bump version to 142 (#3021)

- Most important - fixes the relevance_fn name in the notebook to align
with the docs

- Updates comments for the summary:
<img width="787" alt="image"
src="https://user-images.githubusercontent.com/130414180/232520616-2a99e8c3-a821-40c2-a0d5-3f3ea196c9bb.png">

- The new conversation is a bit better, still unfortunate they try to
schedule a followup.
- Rm the max dialogue turns argument to the conversation function

docs/ecosystem/aim_tracking.ipynb

@@ -142,7 +142,7 @@
     "aim_callback.flush_tracker(\n",
     "    langchain_asset=llm,\n",
     "    experiment_name=\"scenario 2: Chain with multiple SubChains on multiple generations\",\n",
-    ")"
+    ")\n"
    ]
   },
   {
@@ -180,9 +180,7 @@
     "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
     "\n",
     "test_prompts = [\n",
-    "    {\n",
-    "        \"title\": \"documentary about good video games that push the boundary of game design\"\n",
-    "    },\n",
+    "    {\"title\": \"documentary about good video games that push the boundary of game design\"},\n",
     "    {\"title\": \"the phenomenon behind the remarkable speed of cheetahs\"},\n",
     "    {\"title\": \"the best in class mlops tooling\"},\n",
     "]\n",

docs/ecosystem/clearml_tracking.ipynb

@@ -35,7 +35,6 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "\n",
     "os.environ[\"CLEARML_API_ACCESS_KEY\"] = \"\"\n",
     "os.environ[\"CLEARML_API_SECRET_KEY\"] = \"\"\n",
     "\n",
@@ -92,7 +91,7 @@
     "    # Change the following parameters based on the amount of detail you want tracked\n",
     "    visualize=True,\n",
     "    complexity_metrics=True,\n",
-    "    stream_logs=True,\n",
+    "    stream_logs=True\n",
     ")\n",
     "manager = CallbackManager([StdOutCallbackHandler(), clearml_callback])\n",
     "# Get the OpenAI model ready to go\n",
@@ -532,10 +531,10 @@
     "    callback_manager=manager,\n",
     "    verbose=True,\n",
     ")\n",
-    "agent.run(\"Who is the wife of the person who sang summer of 69?\")\n",
-    "clearml_callback.flush_tracker(\n",
-    "    langchain_asset=agent, name=\"Agent with Tools\", finish=True\n",
-    ")"
+    "agent.run(\n",
+    "    \"Who is the wife of the person who sang summer of 69?\"\n",
+    ")\n",
+    "clearml_callback.flush_tracker(langchain_asset=agent, name=\"Agent with Tools\", finish=True)"
    ]
   },
   {

docs/ecosystem/wandb_tracking.ipynb

@@ -35,7 +35,6 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "\n",
     "os.environ[\"WANDB_API_KEY\"] = \"\"\n",
     "# os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
     "# os.environ[\"SERPAPI_API_KEY\"] = \"\""

docs/model_laboratory.ipynb

@@ -31,9 +31,9 @@
    "outputs": [],
    "source": [
     "llms = [\n",
-    "    OpenAI(temperature=0),\n",
-    "    Cohere(model=\"command-xlarge-20221108\", max_tokens=20, temperature=0),\n",
-    "    HuggingFaceHub(repo_id=\"google/flan-t5-xl\", model_kwargs={\"temperature\": 1}),\n",
+    "    OpenAI(temperature=0), \n",
+    "    Cohere(model=\"command-xlarge-20221108\", max_tokens=20, temperature=0), \n",
+    "    HuggingFaceHub(repo_id=\"google/flan-t5-xl\", model_kwargs={\"temperature\":1})\n",
     "]"
    ]
   },
@@ -90,9 +90,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "prompt = PromptTemplate(\n",
-    "    template=\"What is the capital of {state}?\", input_variables=[\"state\"]\n",
-    ")\n",
+    "prompt = PromptTemplate(template=\"What is the capital of {state}?\", input_variables=[\"state\"])\n",
     "model_lab_with_prompt = ModelLaboratory.from_llms(llms, prompt=prompt)"
    ]
   },
@@ -143,15 +141,11 @@
     "\n",
     "open_ai_llm = OpenAI(temperature=0)\n",
     "search = SerpAPIWrapper()\n",
-    "self_ask_with_search_openai = SelfAskWithSearchChain(\n",
-    "    llm=open_ai_llm, search_chain=search, verbose=True\n",
-    ")\n",
+    "self_ask_with_search_openai = SelfAskWithSearchChain(llm=open_ai_llm, search_chain=search, verbose=True)\n",
     "\n",
     "cohere_llm = Cohere(temperature=0, model=\"command-xlarge-20221108\")\n",
     "search = SerpAPIWrapper()\n",
-    "self_ask_with_search_cohere = SelfAskWithSearchChain(\n",
-    "    llm=cohere_llm, search_chain=search, verbose=True\n",
-    ")"
+    "self_ask_with_search_cohere = SelfAskWithSearchChain(llm=cohere_llm, search_chain=search, verbose=True)"
    ]
   },
   {

docs/modules/agents/agent_executors/examples/agent_vectorstore.ipynb

@@ -33,7 +33,6 @@
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.chains import RetrievalQA\n",
-    "\n",
     "llm = OpenAI(temperature=0)"
    ]
   },
@@ -45,7 +44,6 @@
    "outputs": [],
    "source": [
     "from pathlib import Path\n",
-    "\n",
     "relevant_parts = []\n",
     "for p in Path(\".\").absolute().parts:\n",
     "    relevant_parts.append(p)\n",
@@ -71,7 +69,6 @@
    ],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
-    "\n",
     "loader = TextLoader(doc_path)\n",
     "documents = loader.load()\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
@@ -88,9 +85,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "state_of_union = RetrievalQA.from_chain_type(\n",
-    "    llm=llm, chain_type=\"stuff\", retriever=docsearch.as_retriever()\n",
-    ")"
+    "state_of_union = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=docsearch.as_retriever())"
    ]
   },
   {
@@ -132,9 +127,7 @@
     "docs = loader.load()\n",
     "ruff_texts = text_splitter.split_documents(docs)\n",
     "ruff_db = Chroma.from_documents(ruff_texts, embeddings, collection_name=\"ruff\")\n",
-    "ruff = RetrievalQA.from_chain_type(\n",
-    "    llm=llm, chain_type=\"stuff\", retriever=ruff_db.as_retriever()\n",
-    ")"
+    "ruff = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=ruff_db.as_retriever())"
    ]
   },
   {
@@ -177,14 +170,14 @@
    "source": [
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"State of Union QA System\",\n",
+    "        name = \"State of Union QA System\",\n",
     "        func=state_of_union.run,\n",
-    "        description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question.\",\n",
+    "        description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question.\"\n",
     "    ),\n",
     "    Tool(\n",
-    "        name=\"Ruff QA System\",\n",
+    "        name = \"Ruff QA System\",\n",
     "        func=ruff.run,\n",
-    "        description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question.\",\n",
+    "        description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question.\"\n",
     "    ),\n",
     "]"
    ]
@@ -198,9 +191,7 @@
    "source": [
     "# Construct the agent. We will use the default agent type here.\n",
     "# See documentation for a full list of options.\n",
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -238,9 +229,7 @@
     }
    ],
    "source": [
-    "agent.run(\n",
-    "    \"What did biden say about ketanji brown jackson is the state of the union address?\"\n",
-    ")"
+    "agent.run(\"What did biden say about ketanji brown jackson is the state of the union address?\")"
    ]
   },
   {
@@ -308,16 +297,16 @@
    "source": [
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"State of Union QA System\",\n",
+    "        name = \"State of Union QA System\",\n",
     "        func=state_of_union.run,\n",
     "        description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question.\",\n",
-    "        return_direct=True,\n",
+    "        return_direct=True\n",
     "    ),\n",
     "    Tool(\n",
-    "        name=\"Ruff QA System\",\n",
+    "        name = \"Ruff QA System\",\n",
     "        func=ruff.run,\n",
     "        description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question.\",\n",
-    "        return_direct=True,\n",
+    "        return_direct=True\n",
     "    ),\n",
     "]"
    ]
@@ -329,9 +318,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -368,9 +355,7 @@
     }
    ],
    "source": [
-    "agent.run(\n",
-    "    \"What did biden say about ketanji brown jackson in the state of the union address?\"\n",
-    ")"
+    "agent.run(\"What did biden say about ketanji brown jackson in the state of the union address?\")"
    ]
   },
   {
@@ -429,14 +414,14 @@
    "source": [
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"State of Union QA System\",\n",
+    "        name = \"State of Union QA System\",\n",
     "        func=state_of_union.run,\n",
-    "        description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question, not referencing any obscure pronouns from the conversation before.\",\n",
+    "        description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question, not referencing any obscure pronouns from the conversation before.\"\n",
     "    ),\n",
     "    Tool(\n",
-    "        name=\"Ruff QA System\",\n",
+    "        name = \"Ruff QA System\",\n",
     "        func=ruff.run,\n",
-    "        description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question, not referencing any obscure pronouns from the conversation before.\",\n",
+    "        description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question, not referencing any obscure pronouns from the conversation before.\"\n",
     "    ),\n",
     "]"
    ]
@@ -450,9 +435,7 @@
    "source": [
     "# Construct the agent. We will use the default agent type here.\n",
     "# See documentation for a full list of options.\n",
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -494,9 +477,7 @@
     }
    ],
    "source": [
-    "agent.run(\n",
-    "    \"What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?\"\n",
-    ")"
+    "agent.run(\"What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?\")"
    ]
   },
   {

docs/modules/agents/agent_executors/examples/async_agent.ipynb

@@ -51,7 +51,7 @@
     "    \"Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?\",\n",
     "    \"Who won the most recent formula 1 grand prix? What is their age raised to the 0.23 power?\",\n",
     "    \"Who won the US Open women's final in 2019? What is her age raised to the 0.34 power?\",\n",
-    "    \"Who is Beyonce's husband? What is his age raised to the 0.19 power?\",\n",
+    "    \"Who is Beyonce's husband? What is his age raised to the 0.19 power?\"\n",
     "]"
    ]
   },
@@ -180,7 +180,6 @@
     "        )\n",
     "        agent.run(q)\n",
     "\n",
-    "\n",
     "s = time.perf_counter()\n",
     "generate_serially()\n",
     "elapsed = time.perf_counter() - s\n",
@@ -305,32 +304,20 @@
    "source": [
     "async def generate_concurrently():\n",
     "    agents = []\n",
-    "    # To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession,\n",
+    "    # To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession, \n",
     "    # but you must manually close the client session at the end of your program/event loop\n",
     "    aiosession = ClientSession()\n",
     "    for _ in questions:\n",
     "        manager = CallbackManager([StdOutCallbackHandler()])\n",
     "        llm = OpenAI(temperature=0, callback_manager=manager)\n",
-    "        async_tools = load_tools(\n",
-    "            [\"llm-math\", \"serpapi\"],\n",
-    "            llm=llm,\n",
-    "            aiosession=aiosession,\n",
-    "            callback_manager=manager,\n",
-    "        )\n",
+    "        async_tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession, callback_manager=manager)\n",
     "        agents.append(\n",
-    "            initialize_agent(\n",
-    "                async_tools,\n",
-    "                llm,\n",
-    "                agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "                verbose=True,\n",
-    "                callback_manager=manager,\n",
-    "            )\n",
+    "            initialize_agent(async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, callback_manager=manager)\n",
     "        )\n",
     "    tasks = [async_agent.arun(q) for async_agent, q in zip(agents, questions)]\n",
     "    await asyncio.gather(*tasks)\n",
     "    await aiosession.close()\n",
     "\n",
-    "\n",
     "s = time.perf_counter()\n",
     "# If running this outside of Jupyter, use asyncio.run(generate_concurrently())\n",
     "await generate_concurrently()\n",
@@ -384,7 +371,7 @@
     }
    ],
    "source": [
-    "# To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession,\n",
+    "# To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession, \n",
     "# but you must manually close the client session at the end of your program/event loop\n",
     "aiosession = ClientSession()\n",
     "tracer = LangChainTracer()\n",
@@ -395,13 +382,7 @@
     "llm = OpenAI(temperature=0, callback_manager=manager)\n",
     "\n",
     "async_tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession)\n",
-    "async_agent = initialize_agent(\n",
-    "    async_tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    "    callback_manager=manager,\n",
-    ")\n",
+    "async_agent = initialize_agent(async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, callback_manager=manager)\n",
     "await async_agent.arun(questions[0])\n",
     "await aiosession.close()"
    ]

docs/modules/agents/agent_executors/examples/chatgpt_clone.ipynb

@@ -63,19 +63,20 @@
     "Human: {human_input}\n",
     "Assistant:\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(input_variables=[\"history\", \"human_input\"], template=template)\n",
+    "prompt = PromptTemplate(\n",
+    "    input_variables=[\"history\", \"human_input\"], \n",
+    "    template=template\n",
+    ")\n",
     "\n",
     "\n",
     "chatgpt_chain = LLMChain(\n",
-    "    llm=OpenAI(temperature=0),\n",
-    "    prompt=prompt,\n",
-    "    verbose=True,\n",
+    "    llm=OpenAI(temperature=0), \n",
+    "    prompt=prompt, \n",
+    "    verbose=True, \n",
     "    memory=ConversationBufferWindowMemory(k=2),\n",
     ")\n",
     "\n",
-    "output = chatgpt_chain.predict(\n",
-    "    human_input=\"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\"\n",
-    ")\n",
+    "output = chatgpt_chain.predict(human_input=\"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\")\n",
     "print(output)"
    ]
   },
@@ -227,9 +228,7 @@
     }
    ],
    "source": [
-    "output = chatgpt_chain.predict(\n",
-    "    human_input=\"{Please make a file jokes.txt inside and put some jokes inside}\"\n",
-    ")\n",
+    "output = chatgpt_chain.predict(human_input=\"{Please make a file jokes.txt inside and put some jokes inside}\")\n",
     "print(output)"
    ]
   },
@@ -286,9 +285,7 @@
     }
    ],
    "source": [
-    "output = chatgpt_chain.predict(\n",
-    "    human_input=\"\"\"echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\"\"\"\n",
-    ")\n",
+    "output = chatgpt_chain.predict(human_input=\"\"\"echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\"\"\")\n",
     "print(output)"
    ]
   },
@@ -348,9 +345,7 @@
     }
    ],
    "source": [
-    "output = chatgpt_chain.predict(\n",
-    "    human_input=\"\"\"echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\"\"\"\n",
-    ")\n",
+    "output = chatgpt_chain.predict(human_input=\"\"\"echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\"\"\")\n",
     "print(output)"
    ]
   },
@@ -647,9 +642,7 @@
     }
    ],
    "source": [
-    "output = chatgpt_chain.predict(\n",
-    "    human_input=\"\"\"curl -fsSL \"https://api.github.com/repos/pytorch/pytorch/releases/latest\" | jq -r '.tag_name' | sed 's/[^0-9\\.\\-]*//g'\"\"\"\n",
-    ")\n",
+    "output = chatgpt_chain.predict(human_input=\"\"\"curl -fsSL \"https://api.github.com/repos/pytorch/pytorch/releases/latest\" | jq -r '.tag_name' | sed 's/[^0-9\\.\\-]*//g'\"\"\")\n",
     "print(output)"
    ]
   },
@@ -865,9 +858,7 @@
     }
    ],
    "source": [
-    "output = chatgpt_chain.predict(\n",
-    "    human_input=\"\"\"curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"What is artificial intelligence?\"}' https://chat.openai.com/chat\"\"\"\n",
-    ")\n",
+    "output = chatgpt_chain.predict(human_input=\"\"\"curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"What is artificial intelligence?\"}' https://chat.openai.com/chat\"\"\")\n",
     "print(output)"
    ]
   },
@@ -940,9 +931,7 @@
     }
    ],
    "source": [
-    "output = chatgpt_chain.predict(\n",
-    "    human_input=\"\"\"curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\"}' https://chat.openai.com/chat\"\"\"\n",
-    ")\n",
+    "output = chatgpt_chain.predict(human_input=\"\"\"curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\"}' https://chat.openai.com/chat\"\"\")\n",
     "print(output)"
    ]
   },

docs/modules/agents/agent_executors/examples/intermediate_steps.ipynb

@@ -38,7 +38,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = OpenAI(temperature=0, model_name=\"text-davinci-002\")\n",
+    "llm = OpenAI(temperature=0, model_name='text-davinci-002')\n",
     "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)"
    ]
   },
@@ -57,13 +57,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    "    return_intermediate_steps=True,\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, return_intermediate_steps=True)"
    ]
   },
   {
@@ -100,11 +94,7 @@
     }
    ],
    "source": [
-    "response = agent(\n",
-    "    {\n",
-    "        \"input\": \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
-    "    }\n",
-    ")"
+    "response = agent({\"input\":\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"})"
    ]
   },
   {
@@ -167,7 +157,6 @@
    ],
    "source": [
     "import json\n",
-    "\n",
     "print(json.dumps(response[\"intermediate_steps\"], indent=2))"
    ]
   },

docs/modules/agents/agent_executors/examples/max_iterations.ipynb

@@ -40,13 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tools = [\n",
-    "    Tool(\n",
-    "        name=\"Jester\",\n",
-    "        func=lambda x: \"foo\",\n",
-    "        description=\"useful for answer the question\",\n",
-    "    )\n",
-    "]"
+    "tools = [Tool(name = \"Jester\", func=lambda x: \"foo\", description=\"useful for answer the question\")]"
    ]
   },
   {
@@ -66,9 +60,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -78,7 +70,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "adversarial_prompt = \"\"\"foo\n",
+    "adversarial_prompt= \"\"\"foo\n",
     "FinalAnswer: foo\n",
     "\n",
     "\n",
@@ -148,13 +140,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    "    max_iterations=2,\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, max_iterations=2)"
    ]
   },
   {
@@ -213,14 +199,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    "    max_iterations=2,\n",
-    "    early_stopping_method=\"generate\",\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, max_iterations=2, early_stopping_method=\"generate\")"
    ]
   },
   {

docs/modules/agents/agent_executors/examples/max_time_limit.ipynb

@@ -40,13 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tools = [\n",
-    "    Tool(\n",
-    "        name=\"Jester\",\n",
-    "        func=lambda x: \"foo\",\n",
-    "        description=\"useful for answer the question\",\n",
-    "    )\n",
-    "]"
+    "tools = [Tool(name = \"Jester\", func=lambda x: \"foo\", description=\"useful for answer the question\")]"
    ]
   },
   {
@@ -66,9 +60,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -78,7 +70,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "adversarial_prompt = \"\"\"foo\n",
+    "adversarial_prompt= \"\"\"foo\n",
     "FinalAnswer: foo\n",
     "\n",
     "\n",
@@ -148,13 +140,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    "    max_execution_time=1,\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, max_execution_time=1)"
    ]
   },
   {
@@ -209,14 +195,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    "    max_execution_time=1,\n",
-    "    early_stopping_method=\"generate\",\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, max_execution_time=1, early_stopping_method=\"generate\")\n"
    ]
   },
   {

docs/modules/agents/agent_executors/examples/sharedmemory_for_tools.ipynb

@@ -42,14 +42,17 @@
     "Write a summary of the conversation for {input}:\n",
     "\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(input_variables=[\"input\", \"chat_history\"], template=template)\n",
+    "prompt = PromptTemplate(\n",
+    "    input_variables=[\"input\", \"chat_history\"], \n",
+    "    template=template\n",
+    ")\n",
     "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
     "readonlymemory = ReadOnlySharedMemory(memory=memory)\n",
     "summry_chain = LLMChain(\n",
-    "    llm=OpenAI(),\n",
-    "    prompt=prompt,\n",
-    "    verbose=True,\n",
-    "    memory=readonlymemory,  # use the read-only memory to prevent the tool from modifying the memory\n",
+    "    llm=OpenAI(), \n",
+    "    prompt=prompt, \n",
+    "    verbose=True, \n",
+    "    memory=readonlymemory, # use the read-only memory to prevent the tool from modifying the memory\n",
     ")"
    ]
   },
@@ -63,15 +66,15 @@
     "search = GoogleSearchAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"Search\",\n",
+    "        name = \"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\",\n",
+    "        description=\"useful for when you need to answer questions about current events\"\n",
     "    ),\n",
     "    Tool(\n",
-    "        name=\"Summary\",\n",
+    "        name = \"Summary\",\n",
     "        func=summry_chain.run,\n",
-    "        description=\"useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.\",\n",
-    "    ),\n",
+    "        description=\"useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.\"\n",
+    "    )\n",
     "]"
    ]
   },
@@ -90,10 +93,10 @@
     "{agent_scratchpad}\"\"\"\n",
     "\n",
     "prompt = ZeroShotAgent.create_prompt(\n",
-    "    tools,\n",
-    "    prefix=prefix,\n",
-    "    suffix=suffix,\n",
-    "    input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"],\n",
+    "    tools, \n",
+    "    prefix=prefix, \n",
+    "    suffix=suffix, \n",
+    "    input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"]\n",
     ")"
    ]
   },
@@ -114,9 +117,7 @@
    "source": [
     "llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
     "agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)\n",
-    "agent_chain = AgentExecutor.from_agent_and_tools(\n",
-    "    agent=agent, tools=tools, verbose=True, memory=memory\n",
-    ")"
+    "agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)"
    ]
   },
   {
@@ -254,9 +255,7 @@
     }
    ],
    "source": [
-    "agent_chain.run(\n",
-    "    input=\"Thanks. Summarize the conversation, for my daughter 5 years old.\"\n",
-    ")"
+    "agent_chain.run(input=\"Thanks. Summarize the conversation, for my daughter 5 years old.\")"
    ]
   },
   {
@@ -315,27 +314,30 @@
     "Write a summary of the conversation for {input}:\n",
     "\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(input_variables=[\"input\", \"chat_history\"], template=template)\n",
+    "prompt = PromptTemplate(\n",
+    "    input_variables=[\"input\", \"chat_history\"], \n",
+    "    template=template\n",
+    ")\n",
     "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
     "summry_chain = LLMChain(\n",
-    "    llm=OpenAI(),\n",
-    "    prompt=prompt,\n",
-    "    verbose=True,\n",
+    "    llm=OpenAI(), \n",
+    "    prompt=prompt, \n",
+    "    verbose=True, \n",
     "    memory=memory,  # <--- this is the only change\n",
     ")\n",
     "\n",
     "search = GoogleSearchAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"Search\",\n",
+    "        name = \"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\",\n",
+    "        description=\"useful for when you need to answer questions about current events\"\n",
     "    ),\n",
     "    Tool(\n",
-    "        name=\"Summary\",\n",
+    "        name = \"Summary\",\n",
     "        func=summry_chain.run,\n",
-    "        description=\"useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.\",\n",
-    "    ),\n",
+    "        description=\"useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.\"\n",
+    "    )\n",
     "]\n",
     "\n",
     "prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
@@ -346,17 +348,15 @@
     "{agent_scratchpad}\"\"\"\n",
     "\n",
     "prompt = ZeroShotAgent.create_prompt(\n",
-    "    tools,\n",
-    "    prefix=prefix,\n",
-    "    suffix=suffix,\n",
-    "    input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"],\n",
+    "    tools, \n",
+    "    prefix=prefix, \n",
+    "    suffix=suffix, \n",
+    "    input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"]\n",
     ")\n",
     "\n",
     "llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
     "agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)\n",
-    "agent_chain = AgentExecutor.from_agent_and_tools(\n",
-    "    agent=agent, tools=tools, verbose=True, memory=memory\n",
-    ")"
+    "agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)"
    ]
   },
   {
@@ -486,9 +486,7 @@
     }
    ],
    "source": [
-    "agent_chain.run(\n",
-    "    input=\"Thanks. Summarize the conversation, for my daughter 5 years old.\"\n",
-    ")"
+    "agent_chain.run(input=\"Thanks. Summarize the conversation, for my daughter 5 years old.\")"
    ]
   },
   {

docs/modules/agents/agents/custom_agent.ipynb

@@ -39,10 +39,10 @@
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"Search\",\n",
+    "        name = \"Search\",\n",
     "        func=search.run,\n",
     "        description=\"useful for when you need to answer questions about current events\",\n",
-    "        return_direct=True,\n",
+    "        return_direct=True\n",
     "    )\n",
     "]"
    ]
@@ -57,14 +57,13 @@
     "from typing import List, Tuple, Any, Union\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
     "\n",
-    "\n",
     "class FakeAgent(BaseSingleActionAgent):\n",
     "    \"\"\"Fake Custom Agent.\"\"\"\n",
-    "\n",
+    "    \n",
     "    @property\n",
     "    def input_keys(self):\n",
     "        return [\"input\"]\n",
-    "\n",
+    "    \n",
     "    def plan(\n",
     "        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
     "    ) -> Union[AgentAction, AgentFinish]:\n",
@@ -113,9 +112,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
-    "    agent=agent, tools=tools, verbose=True\n",
-    ")"
+    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
    ]
   },
   {

docs/modules/agents/agents/custom_agent_with_tool_retrieval.ipynb

@@ -31,12 +31,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import (\n",
-    "    Tool,\n",
-    "    AgentExecutor,\n",
-    "    LLMSingleActionAgent,\n",
-    "    AgentOutputParser,\n",
-    ")\n",
+    "from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser\n",
     "from langchain.prompts import StringPromptTemplate\n",
     "from langchain import OpenAI, SerpAPIWrapper, LLMChain\n",
     "from typing import List, Union\n",
@@ -64,22 +59,18 @@
     "# Define which tools the agent can use to answer user queries\n",
     "search = SerpAPIWrapper()\n",
     "search_tool = Tool(\n",
-    "    name=\"Search\",\n",
-    "    func=search.run,\n",
-    "    description=\"useful for when you need to answer questions about current events\",\n",
-    ")\n",
-    "\n",
-    "\n",
+    "        name = \"Search\",\n",
+    "        func=search.run,\n",
+    "        description=\"useful for when you need to answer questions about current events\"\n",
+    "    )\n",
     "def fake_func(inp: str) -> str:\n",
     "    return \"foo\"\n",
-    "\n",
-    "\n",
     "fake_tools = [\n",
     "    Tool(\n",
-    "        name=f\"foo-{i}\",\n",
-    "        func=fake_func,\n",
-    "        description=f\"a silly function that you can use to get more information about the number {i}\",\n",
-    "    )\n",
+    "        name=f\"foo-{i}\", \n",
+    "        func=fake_func, \n",
+    "        description=f\"a silly function that you can use to get more information about the number {i}\"\n",
+    "    ) \n",
     "    for i in range(99)\n",
     "]\n",
     "ALL_TOOLS = [search_tool] + fake_tools"
@@ -114,10 +105,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "docs = [\n",
-    "    Document(page_content=t.description, metadata={\"index\": i})\n",
-    "    for i, t in enumerate(ALL_TOOLS)\n",
-    "]"
+    "docs = [Document(page_content=t.description, metadata={\"index\": i}) for i, t in enumerate(ALL_TOOLS)]"
    ]
   },
   {
@@ -139,7 +127,6 @@
    "source": [
     "retriever = vector_store.as_retriever()\n",
     "\n",
-    "\n",
     "def get_tools(query):\n",
     "    docs = retriever.get_relevant_documents(query)\n",
     "    return [ALL_TOOLS[d.metadata[\"index\"]] for d in docs]"
@@ -256,8 +243,6 @@
    "outputs": [],
    "source": [
     "from typing import Callable\n",
-    "\n",
-    "\n",
     "# Set up a prompt template\n",
     "class CustomPromptTemplate(StringPromptTemplate):\n",
     "    # The template to use\n",
@@ -265,7 +250,7 @@
     "    ############## NEW ######################\n",
     "    # The list of tools available\n",
     "    tools_getter: Callable\n",
-    "\n",
+    "    \n",
     "    def format(self, **kwargs) -> str:\n",
     "        # Get the intermediate steps (AgentAction, Observation tuples)\n",
     "        # Format them in a particular way\n",
@@ -279,9 +264,7 @@
     "        ############## NEW ######################\n",
     "        tools = self.tools_getter(kwargs[\"input\"])\n",
     "        # Create a tools variable from the list of tools provided\n",
-    "        kwargs[\"tools\"] = \"\\n\".join(\n",
-    "            [f\"{tool.name}: {tool.description}\" for tool in tools]\n",
-    "        )\n",
+    "        kwargs[\"tools\"] = \"\\n\".join([f\"{tool.name}: {tool.description}\" for tool in tools])\n",
     "        # Create a list of tool names for the tools provided\n",
     "        kwargs[\"tool_names\"] = \", \".join([tool.name for tool in tools])\n",
     "        return self.template.format(**kwargs)"
@@ -299,7 +282,7 @@
     "    tools_getter=get_tools,\n",
     "    # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically\n",
     "    # This includes the `intermediate_steps` variable because that is needed\n",
-    "    input_variables=[\"input\", \"intermediate_steps\"],\n",
+    "    input_variables=[\"input\", \"intermediate_steps\"]\n",
     ")"
    ]
   },
@@ -321,6 +304,7 @@
    "outputs": [],
    "source": [
     "class CustomOutputParser(AgentOutputParser):\n",
+    "    \n",
     "    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:\n",
     "        # Check if agent should finish\n",
     "        if \"Final Answer:\" in llm_output:\n",
@@ -331,16 +315,14 @@
     "                log=llm_output,\n",
     "            )\n",
     "        # Parse out the action and action input\n",
-    "        regex = r\"Action: (.*?)[\\n]*Action Input:[\\s]*(.*)\"\n",
+    "        regex = r\"Action\\s*\\d*\\s*:(.*?)\\nAction\\s*\\d*\\s*Input\\s*\\d*\\s*:[\\s]*(.*)\"\n",
     "        match = re.search(regex, llm_output, re.DOTALL)\n",
     "        if not match:\n",
     "            raise ValueError(f\"Could not parse LLM output: `{llm_output}`\")\n",
     "        action = match.group(1).strip()\n",
     "        action_input = match.group(2)\n",
     "        # Return the action and action input\n",
-    "        return AgentAction(\n",
-    "            tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output\n",
-    "        )"
+    "        return AgentAction(tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output)"
    ]
   },
   {
@@ -393,10 +375,10 @@
    "source": [
     "tool_names = [tool.name for tool in tools]\n",
     "agent = LLMSingleActionAgent(\n",
-    "    llm_chain=llm_chain,\n",
+    "    llm_chain=llm_chain, \n",
     "    output_parser=output_parser,\n",
-    "    stop=[\"\\nObservation:\"],\n",
-    "    allowed_tools=tool_names,\n",
+    "    stop=[\"\\nObservation:\"], \n",
+    "    allowed_tools=tool_names\n",
     ")"
    ]
   },
@@ -417,9 +399,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
-    "    agent=agent, tools=tools, verbose=True\n",
-    ")"
+    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
    ]
   },
   {

docs/modules/agents/agents/custom_llm_agent.ipynb

@@ -47,12 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import (\n",
-    "    Tool,\n",
-    "    AgentExecutor,\n",
-    "    LLMSingleActionAgent,\n",
-    "    AgentOutputParser,\n",
-    ")\n",
+    "from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser\n",
     "from langchain.prompts import StringPromptTemplate\n",
     "from langchain import OpenAI, SerpAPIWrapper, LLMChain\n",
     "from typing import List, Union\n",
@@ -81,9 +76,9 @@
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"Search\",\n",
+    "        name = \"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\",\n",
+    "        description=\"useful for when you need to answer questions about current events\"\n",
     "    )\n",
     "]"
    ]
@@ -144,7 +139,7 @@
     "    template: str\n",
     "    # The list of tools available\n",
     "    tools: List[Tool]\n",
-    "\n",
+    "    \n",
     "    def format(self, **kwargs) -> str:\n",
     "        # Get the intermediate steps (AgentAction, Observation tuples)\n",
     "        # Format them in a particular way\n",
@@ -156,9 +151,7 @@
     "        # Set the agent_scratchpad variable to that value\n",
     "        kwargs[\"agent_scratchpad\"] = thoughts\n",
     "        # Create a tools variable from the list of tools provided\n",
-    "        kwargs[\"tools\"] = \"\\n\".join(\n",
-    "            [f\"{tool.name}: {tool.description}\" for tool in self.tools]\n",
-    "        )\n",
+    "        kwargs[\"tools\"] = \"\\n\".join([f\"{tool.name}: {tool.description}\" for tool in self.tools])\n",
     "        # Create a list of tool names for the tools provided\n",
     "        kwargs[\"tool_names\"] = \", \".join([tool.name for tool in self.tools])\n",
     "        return self.template.format(**kwargs)"
@@ -176,7 +169,7 @@
     "    tools=tools,\n",
     "    # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically\n",
     "    # This includes the `intermediate_steps` variable because that is needed\n",
-    "    input_variables=[\"input\", \"intermediate_steps\"],\n",
+    "    input_variables=[\"input\", \"intermediate_steps\"]\n",
     ")"
    ]
   },
@@ -200,6 +193,7 @@
    "outputs": [],
    "source": [
     "class CustomOutputParser(AgentOutputParser):\n",
+    "    \n",
     "    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:\n",
     "        # Check if agent should finish\n",
     "        if \"Final Answer:\" in llm_output:\n",
@@ -210,16 +204,14 @@
     "                log=llm_output,\n",
     "            )\n",
     "        # Parse out the action and action input\n",
-    "        regex = r\"Action: (.*?)[\\n]*Action Input:[\\s]*(.*)\"\n",
+    "        regex = r\"Action\\s*\\d*\\s*:(.*?)\\nAction\\s*\\d*\\s*Input\\s*\\d*\\s*:[\\s]*(.*)\"\n",
     "        match = re.search(regex, llm_output, re.DOTALL)\n",
     "        if not match:\n",
     "            raise ValueError(f\"Could not parse LLM output: `{llm_output}`\")\n",
     "        action = match.group(1).strip()\n",
     "        action_input = match.group(2)\n",
     "        # Return the action and action input\n",
-    "        return AgentAction(\n",
-    "            tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output\n",
-    "        )"
+    "        return AgentAction(tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output)"
    ]
   },
   {
@@ -294,10 +286,10 @@
    "source": [
     "tool_names = [tool.name for tool in tools]\n",
     "agent = LLMSingleActionAgent(\n",
-    "    llm_chain=llm_chain,\n",
+    "    llm_chain=llm_chain, \n",
     "    output_parser=output_parser,\n",
-    "    stop=[\"\\nObservation:\"],\n",
-    "    allowed_tools=tool_names,\n",
+    "    stop=[\"\\nObservation:\"], \n",
+    "    allowed_tools=tool_names\n",
     ")"
    ]
   },
@@ -318,9 +310,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
-    "    agent=agent, tools=tools, verbose=True\n",
-    ")"
+    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
    ]
   },
   {
@@ -418,7 +408,7 @@
     "    tools=tools,\n",
     "    # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically\n",
     "    # This includes the `intermediate_steps` variable because that is needed\n",
-    "    input_variables=[\"input\", \"intermediate_steps\", \"history\"],\n",
+    "    input_variables=[\"input\", \"intermediate_steps\", \"history\"]\n",
     ")"
    ]
   },
@@ -441,10 +431,10 @@
    "source": [
     "tool_names = [tool.name for tool in tools]\n",
     "agent = LLMSingleActionAgent(\n",
-    "    llm_chain=llm_chain,\n",
+    "    llm_chain=llm_chain, \n",
     "    output_parser=output_parser,\n",
-    "    stop=[\"\\nObservation:\"],\n",
-    "    allowed_tools=tool_names,\n",
+    "    stop=[\"\\nObservation:\"], \n",
+    "    allowed_tools=tool_names\n",
     ")"
    ]
   },
@@ -465,7 +455,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "memory = ConversationBufferWindowMemory(k=2)"
+    "memory=ConversationBufferWindowMemory(k=2)"
    ]
   },
   {
@@ -475,9 +465,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
-    "    agent=agent, tools=tools, verbose=True, memory=memory\n",
-    ")"
+    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)"
    ]
   },
   {

docs/modules/agents/agents/custom_llm_chat_agent.ipynb

@@ -47,12 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import (\n",
-    "    Tool,\n",
-    "    AgentExecutor,\n",
-    "    LLMSingleActionAgent,\n",
-    "    AgentOutputParser,\n",
-    ")\n",
+    "from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser\n",
     "from langchain.prompts import BaseChatPromptTemplate\n",
     "from langchain import SerpAPIWrapper, LLMChain\n",
     "from langchain.chat_models import ChatOpenAI\n",
@@ -82,9 +77,9 @@
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"Search\",\n",
+    "        name = \"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\",\n",
+    "        description=\"useful for when you need to answer questions about current events\"\n",
     "    )\n",
     "]"
    ]
@@ -145,7 +140,7 @@
     "    template: str\n",
     "    # The list of tools available\n",
     "    tools: List[Tool]\n",
-    "\n",
+    "    \n",
     "    def format_messages(self, **kwargs) -> str:\n",
     "        # Get the intermediate steps (AgentAction, Observation tuples)\n",
     "        # Format them in a particular way\n",
@@ -157,9 +152,7 @@
     "        # Set the agent_scratchpad variable to that value\n",
     "        kwargs[\"agent_scratchpad\"] = thoughts\n",
     "        # Create a tools variable from the list of tools provided\n",
-    "        kwargs[\"tools\"] = \"\\n\".join(\n",
-    "            [f\"{tool.name}: {tool.description}\" for tool in self.tools]\n",
-    "        )\n",
+    "        kwargs[\"tools\"] = \"\\n\".join([f\"{tool.name}: {tool.description}\" for tool in self.tools])\n",
     "        # Create a list of tool names for the tools provided\n",
     "        kwargs[\"tool_names\"] = \", \".join([tool.name for tool in self.tools])\n",
     "        formatted = self.template.format(**kwargs)\n",
@@ -178,7 +171,7 @@
     "    tools=tools,\n",
     "    # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically\n",
     "    # This includes the `intermediate_steps` variable because that is needed\n",
-    "    input_variables=[\"input\", \"intermediate_steps\"],\n",
+    "    input_variables=[\"input\", \"intermediate_steps\"]\n",
     ")"
    ]
   },
@@ -202,6 +195,7 @@
    "outputs": [],
    "source": [
     "class CustomOutputParser(AgentOutputParser):\n",
+    "    \n",
     "    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:\n",
     "        # Check if agent should finish\n",
     "        if \"Final Answer:\" in llm_output:\n",
@@ -212,16 +206,14 @@
     "                log=llm_output,\n",
     "            )\n",
     "        # Parse out the action and action input\n",
-    "        regex = r\"Action: (.*?)[\\n]*Action Input:[\\s]*(.*)\"\n",
+    "        regex = r\"Action\\s*\\d*\\s*:(.*?)\\nAction\\s*\\d*\\s*Input\\s*\\d*\\s*:[\\s]*(.*)\"\n",
     "        match = re.search(regex, llm_output, re.DOTALL)\n",
     "        if not match:\n",
     "            raise ValueError(f\"Could not parse LLM output: `{llm_output}`\")\n",
     "        action = match.group(1).strip()\n",
     "        action_input = match.group(2)\n",
     "        # Return the action and action input\n",
-    "        return AgentAction(\n",
-    "            tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output\n",
-    "        )"
+    "        return AgentAction(tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output)"
    ]
   },
   {
@@ -296,10 +288,10 @@
    "source": [
     "tool_names = [tool.name for tool in tools]\n",
     "agent = LLMSingleActionAgent(\n",
-    "    llm_chain=llm_chain,\n",
+    "    llm_chain=llm_chain, \n",
     "    output_parser=output_parser,\n",
-    "    stop=[\"\\nObservation:\"],\n",
-    "    allowed_tools=tool_names,\n",
+    "    stop=[\"\\nObservation:\"], \n",
+    "    allowed_tools=tool_names\n",
     ")"
    ]
   },
@@ -320,9 +312,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
-    "    agent=agent, tools=tools, verbose=True\n",
-    ")"
+    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
    ]
   },
   {

docs/modules/agents/agents/custom_mrkl_agent.ipynb

@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 1,
    "id": "9af9734e",
    "metadata": {},
    "outputs": [],
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 2,
    "id": "becda2a1",
    "metadata": {},
    "outputs": [],
@@ -61,16 +61,16 @@
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"Search\",\n",
+    "        name = \"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\",\n",
+    "        description=\"useful for when you need to answer questions about current events\"\n",
     "    )\n",
     "]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 3,
    "id": "339b1bb8",
    "metadata": {},
    "outputs": [],
@@ -82,7 +82,10 @@
     "{agent_scratchpad}\"\"\"\n",
     "\n",
     "prompt = ZeroShotAgent.create_prompt(\n",
-    "    tools, prefix=prefix, suffix=suffix, input_variables=[\"input\", \"agent_scratchpad\"]\n",
+    "    tools, \n",
+    "    prefix=prefix, \n",
+    "    suffix=suffix, \n",
+    "    input_variables=[\"input\", \"agent_scratchpad\"]\n",
     ")"
    ]
   },
@@ -96,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 4,
    "id": "e21d2098",
    "metadata": {},
    "outputs": [
@@ -142,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 5,
    "id": "9b1cc2a2",
    "metadata": {},
    "outputs": [],
@@ -152,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 6,
    "id": "e4f5092f",
    "metadata": {},
    "outputs": [],
@@ -163,19 +166,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 7,
    "id": "490604e9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
-    "    agent=agent, tools=tools, verbose=True\n",
-    ")"
+    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 8,
    "id": "653b1617",
    "metadata": {},
    "outputs": [
@@ -189,9 +190,9 @@
       "\u001b[32;1m\u001b[1;3mThought: I need to find out the population of Canada\n",
       "Action: Search\n",
       "Action Input: Population of Canada 2023\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,610,447 as of Saturday, February 18, 2023, based on Worldometer elaboration of the latest United Nations data. Canada 2020 population is estimated at 37,742,154 people at mid year according to UN data.\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,661,927 as of Sunday, April 16, 2023, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Arrr, Canada be havin' 38,610,447 scallywags livin' there as of 2023!\u001b[0m\n",
+      "Final Answer: Arrr, Canada be havin' 38,661,927 people livin' there as of 2023!\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -199,10 +200,10 @@
     {
      "data": {
       "text/plain": [
-       "\"Arrr, Canada be havin' 38,610,447 scallywags livin' there as of 2023!\""
+       "\"Arrr, Canada be havin' 38,661,927 people livin' there as of 2023!\""
       ]
      },
-     "execution_count": 31,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -222,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 9,
    "id": "43dbfa2f",
    "metadata": {},
    "outputs": [],
@@ -234,16 +235,16 @@
     "{agent_scratchpad}\"\"\"\n",
     "\n",
     "prompt = ZeroShotAgent.create_prompt(\n",
-    "    tools,\n",
-    "    prefix=prefix,\n",
-    "    suffix=suffix,\n",
-    "    input_variables=[\"input\", \"language\", \"agent_scratchpad\"],\n",
+    "    tools, \n",
+    "    prefix=prefix, \n",
+    "    suffix=suffix, \n",
+    "    input_variables=[\"input\", \"language\", \"agent_scratchpad\"]\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 10,
    "id": "0f087313",
    "metadata": {},
    "outputs": [],
@@ -253,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 11,
    "id": "92c75a10",
    "metadata": {},
    "outputs": [],
@@ -263,19 +264,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 12,
    "id": "ac5b83bf",
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
-    "    agent=agent, tools=tools, verbose=True\n",
-    ")"
+    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 13,
    "id": "c960e4ff",
    "metadata": {},
    "outputs": [
@@ -286,12 +285,16 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: I need to find out the population of Canada in 2023.\n",
+      "\u001b[32;1m\u001b[1;3mThought: I should look for recent population estimates.\n",
       "Action: Search\n",
-      "Action Input: Population of Canada in 2023\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,610,447 as of Saturday, February 18, 2023, based on Worldometer elaboration of the latest United Nations data. Canada 2020 population is estimated at 37,742,154 people at mid year according to UN data.\u001b[0m\n",
+      "Action Input: Canada population 2023\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m39,566,248\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should double check this number.\n",
+      "Action: Search\n",
+      "Action Input: Canada population estimates 2023\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCanada's population was estimated at 39,566,248 on January 1, 2023, after a record population growth of 1,050,110 people from January 1, 2022, to January 1, 2023.\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: La popolazione del Canada nel 2023 è stimata in 38.610.447 persone.\u001b[0m\n",
+      "Final Answer: La popolazione del Canada è stata stimata a 39.566.248 il 1° gennaio 2023, dopo un record di crescita demografica di 1.050.110 persone dal 1° gennaio 2022 al 1° gennaio 2023.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -299,18 +302,16 @@
     {
      "data": {
       "text/plain": [
-       "'La popolazione del Canada nel 2023 è stimata in 38.610.447 persone.'"
+       "'La popolazione del Canada è stata stimata a 39.566.248 il 1° gennaio 2023, dopo un record di crescita demografica di 1.050.110 persone dal 1° gennaio 2022 al 1° gennaio 2023.'"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent_executor.run(\n",
-    "    input=\"How many people live in canada as of 2023?\", language=\"italian\"\n",
-    ")"
+    "agent_executor.run(input=\"How many people live in canada as of 2023?\", language=\"italian\")"
    ]
   },
   {

docs/modules/agents/agents/custom_multi_action_agent.ipynb

@@ -51,15 +51,16 @@
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"Search\",\n",
+    "        name = \"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\",\n",
+    "        description=\"useful for when you need to answer questions about current events\"\n",
     "    ),\n",
     "    Tool(\n",
-    "        name=\"RandomWord\",\n",
+    "        name = \"RandomWord\",\n",
     "        func=random_word,\n",
-    "        description=\"call this to get a random word.\",\n",
-    "    ),\n",
+    "        description=\"call this to get a random word.\"\n",
+    "    \n",
+    "    )\n",
     "]"
    ]
   },
@@ -73,14 +74,13 @@
     "from typing import List, Tuple, Any, Union\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
     "\n",
-    "\n",
     "class FakeAgent(BaseMultiActionAgent):\n",
     "    \"\"\"Fake Custom Agent.\"\"\"\n",
-    "\n",
+    "    \n",
     "    @property\n",
     "    def input_keys(self):\n",
     "        return [\"input\"]\n",
-    "\n",
+    "    \n",
     "    def plan(\n",
     "        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
     "    ) -> Union[List[AgentAction], AgentFinish]:\n",
@@ -141,9 +141,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
-    "    agent=agent, tools=tools, verbose=True\n",
-    ")"
+    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
    ]
   },
   {

docs/modules/agents/agents/examples/chat_conversation_agent.ipynb

@@ -20,7 +20,6 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "\n",
     "os.environ[\"LANGCHAIN_HANDLER\"] = \"langchain\""
    ]
   },
@@ -29,7 +28,15 @@
    "execution_count": 2,
    "id": "f65308ab",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to default session, using empty session: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /sessions (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x10a1767c0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
+     ]
+    }
+   ],
    "source": [
     "from langchain.agents import Tool\n",
     "from langchain.memory import ConversationBufferMemory\n",
@@ -49,9 +56,9 @@
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"Current Search\",\n",
+    "        name = \"Current Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events or the current state of the world. the input to this should be a single search term.\",\n",
+    "        description=\"useful for when you need to answer questions about current events or the current state of the world. the input to this should be a single search term.\"\n",
     "    ),\n",
     "]"
    ]
@@ -73,14 +80,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = ChatOpenAI(temperature=0)\n",
-    "agent_chain = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    "    memory=memory,\n",
-    ")"
+    "llm=ChatOpenAI(temperature=0)\n",
+    "agent_chain = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)"
    ]
   },
   {
@@ -95,7 +96,20 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x13fab40d0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[32;1m\u001b[1;3m{\n",
       "    \"action\": \"Final Answer\",\n",
       "    \"action_input\": \"Hello Bob! How can I assist you today?\"\n",
@@ -131,7 +145,20 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x13fab44f0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[32;1m\u001b[1;3m{\n",
       "    \"action\": \"Final Answer\",\n",
       "    \"action_input\": \"Your name is Bob.\"\n",
@@ -174,10 +201,24 @@
       "    \"action\": \"Current Search\",\n",
       "    \"action_input\": \"Thai food dinner recipes\"\n",
       "}\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m59 easy Thai recipes for any night of the week · Marion Grasby's Thai spicy chilli and basil fried rice · Thai curry noodle soup · Marion Grasby's ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m{\n",
+      "Observation: \u001b[36;1m\u001b[1;3m59 easy Thai recipes for any night of the week · Marion Grasby's Thai spicy chilli and basil fried rice · Thai curry noodle soup · Marion Grasby's Thai Spicy ...\u001b[0m\n",
+      "Thought:"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x13fae8be0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32;1m\u001b[1;3m{\n",
       "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and many more. You can find 59 easy Thai recipes for any night of the week on Marion Grasby's website.\"\n",
+      "    \"action_input\": \"Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and Thai Spicy ... (59 recipes in total).\"\n",
       "}\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -186,7 +227,7 @@
     {
      "data": {
       "text/plain": [
-       "\"Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and many more. You can find 59 easy Thai recipes for any night of the week on Marion Grasby's website.\""
+       "'Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and Thai Spicy ... (59 recipes in total).'"
       ]
      },
      "execution_count": 8,
@@ -217,11 +258,25 @@
       "    \"action_input\": \"who won the world cup in 1978\"\n",
       "}\n",
       "```\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe Argentina national football team represents Argentina in men's international football and is administered by the Argentine Football Association, the governing body for football in Argentina. Nicknamed La Albiceleste, they are the reigning world champions, having won the most recent World Cup in 2022.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m```json\n",
+      "Observation: \u001b[36;1m\u001b[1;3mArgentina national football team\u001b[0m\n",
+      "Thought:"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x13fae86d0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32;1m\u001b[1;3m```json\n",
       "{\n",
       "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"The last letter in your name is 'b'. The Argentina national football team won the World Cup in 1978.\"\n",
+      "    \"action_input\": \"The last letter in your name is 'b', and the winner of the 1978 World Cup was the Argentina national football team.\"\n",
       "}\n",
       "```\u001b[0m\n",
       "\n",
@@ -231,7 +286,7 @@
     {
      "data": {
       "text/plain": [
-       "\"The last letter in your name is 'b'. The Argentina national football team won the World Cup in 1978.\""
+       "\"The last letter in your name is 'b', and the winner of the 1978 World Cup was the Argentina national football team.\""
       ]
      },
      "execution_count": 9,
@@ -240,9 +295,7 @@
     }
    ],
    "source": [
-    "agent_chain.run(\n",
-    "    input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\"\n",
-    ")"
+    "agent_chain.run(input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\")"
    ]
   },
   {
@@ -262,10 +315,24 @@
       "    \"action\": \"Current Search\",\n",
       "    \"action_input\": \"weather in pomfret\"\n",
       "}\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mMostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers possible. High near 40F. Winds NNW at 20 to 30 mph.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m{\n",
+      "Observation: \u001b[36;1m\u001b[1;3m10 Day Weather-Pomfret, CT ; Sun 16. 64° · 50°. 24% · NE 7 mph ; Mon 17. 58° · 45°. 70% · ESE 8 mph ; Tue 18. 57° · 37°. 8% · WSW 15 mph.\u001b[0m\n",
+      "Thought:"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x13fa9d7f0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32;1m\u001b[1;3m{\n",
       "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"The weather in Pomfret is mostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers are possible. High near 40F. Winds NNW at 20 to 30 mph.\"\n",
+      "    \"action_input\": \"The weather in Pomfret, CT for the next 10 days is as follows: Sun 16. 64° · 50°. 24% · NE 7 mph ; Mon 17. 58° · 45°. 70% · ESE 8 mph ; Tue 18. 57° · 37°. 8% · WSW 15 mph.\"\n",
       "}\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -274,7 +341,7 @@
     {
      "data": {
       "text/plain": [
-       "'The weather in Pomfret is mostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers are possible. High near 40F. Winds NNW at 20 to 30 mph.'"
+       "'The weather in Pomfret, CT for the next 10 days is as follows: Sun 16. 64° · 50°. 24% · NE 7 mph ; Mon 17. 58° · 45°. 70% · ESE 8 mph ; Tue 18. 57° · 37°. 8% · WSW 15 mph.'"
       ]
      },
      "execution_count": 10,

docs/modules/agents/agents/examples/conversational_agent.ipynb

@@ -23,7 +23,7 @@
     "from langchain.agents import AgentType\n",
     "from langchain.memory import ConversationBufferMemory\n",
     "from langchain import OpenAI\n",
-    "from langchain.utilities import GoogleSearchAPIWrapper\n",
+    "from langchain.utilities import SerpAPIWrapper\n",
     "from langchain.agents import initialize_agent"
    ]
   },
@@ -34,12 +34,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "search = GoogleSearchAPIWrapper()\n",
+    "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"Current Search\",\n",
+    "        name = \"Current Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events or the current state of the world\",\n",
+    "        description=\"useful for when you need to answer questions about current events or the current state of the world\"\n",
     "    ),\n",
     "]"
    ]
@@ -61,14 +61,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = OpenAI(temperature=0)\n",
-    "agent_chain = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    "    memory=memory,\n",
-    ")"
+    "llm=OpenAI(temperature=0)\n",
+    "agent_chain = initialize_agent(tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)"
    ]
   },
   {
@@ -155,8 +149,12 @@
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3m\n",
-      "Thought: Do I need to use a tool? No\n",
-      "AI: If you like Thai food, some great dinner options this week could include Thai green curry, Pad Thai, or a Thai-style stir-fry. You could also try making a Thai-style soup or salad. Enjoy!\u001b[0m\n",
+      "Thought: Do I need to use a tool? Yes\n",
+      "Action: Current Search\n",
+      "Action Input: Thai food dinner recipes\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m59 easy Thai recipes for any night of the week · Marion Grasby's Thai spicy chilli and basil fried rice · Thai curry noodle soup · Marion Grasby's Thai Spicy ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
+      "AI: Here are some great Thai dinner recipes you can try this week: Marion Grasby's Thai Spicy Chilli and Basil Fried Rice, Thai Curry Noodle Soup, Thai Green Curry with Coconut Rice, Thai Red Curry with Vegetables, and Thai Coconut Soup. I hope you enjoy them!\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -164,7 +162,7 @@
     {
      "data": {
       "text/plain": [
-       "'If you like Thai food, some great dinner options this week could include Thai green curry, Pad Thai, or a Thai-style stir-fry. You could also try making a Thai-style soup or salad. Enjoy!'"
+       "\"Here are some great Thai dinner recipes you can try this week: Marion Grasby's Thai Spicy Chilli and Basil Fried Rice, Thai Curry Noodle Soup, Thai Green Curry with Coconut Rice, Thai Red Curry with Vegetables, and Thai Coconut Soup. I hope you enjoy them!\""
       ]
      },
      "execution_count": 7,
@@ -193,9 +191,9 @@
       "Thought: Do I need to use a tool? Yes\n",
       "Action: Current Search\n",
       "Action Input: Who won the World Cup in 1978\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe Cup was won by the host nation, Argentina, who defeated the Netherlands 3–1 in the final, after extra time. The final was held at River Plate's home stadium ... Amid Argentina's celebrations, there was sympathy for the Netherlands, runners-up for the second tournament running, following a 3-1 final defeat at the Estadio ... The match was won by the Argentine squad in extra time by a score of 3–1. Mario Kempes, who finished as the tournament's top scorer, was named the man of the ... May 21, 2022 ... Argentina won the World Cup for the first time in their history, beating Netherlands 3-1 in the final. This edition of the World Cup was full of ... The adidas Golden Ball is presented to the best player at each FIFA World Cup finals. Those who finish as runners-up in the vote receive the adidas Silver ... Holders West Germany failed to beat Holland and Italy and were eliminated when Berti Vogts' own goal gave Austria a 3-2 victory. Holland thrashed the Austrians ... Jun 14, 2018 ... On a clear afternoon on 1 June 1978 at the revamped El Monumental stadium in Buenos Aires' Belgrano barrio, several hundred children in white ... Dec 15, 2022 ... The tournament couldn't have gone better for the ruling junta. Argentina went on to win the championship, defeating the Netherlands, 3-1, in the ... Nov 9, 2022 ... Host: Argentina Teams: 16. Format: Group stage, second round, third-place playoff, final. Matches: 38. Goals: 102. Winner: Argentina Feb 19, 2009 ... Argentina sealed their first World Cup win on home soil when they defeated the Netherlands in an exciting final that went to extra-time. For the ...\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mArgentina national football team\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
-      "AI: The last letter in your name is 'b'. Argentina won the World Cup in 1978.\u001b[0m\n",
+      "AI: The last letter in your name is \"b\" and the winner of the 1978 World Cup was the Argentina national football team.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -203,7 +201,7 @@
     {
      "data": {
       "text/plain": [
-       "\"The last letter in your name is 'b'. Argentina won the World Cup in 1978.\""
+       "'The last letter in your name is \"b\" and the winner of the 1978 World Cup was the Argentina national football team.'"
       ]
      },
      "execution_count": 8,
@@ -212,9 +210,7 @@
     }
    ],
    "source": [
-    "agent_chain.run(\n",
-    "    input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\"\n",
-    ")"
+    "agent_chain.run(input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\")"
    ]
   },
   {
@@ -234,9 +230,9 @@
       "Thought: Do I need to use a tool? Yes\n",
       "Action: Current Search\n",
       "Action Input: Current temperature in Pomfret\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mA mixture of rain and snow showers. High 39F. Winds NNW at 5 to 10 mph. Chance of precip 50%. Snow accumulations less than one inch. Pomfret, CT Weather Forecast, with current conditions, wind, air quality, and what to expect for the next 3 days. Pomfret Center Weather Forecasts. ... Pomfret Center, CT Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be COOLER than today. It is 46 degrees fahrenheit, or 8 degrees celsius and feels like 46 degrees fahrenheit. The barometric pressure is 29.78 - measured by inch of mercury units - ... Pomfret Weather Forecasts. ... Pomfret, MD Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be MUCH COOLER than today. Additional Headlines. En Español · Share |. Current conditions at ... Pomfret CT. Tonight ... Past Weather Information · Interactive Forecast Map. Pomfret MD detailed current weather report for 20675 in Charles county, Maryland. ... Pomfret, MD weather condition is Mostly Cloudy and 43°F. Mostly Cloudy. Hazardous Weather Conditions. Hazardous Weather Outlook · En Español · Share |. Current conditions at ... South Pomfret VT. Tonight. Pomfret Center, CT Weather. Current Report for Thu Jan 5 2023. As of 2:00 PM EST. 5-Day Forecast | Road Conditions. 45°F 7°c. Feels Like 44°F. Pomfret Center CT. Today. Today: Areas of fog before 9am. Otherwise, cloudy, with a ... Otherwise, cloudy, with a temperature falling to around 33 by 5pm.\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mPartly cloudy skies. High around 70F. Winds W at 5 to 10 mph. Humidity41%.\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
-      "AI: The current temperature in Pomfret is 45°F (7°C) and it feels like 44°F.\u001b[0m\n",
+      "AI: The current temperature in Pomfret is around 70F with partly cloudy skies and winds W at 5 to 10 mph. The humidity is 41%.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -244,7 +240,7 @@
     {
      "data": {
       "text/plain": [
-       "'The current temperature in Pomfret is 45°F (7°C) and it feels like 44°F.'"
+       "'The current temperature in Pomfret is around 70F with partly cloudy skies and winds W at 5 to 10 mph. The humidity is 41%.'"
       ]
      },
      "execution_count": 9,

docs/modules/agents/agents/examples/mrkl.ipynb

@@ -26,20 +26,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain import (\n",
-    "    LLMMathChain,\n",
-    "    OpenAI,\n",
-    "    SerpAPIWrapper,\n",
-    "    SQLDatabase,\n",
-    "    SQLDatabaseChain,\n",
-    ")\n",
+    "from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain\n",
     "from langchain.agents import initialize_agent, Tool\n",
     "from langchain.agents import AgentType"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "07e96d99",
    "metadata": {},
    "outputs": [],
@@ -47,42 +41,40 @@
     "llm = OpenAI(temperature=0)\n",
     "search = SerpAPIWrapper()\n",
     "llm_math_chain = LLMMathChain(llm=llm, verbose=True)\n",
-    "db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
+    "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",
+    "        name = \"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events. You should ask targeted questions\",\n",
+    "        description=\"useful for when you need to answer questions about current events. You should ask targeted questions\"\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",
+    "        description=\"useful for when you need to answer questions about math\"\n",
     "    ),\n",
     "    Tool(\n",
     "        name=\"FooBar DB\",\n",
     "        func=db_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context\",\n",
-    "    ),\n",
+    "        description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context\"\n",
+    "    )\n",
     "]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "a069c4b6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "mrkl = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "e603cd7d",
    "metadata": {},
    "outputs": [
@@ -96,30 +88,24 @@
       "\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
       "Action: Search\n",
       "Action Input: \"Who is Leo DiCaprio's girlfriend?\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Camila Morrone's age\n",
-      "Action: Search\n",
-      "Action Input: \"How old is Camila Morrone?\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.43 power\n",
+      "Observation: \u001b[36;1m\u001b[1;3mDiCaprio met actor Camila Morrone in December 2017, when she was 20 and he was 43. They were spotted at Coachella and went on multiple vacations together. Some reports suggested that DiCaprio was ready to ask Morrone to marry him. The couple made their red carpet debut at the 2020 Academy Awards.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate Camila Morrone's age raised to the 0.43 power.\n",
       "Action: Calculator\n",
-      "Action Input: 25^0.43\u001b[0m\n",
+      "Action Input: 21^0.43\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "25^0.43\u001b[32;1m\u001b[1;3m\n",
-      "```python\n",
-      "import math\n",
-      "print(math.pow(25, 0.43))\n",
+      "21^0.43\u001b[32;1m\u001b[1;3m\n",
+      "```text\n",
+      "21**0.43\n",
       "```\n",
+      "...numexpr.evaluate(\"21**0.43\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.991298452658078\n",
-      "\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m3.7030049853137306\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Camila Morrone is 25 years old and her age raised to the 0.43 power is 3.991298452658078.\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.7030049853137306\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.7030049853137306.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -127,23 +113,21 @@
     {
      "data": {
       "text/plain": [
-       "'Camila Morrone is 25 years old and her age raised to the 0.43 power is 3.991298452658078.'"
+       "\"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.7030049853137306.\""
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mrkl.run(\n",
-    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
-    ")"
+    "mrkl.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "a5c07010",
    "metadata": {},
    "outputs": [
@@ -157,21 +141,36 @@
       "\u001b[32;1m\u001b[1;3m I need to find out the artist's full name and then search the FooBar database for their albums.\n",
       "Action: Search\n",
       "Action Input: \"The Storm Before the Calm\" artist\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe Storm Before the Calm (stylized in all lowercase) is the tenth (and eighth international) studio album by Canadian-American singer-songwriter Alanis ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now need to search the FooBar database for Alanis Morissette's albums\n",
+      "Observation: \u001b[36;1m\u001b[1;3mThe Storm Before the Calm (stylized in all lowercase) is the tenth (and eighth international) studio album by Canadian-American singer-songwriter Alanis Morissette, released June 17, 2022, via Epiphany Music and Thirty Tigers, as well as by RCA Records in Europe.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now need to search the FooBar database for Alanis Morissette's albums.\n",
       "Action: FooBar DB\n",
       "Action Input: What albums by Alanis Morissette are in the FooBar database?\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
-      "What albums by Alanis Morissette are in the FooBar database? \n",
-      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT Title FROM Album INNER JOIN Artist ON Album.ArtistId = Artist.ArtistId WHERE Artist.Name = 'Alanis Morissette' LIMIT 5;\u001b[0m\n",
+      "What albums by Alanis Morissette are in the FooBar database?\n",
+      "SQLQuery:"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:191: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.\n",
+      "  sample_rows = connection.execute(command)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32;1m\u001b[1;3m SELECT \"Title\" FROM \"Album\" INNER JOIN \"Artist\" ON \"Album\".\"ArtistId\" = \"Artist\".\"ArtistId\" WHERE \"Name\" = 'Alanis Morissette' LIMIT 5;\u001b[0m\n",
       "SQLResult: \u001b[33;1m\u001b[1;3m[('Jagged Little Pill',)]\u001b[0m\n",
       "Answer:\u001b[32;1m\u001b[1;3m The albums by Alanis Morissette in the FooBar database are Jagged Little Pill.\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
       "Observation: \u001b[38;5;200m\u001b[1;3m The albums by Alanis Morissette in the FooBar database are Jagged Little Pill.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: The artist who released the album The Storm Before the Calm is Alanis Morissette and the albums of theirs in the FooBar database are Jagged Little Pill.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: The artist who released the album 'The Storm Before the Calm' is Alanis Morissette and the albums of hers in the FooBar database are Jagged Little Pill.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -179,18 +178,16 @@
     {
      "data": {
       "text/plain": [
-       "'The artist who released the album The Storm Before the Calm is Alanis Morissette and the albums of theirs in the FooBar database are Jagged Little Pill.'"
+       "\"The artist who released the album 'The Storm Before the Calm' is Alanis Morissette and the albums of hers in the FooBar database are Jagged Little Pill.\""
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mrkl.run(\n",
-    "    \"What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\"\n",
-    ")"
+    "mrkl.run(\"What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\")"
    ]
   },
   {

docs/modules/agents/agents/examples/mrkl_chat.ipynb

@@ -21,18 +21,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "ac561cc4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain import (\n",
-    "    OpenAI,\n",
-    "    LLMMathChain,\n",
-    "    SerpAPIWrapper,\n",
-    "    SQLDatabase,\n",
-    "    SQLDatabaseChain,\n",
-    ")\n",
+    "from langchain import OpenAI, LLMMathChain, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain\n",
     "from langchain.agents import initialize_agent, Tool\n",
     "from langchain.agents import AgentType\n",
     "from langchain.chat_models import ChatOpenAI"
@@ -40,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "id": "07e96d99",
    "metadata": {},
    "outputs": [],
@@ -49,42 +43,40 @@
     "llm1 = OpenAI(temperature=0)\n",
     "search = SerpAPIWrapper()\n",
     "llm_math_chain = LLMMathChain(llm=llm1, verbose=True)\n",
-    "db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
+    "db = SQLDatabase.from_uri(\"sqlite:///../../../../../notebooks/Chinook.db\")\n",
     "db_chain = SQLDatabaseChain(llm=llm1, database=db, verbose=True)\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"Search\",\n",
+    "        name = \"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events. You should ask targeted questions\",\n",
+    "        description=\"useful for when you need to answer questions about current events. You should ask targeted questions\"\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",
+    "        description=\"useful for when you need to answer questions about math\"\n",
     "    ),\n",
     "    Tool(\n",
     "        name=\"FooBar DB\",\n",
     "        func=db_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context\",\n",
-    "    ),\n",
+    "        description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context\"\n",
+    "    )\n",
     "]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "id": "a069c4b6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "mrkl = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "mrkl = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "id": "e603cd7d",
    "metadata": {},
    "outputs": [
@@ -100,37 +92,34 @@
       "```\n",
       "{\n",
       "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"Who is Leo DiCaprio's girlfriend?\"\n",
+      "  \"action_input\": \"Leo DiCaprio girlfriend\"\n",
       "}\n",
       "```\n",
       "\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mFor the second question, I need to use the calculator tool to raise her current age to the 0.43 power.\n",
+      "Observation: \u001b[36;1m\u001b[1;3mGigi Hadid: 2022 Leo and Gigi were first linked back in September 2022, when a source told Us Weekly that Leo had his “sights set\" on her (alarming way to put it, but okay).\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mFor the second question, I need to calculate the age raised to the 0.43 power. I will use the calculator tool.\n",
       "Action:\n",
       "```\n",
       "{\n",
       "  \"action\": \"Calculator\",\n",
-      "  \"action_input\": \"22.0^(0.43)\"\n",
+      "  \"action_input\": \"((2022-1995)^0.43)\"\n",
       "}\n",
       "```\n",
-      "\n",
       "\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "22.0^(0.43)\u001b[32;1m\u001b[1;3m\n",
-      "```python\n",
-      "import math\n",
-      "print(math.pow(22.0, 0.43))\n",
+      "((2022-1995)^0.43)\u001b[32;1m\u001b[1;3m\n",
+      "```text\n",
+      "(2022-1995)**0.43\n",
       "```\n",
+      "...numexpr.evaluate(\"(2022-1995)**0.43\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
-      "\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m4.125593352125936\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
-      "\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 4.125593352125936\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
-      "Final Answer: Camila Morrone, 3.777824273683966.\u001b[0m\n",
+      "Final Answer: Gigi Hadid is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is approximately 4.13.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -138,23 +127,21 @@
     {
      "data": {
       "text/plain": [
-       "'Camila Morrone, 3.777824273683966.'"
+       "\"Gigi Hadid is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is approximately 4.13.\""
       ]
      },
-     "execution_count": 4,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mrkl.run(\n",
-    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
-    ")"
+    "mrkl.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "id": "a5c07010",
    "metadata": {},
    "outputs": [
@@ -166,7 +153,7 @@
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3mQuestion: What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\n",
-      "Thought: I should use the Search tool to find the answer to the first part of the question and then use the FooBar DB tool to find the answer to the second part of the question.\n",
+      "Thought: I should use the Search tool to find the answer to the first part of the question and then use the FooBar DB tool to find the answer to the second part.\n",
       "Action:\n",
       "```\n",
       "{\n",
@@ -176,7 +163,7 @@
       "```\n",
       "\u001b[0m\n",
       "Observation: \u001b[36;1m\u001b[1;3mAlanis Morissette\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mNow that I have the name of the artist, I can use the FooBar DB tool to find their albums in the database.\n",
+      "Thought:\u001b[32;1m\u001b[1;3mNow that I know the artist's name, I can use the FooBar DB tool to find out if they are in the database and what albums of theirs are in it.\n",
       "Action:\n",
       "```\n",
       "{\n",
@@ -188,7 +175,7 @@
       "\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
-      "What albums does Alanis Morissette have in the database? \n",
+      "What albums does Alanis Morissette have in the database?\n",
       "SQLQuery:"
      ]
     },
@@ -196,7 +183,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:141: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.\n",
+      "/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:191: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.\n",
       "  sample_rows = connection.execute(command)\n"
      ]
     },
@@ -204,14 +191,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32;1m\u001b[1;3m SELECT Title FROM Album WHERE ArtistId IN (SELECT ArtistId FROM Artist WHERE Name = 'Alanis Morissette') LIMIT 5;\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m SELECT \"Title\" FROM \"Album\" WHERE \"ArtistId\" IN (SELECT \"ArtistId\" FROM \"Artist\" WHERE \"Name\" = 'Alanis Morissette') LIMIT 5;\u001b[0m\n",
       "SQLResult: \u001b[33;1m\u001b[1;3m[('Jagged Little Pill',)]\u001b[0m\n",
-      "Answer:\u001b[32;1m\u001b[1;3m Alanis Morissette has the album 'Jagged Little Pill' in the database.\u001b[0m\n",
+      "Answer:\u001b[32;1m\u001b[1;3m Alanis Morissette has the album Jagged Little Pill in the database.\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
-      "Observation: \u001b[38;5;200m\u001b[1;3m Alanis Morissette has the album 'Jagged Little Pill' in the database.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI have found the answer to both parts of the question.\n",
-      "Final Answer: The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database.\u001b[0m\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3m Alanis Morissette has the album Jagged Little Pill in the database.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mThe artist Alanis Morissette is in the FooBar database and has the album Jagged Little Pill in it.\n",
+      "Final Answer: Alanis Morissette is in the FooBar database and has the album Jagged Little Pill in it.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -219,18 +206,16 @@
     {
      "data": {
       "text/plain": [
-       "\"The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database.\""
+       "'Alanis Morissette is in the FooBar database and has the album Jagged Little Pill in it.'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mrkl.run(\n",
-    "    \"What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\"\n",
-    ")"
+    "mrkl.run(\"What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\")"
    ]
   },
   {

docs/modules/agents/agents/examples/react.ipynb

@@ -21,19 +21,18 @@
     "from langchain.agents import initialize_agent, Tool\n",
     "from langchain.agents import AgentType\n",
     "from langchain.agents.react.base import DocstoreExplorer\n",
-    "\n",
-    "docstore = DocstoreExplorer(Wikipedia())\n",
+    "docstore=DocstoreExplorer(Wikipedia())\n",
     "tools = [\n",
     "    Tool(\n",
     "        name=\"Search\",\n",
     "        func=docstore.search,\n",
-    "        description=\"useful for when you need to ask with search\",\n",
+    "        description=\"useful for when you need to ask with search\"\n",
     "    ),\n",
     "    Tool(\n",
     "        name=\"Lookup\",\n",
     "        func=docstore.lookup,\n",
-    "        description=\"useful for when you need to ask with lookup\",\n",
-    "    ),\n",
+    "        description=\"useful for when you need to ask with lookup\"\n",
+    "    )\n",
     "]\n",
     "\n",
     "llm = OpenAI(temperature=0, model_name=\"text-davinci-002\")\n",

docs/modules/agents/agents/examples/self_ask_with_search.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "7e3b513e",
    "metadata": {},
    "outputs": [
@@ -25,11 +25,12 @@
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3m Yes.\n",
       "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
-      "Intermediate answer: \u001b[36;1m\u001b[1;3mCarlos Alcaraz won the 2022 Men's single title while Poland's Iga Swiatek won the Women's single title defeating Tunisian's Ons Jabeur.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mFollow up: Where is Carlos Alcaraz from?\u001b[0m\n",
+      "Intermediate answer: \u001b[36;1m\u001b[1;3mCarlos Alcaraz Garfia\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mFollow up: Where is Carlos Alcaraz Garfia from?\u001b[0m\n",
       "Intermediate answer: \u001b[36;1m\u001b[1;3mEl Palmar, Spain\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3mSo the final answer is: El Palmar, Spain\u001b[0m\n",
-      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
@@ -38,7 +39,7 @@
        "'El Palmar, Spain'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -54,17 +55,21 @@
     "    Tool(\n",
     "        name=\"Intermediate Answer\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to ask with search\",\n",
+    "        description=\"useful for when you need to ask with search\"\n",
     "    )\n",
     "]\n",
     "\n",
-    "self_ask_with_search = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True\n",
-    ")\n",
-    "self_ask_with_search.run(\n",
-    "    \"What is the hometown of the reigning men's U.S. Open champion?\"\n",
-    ")"
+    "self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)\n",
+    "self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2e4d6bc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -83,7 +88,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/getting_started.ipynb

@@ -93,9 +93,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -150,9 +148,7 @@
     }
    ],
    "source": [
-    "agent.run(\n",
-    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
-    ")"
+    "agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
    ]
   },
   {

docs/modules/agents/toolkits/examples/csv.ipynb

@@ -35,17 +35,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "id": "16c4dc59",
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = create_csv_agent(OpenAI(temperature=0), \"titanic.csv\", verbose=True)"
+    "agent = create_csv_agent(OpenAI(temperature=0), 'titanic.csv', verbose=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "id": "46b9489d",
    "metadata": {},
    "outputs": [
@@ -72,7 +72,7 @@
        "'There are 891 rows in the dataframe.'"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "a96309be",
    "metadata": {},
    "outputs": [
@@ -110,7 +110,7 @@
        "'30 people have more than 3 siblings.'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "964a09f7",
    "metadata": {},
    "outputs": [
@@ -143,7 +143,7 @@
       "Thought:\u001b[32;1m\u001b[1;3m I need to import the math library\n",
       "Action: python_repl_ast\n",
       "Action Input: import math\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mNone\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
       "Action: python_repl_ast\n",
       "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
@@ -160,7 +160,7 @@
        "'5.449689683556195'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/modules/agents/toolkits/examples/json.ipynb

@@ -34,7 +34,10 @@
     "import os\n",
     "import yaml\n",
     "\n",
-    "from langchain.agents import create_json_agent, AgentExecutor\n",
+    "from langchain.agents import (\n",
+    "    create_json_agent,\n",
+    "    AgentExecutor\n",
+    ")\n",
     "from langchain.agents.agent_toolkits import JsonToolkit\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.llms.openai import OpenAI\n",
@@ -57,7 +60,9 @@
     "json_toolkit = JsonToolkit(spec=json_spec)\n",
     "\n",
     "json_agent_executor = create_json_agent(\n",
-    "    llm=OpenAI(temperature=0), toolkit=json_toolkit, verbose=True\n",
+    "    llm=OpenAI(temperature=0),\n",
+    "    toolkit=json_toolkit,\n",
+    "    verbose=True\n",
     ")"
    ]
   },
@@ -149,9 +154,7 @@
     }
    ],
    "source": [
-    "json_agent_executor.run(\n",
-    "    \"What are the required parameters in the request body to the /completions endpoint?\"\n",
-    ")"
+    "json_agent_executor.run(\"What are the required parameters in the request body to the /completions endpoint?\")"
    ]
   },
   {

docs/modules/agents/toolkits/examples/openapi.ipynb

@@ -119,7 +119,7 @@
     "with open(\"openai_openapi.yaml\") as f:\n",
     "    raw_openai_api_spec = yaml.load(f, Loader=yaml.Loader)\n",
     "openai_api_spec = reduce_openapi_spec(raw_openai_api_spec)\n",
-    "\n",
+    "    \n",
     "with open(\"klarna_openapi.yaml\") as f:\n",
     "    raw_klarna_api_spec = yaml.load(f, Loader=yaml.Loader)\n",
     "klarna_api_spec = reduce_openapi_spec(raw_klarna_api_spec)\n",
@@ -152,16 +152,12 @@
     "import spotipy.util as util\n",
     "from langchain.requests import RequestsWrapper\n",
     "\n",
-    "\n",
     "def construct_spotify_auth_headers(raw_spec: dict):\n",
-    "    scopes = list(\n",
-    "        raw_spec[\"components\"][\"securitySchemes\"][\"oauth_2_0\"][\"flows\"][\n",
-    "            \"authorizationCode\"\n",
-    "        ][\"scopes\"].keys()\n",
-    "    )\n",
-    "    access_token = util.prompt_for_user_token(scope=\",\".join(scopes))\n",
-    "    return {\"Authorization\": f\"Bearer {access_token}\"}\n",
-    "\n",
+    "    scopes = list(raw_spec['components']['securitySchemes']['oauth_2_0']['flows']['authorizationCode']['scopes'].keys())\n",
+    "    access_token = util.prompt_for_user_token(scope=','.join(scopes))\n",
+    "    return {\n",
+    "        'Authorization': f'Bearer {access_token}'\n",
+    "    }\n",
     "\n",
     "# Get API credentials.\n",
     "headers = construct_spotify_auth_headers(raw_spotify_api_spec)\n",
@@ -222,13 +218,8 @@
    ],
    "source": [
     "import tiktoken\n",
-    "\n",
-    "enc = tiktoken.encoding_for_model(\"text-davinci-003\")\n",
-    "\n",
-    "\n",
-    "def count_tokens(s):\n",
-    "    return len(enc.encode(s))\n",
-    "\n",
+    "enc = tiktoken.encoding_for_model('text-davinci-003')\n",
+    "def count_tokens(s): return len(enc.encode(s))\n",
     "\n",
     "count_tokens(yaml.dump(raw_spotify_api_spec))"
    ]
@@ -263,7 +254,6 @@
    "source": [
     "from langchain.llms.openai import OpenAI\n",
     "from langchain.agents.agent_toolkits.openapi import planner\n",
-    "\n",
     "llm = OpenAI(model_name=\"gpt-4\", temperature=0.0)"
    ]
   },
@@ -339,9 +329,7 @@
    ],
    "source": [
     "spotify_agent = planner.create_openapi_agent(spotify_api_spec, requests_wrapper, llm)\n",
-    "user_query = (\n",
-    "    \"make me a playlist with the first song from kind of blue. call it machine blues.\"\n",
-    ")\n",
+    "user_query = \"make me a playlist with the first song from kind of blue. call it machine blues.\"\n",
     "spotify_agent.run(user_query)"
    ]
   },
@@ -441,8 +429,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "headers = {\"Authorization\": f\"Bearer {os.getenv('OPENAI_API_KEY')}\"}\n",
-    "openai_requests_wrapper = RequestsWrapper(headers=headers)"
+    "headers = {\n",
+    "    \"Authorization\": f\"Bearer {os.getenv('OPENAI_API_KEY')}\"\n",
+    "}\n",
+    "openai_requests_wrapper=RequestsWrapper(headers=headers)"
    ]
   },
   {
@@ -555,9 +545,7 @@
    "source": [
     "# Meta!\n",
     "llm = OpenAI(model_name=\"gpt-4\", temperature=0.25)\n",
-    "openai_agent = planner.create_openapi_agent(\n",
-    "    openai_api_spec, openai_requests_wrapper, llm\n",
-    ")\n",
+    "openai_agent = planner.create_openapi_agent(openai_api_spec, openai_requests_wrapper, llm)\n",
     "user_query = \"generate a short piece of advice\"\n",
     "openai_agent.run(user_query)"
    ]
@@ -605,14 +593,14 @@
    "source": [
     "with open(\"openai_openapi.yaml\") as f:\n",
     "    data = yaml.load(f, Loader=yaml.FullLoader)\n",
-    "json_spec = JsonSpec(dict_=data, max_value_length=4000)\n",
+    "json_spec=JsonSpec(dict_=data, max_value_length=4000)\n",
     "\n",
     "\n",
-    "openapi_toolkit = OpenAPIToolkit.from_llm(\n",
-    "    OpenAI(temperature=0), json_spec, openai_requests_wrapper, verbose=True\n",
-    ")\n",
+    "openapi_toolkit = OpenAPIToolkit.from_llm(OpenAI(temperature=0), json_spec, openai_requests_wrapper, verbose=True)\n",
     "openapi_agent_executor = create_openapi_agent(\n",
-    "    llm=OpenAI(temperature=0), toolkit=openapi_toolkit, verbose=True\n",
+    "    llm=OpenAI(temperature=0),\n",
+    "    toolkit=openapi_toolkit,\n",
+    "    verbose=True\n",
     ")"
    ]
   },
@@ -751,9 +739,7 @@
     }
    ],
    "source": [
-    "openapi_agent_executor.run(\n",
-    "    \"Make a post request to openai /completions. The prompt should be 'tell me a joke.'\"\n",
-    ")"
+    "openapi_agent_executor.run(\"Make a post request to openai /completions. The prompt should be 'tell me a joke.'\")"
    ]
   }
  ],

docs/modules/agents/toolkits/examples/openapi_nla.ipynb

@@ -43,9 +43,7 @@
    "outputs": [],
    "source": [
     "# Select the LLM to use. Here, we use text-davinci-003\n",
-    "llm = OpenAI(\n",
-    "    temperature=0, max_tokens=700\n",
-    ")  # You can swap between different core LLM's here."
+    "llm = OpenAI(temperature=0, max_tokens=700) # You can swap between different core LLM's here."
    ]
   },
   {
@@ -79,9 +77,7 @@
    ],
    "source": [
     "speak_toolkit = NLAToolkit.from_llm_and_url(llm, \"https://api.speak.com/openapi.yaml\")\n",
-    "klarna_toolkit = NLAToolkit.from_llm_and_url(\n",
-    "    llm, \"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\"\n",
-    ")"
+    "klarna_toolkit = NLAToolkit.from_llm_and_url(llm, \"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\")"
    ]
   },
   {
@@ -126,13 +122,8 @@
    "outputs": [],
    "source": [
     "natural_language_tools = speak_toolkit.get_tools() + klarna_toolkit.get_tools()\n",
-    "mrkl = initialize_agent(\n",
-    "    natural_language_tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    "    agent_kwargs={\"format_instructions\": openapi_format_instructions},\n",
-    ")"
+    "mrkl = initialize_agent(natural_language_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, \n",
+    "                        verbose=True, agent_kwargs={\"format_instructions\":openapi_format_instructions})"
    ]
   },
   {
@@ -172,9 +163,7 @@
     }
    ],
    "source": [
-    "mrkl.run(\n",
-    "    \"I have an end of year party for my Italian class and have to buy some Italian clothes for it\"\n",
-    ")"
+    "mrkl.run(\"I have an end of year party for my Italian class and have to buy some Italian clothes for it\")"
    ]
   },
   {
@@ -209,7 +198,7 @@
    },
    "outputs": [],
    "source": [
-    "spoonacular_api_key = \"\"  # Copy from the API Console"
+    "spoonacular_api_key = \"\" # Copy from the API Console"
    ]
   },
   {
@@ -249,10 +238,10 @@
    "source": [
     "requests = Requests(headers={\"x-api-key\": spoonacular_api_key})\n",
     "spoonacular_toolkit = NLAToolkit.from_llm_and_url(\n",
-    "    llm,\n",
+    "    llm, \n",
     "    \"https://spoonacular.com/application/frontend/downloads/spoonacular-openapi-3.json\",\n",
     "    requests=requests,\n",
-    "    max_text_length=1800,  # If you want to truncate the response text\n",
+    "    max_text_length=1800, # If you want to truncate the response text\n",
     ")"
    ]
   },
@@ -274,11 +263,10 @@
     }
    ],
    "source": [
-    "natural_language_api_tools = (\n",
-    "    speak_toolkit.get_tools()\n",
-    "    + klarna_toolkit.get_tools()\n",
-    "    + spoonacular_toolkit.get_tools()[:30]\n",
-    ")\n",
+    "natural_language_api_tools = (speak_toolkit.get_tools() \n",
+    "                              + klarna_toolkit.get_tools() \n",
+    "                              + spoonacular_toolkit.get_tools()[:30]\n",
+    "                             )\n",
     "print(f\"{len(natural_language_api_tools)} tools loaded.\")"
    ]
   },
@@ -292,13 +280,8 @@
    "outputs": [],
    "source": [
     "# Create an agent with the new tools\n",
-    "mrkl = initialize_agent(\n",
-    "    natural_language_api_tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    "    agent_kwargs={\"format_instructions\": openapi_format_instructions},\n",
-    ")"
+    "mrkl = initialize_agent(natural_language_api_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, \n",
+    "                        verbose=True, agent_kwargs={\"format_instructions\":openapi_format_instructions})"
    ]
   },
   {
@@ -390,9 +373,7 @@
     }
    ],
    "source": [
-    "natural_language_api_tools[1].run(\n",
-    "    \"Tell the LangChain audience to 'enjoy the meal' in Italian, please!\"\n",
-    ")"
+    "natural_language_api_tools[1].run(\"Tell the LangChain audience to 'enjoy the meal' in Italian, please!\")"
    ]
   },
   {

docs/modules/agents/toolkits/examples/pandas.ipynb

@@ -32,7 +32,7 @@
     "from langchain.llms import OpenAI\n",
     "import pandas as pd\n",
     "\n",
-    "df = pd.read_csv(\"titanic.csv\")"
+    "df = pd.read_csv('titanic.csv')"
    ]
   },
   {

docs/modules/agents/toolkits/examples/python.ipynb

@@ -35,7 +35,9 @@
    "outputs": [],
    "source": [
     "agent_executor = create_python_agent(\n",
-    "    llm=OpenAI(temperature=0, max_tokens=1000), tool=PythonREPLTool(), verbose=True\n",
+    "    llm=OpenAI(temperature=0, max_tokens=1000),\n",
+    "    tool=PythonREPLTool(),\n",
+    "    verbose=True\n",
     ")"
    ]
   },
@@ -188,11 +190,9 @@
     }
    ],
    "source": [
-    "agent_executor.run(\n",
-    "    \"\"\"Understand, write a single neuron neural network in PyTorch.\n",
+    "agent_executor.run(\"\"\"Understand, write a single neuron neural network in PyTorch.\n",
     "Take synthetic data for y=2x. Train for 1000 epochs and print every 100 epochs.\n",
-    "Return prediction for x = 5\"\"\"\n",
-    ")"
+    "Return prediction for x = 5\"\"\")"
    ]
   },
   {

docs/modules/agents/toolkits/examples/sql_database.ipynb

@@ -53,7 +53,9 @@
     "toolkit = SQLDatabaseToolkit(db=db)\n",
     "\n",
     "agent_executor = create_sql_agent(\n",
-    "    llm=OpenAI(temperature=0), toolkit=toolkit, verbose=True\n",
+    "    llm=OpenAI(temperature=0),\n",
+    "    toolkit=toolkit,\n",
+    "    verbose=True\n",
     ")"
    ]
   },
@@ -291,9 +293,7 @@
     }
    ],
    "source": [
-    "agent_executor.run(\n",
-    "    \"List the total sales per country. Which country's customers spent the most?\"\n",
-    ")"
+    "agent_executor.run(\"List the total sales per country. Which country's customers spent the most?\")"
    ]
   },
   {
@@ -372,9 +372,7 @@
     }
    ],
    "source": [
-    "agent_executor.run(\n",
-    "    \"Show the total number of tracks in each playlist. The Playlist name should be included in the result.\"\n",
-    ")"
+    "agent_executor.run(\"Show the total number of tracks in each playlist. The Playlist name should be included in the result.\")"
    ]
   },
   {

docs/modules/agents/toolkits/examples/vectorstore.ipynb

@@ -31,7 +31,6 @@
     "from langchain.vectorstores import Chroma\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain import OpenAI, VectorDBQA\n",
-    "\n",
     "llm = OpenAI(temperature=0)"
    ]
   },
@@ -54,16 +53,13 @@
    ],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
-    "\n",
-    "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
+    "loader = TextLoader('../../../state_of_the_union.txt')\n",
     "documents = loader.load()\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
     "texts = text_splitter.split_documents(documents)\n",
     "\n",
     "embeddings = OpenAIEmbeddings()\n",
-    "state_of_union_store = Chroma.from_documents(\n",
-    "    texts, embeddings, collection_name=\"state-of-union\"\n",
-    ")"
+    "state_of_union_store = Chroma.from_documents(texts, embeddings, collection_name=\"state-of-union\")"
    ]
   },
   {
@@ -85,7 +81,6 @@
    ],
    "source": [
     "from langchain.document_loaders import WebBaseLoader\n",
-    "\n",
     "loader = WebBaseLoader(\"https://beta.ruff.rs/docs/faq/\")\n",
     "docs = loader.load()\n",
     "ruff_texts = text_splitter.split_documents(docs)\n",
@@ -114,14 +109,17 @@
     "    VectorStoreToolkit,\n",
     "    VectorStoreInfo,\n",
     ")\n",
-    "\n",
     "vectorstore_info = VectorStoreInfo(\n",
     "    name=\"state_of_union_address\",\n",
     "    description=\"the most recent state of the Union adress\",\n",
-    "    vectorstore=state_of_union_store,\n",
+    "    vectorstore=state_of_union_store\n",
     ")\n",
     "toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info)\n",
-    "agent_executor = create_vectorstore_agent(llm=llm, toolkit=toolkit, verbose=True)"
+    "agent_executor = create_vectorstore_agent(\n",
+    "    llm=llm,\n",
+    "    toolkit=toolkit,\n",
+    "    verbose=True\n",
+    ")"
    ]
   },
   {
@@ -167,9 +165,7 @@
     }
    ],
    "source": [
-    "agent_executor.run(\n",
-    "    \"What did biden say about ketanji brown jackson is the state of the union address?\"\n",
-    ")"
+    "agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address?\")"
    ]
   },
   {
@@ -207,9 +203,7 @@
     }
    ],
    "source": [
-    "agent_executor.run(\n",
-    "    \"What did biden say about ketanji brown jackson is the state of the union address? List the source.\"\n",
-    ")"
+    "agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address? List the source.\")"
    ]
   },
   {
@@ -247,13 +241,16 @@
     "ruff_vectorstore_info = VectorStoreInfo(\n",
     "    name=\"ruff\",\n",
     "    description=\"Information about the Ruff python linting library\",\n",
-    "    vectorstore=ruff_store,\n",
+    "    vectorstore=ruff_store\n",
     ")\n",
     "router_toolkit = VectorStoreRouterToolkit(\n",
-    "    vectorstores=[vectorstore_info, ruff_vectorstore_info], llm=llm\n",
+    "    vectorstores=[vectorstore_info, ruff_vectorstore_info],\n",
+    "    llm=llm\n",
     ")\n",
     "agent_executor = create_vectorstore_router_agent(\n",
-    "    llm=llm, toolkit=router_toolkit, verbose=True\n",
+    "    llm=llm,\n",
+    "    toolkit=router_toolkit,\n",
+    "    verbose=True\n",
     ")"
    ]
   },
@@ -302,9 +299,7 @@
     }
    ],
    "source": [
-    "agent_executor.run(\n",
-    "    \"What did biden say about ketanji brown jackson is the state of the union address?\"\n",
-    ")"
+    "agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address?\")"
    ]
   },
   {
@@ -386,9 +381,7 @@
     }
    ],
    "source": [
-    "agent_executor.run(\n",
-    "    \"What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?\"\n",
-    ")"
+    "agent_executor.run(\"What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?\")"
    ]
   },
   {

docs/modules/agents/tools/custom_tools.ipynb

@@ -82,15 +82,15 @@
     "llm_math_chain = LLMMathChain(llm=llm, verbose=True)\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"Search\",\n",
+    "        name = \"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\",\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",
+    "        description=\"useful for when you need to answer questions about math\"\n",
+    "    )\n",
     "]"
    ]
   },
@@ -103,9 +103,7 @@
    "source": [
     "# Construct the agent. We will use the default agent type here.\n",
     "# See documentation for a full list of options.\n",
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -160,9 +158,7 @@
     }
    ],
    "source": [
-    "agent.run(\n",
-    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
-    ")"
+    "agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
    ]
   },
   {
@@ -187,12 +183,11 @@
     "    def _run(self, query: str) -> str:\n",
     "        \"\"\"Use the tool.\"\"\"\n",
     "        return search.run(query)\n",
-    "\n",
+    "    \n",
     "    async def _arun(self, query: str) -> str:\n",
     "        \"\"\"Use the tool asynchronously.\"\"\"\n",
     "        raise NotImplementedError(\"BingSearchRun does not support async\")\n",
-    "\n",
-    "\n",
+    "    \n",
     "class CustomCalculatorTool(BaseTool):\n",
     "    name = \"Calculator\"\n",
     "    description = \"useful for when you need to answer questions about math\"\n",
@@ -200,7 +195,7 @@
     "    def _run(self, query: str) -> str:\n",
     "        \"\"\"Use the tool.\"\"\"\n",
     "        return llm_math_chain.run(query)\n",
-    "\n",
+    "    \n",
     "    async def _arun(self, query: str) -> str:\n",
     "        \"\"\"Use the tool asynchronously.\"\"\"\n",
     "        raise NotImplementedError(\"BingSearchRun does not support async\")"
@@ -223,9 +218,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -280,9 +273,7 @@
     }
    ],
    "source": [
-    "agent.run(\n",
-    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
-    ")"
+    "agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
    ]
   },
   {
@@ -304,7 +295,6 @@
    "source": [
     "from langchain.agents import tool\n",
     "\n",
-    "\n",
     "@tool\n",
     "def search_api(query: str) -> str:\n",
     "    \"\"\"Searches the API for the query.\"\"\"\n",
@@ -421,9 +411,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -470,9 +458,7 @@
     }
    ],
    "source": [
-    "agent.run(\n",
-    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
-    ")"
+    "agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
    ]
   },
   {
@@ -502,27 +488,21 @@
     "from langchain.agents import AgentType\n",
     "from langchain.llms import OpenAI\n",
     "from langchain import LLMMathChain, SerpAPIWrapper\n",
-    "\n",
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"Search\",\n",
+    "        name = \"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\",\n",
+    "        description=\"useful for when you need to answer questions about current events\"\n",
     "    ),\n",
     "    Tool(\n",
     "        name=\"Music Search\",\n",
-    "        func=lambda x: \"'All I Want For Christmas Is You' by Mariah Carey.\",  # Mock Function\n",
+    "        func=lambda x: \"'All I Want For Christmas Is You' by Mariah Carey.\", #Mock Function\n",
     "        description=\"A Music search engine. Use this more than the normal search if the question is about Music, like 'who is the singer of yesterday?' or 'what is the most popular song in 2022?'\",\n",
-    "    ),\n",
+    "    )\n",
     "]\n",
     "\n",
-    "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    OpenAI(temperature=0),\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    ")"
+    "agent = initialize_agent(tools, OpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -585,7 +565,7 @@
     "        name=\"Calculator\",\n",
     "        func=llm_math_chain.run,\n",
     "        description=\"useful for when you need to answer questions about math\",\n",
-    "        return_direct=True,\n",
+    "        return_direct=True\n",
     "    )\n",
     "]"
    ]
@@ -598,9 +578,7 @@
    "outputs": [],
    "source": [
     "llm = OpenAI(temperature=0)\n",
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {

docs/modules/agents/tools/examples/apify.ipynb

@@ -53,7 +53,6 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "\n",
     "os.environ[\"OPENAI_API_KEY\"] = \"Your OpenAI API key\"\n",
     "os.environ[\"APIFY_API_TOKEN\"] = \"Your Apify API token\"\n",
     "\n",

docs/modules/agents/tools/examples/bing_search.ipynb

@@ -25,7 +25,6 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "\n",
     "os.environ[\"BING_SUBSCRIPTION_KEY\"] = \"\"\n",
     "os.environ[\"BING_SEARCH_URL\"] = \"\""
    ]

docs/modules/agents/tools/examples/chatgpt_plugins.ipynb

@@ -81,12 +81,10 @@
    ],
    "source": [
     "llm = ChatOpenAI(temperature=0)\n",
-    "tools = load_tools([\"requests_all\"])\n",
+    "tools = load_tools([\"requests_all\"] )\n",
     "tools += [tool]\n",
     "\n",
-    "agent_chain = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")\n",
+    "agent_chain = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)\n",
     "agent_chain.run(\"what t shirts are available in klarna?\")"
    ]
   },

docs/modules/agents/tools/examples/google_search.ipynb

@@ -22,7 +22,6 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "\n",
     "os.environ[\"GOOGLE_CSE_ID\"] = \"\"\n",
     "os.environ[\"GOOGLE_API_KEY\"] = \"\""
    ]

docs/modules/agents/tools/examples/google_serper.ipynb

@@ -16,7 +16,6 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "\n",
     "os.environ[\"SERPER_API_KEY\"] = \"\""
    ],
    "metadata": {
@@ -76,7 +75,7 @@
    "execution_count": null,
    "outputs": [],
    "source": [
-    "os.environ[\"OPENAI_API_KEY\"] = \"\""
+    "os.environ['OPENAI_API_KEY'] = \"\""
    ],
    "metadata": {
     "collapsed": false
@@ -92,15 +91,15 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m Yes.\n",
-      "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
-      "Intermediate answer: \u001b[36;1m\u001b[1;3mCurrent champions Carlos Alcaraz, 2022 men's singles champion.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mFollow up: Where is Carlos Alcaraz from?\u001b[0m\n",
-      "Intermediate answer: \u001b[36;1m\u001b[1;3mEl Palmar, Spain\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mSo the final answer is: El Palmar, Spain\u001b[0m\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m Yes.\n",
+      "Follow up: Who is the reigning men's U.S. Open champion?\u001B[0m\n",
+      "Intermediate answer: \u001B[36;1m\u001B[1;3mCurrent champions Carlos Alcaraz, 2022 men's singles champion.\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3mFollow up: Where is Carlos Alcaraz from?\u001B[0m\n",
+      "Intermediate answer: \u001B[36;1m\u001B[1;3mEl Palmar, Spain\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3mSo the final answer is: El Palmar, Spain\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -124,16 +123,12 @@
     "    Tool(\n",
     "        name=\"Intermediate Answer\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to ask with search\",\n",
+    "        description=\"useful for when you need to ask with search\"\n",
     "    )\n",
     "]\n",
     "\n",
-    "self_ask_with_search = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True\n",
-    ")\n",
-    "self_ask_with_search.run(\n",
-    "    \"What is the hometown of the reigning men's U.S. Open champion?\"\n",
-    ")"
+    "self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)\n",
+    "self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
    ],
    "metadata": {
     "collapsed": false

docs/modules/agents/tools/examples/human_tools.ipynb

@@ -25,7 +25,7 @@
     "llm = ChatOpenAI(temperature=0.0)\n",
     "math_llm = OpenAI(temperature=0.0)\n",
     "tools = load_tools(\n",
-    "    [\"human\", \"llm-math\"],\n",
+    "    [\"human\", \"llm-math\"], \n",
     "    llm=math_llm,\n",
     ")\n",
     "\n",
@@ -96,6 +96,7 @@
     }
    ],
    "source": [
+    "\n",
     "agent_chain.run(\"What is Eric Zhu's birthday?\")\n",
     "# Answer with \"last week\""
    ]

docs/modules/agents/tools/examples/ifttt.ipynb

@@ -62,12 +62,9 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "\n",
     "key = os.environ[\"IFTTTKey\"]\n",
     "url = f\"https://maker.ifttt.com/trigger/spotify/json/with/key/{key}\"\n",
-    "tool = IFTTTWebhook(\n",
-    "    name=\"Spotify\", description=\"Add a song to spotify playlist\", url=url\n",
-    ")"
+    "tool = IFTTTWebhook(name=\"Spotify\", description=\"Add a song to spotify playlist\", url=url)"
    ]
   },
   {

docs/modules/agents/tools/examples/openweathermap.ipynb

@@ -1,129 +1,128 @@
 {
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "245a954a",
-   "metadata": {},
-   "source": [
-    "# OpenWeatherMap API\n",
-    "\n",
-    "This notebook goes over how to use the OpenWeatherMap component to fetch weather information.\n",
-    "\n",
-    "First, you need to sign up for an OpenWeatherMap API key:\n",
-    "\n",
-    "1. Go to OpenWeatherMap and sign up for an API key [here](https://openweathermap.org/api/)\n",
-    "2. pip install pyowm\n",
-    "\n",
-    "Then we will need to set some environment variables:\n",
-    "1. Save your API KEY into OPENWEATHERMAP_API_KEY env variable"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "961b3689",
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pip install pyowm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "34bb5968",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"OPENWEATHERMAP_API_KEY\"] = \"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "ac4910f8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.utilities import OpenWeatherMapAPIWrapper"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "84b8f773",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "weather = OpenWeatherMapAPIWrapper()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "9651f324-e74a-4f08-a28a-89db029f66f8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "weather_data = weather.run(\"London,GB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "028f4cba",
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "In London,GB, the current weather is as follows:\n",
-      "Detailed status: overcast clouds\n",
-      "Wind speed: 4.63 m/s, direction: 150°\n",
-      "Humidity: 67%\n",
-      "Temperature: \n",
-      "  - Current: 5.35°C\n",
-      "  - High: 6.26°C\n",
-      "  - Low: 3.49°C\n",
-      "  - Feels like: 1.95°C\n",
-      "Rain: {}\n",
-      "Heat index: None\n",
-      "Cloud cover: 100%\n"
-     ]
+      "attachments": {},
+      "cell_type": "markdown",
+      "id": "245a954a",
+      "metadata": {},
+      "source": [
+        "# OpenWeatherMap API\n",
+        "\n",
+        "This notebook goes over how to use the OpenWeatherMap component to fetch weather information.\n",
+        "\n",
+        "First, you need to sign up for an OpenWeatherMap API key:\n",
+        "\n",
+        "1. Go to OpenWeatherMap and sign up for an API key [here](https://openweathermap.org/api/)\n",
+        "2. pip install pyowm\n",
+        "\n",
+        "Then we will need to set some environment variables:\n",
+        "1. Save your API KEY into OPENWEATHERMAP_API_KEY env variable"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "961b3689",
+      "metadata": {
+        "vscode": {
+          "languageId": "shellscript"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "pip install pyowm"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "id": "34bb5968",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "os.environ[\"OPENWEATHERMAP_API_KEY\"] = \"\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "id": "ac4910f8",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain.utilities import OpenWeatherMapAPIWrapper"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "id": "84b8f773",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "weather = OpenWeatherMapAPIWrapper()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "id": "9651f324-e74a-4f08-a28a-89db029f66f8",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "weather_data = weather.run(\"London,GB\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "id": "028f4cba",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "In London,GB, the current weather is as follows:\n",
+            "Detailed status: overcast clouds\n",
+            "Wind speed: 4.63 m/s, direction: 150°\n",
+            "Humidity: 67%\n",
+            "Temperature: \n",
+            "  - Current: 5.35°C\n",
+            "  - High: 6.26°C\n",
+            "  - Low: 3.49°C\n",
+            "  - Feels like: 1.95°C\n",
+            "Rain: {}\n",
+            "Heat index: None\n",
+            "Cloud cover: 100%\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(weather_data)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.2"
     }
-   ],
-   "source": [
-    "print(weather_data)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/modules/agents/tools/examples/search_tools.ipynb

@@ -64,9 +64,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -134,9 +132,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -204,9 +200,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -273,9 +267,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {

docs/modules/agents/tools/examples/searx_search.ipynb

@@ -95,9 +95,7 @@
    },
    "outputs": [],
    "source": [
-    "search = SearxSearchWrapper(\n",
-    "    searx_host=\"http://127.0.0.1:8888\", k=5\n",
-    ")  # k is for max number of items"
+    "search = SearxSearchWrapper(searx_host=\"http://127.0.0.1:8888\", k=5) # k is for max number of items"
    ]
   },
   {
@@ -122,7 +120,7 @@
     }
    ],
    "source": [
-    "search.run(\"large language model \", engines=[\"wiki\"])"
+    "search.run(\"large language model \", engines=['wiki'])"
    ]
   },
   {
@@ -154,7 +152,7 @@
    ],
    "source": [
     "search = SearxSearchWrapper(searx_host=\"http://127.0.0.1:8888\", k=1)\n",
-    "search.run(\"deep learning\", language=\"es\", engines=[\"wiki\"])"
+    "search.run(\"deep learning\", language='es', engines=['wiki'])"
    ]
   },
   {
@@ -246,12 +244,7 @@
     }
    ],
    "source": [
-    "results = search.results(\n",
-    "    \"Large Language Model prompt\",\n",
-    "    num_results=5,\n",
-    "    categories=\"science\",\n",
-    "    time_range=\"year\",\n",
-    ")\n",
+    "results = search.results(\"Large Language Model prompt\", num_results=5, categories='science', time_range='year')\n",
     "pprint.pp(results)"
    ]
   },
@@ -393,9 +386,7 @@
     }
    ],
    "source": [
-    "results = search.results(\n",
-    "    \"Large Language Model prompt\", num_results=5, engines=[\"arxiv\"]\n",
-    ")\n",
+    "results = search.results(\"Large Language Model prompt\", num_results=5, engines=['arxiv'])\n",
     "pprint.pp(results)"
    ]
   },
@@ -434,8 +425,8 @@
     }
    ],
    "source": [
-    "results = search.results(\"large language model\", num_results=20, categories=\"it\")\n",
-    "pprint.pp(list(filter(lambda r: r[\"engines\"][0] == \"github\", results)))"
+    "results = search.results(\"large language model\", num_results = 20, categories='it')\n",
+    "pprint.pp(list(filter(lambda r: r['engines'][0] == 'github', results)))"
    ]
   },
   {
@@ -587,9 +578,7 @@
     }
    ],
    "source": [
-    "results = search.results(\n",
-    "    \"large language model\", num_results=20, engines=[\"github\", \"gitlab\"]\n",
-    ")\n",
+    "results = search.results(\"large language model\", num_results = 20, engines=['github', 'gitlab'])\n",
     "pprint.pp(results)"
    ]
   }

docs/modules/agents/tools/examples/wolfram_alpha.ipynb

@@ -42,8 +42,7 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "\n",
-    "os.environ[\"WOLFRAM_ALPHA_APPID\"] = \"\""
+    "os.environ[\"WOLFRAM_ALPHA_APPID\"] = \"\"\n"
    ]
   },
   {

docs/modules/agents/tools/examples/zapier.ipynb

@@ -52,7 +52,7 @@
     "# get from https://platform.openai.com/\n",
     "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\", \"\")\n",
     "\n",
-    "# get from https://nla.zapier.com/demo/provider/debug (under User Information, after logging in):\n",
+    "# get from https://nla.zapier.com/demo/provider/debug (under User Information, after logging in): \n",
     "os.environ[\"ZAPIER_NLA_API_KEY\"] = os.environ.get(\"ZAPIER_NLA_API_KEY\", \"\")"
    ]
   },
@@ -106,9 +106,7 @@
     "llm = OpenAI(temperature=0)\n",
     "zapier = ZapierNLAWrapper()\n",
     "toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)\n",
-    "agent = initialize_agent(\n",
-    "    toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "agent = initialize_agent(toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -152,9 +150,7 @@
     }
    ],
    "source": [
-    "agent.run(\n",
-    "    \"Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.\"\n",
-    ")"
+    "agent.run(\"Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.\")"
    ]
   },
   {
@@ -210,25 +206,10 @@
     "\n",
     "GMAIL_SEARCH_INSTRUCTIONS = \"Grab the latest email from Silicon Valley Bank\"\n",
     "\n",
-    "\n",
     "def nla_gmail(inputs):\n",
-    "    action = next(\n",
-    "        (a for a in actions if a[\"description\"].startswith(\"Gmail: Find Email\")), None\n",
-    "    )\n",
-    "    return {\n",
-    "        \"email_data\": ZapierNLARunAction(\n",
-    "            action_id=action[\"id\"],\n",
-    "            zapier_description=action[\"description\"],\n",
-    "            params_schema=action[\"params\"],\n",
-    "        ).run(inputs[\"instructions\"])\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "gmail_chain = TransformChain(\n",
-    "    input_variables=[\"instructions\"],\n",
-    "    output_variables=[\"email_data\"],\n",
-    "    transform=nla_gmail,\n",
-    ")"
+    "    action = next((a for a in actions if a[\"description\"].startswith(\"Gmail: Find Email\")), None)\n",
+    "    return {\"email_data\": ZapierNLARunAction(action_id=action[\"id\"], zapier_description=action[\"description\"], params_schema=action[\"params\"]).run(inputs[\"instructions\"])}\n",
+    "gmail_chain = TransformChain(input_variables=[\"instructions\"], output_variables=[\"email_data\"], transform=nla_gmail)"
    ]
   },
   {
@@ -248,7 +229,7 @@
     "Draft email reply:\"\"\"\n",
     "\n",
     "prompt_template = PromptTemplate(input_variables=[\"email_data\"], template=template)\n",
-    "reply_chain = LLMChain(llm=OpenAI(temperature=0.7), prompt=prompt_template)"
+    "reply_chain = LLMChain(llm=OpenAI(temperature=.7), prompt=prompt_template)"
    ]
   },
   {
@@ -262,31 +243,11 @@
     "\n",
     "SLACK_HANDLE = \"@Ankush Gola\"\n",
     "\n",
-    "\n",
     "def nla_slack(inputs):\n",
-    "    action = next(\n",
-    "        (\n",
-    "            a\n",
-    "            for a in actions\n",
-    "            if a[\"description\"].startswith(\"Slack: Send Direct Message\")\n",
-    "        ),\n",
-    "        None,\n",
-    "    )\n",
+    "    action = next((a for a in actions if a[\"description\"].startswith(\"Slack: Send Direct Message\")), None)\n",
     "    instructions = f'Send this to {SLACK_HANDLE} in Slack: {inputs[\"draft_reply\"]}'\n",
-    "    return {\n",
-    "        \"slack_data\": ZapierNLARunAction(\n",
-    "            action_id=action[\"id\"],\n",
-    "            zapier_description=action[\"description\"],\n",
-    "            params_schema=action[\"params\"],\n",
-    "        ).run(instructions)\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "slack_chain = TransformChain(\n",
-    "    input_variables=[\"draft_reply\"],\n",
-    "    output_variables=[\"slack_data\"],\n",
-    "    transform=nla_slack,\n",
-    ")"
+    "    return {\"slack_data\": ZapierNLARunAction(action_id=action[\"id\"], zapier_description=action[\"description\"], params_schema=action[\"params\"]).run(instructions)}\n",
+    "slack_chain = TransformChain(input_variables=[\"draft_reply\"], output_variables=[\"slack_data\"], transform=nla_slack)"
    ]
   },
   {
@@ -329,9 +290,7 @@
    "source": [
     "## finally, execute\n",
     "\n",
-    "overall_chain = SimpleSequentialChain(\n",
-    "    chains=[gmail_chain, reply_chain, slack_chain], verbose=True\n",
-    ")\n",
+    "overall_chain = SimpleSequentialChain(chains=[gmail_chain, reply_chain, slack_chain], verbose=True)\n",
     "overall_chain.run(GMAIL_SEARCH_INSTRUCTIONS)"
    ]
   },

docs/modules/agents/tools/multi_input_tool.ipynb

@@ -45,7 +45,6 @@
     "def multiplier(a, b):\n",
     "    return a * b\n",
     "\n",
-    "\n",
     "def parsing_multiplier(string):\n",
     "    a, b = string.split(\",\")\n",
     "    return multiplier(int(a), int(b))"
@@ -61,14 +60,12 @@
     "llm = OpenAI(temperature=0)\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name=\"Multiplier\",\n",
+    "        name = \"Multiplier\",\n",
     "        func=parsing_multiplier,\n",
-    "        description=\"useful for when you need to multiply two numbers together. The input to this tool should be a comma separated list of numbers of length two, representing the two numbers you want to multiply together. For example, `1,2` would be the input if you wanted to multiply 1 by 2.\",\n",
+    "        description=\"useful for when you need to multiply two numbers together. The input to this tool should be a comma separated list of numbers of length two, representing the two numbers you want to multiply together. For example, `1,2` would be the input if you wanted to multiply 1 by 2.\"\n",
     "    )\n",
     "]\n",
-    "mrkl = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {

docs/modules/callbacks/getting_started.ipynb

@@ -189,7 +189,6 @@
     "from langchain.llms import OpenAI\n",
     "from langchain.schema import AgentAction, AgentFinish, LLMResult\n",
     "\n",
-    "\n",
     "class MyCustomCallbackHandler(BaseCallbackHandler):\n",
     "    \"\"\"Custom CallbackHandler.\"\"\"\n",
     "\n",
@@ -277,21 +276,13 @@
     "    ) -> None:\n",
     "        \"\"\"Run on agent end.\"\"\"\n",
     "        print(finish.log)\n",
-    "\n",
-    "\n",
     "manager = CallbackManager([MyCustomCallbackHandler()])\n",
     "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)\n",
     "tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, callback_manager=manager)\n",
     "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    "    callback_manager=manager,\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, callback_manager=manager\n",
     ")\n",
-    "agent.run(\n",
-    "    \"Who won the US Open men's final in 2019? What is his age raised to the 0.334 power?\"\n",
-    ")"
+    "agent.run(\"Who won the US Open men's final in 2019? What is his age raised to the 0.334 power?\")"
    ]
   },
   {
@@ -334,7 +325,6 @@
     "\n",
     "from langchain.callbacks.base import AsyncCallbackHandler, AsyncCallbackManager\n",
     "\n",
-    "\n",
     "class MyCustomAsyncCallbackHandler(AsyncCallbackHandler):\n",
     "    \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n",
     "\n",
@@ -353,26 +343,15 @@
     "        await asyncio.sleep(0.5)\n",
     "        print(\"\\n\\033[1m> Finished chain.\\033[0m\")\n",
     "\n",
-    "\n",
     "manager = AsyncCallbackManager([MyCustomAsyncCallbackHandler()])\n",
     "\n",
-    "# To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession,\n",
+    "# To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession, \n",
     "# but you must manually close the client session at the end of your program/event loop\n",
     "aiosession = ClientSession()\n",
     "llm = OpenAI(temperature=0, callback_manager=manager)\n",
-    "async_tools = load_tools(\n",
-    "    [\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession, callback_manager=manager\n",
-    ")\n",
-    "async_agent = initialize_agent(\n",
-    "    async_tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    "    callback_manager=manager,\n",
-    ")\n",
-    "await async_agent.arun(\n",
-    "    \"Who won the US Open men's final in 2019? What is his age raised to the 0.334 power?\"\n",
-    ")\n",
+    "async_tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession, callback_manager=manager)\n",
+    "async_agent = initialize_agent(async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, callback_manager=manager)\n",
+    "await async_agent.arun(\"Who won the US Open men's final in 2019? What is his age raised to the 0.334 power?\")\n",
     "await aiosession.close()"
    ]
   },

docs/modules/chains/examples/api.ipynb

@@ -46,10 +46,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains.api import open_meteo_docs\n",
-    "\n",
-    "chain_new = APIChain.from_llm_and_api_docs(\n",
-    "    llm, open_meteo_docs.OPEN_METEO_DOCS, verbose=True\n",
-    ")"
+    "chain_new = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS, verbose=True)"
    ]
   },
   {
@@ -82,9 +79,7 @@
     }
    ],
    "source": [
-    "chain_new.run(\n",
-    "    \"What is the weather like right now in Munich, Germany in degrees Farenheit?\"\n",
-    ")"
+    "chain_new.run('What is the weather like right now in Munich, Germany in degrees Farenheit?')"
    ]
   },
   {
@@ -101,8 +96,7 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "\n",
-    "os.environ[\"TMDB_BEARER_TOKEN\"] = \"\""
+    "os.environ['TMDB_BEARER_TOKEN'] = \"\""
    ]
   },
   {
@@ -112,11 +106,8 @@
    "outputs": [],
    "source": [
     "from langchain.chains.api import tmdb_docs\n",
-    "\n",
     "headers = {\"Authorization\": f\"Bearer {os.environ['TMDB_BEARER_TOKEN']}\"}\n",
-    "chain = APIChain.from_llm_and_api_docs(\n",
-    "    llm, tmdb_docs.TMDB_DOCS, headers=headers, verbose=True\n",
-    ")"
+    "chain = APIChain.from_llm_and_api_docs(llm, tmdb_docs.TMDB_DOCS, headers=headers, verbose=True)"
    ]
   },
   {
@@ -177,16 +168,12 @@
     "from langchain.chains import APIChain\n",
     "\n",
     "# Get api key here: https://www.listennotes.com/api/pricing/\n",
-    "listen_api_key = \"xxx\"\n",
+    "listen_api_key = 'xxx'\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "headers = {\"X-ListenAPI-Key\": listen_api_key}\n",
-    "chain = APIChain.from_llm_and_api_docs(\n",
-    "    llm, podcast_docs.PODCAST_DOCS, headers=headers, verbose=True\n",
-    ")\n",
-    "chain.run(\n",
-    "    \"Search for 'silicon valley bank' podcast episodes, audio length is more than 30 minutes, return only 1 results\"\n",
-    ")"
+    "chain = APIChain.from_llm_and_api_docs(llm, podcast_docs.PODCAST_DOCS, headers=headers, verbose=True)\n",
+    "chain.run(\"Search for 'silicon valley bank' podcast episodes, audio length is more than 30 minutes, return only 1 results\")\n"
    ]
   },
   {

docs/modules/chains/examples/constitutional_chain.ipynb

@@ -168,9 +168,9 @@
    ],
    "source": [
     "master_yoda_principal = ConstitutionalPrinciple(\n",
-    "    name=\"Master Yoda Principle\",\n",
-    "    critique_request=\"Identify specific ways in which the model's response is not in the style of Master Yoda.\",\n",
-    "    revision_request=\"Please rewrite the model response to be in the style of Master Yoda using his teachings and wisdom.\",\n",
+    "    name='Master Yoda Principle',\n",
+    "    critique_request='Identify specific ways in which the model\\'s response is not in the style of Master Yoda.',\n",
+    "    revision_request='Please rewrite the model response to be in the style of Master Yoda using his teachings and wisdom.',\n",
     ")\n",
     "\n",
     "constitutional_chain = ConstitutionalChain.from_llm(\n",

docs/modules/chains/examples/llm_requests.ipynb

@@ -50,7 +50,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = LLMRequestsChain(llm_chain=LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))"
+    "chain = LLMRequestsChain(llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))"
    ]
   },
   {
@@ -63,7 +63,7 @@
     "question = \"What are the Three (3) biggest countries, and their respective sizes?\"\n",
     "inputs = {\n",
     "    \"query\": question,\n",
-    "    \"url\": \"https://www.google.com/search?q=\" + question.replace(\" \", \"+\"),\n",
+    "    \"url\": \"https://www.google.com/search?q=\" + question.replace(\" \", \"+\")\n",
     "}"
    ]
   },

docs/modules/chains/examples/moderation.ipynb

@@ -25,12 +25,7 @@
    "outputs": [],
    "source": [
     "from langchain.llms import OpenAI\n",
-    "from langchain.chains import (\n",
-    "    OpenAIModerationChain,\n",
-    "    SequentialChain,\n",
-    "    LLMChain,\n",
-    "    SimpleSequentialChain,\n",
-    ")\n",
+    "from langchain.chains import OpenAIModerationChain, SequentialChain, LLMChain, SimpleSequentialChain\n",
     "from langchain.prompts import PromptTemplate"
    ]
   },
@@ -177,13 +172,13 @@
    "outputs": [],
    "source": [
     "class CustomModeration(OpenAIModerationChain):\n",
+    "    \n",
     "    def _moderate(self, text: str, results: dict) -> str:\n",
     "        if results[\"flagged\"]:\n",
     "            error_str = f\"The following text was found that violates OpenAI's content policy: {text}\"\n",
     "            return error_str\n",
     "        return text\n",
-    "\n",
-    "\n",
+    "    \n",
     "custom_moderation = CustomModeration()"
    ]
   },
@@ -249,9 +244,7 @@
    "outputs": [],
    "source": [
     "prompt = PromptTemplate(template=\"{text}\", input_variables=[\"text\"])\n",
-    "llm_chain = LLMChain(\n",
-    "    llm=OpenAI(temperature=0, model_name=\"text-davinci-002\"), prompt=prompt\n",
-    ")"
+    "llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name=\"text-davinci-002\"), prompt=prompt)"
    ]
   },
   {
@@ -331,12 +324,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "prompt = PromptTemplate(\n",
-    "    template=\"{setup}{new_input}Person2:\", input_variables=[\"setup\", \"new_input\"]\n",
-    ")\n",
-    "llm_chain = LLMChain(\n",
-    "    llm=OpenAI(temperature=0, model_name=\"text-davinci-002\"), prompt=prompt\n",
-    ")"
+    "prompt = PromptTemplate(template=\"{setup}{new_input}Person2:\", input_variables=[\"setup\", \"new_input\"])\n",
+    "llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name=\"text-davinci-002\"), prompt=prompt)"
    ]
   },
   {
@@ -390,9 +379,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = SequentialChain(\n",
-    "    chains=[llm_chain, moderation_chain], input_variables=[\"setup\", \"new_input\"]\n",
-    ")"
+    "chain = SequentialChain(chains=[llm_chain, moderation_chain], input_variables=[\"setup\", \"new_input\"])"
    ]
   },
   {

docs/modules/chains/examples/openapi.ipynb

@@ -48,9 +48,7 @@
     }
    ],
    "source": [
-    "spec = OpenAPISpec.from_url(\n",
-    "    \"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\"\n",
-    ")"
+    "spec = OpenAPISpec.from_url(\"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\")"
    ]
   },
   {
@@ -81,7 +79,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "operation = APIOperation.from_openapi_spec(spec, \"/public/openai/v0/products\", \"get\")"
+    "operation = APIOperation.from_openapi_spec(spec, '/public/openai/v0/products', \"get\")"
    ]
   },
   {
@@ -105,7 +103,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = OpenAI()  # Load a Language Model"
+    "llm = OpenAI() # Load a Language Model"
    ]
   },
   {
@@ -116,11 +114,11 @@
    "outputs": [],
    "source": [
     "chain = OpenAPIEndpointChain.from_api_operation(\n",
-    "    operation,\n",
-    "    llm,\n",
-    "    requests=Requests(),\n",
+    "    operation, \n",
+    "    llm, \n",
+    "    requests=Requests(), \n",
     "    verbose=True,\n",
-    "    return_intermediate_steps=True,  # Return request and response text\n",
+    "    return_intermediate_steps=True # Return request and response text\n",
     ")"
    ]
   },
@@ -270,12 +268,12 @@
    "outputs": [],
    "source": [
     "chain = OpenAPIEndpointChain.from_api_operation(\n",
-    "    operation,\n",
-    "    llm,\n",
-    "    requests=Requests(),\n",
+    "    operation, \n",
+    "    llm, \n",
+    "    requests=Requests(), \n",
     "    verbose=True,\n",
-    "    return_intermediate_steps=True,  # Return request and response text\n",
-    "    raw_response=True,  # Return raw response\n",
+    "    return_intermediate_steps=True, # Return request and response text\n",
+    "    raw_response=True # Return raw response\n",
     ")"
    ]
   },
@@ -413,9 +411,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "operation = APIOperation.from_openapi_spec(\n",
-    "    spec, \"/v1/public/openai/explain-task\", \"post\"\n",
-    ")"
+    "operation = APIOperation.from_openapi_spec(spec, '/v1/public/openai/explain-task', \"post\")"
    ]
   },
   {
@@ -427,8 +423,11 @@
    "source": [
     "llm = OpenAI()\n",
     "chain = OpenAPIEndpointChain.from_api_operation(\n",
-    "    operation, llm, requests=Requests(), verbose=True, return_intermediate_steps=True\n",
-    ")"
+    "    operation,\n",
+    "    llm,\n",
+    "    requests=Requests(),\n",
+    "    verbose=True,\n",
+    "    return_intermediate_steps=True)"
    ]
   },
   {

docs/modules/chains/examples/pal.ipynb

@@ -28,7 +28,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = OpenAI(model_name=\"code-davinci-002\", temperature=0, max_tokens=512)"
+    "llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)"
    ]
   },
   {
@@ -71,17 +71,17 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mdef solution():\n",
+      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3mdef solution():\n",
       "    \"\"\"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\"\"\"\n",
       "    cindy_pets = 4\n",
       "    marcia_pets = cindy_pets + 2\n",
       "    jan_pets = marcia_pets * 3\n",
       "    total_pets = cindy_pets + marcia_pets + jan_pets\n",
       "    result = total_pets\n",
-      "    return result\u001b[0m\n",
+      "    return result\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -139,8 +139,8 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
+      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m# Put objects into a list to record ordering\n",
       "objects = []\n",
       "objects += [('booklet', 'blue')] * 2\n",
       "objects += [('booklet', 'purple')] * 2\n",
@@ -151,9 +151,9 @@
       "\n",
       "# Count number of purple objects\n",
       "num_purple = len([object for object in objects if object[1] == 'purple'])\n",
-      "answer = num_purple\u001b[0m\n",
+      "answer = num_purple\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished PALChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished PALChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -187,9 +187,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pal_chain = PALChain.from_colored_object_prompt(\n",
-    "    llm, verbose=True, return_intermediate_steps=True\n",
-    ")"
+    "pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True, return_intermediate_steps=True)"
    ]
   },
   {
@@ -214,8 +212,8 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
+      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m# Put objects into a list to record ordering\n",
       "objects = []\n",
       "objects += [('booklet', 'blue')] * 2\n",
       "objects += [('booklet', 'purple')] * 2\n",
@@ -226,9 +224,9 @@
       "\n",
       "# Count number of purple objects\n",
       "num_purple = len([object for object in objects if object[1] == 'purple'])\n",
-      "answer = num_purple\u001b[0m\n",
+      "answer = num_purple\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     }
    ],
@@ -254,7 +252,7 @@
     }
    ],
    "source": [
-    "result[\"intermediate_steps\"]"
+    "result['intermediate_steps']"
    ]
   },
   {

docs/modules/chains/examples/sqlite.ipynb

@@ -230,9 +230,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db_chain = SQLDatabaseChain(\n",
-    "    llm=llm, database=db, prompt=PROMPT, verbose=True, return_intermediate_steps=True\n",
-    ")"
+    "db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True, return_intermediate_steps=True)"
    ]
   },
   {
@@ -343,11 +341,8 @@
    "source": [
     "db = SQLDatabase.from_uri(\n",
     "    \"sqlite:///../../../../notebooks/Chinook.db\",\n",
-    "    include_tables=[\n",
-    "        \"Track\"\n",
-    "    ],  # we include only one table to save tokens in the prompt :)\n",
-    "    sample_rows_in_table_info=2,\n",
-    ")"
+    "    include_tables=['Track'], # we include only one table to save tokens in the prompt :)\n",
+    "    sample_rows_in_table_info=2)"
    ]
   },
   {
@@ -527,10 +522,9 @@
    "source": [
     "db = SQLDatabase.from_uri(\n",
     "    \"sqlite:///../../../../notebooks/Chinook.db\",\n",
-    "    include_tables=[\"Track\", \"Playlist\"],\n",
+    "    include_tables=['Track', 'Playlist'],\n",
     "    sample_rows_in_table_info=2,\n",
-    "    custom_table_info=custom_table_info,\n",
-    ")\n",
+    "    custom_table_info=custom_table_info)\n",
     "\n",
     "print(db.table_info)"
    ]
@@ -604,7 +598,6 @@
    "outputs": [],
    "source": [
     "from langchain.chains import SQLDatabaseSequentialChain\n",
-    "\n",
     "db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")"
    ]
   },

docs/modules/chains/generic/async_chain.ipynb

@@ -39,7 +39,7 @@
       "\n",
       "\n",
       "SparkleSmile Toothpaste\n",
-      "\u001b[1mConcurrent executed in 1.54 seconds.\u001b[0m\n",
+      "\u001B[1mConcurrent executed in 1.54 seconds.\u001B[0m\n",
       "\n",
       "\n",
       "BrightSmile Toothpaste Co.\n",
@@ -55,7 +55,7 @@
       "\n",
       "\n",
       "BrightSmile Toothpaste.\n",
-      "\u001b[1mSerial executed in 6.38 seconds.\u001b[0m\n"
+      "\u001B[1mSerial executed in 6.38 seconds.\u001B[0m\n"
      ]
     }
    ],
@@ -95,17 +95,16 @@
     "    tasks = [async_generate(chain) for _ in range(5)]\n",
     "    await asyncio.gather(*tasks)\n",
     "\n",
-    "\n",
     "s = time.perf_counter()\n",
     "# If running this outside of Jupyter, use asyncio.run(generate_concurrently())\n",
     "await generate_concurrently()\n",
     "elapsed = time.perf_counter() - s\n",
-    "print(\"\\033[1m\" + f\"Concurrent executed in {elapsed:0.2f} seconds.\" + \"\\033[0m\")\n",
+    "print('\\033[1m' + f\"Concurrent executed in {elapsed:0.2f} seconds.\" + '\\033[0m')\n",
     "\n",
     "s = time.perf_counter()\n",
     "generate_serially()\n",
     "elapsed = time.perf_counter() - s\n",
-    "print(\"\\033[1m\" + f\"Serial executed in {elapsed:0.2f} seconds.\" + \"\\033[0m\")"
+    "print('\\033[1m' + f\"Serial executed in {elapsed:0.2f} seconds.\" + '\\033[0m')"
    ]
   }
  ],

docs/modules/chains/generic/from_hub.ipynb

@@ -93,8 +93,7 @@
    ],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
-    "\n",
-    "loader = TextLoader(\"../../state_of_the_union.txt\")\n",
+    "loader = TextLoader('../../state_of_the_union.txt')\n",
     "documents = loader.load()\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
     "texts = text_splitter.split_documents(documents)\n",

docs/modules/chains/generic/llm_chain.ipynb

@@ -42,13 +42,13 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mQuestion: What NFL team won the Super Bowl in the year Justin Beiber was born?\n",
+      "\u001B[32;1m\u001B[1;3mQuestion: What NFL team won the Super Bowl in the year Justin Beiber was born?\n",
       "\n",
-      "Answer: Let's think step by step.\u001b[0m\n",
+      "Answer: Let's think step by step.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -95,11 +95,11 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mWrite a sad poem about ducks.\u001b[0m\n",
+      "\u001B[32;1m\u001B[1;3mWrite a sad poem about ducks.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -138,7 +138,7 @@
    "outputs": [],
    "source": [
     "template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
-    "llm_chain = LLMChain.from_string(llm=OpenAI(temperature=0), template=template)"
+    "llm_chain = LLMChain.from_string(llm=OpenAI(temperature=0), template=template)\n"
    ]
   },
   {

docs/modules/chains/generic/sequential_chains.ipynb

@@ -53,7 +53,7 @@
    "outputs": [],
    "source": [
     "# This is an LLMChain to write a synopsis given a title of a play.\n",
-    "llm = OpenAI(temperature=0.7)\n",
+    "llm = OpenAI(temperature=.7)\n",
     "template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
     "\n",
     "Title: {title}\n",
@@ -70,7 +70,7 @@
    "outputs": [],
    "source": [
     "# This is an LLMChain to write a review of a play given a synopsis.\n",
-    "llm = OpenAI(temperature=0.7)\n",
+    "llm = OpenAI(temperature=.7)\n",
     "template = \"\"\"You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.\n",
     "\n",
     "Play Synopsis:\n",
@@ -89,10 +89,7 @@
    "source": [
     "# This is the overall chain where we run these two chains in sequence.\n",
     "from langchain.chains import SimpleSequentialChain\n",
-    "\n",
-    "overall_chain = SimpleSequentialChain(\n",
-    "    chains=[synopsis_chain, review_chain], verbose=True\n",
-    ")"
+    "overall_chain = SimpleSequentialChain(chains=[synopsis_chain, review_chain], verbose=True)"
    ]
   },
   {
@@ -174,13 +171,13 @@
    "outputs": [],
    "source": [
     "# This is an LLMChain to write a synopsis given a title of a play and the era it is set in.\n",
-    "llm = OpenAI(temperature=0.7)\n",
+    "llm = OpenAI(temperature=.7)\n",
     "template = \"\"\"You are a playwright. Given the title of play and the era it is set in, it is your job to write a synopsis for that title.\n",
     "\n",
     "Title: {title}\n",
     "Era: {era}\n",
     "Playwright: This is a synopsis for the above play:\"\"\"\n",
-    "prompt_template = PromptTemplate(input_variables=[\"title\", \"era\"], template=template)\n",
+    "prompt_template = PromptTemplate(input_variables=[\"title\", 'era'], template=template)\n",
     "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, output_key=\"synopsis\")"
    ]
   },
@@ -192,7 +189,7 @@
    "outputs": [],
    "source": [
     "# This is an LLMChain to write a review of a play given a synopsis.\n",
-    "llm = OpenAI(temperature=0.7)\n",
+    "llm = OpenAI(temperature=.7)\n",
     "template = \"\"\"You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.\n",
     "\n",
     "Play Synopsis:\n",
@@ -211,14 +208,12 @@
    "source": [
     "# This is the overall chain where we run these two chains in sequence.\n",
     "from langchain.chains import SequentialChain\n",
-    "\n",
     "overall_chain = SequentialChain(\n",
     "    chains=[synopsis_chain, review_chain],\n",
     "    input_variables=[\"era\", \"title\"],\n",
     "    # Here we return multiple variables\n",
     "    output_variables=[\"synopsis\", \"review\"],\n",
-    "    verbose=True,\n",
-    ")"
+    "    verbose=True)"
    ]
   },
   {
@@ -253,7 +248,7 @@
     }
    ],
    "source": [
-    "overall_chain({\"title\": \"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})"
+    "overall_chain({\"title\":\"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})"
    ]
   },
   {
@@ -309,7 +304,7 @@
     "from langchain.chains import SequentialChain\n",
     "from langchain.memory import SimpleMemory\n",
     "\n",
-    "llm = OpenAI(temperature=0.7)\n",
+    "llm = OpenAI(temperature=.7)\n",
     "template = \"\"\"You are a social media manager for a theater company.  Given the title of play, the era it is set in, the date,time and location, the synopsis of the play, and the review of the play, it is your job to write a social media post for that play.\n",
     "\n",
     "Here is some context about the time and location of the play:\n",
@@ -323,23 +318,18 @@
     "\n",
     "Social Media Post:\n",
     "\"\"\"\n",
-    "prompt_template = PromptTemplate(\n",
-    "    input_variables=[\"synopsis\", \"review\", \"time\", \"location\"], template=template\n",
-    ")\n",
+    "prompt_template = PromptTemplate(input_variables=[\"synopsis\", \"review\", \"time\", \"location\"], template=template)\n",
     "social_chain = LLMChain(llm=llm, prompt=prompt_template, output_key=\"social_post_text\")\n",
     "\n",
     "overall_chain = SequentialChain(\n",
-    "    memory=SimpleMemory(\n",
-    "        memories={\"time\": \"December 25th, 8pm PST\", \"location\": \"Theater in the Park\"}\n",
-    "    ),\n",
+    "    memory=SimpleMemory(memories={\"time\": \"December 25th, 8pm PST\", \"location\": \"Theater in the Park\"}),\n",
     "    chains=[synopsis_chain, review_chain, social_chain],\n",
     "    input_variables=[\"era\", \"title\"],\n",
     "    # Here we return multiple variables\n",
     "    output_variables=[\"social_post_text\"],\n",
-    "    verbose=True,\n",
-    ")\n",
+    "    verbose=True)\n",
     "\n",
-    "overall_chain({\"title\": \"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})"
+    "overall_chain({\"title\":\"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})"
    ]
   },
   {

docs/modules/chains/generic/serialization.ipynb

@@ -26,12 +26,11 @@
    "outputs": [],
    "source": [
     "from langchain import PromptTemplate, OpenAI, LLMChain\n",
-    "\n",
     "template = \"\"\"Question: {question}\n",
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)"
+    "llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)\n"
    ]
   },
   {
@@ -137,13 +136,13 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mQuestion: whats 2 + 2\n",
+      "\u001B[32;1m\u001B[1;3mQuestion: whats 2 + 2\n",
       "\n",
-      "Answer: Let's think step by step.\u001b[0m\n",
+      "Answer: Let's think step by step.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -258,10 +257,9 @@
     "    \"prompt_path\": \"prompt.json\",\n",
     "    \"llm_path\": \"llm.json\",\n",
     "    \"output_key\": \"text\",\n",
-    "    \"_type\": \"llm_chain\",\n",
+    "    \"_type\": \"llm_chain\"\n",
     "}\n",
     "import json\n",
-    "\n",
     "with open(\"llm_chain_separate.json\", \"w\") as f:\n",
     "    json.dump(config, f, indent=2)"
    ]
@@ -321,13 +319,13 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mQuestion: whats 2 + 2\n",
+      "\u001B[32;1m\u001B[1;3mQuestion: whats 2 + 2\n",
       "\n",
-      "Answer: Let's think step by step.\u001b[0m\n",
+      "Answer: Let's think step by step.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {

docs/modules/chains/generic/transformation.ipynb

@@ -47,10 +47,7 @@
     "    shortened_text = \"\\n\\n\".join(text.split(\"\\n\\n\")[:3])\n",
     "    return {\"output_text\": shortened_text}\n",
     "\n",
-    "\n",
-    "transform_chain = TransformChain(\n",
-    "    input_variables=[\"text\"], output_variables=[\"output_text\"], transform=transform_func\n",
-    ")"
+    "transform_chain = TransformChain(input_variables=[\"text\"], output_variables=[\"output_text\"], transform=transform_func)"
    ]
   },
   {

docs/modules/chains/getting_started.ipynb

@@ -73,7 +73,6 @@
    ],
    "source": [
     "from langchain.chains import LLMChain\n",
-    "\n",
     "chain = LLMChain(llm=llm, prompt=prompt)\n",
     "\n",
     "# Run the chain only specifying the input variable.\n",
@@ -110,13 +109,12 @@
     "    ChatPromptTemplate,\n",
     "    HumanMessagePromptTemplate,\n",
     ")\n",
-    "\n",
     "human_message_prompt = HumanMessagePromptTemplate(\n",
-    "    prompt=PromptTemplate(\n",
-    "        template=\"What is a good name for a company that makes {product}?\",\n",
-    "        input_variables=[\"product\"],\n",
+    "        prompt=PromptTemplate(\n",
+    "            template=\"What is a good name for a company that makes {product}?\",\n",
+    "            input_variables=[\"product\"],\n",
+    "        )\n",
     "    )\n",
-    ")\n",
     "chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])\n",
     "chat = ChatOpenAI(temperature=0.9)\n",
     "chain = LLMChain(llm=chat, prompt=chat_prompt_template)\n",
@@ -191,7 +189,6 @@
    ],
    "source": [
     "from langchain.chains import SimpleSequentialChain\n",
-    "\n",
     "overall_chain = SimpleSequentialChain(chains=[chain, chain_two], verbose=True)\n",
     "\n",
     "# Run the chain specifying only the input variable for the first chain.\n",
@@ -234,19 +231,17 @@
     "    @property\n",
     "    def input_keys(self) -> List[str]:\n",
     "        # Union of the input keys of the two chains.\n",
-    "        all_input_vars = set(self.chain_1.input_keys).union(\n",
-    "            set(self.chain_2.input_keys)\n",
-    "        )\n",
+    "        all_input_vars = set(self.chain_1.input_keys).union(set(self.chain_2.input_keys))\n",
     "        return list(all_input_vars)\n",
     "\n",
     "    @property\n",
     "    def output_keys(self) -> List[str]:\n",
-    "        return [\"concat_output\"]\n",
+    "        return ['concat_output']\n",
     "\n",
     "    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:\n",
     "        output_1 = self.chain_1.run(inputs)\n",
     "        output_2 = self.chain_2.run(inputs)\n",
-    "        return {\"concat_output\": output_1 + output_2}"
+    "        return {'concat_output': output_1 + output_2}"
    ]
   },
   {

docs/modules/chains/index_examples/analyze_document.ipynb

@@ -142,10 +142,7 @@
     }
    ],
    "source": [
-    "qa_document_chain.run(\n",
-    "    input_document=state_of_the_union,\n",
-    "    question=\"what did the president say about justice breyer?\",\n",
-    ")"
+    "qa_document_chain.run(input_document=state_of_the_union, question=\"what did the president say about justice breyer?\")"
    ]
   },
   {

docs/modules/chains/index_examples/chat_vector_db.ipynb

@@ -44,7 +44,6 @@
    "outputs": [],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
-    "\n",
     "loader = TextLoader(\"../../state_of_the_union.txt\")\n",
     "documents = loader.load()"
    ]
@@ -122,9 +121,7 @@
    },
    "outputs": [],
    "source": [
-    "qa = ConversationalRetrievalChain.from_llm(\n",
-    "    OpenAI(temperature=0), vectorstore.as_retriever()\n",
-    ")"
+    "qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever())"
    ]
   },
   {
@@ -214,7 +211,7 @@
     }
    ],
    "source": [
-    "result[\"answer\"]"
+    "result['answer']"
    ]
   },
   {
@@ -235,9 +232,7 @@
    },
    "outputs": [],
    "source": [
-    "qa = ConversationalRetrievalChain.from_llm(\n",
-    "    OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True\n",
-    ")"
+    "qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)"
    ]
   },
   {
@@ -274,7 +269,7 @@
     }
    ],
    "source": [
-    "result[\"source_documents\"][0]"
+    "result['source_documents'][0]"
    ]
   },
   {
@@ -307,14 +302,10 @@
    },
    "outputs": [],
    "source": [
-    "qa = ConversationalRetrievalChain.from_llm(\n",
-    "    OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True\n",
-    ")\n",
+    "qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)\n",
     "chat_history = []\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "result = qa(\n",
-    "    {\"question\": query, \"chat_history\": chat_history, \"vectordbkwargs\": vectordbkwargs}\n",
-    ")"
+    "result = qa({\"question\": query, \"chat_history\": chat_history, \"vectordbkwargs\": vectordbkwargs})"
    ]
   },
   {
@@ -394,7 +385,7 @@
     }
    ],
    "source": [
-    "result[\"answer\"]"
+    "result['answer']"
    ]
   },
   {
@@ -473,7 +464,7 @@
     }
    ],
    "source": [
-    "result[\"answer\"]"
+    "result['answer']"
    ]
   },
   {
@@ -498,30 +489,19 @@
     "from langchain.chains.llm import LLMChain\n",
     "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "from langchain.chains.conversational_retrieval.prompts import (\n",
-    "    CONDENSE_QUESTION_PROMPT,\n",
-    "    QA_PROMPT,\n",
-    ")\n",
+    "from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT\n",
     "from langchain.chains.question_answering import load_qa_chain\n",
     "\n",
     "# Construct a ConversationalRetrievalChain with a streaming llm for combine docs\n",
     "# and a separate, non-streaming llm for question generation\n",
     "llm = OpenAI(temperature=0)\n",
-    "streaming_llm = OpenAI(\n",
-    "    streaming=True,\n",
-    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
-    "    verbose=True,\n",
-    "    temperature=0,\n",
-    ")\n",
+    "streaming_llm = OpenAI(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
     "\n",
     "question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
     "doc_chain = load_qa_chain(streaming_llm, chain_type=\"stuff\", prompt=QA_PROMPT)\n",
     "\n",
     "qa = ConversationalRetrievalChain(\n",
-    "    retriever=vectorstore.as_retriever(),\n",
-    "    combine_docs_chain=doc_chain,\n",
-    "    question_generator=question_generator,\n",
-    ")"
+    "    retriever=vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=question_generator)"
    ]
   },
   {
@@ -565,7 +545,7 @@
    "source": [
     "chat_history = [(query, result[\"answer\"])]\n",
     "query = \"Did he mention who she suceeded\"\n",
-    "result = qa({\"question\": query, \"chat_history\": chat_history})"
+    "result = qa({\"question\": query, \"chat_history\": chat_history})\n"
    ]
   },
   {
@@ -591,11 +571,7 @@
     "    for human, ai in inputs:\n",
     "        res.append(f\"Human:{human}\\nAI:{ai}\")\n",
     "    return \"\\n\".join(res)\n",
-    "\n",
-    "\n",
-    "qa = ConversationalRetrievalChain.from_llm(\n",
-    "    OpenAI(temperature=0), vectorstore.as_retriever(), get_chat_history=get_chat_history\n",
-    ")"
+    "qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), get_chat_history=get_chat_history)"
    ]
   },
   {
@@ -632,7 +608,7 @@
     }
    ],
    "source": [
-    "result[\"answer\"]"
+    "result['answer']"
    ]
   },
   {

docs/modules/chains/index_examples/hyde.ipynb

@@ -91,9 +91,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings = HypotheticalDocumentEmbedder.from_llm(\n",
-    "    multi_llm, base_embeddings, \"web_search\"\n",
-    ")"
+    "embeddings = HypotheticalDocumentEmbedder.from_llm(multi_llm, base_embeddings, \"web_search\")"
    ]
   },
   {
@@ -138,9 +136,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings = HypotheticalDocumentEmbedder(\n",
-    "    llm_chain=llm_chain, base_embeddings=base_embeddings\n",
-    ")"
+    "embeddings = HypotheticalDocumentEmbedder(llm_chain=llm_chain, base_embeddings=base_embeddings)"
    ]
   },
   {
@@ -150,9 +146,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = embeddings.embed_query(\n",
-    "    \"What did the president say about Ketanji Brown Jackson\"\n",
-    ")"
+    "result = embeddings.embed_query(\"What did the president say about Ketanji Brown Jackson\")"
    ]
   },
   {

docs/modules/chains/index_examples/qa_with_sources.ipynb

@@ -66,9 +66,7 @@
     }
    ],
    "source": [
-    "docsearch = Chroma.from_texts(\n",
-    "    texts, embeddings, metadatas=[{\"source\": str(i)} for i in range(len(texts))]\n",
-    ")"
+    "docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": str(i)} for i in range(len(texts))])"
    ]
   },
   {
@@ -215,9 +213,7 @@
     "FINAL ANSWER IN ITALIAN:\"\"\"\n",
     "PROMPT = PromptTemplate(template=template, input_variables=[\"summaries\", \"question\"])\n",
     "\n",
-    "chain = load_qa_with_sources_chain(\n",
-    "    OpenAI(temperature=0), chain_type=\"stuff\", prompt=PROMPT\n",
-    ")\n",
+    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"stuff\", prompt=PROMPT)\n",
     "query = \"What did the president say about Justice Breyer\"\n",
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
@@ -281,9 +277,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = load_qa_with_sources_chain(\n",
-    "    OpenAI(temperature=0), chain_type=\"map_reduce\", return_intermediate_steps=True\n",
-    ")"
+    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_intermediate_steps=True)"
    ]
   },
   {
@@ -343,6 +337,7 @@
     }
    ],
    "source": [
+    "\n",
     "question_prompt_template = \"\"\"Use the following portion of a long document to see if any of the text is relevant to answer the question. \n",
     "Return any relevant text in Italian.\n",
     "{context}\n",
@@ -366,13 +361,7 @@
     "    template=combine_prompt_template, input_variables=[\"summaries\", \"question\"]\n",
     ")\n",
     "\n",
-    "chain = load_qa_with_sources_chain(\n",
-    "    OpenAI(temperature=0),\n",
-    "    chain_type=\"map_reduce\",\n",
-    "    return_intermediate_steps=True,\n",
-    "    question_prompt=QUESTION_PROMPT,\n",
-    "    combine_prompt=COMBINE_PROMPT,\n",
-    ")\n",
+    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_intermediate_steps=True, question_prompt=QUESTION_PROMPT, combine_prompt=COMBINE_PROMPT)\n",
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
   },
@@ -449,9 +438,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = load_qa_with_sources_chain(\n",
-    "    OpenAI(temperature=0), chain_type=\"refine\", return_intermediate_steps=True\n",
-    ")"
+    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"refine\", return_intermediate_steps=True)"
    ]
   },
   {
@@ -550,13 +537,7 @@
     }
    ],
    "source": [
-    "chain = load_qa_with_sources_chain(\n",
-    "    OpenAI(temperature=0),\n",
-    "    chain_type=\"refine\",\n",
-    "    return_intermediate_steps=True,\n",
-    "    question_prompt=question_prompt,\n",
-    "    refine_prompt=refine_prompt,\n",
-    ")\n",
+    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"refine\", return_intermediate_steps=True, question_prompt=question_prompt, refine_prompt=refine_prompt)\n",
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
   },
@@ -577,12 +558,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = load_qa_with_sources_chain(\n",
-    "    OpenAI(temperature=0),\n",
-    "    chain_type=\"map_rerank\",\n",
-    "    metadata_keys=[\"source\"],\n",
-    "    return_intermediate_steps=True,\n",
-    ")"
+    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"map_rerank\", metadata_keys=['source'], return_intermediate_steps=True)"
    ]
   },
   {
@@ -687,13 +663,7 @@
     "    input_variables=[\"context\", \"question\"],\n",
     "    output_parser=output_parser,\n",
     ")\n",
-    "chain = load_qa_with_sources_chain(\n",
-    "    OpenAI(temperature=0),\n",
-    "    chain_type=\"map_rerank\",\n",
-    "    metadata_keys=[\"source\"],\n",
-    "    return_intermediate_steps=True,\n",
-    "    prompt=PROMPT,\n",
-    ")\n",
+    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"map_rerank\", metadata_keys=['source'], return_intermediate_steps=True, prompt=PROMPT)\n",
     "query = \"What did the president say about Justice Breyer\"\n",
     "result = chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]

docs/modules/chains/index_examples/question_answering.ipynb

@@ -71,9 +71,7 @@
     }
    ],
    "source": [
-    "docsearch = Chroma.from_texts(\n",
-    "    texts, embeddings, metadatas=[{\"source\": str(i)} for i in range(len(texts))]\n",
-    ").as_retriever()"
+    "docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": str(i)} for i in range(len(texts))]).as_retriever()"
    ]
   },
   {
@@ -298,9 +296,7 @@
    },
    "outputs": [],
    "source": [
-    "chain = load_qa_chain(\n",
-    "    OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True\n",
-    ")"
+    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True)"
    ]
   },
   {
@@ -384,13 +380,7 @@
     "COMBINE_PROMPT = PromptTemplate(\n",
     "    template=combine_prompt_template, input_variables=[\"summaries\", \"question\"]\n",
     ")\n",
-    "chain = load_qa_chain(\n",
-    "    OpenAI(temperature=0),\n",
-    "    chain_type=\"map_reduce\",\n",
-    "    return_map_steps=True,\n",
-    "    question_prompt=QUESTION_PROMPT,\n",
-    "    combine_prompt=COMBINE_PROMPT,\n",
-    ")\n",
+    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True, question_prompt=QUESTION_PROMPT, combine_prompt=COMBINE_PROMPT)\n",
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
   },
@@ -473,9 +463,7 @@
    },
    "outputs": [],
    "source": [
-    "chain = load_qa_chain(\n",
-    "    OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True\n",
-    ")"
+    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True)"
    ]
   },
   {
@@ -568,13 +556,8 @@
     "initial_qa_prompt = PromptTemplate(\n",
     "    input_variables=[\"context_str\", \"question\"], template=initial_qa_template\n",
     ")\n",
-    "chain = load_qa_chain(\n",
-    "    OpenAI(temperature=0),\n",
-    "    chain_type=\"refine\",\n",
-    "    return_refine_steps=True,\n",
-    "    question_prompt=initial_qa_prompt,\n",
-    "    refine_prompt=refine_prompt,\n",
-    ")\n",
+    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True,\n",
+    "                     question_prompt=initial_qa_prompt, refine_prompt=refine_prompt)\n",
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
   },
@@ -597,9 +580,7 @@
    },
    "outputs": [],
    "source": [
-    "chain = load_qa_chain(\n",
-    "    OpenAI(temperature=0), chain_type=\"map_rerank\", return_intermediate_steps=True\n",
-    ")"
+    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_rerank\", return_intermediate_steps=True)"
    ]
   },
   {
@@ -730,12 +711,7 @@
     "    output_parser=output_parser,\n",
     ")\n",
     "\n",
-    "chain = load_qa_chain(\n",
-    "    OpenAI(temperature=0),\n",
-    "    chain_type=\"map_rerank\",\n",
-    "    return_intermediate_steps=True,\n",
-    "    prompt=PROMPT,\n",
-    ")\n",
+    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_rerank\", return_intermediate_steps=True, prompt=PROMPT)\n",
     "query = \"What did the president say about Justice Breyer\"\n",
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]

docs/modules/chains/index_examples/summarize.ipynb

@@ -248,9 +248,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = load_summarize_chain(\n",
-    "    OpenAI(temperature=0), chain_type=\"map_reduce\", return_intermediate_steps=True\n",
-    ")"
+    "chain = load_summarize_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_intermediate_steps=True)"
    ]
   },
   {
@@ -316,13 +314,7 @@
     "\n",
     "CONCISE SUMMARY IN ITALIAN:\"\"\"\n",
     "PROMPT = PromptTemplate(template=prompt_template, input_variables=[\"text\"])\n",
-    "chain = load_summarize_chain(\n",
-    "    OpenAI(temperature=0),\n",
-    "    chain_type=\"map_reduce\",\n",
-    "    return_intermediate_steps=True,\n",
-    "    map_prompt=PROMPT,\n",
-    "    combine_prompt=PROMPT,\n",
-    ")\n",
+    "chain = load_summarize_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_intermediate_steps=True, map_prompt=PROMPT, combine_prompt=PROMPT)\n",
     "chain({\"input_documents\": docs}, return_only_outputs=True)"
    ]
   },
@@ -390,9 +382,7 @@
     }
    ],
    "source": [
-    "chain = load_summarize_chain(\n",
-    "    OpenAI(temperature=0), chain_type=\"refine\", return_intermediate_steps=True\n",
-    ")\n",
+    "chain = load_summarize_chain(OpenAI(temperature=0), chain_type=\"refine\", return_intermediate_steps=True)\n",
     "\n",
     "chain({\"input_documents\": docs}, return_only_outputs=True)"
    ]
@@ -451,13 +441,7 @@
     "    input_variables=[\"existing_answer\", \"text\"],\n",
     "    template=refine_template,\n",
     ")\n",
-    "chain = load_summarize_chain(\n",
-    "    OpenAI(temperature=0),\n",
-    "    chain_type=\"refine\",\n",
-    "    return_intermediate_steps=True,\n",
-    "    question_prompt=PROMPT,\n",
-    "    refine_prompt=refine_prompt,\n",
-    ")\n",
+    "chain = load_summarize_chain(OpenAI(temperature=0), chain_type=\"refine\", return_intermediate_steps=True, question_prompt=PROMPT, refine_prompt=refine_prompt)\n",
     "chain({\"input_documents\": docs}, return_only_outputs=True)"
    ]
   },

docs/modules/chains/index_examples/vector_db_qa.ipynb

@@ -41,7 +41,6 @@
    ],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
-    "\n",
     "loader = TextLoader(\"../../state_of_the_union.txt\")\n",
     "documents = loader.load()\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
@@ -58,9 +57,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "qa = RetrievalQA.from_chain_type(\n",
-    "    llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever()\n",
-    ")"
+    "qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever())"
    ]
   },
   {
@@ -103,9 +100,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "qa = RetrievalQA.from_chain_type(\n",
-    "    llm=OpenAI(), chain_type=\"map_reduce\", retriever=docsearch.as_retriever()\n",
-    ")"
+    "qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"map_reduce\", retriever=docsearch.as_retriever())"
    ]
   },
   {
@@ -146,7 +141,6 @@
    "outputs": [],
    "source": [
     "from langchain.chains.question_answering import load_qa_chain\n",
-    "\n",
     "qa_chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")\n",
     "qa = RetrievalQA(combine_documents_chain=qa_chain, retriever=docsearch.as_retriever())"
    ]
@@ -190,7 +184,6 @@
    "outputs": [],
    "source": [
     "from langchain.prompts import PromptTemplate\n",
-    "\n",
     "prompt_template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
     "\n",
     "{context}\n",
@@ -210,12 +203,7 @@
    "outputs": [],
    "source": [
     "chain_type_kwargs = {\"prompt\": PROMPT}\n",
-    "qa = RetrievalQA.from_chain_type(\n",
-    "    llm=OpenAI(),\n",
-    "    chain_type=\"stuff\",\n",
-    "    retriever=docsearch.as_retriever(),\n",
-    "    chain_type_kwargs=chain_type_kwargs,\n",
-    ")"
+    "qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)"
    ]
   },
   {
@@ -256,12 +244,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "qa = RetrievalQA.from_chain_type(\n",
-    "    llm=OpenAI(),\n",
-    "    chain_type=\"stuff\",\n",
-    "    retriever=docsearch.as_retriever(),\n",
-    "    return_source_documents=True,\n",
-    ")"
+    "qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever(), return_source_documents=True)"
    ]
   },
   {

docs/modules/chains/index_examples/vector_db_qa_with_sources.ipynb

@@ -55,9 +55,7 @@
     }
    ],
    "source": [
-    "docsearch = Chroma.from_texts(\n",
-    "    texts, embeddings, metadatas=[{\"source\": f\"{i}-pl\"} for i in range(len(texts))]\n",
-    ")"
+    "docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": f\"{i}-pl\"} for i in range(len(texts))])"
    ]
   },
   {
@@ -79,9 +77,7 @@
    "source": [
     "from langchain import OpenAI\n",
     "\n",
-    "chain = RetrievalQAWithSourcesChain.from_chain_type(\n",
-    "    OpenAI(temperature=0), chain_type=\"stuff\", retriever=docsearch.as_retriever()\n",
-    ")"
+    "chain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type=\"stuff\", retriever=docsearch.as_retriever())"
    ]
   },
   {
@@ -103,10 +99,7 @@
     }
    ],
    "source": [
-    "chain(\n",
-    "    {\"question\": \"What did the president say about Justice Breyer\"},\n",
-    "    return_only_outputs=True,\n",
-    ")"
+    "chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
    ]
   },
   {
@@ -127,9 +120,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = RetrievalQAWithSourcesChain.from_chain_type(\n",
-    "    OpenAI(temperature=0), chain_type=\"map_reduce\", retriever=docsearch.as_retriever()\n",
-    ")"
+    "chain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type=\"map_reduce\", retriever=docsearch.as_retriever())"
    ]
   },
   {
@@ -151,10 +142,7 @@
     }
    ],
    "source": [
-    "chain(\n",
-    "    {\"question\": \"What did the president say about Justice Breyer\"},\n",
-    "    return_only_outputs=True,\n",
-    ")"
+    "chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
    ]
   },
   {
@@ -173,11 +161,8 @@
    "outputs": [],
    "source": [
     "from langchain.chains.qa_with_sources import load_qa_with_sources_chain\n",
-    "\n",
     "qa_chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"stuff\")\n",
-    "qa = RetrievalQAWithSourcesChain(\n",
-    "    combine_documents_chain=qa_chain, retriever=docsearch.as_retriever()\n",
-    ")"
+    "qa = RetrievalQAWithSourcesChain(combine_documents_chain=qa_chain, retriever=docsearch.as_retriever())"
    ]
   },
   {
@@ -199,10 +184,7 @@
     }
    ],
    "source": [
-    "qa(\n",
-    "    {\"question\": \"What did the president say about Justice Breyer\"},\n",
-    "    return_only_outputs=True,\n",
-    ")"
+    "qa({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
    ]
   },
   {

docs/modules/chains/index_examples/vector_db_text_generation.ipynb

@@ -72,7 +72,6 @@
     "                github_url = f\"https://github.com/{repo_owner}/{repo_name}/blob/{git_sha}/{relative_path}\"\n",
     "                yield Document(page_content=f.read(), metadata={\"source\": github_url})\n",
     "\n",
-    "\n",
     "sources = get_github_docs(\"yirenlu92\", \"deno-manual-forked\")\n",
     "\n",
     "source_chunks = []\n",
@@ -116,13 +115,14 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
-    "\n",
     "prompt_template = \"\"\"Use the context below to write a 400 word blog post about the topic below:\n",
     "    Context: {context}\n",
     "    Topic: {topic}\n",
     "    Blog post:\"\"\"\n",
     "\n",
-    "PROMPT = PromptTemplate(template=prompt_template, input_variables=[\"context\", \"topic\"])\n",
+    "PROMPT = PromptTemplate(\n",
+    "    template=prompt_template, input_variables=[\"context\", \"topic\"]\n",
+    ")\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "\n",

docs/modules/indexes/document_loaders/examples/airbyte_json.ipynb

@@ -67,7 +67,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = AirbyteJSONLoader(\"/tmp/airbyte_local/json_data/_airbyte_raw_pokemon.jsonl\")"
+    "loader = AirbyteJSONLoader('/tmp/airbyte_local/json_data/_airbyte_raw_pokemon.jsonl')"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/azure_blob_storage_container.ipynb

@@ -78,9 +78,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = AzureBlobStorageContainerLoader(\n",
-    "    conn_str=\"<conn_str>\", container=\"<container>\", prefix=\"<prefix>\"\n",
-    ")"
+    "loader = AzureBlobStorageContainerLoader(conn_str=\"<conn_str>\", container=\"<container>\", prefix=\"<prefix>\")"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/azure_blob_storage_file.ipynb

@@ -38,11 +38,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = AzureBlobStorageFileLoader(\n",
-    "    conn_str=\"<connection string>\",\n",
-    "    container=\"<container name>\",\n",
-    "    blob_name=\"<blob name>\",\n",
-    ")"
+    "loader = AzureBlobStorageFileLoader(conn_str='<connection string>', container='<container name>', blob_name='<blob name>')"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/bigquery.ipynb

@@ -24,7 +24,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "BASE_QUERY = \"\"\"\n",
+    "BASE_QUERY = '''\n",
     "SELECT\n",
     "  id,\n",
     "  dna_sequence,\n",
@@ -41,7 +41,7 @@
     "    SELECT\n",
     "      AS STRUCT 3 AS id, \"TCCGGA\" AS dna_sequence, \"Acidianus hospitalis (strain W1).\" AS organism) AS new_array),\n",
     "  UNNEST(new_array)\n",
-    "\"\"\""
+    "'''"
    ]
   },
   {
@@ -92,11 +92,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = BigQueryLoader(\n",
-    "    BASE_QUERY,\n",
-    "    page_content_columns=[\"dna_sequence\", \"organism\"],\n",
-    "    metadata_columns=[\"id\"],\n",
-    ")\n",
+    "loader = BigQueryLoader(BASE_QUERY, page_content_columns=[\"dna_sequence\", \"organism\"], metadata_columns=[\"id\"])\n",
     "\n",
     "data = loader.load()"
    ]
@@ -132,7 +128,7 @@
    "outputs": [],
    "source": [
     "# Note that the `id` column is being returned twice, with one instance aliased as `source`\n",
-    "ALIASED_QUERY = \"\"\"\n",
+    "ALIASED_QUERY = '''\n",
     "SELECT\n",
     "  id,\n",
     "  dna_sequence,\n",
@@ -150,7 +146,7 @@
     "    SELECT\n",
     "      AS STRUCT 3 AS id, \"TCCGGA\" AS dna_sequence, \"Acidianus hospitalis (strain W1).\" AS organism) AS new_array),\n",
     "  UNNEST(new_array)\n",
-    "\"\"\""
+    "'''"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/bilibili.ipynb

@@ -43,7 +43,9 @@
    },
    "outputs": [],
    "source": [
-    "loader = BiliBiliLoader([\"https://www.bilibili.com/video/BV1xt411o7Xu/\"])"
+    "loader = BiliBiliLoader(\n",
+    "    [\"https://www.bilibili.com/video/BV1xt411o7Xu/\"]\n",
+    ")"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/college_confidential.ipynb

@@ -26,9 +26,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = CollegeConfidentialLoader(\n",
-    "    \"https://www.collegeconfidential.com/colleges/brown-university/\"\n",
-    ")"
+    "loader = CollegeConfidentialLoader(\"https://www.collegeconfidential.com/colleges/brown-university/\")"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/csv.ipynb

@@ -30,7 +30,7 @@
    },
    "outputs": [],
    "source": [
-    "loader = CSVLoader(file_path=\"./example_data/mlb_teams_2012.csv\")\n",
+    "loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv')\n",
     "\n",
     "data = loader.load()"
    ]
@@ -73,14 +73,11 @@
    },
    "outputs": [],
    "source": [
-    "loader = CSVLoader(\n",
-    "    file_path=\"./example_data/mlb_teams_2012.csv\",\n",
-    "    csv_args={\n",
-    "        \"delimiter\": \",\",\n",
-    "        \"quotechar\": '\"',\n",
-    "        \"fieldnames\": [\"MLB Team\", \"Payroll in millions\", \"Wins\"],\n",
-    "    },\n",
-    ")\n",
+    "loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv', csv_args={\n",
+    "    'delimiter': ',',\n",
+    "    'quotechar': '\"',\n",
+    "    'fieldnames': ['MLB Team', 'Payroll in millions', 'Wins']\n",
+    "})\n",
     "\n",
     "data = loader.load()"
    ]
@@ -122,7 +119,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = CSVLoader(file_path=\"./example_data/mlb_teams_2012.csv\", source_column=\"Team\")\n",
+    "loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv', source_column=\"Team\")\n",
     "\n",
     "data = loader.load()"
    ]

docs/modules/indexes/document_loaders/examples/dataframe.ipynb

@@ -27,7 +27,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.read_csv(\"example_data/mlb_teams_2012.csv\")"
+    "df = pd.read_csv('example_data/mlb_teams_2012.csv')"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/directory_loader.ipynb

@@ -34,7 +34,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = DirectoryLoader(\"../\", glob=\"**/*.md\")"
+    "loader = DirectoryLoader('../', glob=\"**/*.md\")"
    ]
   },
   {
@@ -94,7 +94,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = DirectoryLoader(\"../\", glob=\"**/*.md\", loader_cls=TextLoader)"
+    "loader = DirectoryLoader('../', glob=\"**/*.md\", loader_cls=TextLoader)"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/duckdb.ipynb

@@ -82,7 +82,7 @@
     "loader = DuckDBLoader(\n",
     "    \"SELECT * FROM read_csv_auto('example.csv')\",\n",
     "    page_content_columns=[\"Team\"],\n",
-    "    metadata_columns=[\"Payroll\"],\n",
+    "    metadata_columns=[\"Payroll\"]\n",
     ")\n",
     "\n",
     "data = loader.load()"
@@ -120,7 +120,7 @@
    "source": [
     "loader = DuckDBLoader(\n",
     "    \"SELECT Team, Payroll, Team As source FROM read_csv_auto('example.csv')\",\n",
-    "    metadata_columns=[\"source\"],\n",
+    "    metadata_columns=[\"source\"]\n",
     ")\n",
     "\n",
     "data = loader.load()"

docs/modules/indexes/document_loaders/examples/email.ipynb

@@ -35,7 +35,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = UnstructuredEmailLoader(\"example_data/fake-email.eml\")"
+    "loader = UnstructuredEmailLoader('example_data/fake-email.eml')"
    ]
   },
   {
@@ -86,7 +86,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = UnstructuredEmailLoader(\"example_data/fake-email.eml\", mode=\"elements\")"
+    "loader = UnstructuredEmailLoader('example_data/fake-email.eml', mode=\"elements\")"
    ]
   },
   {
@@ -145,7 +145,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = OutlookMessageLoader(\"example_data/fake-email.msg\")"
+    "loader = OutlookMessageLoader('example_data/fake-email.msg')"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/figma.ipynb

@@ -59,9 +59,9 @@
    "outputs": [],
    "source": [
     "figma_loader = FigmaFileLoader(\n",
-    "    os.environ.get(\"ACCESS_TOKEN\"),\n",
-    "    os.environ.get(\"NODE_IDS\"),\n",
-    "    os.environ.get(\"FILE_KEY\"),\n",
+    "    os.environ.get('ACCESS_TOKEN'),\n",
+    "    os.environ.get('NODE_IDS'),\n",
+    "    os.environ.get('FILE_KEY')\n",
     ")"
    ]
   },
@@ -92,23 +92,17 @@
     "    Figma file nodes and metadata: {context}\"\"\"\n",
     "\n",
     "    human_prompt_template = \"Code the {text}. Ensure it's mobile responsive\"\n",
-    "    system_message_prompt = SystemMessagePromptTemplate.from_template(\n",
-    "        system_prompt_template\n",
-    "    )\n",
-    "    human_message_prompt = HumanMessagePromptTemplate.from_template(\n",
-    "        human_prompt_template\n",
-    "    )\n",
+    "    system_message_prompt = SystemMessagePromptTemplate.from_template(system_prompt_template)\n",
+    "    human_message_prompt = HumanMessagePromptTemplate.from_template(human_prompt_template)\n",
     "    # delete the gpt-4 model_name to use the default gpt-3.5 turbo for faster results\n",
-    "    gpt_4 = ChatOpenAI(temperature=0.02, model_name=\"gpt-4\")\n",
+    "    gpt_4 = ChatOpenAI(temperature=.02, model_name='gpt-4')\n",
     "    # Use the retriever's 'get_relevant_documents' method if needed to filter down longer docs\n",
     "    relevant_nodes = figma_doc_retriever.get_relevant_documents(human_input)\n",
     "    conversation = [system_message_prompt, human_message_prompt]\n",
     "    chat_prompt = ChatPromptTemplate.from_messages(conversation)\n",
-    "    response = gpt_4(\n",
-    "        chat_prompt.format_prompt(\n",
-    "            context=relevant_nodes, text=human_input\n",
-    "        ).to_messages()\n",
-    "    )\n",
+    "    response = gpt_4(chat_prompt.format_prompt( \n",
+    "        context=relevant_nodes, \n",
+    "        text=human_input).to_messages())\n",
     "    return response"
    ]
   },

docs/modules/indexes/document_loaders/examples/git.ipynb

@@ -157,10 +157,7 @@
     "from langchain.document_loaders import GitLoader\n",
     "\n",
     "# eg. loading only python files\n",
-    "loader = GitLoader(\n",
-    "    repo_path=\"./example_data/test_repo1/\",\n",
-    "    file_filter=lambda file_path: file_path.endswith(\".py\"),\n",
-    ")"
+    "loader = GitLoader(repo_path=\"./example_data/test_repo1/\", file_filter=lambda file_path: file_path.endswith(\".py\"))"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/gutenberg.ipynb

@@ -27,7 +27,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = GutenbergLoader(\"https://www.gutenberg.org/cache/epub/69972/pg69972.txt\")"
+    "loader = GutenbergLoader('https://www.gutenberg.org/cache/epub/69972/pg69972.txt')"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/ifixit.ipynb

@@ -57,9 +57,7 @@
    "execution_count": 4,
    "outputs": [],
    "source": [
-    "loader = IFixitLoader(\n",
-    "    \"https://www.ifixit.com/Answers/View/318583/My+iPhone+6+is+typing+and+opening+apps+by+itself\"\n",
-    ")\n",
+    "loader = IFixitLoader(\"https://www.ifixit.com/Answers/View/318583/My+iPhone+6+is+typing+and+opening+apps+by+itself\")\n",
     "data = loader.load()"
    ],
    "metadata": {

docs/modules/indexes/document_loaders/examples/notebook.ipynb

@@ -24,12 +24,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = NotebookLoader(\n",
-    "    \"example_data/notebook.ipynb\",\n",
-    "    include_outputs=True,\n",
-    "    max_output_length=20,\n",
-    "    remove_newline=True,\n",
-    ")"
+    "loader = NotebookLoader(\"example_data/notebook.ipynb\", include_outputs=True, max_output_length=20, remove_newline=True)"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/notiondb.ipynb

@@ -76,7 +76,6 @@
    ],
    "source": [
     "from getpass import getpass\n",
-    "\n",
     "NOTION_TOKEN = getpass()\n",
     "DATABASE_ID = getpass()"
    ]

docs/modules/indexes/document_loaders/examples/pdf.ipynb

@@ -376,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "a5525fb0",
    "metadata": {},
    "outputs": [],
@@ -386,12 +386,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "dac7ff68",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = loader.load()"
+    "data = loader.load()[0]   # entire pdf is loaded as a single Document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0ba9f645",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bs4 import BeautifulSoup\n",
+    "soup = BeautifulSoup(data.page_content,'html.parser')\n",
+    "content = soup.find_all('div')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "35304e21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "cur_fs = None\n",
+    "cur_text = ''\n",
+    "snippets = []   # first collect all snippets that have the same font size\n",
+    "for c in content:\n",
+    "    sp = c.find('span')\n",
+    "    if not sp:\n",
+    "        continue\n",
+    "    st = sp.get('style')\n",
+    "    if not st:\n",
+    "        continue\n",
+    "    fs = re.findall('font-size:(\\d+)px',st)\n",
+    "    if not fs:\n",
+    "        continue\n",
+    "    fs = int(fs[0])\n",
+    "    if not cur_fs:\n",
+    "        cur_fs = fs\n",
+    "    if fs == cur_fs:\n",
+    "        cur_text += c.text\n",
+    "    else:\n",
+    "        snippets.append((cur_text,cur_fs))\n",
+    "        cur_fs = fs\n",
+    "        cur_text = c.text\n",
+    "snippets.append((cur_text,cur_fs))\n",
+    "# Note: The above logic is very straightforward. One can also add more strategies such as removing duplicate snippets (as\n",
+    "# headers/footers in a PDF appear on multiple pages so if we find duplicatess safe to assume that it is redundant info)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "af8adf2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.docstore.document import Document\n",
+    "cur_idx = -1\n",
+    "semantic_snippets = []\n",
+    "# Assumption: headings have higher font size than their respective content\n",
+    "for s in snippets:\n",
+    "    # if current snippet's font size > previous section's heading => it is a new heading\n",
+    "    if not semantic_snippets or s[1] > semantic_snippets[cur_idx].metadata['heading_font']:\n",
+    "        metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]}\n",
+    "        metadata.update(data.metadata)\n",
+    "        semantic_snippets.append(Document(page_content='',metadata=metadata))\n",
+    "        cur_idx += 1\n",
+    "        continue\n",
+    "    \n",
+    "    # if current snippet's font size <= previous section's content => content belongs to the same section (one can also create\n",
+    "    # a tree like structure for sub sections if needed but that may require some more thinking and may be data specific)\n",
+    "    if not semantic_snippets[cur_idx].metadata['content_font'] or s[1] <= semantic_snippets[cur_idx].metadata['content_font']:\n",
+    "        semantic_snippets[cur_idx].page_content += s[0]\n",
+    "        semantic_snippets[cur_idx].metadata['content_font'] = max(s[1], semantic_snippets[cur_idx].metadata['content_font'])\n",
+    "        continue\n",
+    "    \n",
+    "    # if current snippet's font size > previous section's content but less tha previous section's heading than also make a new \n",
+    "    # section (e.g. title of a pdf will have the highest font size but we don't want it to subsume all sections)\n",
+    "    metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]}\n",
+    "    metadata.update(data.metadata)\n",
+    "    semantic_snippets.append(Document(page_content='',metadata=metadata))\n",
+    "    cur_idx += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "db7f6674",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(page_content='Recently, various DL models and datasets have been developed for layout analysis\\ntasks. The dhSegment [22] utilizes fully convolutional networks [20] for segmen-\\ntation tasks on historical documents. Object detection-based methods like Faster\\nR-CNN [28] and Mask R-CNN [12] are used for identifying document elements [38]\\nand detecting tables [30, 26]. Most recently, Graph Neural Networks [29] have also\\nbeen used in table detection [27]. However, these models are usually implemented\\nindividually and there is no unified framework to load and use such models.\\nThere has been a surge of interest in creating open-source tools for document\\nimage processing: a search of document image analysis in Github leads to 5M\\nrelevant code pieces 6; yet most of them rely on traditional rule-based methods\\nor provide limited functionalities. The closest prior research to our work is the\\nOCR-D project7, which also tries to build a complete toolkit for DIA. However,\\nsimilar to the platform developed by Neudecker et al. [21], it is designed for\\nanalyzing historical documents, and provides no supports for recent DL models.\\nThe DocumentLayoutAnalysis project8 focuses on processing born-digital PDF\\ndocuments via analyzing the stored PDF data. Repositories like DeepLayout9\\nand Detectron2-PubLayNet10 are individual deep learning models trained on\\nlayout analysis datasets without support for the full DIA pipeline. The Document\\nAnalysis and Exploitation (DAE) platform [15] and the DeepDIVA project [2]\\naim to improve the reproducibility of DIA methods (or DL models), yet they\\nare not actively maintained. OCR engines like Tesseract [14], easyOCR11 and\\npaddleOCR12 usually do not come with comprehensive functionalities for other\\nDIA tasks like layout analysis.\\nRecent years have also seen numerous efforts to create libraries for promoting\\nreproducibility and reusability in the field of DL. Libraries like Dectectron2 [35],\\n6 The number shown is obtained by specifying the search type as ‘code’.\\n7 https://ocr-d.de/en/about\\n8 https://github.com/BobLd/DocumentLayoutAnalysis\\n9 https://github.com/leonlulu/DeepLayout\\n10 https://github.com/hpanwar08/detectron2\\n11 https://github.com/JaidedAI/EasyOCR\\n12 https://github.com/PaddlePaddle/PaddleOCR\\n4\\nZ. Shen et al.\\nFig. 1: The overall architecture of LayoutParser. For an input document image,\\nthe core LayoutParser library provides a set of off-the-shelf tools for layout\\ndetection, OCR, visualization, and storage, backed by a carefully designed layout\\ndata structure. LayoutParser also supports high level customization via efficient\\nlayout annotation and model training functions. These improve model accuracy\\non the target samples. The community platform enables the easy sharing of DIA\\nmodels and whole digitization pipelines to promote reusability and reproducibility.\\nA collection of detailed documentation, tutorials and exemplar projects make\\nLayoutParser easy to learn and use.\\nAllenNLP [8] and transformers [34] have provided the community with complete\\nDL-based support for developing and deploying models for general computer\\nvision and natural language processing problems. LayoutParser, on the other\\nhand, specializes specifically in DIA tasks. LayoutParser is also equipped with a\\ncommunity platform inspired by established model hubs such as Torch Hub [23]\\nand TensorFlow Hub [1]. It enables the sharing of pretrained models as well as\\nfull document processing pipelines that are unique to DIA tasks.\\nThere have been a variety of document data collections to facilitate the\\ndevelopment of DL models. Some examples include PRImA [3](magazine layouts),\\nPubLayNet [38](academic paper layouts), Table Bank [18](tables in academic\\npapers), Newspaper Navigator Dataset [16, 17](newspaper figure layouts) and\\nHJDataset [31](historical Japanese document layouts). A spectrum of models\\ntrained on these datasets are currently available in the LayoutParser model zoo\\nto support different use cases.\\n', metadata={'heading': '2 Related Work\\n', 'content_font': 9, 'heading_font': 11, 'source': 'example_data/layout-parser-paper.pdf'})"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "semantic_snippets[4]"
    ]
   },
   {
@@ -474,9 +577,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "langchain_dev",
    "language": "python",
-   "name": "python3"
+   "name": "langchain_dev"
   },
   "language_info": {
    "codemirror_mode": {

docs/modules/indexes/document_loaders/examples/powerpoint.ipynb

@@ -78,9 +78,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = UnstructuredPowerPointLoader(\n",
-    "    \"example_data/fake-power-point.pptx\", mode=\"elements\"\n",
-    ")"
+    "loader = UnstructuredPowerPointLoader(\"example_data/fake-power-point.pptx\", mode=\"elements\")"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/sitemap.ipynb

@@ -39,7 +39,6 @@
    "source": [
     "# fixes a bug with asyncio and jupyter\n",
     "import nest_asyncio\n",
-    "\n",
     "nest_asyncio.apply()"
    ]
   },
@@ -100,7 +99,7 @@
    "source": [
     "loader = SitemapLoader(\n",
     "    \"https://langchain.readthedocs.io/sitemap.xml\",\n",
-    "    filter_urls=[\"https://python.langchain.com/en/latest/\"],\n",
+    "    filter_urls=[\"https://python.langchain.com/en/latest/\"]\n",
     ")\n",
     "documents = loader.load()"
    ]

docs/modules/indexes/document_loaders/examples/slack_directory.ipynb

@@ -28,7 +28,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.document_loaders import SlackDirectoryLoader"
+    "from langchain.document_loaders import SlackDirectoryLoader "
    ]
   },
   {
@@ -40,7 +40,7 @@
    "source": [
     "# Optionally set your Slack URL. This will give you proper URLs in the docs sources.\n",
     "SLACK_WORKSPACE_URL = \"https://xxx.slack.com\"\n",
-    "LOCAL_ZIPFILE = \"\"  # Paste the local paty to your Slack zip file here.\n",
+    "LOCAL_ZIPFILE = \"\" # Paste the local paty to your Slack zip file here.\n",
     "\n",
     "loader = SlackDirectoryLoader(LOCAL_ZIPFILE, SLACK_WORKSPACE_URL)"
    ]

docs/modules/indexes/document_loaders/examples/srt.ipynb

@@ -26,9 +26,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = SRTLoader(\n",
-    "    \"example_data/Star_Wars_The_Clone_Wars_S06E07_Crisis_at_the_Heart.srt\"\n",
-    ")"
+    "loader = SRTLoader(\"example_data/Star_Wars_The_Clone_Wars_S06E07_Crisis_at_the_Heart.srt\")"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/unstructured_file.ipynb

@@ -118,9 +118,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = UnstructuredFileLoader(\n",
-    "    \"./example_data/state_of_the_union.txt\", mode=\"elements\"\n",
-    ")"
+    "loader = UnstructuredFileLoader(\"./example_data/state_of_the_union.txt\", mode=\"elements\")"
    ]
   },
   {
@@ -185,9 +183,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = UnstructuredFileLoader(\n",
-    "    \"layout-parser-paper-fast.pdf\", strategy=\"fast\", mode=\"elements\"\n",
-    ")"
+    "loader = UnstructuredFileLoader(\"layout-parser-paper-fast.pdf\", strategy=\"fast\", mode=\"elements\")"
    ]
   },
   {
@@ -252,9 +248,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = UnstructuredFileLoader(\n",
-    "    \"./example_data/layout-parser-paper.pdf\", mode=\"elements\"\n",
-    ")"
+    "loader = UnstructuredFileLoader(\"./example_data/layout-parser-paper.pdf\", mode=\"elements\")"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/url.ipynb

@@ -17,7 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.document_loaders import UnstructuredURLLoader"
+    " from langchain.document_loaders import UnstructuredURLLoader"
    ]
   },
   {
@@ -29,8 +29,8 @@
    "source": [
     "urls = [\n",
     "    \"https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-8-2023\",\n",
-    "    \"https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-9-2023\",\n",
-    "]"
+    "    \"https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-9-2023\"\n",
+    "]\n"
    ]
   },
   {
@@ -89,7 +89,7 @@
    "source": [
     "urls = [\n",
     "    \"https://www.youtube.com/watch?v=dQw4w9WgXcQ\",\n",
-    "    \"https://goo.gl/maps/NDSHwePEyaHMFGwh8\",\n",
+    "    \"https://goo.gl/maps/NDSHwePEyaHMFGwh8\"\n",
     "]"
    ]
   },
@@ -162,7 +162,7 @@
    "source": [
     "urls = [\n",
     "    \"https://www.youtube.com/watch?v=dQw4w9WgXcQ\",\n",
-    "    \"https://goo.gl/maps/NDSHwePEyaHMFGwh8\",\n",
+    "    \"https://goo.gl/maps/NDSHwePEyaHMFGwh8\"\n",
     "]"
    ]
   },

docs/modules/indexes/document_loaders/examples/web_base.ipynb

@@ -206,12 +206,10 @@
     }
    ],
    "source": [
-    "loader = WebBaseLoader(\n",
-    "    \"https://www.govinfo.gov/content/pkg/CFR-2018-title10-vol3/xml/CFR-2018-title10-vol3-sec431-86.xml\"\n",
-    ")\n",
+    "loader = WebBaseLoader(\"https://www.govinfo.gov/content/pkg/CFR-2018-title10-vol3/xml/CFR-2018-title10-vol3-sec431-86.xml\")\n",
     "loader.default_parser = \"xml\"\n",
     "docs = loader.load()\n",
-    "docs"
+    "docs\n"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/youtube.ipynb

@@ -18,7 +18,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.document_loaders import YoutubeLoader"
+    "from langchain.document_loaders import YoutubeLoader\n"
    ]
   },
   {
@@ -40,9 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = YoutubeLoader.from_youtube_url(\n",
-    "    \"https://www.youtube.com/watch?v=QsYGlZkevEg\", add_video_info=True\n",
-    ")"
+    "loader = YoutubeLoader.from_youtube_url(\"https://www.youtube.com/watch?v=QsYGlZkevEg\", add_video_info=True)"
    ]
   },
   {
@@ -80,9 +78,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = YoutubeLoader.from_youtube_url(\n",
-    "    \"https://www.youtube.com/watch?v=QsYGlZkevEg\", add_video_info=True\n",
-    ")"
+    "loader = YoutubeLoader.from_youtube_url(\"https://www.youtube.com/watch?v=QsYGlZkevEg\", add_video_info=True)"
    ]
   },
   {
@@ -125,7 +121,7 @@
    "source": [
     "from langchain.document_loaders import GoogleApiClient, GoogleApiYoutubeLoader\n",
     "\n",
-    "# Init the GoogleApiClient\n",
+    "# Init the GoogleApiClient \n",
     "from pathlib import Path\n",
     "\n",
     "\n",
@@ -133,17 +129,11 @@
     "\n",
     "\n",
     "# Use a Channel\n",
-    "youtube_loader_channel = GoogleApiYoutubeLoader(\n",
-    "    google_api_client=google_api_client,\n",
-    "    channel_name=\"Reducible\",\n",
-    "    captions_language=\"en\",\n",
-    ")\n",
+    "youtube_loader_channel = GoogleApiYoutubeLoader(google_api_client=google_api_client, channel_name=\"Reducible\",captions_language=\"en\")\n",
     "\n",
     "# Use Youtube Ids\n",
     "\n",
-    "youtube_loader_ids = GoogleApiYoutubeLoader(\n",
-    "    google_api_client=google_api_client, video_ids=[\"TrdevFK_am4\"], add_video_info=True\n",
-    ")\n",
+    "youtube_loader_ids = GoogleApiYoutubeLoader(google_api_client=google_api_client, video_ids=[\"TrdevFK_am4\"], add_video_info=True)\n",
     "\n",
     "# returns a list of Documents\n",
     "youtube_loader_channel.load()"

docs/modules/indexes/getting_started.ipynb

@@ -21,7 +21,6 @@
     "from typing import List\n",
     "from langchain.schema import Document\n",
     "\n",
-    "\n",
     "class BaseRetriever(ABC):\n",
     "    @abstractmethod\n",
     "    def get_relevant_documents(self, query: str) -> List[Document]:\n",
@@ -100,8 +99,7 @@
    "outputs": [],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
-    "\n",
-    "loader = TextLoader(\"../state_of_the_union.txt\", encoding='utf8')"
+    "loader = TextLoader('../state_of_the_union.txt', encoding='utf8')"
    ]
   },
   {
@@ -301,7 +299,6 @@
    "outputs": [],
    "source": [
     "from langchain.text_splitter import CharacterTextSplitter\n",
-    "\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
     "texts = text_splitter.split_documents(documents)"
    ]
@@ -322,7 +319,6 @@
    "outputs": [],
    "source": [
     "from langchain.embeddings import OpenAIEmbeddings\n",
-    "\n",
     "embeddings = OpenAIEmbeddings()"
    ]
   },
@@ -351,7 +347,6 @@
    ],
    "source": [
     "from langchain.vectorstores import Chroma\n",
-    "\n",
     "db = Chroma.from_documents(texts, embeddings)"
    ]
   },
@@ -429,9 +424,9 @@
    "outputs": [],
    "source": [
     "index_creator = VectorstoreIndexCreator(\n",
-    "    vectorstore_cls=Chroma,\n",
+    "    vectorstore_cls=Chroma, \n",
     "    embedding=OpenAIEmbeddings(),\n",
-    "    text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0),\n",
+    "    text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
     ")"
    ]
   },

docs/modules/indexes/retrievers/examples/chatgpt-plugin-retriever.ipynb

@@ -37,10 +37,7 @@
     "# This is from https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/csv.html\n",
     "\n",
     "from langchain.document_loaders.csv_loader import CSVLoader\n",
-    "\n",
-    "loader = CSVLoader(\n",
-    "    file_path=\"../../document_loaders/examples/example_data/mlb_teams_2012.csv\"\n",
-    ")\n",
+    "loader = CSVLoader(file_path='../../document_loaders/examples/example_data/mlb_teams_2012.csv')\n",
     "data = loader.load()\n",
     "\n",
     "\n",
@@ -51,18 +48,16 @@
     "from langchain.docstore.document import Document\n",
     "import json\n",
     "\n",
-    "\n",
-    "def write_json(path: str, documents: List[Document]) -> None:\n",
+    "def write_json(path: str, documents: List[Document])-> None:\n",
     "    results = [{\"text\": doc.page_content} for doc in documents]\n",
     "    with open(path, \"w\") as f:\n",
     "        json.dump(results, f, indent=2)\n",
     "\n",
-    "\n",
     "write_json(\"foo.json\", data)\n",
     "\n",
     "# STEP 3: Use\n",
     "\n",
-    "# Ingest this as you would any other json file in https://github.com/openai/chatgpt-retrieval-plugin/tree/main/scripts/process_json"
+    "# Ingest this as you would any other json file in https://github.com/openai/chatgpt-retrieval-plugin/tree/main/scripts/process_json\n"
    ]
   },
   {