tj* dZddlmZddlmZmZmZmZmZm Z m Z m Z m Z ddl mZmZddlmZmZddlmZmZmZddlmZmZmZddlmZdd lmZmZer$dd l m!Z!dd l"m#Z#dd l$m%Z%dd l&m'Z'ddl(m)Z)ddl*m+Z+ dz SQL agent.) annotations) TYPE_CHECKINGAnyDictListLiteralOptionalSequenceUnioncast) AIMessage SystemMessage)BasePromptTemplatePromptTemplate)ChatPromptTemplateHumanMessagePromptTemplateMessagesPlaceholder)SQL_FUNCTIONS_SUFFIX SQL_PREFIX SQL_SUFFIX)SQLDatabaseToolkit)InfoSQLDatabaseToolListSQLDatabaseTool) AgentExecutor AgentType)BaseCallbackManager)BaseLanguageModel)BaseTool) SQLDatabaseN forceF)dbpromptllmrtoolkitOptional[SQLDatabaseToolkit] agent_typeCOptional[Union[AgentType, Literal['openai-tools', 'tool-calling']]]callback_managerOptional[BaseCallbackManager]prefix Optional[str]suffixformat_instructionsinput_variablesOptional[List[str]]top_kintmax_iterations Optional[int]max_execution_timeOptional[float]early_stopping_methodstrverboseboolagent_executor_kwargsOptional[Dict[str, Any]] extra_toolsSequence[BaseTool]r%Optional[SQLDatabase]r&Optional[BasePromptTemplate]kwargsrreturnrc ddlm}m}m}m}ddlm}m}m}ddl m }||td|r|rtd|pt||}|p|j }|t|z}|t }||j| }nd jvr#t+| d jvr|j t-fddDrv|}djvr(|dd|D}djvr(|dd|D}||j kr`Cddlm}|p|j}d|d||pt8g}t;j||d&|||dgdgd|}nP||jkrptAtCt*|tEjdtG|ptHtKdg}tMj'||d&|||dgdgd|}n|d vrptAtCt*|tEjdtG|ptHtKdg}tMj'||d!kr|||} n |||} |d&| dgdgd|}ntd"|d#|d&d$|||| | | | d%| piS)'aConstruct a SQL agent from an LLM and toolkit or database. !!! warning This agent can execute arbitrary SQL against your database. By default, the agent is allowed to generate SQL strings and run them via the database connection. This is powerful, but it also means the agent can generate expensive or dangerous queries (e.g., long-running queries, large scans/joins, locking queries, or unintended writes depending on your database permissions). ``create_sql_agent`` returns a ``langchain_classic`` ``AgentExecutor``. ``AgentExecutor`` is an agent abstraction that has long been considered legacy and is not actively supported as the recommended foundation for new production applications. For production-grade agent development, prefer building with Deep Agents: https://github.com/langchain-ai/deepagents If you use this in production, coordinate with your security/DB teams and apply server-side controls: - Use least-privilege database roles (ideally read-only, schema-limited). - Enforce statement timeouts / max execution time and other resource limits at the role or session level. - Apply query guardrails (e.g., restrict accessible schemas/tables, limit concurrency, and monitor/alert on slow queries). Client-side timeouts do not always guarantee that a running statement is cancelled on the database server. Args: llm: Language model to use for the agent. If agent_type is "tool-calling" then llm is expected to support tool calling. toolkit: SQLDatabaseToolkit for the agent to use. Must provide exactly one of 'toolkit' or 'db'. Specify 'toolkit' if you want to use a different model for the agent and the toolkit. agent_type: One of "tool-calling", "openai-tools", "openai-functions", or "zero-shot-react-description". Defaults to "zero-shot-react-description". "tool-calling" is recommended over the legacy "openai-tools" and "openai-functions" types. callback_manager: DEPRECATED. Pass "callbacks" key into 'agent_executor_kwargs' instead to pass constructor callbacks to AgentExecutor. prefix: Prompt prefix string. Must contain variables "top_k" and "dialect". suffix: Prompt suffix string. Default depends on agent type. format_instructions: Formatting instructions to pass to ZeroShotAgent.create_prompt() when 'agent_type' is "zero-shot-react-description". Otherwise ignored. input_variables: DEPRECATED. top_k: Number of rows to query for by default. max_iterations: Passed to AgentExecutor init. max_execution_time: Passed to AgentExecutor init. early_stopping_method: Passed to AgentExecutor init. verbose: AgentExecutor verbosity. agent_executor_kwargs: Arbitrary additional AgentExecutor args. extra_tools: Additional tools to give to agent on top of the ones that come with SQLDatabaseToolkit. db: SQLDatabase from which to create a SQLDatabaseToolkit. Toolkit is created using 'db' and 'llm'. Must provide exactly one of 'db' or 'toolkit'. prompt: Complete agent prompt. prompt and {prefix, suffix, format_instructions, input_variables} are mutually exclusive. **kwargs: Arbitrary additional Agent args. Returns: An AgentExecutor with the specified agent_type agent. Example: .. code-block:: python from langchain_openai import ChatOpenAI from langchain_community.agent_toolkits import create_sql_agent from langchain_community.utilities import SQLDatabase db = SQLDatabase.from_uri("sqlite:///Chinook.db") llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) agent_executor = create_sql_agent(llm, db=db, agent_type="tool-calling", verbose=True) r)create_openai_functions_agentcreate_openai_tools_agentcreate_react_agentcreate_tool_calling_agent)r RunnableAgentRunnableMultiActionAgentrNz@Must provide exactly one of 'toolkit' or 'db'. Received neither.z=Must provide exactly one of 'toolkit' or 'db'. Received both.)r'r%)dialectr4r4)r4rM)rMc3*K|] }|jvVdS)N)r2).0keyr&s i/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain_community/agent_toolkits/sql/base.py z#create_sql_agent..s+VVsf,,VVVVVV) table_info table_namesrT)rTc<g|]}t|t|Sr$) isinstancerrOtools rQ z$create_sql_agent..8!*TCV2W2WrSrU)rUc<g|]}t|t|Sr$)rWrrXs rQrZz$create_sql_agent..r[rS)r&z z{tools}inputoutput)runnableinput_keys_argreturn_keys_arg)contentz{input}agent_scratchpad) variable_name) openai-toolsz tool-callingrez Agent type z not supported at the moment. Must be one of 'tool-calling', 'openai-tools', 'openai-functions', or 'zero-shot-react-description'.zSQL Agent Executor)nameagenttoolsr,r<r6r8r:r$)(langchain_classic.agentsrGrHrIrJlangchain_classic.agents.agentrrKrL$langchain_classic.agents.agent_typesr ValueErrorrZERO_SHOT_REACT_DESCRIPTION get_toolslistrformatrMr2partialr;any get_contextlangchain_classic.agents.mrklr&FORMAT_INSTRUCTIONSjoinrr from_templateOPENAI_FUNCTIONSrr rr rrr from_messages)!r'r(r*r,r.r0r1r2r4r6r8r:r<r>r@r%r&rDrGrHrIrJrrKrLrrh db_context react_prompttemplatergmessagesr_s! ` rQcreate_sql_agentr~.sJ  ?>>>>>2: N    2  K   ;+;;;GDyDJ     ${"3"3 3E ~ ~weDD f, , ,^^#e**^55F . . .^^GO^< L L L L L L$G|'G {{'(j H$1(;;F  ''UF;;#9%J      y1 1 1 >d3&7&7888*8CC&"@,@AAA#2DEEE H (5h??F  223vFF#9%J      7 7 7 >d3&7&7888*8CC&"@,@AAA#2DEEE H (5h??F  ' '00eVDDHH00eVDDH(( #9%J      -* - - -   =  !)%-3   ! &B   rS)NNNNNNNr!r"Nr#FNr$)&r'rr(r)r*r+r,r-r.r/r0r/r1r/r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rAr%rBr&rCrDrrEr)-__doc__ __future__rtypingrrrrrr r r r langchain_core.messagesr rlangchain_core.promptsrrlangchain_core.prompts.chatrrr-langchain_community.agent_toolkits.sql.promptrrr.langchain_community.agent_toolkits.sql.toolkitr+langchain_community.tools.sql_database.toolrrrjrrkrlangchain_core.callbacksrlangchain_core.language_modelsrlangchain_core.toolsr*langchain_community.utilities.sql_databaser r~r$rSrQrsA""""""                      =<<<<<<<EEEEEEEE  NMMMMM G<<<<<<>>>>>><<<<<<@@@@@@------FFFFFF -1 6:  )-+/$&*.!(6:&(#_&!%+/)________rS