return sql intermediate steps (#792)

docs/modules/chains/examples/sqlite.ipynb

@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "15ff81df",
    "metadata": {
     "pycharm": {
@@ -96,7 +96,7 @@
        "' There are 9 employees.'"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -188,6 +188,62 @@
     "db_chain.run(\"How many employees are there in the foobar table?\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "88d8b969",
+   "metadata": {},
+   "source": [
+    "## Return Intermediate Steps\n",
+    "\n",
+    "You can also return the intermediate steps of the SQLDatabaseChain. This allows you to access the SQL statement that was generated, as well as the result of running that against the SQL Database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "38559487",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True, return_intermediate_steps=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "78b6af4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
+      "How many employees are there in the foobar table? \n",
+      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Employee;\u001b[0m\n",
+      "SQLResult: \u001b[33;1m\u001b[1;3m[(9,)]\u001b[0m\n",
+      "Answer:\u001b[32;1m\u001b[1;3m There are 9 employees in the foobar table.\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[' SELECT COUNT(*) FROM Employee;', '[(9,)]']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result = db_chain(\"How many employees are there in the foobar table?\")\n",
+    "result[\"intermediate_steps\"]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b408f800",
@@ -405,7 +461,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

langchain/chains/sql_database/base.py

@@ -34,6 +34,7 @@ class SQLDatabaseChain(Chain, BaseModel):
     """Number of results to return from the query"""
     input_key: str = "query"  #: :meta private:
     output_key: str = "result"  #: :meta private:
+    return_intermediate_steps: bool = False
 
     class Config:
         """Configuration for this pydantic object."""
@@ -55,9 +56,12 @@ class SQLDatabaseChain(Chain, BaseModel):
 
         :meta private:
         """
-        return [self.output_key]
+        if not self.return_intermediate_steps:
+            return [self.output_key]
+        else:
+            return [self.output_key, "intermediate_steps"]
 
-    def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+    def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
         llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)
         input_text = f"{inputs[self.input_key]} \nSQLQuery:"
         self.callback_manager.on_text(input_text, verbose=self.verbose)
@@ -71,10 +75,12 @@ class SQLDatabaseChain(Chain, BaseModel):
             "table_info": table_info,
             "stop": ["\nSQLResult:"],
         }
-
+        intermediate_steps = []
         sql_cmd = llm_chain.predict(**llm_inputs)
+        intermediate_steps.append(sql_cmd)
         self.callback_manager.on_text(sql_cmd, color="green", verbose=self.verbose)
         result = self.database.run(sql_cmd)
+        intermediate_steps.append(result)
         self.callback_manager.on_text("\nSQLResult: ", verbose=self.verbose)
         self.callback_manager.on_text(result, color="yellow", verbose=self.verbose)
         self.callback_manager.on_text("\nAnswer:", verbose=self.verbose)
@@ -82,7 +88,10 @@ class SQLDatabaseChain(Chain, BaseModel):
         llm_inputs["input"] = input_text
         final_result = llm_chain.predict(**llm_inputs)
         self.callback_manager.on_text(final_result, color="green", verbose=self.verbose)
-        return {self.output_key: final_result}
+        chain_result: Dict[str, Any] = {self.output_key: final_result}
+        if self.return_intermediate_steps:
+            chain_result["intermediate_steps"] = intermediate_steps
+        return chain_result
 
     @property
     def _chain_type(self) -> str: