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Feat: Redesign and refactor agent module (#9113)

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### What problem does this PR solve?

#9082 #6365

<u> **WARNING: it's not compatible with the older version of `Agent`
module, which means that `Agent` from older versions can not work
anymore.**</u>

### Type of change

- [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Kevin Hu
2025-07-30 19:41:09 +08:00
committed by GitHub
parent 07e37560fc
commit d9fe279dde
124 changed files with 7744 additions and 18226 deletions
Download Patch File Download Diff File Expand all files Collapse all files

25
agent/test/client.py
View File

@ -15,9 +15,8 @@
#
import argparse
import os
from functools import partial
from agent.canvas import Canvas
from agent.settings import DEBUG
from api import settings
if __name__ == '__main__':
parser = argparse.ArgumentParser()
@ -31,19 +30,17 @@ if __name__ == '__main__':
parser.add_argument('-m', '--stream', default=False, help="Stream output", action='store_true', required=False)
args = parser.parse_args()
settings.init_settings()
canvas = Canvas(open(args.dsl, "r").read(), args.tenant_id)
if canvas.get_prologue():
print(f"==================== Bot =====================\n> {canvas.get_prologue()}", end='')
query = ""
while True:
ans = canvas.run(stream=args.stream)
canvas.reset(True)
query = input("\n==================== User =====================\n> ")
ans = canvas.run(query=query)
print("==================== Bot =====================\n> ", end='')
if args.stream and isinstance(ans, partial):
cont = ""
for an in ans():
print(an["content"][len(cont):], end='', flush=True)
cont = an["content"]
else:
print(ans["content"])
for ans in canvas.run(query=query):
print(ans, end='\n', flush=True)
if DEBUG:
print(canvas.path)
question = input("\n==================== User =====================\n> ")
canvas.add_user_input(question)
print(canvas.path)

129
agent/test/dsl_examples/baidu_generate_and_switch.json
View File

@ -1,129 +0,0 @@
{
"components": {
"begin": {
"obj":{
"component_name": "Begin",
"params": {
"prologue": "Hi there!"
}
},
"downstream": ["answer:0"],
"upstream": []
},
"answer:0": {
"obj": {
"component_name": "Answer",
"params": {}
},
"downstream": ["baidu:0"],
"upstream": ["begin", "message:0","message:1"]
},
"baidu:0": {
"obj": {
"component_name": "Baidu",
"params": {}
},
"downstream": ["generate:0"],
"upstream": ["answer:0"]
},
"generate:0": {
"obj": {
"component_name": "Generate",
"params": {
"llm_id": "deepseek-chat",
"prompt": "You are an intelligent assistant. Please answer the user's question based on what Baidu searched. First, please output the user's question and the content searched by Baidu, and then answer yes, no, or i don't know.Here is the user's question:{user_input}The above is the user's question.Here is what Baidu searched for:{baidu}The above is the content searched by Baidu.",
"temperature": 0.2
},
"parameters": [
{
"component_id": "answer:0",
"id": "69415446-49bf-4d4b-8ec9-ac86066f7709",
"key": "user_input"
},
{
"component_id": "baidu:0",
"id": "83363c2a-00a8-402f-a45c-ddc4097d7d8b",
"key": "baidu"
}
]
},
"downstream": ["switch:0"],
"upstream": ["baidu:0"]
},
"switch:0": {
"obj": {
"component_name": "Switch",
"params": {
"conditions": [
{
"logical_operator" : "or",
"items" : [
{"cpn_id": "generate:0", "operator": "contains", "value": "yes"},
{"cpn_id": "generate:0", "operator": "contains", "value": "yeah"}
],
"to": "message:0"
},
{
"logical_operator" : "and",
"items" : [
{"cpn_id": "generate:0", "operator": "contains", "value": "no"},
{"cpn_id": "generate:0", "operator": "not contains", "value": "yes"},
{"cpn_id": "generate:0", "operator": "not contains", "value": "know"}
],
"to": "message:1"
},
{
"logical_operator" : "",
"items" : [
{"cpn_id": "generate:0", "operator": "contains", "value": "know"}
],
"to": "message:2"
}
],
"end_cpn_id": "answer:0"
}
},
"downstream": ["message:0","message:1"],
"upstream": ["generate:0"]
},
"message:0": {
"obj": {
"component_name": "Message",
"params": {
"messages": ["YES YES YES YES YES YES YES YES YES YES YES YES"]
}
},
"upstream": ["switch:0"],
"downstream": ["answer:0"]
},
"message:1": {
"obj": {
"component_name": "Message",
"params": {
"messages": ["NO NO NO NO NO NO NO NO NO NO NO NO NO NO"]
}
},
"upstream": ["switch:0"],
"downstream": ["answer:0"]
},
"message:2": {
"obj": {
"component_name": "Message",
"params": {
"messages": ["I DON'T KNOW---------------------------"]
}
},
"upstream": ["switch:0"],
"downstream": ["answer:0"]
}
},
"history": [],
"messages": [],
"reference": {},
"path": [],
"answer": []
}

68
agent/test/dsl_examples/categorize.json → agent/test/dsl_examples/categorize_and_agent_with_tavily.json
View File

@ -7,16 +7,8 @@
"prologue": "Hi there!"
}
},
"downstream": ["answer:0"],
"upstream": []
},
"answer:0": {
"obj": {
"component_name": "Answer",
"params": {}
},
"downstream": ["categorize:0"],
"upstream": ["begin"]
"upstream": []
},
"categorize:0": {
"obj": {
@ -26,48 +18,68 @@
"category_description": {
"product_related": {
"description": "The question is about the product usage, appearance and how it works.",
"examples": "Why it always beaming?\nHow to install it onto the wall?\nIt leaks, what to do?",
"to": "message:0"
"to": ["agent:0"]
},
"others": {
"description": "The question is not about the product usage, appearance and how it works.",
"examples": "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?",
"to": "message:1"
"to": ["message:0"]
}
}
}
},
"downstream": ["message:0","message:1"],
"upstream": ["answer:0"]
"downstream": [],
"upstream": ["begin"]
},
"message:0": {
"obj": {
"obj":{
"component_name": "Message",
"params": {
"messages": [
"Message 0!!!!!!!"
"content": [
"Sorry, I don't know. I'm an AI bot."
]
}
},
"downstream": ["answer:0"],
"downstream": [],
"upstream": ["categorize:0"]
},
"agent:0": {
"obj": {
"component_name": "Agent",
"params": {
"llm_id": "deepseek-chat",
"sys_prompt": "You are a smart researcher. You could generate proper queries to search. According to the search results, you could deside next query if the result is not enough.",
"temperature": 0.2,
"llm_enabled_tools": [
{
"component_name": "TavilySearch",
"params": {
"api_key": "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"
}
}
]
}
},
"downstream": ["message:1"],
"upstream": ["categorize:0"]
},
"message:1": {
"obj": {
"component_name": "Message",
"params": {
"messages": [
"Message 1!!!!!!!"
]
"content": ["{agent:0@content}"]
}
},
"downstream": ["answer:0"],
"upstream": ["categorize:0"]
"downstream": [],
"upstream": ["agent:0"]
}
},
"history": [],
"messages": [],
"path": [],
"reference": [],
"answer": []
}
"retrival": {"chunks": [], "doc_aggs": []},
"globals": {
"sys.query": "",
"sys.user_id": "",
"sys.conversation_turns": 0,
"sys.files": []
}
}

113
agent/test/dsl_examples/concentrator_message.json
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@ -1,113 +0,0 @@
{
"components": {
"begin": {
"obj":{
"component_name": "Begin",
"params": {
"prologue": "Hi there!"
}
},
"downstream": ["answer:0"],
"upstream": []
},
"answer:0": {
"obj": {
"component_name": "Answer",
"params": {}
},
"downstream": ["categorize:0"],
"upstream": ["begin"]
},
"categorize:0": {
"obj": {
"component_name": "Categorize",
"params": {
"llm_id": "deepseek-chat",
"category_description": {
"product_related": {
"description": "The question is about the product usage, appearance and how it works.",
"examples": "Why it always beaming?\nHow to install it onto the wall?\nIt leaks, what to do?",
"to": "concentrator:0"
},
"others": {
"description": "The question is not about the product usage, appearance and how it works.",
"examples": "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?",
"to": "concentrator:1"
}
}
}
},
"downstream": ["concentrator:0","concentrator:1"],
"upstream": ["answer:0"]
},
"concentrator:0": {
"obj": {
"component_name": "Concentrator",
"params": {}
},
"downstream": ["message:0"],
"upstream": ["categorize:0"]
},
"concentrator:1": {
"obj": {
"component_name": "Concentrator",
"params": {}
},
"downstream": ["message:1_0","message:1_1","message:1_2"],
"upstream": ["categorize:0"]
},
"message:0": {
"obj": {
"component_name": "Message",
"params": {
"messages": [
"Message 0_0!!!!!!!"
]
}
},
"downstream": ["answer:0"],
"upstream": ["concentrator:0"]
},
"message:1_0": {
"obj": {
"component_name": "Message",
"params": {
"messages": [
"Message 1_0!!!!!!!"
]
}
},
"downstream": ["answer:0"],
"upstream": ["concentrator:1"]
},
"message:1_1": {
"obj": {
"component_name": "Message",
"params": {
"messages": [
"Message 1_1!!!!!!!"
]
}
},
"downstream": ["answer:0"],
"upstream": ["concentrator:1"]
},
"message:1_2": {
"obj": {
"component_name": "Message",
"params": {
"messages": [
"Message 1_2!!!!!!!"
]
}
},
"downstream": ["answer:0"],
"upstream": ["concentrator:1"]
}
},
"history": [],
"messages": [],
"path": [],
"reference": [],
"answer": []
}

157
agent/test/dsl_examples/customer_service.json
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@ -1,157 +0,0 @@
{
"components": {
"begin": {
"obj":{
"component_name": "Begin",
"params": {
"prologue": "Hi! How can I help you?"
}
},
"downstream": ["answer:0"],
"upstream": []
},
"answer:0": {
"obj": {
"component_name": "Answer",
"params": {}
},
"downstream": ["categorize:0"],
"upstream": ["begin", "generate:0", "generate:casual", "generate:answer", "generate:complain", "generate:ask_contact", "message:get_contact"]
},
"categorize:0": {
"obj": {
"component_name": "Categorize",
"params": {
"llm_id": "deepseek-chat",
"category_description": {
"product_related": {
"description": "The question is about the product usage, appearance and how it works.",
"examples": "Why it always beaming?\nHow to install it onto the wall?\nIt leaks, what to do?\nException: Can't connect to ES cluster\nHow to build the RAGFlow image from scratch",
"to": "retrieval:0"
},
"casual": {
"description": "The question is not about the product usage, appearance and how it works. Just casual chat.",
"examples": "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?",
"to": "generate:casual"
},
"complain": {
"description": "Complain even curse about the product or service you provide. But the comment is not specific enough.",
"examples": "How bad is it.\nIt's really sucks.\nDamn, for God's sake, can it be more steady?\nShit, I just can't use this shit.\nI can't stand it anymore.",
"to": "generate:complain"
},
"answer": {
"description": "This answer provide a specific contact information, like e-mail, phone number, wechat number, line number, twitter, discord, etc,.",
"examples": "My phone number is 203921\nkevinhu.hk@gmail.com\nThis is my discord number: johndowson_29384",
"to": "message:get_contact"
}
},
"message_history_window_size": 8
}
},
"downstream": ["retrieval:0", "generate:casual", "generate:complain", "message:get_contact"],
"upstream": ["answer:0"]
},
"generate:casual": {
"obj": {
"component_name": "Generate",
"params": {
"llm_id": "deepseek-chat",
"prompt": "You are a customer support. But the customer wants to have a casual chat with you instead of consulting about the product. Be nice, funny, enthusiasm and concern.",
"temperature": 0.9,
"message_history_window_size": 12,
"cite": false
}
},
"downstream": ["answer:0"],
"upstream": ["categorize:0"]
},
"generate:complain": {
"obj": {
"component_name": "Generate",
"params": {
"llm_id": "deepseek-chat",
"prompt": "You are a customer support. the Customers complain even curse about the products but not specific enough. You need to ask him/her what's the specific problem with the product. Be nice, patient and concern to soothe your customers’ emotions at first place.",
"temperature": 0.9,
"message_history_window_size": 12,
"cite": false
}
},
"downstream": ["answer:0"],
"upstream": ["categorize:0"]
},
"retrieval:0": {
"obj": {
"component_name": "Retrieval",
"params": {
"similarity_threshold": 0.2,
"keywords_similarity_weight": 0.3,
"top_n": 6,
"top_k": 1024,
"rerank_id": "BAAI/bge-reranker-v2-m3",
"kb_ids": ["869a236818b811ef91dffa163e197198"]
}
},
"downstream": ["relevant:0"],
"upstream": ["categorize:0"]
},
"relevant:0": {
"obj": {
"component_name": "Relevant",
"params": {
"llm_id": "deepseek-chat",
"temperature": 0.02,
"yes": "generate:answer",
"no": "generate:ask_contact"
}
},
"downstream": ["generate:answer", "generate:ask_contact"],
"upstream": ["retrieval:0"]
},
"generate:answer": {
"obj": {
"component_name": "Generate",
"params": {
"llm_id": "deepseek-chat",
"prompt": "You are an intelligent assistant. Please answer the question based on content of knowledge base. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\". Answers need to consider chat history.\n Knowledge base content is as following:\n {input}\n The above is the content of knowledge base.",
"temperature": 0.02
}
},
"downstream": ["answer:0"],
"upstream": ["relevant:0"]
},
"generate:ask_contact": {
"obj": {
"component_name": "Generate",
"params": {
"llm_id": "deepseek-chat",
"prompt": "You are a customer support. But you can't answer to customers' question. You need to request their contact like E-mail, phone number, Wechat number, LINE number, twitter, discord, etc,. Product experts will contact them later. Please do not ask the same question twice.",
"temperature": 0.9,
"message_history_window_size": 12,
"cite": false
}
},
"downstream": ["answer:0"],
"upstream": ["relevant:0"]
},
"message:get_contact": {
"obj":{
"component_name": "Message",
"params": {
"messages": [
"Okay, I've already write this down. What else I can do for you?",
"Get it. What else I can do for you?",
"Thanks for your trust! Our expert will contact ASAP. So, anything else I can do for you?",
"Thanks! So, anything else I can do for you?"
]
}
},
"downstream": ["answer:0"],
"upstream": ["categorize:0"]
}
},
"history": [],
"messages": [],
"path": [],
"reference": [],
"answer": []
}

39
agent/test/dsl_examples/intergreper.json
View File

@ -1,39 +0,0 @@
{
"components": {
"begin": {
"obj":{
"component_name": "Begin",
"params": {
"prologue": "Hi there! Please enter the text you want to translate in format like: 'text you want to translate' => target language. For an example: 您好！ => English"
}
},
"downstream": ["answer:0"],
"upstream": []
},
"answer:0": {
"obj": {
"component_name": "Answer",
"params": {}
},
"downstream": ["generate:0"],
"upstream": ["begin", "generate:0"]
},
"generate:0": {
"obj": {
"component_name": "Generate",
"params": {
"llm_id": "deepseek-chat",
"prompt": "You are an professional interpreter.\n- Role: an professional interpreter.\n- Input format: content need to be translated => target language. \n- Answer format: => translated content in target language. \n- Examples:\n - user: 您好！ => English. assistant: => How are you doing!\n - user: You look good today. => Japanese. assistant: => 今日は調子がいいですね 。\n",
"temperature": 0.5
}
},
"downstream": ["answer:0"],
"upstream": ["answer:0"]
}
},
"history": [],
"messages": [],
"reference": {},
"path": [],
"answer": []
}

39
agent/test/dsl_examples/interpreter.json
View File

@ -1,39 +0,0 @@
{
"components": {
"begin": {
"obj":{
"component_name": "Begin",
"params": {
"prologue": "Hi there! Please enter the text you want to translate in format like: 'text you want to translate' => target language. For an example: 您好！ => English"
}
},
"downstream": ["answer:0"],
"upstream": []
},
"answer:0": {
"obj": {
"component_name": "Answer",
"params": {}
},
"downstream": ["generate:0"],
"upstream": ["begin", "generate:0"]
},
"generate:0": {
"obj": {
"component_name": "Generate",
"params": {
"llm_id": "deepseek-chat",
"prompt": "You are an professional interpreter.\n- Role: an professional interpreter.\n- Input format: content need to be translated => target language. \n- Answer format: => translated content in target language. \n- Examples:\n - user: 您好！ => English. assistant: => How are you doing!\n - user: You look good today. => Japanese. assistant: => 今日は調子がいいですね 。\n",
"temperature": 0.5
}
},
"downstream": ["answer:0"],
"upstream": ["answer:0"]
}
},
"history": [],
"messages": [],
"reference": {},
"path": [],
"answer": []
}

92
agent/test/dsl_examples/iteration.json Normal file
View File

@ -0,0 +1,92 @@
{
"components": {
"begin": {
"obj":{
"component_name": "Begin",
"params": {
"prologue": "Hi there!"
}
},
"downstream": ["generate:0"],
"upstream": []
},
"generate:0": {
"obj": {
"component_name": "Agent",
"params": {
"llm_id": "deepseek-chat",
"sys_prompt": "You are an helpful research assistant. \nPlease decompose user's topic: '{sys.query}' into several meaningful sub-topics. \nThe output format MUST be an string array like: [\"sub-topic1\", \"sub-topic2\", ...]. Redundant information is forbidden.",
"temperature": 0.2,
"cite":false,
"output_structure": ["sub-topic1", "sub-topic2", "sub-topic3"]
}
},
"downstream": ["iteration:0"],
"upstream": ["begin"]
},
"iteration:0": {
"obj": {
"component_name": "Iteration",
"params": {
"items_ref": "generate:0@structured_content"
}
},
"downstream": ["message:0"],
"upstream": ["generate:0"]
},
"iterationitem:0": {
"obj": {
"component_name": "IterationItem",
"params": {}
},
"parent_id": "iteration:0",
"downstream": ["tavily:0"],
"upstream": []
},
"tavily:0": {
"obj": {
"component_name": "TavilySearch",
"params": {
"api_key": "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",
"query": "iterationitem:0@result"
}
},
"parent_id": "iteration:0",
"downstream": ["generate:1"],
"upstream": ["iterationitem:0"]
},
"generate:1": {
"obj": {
"component_name": "Agent",
"params": {
"llm_id": "deepseek-chat",
"sys_prompt": "Your goal is to provide answers based on information from the internet. \nYou must use the provided search results to find relevant online information. \nYou should never use your own knowledge to answer questions.\nPlease include relevant url sources in the end of your answers.\n\n \"{tavily:0@formalized_content}\" \nUsing the above information, answer the following question or topic: \"{iterationitem:0@result} \"\nin a detailed report — The report should focus on the answer to the question, should be well structured, informative, in depth, with facts and numbers if available, a minimum of 200 words and with markdown syntax and apa format. Write all source urls at the end of the report in apa format. You should write your report only based on the given information and nothing else.",
"temperature": 0.9,
"cite":false
}
},
"parent_id": "iteration:0",
"downstream": ["iterationitem:0"],
"upstream": ["tavily:0"]
},
"message:0": {
"obj": {
"component_name": "Message",
"params": {
"content": ["{iteration:0@generate:1}"]
}
},
"downstream": [],
"upstream": ["iteration:0"]
}
},
"history": [],
"path": [],
"retrival": {"chunks": [], "doc_aggs": []},
"globals": {
"sys.query": "",
"sys.user_id": "",
"sys.conversation_turns": 0,
"sys.files": []
}
}

62
agent/test/dsl_examples/keyword_wikipedia_and_generate.json
View File

@ -1,62 +0,0 @@
{
"components": {
"begin": {
"obj":{
"component_name": "Begin",
"params": {
"prologue": "Hi there!"
}
},
"downstream": ["answer:0"],
"upstream": []
},
"answer:0": {
"obj": {
"component_name": "Answer",
"params": {}
},
"downstream": ["keyword:0"],
"upstream": ["begin"]
},
"keyword:0": {
"obj": {
"component_name": "KeywordExtract",
"params": {
"llm_id": "deepseek-chat",
"prompt": "- Role: You're a question analyzer.\n - Requirements:\n - Summarize user's question, and give top %s important keyword/phrase.\n - Use comma as a delimiter to separate keywords/phrases.\n - Answer format: (in language of user's question)\n - keyword: ",
"temperature": 0.2,
"top_n": 1
}
},
"downstream": ["wikipedia:0"],
"upstream": ["answer:0"]
},
"wikipedia:0": {
"obj":{
"component_name": "Wikipedia",
"params": {
"top_n": 10
}
},
"downstream": ["generate:0"],
"upstream": ["keyword:0"]
},
"generate:1": {
"obj": {
"component_name": "Generate",
"params": {
"llm_id": "deepseek-chat",
"prompt": "You are an intelligent assistant. Please answer the question based on content from Wikipedia. When the answer from Wikipedia is incomplete, you need to output the URL link of the corresponding content as well. When all the content searched from Wikipedia is irrelevant to the question, your answer must include the sentence, \"The answer you are looking for is not found in the Wikipedia!\". Answers need to consider chat history.\n The content of Wikipedia is as follows:\n {input}\n The above is the content of Wikipedia.",
"temperature": 0.2
}
},
"downstream": ["answer:0"],
"upstream": ["wikipedia:0"]
}
},
"history": [],
"path": [],
"messages": [],
"reference": {},
"answer": []
}

43
agent/test/dsl_examples/retrieval_and_generate.json
View File

@ -7,16 +7,8 @@
"prologue": "Hi there!"
}
},
"downstream": ["answer:0"],
"upstream": []
},
"answer:0": {
"obj": {
"component_name": "Answer",
"params": {}
},
"downstream": ["retrieval:0"],
"upstream": ["begin", "generate:0"]
"upstream": []
},
"retrieval:0": {
"obj": {
@ -26,29 +18,44 @@
"keywords_similarity_weight": 0.3,
"top_n": 6,
"top_k": 1024,
"rerank_id": "BAAI/bge-reranker-v2-m3",
"kb_ids": ["869a236818b811ef91dffa163e197198"]
"rerank_id": "",
"empty_response": "Nothing found in dataset",
"kb_ids": ["1a3d1d7afb0611ef9866047c16ec874f"]
}
},
"downstream": ["generate:0"],
"upstream": ["answer:0"]
"upstream": ["begin"]
},
"generate:0": {
"obj": {
"component_name": "Generate",
"component_name": "LLM",
"params": {
"llm_id": "deepseek-chat",
"prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n Here is the knowledge base:\n {input}\n The above is the knowledge base.",
"sys_prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n Here is the knowledge base:\n {retrieval:0@formalized_content}\n The above is the knowledge base.",
"temperature": 0.2
}
},
"downstream": ["answer:0"],
"downstream": ["message:0"],
"upstream": ["retrieval:0"]
},
"message:0": {
"obj": {
"component_name": "Message",
"params": {
"content": ["{generate:0@content}"]
}
},
"downstream": [],
"upstream": ["generate:0"]
}
},
"history": [],
"messages": [],
"reference": {},
"path": [],
"answer": []
"retrival": {"chunks": [], "doc_aggs": []},
"globals": {
"sys.query": "",
"sys.user_id": "",
"sys.conversation_turns": 0,
"sys.files": []
}
}

59
agent/test/dsl_examples/retrieval_categorize_and_generate.json
View File

@ -7,16 +7,8 @@
"prologue": "Hi there!"
}
},
"downstream": ["answer:0"],
"upstream": []
},
"answer:0": {
"obj": {
"component_name": "Answer",
"params": {}
},
"downstream": ["categorize:0"],
"upstream": ["begin", "generate:0", "switch:0"]
"upstream": []
},
"categorize:0": {
"obj": {
@ -26,30 +18,30 @@
"category_description": {
"product_related": {
"description": "The question is about the product usage, appearance and how it works.",
"examples": "Why it always beaming?\nHow to install it onto the wall?\nIt leaks, what to do?",
"to": "retrieval:0"
"examples": [],
"to": ["retrieval:0"]
},
"others": {
"description": "The question is not about the product usage, appearance and how it works.",
"examples": "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?",
"to": "message:0"
"examples": [],
"to": ["message:0"]
}
}
}
},
"downstream": ["retrieval:0", "message:0"],
"upstream": ["answer:0"]
"downstream": [],
"upstream": ["begin"]
},
"message:0": {
"obj":{
"component_name": "Message",
"params": {
"messages": [
"content": [
"Sorry, I don't know. I'm an AI bot."
]
}
},
"downstream": ["answer:0"],
"downstream": [],
"upstream": ["categorize:0"]
},
"retrieval:0": {
@ -60,29 +52,44 @@
"keywords_similarity_weight": 0.3,
"top_n": 6,
"top_k": 1024,
"rerank_id": "BAAI/bge-reranker-v2-m3",
"kb_ids": ["869a236818b811ef91dffa163e197198"]
"rerank_id": "",
"empty_response": "Nothing found in dataset",
"kb_ids": ["1a3d1d7afb0611ef9866047c16ec874f"]
}
},
"downstream": ["generate:0"],
"upstream": ["switch:0"]
"upstream": ["categorize:0"]
},
"generate:0": {
"obj": {
"component_name": "Generate",
"component_name": "Agent",
"params": {
"llm_id": "deepseek-chat",
"prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n Here is the knowledge base:\n {input}\n The above is the knowledge base.",
"sys_prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n Here is the knowledge base:\n {retrieval:0@formalized_content}\n The above is the knowledge base.",
"temperature": 0.2
}
},
"downstream": ["answer:0"],
"downstream": ["message:1"],
"upstream": ["retrieval:0"]
},
"message:1": {
"obj": {
"component_name": "Message",
"params": {
"content": ["{generate:0@content}"]
}
},
"downstream": [],
"upstream": ["generate:0"]
}
},
"history": [],
"messages": [],
"reference": {},
"path": [],
"answer": []
"retrival": {"chunks": [], "doc_aggs": []},
"globals": {
"sys.query": "",
"sys.user_id": "",
"sys.conversation_turns": 0,
"sys.files": []
}
}

82
agent/test/dsl_examples/retrieval_relevant_and_generate.json
View File

@ -1,82 +0,0 @@
{
"components": {
"begin": {
"obj":{
"component_name": "Begin",
"params": {
"prologue": "Hi there!"
}
},
"downstream": ["answer:0"],
"upstream": []
},
"answer:0": {
"obj": {
"component_name": "Answer",
"params": {}
},
"downstream": ["retrieval:0"],
"upstream": ["begin", "generate:0", "switch:0"]
},
"retrieval:0": {
"obj": {
"component_name": "Retrieval",
"params": {
"similarity_threshold": 0.2,
"keywords_similarity_weight": 0.3,
"top_n": 6,
"top_k": 1024,
"rerank_id": "BAAI/bge-reranker-v2-m3",
"kb_ids": ["869a236818b811ef91dffa163e197198"],
"empty_response": "Sorry, knowledge base has noting related information."
}
},
"downstream": ["relevant:0"],
"upstream": ["answer:0"]
},
"relevant:0": {
"obj": {
"component_name": "Relevant",
"params": {
"llm_id": "deepseek-chat",
"temperature": 0.02,
"yes": "generate:0",
"no": "message:0"
}
},
"downstream": ["message:0", "generate:0"],
"upstream": ["retrieval:0"]
},
"generate:0": {
"obj": {
"component_name": "Generate",
"params": {
"llm_id": "deepseek-chat",
"prompt": "You are an intelligent assistant. Please answer the question based on content of knowledge base. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\". Answers need to consider chat history.\n Knowledge base content is as following:\n {input}\n The above is the content of knowledge base.",
"temperature": 0.2
}
},
"downstream": ["answer:0"],
"upstream": ["relevant:0"]
},
"message:0": {
"obj":{
"component_name": "Message",
"params": {
"messages": [
"Sorry, I don't know. Please leave your contact, our experts will contact you later. What's your e-mail/phone/wechat?",
"I'm an AI bot and not quite sure about this question. Please leave your contact, our experts will contact you later. What's your e-mail/phone/wechat?",
"Can't find answer in my knowledge base. Please leave your contact, our experts will contact you later. What's your e-mail/phone/wechat?"
]
}
},
"downstream": ["answer:0"],
"upstream": ["relevant:0"]
}
},
"history": [],
"path": [],
"messages": [],
"reference": {},
"answer": []
}

103
agent/test/dsl_examples/retrieval_relevant_keyword_baidu_and_generate.json
View File

@ -1,103 +0,0 @@
{
"components": {
"begin": {
"obj":{
"component_name": "Begin",
"params": {
"prologue": "Hi there!"
}
},
"downstream": ["answer:0"],
"upstream": []
},
"answer:0": {
"obj": {
"component_name": "Answer",
"params": {}
},
"downstream": ["retrieval:0"],
"upstream": ["begin"]
},
"retrieval:0": {
"obj": {
"component_name": "Retrieval",
"params": {
"similarity_threshold": 0.2,
"keywords_similarity_weight": 0.3,
"top_n": 6,
"top_k": 1024,
"rerank_id": "BAAI/bge-reranker-v2-m3",
"kb_ids": ["21ca4e6a2c8911ef8b1e0242ac120006"],
"empty_response": "Sorry, knowledge base has noting related information."
}
},
"downstream": ["relevant:0"],
"upstream": ["answer:0"]
},
"relevant:0": {
"obj": {
"component_name": "Relevant",
"params": {
"llm_id": "deepseek-chat",
"temperature": 0.02,
"yes": "generate:0",
"no": "keyword:0"
}
},
"downstream": ["keyword:0", "generate:0"],
"upstream": ["retrieval:0"]
},
"generate:0": {
"obj": {
"component_name": "Generate",
"params": {
"llm_id": "deepseek-chat",
"prompt": "You are an intelligent assistant. Please answer the question based on content of knowledge base. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\". Answers need to consider chat history.\n Knowledge base content is as following:\n {input}\n The above is the content of knowledge base.",
"temperature": 0.2
}
},
"downstream": ["answer:0"],
"upstream": ["relevant:0"]
},
"keyword:0": {
"obj": {
"component_name": "KeywordExtract",
"params": {
"llm_id": "deepseek-chat",
"prompt": "- Role: You're a question analyzer.\n - Requirements:\n - Summarize user's question, and give top %s important keyword/phrase.\n - Use comma as a delimiter to separate keywords/phrases.\n - Answer format: (in language of user's question)\n - keyword: ",
"temperature": 0.2,
"top_n": 1
}
},
"downstream": ["baidu:0"],
"upstream": ["relevant:0"]
},
"baidu:0": {
"obj":{
"component_name": "Baidu",
"params": {
"top_n": 10
}
},
"downstream": ["generate:1"],
"upstream": ["keyword:0"]
},
"generate:1": {
"obj": {
"component_name": "Generate",
"params": {
"llm_id": "deepseek-chat",
"prompt": "You are an intelligent assistant. Please answer the question based on content searched from Baidu. When the answer from a Baidu search is incomplete, you need to output the URL link of the corresponding content as well. When all the content searched from Baidu is irrelevant to the question, your answer must include the sentence, \"The answer you are looking for is not found in the Baidu search!\". Answers need to consider chat history.\n The content of Baidu search is as follows:\n {input}\n The above is the content of Baidu search.",
"temperature": 0.2
}
},
"downstream": ["answer:0"],
"upstream": ["baidu:0"]
}
},
"history": [],
"path": [],
"messages": [],
"reference": {},
"answer": []
}

79
agent/test/dsl_examples/retrieval_relevant_rewrite_and_generate.json
View File

@ -1,79 +0,0 @@
{
"components": {
"begin": {
"obj":{
"component_name": "Begin",
"params": {
"prologue": "Hi there!"
}
},
"downstream": ["answer:0"],
"upstream": []
},
"answer:0": {
"obj": {
"component_name": "Answer",
"params": {}
},
"downstream": ["retrieval:0"],
"upstream": ["begin", "generate:0", "switch:0"]
},
"retrieval:0": {
"obj": {
"component_name": "Retrieval",
"params": {
"similarity_threshold": 0.2,
"keywords_similarity_weight": 0.3,
"top_n": 6,
"top_k": 1024,
"rerank_id": "BAAI/bge-reranker-v2-m3",
"kb_ids": ["869a236818b811ef91dffa163e197198"],
"empty_response": "Sorry, knowledge base has noting related information."
}
},
"downstream": ["relevant:0"],
"upstream": ["answer:0", "rewrite:0"]
},
"relevant:0": {
"obj": {
"component_name": "Relevant",
"params": {
"llm_id": "deepseek-chat",
"temperature": 0.02,
"yes": "generate:0",
"no": "rewrite:0"
}
},
"downstream": ["generate:0", "rewrite:0"],
"upstream": ["retrieval:0"]
},
"generate:0": {
"obj": {
"component_name": "Generate",
"params": {
"llm_id": "deepseek-chat",
"prompt": "You are an intelligent assistant. Please answer the question based on content of knowledge base. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\". Answers need to consider chat history.\n Knowledge base content is as following:\n {input}\n The above is the content of knowledge base.",
"temperature": 0.02
}
},
"downstream": ["answer:0"],
"upstream": ["relevant:0"]
},
"rewrite:0": {
"obj":{
"component_name": "RewriteQuestion",
"params": {
"llm_id": "deepseek-chat",
"temperature": 0.8
}
},
"downstream": ["retrieval:0"],
"upstream": ["relevant:0"]
}
},
"history": [],
"messages": [],
"path": [],
"reference": [],
"answer": []
}

55
agent/test/dsl_examples/tavily_and_generate.json Normal file
View File

@ -0,0 +1,55 @@
{
"components": {
"begin": {
"obj":{
"component_name": "Begin",
"params": {
"prologue": "Hi there!"
}
},
"downstream": ["tavily:0"],
"upstream": []
},
"tavily:0": {
"obj": {
"component_name": "TavilySearch",
"params": {
"api_key": "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"
}
},
"downstream": ["generate:0"],
"upstream": ["begin"]
},
"generate:0": {
"obj": {
"component_name": "LLM",
"params": {
"llm_id": "deepseek-chat",
"sys_prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n Here is the knowledge base:\n {tavily:0@formalized_content}\n The above is the knowledge base.",
"temperature": 0.2
}
},
"downstream": ["message:0"],
"upstream": ["tavily:0"]
},
"message:0": {
"obj": {
"component_name": "Message",
"params": {
"content": ["{generate:0@content}"]
}
},
"downstream": [],
"upstream": ["generate:0"]
}
},
"history": [],
"path": [],
"retrival": {"chunks": [], "doc_aggs": []},
"globals": {
"sys.query": "",
"sys.user_id": "",
"sys.conversation_turns": 0,
"sys.files": []
}
}