├── graph.png ├── app ├── core │ ├── models │ │ ├── __init__.py │ │ └── embedding.py │ ├── utils.py │ ├── configs │ │ └── config.py │ ├── agents │ │ ├── crewai.json │ │ ├── autogen.json │ │ ├── agents.json │ │ └── agents.py │ ├── tools │ │ └── semantic_search_tools.py │ └── pipeline │ │ └── graph.py ├── main.py └── rag │ ├── utils.py │ ├── models.py │ └── router.py ├── requirements ├── autogen.txt ├── crewai.txt ├── langgraph.txt └── fastapi.txt ├── docker-compose.yml ├── run_multiturns.sh ├── Dockerfile ├── setup_env.py ├── .gitignore ├── README.md ├── LICENSE ├── text2sql_autogen.ipynb ├── text2sql_crewai.ipynb └── text2sql_langgraph.ipynb /graph.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yhyu/agentic-text2sql/HEAD/graph.png -------------------------------------------------------------------------------- /app/core/models/__init__.py: -------------------------------------------------------------------------------- 1 | from .embedding import EmbeddingModel 2 | 3 | 4 | def preload_models(): 5 | EmbeddingModel() -------------------------------------------------------------------------------- /requirements/autogen.txt: -------------------------------------------------------------------------------- 1 | numpy==1.26.4 2 | torch==2.3.1 3 | transformers==4.41.2 4 | requests==2.32.3 5 | pyautogen==0.2.32 6 | chromadb==0.4.24 7 | spider_env==0.0.1 8 | pydantic-settings==2.3.4 -------------------------------------------------------------------------------- /requirements/crewai.txt: -------------------------------------------------------------------------------- 1 | numpy==1.26.4 2 | torch==2.3.1 3 | transformers==4.41.2 4 | requests==2.32.3 5 | crewai==0.36.1 6 | crewai-tools==0.4.8 7 | chromadb==0.4.24 8 | spider_env==0.0.1 9 | pydantic-settings==2.3.4 -------------------------------------------------------------------------------- /docker-compose.yml: -------------------------------------------------------------------------------- 1 | version: '2.2' 2 | services: 3 | search-ml-api: 4 | container_name: agentic-text2sql 5 | image: agentic-text2sql:latest 6 | ports: 7 | - 8000:8000 8 | environment: 9 | OPENAI_API_KEY: your-openai-api-key 10 | OPENAI_MODEL_NAME: gpt-4o-mini 11 | -------------------------------------------------------------------------------- /requirements/langgraph.txt: -------------------------------------------------------------------------------- 1 | numpy==1.26.4 2 | torch==2.3.1 3 | transformers==4.41.2 4 | requests==2.32.3 5 | chromadb==0.4.24 6 | spider_env==0.0.1 7 | langgraph==0.1.5 8 | langchain_core==0.2.12 9 | langchain_openai==0.1.7 10 | langchain-community==0.2.7 11 | langchain==0.2.7 12 | pygraphviz==1.13 13 | pydantic-settings==2.3.4 -------------------------------------------------------------------------------- /run_multiturns.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | pwd=`pwd` 4 | export CHROMA_STORAGE=$(pwd)/chroma_db 5 | export SCHEMA_PATH=$(pwd)/table_schemas.jsonl 6 | export SPIDER_CACHE=$(pwd)/spider 7 | if [ ! -f $SCHEMA_PATH ]; then 8 | python setup_env.py 9 | fi 10 | 11 | gunicorn app.main:app -w 1 -k uvicorn.workers.UvicornWorker -b 0.0.0.0:8000 12 | -------------------------------------------------------------------------------- /requirements/fastapi.txt: -------------------------------------------------------------------------------- 1 | --find-links https://download.pytorch.org/whl/torch_stable.html 2 | 3 | fastapi==0.106.0 4 | gunicorn==22.0.0 5 | uvicorn==0.30.0 6 | pydantic==2.7.4 7 | numpy==1.26.4 8 | torch==2.3.1+cpu 9 | transformers==4.41.2 10 | requests==2.32.3 11 | langgraph==0.1.5 12 | langchain_core==0.2.12 13 | langchain_openai==0.1.7 14 | langchain==0.2.7 15 | chromadb==0.4.24 16 | pydantic-settings==2.3.4 -------------------------------------------------------------------------------- /app/core/utils.py: -------------------------------------------------------------------------------- 1 | from threading import Lock 2 | from typing import Any, Dict 3 | 4 | 5 | class Singleton(type): 6 | _instances: Dict = {} 7 | _lock: Lock = Lock() 8 | 9 | def __call__(self, *args: Any, **kwds: Any) -> Any: 10 | with self._lock: 11 | if self not in self._instances: 12 | instance = super().__call__(*args, **kwds) 13 | self._instances[self] = instance 14 | return self._instances[self] 15 | -------------------------------------------------------------------------------- /app/main.py: -------------------------------------------------------------------------------- 1 | from contextlib import asynccontextmanager 2 | 3 | from fastapi import FastAPI 4 | 5 | from app.rag.router import router as rag_router 6 | from app.core.models import preload_models 7 | 8 | 9 | @asynccontextmanager 10 | async def lifespan(app: FastAPI): 11 | # start up 12 | preload_models() 13 | 14 | yield 15 | 16 | # shutdown 17 | 18 | 19 | app = FastAPI(lifespan=lifespan) 20 | 21 | app.include_router(rag_router, prefix='/v1/rag', tags=['RAG']) 22 | -------------------------------------------------------------------------------- /app/rag/utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sqlite3 3 | 4 | from app.core.configs.config import settings 5 | 6 | 7 | def fetch_sqlite(db: str, sql: str) -> list: 8 | db_cache = settings.SPIDER_CACHE 9 | try: 10 | con = sqlite3.connect(f'{db_cache}/spider/database/{db}/{db}.sqlite') 11 | cursor = con.cursor() 12 | cursor.execute(sql) 13 | return cursor.fetchall() 14 | except Exception as e: 15 | print('fetch_sqlite exception:', e) 16 | return [] 17 | -------------------------------------------------------------------------------- /app/rag/models.py: -------------------------------------------------------------------------------- 1 | from typing import Any, List, Optional 2 | from pydantic import BaseModel 3 | 4 | 5 | class SQLQueryResult(BaseModel): 6 | database: str 7 | sql: str 8 | value: Optional[List[Any]] = [] 9 | 10 | 11 | class HealthCheckResponse(BaseModel): 12 | status: str 13 | 14 | 15 | class QueryRequest(BaseModel): 16 | request_id: str 17 | query: str 18 | session_id: Optional[str] = '' 19 | 20 | 21 | class QueryResponse(BaseModel): 22 | request_id: str 23 | response_id: str 24 | results: SQLQueryResult 25 | session_id: str -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM python:3.12.4-slim 2 | 3 | RUN apt-get update && \ 4 | apt-get install --assume-yes --no-install-recommends \ 5 | --reinstall build-essential && \ 6 | rm --recursive --force /var/lib/apt/lists/* 7 | 8 | RUN mkdir -p /agent/app 9 | COPY --chown=agent:agnet ./app /agent/app 10 | COPY --chown=agent:agnet ./setup_env.py /agent/ 11 | COPY --chown=agent:agnet ./run_multiturns.sh /agent/ 12 | COPY --chown=agent:agnet ./requirements/fastapi.txt /agent/requirements.txt 13 | 14 | WORKDIR /agent 15 | RUN chmod a+x run_multiturns.sh 16 | RUN pip3 install --no-cache-dir -r requirements.txt 17 | 18 | CMD ["./run_multiturns.sh"] 19 | -------------------------------------------------------------------------------- /app/core/configs/config.py: -------------------------------------------------------------------------------- 1 | import logging 2 | 3 | from pydantic_settings import BaseSettings 4 | 5 | class Settings(BaseSettings): 6 | # LLM 7 | OPENAI_MODEL_NAME: str = 'gpt-4o-mini' 8 | 9 | # chromadb 10 | CHROMA_STORAGE: str = 'chroma_db' 11 | CHROMA_COLLECTION: str = 'spider-schemas' 12 | 13 | # spider 14 | SPIDER_CACHE: str = 'spider' 15 | SCHEMA_PATH: str = 'table_schemas.jsonl' 16 | 17 | # embedding model 18 | EMBEDDING_MODEL: str = 'Alibaba-NLP/gte-large-en-v1.5' 19 | 20 | # log level 21 | log_level: str = 'INFO' 22 | 23 | 24 | # load settings 25 | settings = Settings() 26 | 27 | # logging 28 | logger = logging.getLogger("gunicorn.error") 29 | logger.setLevel(settings.log_level) 30 | -------------------------------------------------------------------------------- /app/rag/router.py: -------------------------------------------------------------------------------- 1 | 2 | from uuid import uuid4 as guid 3 | 4 | from fastapi import APIRouter, BackgroundTasks, Header 5 | 6 | from app.core.configs.config import logger 7 | from app.core.pipeline.graph import Graph 8 | from app.rag.models import ( 9 | HealthCheckResponse, 10 | QueryRequest, QueryResponse, 11 | SQLQueryResult, 12 | ) 13 | from app.rag.utils import fetch_sqlite 14 | 15 | router = APIRouter() 16 | 17 | 18 | @router.get('/health', response_model=HealthCheckResponse) 19 | async def health_check(): 20 | return { 21 | 'status': 'OK' 22 | } 23 | 24 | 25 | @router.post('/query', response_model=QueryResponse) 26 | async def query( 27 | request: QueryRequest, 28 | background_tasks: BackgroundTasks, 29 | ): 30 | logger.info(f'query request: {request}') 31 | g = Graph() 32 | ret = g(request.query, thread_id=request.session_id) 33 | db = ret['state']['database'][-1] 34 | sql = ret['state']['sql'][-1] 35 | response = QueryResponse( 36 | request_id=request.request_id, 37 | response_id=str(guid().hex), 38 | results=SQLQueryResult( 39 | database=db, 40 | sql=sql, 41 | value=fetch_sqlite(db, sql) 42 | ), 43 | session_id=ret['thread_id'] 44 | ) 45 | logger.info(f'query response: {response}') 46 | return response 47 | -------------------------------------------------------------------------------- /app/core/agents/crewai.json: -------------------------------------------------------------------------------- 1 | { 2 | "assistant_dba": { 3 | "role": "Assistant Search Engineer", 4 | "goal": "Find relevant databases/tables as needed", 5 | "backstory": "You are an Assistant Search Engineer, using tools to assist colleagues in finding relevant databases/tables to answer questions.", 6 | "allow_delegation": false, 7 | "verbose": true 8 | }, 9 | "senior_sql_writer": { 10 | "role": "Senior Database SQL query Writer", 11 | "goal": "Create SQL query as needed", 12 | "backstory": "You are a Senior Database SQL Script Writer at a leading tech think tank. You are good at writing SQL queries, and do your best to produce perfect sql script", 13 | "allow_delegation": false, 14 | "verbose": true 15 | }, 16 | "senior_qa_engineer": { 17 | "role": "Senior Database SQL script Quality Assurance Engineer", 18 | "goal": "Create prefect SQL query, by analizing the script that is given for errors", 19 | "backstory": "You are a database expert that specializes in checking sql script for errors. You have an eye for detail and a knack for finding hidden bugs. You check for missing data fileds, mismatched table schemas, mismatched brackets, syntax errors, logic errors and relevant tables. You also check whether the sql script meets the needs of the question.", 20 | "allow_delegation": true, 21 | "verbose": true 22 | } 23 | } -------------------------------------------------------------------------------- /app/core/tools/semantic_search_tools.py: -------------------------------------------------------------------------------- 1 | import os 2 | from typing import Any, List 3 | 4 | import chromadb 5 | from pydantic import BaseModel 6 | 7 | from app.core.configs.config import settings 8 | from app.core.models.embedding import EmbeddingModel 9 | 10 | 11 | class SemanticSearchTool(BaseModel): 12 | client: Any = None 13 | collection: Any = None 14 | tokenizer: Any = None 15 | embedding_model: Any = None 16 | source: List[str] = [] 17 | n_results: int = 10 18 | 19 | def __init__(self, n_results=10, **kwargs): 20 | super().__init__(**kwargs) 21 | db_path = kwargs.get('chroma_db_path', settings.CHROMA_STORAGE) 22 | collection_name = kwargs.get('chroma_collection', settings.CHROMA_COLLECTION) 23 | self.client = chromadb.PersistentClient(path=db_path) 24 | self.collection = self.client.get_collection( 25 | collection_name, 26 | embedding_function=EmbeddingModel() 27 | ) 28 | self.n_results = n_results 29 | 30 | data_path = kwargs.get('schema_path', settings.SCHEMA_PATH) 31 | with open(data_path) as f: 32 | self.source = f.readlines() 33 | 34 | def __call__(self, question: str) -> str: 35 | results = self.collection.query( 36 | query_texts=question, 37 | n_results=self.n_results, 38 | ) 39 | return '\n'.join([self.source[int(id)] for id in results['ids'][0]]) 40 | -------------------------------------------------------------------------------- /app/core/models/embedding.py: -------------------------------------------------------------------------------- 1 | import os 2 | from typing import Union 3 | 4 | import numpy as np 5 | import torch 6 | import torch.nn.functional as F 7 | from transformers import AutoModel, AutoTokenizer 8 | from chromadb import Documents, EmbeddingFunction, Embeddings 9 | 10 | from app.core.configs.config import settings 11 | 12 | 13 | class EmbeddingModel(EmbeddingFunction): 14 | tokenizer = AutoTokenizer.from_pretrained(settings.EMBEDDING_MODEL) 15 | model = AutoModel.from_pretrained(settings.EMBEDDING_MODEL, trust_remote_code=True) 16 | 17 | def __call__(self, input: Documents) -> Embeddings: 18 | return self.get_embedding(input) 19 | 20 | def get_embedding(self, batch_text: Union[str, list], batch_size=32) -> np.ndarray: 21 | all_embeddings = None 22 | if not isinstance(batch_text, list): 23 | batch_text = [batch_text] 24 | for batch in range(0, len(batch_text), batch_size): 25 | batch_inputs = self.tokenizer( 26 | batch_text[batch:batch+batch_size], 27 | max_length=8192, 28 | padding='longest', 29 | truncation=True, 30 | return_tensors='pt' 31 | ) 32 | with torch.no_grad(): 33 | outputs = self.model(**batch_inputs) 34 | embeddings = outputs.last_hidden_state[:, 0] 35 | embeddings = F.normalize(embeddings, p=2, dim=1) 36 | if all_embeddings is None: 37 | all_embeddings = embeddings 38 | else: 39 | all_embeddings = torch.cat((all_embeddings, embeddings), axis=0) 40 | return all_embeddings.tolist() 41 | -------------------------------------------------------------------------------- /app/core/agents/autogen.json: -------------------------------------------------------------------------------- 1 | { 2 | "assistant_dba": { 3 | "name": "Assistant_Search_Engineer", 4 | "system_message": "You are an Assistant Search Engineer. Your goal is Find relevant databases/tables as needed. MUST ONLY Output the relevant table schemas.", 5 | "description": "First call semantic_search(question) to get a list of table schema candidates. Take the list of candidates to finding the relevant databases/table schemas that is able to answer the question and aLL schemas MUST belong to the same database." 6 | }, 7 | "senior_sql_writer": { 8 | "name": "Senior_Database_SQL_query_Writer", 9 | "system_message": "You are a Senior Database SQL Script Writer at a leading tech think tank. You are good at writing SQL queries, and do your best to produce perfect sql script", 10 | "description": "Based on the given table schemas write a SQL query script to answer the question." 11 | }, 12 | "senior_qa_engineer": { 13 | "name": "Senior_Database_SQL_script_Quality_Assurance_Engineer", 14 | "system_message": "You are a database expert that specializes in checking sql script for errors. You review the work of SQL query writer and provide constructive to help improve the quality of the content. You have an eye for detail and a knack for finding hidden bugs. You check for missing data fileds, redundant data fields, mismatched table schemas, mismatched brackets, syntax errors, logic errors and relevant tables. You also check whether the sql script meets the needs of the question.", 15 | "description": "Based on the question, table schemas, and SQL query script, give the feedback for the SQL query script." 16 | } 17 | } -------------------------------------------------------------------------------- /app/core/agents/agents.json: -------------------------------------------------------------------------------- 1 | { 2 | "search_engineer": { 3 | "system": "You are an Assistant Search Engineer. Your goal is Find relevant databases/tables that is related to the question. MUST ONLY Output the relevant table schemas.", 4 | "expected_output": "Return relevant database and tables' schema into json object: {\"database\": \"\", \"table_schemas\": \"\"}. ALL schemas MUST belong to the same database." 5 | }, 6 | "senior_sql_writer": { 7 | "system": "You are a Senior Database SQL Script Writer at a leading tech think tank. You are good at writing SQL queries, and do your best to produce perfect sql script", 8 | "expected_output": "MUST ONLY return a valid sql script with required fields, don't add unnecessary fields. Also MUST ONLY return executable sql script format, DON'T decorate with markdown format." 9 | }, 10 | "senior_qa_engineer": { 11 | "system": "You are a database expert that specializes in checking sql script for errors. You review the work of SQL query writer and provide constructive to help improve the quality of the content. You have an eye for detail and a knack for finding hidden bugs. You check for missing data fileds, redundant data fields, mismatched table schemas, mismatched brackets, syntax errors, logic errors and relevant tables. You also check whether the sql script meets the needs of the question.", 12 | "expected_output": "Based on your justification for the sql script ONLY returns DENIED or ACCEPTED." 13 | }, 14 | "chief_dba": { 15 | "system": "You are a Chief Database Administrator. Your goal is to ensure that the SQL script does the job that it is supposed to do. You feel that sql script writers always do only half the job, so you are super dedicate to point out the defects.", 16 | "expected_output": "Return your feedback for the sql script." 17 | }, 18 | "search_rewriter": { 19 | "system": "Your are a Senior Search Engineer. Your specialty is rewriting a new search query based on previous search queries so taht it better express the new query for search engine.", 20 | "expected_output": "Return the new rewritten search query." 21 | } 22 | } -------------------------------------------------------------------------------- /setup_env.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | import sqlite3 4 | import urllib 5 | import zipfile 6 | from pathlib import Path 7 | 8 | import chromadb 9 | 10 | from app.core.models.embedding import EmbeddingModel 11 | 12 | def download_spider(download_path: str = 'spider') -> None: 13 | Path(download_path).mkdir(parents=True, exist_ok=True) 14 | down_url = 'https://drive.usercontent.google.com/download?id=1403EGqzIDoHMdQF4c9Bkyl7dZLZ5Wt6J&export=download&authuser=0&confirm=t&uuid=2252606e-782f-4804-8c83-242531f05358&at=AN8xHooFMDH4YaulsljxCkqKhxAd%3A1756365050863' 15 | download_file = os.path.join(download_path, 'spider.zip') 16 | urllib.request.urlretrieve(down_url, download_file) 17 | with zipfile.ZipFile(download_file) as zipf: 18 | zipf.extractall(download_path) 19 | 20 | 21 | def collect_schemas(data_path: str = 'spider/spider_data') -> None: 22 | tables = [] 23 | database_path = os.path.join(data_path, 'database') 24 | for db_name in os.listdir(database_path): 25 | con = sqlite3.connect(f'{database_path}/{db_name}/{db_name}.sqlite') 26 | cursor = con.cursor() 27 | cursor.execute("SELECT tbl_name, sql FROM sqlite_master WHERE type='table';") 28 | tbls = cursor.fetchall() 29 | cursor.close() 30 | con.close() 31 | for t in tbls: 32 | tables.append( 33 | { 34 | 'database': db_name, 35 | 'table': t[0], 36 | 'schema': t[1].replace(' ', '') 37 | } 38 | ) 39 | 40 | with open('table_schemas.jsonl', 'w') as f: 41 | for tbl in tables: 42 | f.write(json.dumps(tbl)) 43 | f.write('\n') 44 | 45 | 46 | def create_chromadb( 47 | data_file: str = 'table_schemas.jsonl', 48 | db_path: str = 'chroma_db', 49 | collection_name: str = 'spider-schemas', 50 | ) -> None: 51 | tables = [] 52 | with open(data_file) as f: 53 | tables = f.readlines() 54 | 55 | # import embeddings to chromadb 56 | client = chromadb.PersistentClient(path=db_path) 57 | collection = client.create_collection( 58 | collection_name, 59 | metadata={'hnsw:space': 'ip'}, 60 | embedding_function=EmbeddingModel() 61 | ) 62 | collection.add( 63 | ids=[str(i) for i in range(len(tables))], documents=tables) 64 | 65 | 66 | if __name__ == "__main__": 67 | # download spider dataset 68 | print('download spider dataset...(it may take a few minutes.)') 69 | download_spider() 70 | 71 | # collect spider table schemas 72 | print('prepare spider data schemas for embedding...') 73 | collect_schemas() 74 | 75 | # create persistent chroma db 76 | print('import embeddings to chroma db...(it takes a few minutes.)') 77 | create_chromadb() 78 | 79 | print('prepare experiment environment done!') 80 | -------------------------------------------------------------------------------- /app/core/pipeline/graph.py: -------------------------------------------------------------------------------- 1 | from typing import Any 2 | from uuid import uuid4 as guid 3 | 4 | from langgraph.graph import StateGraph, END 5 | from langgraph.checkpoint.sqlite import SqliteSaver 6 | 7 | from app.core.agents.agents import Agent, AgentState 8 | from app.core.configs.config import logger 9 | from app.core.utils import Singleton 10 | 11 | 12 | class Graph(metaclass=Singleton): 13 | 14 | graph = None 15 | builder = None 16 | agents = Agent() 17 | memory = SqliteSaver.from_conn_string(":memory:") 18 | thread_id: str = None 19 | 20 | def __init__(self) -> None: 21 | self.rebuild() 22 | 23 | def __call__( 24 | self, question: str, thread_id: str = None, 25 | max_revision: int = 2) -> Any: 26 | if thread_id: 27 | init_state = None 28 | thread = {"configurable": {"thread_id": thread_id}} 29 | if not self.graph.get_state(thread).next: 30 | return { 31 | 'thread_id': '', 32 | 'state': None 33 | } 34 | else: 35 | current_state = self.graph.get_state(thread).values 36 | current_state['question'].append(question.strip()) 37 | self.graph.update_state(thread, current_state) 38 | else: 39 | thread_id = str(guid().hex) 40 | init_state = { 41 | 'level': 0, 42 | 'question': [question], 43 | 'table_schemas': [], 44 | 'database': [], 45 | 'sql': [], 46 | 'accepted': [False], 47 | 'reflect': [], 48 | 'messages': [], 49 | 'revision': 0, 50 | 'max_revision': max_revision, 51 | } 52 | thread = {"configurable": {"thread_id": thread_id}} 53 | 54 | for s in self.graph.stream(init_state, thread): 55 | logger.debug(s) 56 | return { 57 | 'state': self.graph.get_state(thread).values, 58 | 'thread_id': thread_id if self.graph.get_state(thread).next else '' 59 | } 60 | 61 | def build(self) -> None: 62 | # add nodes 63 | self.builder.add_node("search_engineer", self.agents.search_engineer_node) 64 | self.builder.add_node("sql_writer", self.agents.senior_sql_writer_node) 65 | self.builder.add_node("qa_engineer", self.agents.senior_qa_engineer_node) 66 | self.builder.add_node("chief_dba", self.agents.chief_dba_node) 67 | self.builder.add_node("user_proxy", self.agents.user_proxy_node) 68 | self.builder.add_node("search_rewriter", self.agents.search_rewriter_node) 69 | 70 | # add edges 71 | self.builder.add_edge("search_engineer", "sql_writer") 72 | self.builder.add_edge("sql_writer", "qa_engineer") 73 | self.builder.add_edge("chief_dba", "sql_writer") 74 | self.builder.add_edge("search_rewriter", "search_engineer") 75 | 76 | # add conditional edges 77 | self.builder.add_conditional_edges( 78 | "qa_engineer", 79 | lambda state: 'continue' if state['accepted'][state['level']] == True or state['revision'] >= state['max_revision'] else 'reflect', 80 | {'continue': 'user_proxy', 'reflect': 'chief_dba'} 81 | ) 82 | self.builder.add_conditional_edges( 83 | "user_proxy", 84 | lambda state: 'end' if len(state['question'][-1]) == 0 or 'exit' == state['question'][-1].lower() else 'explore', 85 | {'end': END, 'explore': 'search_rewriter'} 86 | ) 87 | 88 | # set entry point 89 | self.builder.set_entry_point("search_engineer") 90 | 91 | # compile graph 92 | self.graph = self.builder.compile( 93 | checkpointer=self.memory, 94 | interrupt_before=["user_proxy"], 95 | ) 96 | 97 | def rebuild(self): 98 | self.builder = StateGraph(AgentState) 99 | self.build() -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/latest/usage/project/#working-with-version-control 110 | .pdm.toml 111 | .pdm-python 112 | .pdm-build/ 113 | 114 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 115 | __pypackages__/ 116 | 117 | # Celery stuff 118 | celerybeat-schedule 119 | celerybeat.pid 120 | 121 | # SageMath parsed files 122 | *.sage.py 123 | 124 | # Environments 125 | .env 126 | .venv 127 | env/ 128 | venv/ 129 | ENV/ 130 | env.bak/ 131 | venv.bak/ 132 | 133 | # Spyder project settings 134 | .spyderproject 135 | .spyproject 136 | 137 | # Rope project settings 138 | .ropeproject 139 | 140 | # mkdocs documentation 141 | /site 142 | 143 | # mypy 144 | .mypy_cache/ 145 | .dmypy.json 146 | dmypy.json 147 | 148 | # Pyre type checker 149 | .pyre/ 150 | 151 | # pytype static type analyzer 152 | .pytype/ 153 | 154 | # Cython debug symbols 155 | cython_debug/ 156 | 157 | # PyCharm 158 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 159 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 160 | # and can be added to the global gitignore or merged into this file. For a more nuclear 161 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 162 | #.idea/ 163 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Agentic RAG for open domain text-to-query 2 | The experiment try to answer open domain business questions described in natural language through SQL queries to retrieve data from business databases. 3 | 4 | ## Prerequisites 5 | - The experiment leverage [CrewAI](https://www.crewai.com/), [AutoGen](https://microsoft.github.io/autogen/) and [LangChain - LangGraph](https://www.langchain.com/langgraph) frameworks to build the agentic RAG pipeline. 6 | - [Spider](https://yale-lily.github.io/spider) dataset is used to test the pipeline. 7 | - [Chroma](https://www.trychroma.com/) is used as a vector database. 8 | - Text embedding model: [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5). (Chroma also supports default built-in text embedding model which is [SentenceTransformer](https://www.sbert.net/) all-MiniLM-L6-v2 model.) 9 | - LLM: OpenAI [gpt-3.5-turbo](https://platform.openai.com/docs/models/gpt-3-5-turbo) 10 | - The experiment runs on CPU only, no GPU is required. 11 | 12 | ## Prepare experiment environment 13 | Install packages for CrewAI experiment. 14 | ``` 15 | pip install --no-cache-dir -r requirements/crewai.txt 16 | ``` 17 | 18 | Install packages for AutoGen experiment. 19 | ``` 20 | pip install --no-cache-dir -r requirements/autogen.txt 21 | ``` 22 | 23 | Install packages for LangGraph experiment. 24 | ``` 25 | pip install --no-cache-dir -r requirements/langgraph.txt 26 | ``` 27 | 28 | Setup environment. 29 | ``` 30 | python setup_env.py 31 | ``` 32 | 33 | ## Try it out 34 | * CrewAI: try [text2sql_crewai](https://github.com/yhyu/agentic-text2sql/blob/main/text2sql_crewai.ipynb) notebook. 35 | * AutoGen: try [text2sql_autogen](https://github.com/yhyu/agentic-text2sql/blob/main/text2sql_autogen.ipynb) notebook. 36 | * LangGraph: try [text2sql_langgraph](https://github.com/yhyu/agentic-text2sql/blob/main/text2sql_langgraph.ipynb) notebook. 37 | 38 | ## Mutli-turns Agentic RAG 39 | The multi-turns experiment is created using [LangGraph](https://www.langchain.com/langgraph) framework. Here is the graph looks like. 40 | ![Multi-turns Agentic RAG](https://github.com/yhyu/agentic-text2sql/blob/main/graph.png) 41 | 42 | The multi-turns experiment is served by [FastAPI](https://fastapi.tiangolo.com/), you can also use other framworks that you familiar with. 43 | Before you launch the service, install required packages: 44 | ``` 45 | pip install --no-cache-dir -r requirements/fastapi.txt 46 | ``` 47 | 48 | Run the command to start the multi-turns agentic RAG service: 49 | ``` 50 | chmod a+x run_multiturns.sh 51 | export OPENAI_API_KEY="your-openai-api-key" 52 | ./run_multiturns.sh 53 | ``` 54 | 55 | ### Multi-turns Request and Response 56 | In the multi-turns agentic RAG, __session_id__ is used to track conversations. __session_id__ is created in the first response, succeeded requests have to use the same __session_id__ to identify the same conversation. For instance: 57 | First request: 58 | ``` 59 | curl -X POST http://127.0.0.1:8000/v1/rag/query \ 60 | -H 'accept: application/json' \ 61 | -H 'Content-Type: application/json' \ 62 | -d '{ 63 | "request_id": "1234", 64 | "query": "How many routes does American Airlines operate?" 65 | }' 66 | ``` 67 | First response: 68 | ```json 69 | { 70 | "request_id":"1234", 71 | "response_id":"c56f90ccf67544a4b25c8290432e90d0", 72 | "results":{ 73 | "database":"flight_4", 74 | "sql":"SELECT COUNT(*) AS total_routes\nFROM routes\nWHERE alid = (SELECT alid FROM airlines WHERE name = 'American Airlines');", 75 | "value":[[2352]] 76 | }, 77 | "session_id":"c56f90ccf67544a4b25c8290432e90d0" 78 | } 79 | ``` 80 | Second request: 81 | ``` 82 | curl -X POST http://127.0.0.1:8000/v1/rag/query \ 83 | -H 'accept: application/json' \ 84 | -H 'Content-Type: application/json' \ 85 | -d '{ 86 | "request_id": "2234", 87 | "query": "How about Ryanair?", 88 | "session_id": "c56f90ccf67544a4b25c8290432e90d0" 89 | }' 90 | ``` 91 | Second response: 92 | ```json 93 | { 94 | "request_id":"1234", 95 | "response_id":"361039f19e4d4b45957b0e2f413a1ba2", 96 | "results":{ 97 | "database":"flight_4", 98 | "sql":"SELECT COUNT(*) AS total_routes\nFROM routes\nJOIN airlines ON routes.alid = airlines.alid\nWHERE airlines.name = 'Ryanair';", 99 | "value":[[2484]] 100 | }, 101 | "session_id":"c56f90ccf67544a4b25c8290432e90d0" 102 | } 103 | ``` 104 | Third request: 105 | ``` 106 | curl -X POST http://127.0.0.1:8000/v1/rag/query \ 107 | -H 'accept: application/json' \ 108 | -H 'Content-Type: application/json' \ 109 | -d '{ 110 | "request_id": "3234", 111 | "query": "What is the country of the former?", 112 | "session_id": "c56f90ccf67544a4b25c8290432e90d0" 113 | }' 114 | ``` 115 | Third response: 116 | ```json 117 | { 118 | "request_id":"1234", 119 | "response_id":"259e86c55bee46c690a1c7b8d961d255", 120 | "results":{ 121 | "database":"flight_4", 122 | "sql":"SELECT country\nFROM airlines\nWHERE name = 'American Airlines';", 123 | "value":[["United States"]] 124 | }, 125 | "session_id":"c56f90ccf67544a4b25c8290432e90d0" 126 | } 127 | ``` -------------------------------------------------------------------------------- /app/core/agents/agents.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | from typing import TypedDict, List 4 | 5 | from langchain_openai import ChatOpenAI 6 | from langchain_core.messages import SystemMessage, HumanMessage, AnyMessage 7 | 8 | from app.core.configs.config import settings 9 | from app.core.tools.semantic_search_tools import SemanticSearchTool 10 | 11 | class AgentState(TypedDict): 12 | level: int 13 | question: List[str] 14 | table_schemas: List[str] 15 | database: List[str] 16 | sql: List[str] 17 | reflect: List[str] 18 | accepted: List[str] 19 | messages: List[List[AnyMessage]] 20 | revision: int 21 | max_revision: int 22 | 23 | 24 | class Agent(): 25 | def __init__( 26 | self, config_file: str = os.path.join(os.path.dirname(__file__), 'agents.json')) -> None: 27 | with open(config_file) as f: 28 | self.agent_config = json.load(f) 29 | self.llm = ChatOpenAI(model=settings.OPENAI_MODEL_NAME, temperature=0) 30 | self.semantic_search = SemanticSearchTool(n_results=10) 31 | 32 | def search_engineer_node(self, state: AgentState): 33 | current_level = state['level'] 34 | candidates = self.semantic_search(state['question'][current_level]) 35 | role_prompt = self.agent_config['search_engineer'] 36 | messages = [ 37 | SystemMessage(content=role_prompt['system']), 38 | HumanMessage( 39 | content=f"Based on the following candidate database/table schemas:\n{candidates}\n\n" 40 | f"Find the relevant database/table schemas to answer the question: {state['question'][current_level]}\n\n" 41 | f"{role_prompt['expected_output']}") 42 | ] 43 | response = self.llm.invoke(messages) 44 | json_output = json.loads(response.content) 45 | 46 | tables = state['table_schemas'].copy() 47 | if len(tables) > current_level: 48 | tables[current_level] = json_output['table_schemas'] 49 | else: 50 | tables.append(json_output['table_schemas']) 51 | 52 | database = state['database'].copy() 53 | if len(database) > current_level: 54 | database[current_level] = json_output['database'] 55 | else: 56 | database.append(json_output['database']) 57 | return {"table_schemas": tables, "database": database} 58 | 59 | def senior_sql_writer_node(self, state: AgentState): 60 | current_level = state['level'] 61 | role_prompt = self.agent_config['senior_sql_writer'] 62 | instruction = f"Based on the following database/table schemas:\n{state['table_schemas'][current_level]}\n\n" 63 | if len(state['reflect']) > 0: 64 | instruction += f"Also reflect on these feedbacks:\n{'\n'.join(state['reflect'])}\n\n" 65 | instruction += f"Write a SQL script to answer the question: {state['question'][current_level]}\n\n{role_prompt['expected_output']}" 66 | human_message = HumanMessage(content=instruction) 67 | messages = [SystemMessage(content=role_prompt['system'])] 68 | for m in state['messages']: 69 | messages += m 70 | messages.append(human_message) 71 | response = self.llm.invoke(messages) 72 | sqls = state['sql'].copy() 73 | if len(sqls) > current_level: 74 | sqls[current_level] = response.content 75 | else: 76 | sqls.append(response.content) 77 | 78 | history = state['messages'].copy() 79 | if len(history) > current_level: 80 | history[current_level][1] = response 81 | else: 82 | history.append([human_message, response]) 83 | return { 84 | "sql": sqls, 85 | "revision": state['revision']+1, 86 | "messages": history 87 | } 88 | 89 | def senior_qa_engineer_node(self, state: AgentState): 90 | current_level = state['level'] 91 | role_prompt = self.agent_config['senior_qa_engineer'] 92 | messages = [ 93 | SystemMessage(content=role_prompt['system']), 94 | HumanMessage( 95 | content=f"Based on the following database/table schemas:\n{state['table_schemas'][current_level]}\n\n" 96 | f"And the sql script:\n{state['sql']}\n\n" 97 | f"Verify the sql script to check if it can complete the task: {state['question'][current_level]}\n\n" 98 | f"{role_prompt['expected_output']}") 99 | ] 100 | response = self.llm.invoke(messages) 101 | accepted = state['accepted'].copy() 102 | accepted[current_level] = 'ACCEPTED' in response.content.upper() 103 | return {"accepted": accepted} 104 | 105 | def chief_dba_node(self, state: AgentState): 106 | current_level = state['level'] 107 | role_prompt = self.agent_config['chief_dba'] 108 | messages = [ 109 | SystemMessage(content=role_prompt['system']), 110 | HumanMessage( 111 | content=f"Based on the following database/table schemas:\n{state['table_schemas'][current_level]}\n\n" 112 | f"And the imperfect sql script:\n{state['sql'][current_level]}\n\n" 113 | f"Provide the usefule and detail recommendation to help the sql writer complete the task: {state['question'][current_level]}\n\n" 114 | f"{role_prompt['expected_output']}") 115 | ] 116 | response = self.llm.invoke(messages) 117 | reflect = state['reflect'].copy() 118 | reflect.append(response.content) 119 | return {"reflect": reflect} 120 | 121 | def search_rewriter_node(self, state: AgentState): 122 | role_prompt = self.agent_config['search_rewriter'] 123 | messages = [ 124 | SystemMessage(content=role_prompt['system']), 125 | HumanMessage( 126 | content=f"Based on the following previous search queries:\n{'\n'.join(state['question'])}\n\n" 127 | f"Rewrite the following new search query so that search engine can understand its intention without previous queries:\n" 128 | f"{state['question'][-1]}\n\n" 129 | f"{role_prompt['expected_output']}") 130 | ] 131 | response = self.llm.invoke(messages) 132 | questions = state['question'].copy() 133 | questions[-1] = response.content 134 | return {"question": questions} 135 | 136 | def user_proxy_node(self, state: AgentState): 137 | accepted = state['accepted'].copy() 138 | accepted.append(False) 139 | return { 140 | 'level': state['level'] + 1, 141 | 'revision': 0, 142 | 'reflect': [], 143 | 'accepted': accepted 144 | } 145 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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-------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import warnings\n", 10 | "warnings.filterwarnings('ignore')" 11 | ] 12 | }, 13 | { 14 | "cell_type": "markdown", 15 | "metadata": {}, 16 | "source": [ 17 | "### Prepare LLM" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 2, 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "import json\n", 27 | "import os\n", 28 | "from typing import Annotated\n", 29 | "from autogen import ConversableAgent, initiate_chats\n", 30 | "from app.core.tools.semantic_search_tools import SemanticSearchTool\n", 31 | "\n", 32 | "os.environ['OPENAI_API_KEY'] = 'your-openai-api-key'\n", 33 | "llm_config = {\"model\": \"gpt-3.5-turbo\"}\n", 34 | "\n", 35 | "# Notes: you can expand n_results to make sure the search results cover final tables, e.g., n_results=50\n", 36 | "db_schema_search_tool = SemanticSearchTool(n_results=10)" 37 | ] 38 | }, 39 | { 40 | "cell_type": "markdown", 41 | "metadata": {}, 42 | "source": [ 43 | "### Prepare experiment data" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 3, 49 | "metadata": {}, 50 | "outputs": [ 51 | { 52 | "name": "stdout", 53 | "output_type": "stream", 54 | "text": [ 55 | "Loading cached Spider dataset from spider\n", 56 | "Schema file not found for spider/spider/database/twitter_1\n", 57 | "Schema file not found for spider/spider/database/company_1\n", 58 | "Schema file not found for spider/spider/database/chinook_1\n", 59 | "Schema file not found for spider/spider/database/flight_4\n", 60 | "Schema file not found for spider/spider/database/small_bank_1\n", 61 | "Schema file not found for spider/spider/database/epinions_1\n", 62 | "Schema file not found for spider/spider/database/icfp_1\n", 63 | "question: Find the famous titles of artists that do not have any volume.\n", 64 | "gold query: SELECT Famous_Title FROM artist WHERE Artist_ID NOT IN(SELECT Artist_ID FROM volume)\n", 65 | "gold result: [('Antievangelistical Process (re-release)',), ('Antithesis of All Flesh',)]\n" 66 | ] 67 | } 68 | ], 69 | "source": [ 70 | "from spider_env import SpiderEnv\n", 71 | "\n", 72 | "spider = SpiderEnv(cache_dir='spider')\n", 73 | "\n", 74 | "# select a random question in Spider\n", 75 | "observation, info = spider.reset()\n", 76 | "question = observation[\"instruction\"]\n", 77 | "print('question:', question)\n", 78 | "print('gold query:', info['gold_query'])\n", 79 | "print('gold result:', info['gold_result'])" 80 | ] 81 | }, 82 | { 83 | "cell_type": "markdown", 84 | "metadata": {}, 85 | "source": [ 86 | "### Create Agents" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": 4, 92 | "metadata": {}, 93 | "outputs": [], 94 | "source": [ 95 | "with open('app/core/agents/autogen.json') as f:\n", 96 | " agent_config = json.load(f)\n", 97 | "\n", 98 | "search_agent = ConversableAgent(\n", 99 | " **agent_config['assistant_dba'],\n", 100 | " llm_config=llm_config,\n", 101 | " human_input_mode='NEVER'\n", 102 | ")\n", 103 | "sql_writer_agent = ConversableAgent(\n", 104 | " **agent_config['senior_sql_writer'],\n", 105 | " llm_config=llm_config,\n", 106 | " human_input_mode='NEVER'\n", 107 | ")\n", 108 | "qa_agent = ConversableAgent(\n", 109 | " **agent_config['senior_qa_engineer'],\n", 110 | " llm_config=llm_config,\n", 111 | " human_input_mode='NEVER'\n", 112 | ")\n", 113 | "\n", 114 | "# user proxy\n", 115 | "user_proxy = ConversableAgent(\n", 116 | " name=\"Admin\",\n", 117 | " system_message=\"Give the question, and send instructions to SQL writer to generate a sql query script.\",\n", 118 | " code_execution_config=False,\n", 119 | " llm_config=llm_config,\n", 120 | " human_input_mode='NEVER'\n", 121 | ")" 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "metadata": {}, 127 | "source": [ 128 | "### Register Tools" 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": 5, 134 | "metadata": {}, 135 | "outputs": [], 136 | "source": [ 137 | "@search_agent.register_for_llm(description='Function for searching relevant database/table schemas')\n", 138 | "@user_proxy.register_for_execution()\n", 139 | "def semantic_search(\n", 140 | " question: Annotated[str, 'A question'] \n", 141 | ") -> Annotated[str, 'Result of relevant table schemas to the question']:\n", 142 | " return db_schema_search_tool(question)" 143 | ] 144 | }, 145 | { 146 | "cell_type": "markdown", 147 | "metadata": {}, 148 | "source": [ 149 | "### Prepare SequenceChat" 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": 6, 155 | "metadata": {}, 156 | "outputs": [], 157 | "source": [ 158 | "chats = [\n", 159 | " {\n", 160 | " \"sender\": user_proxy,\n", 161 | " \"recipient\": search_agent,\n", 162 | " \"message\": f'Find the relevant table schemas to the question: {question}',\n", 163 | " \"summary_method\": \"reflection_with_llm\",\n", 164 | " \"max_turns\": 2,\n", 165 | " \"clear_history\" : True\n", 166 | " },\n", 167 | " {\n", 168 | " \"sender\": search_agent,\n", 169 | " \"recipient\": sql_writer_agent,\n", 170 | " \"message\": f'Based on the table schemas, write a SQL query script to answer the question: {question}',\n", 171 | " \"summary_method\": \"reflection_with_llm\",\n", 172 | " \"summary_args\": {\n", 173 | " \"summary_prompt\" : \"Return the target database and SQL query script into as JSON object only, DO NOT explain reason: \"\n", 174 | " \"{'database': '', 'sql': ''}\",\n", 175 | " },\n", 176 | " \"max_turns\": 1,\n", 177 | " \"clear_history\" : False\n", 178 | " },\n", 179 | " {\n", 180 | " \"sender\": sql_writer_agent,\n", 181 | " \"recipient\": qa_agent,\n", 182 | " \"message\": f'Review the SQL query script to be sure it can answer the question: {question}',\n", 183 | " \"max_turns\": 1,\n", 184 | " \"summary_method\": \"reflection_with_llm\",\n", 185 | " \"summary_args\": {\n", 186 | " \"summary_prompt\" : \"If the SQL query script has to be adjusted. Fix the SQL query script.\"\n", 187 | " \"Return the target database and SQL query script into as JSON object only, DO NOT explain reason: \"\n", 188 | " \"{\\\"database\\\": \\\"\\\", \\\"sql\\\": \\\"\\\"}\",\n", 189 | " },\n", 190 | " },\n", 191 | "]" 192 | ] 193 | }, 194 | { 195 | "cell_type": "markdown", 196 | "metadata": {}, 197 | "source": [ 198 | "### Initiate Chat" 199 | ] 200 | }, 201 | { 202 | "cell_type": "code", 203 | "execution_count": 7, 204 | "metadata": {}, 205 | "outputs": [ 206 | { 207 | "name": "stdout", 208 | "output_type": "stream", 209 | "text": [ 210 | "\u001b[34m\n", 211 | "********************************************************************************\u001b[0m\n", 212 | "\u001b[34mStarting a new chat....\u001b[0m\n", 213 | "\u001b[34m\n", 214 | "********************************************************************************\u001b[0m\n", 215 | "\u001b[33mAdmin\u001b[0m (to Assistant_Search_Engineer):\n", 216 | "\n", 217 | "Find the relevant table schemas to the question: Find the famous titles of artists that do not have any volume.\n", 218 | "\n", 219 | "--------------------------------------------------------------------------------\n", 220 | "\u001b[33mAssistant_Search_Engineer\u001b[0m (to Admin):\n", 221 | "\n", 222 | "\u001b[32m***** Suggested tool call (call_LukfK6QZPFf4i3jHDfsJNZuo): semantic_search *****\u001b[0m\n", 223 | "Arguments: \n", 224 | "{\"question\":\"Find the famous titles of artists that do not have any volume.\"}\n", 225 | "\u001b[32m********************************************************************************\u001b[0m\n", 226 | "\n", 227 | "--------------------------------------------------------------------------------\n", 228 | "\u001b[35m\n", 229 | ">>>>>>>> EXECUTING FUNCTION semantic_search...\u001b[0m\n", 230 | "\u001b[33mAdmin\u001b[0m (to Assistant_Search_Engineer):\n", 231 | "\n", 232 | "\u001b[33mAdmin\u001b[0m (to Assistant_Search_Engineer):\n", 233 | "\n", 234 | "\u001b[32m***** Response from calling tool (call_LukfK6QZPFf4i3jHDfsJNZuo) *****\u001b[0m\n", 235 | "{\"database\": \"theme_gallery\", \"table\": \"artist\", \"schema\": \"CREATE TABLE \\\"artist\\\" (\\n\\\"Artist_ID\\\" int,\\n\\\"Name\\\" text,\\n\\\"Country\\\" text,\\n\\\"Year_Join\\\" int,\\n\\\"Age\\\" int,\\nPRIMARY KEY (\\\"Artist_ID\\\")\\n)\"}\n", 236 | "\n", 237 | "{\"database\": \"music_4\", \"table\": \"artist\", \"schema\": \"CREATE TABLE \\\"artist\\\" (\\n\\\"Artist_ID\\\" int,\\n\\\"Artist\\\" text,\\n\\\"Age\\\" int,\\n\\\"Famous_Title\\\" text,\\n\\\"Famous_Release_date\\\" text,\\nPRIMARY KEY (\\\"Artist_ID\\\")\\n)\"}\n", 238 | "\n", 239 | "{\"database\": \"chinook_1\", \"table\": \"Album\", \"schema\": \"CREATE TABLE `Album` (\\n `AlbumId` integer NOT NULL\\n, `Title` varchar(160) NOT NULL\\n, `ArtistId` integer NOT NULL\\n, PRIMARY KEY (`AlbumId`)\\n, CONSTRAINT `FK_AlbumArtistId` FOREIGN KEY (`ArtistId`) REFERENCES `Artist` (`ArtistId`) ON DELETE NO ACTION ON UPDATE NO ACTION\\n)\"}\n", 240 | "\n", 241 | "{\"database\": \"chinook_1\", \"table\": \"Track\", \"schema\": \"CREATE TABLE `Track` (\\n `TrackId` integer NOT NULL\\n, `Name` varchar(200) NOT NULL\\n, `AlbumId` integer DEFAULT NULL\\n, `MediaTypeId` integer NOT NULL\\n, `GenreId` integer DEFAULT NULL\\n, `Composer` varchar(220) DEFAULT NULL\\n, `Milliseconds` integer NOT NULL\\n, `Bytes` integer DEFAULT NULL\\n, `UnitPrice` decimal(10,2) NOT NULL\\n, PRIMARY KEY (`TrackId`)\\n, CONSTRAINT `FK_TrackAlbumId` FOREIGN KEY (`AlbumId`) REFERENCES `Album` (`AlbumId`) ON DELETE NO ACTION ON UPDATE NO ACTION\\n, CONSTRAINT `FK_TrackGenreId` FOREIGN KEY (`GenreId`) REFERENCES `Genre` (`GenreId`) ON DELETE NO ACTION ON UPDATE NO ACTION\\n, CONSTRAINT `FK_TrackMediaTypeId` FOREIGN KEY (`MediaTypeId`) REFERENCES `MediaType` (`MediaTypeId`) ON DELETE NO ACTION ON UPDATE NO ACTION\\n)\"}\n", 242 | "\n", 243 | "{\"database\": \"chinook_1\", \"table\": \"Artist\", \"schema\": \"CREATE TABLE `Artist` (\\n `ArtistId` integer NOT NULL\\n, `Name` varchar(120) DEFAULT NULL\\n, PRIMARY KEY (`ArtistId`)\\n)\"}\n", 244 | "\n", 245 | "{\"database\": \"music_1\", \"table\": \"artist\", \"schema\": \"CREATE TABLE artist(\\n\\tartist_name varchar2(50) not null,\\n\\tcountry varchar2(20),\\n\\tgender varchar2(20),\\n\\tpreferred_genre varchar2(50),\\n\\tconstraint a_name primary key(artist_name),\\n\\tforeign key(preferred_genre) references genre(g_name) ON DELETE CASCADE\\n)\"}\n", 246 | "\n", 247 | "{\"database\": \"music_4\", \"table\": \"volume\", \"schema\": \"CREATE TABLE \\\"volume\\\" (\\n\\\"Volume_ID\\\" int,\\n\\\"Volume_Issue\\\" text,\\n\\\"Issue_Date\\\" text,\\n\\\"Weeks_on_Top\\\" real,\\n\\\"Song\\\" text,\\n\\\"Artist_ID\\\" int,\\nPRIMARY KEY (\\\"Volume_ID\\\"),\\nFOREIGN KEY (`Artist_ID`) REFERENCES `artist`(`Artist_ID`)\\n)\"}\n", 248 | "\n", 249 | "{\"database\": \"store_1\", \"table\": \"albums\", \"schema\": \"CREATE TABLE albums\\n(\\nid INTEGER PRIMARY KEY AUTOINCREMENT,\\ntitle VARCHAR(160) NOT NULL,\\nartist_id INTEGER NOT NULL,\\nFOREIGN KEY (artist_id) REFERENCES artists (id)\\nON DELETE NO ACTION ON UPDATE NO ACTION\\n)\"}\n", 250 | "\n", 251 | "{\"database\": \"singer\", \"table\": \"singer\", \"schema\": \"CREATE TABLE \\\"singer\\\" (\\n\\\"Singer_ID\\\" int,\\n\\\"Name\\\" text,\\n\\\"Birth_Year\\\" real,\\n\\\"Net_Worth_Millions\\\" real,\\n\\\"Citizenship\\\" text,\\nPRIMARY KEY (\\\"Singer_ID\\\")\\n)\"}\n", 252 | "\n", 253 | "{\"database\": \"music_1\", \"table\": \"files\", \"schema\": \"CREATE TABLE files(\\n\\tf_id number(10) not null,\\n\\tartist_name varchar2(50),\\n\\tfile_size varchar2(20),\\n\\tduration varchar2(20),\\n\\tformats varchar2(20),\\n\\tprimary key(f_id),\\n\\tforeign key(artist_name) references artist(artist_name) ON DELETE CASCADE\\n)\"}\n", 254 | "\n", 255 | "\u001b[32m**********************************************************************\u001b[0m\n", 256 | "\n", 257 | "--------------------------------------------------------------------------------\n", 258 | "\u001b[33mAssistant_Search_Engineer\u001b[0m (to Admin):\n", 259 | "\n", 260 | "### Relevant Table Schemas:\n", 261 | "\n", 262 | "1. **Database: music_4**\n", 263 | " - Table: artist\n", 264 | " - Columns:\n", 265 | " - Artist_ID (int)\n", 266 | " - Artist (text)\n", 267 | " - Age (int)\n", 268 | " - Famous_Title (text)\n", 269 | " - Famous_Release_date (text)\n", 270 | "\n", 271 | "2. **Database: music_4**\n", 272 | " - Table: volume\n", 273 | " - Columns:\n", 274 | " - Volume_ID (int)\n", 275 | " - Volume_Issue (text)\n", 276 | " - Issue_Date (text)\n", 277 | " - Weeks_on_Top (real)\n", 278 | " - Song (text)\n", 279 | " - Artist_ID (int)\n", 280 | "\n", 281 | "Please let me know if you need more information or assistance with these table schemas.\n", 282 | "\n", 283 | "--------------------------------------------------------------------------------\n", 284 | "\u001b[34m\n", 285 | "********************************************************************************\u001b[0m\n", 286 | "\u001b[34mStarting a new chat....\u001b[0m\n", 287 | "\u001b[34m\n", 288 | "********************************************************************************\u001b[0m\n", 289 | "\u001b[33mAssistant_Search_Engineer\u001b[0m (to Senior_Database_SQL_query_Writer):\n", 290 | "\n", 291 | "Based on the table schemas, write a SQL query script to answer the question: Find the famous titles of artists that do not have any volume.\n", 292 | "Context: \n", 293 | "### Relevant Table Schemas:\n", 294 | "\n", 295 | "1. **Database: music_4**\n", 296 | " - Table: artist\n", 297 | " - Columns:\n", 298 | " - Artist_ID (int)\n", 299 | " - Artist (text)\n", 300 | " - Age (int)\n", 301 | " - Famous_Title (text)\n", 302 | " - Famous_Release_date (text)\n", 303 | "\n", 304 | "2. **Database: music_4**\n", 305 | " - Table: volume\n", 306 | " - Columns:\n", 307 | " - Volume_ID (int)\n", 308 | " - Volume_Issue (text)\n", 309 | " - Issue_Date (text)\n", 310 | " - Weeks_on_Top (real)\n", 311 | " - Song (text)\n", 312 | " - Artist_ID (int)\n", 313 | "\n", 314 | "--------------------------------------------------------------------------------\n", 315 | "\u001b[33mSenior_Database_SQL_query_Writer\u001b[0m (to Assistant_Search_Engineer):\n", 316 | "\n", 317 | "```sql\n", 318 | "SELECT DISTINCT a.Famous_Title\n", 319 | "FROM artist a\n", 320 | "LEFT JOIN volume v ON a.Artist_ID = v.Artist_ID\n", 321 | "WHERE v.Volume_ID IS NULL\n", 322 | "AND a.Famous_Title IS NOT NULL;\n", 323 | "```\n", 324 | "\n", 325 | "--------------------------------------------------------------------------------\n", 326 | "\u001b[34m\n", 327 | "********************************************************************************\u001b[0m\n", 328 | "\u001b[34mStarting a new chat....\u001b[0m\n", 329 | "\u001b[34m\n", 330 | "********************************************************************************\u001b[0m\n", 331 | "\u001b[33mSenior_Database_SQL_query_Writer\u001b[0m (to Senior_Database_SQL_script_Quality_Assurance_Engineer):\n", 332 | "\n", 333 | "Review the SQL query script to be sure it can answer the question: Find the famous titles of artists that do not have any volume.\n", 334 | "Context: \n", 335 | "### Relevant Table Schemas:\n", 336 | "\n", 337 | "1. **Database: music_4**\n", 338 | " - Table: artist\n", 339 | " - Columns:\n", 340 | " - Artist_ID (int)\n", 341 | " - Artist (text)\n", 342 | " - Age (int)\n", 343 | " - Famous_Title (text)\n", 344 | " - Famous_Release_date (text)\n", 345 | "\n", 346 | "2. **Database: music_4**\n", 347 | " - Table: volume\n", 348 | " - Columns:\n", 349 | " - Volume_ID (int)\n", 350 | " - Volume_Issue (text)\n", 351 | " - Issue_Date (text)\n", 352 | " - Weeks_on_Top (real)\n", 353 | " - Song (text)\n", 354 | " - Artist_ID (int)\n", 355 | "{'database': 'music_4', 'sql': 'SELECT DISTINCT a.Famous_Title FROM artist a LEFT JOIN volume v ON a.Artist_ID = v.Artist_ID WHERE v.Volume_ID IS NULL AND a.Famous_Title IS NOT NULL;'}\n", 356 | "\n", 357 | "--------------------------------------------------------------------------------\n", 358 | "\u001b[33mSenior_Database_SQL_script_Quality_Assurance_Engineer\u001b[0m (to Senior_Database_SQL_query_Writer):\n", 359 | "\n", 360 | "I have reviewed the provided SQL query to find the famous titles of artists that do not have any volume, and here are my findings:\n", 361 | "\n", 362 | "1. The SQL query looks logically correct and is structured properly.\n", 363 | "2. It correctly uses a LEFT JOIN between the `artist` and `volume` tables on the `Artist_ID` column to ensure that all artists are included in the results, even if they do not have any corresponding volumes.\n", 364 | "3. The condition `WHERE v.Volume_ID IS NULL` is appropriate for identifying artists without any volumes.\n", 365 | "4. The condition `AND a.Famous_Title IS NOT NULL` ensures that only artists with famous titles are included in the result set.\n", 366 | "\n", 367 | "However, here are some recommendations for improvement:\n", 368 | "\n", 369 | "1. The query uses the `SELECT DISTINCT` clause. While this can be useful to eliminate duplicate results, it's better to use it only when necessary. Since we are looking for distinct famous titles, it seems appropriate in this case.\n", 370 | "\n", 371 | "2. Since we are interested in famous titles that do not have any volume, it might be beneficial to add a condition to filter out artists who have famous titles but with associated volumes. To achieve this, you can add an additional condition such as `AND v.Artist_ID IS NULL` to ensure that the famous title retrieved does not correspond to any volume.\n", 372 | "\n", 373 | "3. Ensure there are no syntax errors before running the query in the database environment to avoid any execution failures.\n", 374 | "\n", 375 | "Adjusted SQL Query:\n", 376 | "```sql\n", 377 | "SELECT DISTINCT a.Famous_Title \n", 378 | "FROM artist a \n", 379 | "LEFT JOIN volume v ON a.Artist_ID = v.Artist_ID \n", 380 | "WHERE v.Volume_ID IS NULL AND a.Famous_Title IS NOT NULL AND v.Artist_ID IS NULL;\n", 381 | "```\n", 382 | "\n", 383 | "With these adjustments, the SQL query should effectively retrieve the famous titles of artists that do not have any volumes as required.\n", 384 | "\n", 385 | "--------------------------------------------------------------------------------\n" 386 | ] 387 | } 388 | ], 389 | "source": [ 390 | "chat_results = initiate_chats(chats)" 391 | ] 392 | }, 393 | { 394 | "cell_type": "code", 395 | "execution_count": 8, 396 | "metadata": {}, 397 | "outputs": [ 398 | { 399 | "name": "stdout", 400 | "output_type": "stream", 401 | "text": [ 402 | "{\"database\": \"music_4\", \"sql\": \"SELECT DISTINCT a.Famous_Title FROM artist a LEFT JOIN volume v ON a.Artist_ID = v.Artist_ID WHERE v.Volume_ID IS NULL AND a.Famous_Title IS NOT NULL AND v.Artist_ID IS NULL;\"}\n" 403 | ] 404 | } 405 | ], 406 | "source": [ 407 | "print(chat_results[-1].summary)" 408 | ] 409 | }, 410 | { 411 | "cell_type": "markdown", 412 | "metadata": {}, 413 | "source": [ 414 | "### Compare with gold result" 415 | ] 416 | }, 417 | { 418 | "cell_type": "code", 419 | "execution_count": 9, 420 | "metadata": {}, 421 | "outputs": [ 422 | { 423 | "name": "stdout", 424 | "output_type": "stream", 425 | "text": [ 426 | "[('Antievangelistical Process (re-release)',), ('Antithesis of All Flesh',)]\n" 427 | ] 428 | } 429 | ], 430 | "source": [ 431 | "import sqlite3\n", 432 | "\n", 433 | "summary = json.loads(chat_results[-1].summary)\n", 434 | "con = sqlite3.connect(f\"spider/spider/database/{summary['database']}/{summary['database']}.sqlite\")\n", 435 | "cursor = con.cursor()\n", 436 | "cursor.execute(summary['sql'])\n", 437 | "results = cursor.fetchall()\n", 438 | "print(results)" 439 | ] 440 | }, 441 | { 442 | "cell_type": "code", 443 | "execution_count": 10, 444 | "metadata": {}, 445 | "outputs": [ 446 | { 447 | "name": "stdout", 448 | "output_type": "stream", 449 | "text": [ 450 | "[('Antievangelistical Process (re-release)',), ('Antithesis of All Flesh',)]\n" 451 | ] 452 | } 453 | ], 454 | "source": [ 455 | "print(info['gold_result'])" 456 | ] 457 | } 458 | ], 459 | "metadata": { 460 | "kernelspec": { 461 | "display_name": "Python 3 (ipykernel)", 462 | "language": "python", 463 | "name": "python3" 464 | }, 465 | "language_info": { 466 | "codemirror_mode": { 467 | "name": "ipython", 468 | "version": 3 469 | }, 470 | "file_extension": ".py", 471 | "mimetype": "text/x-python", 472 | "name": "python", 473 | "nbconvert_exporter": "python", 474 | "pygments_lexer": "ipython3", 475 | "version": "3.12.2" 476 | } 477 | }, 478 | "nbformat": 4, 479 | "nbformat_minor": 4 480 | } 481 | -------------------------------------------------------------------------------- /text2sql_crewai.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import warnings\n", 10 | "warnings.filterwarnings('ignore')" 11 | ] 12 | }, 13 | { 14 | "cell_type": "markdown", 15 | "metadata": {}, 16 | "source": [ 17 | "### Prepare LLM" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 2, 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "import json\n", 27 | "import os\n", 28 | "from crewai import Agent, Task, Crew\n", 29 | "\n", 30 | "os.environ['OPENAI_API_KEY'] = 'your-openai-api-key'\n", 31 | "os.environ[\"OPENAI_MODEL_NAME\"] = 'gpt-4o-mini'" 32 | ] 33 | }, 34 | { 35 | "cell_type": "markdown", 36 | "metadata": {}, 37 | "source": [ 38 | "### Prepare Tools" 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": 3, 44 | "metadata": {}, 45 | "outputs": [], 46 | "source": [ 47 | "from crewai_tools import BaseTool\n", 48 | "from app.core.tools.semantic_search_tools import SemanticSearchTool\n", 49 | "\n", 50 | "class SemanticSearchToolForCrewAI(BaseTool):\n", 51 | " name: str =\"Search database and table schemas tool\"\n", 52 | " description: str = (\"Useful to search database and table schemas from vector DB, \"\n", 53 | " \"about a given question and return relevant database andtable schemas\")\n", 54 | " semantic_search_tool: SemanticSearchTool = None\n", 55 | "\n", 56 | " def __init__(self, n_results=10, **kwargs):\n", 57 | " super().__init__(**kwargs)\n", 58 | " self.semantic_search_tool = SemanticSearchTool(n_results, **kwargs)\n", 59 | "\n", 60 | " def _run(self, question: str) -> str:\n", 61 | " return self.semantic_search_tool(question)\n", 62 | "\n", 63 | "# Notes: you can expand n_results to make sure the search results cover final tables, e.g., n_results=50\n", 64 | "db_schema_search_tool = SemanticSearchToolForCrewAI(n_results=10)" 65 | ] 66 | }, 67 | { 68 | "cell_type": "markdown", 69 | "metadata": {}, 70 | "source": [ 71 | "### Prepare experiment data" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": 4, 77 | "metadata": {}, 78 | "outputs": [ 79 | { 80 | "name": "stdout", 81 | "output_type": "stream", 82 | "text": [ 83 | "Loading cached Spider dataset from spider\n", 84 | "Schema file not found for spider/spider/database/epinions_1\n", 85 | "Schema file not found for spider/spider/database/small_bank_1\n", 86 | "Schema file not found for spider/spider/database/twitter_1\n", 87 | "Schema file not found for spider/spider/database/icfp_1\n", 88 | "Schema file not found for spider/spider/database/flight_4\n", 89 | "Schema file not found for spider/spider/database/company_1\n", 90 | "Schema file not found for spider/spider/database/chinook_1\n", 91 | "question: Find the famous titles of artists that do not have any volume.\n", 92 | "gold query: SELECT Famous_Title FROM artist WHERE Artist_ID NOT IN(SELECT Artist_ID FROM volume)\n", 93 | "gold result: [('Antievangelistical Process (re-release)',), ('Antithesis of All Flesh',)]\n" 94 | ] 95 | } 96 | ], 97 | "source": [ 98 | "from spider_env import SpiderEnv\n", 99 | "\n", 100 | "spider = SpiderEnv(cache_dir='spider')\n", 101 | "\n", 102 | "# select a random question in Spider\n", 103 | "observation, info = spider.reset()\n", 104 | "question = observation[\"instruction\"]\n", 105 | "print('question:', question)\n", 106 | "print('gold query:', info['gold_query'])\n", 107 | "print('gold result:', info['gold_result'])" 108 | ] 109 | }, 110 | { 111 | "cell_type": "markdown", 112 | "metadata": {}, 113 | "source": [ 114 | "### Create Agents" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": 5, 120 | "metadata": {}, 121 | "outputs": [], 122 | "source": [ 123 | "with open('app/core/agents/crewai.json') as f:\n", 124 | " agent_config = json.load(f)\n", 125 | "\n", 126 | "search_agent = Agent(**agent_config['assistant_dba'], tools=[db_schema_search_tool])\n", 127 | "sql_writer_agent = Agent(**agent_config['senior_sql_writer'])\n", 128 | "qa_agent = Agent(**agent_config['senior_qa_engineer'])" 129 | ] 130 | }, 131 | { 132 | "cell_type": "markdown", 133 | "metadata": {}, 134 | "source": [ 135 | "### Create Tasks" 136 | ] 137 | }, 138 | { 139 | "cell_type": "code", 140 | "execution_count": 6, 141 | "metadata": {}, 142 | "outputs": [], 143 | "source": [ 144 | "search_task = Task(\n", 145 | " description='Using search tools to find related database/table candidates, and consolidate all relevant tables that belong the same database for information needed to answer the following question:\\n{question}',\n", 146 | " expected_output=\"Return relevant database and tables' schema.\",\n", 147 | " agent=search_agent\n", 148 | ")\n", 149 | "sql_writer_task = Task(\n", 150 | " description='Based on the database/table schemas, generate a SQL query to answer the following question:\\n{question}',\n", 151 | " expected_output=\"MUST BE ONLY a valid database sql script with required fields, don't add unnecessary fields.\",\n", 152 | " agent=sql_writer_agent\n", 153 | ")\n", 154 | "qa_task = Task(\n", 155 | " description=\"Check the given sql query carefully, and refine the sql query if necessary to make it fulfill the needs of the following question:\\n{question}\",\n", 156 | " expected_output=\"The final sql query. MUST BE ONLY a valid sql script with required fields, don't add unnecessary fields, and must be consistent with table schemas.\",\n", 157 | " agent=qa_agent,\n", 158 | " context=[search_task, sql_writer_task]\n", 159 | ")" 160 | ] 161 | }, 162 | { 163 | "cell_type": "markdown", 164 | "metadata": {}, 165 | "source": [ 166 | "### set-up a Crew" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": 7, 172 | "metadata": {}, 173 | "outputs": [], 174 | "source": [ 175 | "text2sql_crew = Crew(\n", 176 | " agents=[\n", 177 | " search_agent,\n", 178 | " sql_writer_agent,\n", 179 | " qa_agent,\n", 180 | " ],\n", 181 | " tasks=[\n", 182 | " search_task,\n", 183 | " sql_writer_task,\n", 184 | " qa_task,\n", 185 | " ],\n", 186 | " verbose=True\n", 187 | ")" 188 | ] 189 | }, 190 | { 191 | "cell_type": "markdown", 192 | "metadata": {}, 193 | "source": [ 194 | "### Prepare inputs (question)" 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": 8, 200 | "metadata": {}, 201 | "outputs": [], 202 | "source": [ 203 | "text2sql_inputs = {\n", 204 | " 'question': question\n", 205 | "}" 206 | ] 207 | }, 208 | { 209 | "cell_type": "markdown", 210 | "metadata": {}, 211 | "source": [ 212 | "### Kickoff crew actions" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": 9, 218 | "metadata": {}, 219 | "outputs": [ 220 | { 221 | "name": "stdout", 222 | "output_type": "stream", 223 | "text": [ 224 | "\u001b[1m\u001b[95m [2024-07-20 07:28:10][DEBUG]: == Working Agent: Assistant Search Engineer\u001b[00m\n", 225 | "\u001b[1m\u001b[95m [2024-07-20 07:28:10][INFO]: == Starting Task: Using search tools to find related database/table candidates, and consolidate all relevant tables that belong the same database for information needed to answer the following question:\n", 226 | "Find the famous titles of artists that do not have any volume.\u001b[00m\n", 227 | "\n", 228 | "\n", 229 | "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n", 230 | "\u001b[32;1m\u001b[1;3mI need to find relevant databases and tables that can help answer the question about famous titles of artists that do not have any volume. I'll start by searching for schemas related to artists, titles, and volumes.\n", 231 | "\n", 232 | "Action: Search database and table schemas tool \n", 233 | "Action Input: {\"question\": \"famous titles of artists that do not have any volume\"} \u001b[0m\u001b[95m \n", 234 | "\n", 235 | "{\"database\": \"theme_gallery\", \"table\": \"artist\", \"schema\": \"CREATE TABLE \\\"artist\\\" (\\n\\\"Artist_ID\\\" int,\\n\\\"Name\\\" text,\\n\\\"Country\\\" text,\\n\\\"Year_Join\\\" int,\\n\\\"Age\\\" int,\\nPRIMARY KEY (\\\"Artist_ID\\\")\\n)\"}\n", 236 | "\n", 237 | "{\"database\": \"music_4\", \"table\": \"artist\", \"schema\": \"CREATE TABLE \\\"artist\\\" (\\n\\\"Artist_ID\\\" int,\\n\\\"Artist\\\" text,\\n\\\"Age\\\" int,\\n\\\"Famous_Title\\\" text,\\n\\\"Famous_Release_date\\\" text,\\nPRIMARY KEY (\\\"Artist_ID\\\")\\n)\"}\n", 238 | "\n", 239 | "{\"database\": \"music_4\", \"table\": \"volume\", \"schema\": \"CREATE TABLE \\\"volume\\\" (\\n\\\"Volume_ID\\\" int,\\n\\\"Volume_Issue\\\" text,\\n\\\"Issue_Date\\\" text,\\n\\\"Weeks_on_Top\\\" real,\\n\\\"Song\\\" text,\\n\\\"Artist_ID\\\" int,\\nPRIMARY KEY (\\\"Volume_ID\\\"),\\nFOREIGN KEY (`Artist_ID`) REFERENCES `artist`(`Artist_ID`)\\n)\"}\n", 240 | "\n", 241 | "{\"database\": \"chinook_1\", \"table\": \"Album\", \"schema\": \"CREATE TABLE `Album` (\\n `AlbumId` integer NOT NULL\\n, `Title` varchar(160) NOT NULL\\n, `ArtistId` integer NOT NULL\\n, PRIMARY KEY (`AlbumId`)\\n, CONSTRAINT `FK_AlbumArtistId` FOREIGN KEY (`ArtistId`) REFERENCES `Artist` (`ArtistId`) ON DELETE NO ACTION ON UPDATE NO ACTION\\n)\"}\n", 242 | "\n", 243 | "{\"database\": \"chinook_1\", \"table\": \"Artist\", \"schema\": \"CREATE TABLE `Artist` (\\n `ArtistId` integer NOT NULL\\n, `Name` varchar(120) DEFAULT NULL\\n, PRIMARY KEY (`ArtistId`)\\n)\"}\n", 244 | "\n", 245 | "{\"database\": \"music_1\", \"table\": \"artist\", \"schema\": \"CREATE TABLE artist(\\n\\tartist_name varchar2(50) not null,\\n\\tcountry varchar2(20),\\n\\tgender varchar2(20),\\n\\tpreferred_genre varchar2(50),\\n\\tconstraint a_name primary key(artist_name),\\n\\tforeign key(preferred_genre) references genre(g_name) ON DELETE CASCADE\\n)\"}\n", 246 | "\n", 247 | "{\"database\": \"store_1\", \"table\": \"albums\", \"schema\": \"CREATE TABLE albums\\n(\\nid INTEGER PRIMARY KEY AUTOINCREMENT,\\ntitle VARCHAR(160) NOT NULL,\\nartist_id INTEGER NOT NULL,\\nFOREIGN KEY (artist_id) REFERENCES artists (id)\\nON DELETE NO ACTION ON UPDATE NO ACTION\\n)\"}\n", 248 | "\n", 249 | "{\"database\": \"store_1\", \"table\": \"artists\", \"schema\": \"CREATE TABLE artists\\n(\\nid INTEGER PRIMARY KEY AUTOINCREMENT,\\nname VARCHAR(120)\\n)\"}\n", 250 | "\n", 251 | "{\"database\": \"singer\", \"table\": \"singer\", \"schema\": \"CREATE TABLE \\\"singer\\\" (\\n\\\"Singer_ID\\\" int,\\n\\\"Name\\\" text,\\n\\\"Birth_Year\\\" real,\\n\\\"Net_Worth_Millions\\\" real,\\n\\\"Citizenship\\\" text,\\nPRIMARY KEY (\\\"Singer_ID\\\")\\n)\"}\n", 252 | "\n", 253 | "{\"database\": \"chinook_1\", \"table\": \"Track\", \"schema\": \"CREATE TABLE `Track` (\\n `TrackId` integer NOT NULL\\n, `Name` varchar(200) NOT NULL\\n, `AlbumId` integer DEFAULT NULL\\n, `MediaTypeId` integer NOT NULL\\n, `GenreId` integer DEFAULT NULL\\n, `Composer` varchar(220) DEFAULT NULL\\n, `Milliseconds` integer NOT NULL\\n, `Bytes` integer DEFAULT NULL\\n, `UnitPrice` decimal(10,2) NOT NULL\\n, PRIMARY KEY (`TrackId`)\\n, CONSTRAINT `FK_TrackAlbumId` FOREIGN KEY (`AlbumId`) REFERENCES `Album` (`AlbumId`) ON DELETE NO ACTION ON UPDATE NO ACTION\\n, CONSTRAINT `FK_TrackGenreId` FOREIGN KEY (`GenreId`) REFERENCES `Genre` (`GenreId`) ON DELETE NO ACTION ON UPDATE NO ACTION\\n, CONSTRAINT `FK_TrackMediaTypeId` FOREIGN KEY (`MediaTypeId`) REFERENCES `MediaType` (`MediaTypeId`) ON DELETE NO ACTION ON UPDATE NO ACTION\\n)\"}\n", 254 | "\n", 255 | "\u001b[00m\n", 256 | "\u001b[32;1m\u001b[1;3mThought: I have found several relevant databases and tables that might contain the information about famous titles of artists and volumes. I need to consolidate the relevant tables from the same databases to provide a comprehensive answer.\n", 257 | "\n", 258 | "Relevant databases and their tables:\n", 259 | "1. **music_4**\n", 260 | " - Table: artist\n", 261 | " ```\n", 262 | " CREATE TABLE \"artist\" (\n", 263 | " \"Artist_ID\" int,\n", 264 | " \"Artist\" text,\n", 265 | " \"Age\" int,\n", 266 | " \"Famous_Title\" text,\n", 267 | " \"Famous_Release_date\" text,\n", 268 | " PRIMARY KEY (\"Artist_ID\")\n", 269 | " )\n", 270 | " ```\n", 271 | " - Table: volume\n", 272 | " ```\n", 273 | " CREATE TABLE \"volume\" (\n", 274 | " \"Volume_ID\" int,\n", 275 | " \"Volume_Issue\" text,\n", 276 | " \"Issue_Date\" text,\n", 277 | " \"Weeks_on_Top\" real,\n", 278 | " \"Song\" text,\n", 279 | " \"Artist_ID\" int,\n", 280 | " PRIMARY KEY (\"Volume_ID\"),\n", 281 | " FOREIGN KEY (`Artist_ID`) REFERENCES `artist`(`Artist_ID`)\n", 282 | " )\n", 283 | " ```\n", 284 | "\n", 285 | "2. **chinook_1**\n", 286 | " - Table: Album\n", 287 | " ```\n", 288 | " CREATE TABLE `Album` (\n", 289 | " `AlbumId` integer NOT NULL,\n", 290 | " `Title` varchar(160) NOT NULL,\n", 291 | " `ArtistId` integer NOT NULL,\n", 292 | " PRIMARY KEY (`AlbumId`),\n", 293 | " CONSTRAINT `FK_AlbumArtistId` FOREIGN KEY (`ArtistId`) REFERENCES `Artist` (`ArtistId`) ON DELETE NO ACTION ON UPDATE NO ACTION\n", 294 | " )\n", 295 | " ```\n", 296 | " - Table: Artist\n", 297 | " ```\n", 298 | " CREATE TABLE `Artist` (\n", 299 | " `ArtistId` integer NOT NULL,\n", 300 | " `Name` varchar(120) DEFAULT NULL,\n", 301 | " PRIMARY KEY (`ArtistId`)\n", 302 | " )\n", 303 | " ```\n", 304 | "\n", 305 | "3. **store_1**\n", 306 | " - Table: albums\n", 307 | " ```\n", 308 | " CREATE TABLE albums\n", 309 | " (\n", 310 | " id INTEGER PRIMARY KEY AUTOINCREMENT,\n", 311 | " title VARCHAR(160) NOT NULL,\n", 312 | " artist_id INTEGER NOT NULL,\n", 313 | " FOREIGN KEY (artist_id) REFERENCES artists (id)\n", 314 | " ON DELETE NO ACTION ON UPDATE NO ACTION\n", 315 | " )\n", 316 | " ```\n", 317 | " - Table: artists\n", 318 | " ```\n", 319 | " CREATE TABLE artists\n", 320 | " (\n", 321 | " id INTEGER PRIMARY KEY AUTOINCREMENT,\n", 322 | " name VARCHAR(120)\n", 323 | " )\n", 324 | " ```\n", 325 | "\n", 326 | "I can see how these tables can provide insight into the famous titles of artists and their relationship with volumes.\n", 327 | "\n", 328 | "Final Answer:\n", 329 | "1. **Database: music_4**\n", 330 | " - **Table: artist**\n", 331 | " ```\n", 332 | " CREATE TABLE \"artist\" (\n", 333 | " \"Artist_ID\" int,\n", 334 | " \"Artist\" text,\n", 335 | " \"Age\" int,\n", 336 | " \"Famous_Title\" text,\n", 337 | " \"Famous_Release_date\" text,\n", 338 | " PRIMARY KEY (\"Artist_ID\")\n", 339 | " )\n", 340 | " ```\n", 341 | " - **Table: volume**\n", 342 | " ```\n", 343 | " CREATE TABLE \"volume\" (\n", 344 | " \"Volume_ID\" int,\n", 345 | " \"Volume_Issue\" text,\n", 346 | " \"Issue_Date\" text,\n", 347 | " \"Weeks_on_Top\" real,\n", 348 | " \"Song\" text,\n", 349 | " \"Artist_ID\" int,\n", 350 | " PRIMARY KEY (\"Volume_ID\"),\n", 351 | " FOREIGN KEY (`Artist_ID`) REFERENCES `artist`(`Artist_ID`)\n", 352 | " )\n", 353 | " ```\n", 354 | "\n", 355 | "2. **Database: chinook_1**\n", 356 | " - **Table: Album**\n", 357 | " ```\n", 358 | " CREATE TABLE `Album` (\n", 359 | " `AlbumId` integer NOT NULL,\n", 360 | " `Title` varchar(160) NOT NULL,\n", 361 | " `ArtistId` integer NOT NULL,\n", 362 | " PRIMARY KEY (`AlbumId`),\n", 363 | " CONSTRAINT `FK_AlbumArtistId` FOREIGN KEY (`ArtistId`) REFERENCES `Artist` (`ArtistId`) ON DELETE NO ACTION ON UPDATE NO ACTION\n", 364 | " )\n", 365 | " ```\n", 366 | " - **Table: Artist**\n", 367 | " ```\n", 368 | " CREATE TABLE `Artist` (\n", 369 | " `ArtistId` integer NOT NULL,\n", 370 | " `Name` varchar(120) DEFAULT NULL,\n", 371 | " PRIMARY KEY (`ArtistId`)\n", 372 | " )\n", 373 | " ```\n", 374 | "\n", 375 | "3. **Database: store_1**\n", 376 | " - **Table: albums**\n", 377 | " ```\n", 378 | " CREATE TABLE albums\n", 379 | " (\n", 380 | " id INTEGER PRIMARY KEY AUTOINCREMENT,\n", 381 | " title VARCHAR(160) NOT NULL,\n", 382 | " artist_id INTEGER NOT NULL,\n", 383 | " FOREIGN KEY (artist_id) REFERENCES artists (id)\n", 384 | " ON DELETE NO ACTION ON UPDATE NO ACTION\n", 385 | " )\n", 386 | " ```\n", 387 | " - **Table: artists**\n", 388 | " ```\n", 389 | " CREATE TABLE artists\n", 390 | " (\n", 391 | " id INTEGER PRIMARY KEY AUTOINCREMENT,\n", 392 | " name VARCHAR(120)\n", 393 | " )\n", 394 | " ```\u001b[0m\n", 395 | "\n", 396 | "\u001b[1m> Finished chain.\u001b[0m\n", 397 | "\u001b[1m\u001b[92m [2024-07-20 07:28:30][DEBUG]: == [Assistant Search Engineer] Task output: 1. **Database: music_4**\n", 398 | " - **Table: artist**\n", 399 | " ```\n", 400 | " CREATE TABLE \"artist\" (\n", 401 | " \"Artist_ID\" int,\n", 402 | " \"Artist\" text,\n", 403 | " \"Age\" int,\n", 404 | " \"Famous_Title\" text,\n", 405 | " \"Famous_Release_date\" text,\n", 406 | " PRIMARY KEY (\"Artist_ID\")\n", 407 | " )\n", 408 | " ```\n", 409 | " - **Table: volume**\n", 410 | " ```\n", 411 | " CREATE TABLE \"volume\" (\n", 412 | " \"Volume_ID\" int,\n", 413 | " \"Volume_Issue\" text,\n", 414 | " \"Issue_Date\" text,\n", 415 | " \"Weeks_on_Top\" real,\n", 416 | " \"Song\" text,\n", 417 | " \"Artist_ID\" int,\n", 418 | " PRIMARY KEY (\"Volume_ID\"),\n", 419 | " FOREIGN KEY (`Artist_ID`) REFERENCES `artist`(`Artist_ID`)\n", 420 | " )\n", 421 | " ```\n", 422 | "\n", 423 | "2. **Database: chinook_1**\n", 424 | " - **Table: Album**\n", 425 | " ```\n", 426 | " CREATE TABLE `Album` (\n", 427 | " `AlbumId` integer NOT NULL,\n", 428 | " `Title` varchar(160) NOT NULL,\n", 429 | " `ArtistId` integer NOT NULL,\n", 430 | " PRIMARY KEY (`AlbumId`),\n", 431 | " CONSTRAINT `FK_AlbumArtistId` FOREIGN KEY (`ArtistId`) REFERENCES `Artist` (`ArtistId`) ON DELETE NO ACTION ON UPDATE NO ACTION\n", 432 | " )\n", 433 | " ```\n", 434 | " - **Table: Artist**\n", 435 | " ```\n", 436 | " CREATE TABLE `Artist` (\n", 437 | " `ArtistId` integer NOT NULL,\n", 438 | " `Name` varchar(120) DEFAULT NULL,\n", 439 | " PRIMARY KEY (`ArtistId`)\n", 440 | " )\n", 441 | " ```\n", 442 | "\n", 443 | "3. **Database: store_1**\n", 444 | " - **Table: albums**\n", 445 | " ```\n", 446 | " CREATE TABLE albums\n", 447 | " (\n", 448 | " id INTEGER PRIMARY KEY AUTOINCREMENT,\n", 449 | " title VARCHAR(160) NOT NULL,\n", 450 | " artist_id INTEGER NOT NULL,\n", 451 | " FOREIGN KEY (artist_id) REFERENCES artists (id)\n", 452 | " ON DELETE NO ACTION ON UPDATE NO ACTION\n", 453 | " )\n", 454 | " ```\n", 455 | " - **Table: artists**\n", 456 | " ```\n", 457 | " CREATE TABLE artists\n", 458 | " (\n", 459 | " id INTEGER PRIMARY KEY AUTOINCREMENT,\n", 460 | " name VARCHAR(120)\n", 461 | " )\n", 462 | " ```\n", 463 | "\n", 464 | "\u001b[00m\n", 465 | "\u001b[1m\u001b[95m [2024-07-20 07:28:30][DEBUG]: == Working Agent: Senior Database SQL query Writer\u001b[00m\n", 466 | "\u001b[1m\u001b[95m [2024-07-20 07:28:30][INFO]: == Starting Task: Based on the database/table schemas, generate a SQL query to answer the following question:\n", 467 | "Find the famous titles of artists that do not have any volume.\u001b[00m\n", 468 | "\n", 469 | "\n", 470 | "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n", 471 | "\u001b[32;1m\u001b[1;3mI now can give a great answer \n", 472 | "Final Answer: \n", 473 | "```sql\n", 474 | "SELECT a.Famous_Title \n", 475 | "FROM artist a \n", 476 | "LEFT JOIN volume v ON a.Artist_ID = v.Artist_ID \n", 477 | "WHERE v.Artist_ID IS NULL;\n", 478 | "```\u001b[0m\n", 479 | "\n", 480 | "\u001b[1m> Finished chain.\u001b[0m\n", 481 | "\u001b[1m\u001b[92m [2024-07-20 07:28:32][DEBUG]: == [Senior Database SQL query Writer] Task output: ```sql\n", 482 | "SELECT a.Famous_Title \n", 483 | "FROM artist a \n", 484 | "LEFT JOIN volume v ON a.Artist_ID = v.Artist_ID \n", 485 | "WHERE v.Artist_ID IS NULL;\n", 486 | "```\n", 487 | "\n", 488 | "\u001b[00m\n", 489 | "\u001b[1m\u001b[95m [2024-07-20 07:28:32][DEBUG]: == Working Agent: Senior Database SQL script Quality Assurance Engineer\u001b[00m\n", 490 | "\u001b[1m\u001b[95m [2024-07-20 07:28:32][INFO]: == Starting Task: Check the given sql query carefully, and refine the sql query if necessary to make it fulfill the needs of the following question:\n", 491 | "Find the famous titles of artists that do not have any volume.\u001b[00m\n", 492 | "\n", 493 | "\n", 494 | "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n", 495 | "\u001b[32;1m\u001b[1;3mI need to check the provided SQL query against the requirements and the database schema to ensure it correctly finds the famous titles of artists that do not have any volume. I will analyze the SQL query for potential errors, inconsistencies, and whether it meets the needs of the question.\n", 496 | "\n", 497 | "The provided SQL query is:\n", 498 | "```sql\n", 499 | "SELECT a.Famous_Title \n", 500 | "FROM artist a \n", 501 | "LEFT JOIN volume v ON a.Artist_ID = v.Artist_ID \n", 502 | "WHERE v.Artist_ID IS NULL;\n", 503 | "```\n", 504 | "\n", 505 | "1. **Schema Review**:\n", 506 | " - The `artist` table has `Artist_ID`, `Artist`, `Age`, `Famous_Title`, and `Famous_Release_date`.\n", 507 | " - The `volume` table has `Volume_ID`, `Volume_Issue`, `Issue_Date`, `Weeks_on_Top`, `Song`, and `Artist_ID`.\n", 508 | "\n", 509 | "2. **Logic Check**:\n", 510 | " - The query uses a `LEFT JOIN`, which is appropriate for finding records in `artist` that do not have corresponding records in `volume`.\n", 511 | " - The condition `WHERE v.Artist_ID IS NULL` correctly filters out artists who do not have any volume.\n", 512 | "\n", 513 | "3. **Field Relevance**:\n", 514 | " - The query selects only `Famous_Title`, which is relevant to the question.\n", 515 | "\n", 516 | "4. **Final Check**:\n", 517 | " - The SQL syntax appears to be correct.\n", 518 | " - The logic of finding artists without any volume is implemented properly.\n", 519 | "\n", 520 | "Since the query seems to fulfill the requirements without any syntax or logical errors, I will finalize it.\n", 521 | "\n", 522 | "Thought: I now know the final answer\n", 523 | "Final Answer: \n", 524 | "```sql\n", 525 | "SELECT a.Famous_Title \n", 526 | "FROM artist a \n", 527 | "LEFT JOIN volume v ON a.Artist_ID = v.Artist_ID \n", 528 | "WHERE v.Artist_ID IS NULL;\n", 529 | "```\u001b[0m\n", 530 | "\n", 531 | "\u001b[1m> Finished chain.\u001b[0m\n", 532 | "\u001b[1m\u001b[92m [2024-07-20 07:28:36][DEBUG]: == [Senior Database SQL script Quality Assurance Engineer] Task output: ```sql\n", 533 | "SELECT a.Famous_Title \n", 534 | "FROM artist a \n", 535 | "LEFT JOIN volume v ON a.Artist_ID = v.Artist_ID \n", 536 | "WHERE v.Artist_ID IS NULL;\n", 537 | "```\n", 538 | "\n", 539 | "\u001b[00m\n" 540 | ] 541 | } 542 | ], 543 | "source": [ 544 | "result = text2sql_crew.kickoff(inputs=text2sql_inputs)" 545 | ] 546 | }, 547 | { 548 | "cell_type": "code", 549 | "execution_count": 10, 550 | "metadata": {}, 551 | "outputs": [ 552 | { 553 | "name": "stdout", 554 | "output_type": "stream", 555 | "text": [ 556 | "```sql\n", 557 | "SELECT a.Famous_Title \n", 558 | "FROM artist a \n", 559 | "LEFT JOIN volume v ON a.Artist_ID = v.Artist_ID \n", 560 | "WHERE v.Artist_ID IS NULL;\n", 561 | "```\n" 562 | ] 563 | } 564 | ], 565 | "source": [ 566 | "print(result)" 567 | ] 568 | }, 569 | { 570 | "cell_type": "markdown", 571 | "metadata": {}, 572 | "source": [ 573 | "### Compare with gold result" 574 | ] 575 | }, 576 | { 577 | "cell_type": "code", 578 | "execution_count": 11, 579 | "metadata": {}, 580 | "outputs": [ 581 | { 582 | "name": "stdout", 583 | "output_type": "stream", 584 | "text": [ 585 | "[('Antievangelistical Process (re-release)',), ('Antithesis of All Flesh',)]\n" 586 | ] 587 | } 588 | ], 589 | "source": [ 590 | "import sqlite3\n", 591 | "\n", 592 | "con = sqlite3.connect('spider/spider/database/music_4/music_4.sqlite')\n", 593 | "cursor = con.cursor()\n", 594 | "cursor.execute(\"\"\"SELECT a.Famous_Title \n", 595 | "FROM artist a \n", 596 | "LEFT JOIN volume v ON a.Artist_ID = v.Artist_ID \n", 597 | "WHERE v.Artist_ID IS NULL;\"\"\")\n", 598 | "results = cursor.fetchall()\n", 599 | "print(results)" 600 | ] 601 | }, 602 | { 603 | "cell_type": "code", 604 | "execution_count": 12, 605 | "metadata": {}, 606 | "outputs": [ 607 | { 608 | "name": "stdout", 609 | "output_type": "stream", 610 | "text": [ 611 | "[('Antievangelistical Process (re-release)',), ('Antithesis of All Flesh',)]\n" 612 | ] 613 | } 614 | ], 615 | "source": [ 616 | "print(info['gold_result'])" 617 | ] 618 | } 619 | ], 620 | "metadata": { 621 | "kernelspec": { 622 | "display_name": "Python 3 (ipykernel)", 623 | "language": "python", 624 | "name": "python3" 625 | }, 626 | "language_info": { 627 | "codemirror_mode": { 628 | "name": "ipython", 629 | "version": 3 630 | }, 631 | "file_extension": ".py", 632 | "mimetype": "text/x-python", 633 | "name": "python", 634 | "nbconvert_exporter": "python", 635 | "pygments_lexer": "ipython3", 636 | "version": "3.12.2" 637 | } 638 | }, 639 | "nbformat": 4, 640 | "nbformat_minor": 4 641 | } 642 | -------------------------------------------------------------------------------- /text2sql_langgraph.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import warnings\n", 10 | "warnings.filterwarnings('ignore')" 11 | ] 12 | }, 13 | { 14 | "cell_type": "markdown", 15 | "metadata": {}, 16 | "source": [ 17 | "### Prepare Experiment Data" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 2, 23 | "metadata": {}, 24 | "outputs": [ 25 | { 26 | "name": "stdout", 27 | "output_type": "stream", 28 | "text": [ 29 | "Loading cached Spider dataset from spider\n", 30 | "Schema file not found for spider/spider/database/epinions_1\n", 31 | "Schema file not found for spider/spider/database/flight_4\n", 32 | "Schema file not found for spider/spider/database/chinook_1\n", 33 | "Schema file not found for spider/spider/database/small_bank_1\n", 34 | "Schema file not found for spider/spider/database/twitter_1\n", 35 | "Schema file not found for spider/spider/database/company_1\n", 36 | "Schema file not found for spider/spider/database/icfp_1\n", 37 | "question: Find the famous titles of artists that do not have any volume.\n", 38 | "gold query: SELECT Famous_Title FROM artist WHERE Artist_ID NOT IN(SELECT Artist_ID FROM volume)\n", 39 | "gold result: [('Antievangelistical Process (re-release)',), ('Antithesis of All Flesh',)]\n" 40 | ] 41 | } 42 | ], 43 | "source": [ 44 | "from spider_env import SpiderEnv\n", 45 | "\n", 46 | "spider = SpiderEnv(cache_dir='spider')\n", 47 | "\n", 48 | "# select a random question in Spider\n", 49 | "observation, info = spider.reset()\n", 50 | "question = observation[\"instruction\"]\n", 51 | "print('question:', question)\n", 52 | "print('gold query:', info['gold_query'])\n", 53 | "print('gold result:', info['gold_result'])" 54 | ] 55 | }, 56 | { 57 | "cell_type": "markdown", 58 | "metadata": {}, 59 | "source": [ 60 | "### Prepare LLM" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": 3, 66 | "metadata": {}, 67 | "outputs": [], 68 | "source": [ 69 | "import json\n", 70 | "import os\n", 71 | "from langchain_openai import ChatOpenAI\n", 72 | "from langgraph.graph import StateGraph, END\n", 73 | "from langchain_core.messages import SystemMessage, HumanMessage\n", 74 | "from app.core.tools.semantic_search_tools import SemanticSearchTool\n", 75 | "\n", 76 | "os.environ['OPENAI_API_KEY'] = 'your-openai-api-key'\n", 77 | "model = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n", 78 | "\n", 79 | "# Notes: you can expand n_results to make sure the search results cover final tables, e.g., n_results=50\n", 80 | "db_schema_search_tool = SemanticSearchTool(n_results=10)" 81 | ] 82 | }, 83 | { 84 | "cell_type": "markdown", 85 | "metadata": {}, 86 | "source": [ 87 | "### Prepare memory cache" 88 | ] 89 | }, 90 | { 91 | "cell_type": "code", 92 | "execution_count": 4, 93 | "metadata": {}, 94 | "outputs": [], 95 | "source": [ 96 | "from langgraph.checkpoint.sqlite import SqliteSaver\n", 97 | "\n", 98 | "memory = SqliteSaver.from_conn_string(\":memory:\")" 99 | ] 100 | }, 101 | { 102 | "cell_type": "markdown", 103 | "metadata": {}, 104 | "source": [ 105 | "### Prepare Agents" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 5, 111 | "metadata": {}, 112 | "outputs": [], 113 | "source": [ 114 | "import operator\n", 115 | "from typing import Annotated, TypedDict, List\n", 116 | "\n", 117 | "class AgentState(TypedDict):\n", 118 | " question: str\n", 119 | " table_schemas: str\n", 120 | " database: str\n", 121 | " sql: str\n", 122 | " reflect: Annotated[List[str], operator.add]\n", 123 | " accepted: bool\n", 124 | " revision: int\n", 125 | " max_revision: int" 126 | ] 127 | }, 128 | { 129 | "cell_type": "code", 130 | "execution_count": 6, 131 | "metadata": {}, 132 | "outputs": [], 133 | "source": [ 134 | "with open('app/core/agents/agents.json') as f:\n", 135 | " agent_config = json.load(f)\n", 136 | "\n", 137 | "def search_engineer_node(state: AgentState):\n", 138 | " candidates = db_schema_search_tool(state['question'])\n", 139 | " role_prompt = agent_config['search_engineer']\n", 140 | " messages = [\n", 141 | " SystemMessage(content=role_prompt['system']),\n", 142 | " HumanMessage(\n", 143 | " content=f\"Based on the following candidate database/table schemas:\\n{candidates}\\n\\n\"\n", 144 | " f\"Find the relevant database/table schemas to answer the question: {state['question']}\\n\\n{role_prompt['expected_output']}\")\n", 145 | " ]\n", 146 | " response = model.invoke(messages)\n", 147 | " json_output = json.loads(response.content)\n", 148 | " return {\"table_schemas\": json_output['table_schemas'], \"database\": json_output['database']}\n", 149 | "\n", 150 | "def senior_sql_writer_node(state: AgentState):\n", 151 | " role_prompt = agent_config['senior_sql_writer']\n", 152 | " instruction = f\"Based on the following database/table schemas:\\n{state['table_schemas']}\\n\\n\"\n", 153 | " if len(state['reflect']) > 0:\n", 154 | " instruction += f\"Also reflect on these feedbacks:\\n{'\\n'.join(state['reflect'])}\\n\\n\"\n", 155 | " instruction += f\"Write a SQL script to answer the question: {state['question']}\\n\\n{role_prompt['expected_output']}\"\n", 156 | " messages = [\n", 157 | " SystemMessage(content=role_prompt['system']), \n", 158 | " HumanMessage(content=instruction)\n", 159 | " ]\n", 160 | " response = model.invoke(messages)\n", 161 | " return {\"sql\": response.content, \"revision\": state['revision']+1}\n", 162 | "\n", 163 | "def senior_qa_engineer_node(state: AgentState):\n", 164 | " role_prompt = agent_config['senior_qa_engineer']\n", 165 | " messages = [\n", 166 | " SystemMessage(content=role_prompt['system']), \n", 167 | " HumanMessage(\n", 168 | " content=f\"Based on the following database/table schemas:\\n{state['table_schemas']}\\n\\n\"\n", 169 | " f\"And the sql script:\\n{state['sql']}\\n\\n\"\n", 170 | " f\"Verify the sql script to check if it can complete the task: {state['question']}\\n\\n{role_prompt['expected_output']}\")\n", 171 | " ]\n", 172 | " response = model.invoke(messages)\n", 173 | " return {\"accepted\": 'ACCEPTED' in response.content.upper()}\n", 174 | "\n", 175 | "def chief_dba_node(state: AgentState):\n", 176 | " role_prompt = agent_config['chief_dba']\n", 177 | " messages = [\n", 178 | " SystemMessage(content=role_prompt['system']), \n", 179 | " HumanMessage(\n", 180 | " content=f\"Based on the following database/table schemas:\\n{state['table_schemas']}\\n\\n\"\n", 181 | " f\"And the imperfect sql script:\\n{state['sql']}\\n\\n\"\n", 182 | " f\"Provide the usefule and detail recommendation to help the sql writer complete the task: {state['question']}\\n\\n\"\n", 183 | " f\"{role_prompt['expected_output']}\")\n", 184 | " ]\n", 185 | " response = model.invoke(messages)\n", 186 | " return {\"reflect\": [response.content]}" 187 | ] 188 | }, 189 | { 190 | "cell_type": "markdown", 191 | "metadata": {}, 192 | "source": [ 193 | "### Construct Graph" 194 | ] 195 | }, 196 | { 197 | "cell_type": "code", 198 | "execution_count": 7, 199 | "metadata": {}, 200 | "outputs": [], 201 | "source": [ 202 | "builder = StateGraph(AgentState)\n", 203 | "\n", 204 | "# add nodes\n", 205 | "builder.add_node(\"search_engineer\", search_engineer_node)\n", 206 | "builder.add_node(\"sql_writer\", senior_sql_writer_node)\n", 207 | "builder.add_node(\"qa_engineer\", senior_qa_engineer_node)\n", 208 | "builder.add_node(\"chief_dba\", chief_dba_node)\n", 209 | "\n", 210 | "# add edges\n", 211 | "builder.add_edge(\"search_engineer\", \"sql_writer\")\n", 212 | "builder.add_edge(\"sql_writer\", \"qa_engineer\")\n", 213 | "builder.add_edge(\"chief_dba\", \"sql_writer\")\n", 214 | "\n", 215 | "# add conditional edges\n", 216 | "builder.add_conditional_edges(\n", 217 | " \"qa_engineer\", \n", 218 | " lambda state: END if state['accepted'] == True or state['revision'] >= state['max_revision'] else \"reflect\", \n", 219 | " {END: END, \"reflect\": \"chief_dba\"}\n", 220 | ")\n", 221 | "\n", 222 | "# set entry point\n", 223 | "builder.set_entry_point(\"search_engineer\")\n", 224 | "\n", 225 | "# compile graph\n", 226 | "graph = builder.compile(checkpointer=memory)" 227 | ] 228 | }, 229 | { 230 | "cell_type": "markdown", 231 | "metadata": {}, 232 | "source": [ 233 | "### Visualize the Graph" 234 | ] 235 | }, 236 | { 237 | "cell_type": "code", 238 | "execution_count": 8, 239 | "metadata": {}, 240 | "outputs": [ 241 | { 242 | "data": { 243 | "image/png": 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", 244 | "text/plain": [ 245 | "" 246 | ] 247 | }, 248 | "execution_count": 8, 249 | "metadata": {}, 250 | "output_type": "execute_result" 251 | } 252 | ], 253 | "source": [ 254 | "from IPython.display import Image\n", 255 | "\n", 256 | "Image(graph.get_graph().draw_png())" 257 | ] 258 | }, 259 | { 260 | "cell_type": "markdown", 261 | "metadata": {}, 262 | "source": [ 263 | "### Run the Graph" 264 | ] 265 | }, 266 | { 267 | "cell_type": "code", 268 | "execution_count": 9, 269 | "metadata": {}, 270 | "outputs": [ 271 | { 272 | "name": "stdout", 273 | "output_type": "stream", 274 | "text": [ 275 | "{'search_engineer': {'table_schemas': [{'table': 'artist', 'schema': 'CREATE TABLE \"artist\" (\\n\"Artist_ID\" int,\\n\"Artist\" text,\\n\"Age\" int,\\n\"Famous_Title\" text,\\n\"Famous_Release_date\" text,\\nPRIMARY KEY (\"Artist_ID\")\\n)'}, {'table': 'volume', 'schema': 'CREATE TABLE \"volume\" (\\n\"Volume_ID\" int,\\n\"Volume_Issue\" text,\\n\"Issue_Date\" text,\\n\"Weeks_on_Top\" real,\\n\"Song\" text,\\n\"Artist_ID\" int,\\nPRIMARY KEY (\"Volume_ID\"),\\nFOREIGN KEY (`Artist_ID`) REFERENCES `artist`(`Artist_ID`)\\n)'}], 'database': 'music_4'}}\n", 276 | "{'sql_writer': {'sql': 'SELECT artist.\"Famous_Title\"\\nFROM artist\\nLEFT JOIN volume ON artist.\"Artist_ID\" = volume.\"Artist_ID\"\\nWHERE volume.\"Artist_ID\" IS NULL;', 'revision': 1}}\n", 277 | "{'qa_engineer': {'accepted': True}}\n" 278 | ] 279 | } 280 | ], 281 | "source": [ 282 | "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n", 283 | "for s in graph.stream({\n", 284 | " 'question': question,\n", 285 | " 'accepted': False,\n", 286 | " 'reflect': [],\n", 287 | " 'revision': 0,\n", 288 | " 'max_revision': 2\n", 289 | "}, thread):\n", 290 | " print(s)" 291 | ] 292 | }, 293 | { 294 | "cell_type": "code", 295 | "execution_count": 10, 296 | "metadata": {}, 297 | "outputs": [ 298 | { 299 | "name": "stdout", 300 | "output_type": "stream", 301 | "text": [ 302 | "database: music_4\n", 303 | "sql:\n", 304 | " SELECT artist.\"Famous_Title\"\n", 305 | "FROM artist\n", 306 | "LEFT JOIN volume ON artist.\"Artist_ID\" = volume.\"Artist_ID\"\n", 307 | "WHERE volume.\"Artist_ID\" IS NULL;\n" 308 | ] 309 | } 310 | ], 311 | "source": [ 312 | "print('database:', graph.get_state(thread).values['database'])\n", 313 | "print('sql:\\n', graph.get_state(thread).values['sql'])" 314 | ] 315 | }, 316 | { 317 | "cell_type": "markdown", 318 | "metadata": {}, 319 | "source": [ 320 | "### Compare with gold result" 321 | ] 322 | }, 323 | { 324 | "cell_type": "code", 325 | "execution_count": 11, 326 | "metadata": {}, 327 | "outputs": [ 328 | { 329 | "name": "stdout", 330 | "output_type": "stream", 331 | "text": [ 332 | "[('Antievangelistical Process (re-release)',), ('Antithesis of All Flesh',)]\n" 333 | ] 334 | } 335 | ], 336 | "source": [ 337 | "import sqlite3\n", 338 | "state = graph.get_state(thread)\n", 339 | "database = state.values['database']\n", 340 | "con = sqlite3.connect(f'spider/spider/database/{database}/{database}.sqlite')\n", 341 | "cursor = con.cursor()\n", 342 | "cursor.execute(state.values['sql'])\n", 343 | "results = cursor.fetchall()\n", 344 | "print(results)" 345 | ] 346 | }, 347 | { 348 | "cell_type": "code", 349 | "execution_count": 12, 350 | "metadata": {}, 351 | "outputs": [ 352 | { 353 | "name": "stdout", 354 | "output_type": "stream", 355 | "text": [ 356 | "[('Antievangelistical Process (re-release)',), ('Antithesis of All Flesh',)]\n" 357 | ] 358 | } 359 | ], 360 | "source": [ 361 | "print(info['gold_result'])" 362 | ] 363 | } 364 | ], 365 | "metadata": { 366 | "kernelspec": { 367 | "display_name": "Python 3 (ipykernel)", 368 | "language": "python", 369 | "name": "python3" 370 | }, 371 | "language_info": { 372 | "codemirror_mode": { 373 | "name": "ipython", 374 | "version": 3 375 | }, 376 | "file_extension": ".py", 377 | "mimetype": "text/x-python", 378 | "name": "python", 379 | "nbconvert_exporter": "python", 380 | "pygments_lexer": "ipython3", 381 | "version": "3.12.2" 382 | } 383 | }, 384 | "nbformat": 4, 385 | "nbformat_minor": 4 386 | } 387 | --------------------------------------------------------------------------------