"""Vector SQL Database Chain Retriever""" from __future__ import annotations from typing import Any, Dict, List, Optional, Sequence, Union from langchain.chains.llm import LLMChain from langchain.chains.sql_database.prompt import PROMPT, SQL_PROMPTS from langchain_community.tools.sql_database.prompt import QUERY_CHECKER from langchain_community.utilities.sql_database import SQLDatabase from langchain_core.callbacks.manager import CallbackManagerForChainRun from langchain_core.embeddings import Embeddings from langchain_core.language_models import BaseLanguageModel from langchain_core.output_parsers import BaseOutputParser from langchain_core.prompts import BasePromptTemplate from langchain_core.prompts.prompt import PromptTemplate from pydantic import ConfigDict from langchain_experimental.sql.base import INTERMEDIATE_STEPS_KEY, SQLDatabaseChain class VectorSQLOutputParser(BaseOutputParser[str]): """Output Parser for Vector SQL. 1. finds for `NeuralArray()` and replace it with the embedding 2. finds for `DISTANCE()` and replace it with the distance name in backend SQL """ model: Embeddings """Embedding model to extract embedding for entity""" distance_func_name: str = "distance" """Distance name for Vector SQL""" model_config = ConfigDict( arbitrary_types_allowed=True, ) @property def _type(self) -> str: return "vector_sql_parser" @classmethod def from_embeddings( cls, model: Embeddings, distance_func_name: str = "distance", **kwargs: Any ) -> VectorSQLOutputParser: return cls(model=model, distance_func_name=distance_func_name, **kwargs) def parse(self, text: str) -> str: text = text.strip() start = text.find("NeuralArray(") _sql_str_compl = text if start > 0: _matched = text[text.find("NeuralArray(") + len("NeuralArray(") :] end = _matched.find(")") + start + len("NeuralArray(") + 1 entity = _matched[: _matched.find(")")] vecs = self.model.embed_query(entity) vecs_str = "[" + ",".join(map(str, vecs)) + "]" _sql_str_compl = text.replace("DISTANCE", self.distance_func_name).replace( text[start:end], vecs_str ) if _sql_str_compl[-1] == ";": _sql_str_compl = _sql_str_compl[:-1] return _sql_str_compl class VectorSQLRetrieveAllOutputParser(VectorSQLOutputParser): """Parser based on VectorSQLOutputParser. It also modifies the SQL to get all columns. """ @property def _type(self) -> str: return "vector_sql_retrieve_all_parser" def parse(self, text: str) -> str: text = text.strip() start = text.upper().find("SELECT") if start >= 0: end = text.upper().find("FROM") text = text.replace(text[start + len("SELECT") + 1 : end - 1], "*") return super().parse(text) def get_result_from_sqldb(db: SQLDatabase, cmd: str) -> Sequence[Dict[str, Any]]: """Get result from SQL Database.""" result = db._execute(cmd, fetch="all") assert isinstance(result, Sequence) return result class VectorSQLDatabaseChain(SQLDatabaseChain): """Chain for interacting with Vector SQL Database. Example: .. code-block:: python from langchain_experimental.sql import SQLDatabaseChain from langchain_community.llms import OpenAI, SQLDatabase, OpenAIEmbeddings db = SQLDatabase(...) db_chain = VectorSQLDatabaseChain.from_llm(OpenAI(), db, OpenAIEmbeddings()) *Security note*: Make sure that the database connection uses credentials that are narrowly-scoped to only include the permissions this chain needs. Failure to do so may result in data corruption or loss, since this chain may attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted. The best way to guard against such negative outcomes is to (as appropriate) limit the permissions granted to the credentials used with this chain. This issue shows an example negative outcome if these steps are not taken: https://github.com/langchain-ai/langchain/issues/5923 """ sql_cmd_parser: VectorSQLOutputParser """Parser for Vector SQL""" native_format: bool = False """If return_direct, controls whether to return in python native format""" def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, Any]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() input_text = f"{inputs[self.input_key]}\nSQLQuery:" _run_manager.on_text(input_text, verbose=self.verbose) # If not present, then defaults to None which is all tables. table_names_to_use = inputs.get("table_names_to_use") table_info = self.database.get_table_info(table_names=table_names_to_use) llm_inputs = { "input": input_text, "top_k": str(self.top_k), "dialect": self.database.dialect, "table_info": table_info, "stop": ["\nSQLResult:"], } intermediate_steps: List = [] try: intermediate_steps.append(llm_inputs) # input: sql generation llm_out = self.llm_chain.predict( callbacks=_run_manager.get_child(), **llm_inputs, ) sql_cmd = self.sql_cmd_parser.parse(llm_out) if self.return_sql: return {self.output_key: sql_cmd} if not self.use_query_checker: _run_manager.on_text(llm_out, color="green", verbose=self.verbose) intermediate_steps.append( llm_out ) # output: sql generation (no checker) intermediate_steps.append({"sql_cmd": llm_out}) # input: sql exec result = get_result_from_sqldb(self.database, sql_cmd) intermediate_steps.append(str(result)) # output: sql exec else: 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) intermediate_steps.append( checked_llm_out ) # output: sql generation (checker) _run_manager.on_text( checked_llm_out, color="green", verbose=self.verbose ) intermediate_steps.append( {"sql_cmd": checked_llm_out} ) # input: sql exec result = get_result_from_sqldb(self.database, checked_sql_command) intermediate_steps.append(str(result)) # output: sql exec llm_out = checked_llm_out sql_cmd = checked_sql_command _run_manager.on_text("\nSQLResult: ", verbose=self.verbose) _run_manager.on_text(str(result), color="yellow", verbose=self.verbose) # If return direct, we just set the final result equal to # the result of the sql query result (`Sequence[Dict[str, Any]]`), # otherwise try to get a human readable final answer (`str`). final_result: Union[str, Sequence[Dict[str, Any]]] if self.return_direct: final_result = result else: _run_manager.on_text("\nAnswer:", verbose=self.verbose) input_text += f"{llm_out}\nSQLResult: {result}\nAnswer:" llm_inputs["input"] = input_text intermediate_steps.append(llm_inputs) # input: final answer final_result = self.llm_chain.predict( callbacks=_run_manager.get_child(), **llm_inputs, ).strip() intermediate_steps.append(final_result) # output: final answer _run_manager.on_text(final_result, color="green", verbose=self.verbose) chain_result: Dict[str, Any] = {self.output_key: final_result} if self.return_intermediate_steps: chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps return chain_result except Exception as exc: # Append intermediate steps to exception, to aid in logging and later # improvement of few shot prompt seeds exc.intermediate_steps = intermediate_steps # type: ignore raise exc @property def _chain_type(self) -> str: return "vector_sql_database_chain" @classmethod def from_llm( cls, llm: BaseLanguageModel, db: SQLDatabase, prompt: Optional[BasePromptTemplate] = None, sql_cmd_parser: Optional[VectorSQLOutputParser] = None, **kwargs: Any, ) -> VectorSQLDatabaseChain: assert sql_cmd_parser, "`sql_cmd_parser` must be set in VectorSQLDatabaseChain." prompt = prompt or SQL_PROMPTS.get(db.dialect, PROMPT) llm_chain = LLMChain(llm=llm, prompt=prompt) return cls( llm_chain=llm_chain, database=db, sql_cmd_parser=sql_cmd_parser, **kwargs )