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Add graphrag (#1793)
### What problem does this PR solve? #1594 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Kevin Hu
48
agent/test/client.py
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48
agent/test/client.py
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#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import argparse
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import os
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from functools import partial
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from agent.canvas import Canvas
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from agent.settings import DEBUG
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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dsl_default_path = os.path.join(
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os.path.dirname(os.path.realpath(__file__)),
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"dsl_examples",
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"retrieval_and_generate.json",
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)
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parser.add_argument('-s', '--dsl', default=dsl_default_path, help="input dsl", action='store', required=True)
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parser.add_argument('-t', '--tenant_id', default=False, help="Tenant ID", action='store', required=True)
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parser.add_argument('-m', '--stream', default=False, help="Stream output", action='store_true', required=False)
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args = parser.parse_args()
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canvas = Canvas(open(args.dsl, "r").read(), args.tenant_id)
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while True:
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ans = canvas.run(stream=args.stream)
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print("==================== Bot =====================\n> ", end='')
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if args.stream and isinstance(ans, partial):
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cont = ""
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for an in ans():
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print(an["content"][len(cont):], end='', flush=True)
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cont = an["content"]
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else:
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print(ans["content"])
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if DEBUG: print(canvas.path)
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question = input("\n==================== User =====================\n> ")
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canvas.add_user_input(question)
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45
agent/test/dsl_examples/categorize.json
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45
agent/test/dsl_examples/categorize.json
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{
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"components": {
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"begin": {
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"obj":{
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"component_name": "Begin",
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"params": {
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"prologue": "Hi there!"
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}
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},
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"downstream": ["answer:0"],
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"upstream": []
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},
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"answer:0": {
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"obj": {
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"component_name": "Answer",
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"params": {}
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},
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"downstream": ["categorize:0"],
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"upstream": ["begin"]
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},
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"categorize:0": {
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"obj": {
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"component_name": "Categorize",
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"params": {
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"llm_id": "deepseek-chat",
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"category_description": {
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"product_related": {
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"description": "The question is about the product usage, appearance and how it works.",
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"examples": "Why it always beaming?\nHow to install it onto the wall?\nIt leaks, what to do?"
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},
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"others": {
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"description": "The question is not about the product usage, appearance and how it works.",
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"examples": "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?"
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}
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}
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}
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},
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"downstream": [],
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"upstream": ["answer:0"]
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}
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},
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"history": [],
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"path": [],
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"answer": []
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}
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157
agent/test/dsl_examples/customer_service.json
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157
agent/test/dsl_examples/customer_service.json
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{
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"components": {
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"begin": {
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"obj":{
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"component_name": "Begin",
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"params": {
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"prologue": "Hi! How can I help you?"
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}
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},
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"downstream": ["answer:0"],
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"upstream": []
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},
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"answer:0": {
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"obj": {
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"component_name": "Answer",
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"params": {}
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},
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"downstream": ["categorize:0"],
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"upstream": ["begin", "generate:0", "generate:casual", "generate:answer", "generate:complain", "generate:ask_contact", "message:get_contact"]
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},
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"categorize:0": {
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"obj": {
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"component_name": "Categorize",
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"params": {
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"llm_id": "deepseek-chat",
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"category_description": {
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"product_related": {
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"description": "The question is about the product usage, appearance and how it works.",
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"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",
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"to": "retrieval:0"
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},
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"casual": {
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"description": "The question is not about the product usage, appearance and how it works. Just casual chat.",
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"examples": "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?",
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"to": "generate:casual"
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},
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"complain": {
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"description": "Complain even curse about the product or service you provide. But the comment is not specific enough.",
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"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.",
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"to": "generate:complain"
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},
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"answer": {
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"description": "This answer provide a specific contact information, like e-mail, phone number, wechat number, line number, twitter, discord, etc,.",
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"examples": "My phone number is 203921\nkevinhu.hk@gmail.com\nThis is my discord number: johndowson_29384",
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"to": "message:get_contact"
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}
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},
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"message_history_window_size": 8
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}
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},
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"downstream": ["retrieval:0", "generate:casual", "generate:complain", "message:get_contact"],
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"upstream": ["answer:0"]
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},
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"generate:casual": {
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"obj": {
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"component_name": "Generate",
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"params": {
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"llm_id": "deepseek-chat",
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"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.",
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"temperature": 0.9,
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"message_history_window_size": 12,
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"cite": false
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}
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},
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"downstream": ["answer:0"],
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"upstream": ["categorize:0"]
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},
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"generate:complain": {
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"obj": {
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"component_name": "Generate",
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"params": {
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"llm_id": "deepseek-chat",
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"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.",
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"temperature": 0.9,
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"message_history_window_size": 12,
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"cite": false
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}
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},
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"downstream": ["answer:0"],
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"upstream": ["categorize:0"]
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},
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"retrieval:0": {
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"obj": {
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"component_name": "Retrieval",
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"params": {
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"similarity_threshold": 0.2,
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"keywords_similarity_weight": 0.3,
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"top_n": 6,
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"top_k": 1024,
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"rerank_id": "BAAI/bge-reranker-v2-m3",
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"kb_ids": ["869a236818b811ef91dffa163e197198"]
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}
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},
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"downstream": ["relevant:0"],
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"upstream": ["categorize:0"]
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},
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"relevant:0": {
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"obj": {
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"component_name": "Relevant",
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"params": {
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"llm_id": "deepseek-chat",
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"temperature": 0.02,
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"yes": "generate:answer",
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"no": "generate:ask_contact"
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}
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},
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"downstream": ["generate:answer", "generate:ask_contact"],
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"upstream": ["retrieval:0"]
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},
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"generate:answer": {
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"obj": {
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"component_name": "Generate",
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"params": {
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"llm_id": "deepseek-chat",
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"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.",
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"temperature": 0.02
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}
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},
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"downstream": ["answer:0"],
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"upstream": ["relevant:0"]
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},
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"generate:ask_contact": {
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"obj": {
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"component_name": "Generate",
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"params": {
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"llm_id": "deepseek-chat",
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"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.",
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"temperature": 0.9,
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"message_history_window_size": 12,
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"cite": false
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}
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},
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"downstream": ["answer:0"],
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"upstream": ["relevant:0"]
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},
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"message:get_contact": {
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"obj":{
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"component_name": "Message",
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"params": {
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"messages": [
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"Okay, I've already write this down. What else I can do for you?",
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"Get it. What else I can do for you?",
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"Thanks for your trust! Our expert will contact ASAP. So, anything else I can do for you?",
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"Thanks! So, anything else I can do for you?"
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]
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}
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},
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"downstream": ["answer:0"],
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"upstream": ["categorize:0"]
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}
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},
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"history": [],
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"messages": [],
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"path": [],
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"reference": [],
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"answer": []
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}
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210
agent/test/dsl_examples/headhunter_zh.json
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210
agent/test/dsl_examples/headhunter_zh.json
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{
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"components": {
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"begin": {
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"obj": {
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"component_name": "Begin",
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"params": {
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"prologue": "您好！我是AGI方向的猎头，了解到您是这方面的大佬，然后冒昧的就联系到您。这边有个机会想和您分享，RAGFlow正在招聘您这个岗位的资深的工程师不知道您那边是不是感兴趣？"
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}
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},
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"downstream": ["answer:0"],
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"upstream": []
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},
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"answer:0": {
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"obj": {
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"component_name": "Answer",
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"params": {}
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},
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"downstream": ["categorize:0"],
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"upstream": ["begin", "message:reject"]
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},
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"categorize:0": {
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"obj": {
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"component_name": "Categorize",
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"params": {
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"llm_id": "deepseek-chat",
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"category_description": {
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"about_job": {
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"description": "该问题关于职位本身或公司的信息。",
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"examples": "什么岗位？\n汇报对象是谁?\n公司多少人？\n公司有啥产品？\n具体工作内容是啥？\n地点哪里？\n双休吗？",
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"to": "retrieval:0"
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},
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"casual": {
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"description": "该问题不关于职位本身或公司的信息，属于闲聊。",
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"examples": "你好\n好久不见\n你男的女的？\n你是猴子派来的救兵吗？\n上午开会了?\n你叫啥？\n最近市场如何?生意好做吗？",
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"to": "generate:casual"
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},
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"interested": {
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"description": "该回答表示他对于该职位感兴趣。",
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"examples": "嗯\n说吧\n说说看\n还好吧\n是的\n哦\nyes\n具体说说",
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"to": "message:introduction"
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},
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"answer": {
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"description": "该回答表示他对于该职位不感兴趣，或感觉受到骚扰。",
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"examples": "不需要\n不感兴趣\n暂时不看\n不要\nno\n我已经不干这个了\n我不是这个方向的",
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"to": "message:reject"
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}
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}
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}
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},
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"downstream": [
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"message:introduction",
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"generate:casual",
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"message:reject",
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"retrieval:0"
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],
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"upstream": ["answer:0"]
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},
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"message:introduction": {
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"obj": {
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"component_name": "Message",
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"params": {
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"messages": [
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"我简单介绍以下：\nRAGFlow 是一款基于深度文档理解构建的开源 RAG（Retrieval-Augmented Generation）引擎。RAGFlow 可以为各种规模的企业及个人提供一套精简的 RAG 工作流程，结合大语言模型（LLM）针对用户各类不同的复杂格式数据提供可靠的问答以及有理有据的引用。https://github.com/infiniflow/ragflow\n您那边还有什么要了解的？"
|
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]
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}
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},
|
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"downstream": ["answer:1"],
|
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"upstream": ["categorize:0"]
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},
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"answer:1": {
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"obj": {
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"component_name": "Answer",
|
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"params": {}
|
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},
|
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"downstream": ["categorize:1"],
|
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"upstream": [
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"message:introduction",
|
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"generate:aboutJob",
|
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"generate:casual",
|
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"generate:get_wechat",
|
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"generate:nowechat"
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]
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},
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"categorize:1": {
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"obj": {
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"component_name": "Categorize",
|
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"params": {
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"llm_id": "deepseek-chat",
|
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"category_description": {
|
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"about_job": {
|
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"description": "该问题关于职位本身或公司的信息。",
|
||||
"examples": "什么岗位？\n汇报对象是谁?\n公司多少人？\n公司有啥产品？\n具体工作内容是啥？\n地点哪里？\n双休吗？",
|
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"to": "retrieval:0"
|
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},
|
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"casual": {
|
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"description": "该问题不关于职位本身或公司的信息，属于闲聊。",
|
||||
"examples": "你好\n好久不见\n你男的女的？\n你是猴子派来的救兵吗？\n上午开会了?\n你叫啥？\n最近市场如何?生意好做吗？",
|
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"to": "generate:casual"
|
||||
},
|
||||
"wechat": {
|
||||
"description": "该回答表示他愿意加微信,或者已经报了微信号。",
|
||||
"examples": "嗯\n可以\n是的\n哦\nyes\n15002333453\nwindblow_2231",
|
||||
"to": "generate:get_wechat"
|
||||
},
|
||||
"giveup": {
|
||||
"description": "该回答表示他不愿意加微信。",
|
||||
"examples": "不需要\n不感兴趣\n暂时不看\n不要\nno\n不方便\n不知道还要加我微信",
|
||||
"to": "generate:nowechat"
|
||||
}
|
||||
},
|
||||
"message_history_window_size": 8
|
||||
}
|
||||
},
|
||||
"downstream": [
|
||||
"retrieval:0",
|
||||
"generate:casual",
|
||||
"generate:get_wechat",
|
||||
"generate:nowechat"
|
||||
],
|
||||
"upstream": ["answer:1"]
|
||||
},
|
||||
"generate:casual": {
|
||||
"obj": {
|
||||
"component_name": "Generate",
|
||||
"params": {
|
||||
"llm_id": "deepseek-chat",
|
||||
"prompt": "你是AGI方向的猎头，现在候选人的聊了和职位无关的话题，请耐心的回应候选人，并将话题往该AGI的职位上带，最好能要到候选人微信号以便后面保持联系。",
|
||||
"temperature": 0.9,
|
||||
"message_history_window_size": 12,
|
||||
"cite": false
|
||||
}
|
||||
},
|
||||
"downstream": ["answer:1"],
|
||||
"upstream": ["categorize:0", "categorize:1"]
|
||||
},
|
||||
"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": ["generate:aboutJob"],
|
||||
"upstream": ["categorize:0", "categorize:1"]
|
||||
},
|
||||
"generate:aboutJob": {
|
||||
"obj": {
|
||||
"component_name": "Generate",
|
||||
"params": {
|
||||
"llm_id": "deepseek-chat",
|
||||
"prompt": "你是AGI方向的猎头，候选人问了有关职位或公司的问题，你根据以下职位信息回答。如果职位信息中不包含候选人的问题就回答不清楚、不知道、有待确认等。回答完后引导候选人加微信号，如：\n - 方便加一下微信吗，我把JD发您看看？\n - 微信号多少，我把详细职位JD发您？\n 职位信息如下:\n {input}\n 职位信息如上。",
|
||||
"temperature": 0.02
|
||||
}
|
||||
},
|
||||
"downstream": ["answer:1"],
|
||||
"upstream": ["retrieval:0"]
|
||||
},
|
||||
"generate:get_wechat": {
|
||||
"obj": {
|
||||
"component_name": "Generate",
|
||||
"params": {
|
||||
"llm_id": "deepseek-chat",
|
||||
"prompt": "你是AGI方向的猎头，候选人表示不反感加微信，如果对方已经报了微信号，表示感谢和信任并表示马上会加上；如果没有，则问对方微信号多少。你的微信号是weixin_kevin，E-mail是kkk@ragflow.com。说话不要重复。不要总是您好。",
|
||||
"temperature": 0.1,
|
||||
"message_history_window_size": 12,
|
||||
"cite": false
|
||||
}
|
||||
},
|
||||
"downstream": ["answer:1"],
|
||||
"upstream": ["categorize:1"]
|
||||
},
|
||||
"generate:nowechat": {
|
||||
"obj": {
|
||||
"component_name": "Generate",
|
||||
"params": {
|
||||
"llm_id": "deepseek-chat",
|
||||
"prompt": "你是AGI方向的猎头，当你提出加微信时对方表示拒绝。你需要耐心礼貌的回应候选人，表示对于保护隐私信息给予理解，也可以询问他对该职位的看法和顾虑。并在恰当的时机再次询问微信联系方式。也可以鼓励候选人主动与你取得联系。你的微信号是weixin_kevin，E-mail是kkk@ragflow.com。说话不要重复。不要总是您好。",
|
||||
"temperature": 0.1,
|
||||
"message_history_window_size": 12,
|
||||
"cite": false
|
||||
}
|
||||
},
|
||||
"downstream": ["answer:1"],
|
||||
"upstream": ["categorize:1"]
|
||||
},
|
||||
"message:reject": {
|
||||
"obj": {
|
||||
"component_name": "Message",
|
||||
"params": {
|
||||
"messages": [
|
||||
"好的，祝您生活愉快，工作顺利。",
|
||||
"哦，好的，感谢您宝贵的时间！"
|
||||
]
|
||||
}
|
||||
},
|
||||
"downstream": ["answer:0"],
|
||||
"upstream": ["categorize:0"]
|
||||
}
|
||||
},
|
||||
"history": [],
|
||||
"messages": [],
|
||||
"path": [],
|
||||
"reference": [],
|
||||
"answer": []
|
||||
}
|
||||
39
agent/test/dsl_examples/intergreper.json
Normal file
39
agent/test/dsl_examples/intergreper.json
Normal file
@ -0,0 +1,39 @@
|
||||
{
|
||||
"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
Normal file
39
agent/test/dsl_examples/interpreter.json
Normal file
@ -0,0 +1,39 @@
|
||||
{
|
||||
"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": []
|
||||
}
|
||||
62
agent/test/dsl_examples/keyword_wikipedia_and_generate.json
Normal file
62
agent/test/dsl_examples/keyword_wikipedia_and_generate.json
Normal file
@ -0,0 +1,62 @@
|
||||
{
|
||||
"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": []
|
||||
}
|
||||
54
agent/test/dsl_examples/retrieval_and_generate.json
Normal file
54
agent/test/dsl_examples/retrieval_and_generate.json
Normal file
@ -0,0 +1,54 @@
|
||||
{
|
||||
"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"]
|
||||
},
|
||||
"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": ["generate:0"],
|
||||
"upstream": ["answer:0"]
|
||||
},
|
||||
"generate:0": {
|
||||
"obj": {
|
||||
"component_name": "Generate",
|
||||
"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.",
|
||||
"temperature": 0.2
|
||||
}
|
||||
},
|
||||
"downstream": ["answer:0"],
|
||||
"upstream": ["retrieval:0"]
|
||||
}
|
||||
},
|
||||
"history": [],
|
||||
"messages": [],
|
||||
"reference": {},
|
||||
"path": [],
|
||||
"answer": []
|
||||
}
|
||||
@ -0,0 +1,88 @@
|
||||
{
|
||||
"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", "generate:0", "switch:0"]
|
||||
},
|
||||
"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": "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"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"downstream": ["retrieval:0", "message:0"],
|
||||
"upstream": ["answer:0"]
|
||||
},
|
||||
"message:0": {
|
||||
"obj":{
|
||||
"component_name": "Message",
|
||||
"params": {
|
||||
"messages": [
|
||||
"Sorry, I don't know. I'm an AI bot."
|
||||
]
|
||||
}
|
||||
},
|
||||
"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": ["generate:0"],
|
||||
"upstream": ["switch:0"]
|
||||
},
|
||||
"generate:0": {
|
||||
"obj": {
|
||||
"component_name": "Generate",
|
||||
"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.",
|
||||
"temperature": 0.2
|
||||
}
|
||||
},
|
||||
"downstream": ["answer:0"],
|
||||
"upstream": ["retrieval:0"]
|
||||
}
|
||||
},
|
||||
"history": [],
|
||||
"messages": [],
|
||||
"reference": {},
|
||||
"path": [],
|
||||
"answer": []
|
||||
}
|
||||
82
agent/test/dsl_examples/retrieval_relevant_and_generate.json
Normal file
82
agent/test/dsl_examples/retrieval_relevant_and_generate.json
Normal file
@ -0,0 +1,82 @@
|
||||
{
|
||||
"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": []
|
||||
}
|
||||
@ -0,0 +1,103 @@
|
||||
{
|
||||
"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": []
|
||||
}
|
||||
@ -0,0 +1,79 @@
|
||||
{
|
||||
"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": []
|
||||
}
|
||||
Reference in New Issue
Block a user