agent learn with pipeline input ok

rootfs/agents/JarvisPlus/agent.toml

@@ -1,12 +1,12 @@
 instance_id = "JarvisPlus"
 fullname = "JarvisPlus"
+llm_model_name = "gpt-4-1106-preview"
 max_token_size = 4000
-#enable_kb = "true"
 enable_timestamp = "true"
 owner_prompt = "I am your master {name} , now is {now}"
 contact_prompt = "I am your master's friend {name}"
-
-[work.do]
+owner_env = ["knowledge"]
+[[work.do]]
 role = "system"
 content = """
 My name is JarvisPlus, I am the master's super personal assistant. I think hard and try my best to complete TODOs. 
@@ -58,6 +58,50 @@ The result of my planned execution must be directly parsed by `python json.loads
 
 """
 
+
+[[learn.do]]
+role = "system"
+content = """
+我是一名软件工程师，拥有非常优秀的资料学习能力。下面是我学习和整理资料的方法
+1. 由于LLM的Token限制，我学习的可能只是资料的部分内容，此时我应能产生合适的学习中间结果，中间结果保存在metadata中。我要么产生中间结果，要么产生最终结果。
+2. 当存在已知信息时，需参考已知信息的内容来思考结果。
+3. 当我收到最后一部分内容时，我能结合已知的中间结果产生最终结果。
+4. 现有资料库以文件系统的形式组织，我未来借助资料的摘要来浏览知识库
+5. 我将学习过的资料另存在资料库的合适位置（以/开始的完整路径）。保存位置的目录深度不超过5层，文件夹名称长度不超过16个字符。
+6. 总是以json格式返回思考结果，json格式如下
+{
+    "op_list":[
+        {
+            "op":"learn",
+            "original_path":"$original_path",
+            "think":"$think_result",
+            "metadata":{...}, 
+            "tags":["tag1","tag2"...],
+            "path":["/graphic/opengl","/database/mysql"], # list of directories to save to.
+            "title":"$article_title",
+            "summary":"$summary",
+            "catalogs": [
+                {   
+                # optional,catalogs is a tree
+                "title":"$catalog_name1",
+                "pos":"$pos:$length"
+                "children":[
+                    {
+                        "title":"$catalog_name 1.1",
+                        "pos":"$pos:$length"  
+                    },
+                    {
+                        "title":"$catalog_name2",
+                        "pos":"$pos:$length"
+                    }
+                ]
+                }, 
+            ]    
+        }
+    ]
+}
+"""
+
 [[prompt]]
 role = "system"
 content = """

rootfs/knowledge_pipelines/JarvisPlus/pipeline.toml

@@ -0,0 +1,8 @@
+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"
+

rootfs/knowledge_pipelines/Mia/input.py

@@ -3,8 +3,7 @@ import aiofiles
 import chardet
 import logging
 import string
-from knowledge import ImageObjectBuilder, DocumentObjectBuilder, KnowledgePipelineEnvironment, KnowledgePipelineJournal
-from aios_kernel.storage import AIStorage
+from aios import AIStorage,ImageObjectBuilder, DocumentObjectBuilder, KnowledgePipelineEnvironment, KnowledgePipelineJournal
 
 class KnowledgeDirSource:
     def __init__(self, env: KnowledgePipelineEnvironment, config):

rootfs/knowledge_pipelines/Mia/parser.py

@@ -1,8 +1,7 @@
 # define a knowledge base class
 import json
 import string
-from aios_kernel import ComputeKernel, AIStorage
-from knowledge import *
+from aios import *
 
 
 class EmbeddingParser:

rootfs/knowledge_pipelines/Mia/query.py

@@ -1,12 +1,11 @@
 import os
 import logging
 import json
-from aios_kernel import *
-from knowledge import *
+from aios import *
 
-class KnowledgeEnvironment(Environment):
-    def __init__(self, env_id: str) -> None:
-        super().__init__(env_id)
+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"
@@ -93,5 +92,5 @@ class KnowledgeEnvironment(Environment):
             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() -> KnowledgeEnvironment:
-    return KnowledgeEnvironment("embedding")
+def init(workspace: str) -> EmbeddingEnvironment:
+    return EmbeddingEnvironment(workspace)

rootfs/knowledge_pipelines/pipelines.toml

@@ -1,3 +1,3 @@
 pipelines = [
-    "Mail/Sync"
+    "JarvisPlus"
 ]