gyt/opendan
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

test prompt from knowledge object

Browse Source
This commit is contained in:
tsukasa
2023-09-18 09:51:51 +08:00
parent b59af5d4a7
commit 805dc88de2
6 changed files with 98 additions and 99 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/aios_kernel/compute_kernel.py
+1 -1
View File
@@ -121,7 +121,7 @@ class ComputeKernel:
def text_embedding(self,input:str,model_name:Optional[str] = None):
task_req = ComputeTask()
task_req.set_text_embeding_params(input,model_name)
task_req.set_text_embedding_params(input,model_name)
self.run(task_req)
return task_req
src/aios_kernel/compute_task.py
+1 -1
View File
@@ -38,7 +38,7 @@ class ComputeTask:
self.params["model_name"] = "gpt-4-0613"
self.params["max_token_size"] = max_token_size
def set_text_embeding_params(self, input, model_name=None, callchain_id = None):
def set_text_embedding_params(self, input, model_name=None, callchain_id = None):
self.task_type = "text_embedding"
self.create_time = time.time()
self.task_id = uuid.uuid4().hex
src/aios_kernel/knowledge_base.py
+78 -73
View File
@@ -1,7 +1,7 @@
# define a knowledge base class
import json
from . import AgentPrompt, ComputeKernel
from ..knowledge import *
from knowledge import *
class KnowledgeBase:
@@ -62,7 +62,7 @@ class KnowledgeBase:
async def __embedding_email(self, email: EmailObject):
vector = await self.compute_kernel.do_text_embedding(json.dumps(email.get_desc()))
self.store.get_vector_store("default").insert(vector, email.calculate_id())
await self.store.get_vector_store("default").insert(vector, email.calculate_id())
await self.__embedding_rich_text(email.get_rich_text())
@@ -80,89 +80,94 @@ class KnowledgeBase:
else:
pass
def __save_document(self, document: DocumentObject):
doc_id = document.calculate_id()
self.store.get_object_store().put_object(doc_id, document.encode())
for chunk_id in document.get_chunk_list():
self.store.get_relation_store().add_relation(chunk_id, doc_id)
# def __save_document(self, document: DocumentObject):
# doc_id = document.calculate_id()
# self.store.get_object_store().put_object(doc_id, document.encode())
# for chunk_id in document.get_chunk_list():
# self.store.get_relation_store().add_relation(chunk_id, doc_id)
def __save_image(self, image: ImageObject):
image_id = image.calculate_id()
self.store.get_object_store().put_object(image_id, image.encode())
# def __save_image(self, image: ImageObject):
# image_id = image.calculate_id()
# self.store.get_object_store().put_object(image_id, image.encode())
def __save_video(self, video: VideoObject):
video_id = video.calculate_id()
self.store.get_object_store().put_object(video_id, video.encode())
# def __save_video(self, video: VideoObject):
# video_id = video.calculate_id()
# self.store.get_object_store().put_object(video_id, video.encode())
def __save_rich_text(self, rich_text: RichTextObject):
rich_text_id = rich_text.calculate_id()
# rich_text_enc = dict()
# rich_text_enc["desc"] = rich_text.desc
# rich_text_enc["body"] = {"documents": {}, "images": {}, "videos": {}, "rich_texts": {}}
for key, document in rich_text.get_documents().items():
self.__save_document(document)
doc_id = document.calculate_id()
self.store.get_relation_store().add_relation(doc_id, rich_text_id)
# rich_text_enc["body"]["documents"][key] = doc_id
for key, image in rich_text.get_images().items():
self.__save_image(image)
image_id = image.calculate_id()
self.store.get_relation_store().add_relation(image_id, rich_text_id)
# rich_text_enc["body"]["images"][key] = image_id
for key, video in rich_text.get_videos().items():
self.__save_video(video)
video_id = video.calculate_id()
self.store.get_relation_store().add_relation(video_id, rich_text_id)
# rich_text_enc["body"]["videos"][key] = video_id
for key, rich_text in rich_text.get_rich_texts().items():
self.__save_rich_text(rich_text)
rich_text_id = rich_text.calculate_id()
self.store.get_relation_store().add_relation(rich_text_id, rich_text_id)
# rich_text_enc["body"]["rich_texts"][key] = rich_text_id
# def __save_rich_text(self, rich_text: RichTextObject):
# rich_text_id = rich_text.calculate_id()
# # rich_text_enc = dict()
# # rich_text_enc["desc"] = rich_text.desc
# # rich_text_enc["body"] = {"documents": {}, "images": {}, "videos": {}, "rich_texts": {}}
# for key, document in rich_text.get_documents().items():
# self.__save_document(document)
# doc_id = document.calculate_id()
# self.store.get_relation_store().add_relation(doc_id, rich_text_id)
# # rich_text_enc["body"]["documents"][key] = doc_id
# for key, image in rich_text.get_images().items():
# self.__save_image(image)
# image_id = image.calculate_id()
# self.store.get_relation_store().add_relation(image_id, rich_text_id)
# # rich_text_enc["body"]["images"][key] = image_id
# for key, video in rich_text.get_videos().items():
# self.__save_video(video)
# video_id = video.calculate_id()
# self.store.get_relation_store().add_relation(video_id, rich_text_id)
# # rich_text_enc["body"]["videos"][key] = video_id
# for key, rich_text in rich_text.get_rich_texts().items():
# self.__save_rich_text(rich_text)
# rich_text_id = rich_text.calculate_id()
# self.store.get_relation_store().add_relation(rich_text_id, rich_text_id)
# # rich_text_enc["body"]["rich_texts"][key] = rich_text_id
self.store.get_object_store().put_object(rich_text_id, rich_text.encode())
# self.store.get_object_store().put_object(rich_text_id, rich_text.encode())
def __save_email(self, email: EmailObject):
email_id = email.calculate_id()
# email_enc = dict()
# email_enc["desc"] = email.desc
# email_enc["body"] = {"content": None}
self.__save_rich_text(email.get_rich_text())
rich_text_id = email.get_rich_text().calculate_id()
self.store.get_relation_store().add_relation(rich_text_id, email_id)
# email_enc["body"]["content"] = rich_text_id
self.store.get_object_store().put_object(email_id, email.encode())
# def __save_email(self, email: EmailObject):
# email_id = email.calculate_id()
# # email_enc = dict()
# # email_enc["desc"] = email.desc
# # email_enc["body"] = {"content": None}
# self.__save_rich_text(email.get_rich_text())
# rich_text_id = email.get_rich_text().calculate_id()
# self.store.get_relation_store().add_relation(rich_text_id, email_id)
# # email_enc["body"]["content"] = rich_text_id
# self.store.get_object_store().put_object(email_id, email.encode())
def __save_object(self, object: KnowledgeObject):
if object.get_object_type() == ObjectType.Document:
self.__save_document(object)
if object.get_object_type() == ObjectType.Image:
self.__save_image(object)
if object.get_object_type() == ObjectType.Video:
self.__save_video(object)
if object.get_object_type() == ObjectType.RichText:
self.__save_rich_text(object)
if object.get_object_type() == ObjectType.Email:
self.__save_email(object)
else:
pass
# def __save_object(self, object: KnowledgeObject):
# if object.get_object_type() == ObjectType.Document:
# self.__save_document(object)
# if object.get_object_type() == ObjectType.Image:
# self.__save_image(object)
# if object.get_object_type() == ObjectType.Video:
# self.__save_video(object)
# if object.get_object_type() == ObjectType.RichText:
# self.__save_rich_text(object)
# if object.get_object_type() == ObjectType.Email:
# self.__save_email(object)
# else:
# pass
async def insert_object(self, object: KnowledgeObject):
self.__save_object(object)
self.__do_embedding(object)
# self.__save_object(object)
await self.__do_embedding(object)
async def query_prompt(self, prompt: AgentPrompt):
objects = await self.query_objects(prompt)
knowledge_prompt = self.prompt_from_objects(objects)
prompt.append(knowledge_prompt)
async def query_objects(self, prompt: AgentPrompt) -> [ObjectID]:
results = []
for msg in prompt.messages:
if msg.role == "user":
vector = await self.compute_kernel.do_text_embedding(msg.content)
object_ids = self.store.get_vector_store("default").query(vector, 10)
object_ids = await self.store.get_vector_store("default").query(vector, 10)
results.append(object_ids)
return results
async def __load_object(self, object_id: ObjectID) -> KnowledgeObject:
def __load_object(self, object_id: ObjectID) -> KnowledgeObject:
if object_id.get_object_type() == ObjectType.Document:
return DocumentObject.decode(self.store.get_object_store().get_object(object_id))
if object_id.get_object_type() == ObjectType.Image:
@@ -177,10 +182,10 @@ class KnowledgeBase:
pass
async def prompt_from_objects(self, object_ids: [ObjectID]) -> AgentPrompt:
def prompt_from_objects(self, object_ids: [ObjectID]) -> AgentPrompt:
results = dict()
for object_id in object_ids:
parents = self.store.get_relation_store().get_related_objects(object_id)
parents = self.store.get_relation_store().get_related_root_objects(object_id)
# last parent is the root object
root_object_id = parents[-1]
if results[root_object_id] is None:
@@ -192,9 +197,9 @@ class KnowledgeBase:
result_desc = []
for result in results.values():
# first element in result is the root object
root_object = await self.__load_object(result[0])
if root_object.get_object_type() == ObjectType.Email:
email = await self.__load_object(object_id)
root_object_id = result[0]
if root_object_id.get_object_type() == ObjectType.Email:
email = self.__load_object(root_object_id)
desc = email.get_desc()
desc["type"] = "email"
desc["contents"] = []
@@ -208,12 +213,12 @@ class KnowledgeBase:
if object_id.get_object_type() == ObjectType.Chunk:
upper_list.append({"type": "text", "content": self.store.get_chunk_reader().get_chunk(object_id).read().decode("utf-8")})
if object_id.get_object_type() == ObjectType.Image:
image = await self.__load_object(object_id)
image = self.__load_object(object_id)
desc = image.get_desc()
desc["type"] = "image"
upper_list.append(desc)
if object_id.get_object_type() == ObjectType.Video:
video = await self.__load_object(object_id)
video = self.__load_object(object_id)
desc = video.get_desc()
desc["type"] = "video"
upper_list.append(desc)
src/aios_kernel/open_ai_node.py
+1 -1
View File
@@ -82,7 +82,7 @@ class OpenAI_ComputeNode(ComputeNode):
input = task.params["input"]
logger.info(f"call openai {model_name} input: {input}")
resp = openai.Embeding.create(model=model_name,
resp = openai.Embedding.create(model=model_name,
input=input)
logger.info(f"openai response: {resp}")
src/knowledge/core_object/document_object.py
+1
View File
@@ -53,6 +53,7 @@ class DocumentObjectBuilder:
chunk_list = KnowledgeStore().get_chunk_list_writer().create_chunk_list_from_text(
self.text,
1024 * 4,
"."
)
doc = DocumentObject(self.meta, self.tags, chunk_list)
doc_id = doc.calculate_id()
test/test_knowledge_base.py
+15 -22
View File
@@ -18,39 +18,32 @@ root.addHandler(handler)
from knowledge import ObjectID, HashValue, EmailObjectBuilder
from aios_kernel import KnowledgeBase, AgentPrompt
from aios_kernel import KnowledgeBase, AgentPrompt, OpenAI_ComputeNode, ComputeKernel
import asyncio
import unittest
async def test_embedding_email():
data = HashValue.hash_data("1233".encode("utf-8"));
print(data.to_base58())
print(data.to_base36())
async def test_embedding_email(test):
open_ai_node = OpenAI_ComputeNode()
open_ai_node.start()
ComputeKernel().add_compute_node(open_ai_node)
data2 = HashValue.from_base58(data.to_base58())
self.assertEqual(data.to_base36(), data2.to_base36())
email_folder = os.path.join(dir_path, "../rootfs/data/email/")
print("explore emails in folder ", email_folder)
for root, dirs, files in os.walk(email_folder):
for dir in dirs:
email_object = EmailObjectBuilder({}, os.path.join(root, dir)).build()
await KnowledgeBase().insert_object(email_object)
data2 = HashValue.from_base36(data.to_base36())
self.assertEqual(data.to_base58(), data2.to_base58())
email_folder = "F:\\system\\Downloads\\8081ffdb80925f5bff9c6ab9c4756c7d"
email_object = EmailObjectBuilder({}, email_folder).build()
await KnowledgeBase().do_embedding(email_object)
async def test_query_email():
msg_prompt = AgentPrompt()
msg_prompt.messages = [{"role":"user","content":"abcdef"}]
KnowledgeBase().query(msg_prompt)
await KnowledgeBase().query_prompt(msg_prompt)
class TestKnowledgeBase(unittest.TestCase):
def test_embedding(self):
asyncio.run(test_embedding_email())
def test_query(self):
asyncio.run(test_query_email())
asyncio.run(test_embedding_email(self))
if __name__ == "__main__":