stash

docs/examples/agents/mrkl.ipynb

@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "ac561cc4",
    "metadata": {},
    "outputs": [],
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "id": "07e96d99",
    "metadata": {},
    "outputs": [],
@@ -43,16 +43,16 @@
     "db = SQLDatabase.from_uri(\"sqlite:///../../../notebooks/Chinook.db\")\n",
     "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)\n",
     "tools = [\n",
-    "    Tool(\n",
-    "        name = \"Search\",\n",
-    "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\"\n",
-    "    ),\n",
-    "    Tool(\n",
-    "        name=\"Calculator\",\n",
-    "        func=llm_math_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about math\"\n",
-    "    ),\n",
+    "#     Tool(\n",
+    "#         name = \"Search\",\n",
+    "#         func=search.run,\n",
+    "#         description=\"useful for when you need to answer questions about current events\"\n",
+    "#     ),\n",
+    "#     Tool(\n",
+    "#         name=\"Calculator\",\n",
+    "#         func=llm_math_chain.run,\n",
+    "#         description=\"useful for when you need to answer questions about math\"\n",
+    "#     ),\n",
     "    Tool(\n",
     "        name=\"FooBar DB\",\n",
     "        func=db_chain.run,\n",
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "id": "a069c4b6",
    "metadata": {},
    "outputs": [],
@@ -71,6 +71,153 @@
     "mrkl = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "356a1396",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "What was our total number of sales and total revenue in 2013?\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out how many sales and how much revenue we had in 2013\n",
+      "Action: FooBar DB\n",
+      "Action Input: sales 2013\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "sales 2013\n",
+      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT SUM(Total) FROM Invoice WHERE InvoiceDate BETWEEN '2013-01-01' AND '2013-12-31'\u001b[0m\n",
+      "SQLResult: \u001b[33;1m\u001b[1;3m[(450.58000000000027,)]\u001b[0m\n",
+      "Answer:\u001b[32;1m\u001b[1;3m 450.58\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[36;1m\u001b[1;3m 450.58\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out how many sales and how much revenue we had in 2013\n",
+      "Action: FooBar DB\n",
+      "Action Input: revenue 2013\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "revenue 2013\n",
+      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT SUM(Total) FROM Invoice WHERE strftime('%Y', InvoiceDate) = '2013'\u001b[0m\n",
+      "SQLResult: \u001b[33;1m\u001b[1;3m[(450.58000000000027,)]\u001b[0m\n",
+      "Answer:\u001b[32;1m\u001b[1;3m 450.58\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[36;1m\u001b[1;3m 450.58\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: We had 450.58 sales and 450.58 revenue in 2013\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'We had 450.58 sales and 450.58 revenue in 2013'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mrkl.run(\"What was our total number of sales and total revenue in 2013?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4e9c9b23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import ZeroShotAgent\n",
+    "from langchain import PromptTemplate\n",
+    "from langchain import LLMChain\n",
+    "TEMPLATE = \"\"\"You are a data engineer answering questions using a SQL database.\n",
+    "\n",
+    "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. If a previous query produced an error, do NOT try it again.\n",
+    "\n",
+    "Only use the following tables:\n",
+    "\n",
+    "{table_info}\n",
+    "\n",
+    "Use the following format:\n",
+    "\n",
+    "Question: the input question you must answer\n",
+    "Thought: you should always think about what to do\n",
+    "Action: SQLDB\n",
+    "Action Input: the query to run against the SQL database\n",
+    "Observation: the result of the action\n",
+    "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
+    "Thought: I now know the final answer\n",
+    "Final Answer: the final answer to the original input question\n",
+    "\n",
+    "Begin!\n",
+    "\n",
+    "Question: {{input}}\"\"\".format(**{\n",
+    "            \"dialect\": db.dialect,\n",
+    "            \"table_info\": db.table_info})\n",
+    "prompt = PromptTemplate(template=TEMPLATE, input_variables=[\"input\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a6fb1b4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
+    "tools = [\n",
+    "    Tool(\"SQLDB\", db.run, \"foo\")\n",
+    "]\n",
+    "agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "64578802",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "What was our total number of sales and total revenue in 2013?\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find the total number of sales and total revenue\n",
+      "Action: SQLDB\n",
+      "Action Input: SELECT SUM(Total) AS \"Total Sales\", SUM(Total) AS \"Total Revenue\" FROM Invoice WHERE InvoiceDate LIKE '2013%'\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m[(450.58000000000027, 450.58000000000027)]\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 450.58\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'450.58'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"What was our total number of sales and total revenue in 2013?\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,

langchain/agents/sql_agent.py

@@ -0,0 +1,22 @@
+TEMPLATE = """You are a data engineer answering questions using a SQL database.
+
+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.
+
+Only use the following tables:
+
+{table_info}
+
+Use the following format:
+
+Question: the input question you must answer
+Thought: you should always think about what to do
+Action: SQLDB
+Action Input: the query to run against the SQL database
+Observation: the result of the action
+... (this Thought/Action/Action Input/Observation can repeat N times)
+Thought: I now know the final answer
+Final Answer: the final answer to the original input question
+
+Begin!
+
+Question: {input}"""

langchain/chains/sql_database/base.py

@@ -64,7 +64,10 @@ class SQLDatabaseChain(Chain, BaseModel):
         }
         sql_cmd = llm_chain.predict(**llm_inputs)
         chained_input.add(sql_cmd, color="green")
-        result = self.database.run(sql_cmd)
+        try:
+            result = self.database.run(sql_cmd)
+        except Exception as e:
+            result = str(e)
         chained_input.add("\nSQLResult: ")
         chained_input.add(result, color="yellow")
         chained_input.add("\nAnswer:")

langchain/sql_database.py

@@ -67,5 +67,8 @@ class SQLDatabase:
 
     def run(self, command: str) -> str:
         """Execute a SQL command and return a string of the results."""
-        result = self._engine.execute(command).fetchall()
+        try:
+            result = self._engine.execute(command).fetchall()
+        except Exception as e:
+            result = e
         return str(result)