Langchain Sqlalchmy Query error when using sqlite

[rm17tink]

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Example Code

from langchain_google_genai import ChatGoogleGenerativeAI
from dotenv import load_dotenv
import os
load_dotenv()

from langchain.sql_database import SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
from langchain.chains import create_sql_query_chain
llm = ChatGoogleGenerativeAI(model="gemini-pro")

chain = create_sql_query_chain(llm, input_db)
response = chain.invoke({"question": "How many rows are in the database "})
response

Description

---

OperationalError Traceback (most recent call last)
File C:\ProgramData\anaconda3\Lib\site-packages\sqlalchemy\engine\base.py:1967, in Connection._exec_single_context(self, dialect, context, statement, parameters)
1966 if not evt_handled:
-> 1967 self.dialect.do_execute(
1968 cursor, str_statement, effective_parameters, context
1969 )
1971 if self._has_events or self.engine._has_events:

File C:\ProgramData\anaconda3\Lib\site-packages\sqlalchemy\engine\default.py:941, in DefaultDialect.do_execute(self, cursor, statement, parameters, context)
940 def do_execute(self, cursor, statement, parameters, context=None):
--> 941 cursor.execute(statement, parameters)

OperationalError: near "SQLQuery": syntax error

The above exception was the direct cause of the following exception:

OperationalError Traceback (most recent call last)
Cell In[16], line 1
----> 1 input_db.run(response)

File C:\ProgramData\anaconda3\Lib\site-packages\langchain_community\utilities\sql_database.py:502, in SQLDatabase.run(self, command, fetch, include_columns, parameters, execution_options)
488 def run(
489 self,
490 command: Union[str, Executable],
(...)
495 execution_options: Optional[Dict[str, Any]] = None,
496 ) -> Union[str, Sequence[Dict[str, Any]], Result[Any]]:
497 """Execute a SQL command and return a string representing the results.
498
499 If the statement returns rows, a string of the results is returned.
500 If the statement returns no rows, an empty string is returned.
501 """
--> 502 result = self._execute(
503 command, fetch, parameters=parameters, execution_options=execution_options
504 )
506 if fetch == "cursor":
507 return result

File C:\ProgramData\anaconda3\Lib\site-packages\langchain_community\utilities\sql_database.py:467, in SQLDatabase._execute(self, command, fetch, parameters, execution_options)
465 else:
466 raise TypeError(f"Query expression has unknown type: {type(command)}")
--> 467 cursor = connection.execute(
468 command,
469 parameters,
470 execution_options=execution_options,
471 )
473 if cursor.returns_rows:
474 if fetch == "all":

File C:\ProgramData\anaconda3\Lib\site-packages\sqlalchemy\engine\base.py:1418, in Connection.execute(self, statement, parameters, execution_options)
1416 raise exc.ObjectNotExecutableError(statement) from err
1417 else:
-> 1418 return meth(
1419 self,
1420 distilled_parameters,
1421 execution_options or NO_OPTIONS,
1422 )

File C:\ProgramData\anaconda3\Lib\site-packages\sqlalchemy\sql\elements.py:515, in ClauseElement._execute_on_connection(self, connection, distilled_params, execution_options)
513 if TYPE_CHECKING:
514 assert isinstance(self, Executable)
--> 515 return connection._execute_clauseelement(
516 self, distilled_params, execution_options
517 )
518 else:
519 raise exc.ObjectNotExecutableError(self)

File C:\ProgramData\anaconda3\Lib\site-packages\sqlalchemy\engine\base.py:1640, in Connection._execute_clauseelement(self, elem, distilled_parameters, execution_options)
1628 compiled_cache: Optional[CompiledCacheType] = execution_options.get(
1629 "compiled_cache", self.engine._compiled_cache
1630 )
1632 compiled_sql, extracted_params, cache_hit = elem._compile_w_cache(
1633 dialect=dialect,
1634 compiled_cache=compiled_cache,
(...)
1638 linting=self.dialect.compiler_linting | compiler.WARN_LINTING,
1639 )
-> 1640 ret = self._execute_context(
1641 dialect,
1642 dialect.execution_ctx_cls._init_compiled,
1643 compiled_sql,
1644 distilled_parameters,
1645 execution_options,
1646 compiled_sql,
1647 distilled_parameters,
1648 elem,
1649 extracted_params,
1650 cache_hit=cache_hit,
1651 )
1652 if has_events:
1653 self.dispatch.after_execute(
1654 self,
1655 elem,
(...)
1659 ret,
1660 )

File C:\ProgramData\anaconda3\Lib\site-packages\sqlalchemy\engine\base.py:1846, in Connection._execute_context(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)
1844 return self._exec_insertmany_context(dialect, context)
1845 else:
-> 1846 return self._exec_single_context(
1847 dialect, context, statement, parameters
1848 )

File C:\ProgramData\anaconda3\Lib\site-packages\sqlalchemy\engine\base.py:1986, in Connection._exec_single_context(self, dialect, context, statement, parameters)
1983 result = context._setup_result_proxy()
1985 except BaseException as e:
-> 1986 self._handle_dbapi_exception(
1987 e, str_statement, effective_parameters, cursor, context
1988 )
1990 return result

File C:\ProgramData\anaconda3\Lib\site-packages\sqlalchemy\engine\base.py:2355, in Connection._handle_dbapi_exception(self, e, statement, parameters, cursor, context, is_sub_exec)
2353 elif should_wrap:
2354 assert sqlalchemy_exception is not None
-> 2355 raise sqlalchemy_exception.with_traceback(exc_info[2]) from e
2356 else:
2357 assert exc_info[1] is not None

File C:\ProgramData\anaconda3\Lib\site-packages\sqlalchemy\engine\base.py:1967, in Connection._exec_single_context(self, dialect, context, statement, parameters)
1965 break
1966 if not evt_handled:
-> 1967 self.dialect.do_execute(
1968 cursor, str_statement, effective_parameters, context
1969 )
1971 if self._has_events or self.engine._has_events:
1972 self.dispatch.after_cursor_execute(
1973 self,
1974 cursor,
(...)
1978 context.executemany,
1979 )

File C:\ProgramData\anaconda3\Lib\site-packages\sqlalchemy\engine\default.py:941, in DefaultDialect.do_execute(self, cursor, statement, parameters, context)
940 def do_execute(self, cursor, statement, parameters, context=None):
--> 941 cursor.execute(statement, parameters)
I keep getting sql query errors using langchain with gemini kicking me a sqlalchemy error when the query gets passed

OperationalError: (sqlite3.OperationalError) near "SQLQuery": syntax error
[SQL: SQLQuery: SELECT
COUNT(*) AS "Total number of rows"
FROM
"CNS_SEX_AGE_RACE"]
(Background on this error at: https://sqlalche.me/e/20/e3q8)

System Info

System Information

OS: Windows
OS Version: 10.0.19045
Python Version: 3.11.3 | packaged by Anaconda, Inc. | (main, Apr 19 2023, 23:46:34) [MSC v.1916 64 bit (AMD64)]

Package Information

langchain_core: 0.2.33
langchain: 0.2.14
langchain_community: 0.2.12
langsmith: 0.1.81
langchain_experimental: 0.0.64
langchain_google_genai: 1.0.9
langchain_openai: 0.1.22
langchain_text_splitters: 0.2.1

Optional packages not installed

langgraph
langserve

Other Dependencies

aiohttp: 3.8.3
async-timeout: 4.0.2
beautifulsoup4: 4.12.2
dataclasses-json: 0.6.7
google-generativeai: 0.7.2
jsonpatch: 1.33
lxml: 4.9.2
numpy: 1.24.3
openai: 1.41.1
orjson: 3.10.5
packaging: 24.1
pillow: 10.4.0
pydantic: 2.7.4
PyYAML: 6.0
requests: 2.32.3
SQLAlchemy: 2.0.32
tenacity: 8.4.1
tiktoken: 0.7.0
typing-extensions: 4.12.2

[dosubot[bot]]

Hey there, @rm17tink! I'm here to help you with any bugs, questions, or contributions you might have. Let's squash those bugs together! 🤖

To resolve the OperationalError related to SQLAlchemy when using LangChain with SQLite, ensure that the database path is correctly specified and that the SQLite database file is accessible. Here is an example of how to initialize the SQLiteCache correctly:

from sqlalchemy import create_engine
from langchain_community.cache import SQLiteCache

# Initialize the SQLiteCache with the correct database path
database_path = "path/to/your/database.db"
cache = SQLiteCache(database_path=database_path)

# Use the cache in your application
# Example: cache.lookup(prompt, llm_string)

Make sure that the database_path points to a valid location where the SQLite database file can be created or accessed. If the file does not exist, SQLAlchemy will create it. If you encounter permission issues, ensure that the directory is writable.

Additionally, ensure that the FullLLMCache table schema is correctly defined and that the database is properly initialized:

from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, String, Integer

Base = declarative_base()

class FullLLMCache(Base):
    """SQLite table for full LLM Cache (all generations)."""
    __tablename__ = "full_llm_cache"
    prompt = Column(String, primary_key=True)
    llm = Column(String, primary_key=True)
    idx = Column(Integer, primary_key=True)
    response = Column(String)

# Create the engine and initialize the tables
engine = create_engine(f"sqlite:///{database_path}")
Base.metadata.create_all(engine)

By ensuring the correct database path and initializing the tables, you should be able to resolve the OperationalError when using SQLAlchemy with SQLite in LangChain.

For further reference, you can also look at the example provided in the LangChain documentation for using the SQLDatabaseChain with SQLite:

from langchain_openai import OpenAI
from langchain_community.utilities import SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain

# Initialize the database connection
db = SQLDatabase.from_uri("sqlite:///path/to/your/database.db")
llm = OpenAI(temperature=0, verbose=True)

# Create the SQLDatabaseChain
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)

# Run a query
result = db_chain.run("How many employees are there?")
print(result)

This example demonstrates how to set up and use the SQLDatabaseChain with SQLite, ensuring that the database connection is properly established and queries can be executed without errors ^[1][2].

To continue talking to Dosu, mention @dosu.

[rm17tink]

Ia this new? Seems many tutorials only focus on db import and response

[dosubot[bot]]

The issue you're encountering with the OperationalError in SQLAlchemy when using LangChain with SQLite is likely due to the SQL query not being correctly formatted. This can happen if the language model generates an incorrect SQL statement.

To address this, you can use the run_no_throw method, which is designed to handle SQL execution errors gracefully by returning the error message instead of raising an exception. Here is an example of how to use it:

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.sql_database import SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
from langchain.chains import create_sql_query_chain

# Initialize the language model
llm = ChatGoogleGenerativeAI(model="gemini-pro")

# Initialize the database connection
input_db = SQLDatabase.from_uri("sqlite:///path/to/your/database.db")

# Create the SQL query chain
chain = create_sql_query_chain(llm, input_db)

# Run the query using run_no_throw to handle errors gracefully
response = chain.invoke({"question": "How many rows are in the database"})
print(response)

Ensure that the database path is correctly specified and accessible. If the issue persists, you might want to check the generated SQL query for syntax errors and adjust the prompt or the language model's configuration accordingly.

For more detailed setup steps and examples, you can refer to the LangChain documentation on using SQLDatabaseChain with SQLite ^[1].

To continue talking to Dosu, mention @dosu.

[rm17tink]

So I followed all the steps but it seems the chain.invoke produces a result string like

chain = create_sql_query_chain(llm, input_db)
response = chain.invoke({"question": "How many rows are in the database "})
response

:'sql\nSELECT COUNT(*) AS "row_count"\nFROM CNS_SEX_AGE_RACE;\n'

when i pass in normal sql syntax to the input_db. it runs fine

response ="SELECT COUNT(*) AS count FROM CNS_SEX_AGE_RACE"
response

input_db.run(response)

'[(264368,)]'

why is producing poor syntax strings?

[dosubot[bot]]

The chain.invoke method in LangChain with SQLite produces poorly formatted SQL syntax strings because it relies on the language model (LLM) to generate the SQL commands. The LLM might not always produce syntactically correct SQL, especially if the prompt or the model's understanding is not perfect. This is evident from the _call method in the SQLDatabaseChain class, where the SQL command is generated by the llm_chain.predict method and then parsed by the sql_cmd_parser:

llm_out = self.llm_chain.predict(
    callbacks=_run_manager.get_child(),
    **llm_inputs,
)
sql_cmd = self.sql_cmd_parser.parse(llm_out)

In contrast, directly passing normal SQL syntax to input_db.run works fine because it bypasses the LLM and directly executes the provided SQL command, which is assumed to be correctly formatted.

To mitigate this issue, you can use the query_checker tool, which attempts to fix the initial SQL generated by the LLM:

if self.use_query_checker:
    query_checker_prompt = self.query_checker_prompt or PromptTemplate(
        template=QUERY_CHECKER, input_variables=["query", "dialect"]
    )
    query_checker_chain = LLMChain(
        llm=self.llm_chain.llm,
        prompt=query_checker_prompt,
        output_parser=self.llm_chain.output_parser,
    )
    query_checker_inputs = {
        "query": llm_out,
        "dialect": self.database.dialect,
    }
    checked_llm_out = query_checker_chain.predict(
        callbacks=_run_manager.get_child(), **query_checker_inputs
    )
    checked_sql_command = self.sql_cmd_parser.parse(checked_llm_out)
    sql_cmd = checked_sql_command

However, even with the query checker, there is no guarantee that the SQL will always be perfectly formatted, as it still depends on the LLM's capabilities ^[1][2].

To continue talking to Dosu, mention @dosu.