more complex sql chain (#619)

add a more complex sql chain that first subsets the necessary tables

docs/modules/chains/examples/sqlite.ipynb

@@ -179,10 +179,80 @@
     "db_chain.run(\"How many employees are there in the foobar table?\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c12ae15a",
+   "metadata": {},
+   "source": [
+    "## SQLDatabaseSequentialChain\n",
+    "\n",
+    "Chain for querying SQL database that is a sequential chain.\n",
+    "\n",
+    "The chain is as follows:\n",
+    "\n",
+    "    1. Based on the query, determine which tables to use.\n",
+    "    2. Based on those tables, call the normal SQL database chain.\n",
+    "\n",
+    "This is useful in cases where the number of tables in the database is large."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e59a4740",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.sql_database.base import SQLDatabaseSequentialChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "58bb49b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = SQLDatabaseSequentialChain.from_llm(llm, db, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "95017b1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new SQLDatabaseSequentialChain chain...\u001b[0m\n",
+      "Table names to use:\n",
+      "\u001b[33;1m\u001b[1;3m['Employee', 'Customer']\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' 0 employees are also customers.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"How many employees are also customers?\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e59a4740",
+   "id": "b2998b03",
    "metadata": {},
    "outputs": [],
    "source": []

langchain/chains/sequential.py

@@ -47,7 +47,10 @@ class SequentialChain(Chain, BaseModel):
         for chain in chains:
             missing_vars = set(chain.input_keys).difference(known_variables)
             if missing_vars:
-                raise ValueError(f"Missing required input keys: {missing_vars}")
+                raise ValueError(
+                    f"Missing required input keys: {missing_vars}, "
+                    f"only had {known_variables}"
+                )
             overlapping_keys = known_variables.intersection(chain.output_keys)
             if overlapping_keys:
                 raise ValueError(

langchain/chains/sql_database/base.py

@@ -1,11 +1,13 @@
 """Chain for interacting with SQL Database."""
-from typing import Dict, List
+from __future__ import annotations
+
+from typing import Any, Dict, List
 
 from pydantic import BaseModel, Extra
 
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
-from langchain.chains.sql_database.prompt import PROMPT
+from langchain.chains.sql_database.prompt import DECIDER_PROMPT, PROMPT
 from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 from langchain.sql_database import SQLDatabase
@@ -53,15 +55,18 @@ class SQLDatabaseChain(Chain, BaseModel):
         """
         return [self.output_key]
 
-    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
+    def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
         llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)
         input_text = f"{inputs[self.input_key]} \nSQLQuery:"
         if self.verbose:
             self.callback_manager.on_text(input_text)
+        # 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,
             "dialect": self.database.dialect,
-            "table_info": self.database.table_info,
+            "table_info": table_info,
             "stop": ["\nSQLResult:"],
         }
         sql_cmd = llm_chain.predict(**llm_inputs)
@@ -78,3 +83,68 @@ class SQLDatabaseChain(Chain, BaseModel):
         if self.verbose:
             self.callback_manager.on_text(final_result, color="green")
         return {self.output_key: final_result}
+
+
+class SQLDatabaseSequentialChain(Chain, BaseModel):
+    """Chain for querying SQL database that is a sequential chain.
+
+    The chain is as follows:
+    1. Based on the query, determine which tables to use.
+    2. Based on those tables, call the normal SQL database chain.
+
+    This is useful in cases where the number of tables in the database is large.
+    """
+
+    @classmethod
+    def from_llm(
+        cls,
+        llm: BaseLLM,
+        database: SQLDatabase,
+        query_prompt: BasePromptTemplate = PROMPT,
+        decider_prompt: BasePromptTemplate = DECIDER_PROMPT,
+        **kwargs: Any,
+    ) -> SQLDatabaseSequentialChain:
+        """Load the necessary chains."""
+        sql_chain = SQLDatabaseChain(llm=llm, database=database, prompt=query_prompt)
+        decider_chain = LLMChain(
+            llm=llm, prompt=decider_prompt, output_key="table_names"
+        )
+        return cls(sql_chain=sql_chain, decider_chain=decider_chain, **kwargs)
+
+    decider_chain: LLMChain
+    sql_chain: SQLDatabaseChain
+    input_key: str = "query"  #: :meta private:
+    output_key: str = "result"  #: :meta private:
+
+    @property
+    def input_keys(self) -> List[str]:
+        """Return the singular input key.
+
+        :meta private:
+        """
+        return [self.input_key]
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Return the singular output key.
+
+        :meta private:
+        """
+        return [self.output_key]
+
+    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
+        _table_names = self.sql_chain.database.get_table_names()
+        table_names = ", ".join(_table_names)
+        llm_inputs = {
+            "query": inputs[self.input_key],
+            "table_names": table_names,
+        }
+        table_names_to_use = self.decider_chain.predict_and_parse(**llm_inputs)
+        if self.verbose:
+            self.callback_manager.on_text("Table names to use:", end="\n")
+            self.callback_manager.on_text(str(table_names_to_use), color="yellow")
+        new_inputs = {
+            self.sql_chain.input_key: inputs[self.input_key],
+            "table_names_to_use": table_names_to_use,
+        }
+        return self.sql_chain(new_inputs, return_only_outputs=True)

langchain/chains/sql_database/prompt.py

@@ -1,4 +1,5 @@
 # flake8: noqa
+from langchain.prompts.base import CommaSeparatedListOutputParser
 from langchain.prompts.prompt import PromptTemplate
 
 _DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
@@ -17,3 +18,16 @@ Question: {input}"""
 PROMPT = PromptTemplate(
     input_variables=["input", "table_info", "dialect"], template=_DEFAULT_TEMPLATE
 )
+
+_DECIDER_TEMPLATE = """Given the below input question and list of potential tables, output a comma separated list of the table names that may be neccessary to answer this question.
+
+Question: {query}
+
+Table Names: {table_names}
+
+Relevant Table Names:"""
+DECIDER_PROMPT = PromptTemplate(
+    input_variables=["query", "table_names"],
+    template=_DECIDER_TEMPLATE,
+    output_parser=CommaSeparatedListOutputParser(),
+)

langchain/prompts/base.py

@@ -64,6 +64,14 @@ class ListOutputParser(BaseOutputParser):
         """Parse the output of an LLM call."""
 
 
+class CommaSeparatedListOutputParser(ListOutputParser):
+    """Parse out comma separated lists."""
+
+    def parse(self, text: str) -> List[str]:
+        """Parse the output of an LLM call."""
+        return text.strip().split(", ")
+
+
 class RegexParser(BaseOutputParser, BaseModel):
     """Class to parse the output into a dictionary."""
 

langchain/sql_database.py

@@ -50,7 +50,8 @@ class SQLDatabase:
         """Return string representation of dialect to use."""
         return self._engine.dialect.name
 
-    def _get_table_names(self) -> Iterable[str]:
+    def get_table_names(self) -> Iterable[str]:
+        """Get names of tables available."""
         if self._include_tables:
             return self._include_tables
         return set(self._all_tables) - set(self._ignore_tables)
@@ -58,9 +59,19 @@ class SQLDatabase:
     @property
     def table_info(self) -> str:
         """Information about all tables in the database."""
+        return self.get_table_info()
+
+    def get_table_info(self, table_names: Optional[List[str]] = None) -> str:
+        """Get information about specified tables."""
+        all_table_names = self.get_table_names()
+        if table_names is not None:
+            missing_tables = set(table_names).difference(all_table_names)
+            if missing_tables:
+                raise ValueError(f"table_names {missing_tables} not found in database")
+            all_table_names = table_names
         template = "Table '{table_name}' has columns: {columns}."
         tables = []
-        for table_name in self._get_table_names():
+        for table_name in all_table_names:
             columns = []
             for column in self._inspector.get_columns(table_name, schema=self._schema):
                 columns.append(f"{column['name']} ({str(column['type'])})")