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Created January 2, 2025 22:37
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LangGraph Exploration
Raw
README.md

A modification of the LangChain SQL Q&A tutorial https://python.langchain.com/docs/tutorials/sql_qa/.

The changes are:

  • uses Pydantic to type the state and inputs/outputs
  • uses duckDB on the palmerpenguin dataset
  • uses a Nvidia NIM for the LLM
  • instead of a sequence write_query -> run_query -> gen_answer, this graph adds a LLM that checks the write_query output for validity and to see if it answers the question, leading to a more dynamic graph that looks like this:

graph

Setup:

  1. Use uv to create the project based on the lock file
  2. Run
uv run sql_agent.py
  1. Sample output:
WRITING QUERY FROM QUESTION how many penguins are there by type?
PROPOSED QUERY IS sql_query='SELECT species, COUNT(*) FROM penguins GROUP BY species ORDER BY COUNT(*) DESC;'
CHECKING QUERY: sql_query='SELECT species, COUNT(*) FROM penguins GROUP BY species ORDER BY COUNT(*) DESC;'
VALID QUERY: True
ATTEMPTING TO RUN QUERY SELECT species, COUNT(*) FROM penguins GROUP BY species ORDER BY COUNT(*) DESC;
{'question': 'how many penguins are there by type?', 'sql_query': SQLCandidateOutput(sql_query='SELECT species, COUNT(*) FROM penguins GROUP BY species ORDER BY COUNT(*) DESC;'), 'valid_sql': SQLCheck(valid_sql=True), 'result': "[('Adelie', 152), ('Gentoo', 124), ('Chinstrap', 68)]"}
Raw
sql_agent.py
import palmerpenguins as p
import duckdb
from langchain_nvidia_ai_endpoints import ChatNVIDIA
from pydantic import BaseModel, Field
from langchain.tools import tool
from langchain_core.prompts import PromptTemplate
from typing import Optional
from langgraph.graph import START, END, StateGraph
# Create a SQL database to use
data = p.load_penguins()
conn = duckdb.connect("example.duckdb")
table_name = "penguins"
conn.execute(f"CREATE TABLE IF NOT EXISTS {table_name} AS SELECT * FROM data")
columns_info = conn.execute(f"PRAGMA table_info('{table_name}')").fetchall()
columns = " ".join([column[1] for column in columns_info])
conn.close()
# Initialize the model
API_KEY = ""
llm = ChatNVIDIA(api_key=API_KEY, model="meta/llama-3.3-70b-instruct")
prompt = PromptTemplate.from_template(
"""
Return a syntax correct SQL query for DuckDB that answers the users question. Use the table name and columns available below.
TABLE NAME:
{table_name}
COLUMNS:
{columns}
QUESTION:
{question}
"""
)
# LLM for creating the sql
class SQLCandidateOutput(BaseModel):
sql_query: str = Field(description="a valid duckdb sql query")
class SQLCheck(BaseModel):
valid_sql: bool = Field(
description="is the generated sql valid and answers the user question"
)
class SQLResult(BaseModel):
result: str = Field(description="the result of the sql query coerced to a string")
# Agent state
class SQLAgentState(BaseModel):
question: str = Field(description="user supplied question about the data")
sql_query: Optional[SQLCandidateOutput] = None
valid_sql: Optional[SQLCheck] = None
result: Optional[SQLResult] = None
def write_query(state: SQLAgentState):
print(f"WRITING QUERY FROM QUESTION {state.question}")
sql_proposer = prompt | llm.with_structured_output(SQLCandidateOutput)
response = sql_proposer.invoke(
{"question": state.question, "columns": columns, "table_name": table_name}
)
state.sql_query = SQLCandidateOutput(sql_query=response.sql_query)
print(f"PROPOSED QUERY IS {state.sql_query}")
return state
validate_prompt = PromptTemplate.from_template(
"""
Is the sql statement below valid SQL that also answers the user's question?
SQL: {sql}
QUESTION: {question}
"""
)
def check_query(state: SQLAgentState):
print(f"CHECKING QUERY: {state.sql_query}")
sql_proposer = validate_prompt | llm.with_structured_output(SQLCheck)
response = sql_proposer.invoke(
{
"question": state.question,
"sql": state.sql_query,
}
)
state.valid_sql = SQLCheck(valid_sql=response.valid_sql)
print(f"VALID QUERY: {state.valid_sql.valid_sql}")
return state
@tool
def run_sql_tool(sql: str):
"""Run the supplied sql string in a hard coded duckdb database"""
print(f"ATTEMPTING TO RUN QUERY {sql}")
# todo, figure out a better way to handle conn across many invocations
conn = duckdb.connect("example.duckdb")
try:
result = conn.execute(sql).fetchall()
finally:
conn.close()
return str(result)
def run_sql(state: SQLAgentState):
state.result = run_sql_tool.invoke(
str(state.sql_query.sql_query)
) # types arent happy here?
return state
def decide_to_execute(state: SQLAgentState):
if state.valid_sql.valid_sql:
return "run_sql"
return "write_query"
sql_agent = (
StateGraph(SQLAgentState)
.add_node(write_query)
.add_node(check_query)
.add_node(run_sql)
)
sql_agent.add_edge(START, "write_query")
sql_agent.add_edge("write_query", "check_query")
sql_agent.add_conditional_edges(
"check_query",
decide_to_execute,
{"run_sql": "run_sql", "write_query": "write_query"},
)
sql_agent.add_edge("run_sql", END)
app = sql_agent.compile()
app.get_graph().draw_mermaid_png(output_file_path="graph.png")
result = app.invoke(SQLAgentState(question="how many penguins are there by type?"))
print(result)
# print(check_query(write_query(SQLAgentState(question="how many penguins are there by type?"))))
Raw
uv.lock
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