input.params]
-path = "${myai_dir}/mail"
-imap_server = "imap.qq.com"
-imap_port = 993
-address = "115620204@qq.com"
-password = "zbbjpbukeonqbjja"
-[input.params.fields]
-from = "from"
-to = "to"
-subject = "subject"
-
-
@@ -1,211 +0,0 @@
-import copy
-import os
-from typing import List
-
-import aiofiles
-import chardet
-import logging
-import string
-import docx2txt
-from PyPDF2 import PdfReader
-
-from aios import KnowledgePipelineEnvironment, ImageObjectBuilder, DocumentObjectBuilder, KnowledgeStore, RichTextObject
-from aios.agent.agent_base import AgentPrompt
-from aios.frame.compute_kernel import ComputeKernel
-from aios.knowledge.data.writer import split_text
-from aios.proto.compute_task import ComputeTaskResult, ComputeTaskResultCode
-from aios.storage.storage import AIStorage
-from aios.utils import video_utils, image_utils
-
-
-class KnowledgeDirSource:
-    def __init__(self, env: KnowledgePipelineEnvironment, config):
-        self.env = env
-        path = string.Template(config["path"]).substitute(myai_dir=AIStorage.get_instance().get_myai_dir())
-        config["path"] = path
-        self.config = config
-
-    # @classmethod
-    # def user_config_items(cls):
-    #     return [("path", "local dir path")]
-
-    def path(self):
-        return self.config["path"]
-
-    @staticmethod
-    async def read_txt_file(file_path:str)->str:
-        cur_encode = "utf-8"
-        async with aiofiles.open(file_path,'rb') as f:
-            cur_encode = chardet.detect(await f.read())['encoding']
-
-        async with aiofiles.open(file_path,'r',encoding=cur_encode) as f:
-            return await f.read()
-
-    async def next(self):
-        while True:
-            journals = self.env.journal.latest_journals(1)
-            from_time = 0
-            if len(journals) == 1:
-                latest_journal = journals[0]
-                if latest_journal.is_finish():
-                    yield None
-                    continue
-                from_time = os.path.getctime(latest_journal.get_input())
-                if os.path.getmtime(self.path()) <= from_time:
-                    yield (None, None)
-                    continue
-
-            file_pathes = sorted(os.listdir(self.path()), key=lambda x: os.path.getctime(os.path.join(self.path(), x)))
-            for rel_path in file_pathes:
-                file_path = os.path.join(self.path(), rel_path)
-                timestamp = os.path.getctime(file_path)
-                if timestamp <= from_time:
-                    continue
-                ext = os.path.splitext(file_path)[1].lower()
-                if ext in ['.jpg', '.jpeg', '.png', '.gif', '.bmp']:
-                    logging.info(f"knowledge dir source found image file {file_path}")
-                    image = ImageObjectBuilder({}, {}, file_path).build(self.env.get_knowledge_store())
-                    await self.env.get_knowledge_store().insert_object(image)
-                    yield (image.calculate_id(), file_path)
-                if ext in ['.txt']:
-                    logging.info(f"knowledge dir source found text file {file_path}")
-                    text = await self.read_txt_file(file_path)
-                    document = DocumentObjectBuilder({}, {}, text).build(self.env.get_knowledge_store())
-                    await self.env.get_knowledge_store().insert_object(document)
-                    yield (document.calculate_id(), file_path)
-            yield (None, None)
-
-
-def init(env: KnowledgePipelineEnvironment, params: dict) -> KnowledgeDirSource:
-    return KnowledgeDirSource(env, params)
-
-
-async def image_to_text(images: List[str]) -> str:
-    msg_prompt = AgentPrompt()
-    image_prompt = "What's in this image?"
-    content = [{"type": "text", "text": image_prompt}]
-    content.extend([{"type": "image_url", "image_url": {"url": image_utils.to_base64(image)}} for image in images])
-    msg_prompt.messages = [{"role": "user", "content": content}]
-
-    resp: ComputeTaskResult = await (ComputeKernel.get_instance()
-                                     .do_llm_completion(prompt=msg_prompt,
-                                                        resp_mode="text",
-                                                        mode_name="gpt-4-vision-preview",
-                                                        max_token=4000,
-                                                        inner_functions=None,
-                                                        timeout=None))
-    if resp.result_code != ComputeTaskResultCode.OK:
-        raise Exception(f"image_to_text error: {resp.result_code} msg:{resp.error_str}")
-    return resp.result_str
-
-
-async def video_to_text(video: str) -> str:
-    prompt = "These pictures are key frames extracted from the video. Please describe the content of the video based on these key frames."
-    frames = video_utils.extract_frames(video, (1024, 1024))
-    msg_prompt = AgentPrompt()
-    content = [{"type": "text", "text": prompt}]
-    content.extend([{"type": "image_url", "image_url": {"url": frame}} for frame in frames])
-    msg_prompt.messages = [{"role": "user", "content": content}]
-    resp: ComputeTaskResult = await (ComputeKernel.get_instance()
-                                     .do_llm_completion(prompt=msg_prompt,
-                                                        resp_mode="text",
-                                                        mode_name="gpt-4-vision-preview",
-                                                        max_token=4000,
-                                                        inner_functions=None,
-                                                        timeout=None))
-    if resp.result_code != ComputeTaskResultCode.OK:
-        raise Exception(f"video_to_text error: {resp.result_code} msg:{resp.error_str}")
-    return resp.result_str
-
-
-async def summary_document(text: str, separators: List[str]=["\n\n", "\n"]) -> str:
-    chunks = split_text(text, separators=separators, chunk_size=4000, chunk_overlap=200, length_function=len)
-
-    prompt = AgentPrompt()
-    prompt.system_message = {"role":"system","content":"Your job is to generate a summary based on the input."}
-    if len(chunks) == 1:
-        prompt.append(AgentPrompt(chunks[0]))
-        resp = await (ComputeKernel.get_instance()
-                      .do_llm_completion(prompt=prompt,
-                                         resp_mode="text",
-                                         mode_name="gpt-4-1106-preview",
-                                         max_token=4000,
-                                         inner_functions=None,
-                                         timeout=None))
-        if resp.result_code != ComputeTaskResultCode.OK:
-            raise Exception(f"summary_document error: {resp.result_code} msg:{resp.error_str}")
-        return resp.result_str
-
-    segments = []
-    for i, chunk in enumerate(chunks):
-        seg_prompt = copy.deepcopy(prompt)
-        seg_prompt.append(AgentPrompt(chunk))
-        resp = await (ComputeKernel.get_instance()
-                      .do_llm_completion(prompt=seg_prompt,
-                                         resp_mode="text",
-                                         mode_name="gpt-4-1106-preview",
-                                         max_token=4000,
-                                         inner_functions=None,
-                                         timeout=None))
-        if resp.result_code != ComputeTaskResultCode.OK:
-            raise Exception(f"summary_document error: {resp.result_code} msg:{resp.error_str}")
-        segments.append(resp.result_str)
-
-    segments_str = "\n".join(segments)
-    prompt.append(AgentPrompt(f"Please combine the summaries of the following paragraphs into one complete summary:\n{segments_str}"))
-    resp = await (ComputeKernel.get_instance()
-                  .do_llm_completion(prompt=prompt,
-                                     resp_mode="text",
-                                     mode_name="gpt-4-1106-preview",
-                                     max_token=4000,
-                                     inner_functions=None,
-                                     timeout=None))
-    if resp.result_code != ComputeTaskResultCode.OK:
-        raise Exception(f"summary_document error: {resp.result_code} msg:{resp.error_str}")
-    return resp.result_str
-
-
-
-def pdf_to_rich_text_object(pdf: str, store: KnowledgeStore) -> RichTextObject:
-    base_name = os.path.basename(pdf)
-    cache_path = os.path.join(AIStorage.get_instance().get_myai_dir(), "knowledge", "doc_cache", base_name)
-    if not os.path.exists(cache_path):
-        os.makedirs(cache_path)
-
-    reader = PdfReader(pdf)
-    rich_text = RichTextObject()
-    page_texts = []
-    image_count = 0
-    for page in reader.pages:
-        text = page.extract_text()
-        page_texts.append(text)
-        for image in page.images:
-            image_path = os.path.join(cache_path, f"{image_count}_{image.name}")
-            with open(image_path, "wb") as f:
-                f.write(image.data)
-            image_object = ImageObjectBuilder({}, {}, image_path).build(store)
-            rich_text.add_image(image_object)
-
-    document = DocumentObjectBuilder({}, {}, "".join(page_texts)).build(store)
-    rich_text.add_document(document)
-
-    return rich_text
-
-
-def doc_to_rich_text_object(doc: str, store: KnowledgeStore) -> RichTextObject:
-    base_name = os.path.basename(doc)
-    cache_path = os.path.join(AIStorage.get_instance().get_myai_dir(), "knowledge", "doc_cache", base_name)
-    if not os.path.exists(cache_path):
-        os.makedirs(cache_path)
-    text = docx2txt.process(doc, cache_path)
-
-    rich_text = RichTextObject()
-    for image in os.listdir(cache_path):
-        image_path = os.path.join(cache_path, image)
-        image_object = ImageObjectBuilder({}, {}, image_path).build(store)
-        rich_text.add_image(image_object)
-
-    document = DocumentObjectBuilder({}, {}, text).build(store)
-    rich_text.add_document(document)
-
-    return rich_text
@@ -1,101 +0,0 @@
-# define a knowledge base class
-import json
-import string
-from aios 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 str(object)
-
-def init(env: KnowledgePipelineEnvironment, params: dict) -> EmbeddingParser:
-    return EmbeddingParser(env, params)
@@ -1,6 +0,0 @@
-name = "Mia"
-input.module = "input.py"
-input.params.path = "${myai_dir}/data"
-parser.module = "parser.py"
-parser.params.path = "${myai_dir}/knowledge/indices/embedding"
-
@@ -1,96 +0,0 @@
-import os
-import logging
-import json
-from aios import *
-
-class EmbeddingEnvironment(SimpleEnvironment):
-    def __init__(self, workspace: str) -> None:
-        super().__init__(workspace)
-        self.path = os.path.join(AIStorage.get_instance().get_myai_dir(), "knowledge/indices/embedding")
-        self._default_text_model = "all-MiniLM-L6-v2"
-        self._default_image_model = "clip-ViT-B-32"
-
-        query_param = {
-            "tokens": "key words to query", 
-            "types": "prefered knowledge types, one or more of [text, image]",
-            "limit": "index of query result"
-        }
-        self.add_ai_function(SimpleAIFunction("query_knowledge", 
-                                            "vector query content from local knowledge base",
-                                            self._query, 
-                                            query_param))
-        
-    def __get_vector_store(self, model_name: str) -> ChromaVectorStore:
-        return ChromaVectorStore(self.path, model_name)
-    
-    async def query_objects(self, tokens: str, types: list[str], topk: int) -> [ObjectID]:
-        texts = []
-        if "text" in types:
-            vector = await ComputeKernel.get_instance().do_text_embedding(tokens, self._default_text_model)
-            texts = await self.__get_vector_store(self._default_text_model).query(vector, topk)
-        images = []
-        if "image" in types:
-            vector = await ComputeKernel.get_instance().do_text_embedding(tokens, self._default_image_model)
-            images = await self.__get_vector_store(self._default_image_model).query(vector, topk)
-        return texts + images
-    
-        
-    def tokens_from_objects(self, object_ids: [ObjectID]) -> list[str]:
-        results = dict()
-        for object_id in object_ids:
-            parents = KnowledgeStore().get_relation_store().get_related_root_objects(object_id)
-            # last parent is the root object
-            root_object_id = parents[0] if parents else object_id
-            logging.info(f"object_id: {str(object_id)} root_object_id: {str(root_object_id)}")
-            if str(root_object_id) in results:
-                results[str(root_object_id)].append(object_id)
-            else:
-                results[str(root_object_id)] = [root_object_id, object_id]
-        content = ""
-        result_desc = []
-        for result in results.values():
-            # first element in result is the root object
-            root_object_id = result[0]
-            if root_object_id.get_object_type() == ObjectType.Email:
-                email = KnowledgeStore().load_object(root_object_id)
-                desc = email.get_desc()
-                desc["type"] = "email"
-                desc["contents"] = []
-                result_desc.append(desc)
-                upper_list = desc["contents"]
-                result = result[1:]
-            else:
-                upper_list = result_desc
-            
-            for object_id in result:
-                if object_id.get_object_type() == ObjectType.Chunk:
-                    upper_list.append({"type": "text", "content": KnowledgeStore().get_chunk_reader().get_chunk(object_id).read().decode("utf-8")})
-                if object_id.get_object_type() == ObjectType.Image:
-                    # image = self.load_object(object_id)
-                    desc = dict()
-                    desc["id"] = str(object_id)
-                    desc["type"] = "image"
-                    upper_list.append(desc)
-                if object_id.get_object_type() == ObjectType.Video:
-                    video = KnowledgeStore().load_object(object_id)
-                    desc = video.get_desc()
-                    desc["type"] = "video"
-                    upper_list.append(desc)
-                else:
-                    pass
-        content += json.dumps(result_desc)
-        content += ".\n"  
-
-        return content
-    
-    async def _query(self, tokens: str, types: list[str] = ["text"], index: str=0):
-        index = int(index)
-        object_ids = await self.query_objects(tokens, types, 4)
-        if len(object_ids) <= index:
-            return "*** I have no more information for your reference.\n"
-        else:
-            content = "*** I have provided the following known information for your reference with json format:\n"
-            return content + self.tokens_from_objects(object_ids[index:index+1])
-
-def init(workspace: str) -> EmbeddingEnvironment:
-    return EmbeddingEnvironment(workspace)
@@ -1,3 +0,0 @@
-pipelines = [
-    "JarvisPlus"
-]
@@ -36,7 +36,7 @@ logger = logging.getLogger(__name__)


 class AgentMemory:
-    def __init__(self,agent_id:str,base_dir:str,enable_knowledge_graph = True) -> None:
+    def __init__(self,agent_id:str,base_dir:str,enable_knowledge_graph = False) -> None:
        self.agent_memory_base_dir = base_dir
        self.agent_id:str= agent_id

@@ -52,15 +52,17 @@ class AgentMemory:
        self.last_think_time : float = 0.0
        self.enable_knowledge_graph : bool = enable_knowledge_graph
        if self.enable_knowledge_graph:
-            kb_desc = """The Knowledgegraph is used to store important information obtained by Agent in the conversation.Use the following ways to store information:
-            /contacts/$name:Related information of the contact
-            /relations/$obj1/$obj2:The relationship between obj2 and obj1
-            /summary/$topic:Based on topic summary
+            kb_desc = """
+The Knowledgegraph is used to store important information obtained by Agent in the conversation.Use the following ways to store information:
+- /contacts/$name:Related information of the contact
+- /relations/$obj1/$obj2:The relationship between obj2 and obj1
+- /summary/$topic:Based on topic summary
            """

            self.knowledge_graph = ObjFSKnowledgeGrpah(f"{self.agent_id}.memory",self.memory_db,kb_desc)
            BaseKnowledgeGraph.add_kb(self.knowledge_graph)
            self.simple_memory_sentences = None
+            
        else:
            self.knowledge_graph = None
            self.simple_memory_sentences : List[str] = []
@@ -1,7 +1,7 @@
 from .object import *
-from .vector import *
+#from .vector import *
 from .data import *
 from .store import KnowledgeStore
 from .core_object import *
-from .pipeline import *
+#from .pipeline import *
 from .knowledge_base import *
@@ -1,133 +0,0 @@
-import datetime
-import sqlite3
-import os
-import logging
-from . import ObjectID, KnowledgeStore
-from enum import Enum
-
-class KnowledgePipelineJournal:
-    def __init__(self, time: datetime.datetime, input: str, parser: str):
-        self.time = time
-        self.input = input
-        self.parser = parser
-    
-    def is_finish(self) -> bool:
-        return self.input is None
-
-    def get_input(self) -> str:
-        return self.input
-    
-    def get_parser(self) -> str:
-        return self.parser
-    
-    def __str__(self) -> str:
-        if self.is_finish():
-            return f"{self.time}: finished)"
-        else:
-            return f"{self.time}: input:{self.input}, parser:{self.parser})"
-
-# init sqlite3 client
-class KnowledgePipelineJournalClient:
-    def __init__(self, pipeline_path: str = None):
-        if not os.path.exists(pipeline_path):
-            os.makedirs(pipeline_path)
-        self.journal_path = os.path.join(pipeline_path, "journal.db")
-    
-        conn = sqlite3.connect(self.journal_path)
-        conn.execute(
-            '''CREATE TABLE IF NOT EXISTS journal (
-                id INTEGER PRIMARY KEY AUTOINCREMENT, 
-                time DATETIME DEFAULT CURRENT_TIMESTAMP,  
-                input TEXT, 
-                parser TEXT)'''
-        )
-        conn.commit()
-
-    def insert(self, input: str, parser: str, timestamp: datetime.datetime = None):
-        timestamp = datetime.datetime.now() if timestamp is None else timestamp
-        conn = sqlite3.connect(self.journal_path)
-        conn.execute(
-            "INSERT INTO journal (time, input, parser) VALUES (?, ?, ?)",
-            (timestamp, input, parser),
-        )
-        conn.commit()
-          
-    def latest_journals(self, topn) -> [KnowledgePipelineJournal]:
-        conn = sqlite3.connect(self.journal_path)
-        cursor = conn.cursor()
-        cursor.execute("SELECT * FROM journal ORDER BY id DESC LIMIT ?", (topn,))
-        return [KnowledgePipelineJournal(time, input, parser) for (_, time, input, parser) in cursor.fetchall()]
-
-class KnowledgePipelineEnvironment:
-    def __init__(self, pipeline_path: str):
-        self.knowledge_store = KnowledgeStore()
-        if not os.path.exists(pipeline_path):
-            os.makedirs(pipeline_path)
-        self.pipeline_path = pipeline_path
-        self.journal = KnowledgePipelineJournalClient(pipeline_path)
-        self.logger = logging.getLogger()
-
-    def get_journal(self) -> KnowledgePipelineJournalClient:
-        return self.journal
-    
-    def get_knowledge_store(self) -> KnowledgeStore:
-        return self.knowledge_store
-    
-    def get_logger(self) -> logging.Logger:
-        return self.logger
-
-class KnowledgePipelineState(Enum):
-    INIT = 0
-    RUNNING = 1
-    STOPPED = 2
-    FINISHED = 3
-
-class NullParser:
-    async def parse(self, object_id):
-        return ""
-
-class KnowledgePipeline:
-    def __init__(self, name: str, env: KnowledgePipelineEnvironment, input_init, input_params=None, parser_init=None, parser_params=None):
-        self.name = name
-        self.state = KnowledgePipelineState.INIT
-        self.input_init = input_init
-        self.input_params = input_params
-        self.parser_init = parser_init
-        self.parser_params = parser_params
-        self.env = env
-        self.input = None
-        self.parser = None
-
-    def get_name(self):
-        return self.name
-
-    def get_journal(self) -> KnowledgePipelineJournalClient:
-        return self.env.journal
-        
-    async def run(self):
-        if self.state == KnowledgePipelineState.INIT:
-            self.input = self.input_init(self.env, self.input_params)
-            if self.parser_init is None:
-                self.parser = NullParser()
-            else:
-                self.parser = self.parser_init(self.env, self.parser_params)
-            self.state = KnowledgePipelineState.RUNNING
-        if self.state == KnowledgePipelineState.RUNNING:
-            async for input in self.input.next():
-                if input is None:
-                    self.state = KnowledgePipelineState.FINISHED
-                    self.env.journal.insert(None, None)
-                    return 
-                (object_id, input_journal) = input
-                if object_id is not None:
-                    parser_journal = await self.parser.parse(object_id)
-                    self.env.journal.insert(input_journal, parser_journal)
-                else:
-                    return
-        if self.state == KnowledgePipelineState.STOPPED:
-            return 
-        if self.state == KnowledgePipelineState.FINISHED:
-            return 
-
-
-    
@@ -1,2 +0,0 @@
-from .vector_base import VectorBase
-from .chroma_store import ChromaVectorStore
@@ -1,51 +0,0 @@
-from .vector_base import VectorBase
-from ..object import ObjectID
-import chromadb
-import logging
-import os
-
-
-class ChromaVectorStore(VectorBase):
-    def __init__(self, root_dir, model_name: str) -> None:
-        super().__init__(model_name)
-
-        logging.info(
-            "will init chroma vector store, model={}".format(model_name)
-        )
-
-        directory = os.path.join(root_dir, "vector")
-        logging.info("will use vector store: {}".format(directory))
-
-        client = chromadb.PersistentClient(
-            path=directory, settings=chromadb.Settings(anonymized_telemetry=False)
-        )
-        # client = chromadb.Client()
-
-        collection_name = "coll_{}".format(model_name)
-        logging.info("will init chroma colletion: %s", collection_name)
-
-        collection = client.get_or_create_collection(collection_name)
-        self.collection = collection
-
-    async def insert(self, vector: [float], id: ObjectID):
-        logging.info(f"will insert vector: {len(vector)} id: {str(id)}")
-        logging.debug(f"vector is {vector}")
-        self.collection.add(
-            embeddings=vector,
-            ids=str(id),
-        )
-
-    async def query(self, vector: [float], top_k: int) -> [ObjectID]:
-        ret = self.collection.query(
-            query_embeddings=vector,
-            n_results=top_k,
-        )
-        logging.info(f"query result {ret}")
-        if len(ret['ids']) == 0:
-            return []
-        return list(map(ObjectID.from_base58, ret["ids"][0]))
-
-    async def delete(self, id: ObjectID):
-        self.collection.delete(
-            ids=id,
-        )
@@ -1,16 +0,0 @@
-# import the ObjectID class
-from ..object import ObjectID
-
-# define a vector base class
-class VectorBase:
-    def __init__(self, model_name) -> None:
-        self.model_name = model_name
-
-    async def insert(self, vector: [float], id: ObjectID):
-        pass
-
-    async def query(self, vector: [float], top_k: int) -> [ObjectID]:
-        pass
-    
-    async def delete(self, id: ObjectID):
-        pass
@@ -61,7 +61,7 @@ class AgentMsg:
        self.mentions:[] = None #use in group chat only
        #self.title:str = None
        self.body:str = None
-        self.body_mime:str = None #//default is "text/plain",encode is utf8
+        self.body_mime:str = "text/plain" #//default is "text/plain",encode is utf8

        #type is call / action
        self.func_name = None
@@ -1,3 +1,3 @@
-from .local_document import LocalKnowledgeBase, ScanLocalDocument, ParseLocalDocument
+#from .local_document import LocalKnowledgeBase, ScanLocalDocument, ParseLocalDocument
 from .local_file_system import FilesystemEnvironment
 from .shell import ShellEnvironment
@@ -9,7 +9,7 @@ import aiofiles
 from urllib.parse import urlparse
 from typing import Optional

-from aios import KnowledgeStore, ObjectType
+#from aios import KnowledgeStore, ObjectType
 from aios.frame.tunnel import AgentTunnel
 from aios.proto.agent_msg import AgentMsg, AgentMsgType
 import discord
@@ -165,26 +165,26 @@ class DiscordTunnel(AgentTunnel):
                    if len(resp_msg.body) < 1:
                        return

-                    knownledge_object = KnowledgeStore().parse_object_in_message(resp_msg.body)
-                    if knownledge_object is not None:
-                        if knownledge_object.get_object_type() == ObjectType.Image:
-                            image = KnowledgeStore().bytes_from_object(knownledge_object)
-                            try:
-                                async with aiofiles.open("image.jpg", "wb") as f:
-                                    await f.write(image)
-                                    await message.channel.send(file=discord.File("image.jpg"))
-                            except Exception as e:
-                                logger.error(f"save image error:{e}")
-                                logger.exception(e)
-                            return
-                    else:
-                        pos = resp_msg.body.find("audio file")
-                        if pos != -1:
-                            audio_file = resp_msg.body[pos+11:].strip()
-                            if audio_file.startswith("\""):
-                                audio_file = audio_file[1:-1]
-                            await message.channel.send(file=discord.File(audio_file))
-                            return
+                    # knownledge_object = KnowledgeStore().parse_object_in_message(resp_msg.body)
+                    # if knownledge_object is not None:
+                    #     if knownledge_object.get_object_type() == ObjectType.Image:
+                    #         image = KnowledgeStore().bytes_from_object(knownledge_object)
+                    #         try:
+                    #             async with aiofiles.open("image.jpg", "wb") as f:
+                    #                 await f.write(image)
+                    #                 await message.channel.send(file=discord.File("image.jpg"))
+                    #         except Exception as e:
+                    #             logger.error(f"save image error:{e}")
+                    #             logger.exception(e)
+                    #         return
+                    # else:
+                    #     pos = resp_msg.body.find("audio file")
+                    #     if pos != -1:
+                    #         audio_file = resp_msg.body[pos+11:].strip()
+                    #         if audio_file.startswith("\""):
+                    #             audio_file = audio_file[1:-1]
+                    #         await message.channel.send(file=discord.File(audio_file))
+                    #         return
                    await message.channel.send(resp_msg.body)
                else:
                    if resp_msg.is_image_msg():
@@ -12,7 +12,7 @@ import aiohttp
 from slack_bolt.adapter.socket_mode.websockets import AsyncSocketModeHandler
 from slack_bolt.app.async_app import AsyncApp

-from aios import KnowledgeStore, ObjectType
+#from aios import KnowledgeStore, ObjectType
 from aios.frame.tunnel import AgentTunnel
 from aios.proto.agent_msg import AgentMsg, AgentMsgType
 from aios.storage.storage import AIStorage
@@ -189,26 +189,26 @@ class SlackTunnel(AgentTunnel):
                    if len(resp_msg.body) < 1:
                        return

-                    knownledge_object = KnowledgeStore().parse_object_in_message(resp_msg.body)
-                    if knownledge_object is not None:
-                        if knownledge_object.get_object_type() == ObjectType.Image:
-                            image = KnowledgeStore().bytes_from_object(knownledge_object)
-                            try:
-                                async with aiofiles.open("image.jpg", "wb") as f:
-                                    await f.write(image)
-                                    await app.client.files_upload_v2(channel=event["channel"], file="image.jpg")
-                            except Exception as e:
-                                logger.error(f"save image error:{e}")
-                                logger.exception(e)
-                            return
-                    else:
-                        pos = resp_msg.body.find("audio file")
-                        if pos != -1:
-                            audio_file = resp_msg.body[pos+11:].strip()
-                            if audio_file.startswith("\""):
-                                audio_file = audio_file[1:-1]
-                            await app.client.files_upload_v2(channel=event["channel"], file=audio_file)
-                            return
+                    # knownledge_object = KnowledgeStore().parse_object_in_message(resp_msg.body)
+                    # if knownledge_object is not None:
+                    #     if knownledge_object.get_object_type() == ObjectType.Image:
+                    #         image = KnowledgeStore().bytes_from_object(knownledge_object)
+                    #         try:
+                    #             async with aiofiles.open("image.jpg", "wb") as f:
+                    #                 await f.write(image)
+                    #                 await app.client.files_upload_v2(channel=event["channel"], file="image.jpg")
+                    #         except Exception as e:
+                    #             logger.error(f"save image error:{e}")
+                    #             logger.exception(e)
+                    #         return
+                    # else:
+                    #     pos = resp_msg.body.find("audio file")
+                    #     if pos != -1:
+                    #         audio_file = resp_msg.body[pos+11:].strip()
+                    #         if audio_file.startswith("\""):
+                    #             audio_file = audio_file[1:-1]
+                    #         await app.client.files_upload_v2(channel=event["channel"], file=audio_file)
+                    #         return
                    await app.client.chat_postMessage(channel=event["channel"], text=resp_msg.body)
                else:
                    if resp_msg.is_image_msg():
@@ -1 +0,0 @@
-from .local_st_compute_node import *
@@ -1,212 +0,0 @@
-import logging
-import requests
-from typing import Optional, List
-from pydantic import BaseModel
-from typing import Union
-from PIL import Image
-import io
-
-from aios import ComputeTask, ComputeTaskResult, ComputeTaskState, ComputeTaskType,ComputeTaskResultCode,ComputeNode,AIStorage,UserConfig,ObjectID,Queue_ComputeNode
-
-logger = logging.getLogger(__name__)
-
-class LocalSentenceTransformer_Text_ComputeNode(Queue_ComputeNode):
-    # For valid pretrained models, see https://www.sbert.net/docs/pretrained_models.html
-    def __init__(self, model_name: str = "all-MiniLM-L6-v2"):
-        super().__init__()
-
-        self.node_id = "local_sentence_transformer_text_embedding_node"
-        self.model_name = model_name
-        self.model = None
-
-    def initial(self) -> bool:
-        logger.info(
-            f"LocalSentenceTransformer_Text_ComputeNode init, model_name: {self.model_name}"
-        )
-
-        assert self.model_name is not None
-        assert self.model is None
-        try:
-            from sentence_transformers import SentenceTransformer
-
-            self.model = SentenceTransformer(self.model_name)
-        except Exception as err:
-            logger.error(f"load model {self.model} failed: {err}")
-            return False
-        self.start()
-        return True
-
-    async def execute_task(self, task: ComputeTask) :
-        result = ComputeTaskResult()
-        result.result_code = ComputeTaskResultCode.ERROR
-        result.set_from_task(task)
-        result.worker_id = self.node_id
-        try:
-            # logger.debug(f"LocalSentenceTransformer_Text_ComputeNode task: {task}")
-            if task.task_type == ComputeTaskType.TEXT_EMBEDDING:
-                input = task.params["input"]
-                logger.debug(
-                    f"LocalSentenceTransformer_Text_ComputeNode task input: {input}"
-                )
-                sentence_embeddings = self.model.encode(input, show_progress_bar=False).tolist()
-                # logger.debug(f"LocalSentenceTransformer_Text_ComputeNode task sentence_embeddings: {sentence_embeddings}")
-                result.result_code = ComputeTaskResultCode.OK
-                result.result["content"] = sentence_embeddings
-
-            else:
-                result.error_str = f"unsupport embedding task type: {task.task_type}"
-        except Exception as err:
-            import traceback
-
-            logger.error(f"{traceback.format_exc()}, error: {err}")
-            result.error_str = f"{traceback.format_exc()}, error: {err}"
-
-        return result
-
-
-    def display(self) -> str:
-        return f"LocalSentenceTransformer_Text_ComputeNode: {self.node_id}, {self.model_name}"
-
-    def get_capacity(self):
-        pass
-
-    def is_support(self, task: ComputeTask) -> bool:
-        return task.task_type == ComputeTaskType.TEXT_EMBEDDING and task.params["model_name"] == "all-MiniLM-L6-v2"
-
-    def is_local(self) -> bool:
-        return True
-
-
-class LocalSentenceTransformer_Image_ComputeNode(Queue_ComputeNode):
-    # For valid pretrained models, see https://www.sbert.net/docs/pretrained_models.html
-    def __init__(
-        self,
-        model_name: str = "clip-ViT-B-32",
-        multi_model_name: str = "clip-ViT-B-32-multilingual-v1",
-    ):
-        super().__init__()
-
-        self.node_id = "local_sentence_transformer_image_embedding_node"
-        self.model_name = model_name
-        self.multi_model_name = multi_model_name
-        self.model = None
-        self.multi_model = None
-
-    def initial(self) -> bool:
-        logger.info(
-            f"LocalSentenceTransformer_Image_ComputeNode init, model_name: {self.model_name} {self.multi_model_name}"
-        )
-
-        assert self.model_name is not None
-        assert self.multi_model_name is not None
-        assert self.model is None
-        assert self.multi_model is None
-
-        try:
-            from sentence_transformers import SentenceTransformer
-
-            self.model = SentenceTransformer(self.model_name)
-            self.multi_model = SentenceTransformer(self.multi_model_name)
-        except Exception as err:
-            logger.error(f"load model {self.model} failed: {err}")
-            return False
-        self.start()
-        return True
-
-    def _load_image(self, source: Union[ObjectID, bytes]) -> Optional[Image]:
-        image_data = None
-        if isinstance(source, ObjectID):
-            from aios import KnowledgeStore, ImageObject
-
-            buf = KnowledgeStore().get_object_store().get_object(source)
-            if buf is None:
-                logger.error(f"load image object but not found! {source}")
-                return None
-
-            try:
-                image_obj = ImageObject.decode(buf)
-            except Exception as err:
-                logger.error(f"decode ImageObject from buffer failed: {source}, {err}")
-                return None
-
-            file_size = image_obj.get_file_size()
-            # print(f"got image object: {source.to_base58()}, size: {file_size}")
-
-            image_data = (
-                KnowledgeStore()
-                .get_chunk_reader()
-                .read_chunk_list_to_single_bytes(image_obj.get_chunk_list())
-            )
-
-        elif isinstance(source, bytes):
-            image_data = source
-        else:
-            logger.error(f"unsupport image source type: {type(source)}, {source}")
-            return None
-
-        try:
-            img = Image.open(io.BytesIO(image_data))
-
-            return img
-        except Exception as err:
-            logger.error(f"load image from buffer failed: {source}, {err}")
-            return None
-
-    async def execute_task(
-        self, task: ComputeTask
-    ) -> ComputeTaskResult:
-        result = ComputeTaskResult()
-        result.result_code = ComputeTaskResultCode.ERROR
-        result.set_from_task(task)
-        result.worker_id = self.node_id
-        try:
-            # logger.debug(f"LocalSentenceTransformer_Text_ComputeNode task: {task}")
-            if task.task_type == ComputeTaskType.TEXT_EMBEDDING:
-                input = task.params["input"]
-                logger.debug(
-                    f"LocalSentenceTransformer_Text_ComputeNode task text input: {input}"
-                )
-                sentence_embeddings = self.multi_model.encode(input, show_progress_bar=False).tolist()
-                # logger.debug(f"LocalSentenceTransformer_Text_ComputeNode task sentence_embeddings: {sentence_embeddings}")
-                result.result_code = ComputeTaskResultCode.OK
-                result.result["content"] = sentence_embeddings
-
-            elif task.task_type == ComputeTaskType.IMAGE_EMBEDDING:
-                input = task.params["input"]
-                logger.debug(
-                    f"LocalSentenceTransformer_Image_ComputeNode task image input: {input}"
-                )
-
-                img = self._load_image(input)
-                if img is None:
-                    result.error_str = f"load image failed: {input}"
-                    return result   
-
-                sentence_embeddings = self.model.encode(img, show_progress_bar=False).tolist()
-                result.result_code = ComputeTaskResultCode.OK
-                result.result["content"] = sentence_embeddings
-            else:
-                result.error_str = f"unsupport embedding task type: {task.task_type}"
-        except Exception as err:
-            import traceback
-
-            logger.error(f"{traceback.format_exc()}, error: {err}")
-            result.error_str = f"{traceback.format_exc()}, error: {err}"
- 
-        
-        return result
-
-    def display(self) -> str:
-        return f"LocalSentenceTransformer_Image_ComputeNode: {self.node_id}, {self.model_name}"
-
-    def get_capacity(self):
-        pass
-
-    def is_support(self, task: ComputeTask) -> bool:
-        return (
-            (task.task_type == ComputeTaskType.TEXT_EMBEDDING and task.params["model_name"] == "clip-ViT-B-32")
-            or task.task_type == ComputeTaskType.IMAGE_EMBEDDING
-        )
-
-    def is_local(self) -> bool:
-        return True
@@ -12,7 +12,7 @@ from telegram import Bot
 from telegram.ext import Updater
 from telegram.error import Forbidden, NetworkError

-from aios import ObjectType, KnowledgeStore,AgentTunnel,AIStorage,ContactManager,Contact,AgentMsg,AgentMsgType
+from aios import AgentTunnel,AIStorage,ContactManager,Contact,AgentMsg,AgentMsgType

 logger = logging.getLogger(__name__)

@@ -160,6 +160,68 @@ class TelegramTunnel(AgentTunnel):
        if not os.path.exists(path):
            os.makedirs(path)
        return path
+    
+    async def conver_agent_msg_to_tg_msg(self,resp_msg:AgentMsg,update: Update):
+        
+        if resp_msg.body_mime is None:
+            if resp_msg.body is None:
+                return
+
+            if len(resp_msg.body) < 1:
+                await update.message.reply_text("")
+                return
+
+            # knowledge_object = KnowledgeStore().parse_object_in_message(resp_msg.body)
+            # knowledge_object = None
+            # if knowledge_object is not None:
+            #     if knowledge_object.get_object_type() == ObjectType.Image:
+            #         image = KnowledgeStore().bytes_from_object(knowledge_object)
+            #         try:
+            #             async with aiofiles.open("tg_send_temp.png", mode='wb') as local_file:
+            #                 if local_file:
+            #                     await local_file.write(image)
+            #                     await update.message.reply_photo("tg_send_temp.png")
+            #         except Exception as e:
+            #             logger.error(f"save image error: {e}")
+            #         return
+            # else:
+            #     pos = resp_msg.body.find("audio file")
+            #     if pos != -1:
+            #         audio_file = resp_msg.body[pos+11:].strip()
+            #         if audio_file.startswith("\""):
+            #             audio_file = audio_file[1:-1]
+            #         await update.message.reply_voice(audio_file)
+            #         return
+            await update.message.reply_text(resp_msg.body)
+        else:
+            if resp_msg.is_image_msg():
+                text, images = resp_msg.get_image_body()
+                if text is not None:
+                    await update.message.reply_text(text)
+                for image in images:
+                    if os.path.exists(image):
+                        await update.message.reply_photo(image)
+                    else:
+                        await update.message.reply_text(image)
+            elif resp_msg.is_video_msg():
+                text, video_file = resp_msg.get_video_body()
+                if text is not None:
+                    await update.message.reply_text(text)
+                if os.path.exists(video_file):
+                    await update.message.reply_video(video_file)
+                else:
+                    await update.message.reply_text(video_file)
+            elif resp_msg.is_audio_msg():
+                text, audio_file = resp_msg.get_audio_body()
+                if text is not None:
+                    await update.message.reply_text(text)
+
+                if os.path.exists(audio_file):
+                    await update.message.reply_voice(audio_file)
+                else:
+                    await update.message.reply_text(audio_file)
+            else:
+                await update.message.reply_text(resp_msg.body)

    async def conver_tg_msg_to_agent_msg(self,message:Message) -> AgentMsg:
        agent_msg = AgentMsg()
@@ -246,6 +308,7 @@ class TelegramTunnel(AgentTunnel):

        return False

+    # main entry for telegram message
    async def on_message(self, bot:Bot, update: Update) -> None:
        message = update.message
        logger.info(f"on_message: {message.message_id} from {message.from_user.username} ({update.effective_user.username}) to {message.chat.title}({message.chat.id})")
@@ -312,63 +375,8 @@ class TelegramTunnel(AgentTunnel):
        if resp_msg is None:
            await update.message.reply_text(f"System Error: Timeout,{self.target_id}  no resopnse! Please check logs/aios.log for more details!")
        else:
-            if resp_msg.body_mime is None:
-                if resp_msg.body is None:
-                    return
+            await self.conver_agent_msg_to_tg_msg(resp_msg,update)

-                if len(resp_msg.body) < 1:
-                    await update.message.reply_text("")
-                    return
-
-                knowledge_object = KnowledgeStore().parse_object_in_message(resp_msg.body)
-                if knowledge_object is not None:
-                    if knowledge_object.get_object_type() == ObjectType.Image:
-                        image = KnowledgeStore().bytes_from_object(knowledge_object)
-                        try:
-                            async with aiofiles.open("tg_send_temp.png", mode='wb') as local_file:
-                                if local_file:
-                                    await local_file.write(image)
-                                    await update.message.reply_photo("tg_send_temp.png")
-                        except Exception as e:
-                            logger.error(f"save image error: {e}")
-                        return
-                else:
-                    pos = resp_msg.body.find("audio file")
-                    if pos != -1:
-                        audio_file = resp_msg.body[pos+11:].strip()
-                        if audio_file.startswith("\""):
-                            audio_file = audio_file[1:-1]
-                        await update.message.reply_voice(audio_file)
-                        return
-                await update.message.reply_text(resp_msg.body)
-            else:
-                if resp_msg.is_image_msg():
-                    text, images = resp_msg.get_image_body()
-                    if text is not None:
-                        await update.message.reply_text(text)
-                    for image in images:
-                        if os.path.exists(image):
-                            await update.message.reply_photo(image)
-                        else:
-                            await update.message.reply_text(image)
-                elif resp_msg.is_video_msg():
-                    text, video_file = resp_msg.get_video_body()
-                    if text is not None:
-                        await update.message.reply_text(text)
-                    if os.path.exists(video_file):
-                        await update.message.reply_video(video_file)
-                    else:
-                        await update.message.reply_text(video_file)
-                elif resp_msg.is_audio_msg():
-                    text, audio_file = resp_msg.get_audio_body()
-                    if text is not None:
-                        await update.message.reply_text(text)
-
-                    if os.path.exists(audio_file):
-                        await update.message.reply_voice(audio_file)
-                    else:
-                        await update.message.reply_text(audio_file)
-                else:
-                    await update.message.reply_text(resp_msg.body)
+           


@@ -0,0 +1,159 @@
+aiofiles>=23.2.1
+aiohttp>=3.8.5
+aioimaplib>=1.0.1
+aiosignal>=1.3.1
+aiosmtplib>=2.0.2
+anyio>=4.0.0
+async-timeout>=4.0.3
+attrs>=23.1.0
+backoff>=2.2.1
+base36>=0.1.1
+base58>=2.1.1
+beautifulsoup4>=4.12.2
+cachetools>=5.3.1
+certifi>=2023.7.22
+charset-normalizer>=3.2.0
+chroma-hnswlib>=0.7.1
+chromadb>=0.4.0
+click>=8.1.7
+colorama>=0.4.6
+coloredlogs>=15.0.1
+decorator>=4.4.2
+fastapi
+filelock>=3.12.3
+flatbuffers>=23.5.26
+frozenlist>=1.4.0
+fsspec>=2023.9.0
+google>=3.0.0
+google-api-core>=2.11.1
+google-auth>=2.23.0
+google-cloud>=0.34.0
+google-cloud-texttospeech>=2.14.1
+googleapis-common-protos>=1.60.0
+h11>=0.14.0
+httpcore>=0.17.3
+httptools>=0.6.0
+httpx>=0.24.1
+huggingface-hub>=0.16.4
+humanfriendly>=10.0
+idna>=3.4
+imageio>=2.31.3
+imageio-ffmpeg>=0.4.8
+importlib-resources>=6.0.1
+mail-parser>=3.15.0
+monotonic>=1.6
+moviepy>=1.0.0
+mpmath>=1.3.0
+multidict>=6.0.4
+numpy>=1.25.2
+onnxruntime>=1.15.1
+
+overrides>=7.4.0
+packaging>=23.1
+pandas>=2.1.0
+Pillow>=10.0.0
+posthog>=3.0.2
+proglog>=0.1.10
+prompt-toolkit>=3.0.39
+proto-plus>=1.22.3
+pulsar-client>=3.3.0
+pyasn1>=0.5.0
+pyasn1-modules>=0.3.0
+pydantic
+PyPika>=0.48.9
+pyreadline3>=3.4.1
+python-dateutil>=2.8.2
+python-dotenv>=1.0.0
+python-telegram-bot>=20.5
+pytz>=2023.3.post1
+PyYAML>=6.0.1
+requests>=2.31.0
+rsa>=4.9
+simplejson>=3.19.1
+six>=1.16.0
+sniffio>=1.3.0
+soupsieve>=2.5
+starlette>=0.27.0
+sympy>=1.12
+tokenizers>=0.14.0
+toml>=0.10.0
+protobuf
+grpcio
+grpcio-status
+h11==0.14.0
+httpcore==0.17.3
+httptools==0.6.0
+httpx==0.24.1
+huggingface-hub==0.16.4
+humanfriendly==10.0
+idna==3.4
+imageio==2.31.3
+imageio-ffmpeg==0.4.8
+importlib-resources==6.0.1
+mail-parser==3.15.0
+monotonic==1.6
+moviepy==1.0.0
+mpmath==1.3.0
+multidict==6.0.4
+numpy==1.25.2
+onnxruntime==1.15.1
+overrides==7.4.0
+packaging==23.1
+pandas==2.1.0
+Pillow==10.0.0
+posthog==3.0.2
+proglog==0.1.10
+prompt-toolkit==3.0.39
+proto-plus==1.22.3
+protobuf
+pulsar-client==3.3.0
+pyasn1==0.5.0
+pyasn1-modules==0.3.0
+pydantic==1.10.12
+PyPika==0.48.9
+pyreadline3==3.4.1
+python-dateutil==2.8.2
+python-dotenv==1.0.0
+python-telegram-bot==20.5
+pytz==2023.3.post1
+PyYAML==6.0.1
+requests==2.31.0
+rsa==4.9
+simplejson==3.19.1
+six==1.16.0
+sniffio==1.3.0
+soupsieve==2.5
+starlette==0.27.0
+sympy==1.12
+telegram==0.0.1
+tokenizers==0.14.0
+toml==0.10.0
+pysocks
+chardet
+pydub
+aiosqlite
+python-telegram-bot
+pydub
+stability_sdk
+sentence-transformers==2.2.2
+tiktoken
+markdown
+PyPDF2
+srt
+webvtt-py
+openai
+docker
+generic_escape
+duckduckgo-search
+SQLAlchemy
+mysqlclient
+psycopg2-binary
+pyodbc
+oracledb
+html2text
+docx2txt
+opencv-python
+discord.py
+slack_bolt
+wget
+moviepy
@@ -38,16 +38,16 @@ from google_node import *
 from llama_node import *
 from openai_node import *
 from sd_node import *
-from st_node import *

 from agent_manager import AgentManager
 from workflow_manager import WorkflowManager
-from knowledge_manager import KnowledgePipelineManager
+#from knowledge_manager import KnowledgePipelineManager
 from tg_tunnel import TelegramTunnel
 from email_tunnel import EmailTunnel
 from discord_tunnel import DiscordTunnel
 from slack_tunnel import SlackTunnel
-from common_environment import LocalKnowledgeBase, FilesystemEnvironment, ShellEnvironment, ScanLocalDocument, ParseLocalDocument
+from common_environment import FilesystemEnvironment, ShellEnvironment
+#from common_environment import ScanLocalDocument, ParseLocalDocument

 from compute_node_config import *

@@ -214,17 +214,17 @@ class AIOS_Shell:



-        local_st_text_compute_node = LocalSentenceTransformer_Text_ComputeNode()
-        if local_st_text_compute_node.initial() is not True:
-            logger.error("local sentence transformer text embedding node initial failed!")
-        else:
-            ComputeKernel.get_instance().add_compute_node(local_st_text_compute_node)
+        # local_st_text_compute_node = LocalSentenceTransformer_Text_ComputeNode()
+        # if local_st_text_compute_node.initial() is not True:
+        #     logger.error("local sentence transformer text embedding node initial failed!")
+        # else:
+        #     ComputeKernel.get_instance().add_compute_node(local_st_text_compute_node)

-        local_st_image_compute_node = LocalSentenceTransformer_Image_ComputeNode()
-        if local_st_image_compute_node.initial() is not True:
-            logger.error("local sentence transformer image embedding node initial failed!")
-        else:
-            ComputeKernel.get_instance().add_compute_node(local_st_image_compute_node)
+        # local_st_image_compute_node = LocalSentenceTransformer_Image_ComputeNode()
+        # if local_st_image_compute_node.initial() is not True:
+        #     logger.error("local sentence transformer image embedding node initial failed!")
+        # else:
+        #     ComputeKernel.get_instance().add_compute_node(local_st_image_compute_node)


        await ComputeKernel.get_instance().start()
@@ -233,12 +233,12 @@ class AIOS_Shell:
        #AIBus().get_default_bus().register_message_handler(self.username,self._user_process_msg)


-        pipelines = KnowledgePipelineManager.initial(os.path.join(AIStorage().get_instance().get_myai_dir(), "knowledge/pipelines"))
-        pipelines.register_input("scan_local", ScanLocalDocument)
-        pipelines.register_parser("parse_local", ParseLocalDocument)
-        pipelines.load_dir(os.path.join(AIStorage().get_instance().get_system_app_dir(), "knowledge_pipelines"))
-        pipelines.load_dir(os.path.join(AIStorage().get_instance().get_myai_dir(), "knowledge_pipelines"))
-        asyncio.create_task(pipelines.run())
+        #pipelines = KnowledgePipelineManager.initial(os.path.join(AIStorage().get_instance().get_myai_dir(), "knowledge/pipelines"))
+        #pipelines.register_input("scan_local", ScanLocalDocument)
+        #pipelines.register_parser("parse_local", ParseLocalDocument)
+        #pipelines.load_dir(os.path.join(AIStorage().get_instance().get_system_app_dir(), "knowledge_pipelines"))
+        #pipelines.load_dir(os.path.join(AIStorage().get_instance().get_myai_dir(), "knowledge_pipelines"))
+        #asyncio.create_task(pipelines.run())

        TelegramTunnel.register_to_loader()
        EmailTunnel.register_to_loader()