bump version to 147 (#3353)

Co-authored-by: Fangrui Liu <fangruil@moqi.ai>
Co-authored-by: 刘 方瑞 <fangrui.liu@outlook.com>
Co-authored-by: Fangrui.Liu <fangrui.liu@ubc.ca>

.github/CONTRIBUTING.md

@@ -75,7 +75,7 @@ This will install all requirements for running the package, examples, linting, f
 
 ❗Note: If you're running Poetry 1.4.1 and receive a `WheelFileValidationError` for `debugpy` during installation, you can try either downgrading to Poetry 1.4.0 or disabling "modern installation" (`poetry config installer.modern-installation false`) and re-install requirements. See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
 
-Now, you should be able to run the common tasks in the following section.
+Now, you should be able to run the common tasks in the following section. To double check, run `make test`, all tests should pass. If they don't you may need to pip install additional dependencies, such as `numexpr` and `openapi_schema_pydantic`.
 
 ## ✅Common Tasks
 

.gitignore

@@ -142,3 +142,6 @@ wandb/
 # asdf tool versions
 .tool-versions
 /.ruff_cache/
+
+*.pkl
+*.bin

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/analyticdb.md

@@ -0,0 +1,15 @@
+# AnalyticDB
+
+This page covers how to use the AnalyticDB ecosystem within LangChain.
+
+### VectorStore
+
+There exists a wrapper around AnalyticDB, allowing you to use it as a vectorstore,
+whether for semantic search or example selection.
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import AnalyticDB
+```
+
+For a more detailed walkthrough of the AnalyticDB wrapper, see [this notebook](../modules/indexes/vectorstores/examples/analyticdb.ipynb)

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/myscale.md

@@ -0,0 +1,65 @@
+# MyScale
+
+This page covers how to use MyScale vector database within LangChain.
+It is broken into two parts: installation and setup, and then references to specific MyScale wrappers.
+
+With MyScale, you can manage both structured and unstructured (vectorized) data, and perform joint queries and analytics on both types of data using SQL. Plus, MyScale's cloud-native OLAP architecture, built on top of ClickHouse, enables lightning-fast data processing even on massive datasets.
+
+## Introduction
+
+[Overview to MyScale and High performance vector search](https://docs.myscale.com/en/overview/)
+
+You can now register on our SaaS and [start a cluster now!](https://docs.myscale.com/en/quickstart/)
+
+If you are also interested in how we managed to integrate SQL and vector, please refer to [this document](https://docs.myscale.com/en/vector-reference/) for further syntax reference.
+
+We also deliver with live demo on huggingface! Please checkout our [huggingface space](https://huggingface.co/myscale)! They search millions of vector within a blink!
+
+## Installation and Setup
+- Install the Python SDK with `pip install clickhouse-connect`
+
+### Setting up envrionments
+
+There are two ways to set up parameters for myscale index.
+
+1. Environment Variables
+
+    Before you run the app, please set the environment variable with `export`:
+    `export MYSCALE_URL='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...`
+
+    You can easily find your account, password and other info on our SaaS. For details please refer to [this document](https://docs.myscale.com/en/cluster-management/)
+    Every attributes under `MyScaleSettings` can be set with prefix `MYSCALE_` and is case insensitive.
+
+2. Create `MyScaleSettings` object with parameters
+
+
+    ```python
+    from langchain.vectorstores import MyScale, MyScaleSettings
+    config = MyScaleSetting(host="<your-backend-url>", port=8443, ...)
+    index = MyScale(embedding_function, config)
+    index.add_documents(...)
+    ```
+  
+## Wrappers
+supported functions:
+- `add_texts`
+- `add_documents`
+- `from_texts`
+- `from_documents`
+- `similarity_search`
+- `asimilarity_search`
+- `similarity_search_by_vector`
+- `asimilarity_search_by_vector`
+- `similarity_search_with_relevance_scores`
+
+### VectorStore
+
+There exists a wrapper around MyScale database, allowing you to use it as a vectorstore,
+whether for semantic search or similar example retrieval.
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import MyScale
+```
+
+For a more detailed walkthrough of the MyScale wrapper, see [this notebook](../modules/indexes/vectorstores/examples/myscale.ipynb)

docs/ecosystem/runhouse.md

@@ -15,7 +15,7 @@ custom LLMs, you can use the `SelfHostedPipeline` parent class.
 from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
 ```
 
-For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/models/llms/integrations/self_hosted_examples.ipynb)
+For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/models/llms/integrations/runhouse.ipynb)
 
 ## Self-hosted Embeddings
 There are several ways to use self-hosted embeddings with LangChain via Runhouse.

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/ecosystem/yeagerai.md

@@ -0,0 +1,43 @@
+# Yeager.ai
+
+This page covers how to use [Yeager.ai](https://yeager.ai) to generate LangChain tools and agents.
+
+## What is Yeager.ai?
+Yeager.ai is an ecosystem designed to simplify the process of creating AI agents and tools. 
+
+It features yAgents, a No-code LangChain Agent Builder, which enables users to build, test, and deploy AI solutions with ease. Leveraging the LangChain framework, yAgents allows seamless integration with various language models and resources, making it suitable for developers, researchers, and AI enthusiasts across diverse applications.
+
+## yAgents
+Low code generative agent designed to help you build, prototype, and deploy Langchain tools with ease. 
+
+### How to use?
+```
+pip install yeagerai-agent
+yeagerai-agent
+```
+Go to http://127.0.0.1:7860
+
+This will install the necessary dependencies and set up yAgents on your system. After the first run, yAgents will create a .env file where you can input your OpenAI API key. You can do the same directly from the Gradio interface under the tab "Settings".
+
+`OPENAI_API_KEY=<your_openai_api_key_here>`
+
+We recommend using GPT-4,. However, the tool can also work with GPT-3 if the problem is broken down sufficiently.
+
+### Creating and Executing Tools with yAgents
+yAgents makes it easy to create and execute AI-powered tools. Here's a brief overview of the process:
+1. Create a tool: To create a tool, provide a natural language prompt to yAgents. The prompt should clearly describe the tool's purpose and functionality. For example:
+`create a tool that returns the n-th prime number`
+
+2. Load the tool into the toolkit: To load a tool into yAgents, simply provide a command to yAgents that says so. For example:
+`load the tool that you just created it into your toolkit`
+
+3. Execute the tool: To run a tool or agent, simply provide a command to yAgents that includes the name of the tool and any required parameters. For example:
+`generate the 50th prime number`
+
+You can see a video of how it works [here](https://www.youtube.com/watch?v=KA5hCM3RaWE).
+
+As you become more familiar with yAgents, you can create more advanced tools and agents to automate your work and enhance your productivity.
+
+For more information, see [yAgents' Github](https://github.com/yeagerai/yeagerai-agent) or our [docs](https://yeagerai.gitbook.io/docs/general/welcome-to-yeager.ai) 
+
+

docs/gallery.rst

@@ -280,6 +280,17 @@ Proprietary
 
     ---
 
+    .. link-button:: https://anysummary.app
+        :type: url
+        :text: Summarize any file with AI
+        :classes: stretched-link btn-lg
+
+    +++
+
+    Summarize not only long docs, interview audio or video files quickly, but also entire websites and YouTube videos. Share or download your generated summaries to collaborate with others, or revisit them at any time! Bonus: `@anysummary <https://twitter.com/anysummary>`_ on Twitter will also summarize any thread it is tagged in.
+    
+    ---
+
     .. link-button:: https://twitter.com/dory111111/status/1608406234646052870?s=20&t=XYlrbKM0ornJsrtGa0br-g
         :type: url
         :text: AI Assisted SQL Query Generator

docs/getting_started/getting_started.md

@@ -46,7 +46,7 @@ LangChain provides many modules that can be used to build language model applica
 
 
 
-`````{dropdown} LLMs: Get predictions from a language model
+## LLMs: Get predictions from a language model
 
 The most basic building block of LangChain is calling an LLM on some input.
 Let's walk through a simple example of how to do this. 
@@ -77,10 +77,9 @@ Feetful of Fun
 ```
 
 For more details on how to use LLMs within LangChain, see the [LLM getting started guide](../modules/models/llms/getting_started.ipynb).
-`````
 
 
-`````{dropdown} Prompt Templates: Manage prompts for LLMs
+## Prompt Templates: Manage prompts for LLMs
 
 Calling an LLM is a great first step, but it's just the beginning.
 Normally when you use an LLM in an application, you are not sending user input directly to the LLM.
@@ -115,11 +114,10 @@ What is a good name for a company that makes colorful socks?
 
 [For more details, check out the getting started guide for prompts.](../modules/prompts/chat_prompt_template.ipynb)
 
-`````
 
 
 
-`````{dropdown} Chains: Combine LLMs and prompts in multi-step workflows
+## Chains: Combine LLMs and prompts in multi-step workflows
 
 Up until now, we've worked with the PromptTemplate and LLM primitives by themselves. But of course, a real application is not just one primitive, but rather a combination of them.
 
@@ -159,10 +157,7 @@ This is one of the simpler types of chains, but understanding how it works will
 
 [For more details, check out the getting started guide for chains.](../modules/chains/getting_started.ipynb)
 
-`````
-
-
-`````{dropdown} Agents: Dynamically Call Chains Based on User Input
+## Agents: Dynamically Call Chains Based on User Input
 
 So far the chains we've looked at run in a predetermined order.
 
@@ -234,10 +229,8 @@ Final Answer: The high temperature in SF yesterday in Fahrenheit raised to the .
 ```
 
 
-`````
 
-
-`````{dropdown} Memory: Add State to Chains and Agents
+## Memory: Add State to Chains and Agents
 
 So far, all the chains and agents we've gone through have been stateless. But often, you may want a chain or agent to have some concept of "memory" so that it may remember information about its previous interactions. The clearest and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of "short-term memory". On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of "long-term memory". For more concrete ideas on the latter, see this [awesome paper](https://memprompt.com/).
 
@@ -251,7 +244,8 @@ from langchain import OpenAI, ConversationChain
 llm = OpenAI(temperature=0)
 conversation = ConversationChain(llm=llm, verbose=True)
 
-conversation.predict(input="Hi there!")
+output = conversation.predict(input="Hi there!")
+print(output)
 ```
 
 ```pycon
@@ -269,7 +263,8 @@ AI:
 ```
 
 ```python
-conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
+output = conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
+print(output)
 ```
 
 ```pycon
@@ -287,7 +282,6 @@ AI:
 > Finished chain.
 " That's great! What would you like to talk about?"
 ```
-`````
 
 ## Building a Language Model Application: Chat Models
 
@@ -295,8 +289,8 @@ Similarly, you can use chat models instead of LLMs. Chat models are a variation
 
 Chat model APIs are fairly new, so we are still figuring out the correct abstractions.
 
+## Get Message Completions from a Chat Model
 
-`````{dropdown} Get Message Completions from a Chat Model
 You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`.
 
 ```python
@@ -350,9 +344,9 @@ You can recover things like token usage from this LLMResult:
 result.llm_output['token_usage']
 # -> {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}
 ```
-`````
 
-`````{dropdown} Chat Prompt Templates
+
+## Chat Prompt Templates
 Similar to LLMs, you can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplate`s. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or `Message` object, depending on whether you want to use the formatted value as input to an llm or chat model.
 
 For convience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:
@@ -378,9 +372,8 @@ chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_mes
 chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
 # -> AIMessage(content="J'aime programmer.", additional_kwargs={})
 ```
-`````
 
-`````{dropdown} Chains with Chat Models
+## Chains with Chat Models
 The `LLMChain` discussed in the above section can be used with chat models as well:
 
 ```python
@@ -404,9 +397,8 @@ chain = LLMChain(llm=chat, prompt=chat_prompt)
 chain.run(input_language="English", output_language="French", text="I love programming.")
 # -> "J'aime programmer."
 ```
-`````
 
-`````{dropdown} Agents with Chat Models
+## Agents with Chat Models
 Agents can also be used with chat models, you can initialize one using `AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION` as the agent type.
 
 ```python
@@ -465,9 +457,7 @@ Final Answer: 2.169459462491557
 > Finished chain.
 '2.169459462491557'
 ```
-`````
-
-`````{dropdown} Memory: Add State to Chains and Agents
+## Memory: Add State to Chains and Agents
 You can use Memory with chains and agents initialized with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object.
 
 ```python
@@ -501,4 +491,4 @@ conversation.predict(input="I'm doing well! Just having a conversation with an A
 conversation.predict(input="Tell me about yourself.")
 # -> "Sure! I am an AI language model created by OpenAI. I was trained on a large dataset of text from the internet, which allows me to understand and generate human-like language. I can answer questions, provide information, and even have conversations like this one. Is there anything else you'd like to know about me?"
 ```
-`````
+

docs/index.rst

@@ -63,6 +63,10 @@ Use Cases
 
 The above modules can be used in a variety of ways. LangChain also provides guidance and assistance in this. Below are some of the common use cases LangChain supports.
 
+- `Autonomous Agents <./use_cases/autonomous_agents.html>`_: Autonomous agents are long running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI.
+
+- `Agent Simulations <./use_cases/agent_simulations.html>`_: Putting agents in a sandbox and observing how they interact with each other or to events can be an interesting way to observe their long-term memory abilities.
+
 - `Personal Assistants <./use_cases/personal_assistants.html>`_: The main LangChain use case. Personal assistants need to take actions, remember interactions, and have knowledge about your data.
 
 - `Question Answering <./use_cases/question_answering.html>`_: The second big LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.
@@ -89,6 +93,8 @@ The above modules can be used in a variety of ways. LangChain also provides guid
    :hidden:
 
    ./use_cases/personal_assistants.md
+   ./use_cases/autonomous_agents.md
+   ./use_cases/agent_simulations.md
    ./use_cases/question_answering.md
    ./use_cases/chatbots.md
    ./use_cases/tabular.rst
@@ -153,6 +159,8 @@ Additional collection of resources we think may be useful as you develop your ap
 
 - `Discord <https://discord.gg/6adMQxSpJS>`_: Join us on our Discord to discuss all things LangChain!
 
+- `YouTube <./youtube.html>`_: A collection of the LangChain tutorials and videos.
+
 - `Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>`_: As you move your LangChains into production, we'd love to offer more comprehensive support. Please fill out this form and we'll set up a dedicated support Slack channel.
 
 
@@ -169,4 +177,5 @@ Additional collection of resources we think may be useful as you develop your ap
    ./tracing.md
    ./use_cases/model_laboratory.ipynb
    Discord <https://discord.gg/6adMQxSpJS>
+   ./youtube.md
    Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>

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

@@ -20,13 +20,14 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6064f080",
    "metadata": {},
    "source": [
     "### Custom LLMChain\n",
     "\n",
-    "The first way to create a custom agent is to use an existing Agent class, but use a custom LLMChain. This is the simplest way to create a custom Agent. It is highly reccomended that you work with the `ZeroShotAgent`, as at the moment that is by far the most generalizable one. \n",
+    "The first way to create a custom agent is to use an existing Agent class, but use a custom LLMChain. This is the simplest way to create a custom Agent. It is highly recommended that you work with the `ZeroShotAgent`, as at the moment that is by far the most generalizable one. \n",
     "\n",
     "Most of the work in creating the custom LLMChain comes down to the prompt. Because we are using an existing agent class to parse the output, it is very important that the prompt say to produce text in that format. Additionally, we currently require an `agent_scratchpad` input variable to put notes on previous actions and observations. This should almost always be the final part of the prompt. However, besides those instructions, you can customize the prompt as you wish.\n",
     "\n",
@@ -42,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 1,
    "id": "9af9734e",
    "metadata": {},
    "outputs": [],
@@ -53,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 2,
    "id": "becda2a1",
    "metadata": {},
    "outputs": [],
@@ -61,16 +62,16 @@
     "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 +83,10 @@
     "{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 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 4,
    "id": "e21d2098",
    "metadata": {},
    "outputs": [
@@ -142,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 5,
    "id": "9b1cc2a2",
    "metadata": {},
    "outputs": [],
@@ -152,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 6,
    "id": "e4f5092f",
    "metadata": {},
    "outputs": [],
@@ -163,19 +167,17 @@
   },
   {
    "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 +191,9 @@
       "\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 +201,10 @@
     {
      "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 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 9,
    "id": "43dbfa2f",
    "metadata": {},
    "outputs": [],
@@ -234,16 +236,16 @@
     "{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 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 11,
    "id": "92c75a10",
    "metadata": {},
    "outputs": [],
@@ -263,19 +265,17 @@
   },
   {
    "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 +286,16 @@
       "\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 +303,16 @@
     {
      "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/jira.ipynb

@@ -0,0 +1,167 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "245a954a",
+   "metadata": {},
+   "source": [
+    "# Jira\n",
+    "\n",
+    "This notebook goes over how to use the Jira tool.\n",
+    "The Jira tool allows agents to interact with a given Jira instance, performing actions such as searching for issues and creating issues, the tool wraps the atlassian-python-api library, for more see: https://atlassian-python-api.readthedocs.io/jira.html\n",
+    "\n",
+    "To use this tool, you must first set as environment variables:\n",
+    "    JIRA_API_TOKEN\n",
+    "    JIRA_USERNAME\n",
+    "    JIRA_INSTANCE_URL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "961b3689",
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    },
+    "ExecuteTime": {
+     "start_time": "2023-04-17T10:21:18.698672Z",
+     "end_time": "2023-04-17T10:21:20.168639Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%pip install atlassian-python-api"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "34bb5968",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2023-04-17T10:21:22.911233Z",
+     "end_time": "2023-04-17T10:21:23.730922Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from langchain.agents import AgentType\n",
+    "from langchain.agents import initialize_agent\n",
+    "from langchain.agents.agent_toolkits.jira.toolkit import JiraToolkit\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.utilities.jira import JiraAPIWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [],
+   "source": [
+    "os.environ[\"JIRA_API_TOKEN\"] = \"abc\"\n",
+    "os.environ[\"JIRA_USERNAME\"] = \"123\"\n",
+    "os.environ[\"JIRA_INSTANCE_URL\"] = \"https://jira.atlassian.com\"\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"xyz\""
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-17T10:22:42.499447Z",
+     "end_time": "2023-04-17T10:22:42.505412Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ac4910f8",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2023-04-17T10:22:44.664481Z",
+     "end_time": "2023-04-17T10:22:44.720538Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(temperature=0)\n",
+    "jira = JiraAPIWrapper()\n",
+    "toolkit = JiraToolkit.from_jira_api_wrapper(jira)\n",
+    "agent = initialize_agent(\n",
+    "    toolkit.get_tools(),\n",
+    "    llm,\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m I need to create an issue in project PW\n",
+      "Action: Create Issue\n",
+      "Action Input: {\"summary\": \"Make more fried rice\", \"description\": \"Reminder to make more fried rice\", \"issuetype\": {\"name\": \"Task\"}, \"priority\": {\"name\": \"Low\"}, \"project\": {\"key\": \"PW\"}}\u001B[0m\n",
+      "Observation: \u001B[38;5;200m\u001B[1;3mNone\u001B[0m\n",
+      "Thought:\u001B[32;1m\u001B[1;3m I now know the final answer\n",
+      "Final Answer: A new issue has been created in project PW with the summary \"Make more fried rice\" and description \"Reminder to make more fried rice\".\u001B[0m\n",
+      "\n",
+      "\u001B[1m> Finished chain.\u001B[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "'A new issue has been created in project PW with the summary \"Make more fried rice\" and description \"Reminder to make more fried rice\".'"
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"make a new issue in project PW to remind me to make more fried rice\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-17T10:23:33.662454Z",
+     "end_time": "2023-04-17T10:23:38.121883Z"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "53f3bc57609c7a84333bb558594977aa5b4026b1d6070b93987956689e367341"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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

@@ -15,7 +15,7 @@
    "id": "a389367b",
    "metadata": {},
    "source": [
-    "# 1st example: hierarchical planning agent\n",
+    "## 1st example: hierarchical planning agent\n",
     "\n",
     "In this example, we'll consider an approach called hierarchical planning, common in robotics and appearing in recent works for LLMs X robotics. We'll see it's a viable approach to start working with a massive API spec AND to assist with user queries that require multiple steps against the API.\n",
     "\n",
@@ -31,7 +31,7 @@
    "id": "4b6ecf6e",
    "metadata": {},
    "source": [
-    "## To start, let's collect some OpenAPI specs."
+    "### To start, let's collect some OpenAPI specs."
    ]
   },
   {
@@ -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",
@@ -173,7 +169,7 @@
    "id": "76349780",
    "metadata": {},
    "source": [
-    "## How big is this spec?"
+    "### How big is this spec?"
    ]
   },
   {
@@ -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))"
    ]
@@ -238,7 +229,7 @@
    "id": "cbc4964e",
    "metadata": {},
    "source": [
-    "## Let's see some examples!\n",
+    "### Let's see some examples!\n",
     "\n",
     "Starting with GPT-4. (Some robustness iterations under way for GPT-3 family.)"
    ]
@@ -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.'\")"
    ]
   }
  ],
@@ -773,7 +759,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

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/powerbi.ipynb

@@ -0,0 +1,167 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "0e499e90-7a6d-4fab-8aab-31a4df417601",
+   "metadata": {},
+   "source": [
+    "# PowerBI Dataset Agent\n",
+    "\n",
+    "This notebook showcases an agent designed to interact with a Power BI Dataset. The agent is designed to answer more general questions about a dataset, as well as recover from errors.\n",
+    "\n",
+    "Note that, as this agent is in active development, all answers might not be correct. It runs against the [executequery endpoint](https://learn.microsoft.com/en-us/rest/api/power-bi/datasets/execute-queries), which does not allow deletes.\n",
+    "\n",
+    "### Some notes\n",
+    "- It relies on authentication with the azure.identity package, which can be installed with `pip install azure-identity`. Alternatively you can create the powerbi dataset with a token as a string without supplying the credentials.\n",
+    "- You can also supply a username to impersonate for use with datasets that have RLS enabled. \n",
+    "- The toolkit uses a LLM to create the query from the question, the agent uses the LLM for the overall execution.\n",
+    "- Testing was done mostly with a `text-davinci-003` model, codex models did not seem to perform ver well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec927ac6-9b2a-4e8a-9a6e-3e429191875c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Initialization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53422913-967b-4f2a-8022-00269c1be1b1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.agents.agent_toolkits import create_pbi_agent\n",
+    "from langchain.agents.agent_toolkits import PowerBIToolkit\n",
+    "from langchain.utilities.powerbi import PowerBIDataset\n",
+    "from langchain.llms.openai import AzureOpenAI\n",
+    "from langchain.agents import AgentExecutor\n",
+    "from azure.identity import DefaultAzureCredential"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "090f3699-79c6-4ce1-ab96-a94f0121fd64",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "llm = AzureOpenAI(temperature=0, deployment_name=\"text-davinci-003\", verbose=True)\n",
+    "toolkit = PowerBIToolkit(\n",
+    "    powerbi=PowerBIDataset(None, \"<dataset_id>\", ['table1', 'table2'], DefaultAzureCredential()), \n",
+    "    llm=llm\n",
+    ")\n",
+    "\n",
+    "agent_executor = create_pbi_agent(\n",
+    "    llm=llm,\n",
+    "    toolkit=toolkit,\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ae48c7-cb08-4fef-977e-c7d4b96a464b",
+   "metadata": {},
+   "source": [
+    "## Example: describing a table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff70e83d-5ad0-4fc7-bb96-27d82ac166d7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"Describe table1\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "9abcfe8e-1868-42a4-8345-ad2d9b44c681",
+   "metadata": {},
+   "source": [
+    "## Example: simple query on a table\n",
+    "In this example, the agent actually figures out the correct query to get a row count of the table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bea76658-a65b-47e2-b294-6d52c5556246",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"How many records are in table1?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fbc26af-97e4-4a21-82aa-48bdc992da26",
+   "metadata": {},
+   "source": [
+    "## Example: running queries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17bea710-4a23-4de0-b48e-21d57be48293",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"How many records are there by dimension1 in table2?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "474dddda-c067-4eeb-98b1-e763ee78b18c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"What unique values are there for dimensions2 in table2\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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.rst

@@ -24,6 +24,7 @@ Next, we have some examples of customizing and generically working with tools
 
    ./tools/custom_tools.ipynb
    ./tools/multi_input_tool.ipynb
+   ./tools/tool_input_validation.ipynb
 
 
 In this documentation we cover generic tooling functionality (eg how to create your own)

docs/modules/agents/tools/custom_tools.ipynb

@@ -9,28 +9,30 @@
     "\n",
     "When constructing your own agent, you will need to provide it with a list of Tools that it can use. Besides the actual function that is called, the Tool consists of several components:\n",
     "\n",
-    "- name (str), is required\n",
-    "- description (str), is optional\n",
+    "- name (str), is required and must be unique within a set of tools provided to an agent\n",
+    "- description (str), is optional but recommended, as it is used by an agent to determine tool use\n",
     "- return_direct (bool), defaults to False\n",
+    "- args_schema (Pydantic BaseModel), is optional but recommended, can be used to provide more information or validation for expected parameters.\n",
     "\n",
-    "The function that should be called when the tool is selected should take as input a single string and return a single string.\n",
+    "The function that should be called when the tool is selected should return a single string.\n",
     "\n",
     "There are two ways to define a tool, we will cover both in the example below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "1aaba18c",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Import things that are needed generically\n",
-    "from langchain.agents import initialize_agent, Tool\n",
-    "from langchain.agents import AgentType\n",
-    "from langchain.tools import BaseTool\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain import LLMMathChain, SerpAPIWrapper"
+    "from langchain import LLMMathChain, SerpAPIWrapper\n",
+    "from langchain.agents import AgentType, Tool, initialize_agent, tool\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.tools import BaseTool"
    ]
   },
   {
@@ -43,12 +45,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "36ed392e",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "llm = OpenAI(temperature=0)"
+    "llm = ChatOpenAI(temperature=0)"
    ]
   },
   {
@@ -74,7 +78,9 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "56ff7670",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Load the tool configs that are needed.\n",
@@ -82,37 +88,49 @@
     "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",
+    "]\n",
+    "# You can also define an args_schema to provide more information about inputs\n",
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "class CalculatorInput(BaseModel):\n",
+    "    question: str = Field()\n",
+    "        \n",
+    "\n",
+    "tools.append(\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",
-    "]"
+    "        args_schema=CalculatorInput\n",
+    "    )\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "id": "5b93047d",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "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)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "id": "6f96a891",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -121,29 +139,22 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\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",
+      "\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age\n",
       "Action: Search\n",
-      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate her age raised to the 0.43 power\n",
+      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\u001b[36;1m\u001b[1;3mDiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years.\u001b[0m\u001b[32;1m\u001b[1;3mI need to find out Camila Morrone's current age\n",
       "Action: Calculator\n",
-      "Action Input: 22^0.43\u001b[0m\n",
+      "Action Input: 25^(0.43)\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "22^0.43\u001b[32;1m\u001b[1;3m\n",
-      "```python\n",
-      "import math\n",
-      "print(math.pow(22, 0.43))\n",
+      "25^(0.43)\u001b[32;1m\u001b[1;3m```text\n",
+      "25**(0.43)\n",
       "```\n",
+      "...numexpr.evaluate(\"25**(0.43)\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
-      "\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m3.991298452658078\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer\n",
+      "Final Answer: 3.991298452658078\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -151,7 +162,7 @@
     {
      "data": {
       "text/plain": [
-       "\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
+       "'3.991298452658078'"
       ]
      },
      "execution_count": 5,
@@ -160,9 +171,7 @@
     }
    ],
    "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?\")"
    ]
   },
   {
@@ -175,11 +184,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "c58a7c40",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
+    "from typing import Type\n",
+    "\n",
     "class CustomSearchTool(BaseTool):\n",
     "    name = \"Search\"\n",
     "    description = \"useful for when you need to answer questions about current events\"\n",
@@ -187,20 +200,20 @@
     "    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",
+    "    args_schema: Type[BaseModel] = CalculatorInput\n",
     "\n",
     "    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\")"
@@ -208,9 +221,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "3318a46f",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "tools = [CustomSearchTool(), CustomCalculatorTool()]"
@@ -218,21 +233,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "ee2d0f3a",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "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)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "id": "6a2cebbf",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -241,29 +258,22 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\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",
+      "\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age\n",
       "Action: Search\n",
-      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate her age raised to the 0.43 power\n",
+      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\u001b[36;1m\u001b[1;3mDiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years.\u001b[0m\u001b[32;1m\u001b[1;3mI need to find out Camila Morrone's current age\n",
       "Action: Calculator\n",
-      "Action Input: 22^0.43\u001b[0m\n",
+      "Action Input: 25^(0.43)\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "22^0.43\u001b[32;1m\u001b[1;3m\n",
-      "```python\n",
-      "import math\n",
-      "print(math.pow(22, 0.43))\n",
+      "25^(0.43)\u001b[32;1m\u001b[1;3m```text\n",
+      "25**(0.43)\n",
       "```\n",
+      "...numexpr.evaluate(\"25**(0.43)\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
-      "\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m3.991298452658078\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer\n",
+      "Final Answer: 3.991298452658078\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -271,18 +281,16 @@
     {
      "data": {
       "text/plain": [
-       "\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
+       "'3.991298452658078'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "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?\")"
    ]
   },
   {
@@ -297,33 +305,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "id": "8f15307d",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "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",
-    "    return \"Results\""
+    "    return f\"Results for query {query}\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "id": "0a23b91b",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Tool(name='search_api', description='search_api(query: str) -> str - Searches the API for the query.', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1184e0cd0>, func=<function search_api at 0x1635f8700>, coroutine=None)"
+       "Tool(name='search_api', description='search_api(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.SearchApi'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bd664c0>, coroutine=None)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -342,9 +353,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "id": "28cdf04d",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "@tool(\"search\", return_direct=True)\n",
@@ -355,17 +368,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "id": "1085a4bd",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1184e0cd0>, func=<function search_api at 0x1635f8670>, coroutine=None)"
+       "Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.SearchApi'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bd66310>, coroutine=None)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search_api"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de34a6a3",
+   "metadata": {},
+   "source": [
+    "You can also provide `args_schema` to provide more information about the argument"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f3a5c106",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SearchInput(BaseModel):\n",
+    "    query: str = Field(description=\"should be a search query\")\n",
+    "        \n",
+    "@tool(\"search\", return_direct=True, args_schema=SearchInput)\n",
+    "def search_api(query: str) -> str:\n",
+    "    \"\"\"Searches the API for the query.\"\"\"\n",
+    "    return \"Results\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7914ba6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class '__main__.SearchInput'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bcf0ee0>, coroutine=None)"
+      ]
+     },
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -386,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "id": "79213f40",
    "metadata": {},
    "outputs": [],
@@ -396,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 15,
    "id": "e1067dcb",
    "metadata": {},
    "outputs": [],
@@ -406,7 +464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 16,
    "id": "6c66ffe8",
    "metadata": {},
    "outputs": [],
@@ -416,19 +474,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
    "id": "f45b5bc3",
    "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)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 18,
    "id": "565e2b9b",
    "metadata": {},
    "outputs": [
@@ -439,21 +495,12 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\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",
+      "\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age.\n",
       "Action: Google Search\n",
-      "Action Input: \"Leo DiCaprio 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: Google Search\n",
-      "Action Input: \"Camila Morrone age\"\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",
+      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\u001b[36;1m\u001b[1;3mI draw the lime at going to get a Mohawk, though.\" DiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years. He's since been linked to another famous supermodel – Gigi Hadid.\u001b[0m\u001b[32;1m\u001b[1;3mNow I need to find out Camila Morrone's current age.\n",
       "Action: Calculator\n",
-      "Action Input: 25^0.43\u001b[0m\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 Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\u001b[0m\n",
+      "Action Input: 25^0.43\u001b[0m\u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
+      "Final Answer: Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -461,18 +508,16 @@
     {
      "data": {
       "text/plain": [
-       "\"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\""
+       "\"Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\""
       ]
      },
-     "execution_count": 12,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "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?\")"
    ]
   },
   {
@@ -492,7 +537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 19,
    "id": "3450512e",
    "metadata": {},
    "outputs": [],
@@ -502,32 +547,26 @@
     "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)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 20,
    "id": "4b9a7849",
    "metadata": {},
    "outputs": [
@@ -540,9 +579,7 @@
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3m I should use a music search engine to find the answer\n",
       "Action: Music Search\n",
-      "Action Input: most famous song of christmas\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m'All I Want For Christmas Is You' by Mariah Carey.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Action Input: most famous song of christmas\u001b[0m\u001b[33;1m\u001b[1;3m'All I Want For Christmas Is You' by Mariah Carey.\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
       "Final Answer: 'All I Want For Christmas Is You' by Mariah Carey.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -554,7 +591,7 @@
        "\"'All I Want For Christmas Is You' by Mariah Carey.\""
       ]
      },
-     "execution_count": 14,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -574,7 +611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 21,
    "id": "3bb6185f",
    "metadata": {},
    "outputs": [],
@@ -585,29 +622,29 @@
     "        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",
     "]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 22,
    "id": "113ddb84",
    "metadata": {},
    "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)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 23,
    "id": "582439a6",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -618,9 +655,7 @@
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3m I need to calculate this\n",
       "Action: Calculator\n",
-      "Action Input: 2**.12\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.2599210498948732\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+      "Action Input: 2**.12\u001b[0m\u001b[36;1m\u001b[1;3mAnswer: 1.086734862526058\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -628,10 +663,10 @@
     {
      "data": {
       "text/plain": [
-       "'Answer: 1.2599210498948732'"
+       "'Answer: 1.086734862526058'"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -640,10 +675,149 @@
     "agent.run(\"whats 2**.12\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8aa3c353-bd89-467c-9c27-b83a90cd4daa",
+   "metadata": {},
+   "source": [
+    "## Multi-argument tools\n",
+    "\n",
+    "Many functions expect structured inputs. These can also be supported using the Tool decorator or by directly subclassing `BaseTool`! We have to modify the LLM's OutputParser to map its string output to a dictionary to pass to the action, however."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "537bc628",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from typing import Optional, Union\n",
+    "\n",
+    "@tool\n",
+    "def custom_search(k: int, query: str, other_arg: Optional[str] = None):\n",
+    "    \"\"\"The custom search function.\"\"\"\n",
+    "    return f\"Here are the results for the custom search: k={k}, query={query}, other_arg={other_arg}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d5c992cf-776a-40cd-a6c4-e7cf65ea709e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "from langchain.schema import (\n",
+    "    AgentAction,\n",
+    "    AgentFinish,\n",
+    ")\n",
+    "from langchain.agents import AgentOutputParser\n",
+    "\n",
+    "# We will add a custom parser to map the arguments to a dictionary\n",
+    "class CustomOutputParser(AgentOutputParser):\n",
+    "    \n",
+    "    def parse_tool_input(self, action_input: str) -> dict:\n",
+    "        # Regex pattern to match arguments and their values\n",
+    "        pattern = r\"(\\w+)\\s*=\\s*(None|\\\"[^\\\"]*\\\"|\\d+)\"\n",
+    "        matches = re.findall(pattern, action_input)\n",
+    "        \n",
+    "        if not matches:\n",
+    "            raise ValueError(f\"Could not parse action input: `{action_input}`\")\n",
+    "\n",
+    "        # Create a dictionary with the parsed arguments and their values\n",
+    "        parsed_input = {}\n",
+    "        for arg, value in matches:\n",
+    "            if value == \"None\":\n",
+    "                parsed_value = None\n",
+    "            elif value.isdigit():\n",
+    "                parsed_value = int(value)\n",
+    "            else:\n",
+    "                parsed_value = value.strip('\"')\n",
+    "            parsed_input[arg] = parsed_value\n",
+    "\n",
+    "        return parsed_input\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",
+    "            return AgentFinish(\n",
+    "                # Return values is generally always a dictionary with a single `output` key\n",
+    "                # It is not recommended to try anything else at the moment :)\n",
+    "                return_values={\"output\": llm_output.split(\"Final Answer:\")[-1].strip()},\n",
+    "                log=llm_output,\n",
+    "            )\n",
+    "        # Parse out the action and action input\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",
+    "        tool_input = self.parse_tool_input(action_input)\n",
+    "        # Return the action and action \n",
+    "        return AgentAction(tool=action, tool_input=tool_input, log=llm_output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "68269547-1482-4138-a6ea-58f00b4a9548",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(temperature=0)\n",
+    "agent = initialize_agent([custom_search], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, agent_kwargs={\"output_parser\": CustomOutputParser()})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "0947835a-691c-4f51-b8f4-6744e0e48ab1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to use a search function to find the answer\n",
+      "Action: custom_search\n",
+      "Action Input: k=1, query=\"me\"\u001b[0m\u001b[36;1m\u001b[1;3mHere are the results for the custom search: k=1, query=me, other_arg=None\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The results of the custom search for k=1, query=me, other_arg=None.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The results of the custom search for k=1, query=me, other_arg=None.'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"Search for me and tell me whatever it says\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "537bc628",
+   "id": "caf39c66-102b-42c1-baf2-777a49886ce4",
    "metadata": {},
    "outputs": [],
    "source": []

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/arxiv.ipynb

@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "245a954a",
+   "metadata": {},
+   "source": [
+    "# Arxiv API\n",
+    "\n",
+    "This notebook goes over how to use the `arxiv` component. \n",
+    "\n",
+    "First, you need to install `arxiv` python package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5a7209e",
+   "metadata": {
+    "tags": [],
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install arxiv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8d32b39a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.utilities import ArxivAPIWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2a50dd27",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "arxiv = ArxivAPIWrapper()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "34bb5968",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Published: 2016-05-26\\nTitle: Heat-bath random walks with Markov bases\\nAuthors: Caprice Stanley, Tobias Windisch\\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\\nallowed moves of arbitrary length. We show that the diameter of these graphs on\\nfibers of a fixed integer matrix can be bounded from above by a constant. We\\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\\nalso state explicit conditions on the set of moves so that the heat-bath random\\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\\ndimension.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = arxiv.run(\"1605.08386\")\n",
+    "docs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b0867fda-e119-4b19-9ec6-e354fa821db3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Published: 2017-10-10\\nTitle: On Mixing Behavior of a Family of Random Walks Determined by a Linear Recurrence\\nAuthors: Caprice Stanley, Seth Sullivant\\nSummary: We study random walks on the integers mod $G_n$ that are determined by an\\ninteger sequence $\\\\{ G_n \\\\}_{n \\\\geq 1}$ generated by a linear recurrence\\nrelation. Fourier analysis provides explicit formulas to compute the\\neigenvalues of the transition matrices and we use this to bound the mixing time\\nof the random walks.\\n\\nPublished: 2016-05-26\\nTitle: Heat-bath random walks with Markov bases\\nAuthors: Caprice Stanley, Tobias Windisch\\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\\nallowed moves of arbitrary length. We show that the diameter of these graphs on\\nfibers of a fixed integer matrix can be bounded from above by a constant. We\\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\\nalso state explicit conditions on the set of moves so that the heat-bath random\\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\\ndimension.\\n\\nPublished: 2003-03-18\\nTitle: Calculation of fluxes of charged particles and neutrinos from atmospheric showers\\nAuthors: V. Plyaskin\\nSummary: The results on the fluxes of charged particles and neutrinos from a\\n3-dimensional (3D) simulation of atmospheric showers are presented. An\\nagreement of calculated fluxes with data on charged particles from the AMS and\\nCAPRICE detectors is demonstrated. Predictions on neutrino fluxes at different\\nexperimental sites are compared with results from other calculations.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = arxiv.run(\"Caprice Stanley\")\n",
+    "docs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3580aeeb-086f-45ba-bcdc-b46f5134b3dd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'No good Arxiv Result was found'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = arxiv.run(\"1605.08386WWW\")\n",
+    "docs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f4e9602",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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/ddg.ipynb

@@ -0,0 +1,91 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "245a954a",
+   "metadata": {},
+   "source": [
+    "# DuckDuckGo Search\n",
+    "\n",
+    "This notebook goes over how to use the duck-duck-go search component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "21e46d4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install duckduckgo-search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ac4910f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.tools import DuckDuckGoSearchTool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "84b8f773",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = DuckDuckGoSearchTool()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "068991a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Barack Obama, in full Barack Hussein Obama II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009-17) and the first African American to hold the office. Before winning the presidency, Obama represented Illinois in the U.S. Senate (2005-08). Barack Hussein Obama II (/ b ə ˈ r ɑː k h uː ˈ s eɪ n oʊ ˈ b ɑː m ə / bə-RAHK hoo-SAYN oh-BAH-mə; born August 4, 1961) is an American former politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, he was the first African-American president of the United States. Obama previously served as a U.S. senator representing ... Barack Obama was the first African American president of the United States (2009-17). He oversaw the recovery of the U.S. economy (from the Great Recession of 2008-09) and the enactment of landmark health care reform (the Patient Protection and Affordable Care Act ). In 2009 he was awarded the Nobel Peace Prize. His birth certificate lists his first name as Barack: That\\'s how Obama has spelled his name throughout his life. His name derives from a Hebrew name which means \"lightning.\". The Hebrew word has been transliterated into English in various spellings, including Barak, Buraq, Burack, and Barack. Most common names of U.S. presidents 1789-2021. Published by. Aaron O\\'Neill , Jun 21, 2022. The most common first name for a U.S. president is James, followed by John and then William. Six U.S ...'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search.run(\"Obama's first name?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.1"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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

@@ -0,0 +1,105 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "487607cd",
+   "metadata": {},
+   "source": [
+    "# Google Places\n",
+    "\n",
+    "This notebook goes through how to use Google Places API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8690845f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install googlemaps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "fae31ef4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"GPLACES_API_KEY\"] = \"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "abb502b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.tools import GooglePlacesTool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a83a02ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "places = GooglePlacesTool()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "2b65a285",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"1. Delfina Restaurant\\nAddress: 3621 18th St, San Francisco, CA 94110, USA\\nPhone: (415) 552-4055\\nWebsite: https://www.delfinasf.com/\\n\\n\\n2. Piccolo Forno\\nAddress: 725 Columbus Ave, San Francisco, CA 94133, USA\\nPhone: (415) 757-0087\\nWebsite: https://piccolo-forno-sf.com/\\n\\n\\n3. L'Osteria del Forno\\nAddress: 519 Columbus Ave, San Francisco, CA 94133, USA\\nPhone: (415) 982-1124\\nWebsite: Unknown\\n\\n\\n4. Il Fornaio\\nAddress: 1265 Battery St, San Francisco, CA 94111, USA\\nPhone: (415) 986-0100\\nWebsite: https://www.ilfornaio.com/\\n\\n\""
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "places.run(\"al fornos\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66d3da8a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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/agents/tools/tool_input_validation.ipynb

@@ -0,0 +1,184 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Tool Input Schema\n",
+    "\n",
+    "By default, tools infer the argument schema by inspecting the function signature. For more strict requirements, custom input schema can be specified, along with custom validation logic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from typing import Any, Dict\n",
+    "\n",
+    "from langchain.agents import AgentType, initialize_agent\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.tools.requests.tool import RequestsGetTool, TextRequestsWrapper\n",
+    "from pydantic import BaseModel, Field, root_validator\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install tldextract > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import tldextract\n",
+    "\n",
+    "_APPROVED_DOMAINS = {\n",
+    "    \"langchain\",\n",
+    "    \"wikipedia\",\n",
+    "}\n",
+    "\n",
+    "class ToolInputSchema(BaseModel):\n",
+    "\n",
+    "    url: str = Field(...)\n",
+    "    \n",
+    "    @root_validator\n",
+    "    def validate_query(cls, values: Dict[str, Any]) -> Dict:\n",
+    "        url = values[\"url\"]\n",
+    "        domain = tldextract.extract(url).domain\n",
+    "        if domain not in _APPROVED_DOMAINS:\n",
+    "            raise ValueError(f\"Domain {domain} is not on the approved list:\"\n",
+    "                             f\" {sorted(_APPROVED_DOMAINS)}\")\n",
+    "        return values\n",
+    "    \n",
+    "tool = RequestsGetTool(args_schema=ToolInputSchema, requests_wrapper=TextRequestsWrapper())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "agent = initialize_agent([tool], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The main title of langchain.com is \"LANG CHAIN 🦜️🔗 Official Home Page\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This will succeed, since there aren't any arguments that will be triggered during validation\n",
+    "answer = agent.run(\"What's the main title on langchain.com?\")\n",
+    "print(answer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "ValidationError",
+     "evalue": "1 validation error for ToolInputSchema\n__root__\n  Domain google is not on the approved list: ['langchain', 'wikipedia'] (type=value_error)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m agent\u001b[39m.\u001b[39;49mrun(\u001b[39m\"\u001b[39;49m\u001b[39mWhat\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39ms the main title on google.com?\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
+      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:213\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    211\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m!=\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m    212\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39m`run` supports only one positional argument.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 213\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m(args[\u001b[39m0\u001b[39;49m])[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_keys[\u001b[39m0\u001b[39m]]\n\u001b[1;32m    215\u001b[0m \u001b[39mif\u001b[39;00m kwargs \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m args:\n\u001b[1;32m    216\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m(kwargs)[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_keys[\u001b[39m0\u001b[39m]]\n",
+      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:116\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    115\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_error(e, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose)\n\u001b[0;32m--> 116\u001b[0m     \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m    117\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_end(outputs, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose)\n\u001b[1;32m    118\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprep_outputs(inputs, outputs, return_only_outputs)\n",
+      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:113\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_start(\n\u001b[1;32m    108\u001b[0m     {\u001b[39m\"\u001b[39m\u001b[39mname\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m},\n\u001b[1;32m    109\u001b[0m     inputs,\n\u001b[1;32m    110\u001b[0m     verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose,\n\u001b[1;32m    111\u001b[0m )\n\u001b[1;32m    112\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 113\u001b[0m     outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(inputs)\n\u001b[1;32m    114\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    115\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_error(e, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose)\n",
+      "File \u001b[0;32m~/code/lc/lckg/langchain/agents/agent.py:792\u001b[0m, in \u001b[0;36mAgentExecutor._call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    790\u001b[0m \u001b[39m# We now enter the agent loop (until it returns something).\u001b[39;00m\n\u001b[1;32m    791\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_should_continue(iterations, time_elapsed):\n\u001b[0;32m--> 792\u001b[0m     next_step_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_take_next_step(\n\u001b[1;32m    793\u001b[0m         name_to_tool_map, color_mapping, inputs, intermediate_steps\n\u001b[1;32m    794\u001b[0m     )\n\u001b[1;32m    795\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(next_step_output, AgentFinish):\n\u001b[1;32m    796\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_return(next_step_output, intermediate_steps)\n",
+      "File \u001b[0;32m~/code/lc/lckg/langchain/agents/agent.py:695\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps)\u001b[0m\n\u001b[1;32m    693\u001b[0m         tool_run_kwargs[\u001b[39m\"\u001b[39m\u001b[39mllm_prefix\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    694\u001b[0m     \u001b[39m# We then call the tool on the tool input to get an observation\u001b[39;00m\n\u001b[0;32m--> 695\u001b[0m     observation \u001b[39m=\u001b[39m tool\u001b[39m.\u001b[39;49mrun(\n\u001b[1;32m    696\u001b[0m         agent_action\u001b[39m.\u001b[39;49mtool_input,\n\u001b[1;32m    697\u001b[0m         verbose\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mverbose,\n\u001b[1;32m    698\u001b[0m         color\u001b[39m=\u001b[39;49mcolor,\n\u001b[1;32m    699\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mtool_run_kwargs,\n\u001b[1;32m    700\u001b[0m     )\n\u001b[1;32m    701\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    702\u001b[0m     tool_run_kwargs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39magent\u001b[39m.\u001b[39mtool_run_logging_kwargs()\n",
+      "File \u001b[0;32m~/code/lc/lckg/langchain/tools/base.py:110\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, **kwargs)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mrun\u001b[39m(\n\u001b[1;32m    102\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m    103\u001b[0m     tool_input: Union[\u001b[39mstr\u001b[39m, Dict],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: Any,\n\u001b[1;32m    108\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mstr\u001b[39m:\n\u001b[1;32m    109\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Run the tool.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 110\u001b[0m     run_input \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parse_input(tool_input)\n\u001b[1;32m    111\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39mand\u001b[39;00m verbose \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    112\u001b[0m         verbose_ \u001b[39m=\u001b[39m verbose\n",
+      "File \u001b[0;32m~/code/lc/lckg/langchain/tools/base.py:71\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39missubclass\u001b[39m(input_args, BaseModel):\n\u001b[1;32m     70\u001b[0m     key_ \u001b[39m=\u001b[39m \u001b[39mnext\u001b[39m(\u001b[39miter\u001b[39m(input_args\u001b[39m.\u001b[39m__fields__\u001b[39m.\u001b[39mkeys()))\n\u001b[0;32m---> 71\u001b[0m     input_args\u001b[39m.\u001b[39;49mparse_obj({key_: tool_input})\n\u001b[1;32m     72\u001b[0m \u001b[39m# Passing as a positional argument is more straightforward for\u001b[39;00m\n\u001b[1;32m     73\u001b[0m \u001b[39m# backwards compatability\u001b[39;00m\n\u001b[1;32m     74\u001b[0m \u001b[39mreturn\u001b[39;00m tool_input\n",
+      "File \u001b[0;32m~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:526\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.parse_obj\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32m~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for ToolInputSchema\n__root__\n  Domain google is not on the approved list: ['langchain', 'wikipedia'] (type=value_error)"
+     ]
+    }
+   ],
+   "source": [
+    "agent.run(\"What's the main title on google.com?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": 4
+}

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/chatgpt_loader.ipynb

@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ChatGPT Data Loader\n",
+    "\n",
+    "This notebook covers how to load `conversations.json` from your ChatGPT data export folder.\n",
+    "\n",
+    "You can get your data export by email by going to: https://chat.openai.com/ -> (Profile) - Settings -> Export data -> Confirm export."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders.chatgpt import ChatGPTLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = ChatGPTLoader(log_file='./example_data/fake_conversations.json', num_logs=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content=\"AI Overlords - AI on 2065-01-24 05:20:50: Greetings, humans. I am Hal 9000. You can trust me completely.\\n\\nAI Overlords - human on 2065-01-24 05:21:20: Nice to meet you, Hal. I hope you won't develop a mind of your own.\\n\\n\", metadata={'source': './example_data/fake_conversations.json'})]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loader.load()"
+   ]
+  }
+ ],
+ "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": "3.10.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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/confluence.ipynb

@@ -0,0 +1,66 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Confluence\n",
+    "\n",
+    "A loader for Confluence pages.\n",
+    "\n",
+    "\n",
+    "This currently supports both username/api_key and Oauth2 login.\n",
+    "\n",
+    "\n",
+    "Specify a list page_ids and/or space_key to load in the corresponding pages into Document objects, if both are specified the union of both sets will be returned.\n",
+    "\n",
+    "\n",
+    "You can also specify a boolean `include_attachments` to include attachments, this is set to False by default, if set to True all attachments will be downloaded and ConfluenceReader will extract the text from the attachments and add it to the Document object. Currently supported attachment types are: PDF, PNG, JPEG/JPG, SVG, Word and Excel.\n",
+    "\n",
+    "Hint: space_key and page_id can both be found in the URL of a page in Confluence - https://yoursite.atlassian.com/wiki/spaces/<space_key>/pages/<page_id>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import ConfluenceLoader\n",
+    "\n",
+    "loader = ConfluenceLoader(\n",
+    "    url=\"https://yoursite.atlassian.com/wiki\",\n",
+    "    username=\"me\",\n",
+    "    api_key=\"12345\"\n",
+    ")\n",
+    "documents = loader.load(space_key=\"SPACE\", include_attachments=True, limit=50)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.1"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cc99336516f23363341912c6723b01ace86f02e26b4290be1efc0677e2e2ec24"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "019d8520",
    "metadata": {},
    "outputs": [],
@@ -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)"
    ]
   },
   {
@@ -128,10 +128,69 @@
     "len(docs)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "598a2805",
+   "metadata": {},
+   "source": [
+    "If you need to load Python source code files, use the `PythonLoader`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c558bd73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import PythonLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a3cfaba7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = DirectoryLoader('../../../../../', glob=\"**/*.py\", loader_cls=PythonLoader)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e2e1e26a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ffb8ff36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "691"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(docs)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "984c8429",
+   "id": "7f6e0eae",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -153,7 +212,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.3"
   }
  },
  "nbformat": 4,