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tuyang.yhj
@@ -6,20 +6,20 @@ from enum import auto, Enum
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from typing import List, Any
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from pilot.configs.model_config import DB_SETTINGS
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class SeparatorStyle(Enum):
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SINGLE = auto()
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TWO = auto()
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THREE = auto()
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FOUR = auto()
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@dataclasses.dataclass
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@ dataclasses.dataclass
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class Conversation:
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"""This class keeps all conversation history. """
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system: str
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roles: List[str]
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messages: List[List[str]]
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messages: List[List[str]]
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offset: int
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sep_style: SeparatorStyle = SeparatorStyle.SINGLE
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sep: str = "###"
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@@ -34,7 +34,7 @@ class Conversation:
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ret = self.system + self.sep
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for role, message in self.messages:
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if message:
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ret += role + ": " + message + self.sep
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ret += role + ": " + message + self.sep
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else:
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ret += role + ":"
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return ret
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@@ -51,11 +51,10 @@ class Conversation:
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else:
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raise ValueError(f"Invalid style: {self.sep_style}")
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def append_message(self, role, message):
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self.messages.append([role, message])
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def to_gradio_chatbot(self):
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ret = []
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for i, (role, msg) in enumerate(self.messages[self.offset:]):
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@@ -103,9 +102,10 @@ def gen_sqlgen_conversation(dbname):
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message += s["schema_info"] + ";"
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return f"数据库{dbname}的Schema信息如下: {message}\n"
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conv_one_shot = Conversation(
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system="A chat between a curious human and an artificial intelligence assistant, who very familiar with database related knowledge. "
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"The assistant gives helpful, detailed, professional and polite answers to the human's questions. ",
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"The assistant gives helpful, detailed, professional and polite answers to the human's questions. ",
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roles=("Human", "Assistant"),
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messages=(
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(
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@@ -136,10 +136,10 @@ conv_one_shot = Conversation(
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sep_style=SeparatorStyle.SINGLE,
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sep="###"
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)
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conv_vicuna_v1 = Conversation(
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system = "A chat between a curious user and an artificial intelligence assistant. who very familiar with database related knowledge. "
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"The assistant gives helpful, detailed, professional and polite answers to the user's questions. ",
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system="A chat between a curious user and an artificial intelligence assistant. who very familiar with database related knowledge. "
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"The assistant gives helpful, detailed, professional and polite answers to the user's questions. ",
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roles=("USER", "ASSISTANT"),
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messages=(),
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offset=0,
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@@ -150,60 +150,60 @@ conv_vicuna_v1 = Conversation(
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auto_dbgpt_one_shot = Conversation(
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system="You are DB-GPT, an AI designed to answer questions about HackerNews by query `hackerbews` database in MySQL. "
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"Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.",
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"Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.",
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roles=("USER", "ASSISTANT"),
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messages=(
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(
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"USER",
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""" Answer how many users does hackernews have by query mysql database
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Constraints:
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1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.
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2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
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3. No user assistance
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4. Exclusively use the commands listed in double quotes e.g. "command name"
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Commands:
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1. analyze_code: Analyze Code, args: "code": "<full_code_string>"
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2. execute_python_file: Execute Python File, args: "filename": "<filename>"
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3. append_to_file: Append to file, args: "filename": "<filename>", "text": "<text>"
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4. delete_file: Delete file, args: "filename": "<filename>"
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5. list_files: List Files in Directory, args: "directory": "<directory>"
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6. read_file: Read file, args: "filename": "<filename>"
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7. write_to_file: Write to file, args: "filename": "<filename>", "text": "<text>"
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8. tidb_sql_executor: "Execute SQL in TiDB Database.", args: "sql": "<sql>"
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Resources:
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1. Internet access for searches and information gathering.
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2. Long Term memory management.
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3. vicuna powered Agents for delegation of simple tasks.
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4. File output.
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Performance Evaluation:
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1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
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2. Constructively self-criticize your big-picture behavior constantly.
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3. Reflect on past decisions and strategies to refine your approach.
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4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
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5. Write all code to a file.
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You should only respond in JSON format as described below
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Response Format:
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{
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"thoughts": {
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"text": "thought",
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"reasoning": "reasoning",
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"plan": "- short bulleted\n- list that conveys\n- long-term plan",
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"criticism": "constructive self-criticism",
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"speak": "thoughts summary to say to user"
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},
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"command": {
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"name": "command name",
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"args": {
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"arg name": "value"
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}
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}
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}
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Ensure the response can be parsed by Python json.loads
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"""
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"USER",
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""" Answer how many users does hackernews have by query mysql database
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Constraints:
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1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.
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2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
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3. No user assistance
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4. Exclusively use the commands listed in double quotes e.g. "command name"
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Commands:
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1. analyze_code: Analyze Code, args: "code": "<full_code_string>"
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2. execute_python_file: Execute Python File, args: "filename": "<filename>"
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3. append_to_file: Append to file, args: "filename": "<filename>", "text": "<text>"
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4. delete_file: Delete file, args: "filename": "<filename>"
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5. list_files: List Files in Directory, args: "directory": "<directory>"
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6. read_file: Read file, args: "filename": "<filename>"
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7. write_to_file: Write to file, args: "filename": "<filename>", "text": "<text>"
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8. tidb_sql_executor: "Execute SQL in TiDB Database.", args: "sql": "<sql>"
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Resources:
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1. Internet access for searches and information gathering.
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2. Long Term memory management.
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3. vicuna powered Agents for delegation of simple tasks.
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4. File output.
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Performance Evaluation:
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1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
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2. Constructively self-criticize your big-picture behavior constantly.
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3. Reflect on past decisions and strategies to refine your approach.
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4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
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5. Write all code to a file.
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You should only respond in JSON format as described below
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Response Format:
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{
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"thoughts": {
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"text": "thought",
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"reasoning": "reasoning",
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"plan": "- short bulleted\n- list that conveys\n- long-term plan",
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"criticism": "constructive self-criticism",
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"speak": "thoughts summary to say to user"
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},
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"command": {
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"name": "command name",
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"args": {
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"arg name": "value"
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}
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}
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}
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Ensure the response can be parsed by Python json.loads
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"""
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),
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(
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"ASSISTANT",
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@@ -234,7 +234,7 @@ auto_dbgpt_one_shot = Conversation(
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auto_dbgpt_without_shot = Conversation(
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system="You are DB-GPT, an AI designed to answer questions about HackerNews by query `hackerbews` database in MySQL. "
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"Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.",
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"Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.",
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roles=("USER", "ASSISTANT"),
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messages=(),
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offset=0,
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@@ -251,14 +251,12 @@ conv_qa_prompt_template = """ 基于以下已知的信息, 专业、详细的回
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{question}
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"""
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default_conversation = conv_one_shot
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conversation_types = {
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"native": "LLM原生对话",
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"default_knownledge": "默认知识库对话",
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"custome": "新增知识库对话",
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"custome": "新增知识库对话",
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}
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conv_templates = {
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@@ -266,7 +264,6 @@ conv_templates = {
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"vicuna_v1": conv_vicuna_v1,
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}
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if __name__ == "__main__":
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message = gen_sqlgen_conversation("dbgpt")
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print(message)
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print(message)
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