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harrison/s
Author | SHA1 | Date | |
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3665c98158 |
@ -21,7 +21,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 8,
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"id": "ac561cc4",
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"metadata": {},
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"outputs": [],
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@ -32,7 +32,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 9,
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"id": "07e96d99",
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"metadata": {},
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"outputs": [],
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@ -43,16 +43,16 @@
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"db = SQLDatabase.from_uri(\"sqlite:///../../../notebooks/Chinook.db\")\n",
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"db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)\n",
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"tools = [\n",
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" Tool(\n",
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" name = \"Search\",\n",
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" func=search.run,\n",
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" description=\"useful for when you need to answer questions about current events\"\n",
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" ),\n",
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" Tool(\n",
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" name=\"Calculator\",\n",
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" func=llm_math_chain.run,\n",
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" description=\"useful for when you need to answer questions about math\"\n",
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" ),\n",
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"# Tool(\n",
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"# name = \"Search\",\n",
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"# func=search.run,\n",
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"# description=\"useful for when you need to answer questions about current events\"\n",
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"# ),\n",
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"# Tool(\n",
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"# name=\"Calculator\",\n",
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"# func=llm_math_chain.run,\n",
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"# description=\"useful for when you need to answer questions about math\"\n",
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"# ),\n",
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" Tool(\n",
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" name=\"FooBar DB\",\n",
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" func=db_chain.run,\n",
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@ -63,7 +63,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 10,
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"id": "a069c4b6",
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"metadata": {},
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"outputs": [],
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@ -71,6 +71,153 @@
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"mrkl = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "356a1396",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"What was our total number of sales and total revenue in 2013?\n",
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"Thought:\u001b[32;1m\u001b[1;3m I need to find out how many sales and how much revenue we had in 2013\n",
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"Action: FooBar DB\n",
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"Action Input: sales 2013\u001b[0m\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"sales 2013\n",
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"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",
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"SQLResult: \u001b[33;1m\u001b[1;3m[(450.58000000000027,)]\u001b[0m\n",
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"Answer:\u001b[32;1m\u001b[1;3m 450.58\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\n",
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"Observation: \u001b[36;1m\u001b[1;3m 450.58\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I need to find out how many sales and how much revenue we had in 2013\n",
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"Action: FooBar DB\n",
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"Action Input: revenue 2013\u001b[0m\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"revenue 2013\n",
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"SQLQuery:\u001b[32;1m\u001b[1;3m SELECT SUM(Total) FROM Invoice WHERE strftime('%Y', InvoiceDate) = '2013'\u001b[0m\n",
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"SQLResult: \u001b[33;1m\u001b[1;3m[(450.58000000000027,)]\u001b[0m\n",
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"Answer:\u001b[32;1m\u001b[1;3m 450.58\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\n",
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"Observation: \u001b[36;1m\u001b[1;3m 450.58\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: We had 450.58 sales and 450.58 revenue in 2013\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'We had 450.58 sales and 450.58 revenue in 2013'"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mrkl.run(\"What was our total number of sales and total revenue in 2013?\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "4e9c9b23",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import ZeroShotAgent\n",
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"from langchain import PromptTemplate\n",
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"from langchain import LLMChain\n",
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"TEMPLATE = \"\"\"You are a data engineer answering questions using a SQL database.\n",
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"\n",
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"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",
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"\n",
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"Only use the following tables:\n",
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"\n",
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"{table_info}\n",
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"\n",
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"Use the following format:\n",
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"\n",
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"Question: the input question you must answer\n",
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"Thought: you should always think about what to do\n",
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"Action: SQLDB\n",
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"Action Input: the query to run against the SQL database\n",
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"Observation: the result of the action\n",
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"... (this Thought/Action/Action Input/Observation can repeat N times)\n",
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"Thought: I now know the final answer\n",
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"Final Answer: the final answer to the original input question\n",
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"\n",
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"Begin!\n",
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"\n",
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"Question: {{input}}\"\"\".format(**{\n",
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" \"dialect\": db.dialect,\n",
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" \"table_info\": db.table_info})\n",
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"prompt = PromptTemplate(template=TEMPLATE, input_variables=[\"input\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "a6fb1b4f",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
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"tools = [\n",
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" Tool(\"SQLDB\", db.run, \"foo\")\n",
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"]\n",
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"agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "64578802",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"What was our total number of sales and total revenue in 2013?\n",
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"Thought:\u001b[32;1m\u001b[1;3m I need to find the total number of sales and total revenue\n",
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"Action: SQLDB\n",
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"Action Input: SELECT SUM(Total) AS \"Total Sales\", SUM(Total) AS \"Total Revenue\" FROM Invoice WHERE InvoiceDate LIKE '2013%'\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m[(450.58000000000027, 450.58000000000027)]\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: 450.58\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'450.58'"
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]
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\"What was our total number of sales and total revenue in 2013?\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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22
langchain/agents/sql_agent.py
Normal file
22
langchain/agents/sql_agent.py
Normal file
@ -0,0 +1,22 @@
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TEMPLATE = """You are a data engineer answering questions using a SQL database.
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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.
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Only use the following tables:
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{table_info}
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Use the following format:
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Question: the input question you must answer
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Thought: you should always think about what to do
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Action: SQLDB
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Action Input: the query to run against the SQL database
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Observation: the result of the action
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... (this Thought/Action/Action Input/Observation can repeat N times)
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Thought: I now know the final answer
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Final Answer: the final answer to the original input question
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Begin!
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Question: {input}"""
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@ -64,7 +64,10 @@ class SQLDatabaseChain(Chain, BaseModel):
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}
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sql_cmd = llm_chain.predict(**llm_inputs)
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chained_input.add(sql_cmd, color="green")
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try:
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result = self.database.run(sql_cmd)
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except Exception as e:
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result = str(e)
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chained_input.add("\nSQLResult: ")
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chained_input.add(result, color="yellow")
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chained_input.add("\nAnswer:")
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@ -67,5 +67,8 @@ class SQLDatabase:
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def run(self, command: str) -> str:
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"""Execute a SQL command and return a string of the results."""
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try:
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result = self._engine.execute(command).fetchall()
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except Exception as e:
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result = e
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return str(result)
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Block a user