mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-05 06:33:20 +00:00
bf5193bb99
Upgrade to using a literal for specifying the extra which is the
recommended approach in pydantic 2.
This works correctly also in pydantic v1.
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel, extra="forbid"):
x: int
Foo(x=5, y=1)
```
And
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel):
x: int
class Config:
extra = "forbid"
Foo(x=5, y=1)
```
## Enum -> literal using grit pattern:
```
engine marzano(0.1)
language python
or {
`extra=Extra.allow` => `extra="allow"`,
`extra=Extra.forbid` => `extra="forbid"`,
`extra=Extra.ignore` => `extra="ignore"`
}
```
Resorted attributes in config and removed doc-string in case we will
need to deal with going back and forth between pydantic v1 and v2 during
the 0.3 release. (This will reduce merge conflicts.)
## Sort attributes in Config:
```
engine marzano(0.1)
language python
function sort($values) js {
return $values.text.split(',').sort().join("\n");
}
class_definition($name, $body) as $C where {
$name <: `Config`,
$body <: block($statements),
$values = [],
$statements <: some bubble($values) assignment() as $A where {
$values += $A
},
$body => sort($values),
}
```
131 lines
4.5 KiB
Python
131 lines
4.5 KiB
Python
"""Toolkit for interacting with an SQL database."""
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from typing import List
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.pydantic_v1 import Field
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from langchain_core.tools import BaseToolkit
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from langchain_community.tools import BaseTool
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from langchain_community.tools.sql_database.tool import (
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InfoSQLDatabaseTool,
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ListSQLDatabaseTool,
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QuerySQLCheckerTool,
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QuerySQLDataBaseTool,
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)
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from langchain_community.utilities.sql_database import SQLDatabase
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class SQLDatabaseToolkit(BaseToolkit):
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"""SQLDatabaseToolkit for interacting with SQL databases.
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Setup:
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Install ``langchain-community``.
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.. code-block:: bash
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pip install -U langchain-community
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Key init args:
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db: SQLDatabase
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The SQL database.
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llm: BaseLanguageModel
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The language model (for use with QuerySQLCheckerTool)
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Instantiate:
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.. code-block:: python
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from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
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from langchain_community.utilities.sql_database import SQLDatabase
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from langchain_openai import ChatOpenAI
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db = SQLDatabase.from_uri("sqlite:///Chinook.db")
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llm = ChatOpenAI(temperature=0)
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toolkit = SQLDatabaseToolkit(db=db, llm=llm)
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Tools:
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.. code-block:: python
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toolkit.get_tools()
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Use within an agent:
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.. code-block:: python
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from langchain import hub
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from langgraph.prebuilt import create_react_agent
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# Pull prompt (or define your own)
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prompt_template = hub.pull("langchain-ai/sql-agent-system-prompt")
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system_message = prompt_template.format(dialect="SQLite", top_k=5)
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# Create agent
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agent_executor = create_react_agent(
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llm, toolkit.get_tools(), state_modifier=system_message
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)
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# Query agent
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example_query = "Which country's customers spent the most?"
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events = agent_executor.stream(
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{"messages": [("user", example_query)]},
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stream_mode="values",
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)
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for event in events:
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event["messages"][-1].pretty_print()
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""" # noqa: E501
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db: SQLDatabase = Field(exclude=True)
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llm: BaseLanguageModel = Field(exclude=True)
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@property
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def dialect(self) -> str:
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"""Return string representation of SQL dialect to use."""
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return self.db.dialect
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class Config:
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arbitrary_types_allowed = True
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def get_tools(self) -> List[BaseTool]:
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"""Get the tools in the toolkit."""
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list_sql_database_tool = ListSQLDatabaseTool(db=self.db)
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info_sql_database_tool_description = (
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"Input to this tool is a comma-separated list of tables, output is the "
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"schema and sample rows for those tables. "
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"Be sure that the tables actually exist by calling "
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f"{list_sql_database_tool.name} first! "
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"Example Input: table1, table2, table3"
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)
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info_sql_database_tool = InfoSQLDatabaseTool(
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db=self.db, description=info_sql_database_tool_description
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)
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query_sql_database_tool_description = (
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"Input to this tool is a detailed and correct SQL query, output is a "
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"result from the database. If the query is not correct, an error message "
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"will be returned. If an error is returned, rewrite the query, check the "
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"query, and try again. If you encounter an issue with Unknown column "
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f"'xxxx' in 'field list', use {info_sql_database_tool.name} "
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"to query the correct table fields."
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)
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query_sql_database_tool = QuerySQLDataBaseTool(
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db=self.db, description=query_sql_database_tool_description
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)
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query_sql_checker_tool_description = (
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"Use this tool to double check if your query is correct before executing "
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"it. Always use this tool before executing a query with "
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f"{query_sql_database_tool.name}!"
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)
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query_sql_checker_tool = QuerySQLCheckerTool(
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db=self.db, llm=self.llm, description=query_sql_checker_tool_description
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)
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return [
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query_sql_database_tool,
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info_sql_database_tool,
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list_sql_database_tool,
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query_sql_checker_tool,
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]
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def get_context(self) -> dict:
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"""Return db context that you may want in agent prompt."""
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return self.db.get_context()
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