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12861273e1
**Description:**
When using the SQLDatabaseChain with Llama2-70b LLM and, SQLite
database. I was getting `Warning: You can only execute one statement at
a time.`.
```
from langchain.sql_database import SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
sql_database_path = '/dccstor/mmdataretrieval/mm_dataset/swimming_record/rag_data/swimmingdataset.db'
sql_db = get_database(sql_database_path)
db_chain = SQLDatabaseChain.from_llm(mistral, sql_db, verbose=True, callbacks = [callback_obj])
db_chain.invoke({
"query": "What is the best time of Lance Larson in men's 100 meter butterfly competition?"
})
```
Error:
```
Warning Traceback (most recent call last)
Cell In[31], line 3
1 import langchain
2 langchain.debug=False
----> 3 db_chain.invoke({
4 "query": "What is the best time of Lance Larson in men's 100 meter butterfly competition?"
5 })
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain/chains/base.py:162, in Chain.invoke(self, input, config, **kwargs)
160 except BaseException as e:
161 run_manager.on_chain_error(e)
--> 162 raise e
163 run_manager.on_chain_end(outputs)
164 final_outputs: Dict[str, Any] = self.prep_outputs(
165 inputs, outputs, return_only_outputs
166 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain/chains/base.py:156, in Chain.invoke(self, input, config, **kwargs)
149 run_manager = callback_manager.on_chain_start(
150 dumpd(self),
151 inputs,
152 name=run_name,
153 )
154 try:
155 outputs = (
--> 156 self._call(inputs, run_manager=run_manager)
157 if new_arg_supported
158 else self._call(inputs)
159 )
160 except BaseException as e:
161 run_manager.on_chain_error(e)
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_experimental/sql/base.py:198, in SQLDatabaseChain._call(self, inputs, run_manager)
194 except Exception as exc:
195 # Append intermediate steps to exception, to aid in logging and later
196 # improvement of few shot prompt seeds
197 exc.intermediate_steps = intermediate_steps # type: ignore
--> 198 raise exc
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_experimental/sql/base.py:143, in SQLDatabaseChain._call(self, inputs, run_manager)
139 intermediate_steps.append(
140 sql_cmd
141 ) # output: sql generation (no checker)
142 intermediate_steps.append({"sql_cmd": sql_cmd}) # input: sql exec
--> 143 result = self.database.run(sql_cmd)
144 intermediate_steps.append(str(result)) # output: sql exec
145 else:
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_community/utilities/sql_database.py:436, in SQLDatabase.run(self, command, fetch, include_columns)
425 def run(
426 self,
427 command: str,
428 fetch: Literal["all", "one"] = "all",
429 include_columns: bool = False,
430 ) -> str:
431 """Execute a SQL command and return a string representing the results.
432
433 If the statement returns rows, a string of the results is returned.
434 If the statement returns no rows, an empty string is returned.
435 """
--> 436 result = self._execute(command, fetch)
438 res = [
439 {
440 column: truncate_word(value, length=self._max_string_length)
(...)
443 for r in result
444 ]
446 if not include_columns:
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_community/utilities/sql_database.py:413, in SQLDatabase._execute(self, command, fetch)
410 elif self.dialect == "postgresql": # postgresql
411 connection.exec_driver_sql("SET search_path TO %s", (self._schema,))
--> 413 cursor = connection.execute(text(command))
414 if cursor.returns_rows:
415 if fetch == "all":
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1416, in Connection.execute(self, statement, parameters, execution_options)
1414 raise exc.ObjectNotExecutableError(statement) from err
1415 else:
-> 1416 return meth(
1417 self,
1418 distilled_parameters,
1419 execution_options or NO_OPTIONS,
1420 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/sql/elements.py:516, in ClauseElement._execute_on_connection(self, connection, distilled_params, execution_options)
514 if TYPE_CHECKING:
515 assert isinstance(self, Executable)
--> 516 return connection._execute_clauseelement(
517 self, distilled_params, execution_options
518 )
519 else:
520 raise exc.ObjectNotExecutableError(self)
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1639, in Connection._execute_clauseelement(self, elem, distilled_parameters, execution_options)
1627 compiled_cache: Optional[CompiledCacheType] = execution_options.get(
1628 "compiled_cache", self.engine._compiled_cache
1629 )
1631 compiled_sql, extracted_params, cache_hit = elem._compile_w_cache(
1632 dialect=dialect,
1633 compiled_cache=compiled_cache,
(...)
1637 linting=self.dialect.compiler_linting | compiler.WARN_LINTING,
1638 )
-> 1639 ret = self._execute_context(
1640 dialect,
1641 dialect.execution_ctx_cls._init_compiled,
1642 compiled_sql,
1643 distilled_parameters,
1644 execution_options,
1645 compiled_sql,
1646 distilled_parameters,
1647 elem,
1648 extracted_params,
1649 cache_hit=cache_hit,
1650 )
1651 if has_events:
1652 self.dispatch.after_execute(
1653 self,
1654 elem,
(...)
1658 ret,
1659 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1848, in Connection._execute_context(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)
1843 return self._exec_insertmany_context(
1844 dialect,
1845 context,
1846 )
1847 else:
-> 1848 return self._exec_single_context(
1849 dialect, context, statement, parameters
1850 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1988, in Connection._exec_single_context(self, dialect, context, statement, parameters)
1985 result = context._setup_result_proxy()
1987 except BaseException as e:
-> 1988 self._handle_dbapi_exception(
1989 e, str_statement, effective_parameters, cursor, context
1990 )
1992 return result
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:2346, in Connection._handle_dbapi_exception(self, e, statement, parameters, cursor, context, is_sub_exec)
2344 else:
2345 assert exc_info[1] is not None
-> 2346 raise exc_info[1].with_traceback(exc_info[2])
2347 finally:
2348 del self._reentrant_error
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1969, in Connection._exec_single_context(self, dialect, context, statement, parameters)
1967 break
1968 if not evt_handled:
-> 1969 self.dialect.do_execute(
1970 cursor, str_statement, effective_parameters, context
1971 )
1973 if self._has_events or self.engine._has_events:
1974 self.dispatch.after_cursor_execute(
1975 self,
1976 cursor,
(...)
1980 context.executemany,
1981 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/default.py:922, in DefaultDialect.do_execute(self, cursor, statement, parameters, context)
921 def do_execute(self, cursor, statement, parameters, context=None):
--> 922 cursor.execute(statement, parameters)
Warning: You can only execute one statement at a time.
```
**Issue:**
The Error occurs because when generating the SQLQuery, the llm_input
includes the stop character of "\nSQLResult:", so for this user query
the LLM generated response is **SELECT Time FROM men_butterfly_100m
WHERE Swimmer = 'Lance Larson';\nSQLResult:** it is required to remove
the SQLResult suffix on the llm response before executing it on the
database.
```
llm_inputs = {
"input": input_text,
"top_k": str(self.top_k),
"dialect": self.database.dialect,
"table_info": table_info,
"stop": ["\nSQLResult:"],
}
sql_cmd = self.llm_chain.predict(
callbacks=_run_manager.get_child(),
**llm_inputs,
).strip()
if SQL_RESULT in sql_cmd:
sql_cmd = sql_cmd.split(SQL_RESULT)[0].strip()
result = self.database.run(sql_cmd)
```
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---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
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README.md
🦜️🔗 LangChain
⚡ Build context-aware reasoning applications ⚡
Looking for the JS/TS library? Check out LangChain.js.
To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications. Fill out this form to speak with our sales team.
Quick Install
With pip:
pip install langchain
With conda:
conda install langchain -c conda-forge
🤔 What is LangChain?
LangChain is a framework for developing applications powered by language models. It enables applications that:
- Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
- Reason: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)
This framework consists of several parts.
- LangChain Libraries: The Python and JavaScript libraries. Contains interfaces and integrations for a myriad of components, a basic run time for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.
- LangChain Templates: A collection of easily deployable reference architectures for a wide variety of tasks.
- LangServe: A library for deploying LangChain chains as a REST API.
- LangSmith: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
- LangGraph: LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner.
The LangChain libraries themselves are made up of several different packages.
langchain-core: Base abstractions and LangChain Expression Language.langchain-community: Third party integrations.langchain: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
🧱 What can you build with LangChain?
❓ Retrieval augmented generation
- Documentation
- End-to-end Example: Chat LangChain and repo
💬 Analyzing structured data
- Documentation
- End-to-end Example: SQL Llama2 Template
🤖 Chatbots
- Documentation
- End-to-end Example: Web LangChain (web researcher chatbot) and repo
And much more! Head to the Use cases section of the docs for more.
🚀 How does LangChain help?
The main value props of the LangChain libraries are:
- Components: composable tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
- Off-the-shelf chains: built-in assemblages of components for accomplishing higher-level tasks
Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.
Components fall into the following modules:
📃 Model I/O:
This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.
📚 Retrieval:
Data Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.
🤖 Agents:
Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.
📖 Documentation
Please see here for full documentation, which includes:
- Getting started: installation, setting up the environment, simple examples
- Overview of the interfaces, modules, and integrations
- Use case walkthroughs and best practice guides
- LangSmith, LangServe, and LangChain Template overviews
- Reference: full API docs
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see here.