fmt

@vowelparrot @hwchase17 Here a new implementation of
`acompress_documents` for `LLMChainExtractor ` without changes to the
sync-version, as you suggested in #3587 / [Async Support for
LLMChainExtractor](https://github.com/hwchase17/langchain/pull/3587) .

I created a new PR to avoid cluttering history with reverted commits,
hope that is the right way.
Happy for any improvements/suggestions.

(PS:
I also tried an alternative implementation with a nested helper function
like

``` python
  async def acompress_documents_old(
      self, documents: Sequence[Document], query: str
  ) -> Sequence[Document]:
      """Compress page content of raw documents."""
      async def _compress_concurrently(doc):
          _input = self.get_input(query, doc)
          output = await self.llm_chain.apredict_and_parse(**_input)
          return Document(page_content=output, metadata=doc.metadata)
      outputs=await asyncio.gather(*[_compress_concurrently(doc) for doc in documents])
      compressed_docs=list(filter(lambda x: len(x.page_content)>0,outputs))
      return compressed_docs
```

But in the end I found the commited version to be better readable and
more "canonical" - hope you agree.

.gitignore

@@ -1,3 +1,4 @@
+.vs/
 .vscode/
 .idea/
 # Byte-compiled / optimized / DLL files

Dockerfile

@@ -1,5 +1,7 @@
 # This is a Dockerfile for running unit tests
 
+ARG POETRY_HOME=/opt/poetry
+
 # Use the Python base image
 FROM python:3.11.2-bullseye AS builder
 
@@ -7,7 +9,7 @@ FROM python:3.11.2-bullseye AS builder
 ARG POETRY_VERSION=1.4.2
 
 # Define the directory to install Poetry to (default is /opt/poetry)
-ARG POETRY_HOME=/opt/poetry
+ARG POETRY_HOME
 
 # Create a Python virtual environment for Poetry and install it
 RUN python3 -m venv ${POETRY_HOME} && \
@@ -23,6 +25,8 @@ WORKDIR /app
 # Use a multi-stage build to install dependencies
 FROM builder AS dependencies
 
+ARG POETRY_HOME
+
 # Copy only the dependency files for installation
 COPY pyproject.toml poetry.lock poetry.toml ./
 

docs/ecosystem/aim_tracking.ipynb

@@ -61,7 +61,6 @@
     "from datetime import datetime\n",
     "\n",
     "from langchain.llms import OpenAI\n",
-    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.callbacks import AimCallbackHandler, StdOutCallbackHandler"
    ]
   },
@@ -109,8 +108,8 @@
     "    experiment_name=\"scenario 1: OpenAI LLM\",\n",
     ")\n",
     "\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), aim_callback])\n",
-    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
+    "callbacks = [StdOutCallbackHandler(), aim_callback]\n",
+    "llm = OpenAI(temperature=0, callbacks=callbacks)"
    ]
   },
   {
@@ -177,7 +176,7 @@
     "Title: {title}\n",
     "Playwright: This is a synopsis for the above play:\"\"\"\n",
     "prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
+    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)\n",
     "\n",
     "test_prompts = [\n",
     "    {\"title\": \"documentary about good video games that push the boundary of game design\"},\n",
@@ -249,13 +248,12 @@
    ],
    "source": [
     "# scenario 3 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=callbacks)\n",
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
     "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    callback_manager=manager,\n",
-    "    verbose=True,\n",
+    "    callbacks=callbacks,\n",
     ")\n",
     "agent.run(\n",
     "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",

docs/ecosystem/clearml_tracking.ipynb

@@ -79,7 +79,6 @@
    "source": [
     "from datetime import datetime\n",
     "from langchain.callbacks import ClearMLCallbackHandler, StdOutCallbackHandler\n",
-    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.llms import OpenAI\n",
     "\n",
     "# Setup and use the ClearML Callback\n",
@@ -93,9 +92,9 @@
     "    complexity_metrics=True,\n",
     "    stream_logs=True\n",
     ")\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), clearml_callback])\n",
+    "callbacks = [StdOutCallbackHandler(), clearml_callback]\n",
     "# Get the OpenAI model ready to go\n",
-    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
+    "llm = OpenAI(temperature=0, callbacks=callbacks)"
    ]
   },
   {
@@ -523,13 +522,12 @@
     "from langchain.agents import AgentType\n",
     "\n",
     "# SCENARIO 2 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=callbacks)\n",
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
     "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    callback_manager=manager,\n",
-    "    verbose=True,\n",
+    "    callbacks=callbacks,\n",
     ")\n",
     "agent.run(\n",
     "    \"Who is the wife of the person who sang summer of 69?\"\n",

docs/ecosystem/comet_tracking.ipynb

@@ -121,7 +121,6 @@
     "from datetime import datetime\n",
     "\n",
     "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
-    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.llms import OpenAI\n",
     "\n",
     "comet_callback = CometCallbackHandler(\n",
@@ -131,8 +130,8 @@
     "    tags=[\"llm\"],\n",
     "    visualizations=[\"dep\"],\n",
     ")\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
-    "llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
+    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
+    "llm = OpenAI(temperature=0.9, callbacks=callbacks, verbose=True)\n",
     "\n",
     "llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\", \"Tell me a fact\"] * 3)\n",
     "print(\"LLM result\", llm_result)\n",
@@ -153,7 +152,6 @@
    "outputs": [],
    "source": [
     "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
-    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
@@ -164,15 +162,14 @@
     "    stream_logs=True,\n",
     "    tags=[\"synopsis-chain\"],\n",
     ")\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
-    "\n",
-    "llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
+    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
+    "llm = OpenAI(temperature=0.9, callbacks=callbacks)\n",
     "\n",
     "template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
     "Title: {title}\n",
     "Playwright: This is a synopsis for the above play:\"\"\"\n",
     "prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
+    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)\n",
     "\n",
     "test_prompts = [{\"title\": \"Documentary about Bigfoot in Paris\"}]\n",
     "print(synopsis_chain.apply(test_prompts))\n",
@@ -194,7 +191,6 @@
    "source": [
     "from langchain.agents import initialize_agent, load_tools\n",
     "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
-    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.llms import OpenAI\n",
     "\n",
     "comet_callback = CometCallbackHandler(\n",
@@ -203,15 +199,15 @@
     "    stream_logs=True,\n",
     "    tags=[\"agent\"],\n",
     ")\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
-    "llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
+    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
+    "llm = OpenAI(temperature=0.9, callbacks=callbacks)\n",
     "\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=callbacks)\n",
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
     "    agent=\"zero-shot-react-description\",\n",
-    "    callback_manager=manager,\n",
+    "    callbacks=callbacks,\n",
     "    verbose=True,\n",
     ")\n",
     "agent.run(\n",
@@ -255,7 +251,6 @@
     "from rouge_score import rouge_scorer\n",
     "\n",
     "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
-    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
@@ -298,10 +293,10 @@
     "    tags=[\"custom_metrics\"],\n",
     "    custom_metrics=rouge_score.compute_metric,\n",
     ")\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
-    "llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
+    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
+    "llm = OpenAI(temperature=0.9)\n",
     "\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
+    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)\n",
     "\n",
     "test_prompts = [\n",
     "    {\n",
@@ -323,7 +318,7 @@
     "                 \"\"\"\n",
     "    }\n",
     "]\n",
-    "print(synopsis_chain.apply(test_prompts))\n",
+    "print(synopsis_chain.apply(test_prompts, callbacks=callbacks))\n",
     "comet_callback.flush_tracker(synopsis_chain, finish=True)"
    ]
   }

docs/ecosystem/gpt4all.md

@@ -3,6 +3,7 @@
 This page covers how to use the `GPT4All` wrapper within LangChain. The tutorial is divided into two parts: installation and setup, followed by usage with an example.
 
 ## Installation and Setup
+
 - Install the Python package with `pip install pyllamacpp`
 - Download a [GPT4All model](https://github.com/nomic-ai/pyllamacpp#supported-model) and place it in your desired directory
 
@@ -28,16 +29,16 @@ To stream the model's predictions, add in a CallbackManager.
 
 ```python
 from langchain.llms import GPT4All
-from langchain.callbacks.base import CallbackManager
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+
 # There are many CallbackHandlers supported, such as
 # from langchain.callbacks.streamlit import StreamlitCallbackHandler
 
-callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
-model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8, callback_handler=callback_handler, verbose=True)
+callbacks = [StreamingStdOutCallbackHandler()]
+model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)
 
 # Generate text. Tokens are streamed through the callback manager.
-model("Once upon a time, ")
+model("Once upon a time, ", callbacks=callbacks)
 ```
 
 ## Model File

docs/ecosystem/redis.md

@@ -0,0 +1,79 @@
+# Redis
+
+This page covers how to use the [Redis](https://redis.com) ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific Redis wrappers.
+
+## Installation and Setup
+- Install the Redis Python SDK with `pip install redis`
+
+## Wrappers
+
+### Cache
+
+The Cache wrapper allows for [Redis](https://redis.io) to be used as a remote, low-latency, in-memory cache for LLM prompts and responses.
+
+#### Standard Cache
+The standard cache is the Redis bread & butter of use case in production for both [open source](https://redis.io) and [enterprise](https://redis.com) users globally.
+
+To import this cache:
+```python
+from langchain.cache import RedisCache
+```
+
+To use this cache with your LLMs:
+```python
+import langchain
+import redis
+
+redis_client = redis.Redis.from_url(...)
+langchain.llm_cache = RedisCache(redis_client)
+```
+
+#### Semantic Cache
+Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached results. Under the hood it blends Redis as both a cache and a vectorstore.
+
+To import this cache:
+```python
+from langchain.cache import RedisSemanticCache
+```
+
+To use this cache with your LLMs:
+```python
+import langchain
+import redis
+
+# use any embedding provider...
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+
+redis_url = "redis://localhost:6379"
+
+langchain.llm_cache = RedisSemanticCache(
+    embedding=FakeEmbeddings(),
+    redis_url=redis_url
+)
+```
+
+### VectorStore
+
+The vectorstore wrapper turns Redis into a low-latency [vector database](https://redis.com/solutions/use-cases/vector-database/) for semantic search or LLM content retrieval.
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import Redis
+```
+
+For a more detailed walkthrough of the Redis vectorstore wrapper, see [this notebook](../modules/indexes/vectorstores/examples/redis.ipynb).
+
+### Retriever
+
+The Redis vector store retriever wrapper generalizes the vectorstore class to perform low-latency document retrieval. To create the retriever, simply call `.as_retriever()` on the base vectorstore class.
+
+### Memory
+Redis can be used to persist LLM conversations.
+
+#### Vector Store Retriever Memory
+
+For a more detailed walkthrough of the `VectorStoreRetrieverMemory` wrapper, see [this notebook](../modules/memory/types/vectorstore_retriever_memory.ipynb).
+
+#### Chat Message History Memory
+For a detailed example of Redis to cache conversation message history, see [this notebook](../modules/memory/examples/redis_chat_message_history.ipynb).

docs/ecosystem/tair.md

@@ -0,0 +1,22 @@
+# Tair
+
+This page covers how to use the Tair ecosystem within LangChain.
+
+## Installation and Setup
+
+Install Tair Python SDK with `pip install tair`.
+
+## Wrappers
+
+### VectorStore
+
+There exists a wrapper around TairVector, allowing you to use it as a vectorstore,
+whether for semantic search or example selection.
+
+To import this vectorstore:
+
+```python
+from langchain.vectorstores import Tair
+```
+
+For a more detailed walkthrough of the Tair wrapper, see [this notebook](../modules/indexes/vectorstores/examples/tair.ipynb)

docs/ecosystem/unstructured.md

@@ -10,6 +10,10 @@ This page is broken into two parts: installation and setup, and then references
 `unstructured` wrappers.
 
 ## Installation and Setup
+
+If you are using a loader that runs locally, use the following steps to get `unstructured` and
+its dependencies running locally.
+
 - Install the Python SDK with `pip install "unstructured[local-inference]"`
 - Install the following system dependencies if they are not already available on your system.
   Depending on what document types you're parsing, you may not need all of these.
@@ -25,6 +29,15 @@ This page is broken into two parts: installation and setup, and then references
       using the `"fast"` strategy, which uses `pdfminer` directly and doesn't require
       `detectron2`.
 
+If you want to get up and running with less set up, you can
+simply run `pip install unstructured` and use `UnstructuredAPIFileLoader` or
+`UnstructuredAPIFileIOLoader`. That will process your document using the hosted Unstructured API.
+Note that currently (as of 1 May 2023) the Unstructured API is open, but it will soon require
+an API. The [Unstructured documentation page](https://unstructured-io.github.io/) will have
+instructions on how to generate an API key once they're available. Check out the instructions
+[here](https://github.com/Unstructured-IO/unstructured-api#dizzy-instructions-for-using-the-docker-image)
+if you'd like to self-host the Unstructured API or run it locally.
+
 ## Wrappers
 
 ### Data Loaders

docs/ecosystem/wandb_tracking.ipynb

@@ -50,7 +50,6 @@
    "source": [
     "from datetime import datetime\n",
     "from langchain.callbacks import WandbCallbackHandler, StdOutCallbackHandler\n",
-    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.llms import OpenAI"
    ]
   },
@@ -196,8 +195,8 @@
     "    name=\"llm\",\n",
     "    tags=[\"test\"],\n",
     ")\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), wandb_callback])\n",
-    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
+    "callbacks = [StdOutCallbackHandler(), wandb_callback]\n",
+    "llm = OpenAI(temperature=0, callbacks=callbacks)"
    ]
   },
   {
@@ -484,7 +483,7 @@
     "Title: {title}\n",
     "Playwright: This is a synopsis for the above play:\"\"\"\n",
     "prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
+    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)\n",
     "\n",
     "test_prompts = [\n",
     "    {\n",
@@ -577,16 +576,15 @@
    ],
    "source": [
     "# SCENARIO 3 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
     "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    callback_manager=manager,\n",
-    "    verbose=True,\n",
     ")\n",
     "agent.run(\n",
-    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
+    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\",\n",
+    "    callbacks=callbacks,\n",
     ")\n",
     "wandb_callback.flush_tracker(agent, reset=False, finish=True)"
    ]

docs/getting_started/getting_started.md

@@ -172,9 +172,9 @@ In order to load agents, you should understand the following concepts:
 - LLM: The language model powering the agent.
 - Agent: The agent to use. This should be a string that references a support agent class. Because this notebook focuses on the simplest, highest level API, this only covers using the standard supported agents. If you want to implement a custom agent, see the documentation for custom agents (coming soon).
 
-**Agents**: For a list of supported agents and their specifications, see [here](../modules/agents/agents.md).
+**Agents**: For a list of supported agents and their specifications, see [here](../modules/agents/getting_started.ipynb).
 
-**Tools**: For a list of predefined tools and their specifications, see [here](../modules/agents/tools.md).
+**Tools**: For a list of predefined tools and their specifications, see [here](../modules/agents/tools/getting_started.md).
 
 For this example, you will also need to install the SerpAPI Python package.
 
@@ -349,7 +349,7 @@ result.llm_output['token_usage']
 ## 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:
+For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:
 
 ```python
 from langchain.chat_models import ChatOpenAI
@@ -361,9 +361,9 @@ from langchain.prompts.chat import (
 
 chat = ChatOpenAI(temperature=0)
 
-template="You are a helpful assistant that translates {input_language} to {output_language}."
+template = "You are a helpful assistant that translates {input_language} to {output_language}."
 system_message_prompt = SystemMessagePromptTemplate.from_template(template)
-human_template="{text}"
+human_template = "{text}"
 human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
 
 chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
@@ -387,9 +387,9 @@ from langchain.prompts.chat import (
 
 chat = ChatOpenAI(temperature=0)
 
-template="You are a helpful assistant that translates {input_language} to {output_language}."
+template = "You are a helpful assistant that translates {input_language} to {output_language}."
 system_message_prompt = SystemMessagePromptTemplate.from_template(template)
-human_template="{text}"
+human_template = "{text}"
 human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
 chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
 

docs/index.rst

@@ -44,6 +44,8 @@ These modules are, in increasing order of complexity:
 
 - `Agents <./modules/agents.html>`_: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.
 
+- `Callbacks <./modules/callbacks/getting_started.html>`_: It can be difficult to track all that occurs inside a chain or agent - callbacks help add a level of observability and introspection.
+
 
 .. toctree::
    :maxdepth: 1
@@ -57,6 +59,7 @@ These modules are, in increasing order of complexity:
    ./modules/memory.md
    ./modules/chains.md
    ./modules/agents.md
+   ./modules/callbacks/getting_started.ipynb
 
 Use Cases
 ----------

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

@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "id": "da5df06c-af6f-4572-b9f5-0ab971c16487",
    "metadata": {
     "tags": []
@@ -42,7 +42,6 @@
     "from langchain.agents import AgentType\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.callbacks.stdout import StdOutCallbackHandler\n",
-    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.callbacks.tracers import LangChainTracer\n",
     "from aiohttp import ClientSession\n",
     "\n",
@@ -57,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "id": "fd4c294e-b1d6-44b8-b32e-2765c017e503",
    "metadata": {
     "tags": []
@@ -73,16 +72,15 @@
       "\u001b[32;1m\u001b[1;3m I need to find out who won the US Open men's final in 2019 and then calculate his age raised to the 0.334 power.\n",
       "Action: Search\n",
       "Action Input: \"US Open men's final 2019 winner\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mRafael Nadal\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Rafael Nadal's age\n",
+      "Observation: \u001b[33;1m\u001b[1;3mRafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out the age of the winner\n",
       "Action: Search\n",
       "Action Input: \"Rafael Nadal age\"\u001b[0m\n",
       "Observation: \u001b[33;1m\u001b[1;3m36 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 36 raised to the 0.334 power\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate his age raised to the 0.334 power\n",
       "Action: Calculator\n",
       "Action Input: 36^0.334\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\n",
-      "\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
       "Final Answer: Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484.\u001b[0m\n",
       "\n",
@@ -93,18 +91,17 @@
       "\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
       "Action: Search\n",
       "Action Input: \"Olivia Wilde boyfriend\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mJason Sudeikis\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Jason Sudeikis' age\n",
+      "Observation: \u001b[33;1m\u001b[1;3mOlivia Wilde started dating Harry Styles after ending her years-long engagement to Jason Sudeikis — see their relationship timeline.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Harry Styles' age.\n",
       "Action: Search\n",
-      "Action Input: \"Jason Sudeikis age\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m47 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 47 raised to the 0.23 power\n",
+      "Action Input: \"Harry Styles age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m29 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 29 raised to the 0.23 power.\n",
       "Action: Calculator\n",
-      "Action Input: 47^0.23\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.4242784855673896\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896.\u001b[0m\n",
+      "Action Input: 29^0.23\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.169459462491557\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Harry Styles, Olivia Wilde's boyfriend, is 29 years old and his age raised to the 0.23 power is 2.169459462491557.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
@@ -113,17 +110,17 @@
       "\u001b[32;1m\u001b[1;3m I need to find out who won the grand prix and then calculate their age raised to the 0.23 power.\n",
       "Action: Search\n",
       "Action Input: \"Formula 1 Grand Prix Winner\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mMax Verstappen\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Max Verstappen's age\n",
+      "Observation: \u001b[33;1m\u001b[1;3mMichael Schumacher (top left) and Lewis Hamilton (top right) have each won the championship a record seven times during their careers, while Sebastian Vettel ( ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out the age of the winner\n",
       "Action: Search\n",
-      "Action Input: \"Max Verstappen Age\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m25 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.23 power\n",
+      "Action Input: \"Michael Schumacher age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m54 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate the age raised to the 0.23 power\n",
       "Action: Calculator\n",
-      "Action Input: 25^0.23\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.84599359907945\u001b[0m\n",
+      "Action Input: 54^0.23\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.502940725307012\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Max Verstappen, 25 years old, raised to the 0.23 power is 1.84599359907945.\u001b[0m\n",
+      "Final Answer: Michael Schumacher, aged 54, raised to the 0.23 power is 2.502940725307012.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
@@ -132,18 +129,17 @@
       "\u001b[32;1m\u001b[1;3m I need to find out who won the US Open women's final in 2019 and then calculate her age raised to the 0.34 power.\n",
       "Action: Search\n",
       "Action Input: \"US Open women's final 2019 winner\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mBianca Andreescu defeated Serena Williams in the final, 6–3, 7–5 to win the women's singles tennis title at the 2019 US Open. It was her first major title, and she became the first Canadian, as well as the first player born in the 2000s, to win a major singles title.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Bianca Andreescu's age.\n",
+      "Observation: \u001b[33;1m\u001b[1;3mBianca Andreescu\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out her age\n",
       "Action: Search\n",
       "Action Input: \"Bianca Andreescu age\"\u001b[0m\n",
       "Observation: \u001b[33;1m\u001b[1;3m22 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the age of Bianca Andreescu and can calculate her age raised to the 0.34 power.\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate her age raised to the 0.34 power\n",
       "Action: Calculator\n",
       "Action Input: 22^0.34\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.8603798598506933\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: Bianca Andreescu won the US Open women's final in 2019 and her age raised to the 0.34 power is 2.8603798598506933.\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.8603798598506933\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Bianca Andreescu, aged 22, won the US Open women's final in 2019 and her age raised to the 0.34 power is 2.86.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
@@ -160,35 +156,32 @@
       "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 53 raised to the 0.19 power\n",
       "Action: Calculator\n",
       "Action Input: 53^0.19\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.12624064206896\n",
-      "\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.12624064206896\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
       "Final Answer: Jay-Z is Beyonce's husband and his age raised to the 0.19 power is 2.12624064206896.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
-      "Serial executed in 65.11 seconds.\n"
+      "Serial executed in 52.47 seconds.\n"
      ]
     }
    ],
    "source": [
-    "def generate_serially():\n",
-    "    for q in questions:\n",
-    "        llm = OpenAI(temperature=0)\n",
-    "        tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm)\n",
-    "        agent = initialize_agent(\n",
-    "            tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    "        )\n",
-    "        agent.run(q)\n",
+    "llm = OpenAI(temperature=0)\n",
+    "tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm)\n",
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")\n",
     "\n",
     "s = time.perf_counter()\n",
-    "generate_serially()\n",
+    "for q in questions:\n",
+    "    agent.run(q)\n",
     "elapsed = time.perf_counter() - s\n",
     "print(f\"Serial executed in {elapsed:0.2f} seconds.\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "id": "076d7b85-45ec-465d-8b31-c2ad119c3438",
    "metadata": {
     "tags": []
@@ -202,6 +195,9 @@
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\n",
+      "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\n",
@@ -210,182 +206,94 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
-      "Action: Search\n",
-      "Action Input: \"Olivia Wilde boyfriend\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.\n",
-      "Action: Search\n",
-      "Action Input: \"Who is Beyonce's husband?\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mJay-Z\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out who won the grand prix and then calculate their age raised to the 0.23 power.\n",
-      "Action: Search\n",
-      "Action Input: \"Formula 1 Grand Prix Winner\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out who won the US Open women's final in 2019 and then calculate her age raised to the 0.34 power.\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who won the US Open women's final in 2019 and then calculate her age raised to the 0.34 power.\n",
       "Action: Search\n",
       "Action Input: \"US Open women's final 2019 winner\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mJason Sudeikis\u001b[0m\n",
-      "Thought:\n",
-      "Observation: \u001b[33;1m\u001b[1;3mMax Verstappen\u001b[0m\n",
-      "Thought:\n",
-      "Observation: \u001b[33;1m\u001b[1;3mBianca Andreescu defeated Serena Williams in the final, 6–3, 7–5 to win the women's singles tennis title at the 2019 US Open. It was her first major title, and she became the first Canadian, as well as the first player born in the 2000s, to win a major singles title.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Jason Sudeikis' age\n",
-      "Action: Search\n",
-      "Action Input: \"Jason Sudeikis age\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out Jay-Z's age\n",
-      "Action: Search\n",
-      "Action Input: \"How old is Jay-Z?\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m53 years\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mBianca Andreescu\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I need to find out who won the US Open men's final in 2019 and then calculate his age raised to the 0.334 power.\n",
       "Action: Search\n",
       "Action Input: \"US Open men's final 2019 winner\"\u001b[0m\n",
       "Observation: \u001b[33;1m\u001b[1;3mRafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Olivia Wilde boyfriend\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out who won the grand prix and then calculate their age raised to the 0.23 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Formula 1 Grand Prix Winner\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Who is Beyonce's husband?\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mOlivia Wilde started dating Harry Styles after ending her years-long engagement to Jason Sudeikis — see their relationship timeline.\u001b[0m\n",
       "Thought:\n",
-      "Observation: \u001b[33;1m\u001b[1;3m47 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Max Verstappen's age\n",
+      "Observation: \u001b[33;1m\u001b[1;3mJay-Z\u001b[0m\n",
+      "Thought:\n",
+      "Observation: \u001b[33;1m\u001b[1;3mMichael Schumacher (top left) and Lewis Hamilton (top right) have each won the championship a record seven times during their careers, while Sebastian Vettel ( ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out her age\n",
       "Action: Search\n",
-      "Action Input: \"Max Verstappen Age\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m25 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Bianca Andreescu's age.\n",
+      "Action Input: \"Bianca Andreescu age\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out Jay-Z's age\n",
       "Action: Search\n",
-      "Action Input: \"Bianca Andreescu age\"\u001b[0m\n",
+      "Action Input: \"How old is Jay-Z?\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m53 years\u001b[0m\n",
+      "Thought:\n",
       "Observation: \u001b[33;1m\u001b[1;3m22 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 53 raised to the 0.19 power\n",
-      "Action: Calculator\n",
-      "Action Input: 53^0.19\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out the age of the winner\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Harry Styles' age.\n",
       "Action: Search\n",
-      "Action Input: \"Rafael Nadal age\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to calculate 47 raised to the 0.23 power\n",
+      "Action Input: \"Harry Styles age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m29 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate her age raised to the 0.34 power\n",
       "Action: Calculator\n",
-      "Action Input: 47^0.23\u001b[0m\n",
+      "Action Input: 22^0.34\u001b[0m\u001b[32;1m\u001b[1;3m I need to calculate 53 raised to the 0.19 power\n",
+      "Action: Calculator\n",
+      "Action Input: 53^0.19\u001b[0m\u001b[32;1m\u001b[1;3m I need to calculate 29 raised to the 0.23 power.\n",
+      "Action: Calculator\n",
+      "Action Input: 29^0.23\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out the age of the winner\n",
+      "Action: Search\n",
+      "Action Input: \"Rafael Nadal age\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out the age of the winner\n",
+      "Action: Search\n",
+      "Action Input: \"Michael Schumacher age\"\u001b[0m\n",
+      "Observation: \n",
       "Observation: \u001b[33;1m\u001b[1;3m36 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.23 power\n",
-      "Action: Calculator\n",
-      "Action Input: 25^0.23\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.12624064206896\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the age of Bianca Andreescu and can calculate her age raised to the 0.34 power.\n",
-      "Action: Calculator\n",
-      "Action Input: 22^0.34\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.84599359907945\u001b[0m\n",
+      "Thought:\u001b[33;1m\u001b[1;3m54 years\u001b[0m\n",
       "Thought:\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.4242784855673896\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate his age raised to the 0.334 power\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.8603798598506933\u001b[0m\n",
+      "Thought:\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.169459462491557\u001b[0m\n",
+      "Thought:\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.12624064206896\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate the age raised to the 0.334 power\n",
       "Action: Calculator\n",
-      "Action Input: 36^0.334\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.8603798598506933\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Jay-Z is Beyonce's husband and his age raised to the 0.19 power is 2.12624064206896.\u001b[0m\n",
-      "\n",
+      "Action Input: 36^0.334\u001b[0m\u001b[32;1m\u001b[1;3m I now need to calculate the age raised to the 0.23 power\n",
+      "Action: Calculator\n",
+      "Action Input: 54^0.23\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Max Verstappen, 25 years old, raised to the 0.23 power is 1.84599359907945.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896.\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\u001b[0m\n",
+      "Thought:\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.502940725307012\u001b[0m\n",
+      "Thought:\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: Bianca Andreescu won the US Open women's final in 2019 and her age raised to the 0.34 power is 2.8603798598506933.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "Concurrent executed in 12.38 seconds.\n"
+      "Concurrent executed in 14.49 seconds.\n"
      ]
     }
    ],
    "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",
-    "    # 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([\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession, callback_manager=manager)\n",
-    "        agents.append(\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",
+    "llm = OpenAI(temperature=0)\n",
+    "tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm)\n",
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")\n",
     "\n",
     "s = time.perf_counter()\n",
-    "# If running this outside of Jupyter, use asyncio.run(generate_concurrently())\n",
-    "await generate_concurrently()\n",
+    "# If running this outside of Jupyter, use asyncio.run or loop.run_until_complete\n",
+    "tasks = [agent.arun(q) for q in questions]\n",
+    "await asyncio.gather(*tasks)\n",
     "elapsed = time.perf_counter() - s\n",
     "print(f\"Concurrent executed in {elapsed:0.2f} seconds.\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "97ef285c-4a43-4a4e-9698-cd52a1bc56c9",
-   "metadata": {},
-   "source": [
-    "## Using Tracing with Asynchronous Agents\n",
-    "\n",
-    "To use tracing with async agents, you must pass in a custom `CallbackManager` with `LangChainTracer` to each agent running asynchronously. This way, you avoid collisions while the trace is being collected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "44bda05a-d33e-4e91-9a71-a0f3f96aae95",
-   "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 find out who won the US Open men's final in 2019 and then calculate his age raised to the 0.334 power.\n",
-      "Action: Search\n",
-      "Action Input: \"US Open men's final 2019 winner\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mRafael Nadal\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Rafael Nadal's age\n",
-      "Action: Search\n",
-      "Action Input: \"Rafael Nadal age\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m36 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 36 raised to the 0.334 power\n",
-      "Action: Calculator\n",
-      "Action Input: 36^0.334\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 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",
-    "tracer.load_default_session()\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), tracer])\n",
-    "\n",
-    "# Pass the manager into the llm if you want llm calls traced.\n",
-    "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(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()"
-   ]
   }
  ],
  "metadata": {
@@ -404,7 +312,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/agents/agents/custom_agent_with_tool_retrieval.ipynb

@@ -373,6 +373,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "tools = get_tools(\"whats the weather?\")\n",
     "tool_names = [tool.name for tool in tools]\n",
     "agent = LLMSingleActionAgent(\n",
     "    llm_chain=llm_chain, \n",

docs/modules/agents/agents/examples/structured_chat.ipynb

@@ -0,0 +1,312 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4658d71a",
+   "metadata": {},
+   "source": [
+    "# Structured Tool Chat Agent\n",
+    "\n",
+    "This notebook walks through using a chat agent capable of using multi-input tools.\n",
+    "\n",
+    "Older agents are configured to specify an action input as a single string, but this agent can use the provided tools' `args_schema` to populate the action input.\n",
+    "\n",
+    "This functionality is natively available in the (`structured-chat-zero-shot-react-description` or `AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f65308ab",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.agents import AgentType\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.agents import initialize_agent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30aaf540-9e8e-436e-af8b-89e610e34120",
+   "metadata": {},
+   "source": [
+    "### Initialize Tools\n",
+    "\n",
+    "We will test the agent using a web browser."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "71027ff2-5d09-49cd-92a1-24b2c454a7ae",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.agents.agent_toolkits import PlayWrightBrowserToolkit\n",
+    "from langchain.tools.playwright.utils import (\n",
+    "    create_async_playwright_browser,\n",
+    "    create_sync_playwright_browser, # A synchronous browser is available, though it isn't compatible with jupyter.\n",
+    ")\n",
+    "\n",
+    "# This import is required only for jupyter notebooks, since they have their own eventloop\n",
+    "import nest_asyncio\n",
+    "nest_asyncio.apply()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5fb14d6d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "async_browser = create_async_playwright_browser()\n",
+    "browser_toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=async_browser)\n",
+    "tools = browser_toolkit.get_tools()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cafe9bc1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "llm = ChatOpenAI(temperature=0) # Also works well with Anthropic models\n",
+    "agent_chain = initialize_agent(tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c4a45575-f3ef-46ba-a943-475584073984",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks import tracing_enabled # This is used to configure tracing for our runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4f4aa234-9746-47d8-bec7-d76081ac3ef6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "Hi Erica! How can I assist you today?\n"
+     ]
+    }
+   ],
+   "source": [
+    "with tracing_enabled(): # If you want to see the traces in the UI\n",
+    "    response = await agent_chain.arun(input=\"Hi I'm Erica.\")\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "23e7dc33-50a5-4685-8e9b-4ac49e12877f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "I'm here to chat! How's your day going?\n"
+     ]
+    }
+   ],
+   "source": [
+    "with tracing_enabled(): # If you want to see the traces in the UI\n",
+    "    response = await agent_chain.arun(input=\"Don't need help really just chatting.\")\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dc70b454",
+   "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;3mAction:\n",
+      "```\n",
+      "{\n",
+      "  \"action\": \"navigate_browser\",\n",
+      "  \"action_input\": {\n",
+      "    \"url\": \"https://blog.langchain.dev/\"\n",
+      "  }\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mNavigating to https://blog.langchain.dev/ returned status code 200\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to extract the text from the webpage to summarize it.\n",
+      "Action:\n",
+      "```\n",
+      "{\n",
+      "  \"action\": \"extract_text\",\n",
+      "  \"action_input\": {}\n",
+      "}\n",
+      "```\n",
+      "\u001b[0m\n",
+      "Observation: \u001b[31;1m\u001b[1;3mLangChain LangChain Home About GitHub Docs LangChain The official LangChain blog. Auto-Evaluator Opportunities Editor's Note: this is a guest blog post by Lance Martin.\n",
+      "\n",
+      "\n",
+      "TL;DR\n",
+      "\n",
+      "We recently open-sourced an auto-evaluator tool for grading LLM question-answer chains. We are now releasing an open source, free to use hosted app and API to expand usability. Below we discuss a few opportunities to further improve May 1, 2023 5 min read Callbacks Improvements TL;DR: We're announcing improvements to our callbacks system, which powers logging, tracing, streaming output, and some awesome third-party integrations. This will better support concurrent runs with independent callbacks, tracing of deeply nested trees of LangChain components, and callback handlers scoped to a single request (which is super useful for May 1, 2023 3 min read Unleashing the power of AI Collaboration with Parallelized LLM Agent Actor Trees Editor's note: the following is a guest blog post from Cyrus at Shaman AI. We use guest blog posts to highlight interesting and novel applciations, and this is certainly that. There's been a lot of talk about agents recently, but most have been discussions around a single agent. If multiple Apr 28, 2023 4 min read Gradio & LLM Agents Editor's note: this is a guest blog post from Freddy Boulton, a software engineer at Gradio. We're excited to share this post because it brings a large number of exciting new tools into the ecosystem. Agents are largely defined by the tools they have, so to be able to equip Apr 23, 2023 4 min read RecAlign - The smart content filter for social media feed [Editor's Note] This is a guest post by Tian Jin. We are highlighting this application as we think it is a novel use case. Specifically, we think recommendation systems are incredibly impactful in our everyday lives and there has not been a ton of discourse on how LLMs will impact Apr 22, 2023 3 min read Improving Document Retrieval with Contextual Compression Note: This post assumes some familiarity with LangChain and is moderately technical.\n",
+      "\n",
+      "💡 TL;DR: We’ve introduced a new abstraction and a new document Retriever to facilitate the post-processing of retrieved documents. Specifically, the new abstraction makes it easy to take a set of retrieved documents and extract from them Apr 20, 2023 3 min read Autonomous Agents & Agent Simulations Over the past two weeks, there has been a massive increase in using LLMs in an agentic manner. Specifically, projects like AutoGPT, BabyAGI, CAMEL, and Generative Agents have popped up. The LangChain community has now implemented some parts of all of those projects in the LangChain framework. While researching and Apr 18, 2023 7 min read AI-Powered Medical Knowledge: Revolutionizing Care for Rare Conditions [Editor's Note]: This is a guest post by Jack Simon, who recently participated in a hackathon at Williams College. He built a LangChain-powered chatbot focused on appendiceal cancer, aiming to make specialized knowledge more accessible to those in need. If you are interested in building a chatbot for another rare Apr 17, 2023 3 min read Auto-Eval of Question-Answering Tasks By Lance Martin\n",
+      "\n",
+      "Context\n",
+      "\n",
+      "LLM ops platforms, such as LangChain, make it easy to assemble LLM components (e.g., models, document retrievers, data loaders) into chains. Question-Answering is one of the most popular applications of these chains. But it is often not always obvious to determine what parameters (e.g. Apr 15, 2023 3 min read Announcing LangChainJS Support for Multiple JS Environments TLDR: We're announcing support for running LangChain.js in browsers, Cloudflare Workers, Vercel/Next.js, Deno, Supabase Edge Functions, alongside existing support for Node.js ESM and CJS. See install/upgrade docs and breaking changes list.\n",
+      "\n",
+      "\n",
+      "Context\n",
+      "\n",
+      "Originally we designed LangChain.js to run in Node.js, which is the Apr 11, 2023 3 min read LangChain x Supabase Supabase is holding an AI Hackathon this week. Here at LangChain we are big fans of both Supabase and hackathons, so we thought this would be a perfect time to highlight the multiple ways you can use LangChain and Supabase together.\n",
+      "\n",
+      "The reason we like Supabase so much is that Apr 8, 2023 2 min read Announcing our $10M seed round led by Benchmark It was only six months ago that we released the first version of LangChain, but it seems like several years. When we launched, generative AI was starting to go mainstream: stable diffusion had just been released and was captivating people’s imagination and fueling an explosion in developer activity, Jasper Apr 4, 2023 4 min read Custom Agents One of the most common requests we've heard is better functionality and documentation for creating custom agents. This has always been a bit tricky - because in our mind it's actually still very unclear what an \"agent\" actually is, and therefor what the \"right\" abstractions for them may be. Recently, Apr 3, 2023 3 min read Retrieval TL;DR: We are adjusting our abstractions to make it easy for other retrieval methods besides the LangChain VectorDB object to be used in LangChain. This is done with the goals of (1) allowing retrievers constructed elsewhere to be used more easily in LangChain, (2) encouraging more experimentation with alternative Mar 23, 2023 4 min read LangChain + Zapier Natural Language Actions (NLA) We are super excited to team up with Zapier and integrate their new Zapier NLA API into LangChain, which you can now use with your agents and chains. With this integration, you have access to the 5k+ apps and 20k+ actions on Zapier's platform through a natural language API interface. Mar 16, 2023 2 min read Evaluation Evaluation of language models, and by extension applications built on top of language models, is hard. With recent model releases (OpenAI, Anthropic, Google) evaluation is becoming a bigger and bigger issue. People are starting to try to tackle this, with OpenAI releasing OpenAI/evals - focused on evaluating OpenAI models. Mar 14, 2023 3 min read LLMs and SQL Francisco Ingham and Jon Luo are two of the community members leading the change on the SQL integrations. We’re really excited to write this blog post with them going over all the tips and tricks they’ve learned doing so. We’re even more excited to announce that we’ Mar 13, 2023 8 min read Origin Web Browser [Editor's Note]: This is the second of hopefully many guest posts. We intend to highlight novel applications building on top of LangChain. If you are interested in working with us on such a post, please reach out to harrison@langchain.dev.\n",
+      "\n",
+      "Authors: Parth Asawa (pgasawa@), Ayushi Batwara (ayushi.batwara@), Jason Mar 8, 2023 4 min read Prompt Selectors One common complaint we've heard is that the default prompt templates do not work equally well for all models. This became especially pronounced this past week when OpenAI released a ChatGPT API. This new API had a completely new interface (which required new abstractions) and as a result many users Mar 8, 2023 2 min read Chat Models Last week OpenAI released a ChatGPT endpoint. It came marketed with several big improvements, most notably being 10x cheaper and a lot faster. But it also came with a completely new API endpoint. We were able to quickly write a wrapper for this endpoint to let users use it like Mar 6, 2023 6 min read Using the ChatGPT API to evaluate the ChatGPT API OpenAI released a new ChatGPT API yesterday. Lots of people were excited to try it. But how does it actually compare to the existing API? It will take some time before there is a definitive answer, but here are some initial thoughts. Because I'm lazy, I also enrolled the help Mar 2, 2023 5 min read Agent Toolkits Today, we're announcing agent toolkits, a new abstraction that allows developers to create agents designed for a particular use-case (for example, interacting with a relational database or interacting with an OpenAPI spec). We hope to continue developing different toolkits that can enable agents to do amazing feats. Toolkits are supported Mar 1, 2023 3 min read TypeScript Support It's finally here... TypeScript support for LangChain.\n",
+      "\n",
+      "What does this mean? It means that all your favorite prompts, chains, and agents are all recreatable in TypeScript natively. Both the Python version and TypeScript version utilize the same serializable format, meaning that artifacts can seamlessly be shared between languages. As an Feb 17, 2023 2 min read Streaming Support in LangChain We’re excited to announce streaming support in LangChain. There's been a lot of talk about the best UX for LLM applications, and we believe streaming is at its core. We’ve also updated the chat-langchain repo to include streaming and async execution. We hope that this repo can serve Feb 14, 2023 2 min read LangChain + Chroma Today we’re announcing LangChain's integration with Chroma, the first step on the path to the Modern A.I Stack.\n",
+      "\n",
+      "\n",
+      "LangChain - The A.I-native developer toolkit\n",
+      "\n",
+      "We started LangChain with the intent to build a modular and flexible framework for developing A.I-native applications. Some of the use cases Feb 13, 2023 2 min read Page 1 of 2 Older Posts → LangChain © 2023 Sign up Powered by Ghost\u001b[0m\n",
+      "Thought:\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "The LangChain blog has recently released an open-source auto-evaluator tool for grading LLM question-answer chains and is now releasing an open-source, free-to-use hosted app and API to expand usability. The blog also discusses various opportunities to further improve the LangChain platform.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with tracing_enabled(): # If you want to see the traces in the UI\n",
+    "    response = await agent_chain.arun(input=\"Browse to blog.langchain.dev and summarize the text, please.\")\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0084efd6",
+   "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;3mThought: I can navigate to the xkcd website and extract the latest comic title and alt text to answer the question.\n",
+      "Action:\n",
+      "```\n",
+      "{\n",
+      "  \"action\": \"navigate_browser\",\n",
+      "  \"action_input\": {\n",
+      "    \"url\": \"https://xkcd.com/\"\n",
+      "  }\n",
+      "}\n",
+      "```\n",
+      "\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mNavigating to https://xkcd.com/ returned status code 200\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI can extract the latest comic title and alt text using CSS selectors.\n",
+      "Action:\n",
+      "```\n",
+      "{\n",
+      "  \"action\": \"get_elements\",\n",
+      "  \"action_input\": {\n",
+      "    \"selector\": \"#ctitle, #comic img\",\n",
+      "    \"attributes\": [\"alt\", \"src\"]\n",
+      "  }\n",
+      "}\n",
+      "``` \n",
+      "\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m[{\"alt\": \"Tapetum Lucidum\", \"src\": \"//imgs.xkcd.com/comics/tapetum_lucidum.png\"}]\u001b[0m\n",
+      "Thought:\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "The latest xkcd comic is titled \"Tapetum Lucidum\" and the image can be found at https://xkcd.com/2565/.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with tracing_enabled(): # If you want to see the traces in the UI\n",
+    "    response = await agent_chain.arun(input=\"What's the latest xkcd comic about?\")\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebd7ae33-f67d-4378-ac79-9d91e0c8f53a",
+   "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": 5
+}

docs/modules/agents/tools/custom_tools.ipynb

@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "5436020b",
    "metadata": {},
@@ -12,11 +13,10 @@
     "- 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",
+    "- args_schema (Pydantic BaseModel), is optional but recommended, can be used to provide more information (e.g., few-shot examples) or validation for expected parameters.\n",
     "\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."
+    "There are two main ways to define a tool, we will cover both in the example below."
    ]
   },
   {
@@ -30,9 +30,9 @@
    "source": [
     "# Import things that are needed generically\n",
     "from langchain import LLMMathChain, SerpAPIWrapper\n",
-    "from langchain.agents import AgentType, Tool, initialize_agent, tool\n",
+    "from langchain.agents import AgentType, initialize_agent\n",
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.tools import BaseTool"
+    "from langchain.tools import BaseTool, StructuredTool, Tool, tool"
    ]
   },
   {
@@ -56,22 +56,27 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "f8bc72c2",
    "metadata": {},
    "source": [
-    "## Completely New Tools \n",
-    "First, we show how to create completely new tools from scratch.\n",
+    "## Completely New Tools - String Input and Output\n",
+    "\n",
+    "The simplest tools accept a single query string and return a string output. If your tool function requires multiple arguments, you might want to skip down to the `StructuredTool` section below.\n",
     "\n",
     "There are two ways to do this: either by using the Tool dataclass, or by subclassing the BaseTool class."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "b63fcc3b",
    "metadata": {},
    "source": [
-    "### Tool dataclass"
+    "### Tool dataclass\n",
+    "\n",
+    "The 'Tool' dataclass wraps functions that accept a single string input and returns a string output."
    ]
   },
   {
@@ -81,19 +86,46 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/wfh/code/lc/lckg/langchain/chains/llm_math/base.py:50: UserWarning: Directly instantiating an LLMMathChain with an llm is deprecated. Please instantiate with llm_chain argument or using the from_llm class method.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "# Load the tool configs that are needed.\n",
     "search = SerpAPIWrapper()\n",
     "llm_math_chain = LLMMathChain(llm=llm, verbose=True)\n",
     "tools = [\n",
-    "    Tool(\n",
-    "        name = \"Search\",\n",
+    "    Tool.from_function(\n",
     "        func=search.run,\n",
+    "        name = \"Search\",\n",
     "        description=\"useful for when you need to answer questions about current events\"\n",
+    "        # coroutine= ... <- you can specify an async method if desired as well\n",
     "    ),\n",
-    "]\n",
-    "# You can also define an args_schema to provide more information about inputs\n",
+    "]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "e9b560f7",
+   "metadata": {},
+   "source": [
+    "You can also define a custom `args_schema`` to provide more information about inputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "631361e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "from pydantic import BaseModel, Field\n",
     "\n",
     "class CalculatorInput(BaseModel):\n",
@@ -101,18 +133,19 @@
     "        \n",
     "\n",
     "tools.append(\n",
-    "    Tool(\n",
-    "        name=\"Calculator\",\n",
+    "    Tool.from_function(\n",
     "        func=llm_math_chain.run,\n",
+    "        name=\"Calculator\",\n",
     "        description=\"useful for when you need to answer questions about math\",\n",
     "        args_schema=CalculatorInput\n",
+    "        # coroutine= ... <- you can specify an async method if desired as well\n",
     "    )\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "5b93047d",
    "metadata": {
     "tags": []
@@ -126,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "6f96a891",
    "metadata": {
     "tags": []
@@ -141,7 +174,17 @@
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\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\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 Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAfter rumours of a romance with Gigi Hadid, the Oscar winner has seemingly moved on. First being linked to the television personality in September 2022, it appears as if his \"age bracket\" has moved up. This follows his rumoured relationship with mere 19-year-old Eden Polani.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI still need to find out his current girlfriend's name and age\n",
+      "Action: Search\n",
+      "Action Input: \"Leo DiCaprio current girlfriend\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mJust Jared on Instagram: “Leonardo DiCaprio & girlfriend Camila Morrone couple up for a lunch date!\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mNow that I know his girlfriend's name is Camila Morrone, I need to find her current age\n",
+      "Action: 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;3mNow that I have her age, I need to calculate her age raised to the 0.43 power\n",
       "Action: Calculator\n",
       "Action Input: 25^(0.43)\u001b[0m\n",
       "\n",
@@ -153,8 +196,10 @@
       "\u001b[0m\n",
       "Answer: \u001b[33;1m\u001b[1;3m3.991298452658078\u001b[0m\n",
       "\u001b[1m> Finished chain.\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",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\n",
+      "Thought:\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"
      ]
@@ -162,10 +207,10 @@
     {
      "data": {
       "text/plain": [
-       "'3.991298452658078'"
+       "\"Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\""
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -175,71 +220,65 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6f12eaf0",
    "metadata": {},
    "source": [
-    "### Subclassing the BaseTool class"
+    "### Subclassing the BaseTool class\n",
+    "\n",
+    "You can also directly subclass `BaseTool`. This is useful if you want more control over the instance variables or if you want to propagate callbacks to nested chains or other tools."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "c58a7c40",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "from typing import Type\n",
+    "from typing import Optional, Type\n",
+    "\n",
+    "from langchain.callbacks.manager import AsyncCallbackManagerForToolRun, CallbackManagerForToolRun\n",
     "\n",
     "class CustomSearchTool(BaseTool):\n",
-    "    name = \"Search\"\n",
+    "    name = \"custom_search\"\n",
     "    description = \"useful for when you need to answer questions about current events\"\n",
     "\n",
-    "    def _run(self, query: str) -> str:\n",
+    "    def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:\n",
     "        \"\"\"Use the tool.\"\"\"\n",
     "        return search.run(query)\n",
     "    \n",
-    "    async def _arun(self, query: str) -> str:\n",
+    "    async def _arun(self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:\n",
     "        \"\"\"Use the tool asynchronously.\"\"\"\n",
-    "        raise NotImplementedError(\"BingSearchRun does not support async\")\n",
+    "        raise NotImplementedError(\"custom_search does not support async\")\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",
+    "    def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:\n",
     "        \"\"\"Use the tool.\"\"\"\n",
     "        return llm_math_chain.run(query)\n",
     "    \n",
-    "    async def _arun(self, query: str) -> str:\n",
+    "    async def _arun(self, query: str,  run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:\n",
     "        \"\"\"Use the tool asynchronously.\"\"\"\n",
-    "        raise NotImplementedError(\"BingSearchRun does not support async\")"
+    "        raise NotImplementedError(\"Calculator does not support async\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "3318a46f",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "tools = [CustomSearchTool(), CustomCalculatorTool()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "ee2d0f3a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
+    "tools = [CustomSearchTool(), CustomCalculatorTool()]\n",
     "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
@@ -258,22 +297,30 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\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\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",
+      "\u001b[32;1m\u001b[1;3mI need to use custom_search to find out who Leo DiCaprio's girlfriend is, and then use the Calculator to raise her age to the 0.43 power.\n",
+      "Action: custom_search\n",
+      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAfter rumours of a romance with Gigi Hadid, the Oscar winner has seemingly moved on. First being linked to the television personality in September 2022, it appears as if his \"age bracket\" has moved up. This follows his rumoured relationship with mere 19-year-old Eden Polani.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to find out the current age of Eden Polani.\n",
+      "Action: custom_search\n",
+      "Action Input: \"Eden Polani age\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m19 years old\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mNow I can use the Calculator to raise her age to the 0.43 power.\n",
       "Action: Calculator\n",
-      "Action Input: 25^(0.43)\u001b[0m\n",
+      "Action Input: 19 ^ 0.43\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "25^(0.43)\u001b[32;1m\u001b[1;3m```text\n",
-      "25**(0.43)\n",
+      "19 ^ 0.43\u001b[32;1m\u001b[1;3m```text\n",
+      "19 ** 0.43\n",
       "```\n",
-      "...numexpr.evaluate(\"25**(0.43)\")...\n",
+      "...numexpr.evaluate(\"19 ** 0.43\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.991298452658078\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m3.547023357958959\u001b[0m\n",
       "\u001b[1m> Finished chain.\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",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.547023357958959\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
+      "Final Answer: 3.547023357958959\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -281,7 +328,7 @@
     {
      "data": {
       "text/plain": [
-       "'3.991298452658078'"
+       "'3.547023357958959'"
       ]
      },
      "execution_count": 9,
@@ -312,34 +359,13 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.agents import tool\n",
+    "from langchain.tools import tool\n",
     "\n",
     "@tool\n",
     "def search_api(query: str) -> str:\n",
     "    \"\"\"Searches the API for the query.\"\"\"\n",
-    "    return f\"Results for query {query}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "0a23b91b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "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": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
+    "    return f\"Results for query {query}\"\n",
+    "\n",
     "search_api"
    ]
   },
@@ -433,18 +459,149 @@
    ]
   },
   {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "61d2e80b",
+   "metadata": {},
+   "source": [
+    "## Custom Structured Tools\n",
+    "\n",
+    "If your functions require more structured arguments, you can use the `StructuredTool` class directly, or still subclass the `BaseTool` class."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "5be41722",
+   "metadata": {},
+   "source": [
+    "### StructuredTool dataclass\n",
+    "\n",
+    "To dynamically generate a structured tool from a given function, the fastest way to get started is with `StructuredTool.from_function()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3c070216",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from langchain.tools import StructuredTool\n",
+    "\n",
+    "def post_message(url: str, body: dict, parameters: Optional[dict] = None) -> str:\n",
+    "    \"\"\"Sends a POST request to the given url with the given body and parameters.\"\"\"\n",
+    "    result = requests.post(url, json=body, params=parameters)\n",
+    "    return f\"Status: {result.status_code} - {result.text}\"\n",
+    "\n",
+    "tool = StructuredTool.from_function(post_message)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "fb0a38eb",
+   "metadata": {},
+   "source": [
+    "## Subclassing the BaseTool\n",
+    "\n",
+    "The BaseTool automatically infers the schema from the _run method's signature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7505c9c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Optional, Type\n",
+    "\n",
+    "from langchain.callbacks.manager import AsyncCallbackManagerForToolRun, CallbackManagerForToolRun\n",
+    "            \n",
+    "class CustomSearchTool(BaseTool):\n",
+    "    name = \"custom_search\"\n",
+    "    description = \"useful for when you need to answer questions about current events\"\n",
+    "\n",
+    "    def _run(self, query: str, engine: str = \"google\", gl: str = \"us\", hl: str = \"en\", run_manager: Optional[CallbackManagerForToolRun] = None) -> str:\n",
+    "        \"\"\"Use the tool.\"\"\"\n",
+    "        search_wrapper = SerpAPIWrapper(params={\"engine\": engine, \"gl\": gl, \"hl\": hl})\n",
+    "        return search_wrapper.run(query)\n",
+    "    \n",
+    "    async def _arun(self, query: str,  engine: str = \"google\", gl: str = \"us\", hl: str = \"en\", run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:\n",
+    "        \"\"\"Use the tool asynchronously.\"\"\"\n",
+    "        raise NotImplementedError(\"custom_search does not support async\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "# You can provide a custom args schema to add descriptions or custom validation\n",
+    "\n",
+    "class SearchSchema(BaseModel):\n",
+    "    query: str = Field(description=\"should be a search query\")\n",
+    "    engine: str = Field(description=\"should be a search engine\")\n",
+    "    gl: str = Field(description=\"should be a country code\")\n",
+    "    hl: str = Field(description=\"should be a language code\")\n",
+    "\n",
+    "class CustomSearchTool(BaseTool):\n",
+    "    name = \"custom_search\"\n",
+    "    description = \"useful for when you need to answer questions about current events\"\n",
+    "    args_schema: Type[SearchSchema] = SearchSchema\n",
+    "\n",
+    "    def _run(self, query: str, engine: str = \"google\", gl: str = \"us\", hl: str = \"en\", run_manager: Optional[CallbackManagerForToolRun] = None) -> str:\n",
+    "        \"\"\"Use the tool.\"\"\"\n",
+    "        search_wrapper = SerpAPIWrapper(params={\"engine\": engine, \"gl\": gl, \"hl\": hl})\n",
+    "        return search_wrapper.run(query)\n",
+    "    \n",
+    "    async def _arun(self, query: str,  engine: str = \"google\", gl: str = \"us\", hl: str = \"en\", run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:\n",
+    "        \"\"\"Use the tool asynchronously.\"\"\"\n",
+    "        raise NotImplementedError(\"custom_search does not support async\")\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "7d68b0ac",
+   "metadata": {},
+   "source": [
+    "## Using the decorator\n",
+    "\n",
+    "The `tool` decorator creates a structured tool automatically if the signature has multiple arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "38d11416",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from langchain.tools import tool\n",
+    "\n",
+    "@tool\n",
+    "def post_message(url: str, body: dict, parameters: Optional[dict] = None) -> str:\n",
+    "    \"\"\"Sends a POST request to the given url with the given body and parameters.\"\"\"\n",
+    "    result = requests.post(url, json=body, params=parameters)\n",
+    "    return f\"Status: {result.status_code} - {result.text}\""
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "1d0430d6",
    "metadata": {},
    "source": [
     "## Modify existing tools\n",
     "\n",
-    "Now, we show how to load existing tools and just modify them. In the example below, we do something really simple and change the Search tool to have the name `Google Search`."
+    "Now, we show how to load existing tools and modify them directly. In the example below, we do something really simple and change the Search tool to have the name `Google Search`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "79213f40",
    "metadata": {},
    "outputs": [],
@@ -454,7 +611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "e1067dcb",
    "metadata": {},
    "outputs": [],
@@ -464,7 +621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "6c66ffe8",
    "metadata": {},
    "outputs": [],
@@ -474,7 +631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "f45b5bc3",
    "metadata": {},
    "outputs": [],
@@ -484,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "565e2b9b",
    "metadata": {},
    "outputs": [
@@ -497,10 +654,18 @@
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\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\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 Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAfter rumours of a romance with Gigi Hadid, the Oscar winner has seemingly moved on. First being linked to the television personality in September 2022, it appears as if his \"age bracket\" has moved up. This follows his rumoured relationship with mere 19-year-old Eden Polani.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI still need to find out his current girlfriend's name and her age.\n",
+      "Action: Google Search\n",
+      "Action Input: \"Leo DiCaprio current girlfriend age\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mLeonardo DiCaprio has been linked with 19-year-old model Eden Polani, continuing the rumour that he doesn't date any women over the age of ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to find out the age of Eden Polani.\n",
       "Action: Calculator\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",
+      "Action Input: 19^(0.43)\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.547023357958959\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
+      "Final Answer: The age of Leo DiCaprio's girlfriend raised to the 0.43 power is approximately 3.55.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -508,10 +673,10 @@
     {
      "data": {
       "text/plain": [
-       "\"Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\""
+       "\"The age of Leo DiCaprio's girlfriend raised to the 0.43 power is approximately 3.55.\""
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -537,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "3450512e",
    "metadata": {},
    "outputs": [],
@@ -674,153 +839,6 @@
    "source": [
     "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": "caf39c66-102b-42c1-baf2-777a49886ce4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -839,7 +857,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.2"
   },
   "vscode": {
    "interpreter": {

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

@@ -5,7 +5,7 @@
    "id": "245a954a",
    "metadata": {},
    "source": [
-    "# Arxiv API\n",
+    "# ArXiv API Tool\n",
     "\n",
     "This notebook goes over how to use the `arxiv` component. \n",
     "\n",
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "d5a7209e",
    "metadata": {
     "tags": [],
@@ -22,7 +22,17 @@
      "languageId": "shellscript"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: arxiv in /Users/wfh/code/lc/lckg/.venv/lib/python3.11/site-packages (1.4.7)\n",
+      "Requirement already satisfied: feedparser in /Users/wfh/code/lc/lckg/.venv/lib/python3.11/site-packages (from arxiv) (6.0.10)\n",
+      "Requirement already satisfied: sgmllib3k in /Users/wfh/code/lc/lckg/.venv/lib/python3.11/site-packages (from feedparser->arxiv) (1.0.0)\n"
+     ]
+    }
+   ],
    "source": [
     "!pip install arxiv"
    ]
@@ -30,6 +40,92 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "ce1a4827-ce89-4f31-a041-3246743e513a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.agents import load_tools, initialize_agent, AgentType\n",
+    "\n",
+    "llm = ChatOpenAI(temperature=0.0)\n",
+    "tools = load_tools(\n",
+    "    [\"arxiv\"], \n",
+    ")\n",
+    "\n",
+    "agent_chain = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ad7dd945-5ae3-49e5-b667-6d86b15050b6",
+   "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;3mI need to use Arxiv to search for the paper.\n",
+      "Action: Arxiv\n",
+      "Action Input: \"1605.08386\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mPublished: 2016-05-26\n",
+      "Title: Heat-bath random walks with Markov bases\n",
+      "Authors: Caprice Stanley, Tobias Windisch\n",
+      "Summary: Graphs on lattice points are studied whose edges come from a finite set of\n",
+      "allowed moves of arbitrary length. We show that the diameter of these graphs on\n",
+      "fibers of a fixed integer matrix can be bounded from above by a constant. We\n",
+      "then study the mixing behaviour of heat-bath random walks on these graphs. We\n",
+      "also state explicit conditions on the set of moves so that the heat-bath random\n",
+      "walk, a generalization of the Glauber dynamics, is an expander in fixed\n",
+      "dimension.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mThe paper is about heat-bath random walks with Markov bases on graphs of lattice points.\n",
+      "Final Answer: The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points.'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_chain.run(\n",
+    "    \"What's the paper 1605.08386 about?\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4183343-d69a-4be0-9b2c-cc98464a6825",
+   "metadata": {},
+   "source": [
+    "## The ArXiv API Wrapper\n",
+    "\n",
+    "The tool wraps the API Wrapper. Below, we can explore some of the features it provides."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "id": "8d32b39a",
    "metadata": {
     "tags": []
@@ -57,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "34bb5968",
    "metadata": {
     "tags": []
@@ -69,12 +165,14 @@
        "'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,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "\n",
+    "arxiv = ArxivAPIWrapper()\n",
     "docs = arxiv.run(\"1605.08386\")\n",
     "docs"
    ]
@@ -91,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "b0867fda-e119-4b19-9ec6-e354fa821db3",
    "metadata": {
     "tags": []
@@ -103,7 +201,7 @@
        "'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,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -123,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "3580aeeb-086f-45ba-bcdc-b46f5134b3dd",
    "metadata": {
     "tags": []
@@ -135,7 +233,7 @@
        "'No good Arxiv Result was found'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -162,7 +260,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,

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

@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## AWS Lambda API"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook goes over how to use the AWS Lambda Tool component.\n",
+    "\n",
+    "AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS), designed to allow developers to build and run applications and services without the need for provisioning or managing servers. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing the infrastructure required to run your applications.\n",
+    "\n",
+    "By including a `awslambda` in the list of tools provided to an Agent, you can grant your Agent the ability to invoke code running in your AWS Cloud for whatever purposes you need.\n",
+    "\n",
+    "When an Agent uses the awslambda tool, it will provide an argument of type string which will in turn be passed into the Lambda function via the event parameter.\n",
+    "\n",
+    "First, you need to install `boto3` python package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install boto3 > /dev/null"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order for an agent to use the tool, you must provide it with the name and description that match the functionality of you lambda function's logic. \n",
+    "\n",
+    "You must also provide the name of your function. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that because this tool is effectively just a wrapper around the boto3 library, you will need to run `aws configure` in order to make use of the tool. For more detail, see [here](https://docs.aws.amazon.com/cli/index.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain import OpenAI\n",
+    "from langchain.agents import load_tools, AgentType\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "\n",
+    "tools = load_tools(\n",
+    "    [\"awslambda\"],\n",
+    "    awslambda_tool_name=\"email-sender\",\n",
+    "    awslambda_tool_description=\"sends an email with the specified content to test@testing123.com\",\n",
+    "    function_name=\"testFunction1\"\n",
+    ")\n",
+    "\n",
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)\n",
+    "\n",
+    "agent.run(\"Send an email to test@testing123.com saying hello world.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.2"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -5,158 +5,158 @@
       "id": "8f210ec3",
       "metadata": {},
       "source": [
-        "# Bash\n",
-        "It can often be useful to have an LLM generate bash commands, and then run them. A common use case for this is letting the LLM interact with your local file system. We provide an easy util to execute bash commands."
+        "# Shell Tool\n",
+        "\n",
+        "Giving agents access to the shell is powerful (though risky outside a sandboxed environment).\n",
+        "\n",
+        "The LLM can use it to execute any shell commands. A common use case for this is letting the LLM interact with your local file system."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": 1,
       "id": "f7b3767b",
-      "metadata": {},
+      "metadata": {
+        "tags": []
+      },
       "outputs": [],
       "source": [
-        "from langchain.utilities import BashProcess"
+        "from langchain.tools import ShellTool\n",
+        "\n",
+        "shell_tool = ShellTool()"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": 2,
-      "id": "cf1c92f0",
-      "metadata": {},
-      "outputs": [],
+      "id": "c92ac832-556b-4f66-baa4-b78f965dfba0",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Hello World!\n",
+            "\n",
+            "real\t0m0.000s\n",
+            "user\t0m0.000s\n",
+            "sys\t0m0.000s\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/Users/wfh/code/lc/lckg/langchain/tools/shell/tool.py:34: UserWarning: The shell tool has no safeguards by default. Use at your own risk.\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
       "source": [
-        "bash = BashProcess()"
+        "print(shell_tool.run({\"commands\": [\"echo 'Hello World!'\", \"time\"]}))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2fa952fc",
+      "metadata": {},
+      "source": [
+        "### Use with Agents\n",
+        "\n",
+        "As with all tools, these can be given to an agent to accomplish more complex tasks. Let's have the agent fetch some links from a web page."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": 3,
-      "id": "2fa952fc",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "apify.ipynb\n",
-            "arxiv.ipynb\n",
-            "bash.ipynb\n",
-            "bing_search.ipynb\n",
-            "chatgpt_plugins.ipynb\n",
-            "ddg.ipynb\n",
-            "google_places.ipynb\n",
-            "google_search.ipynb\n",
-            "google_serper.ipynb\n",
-            "gradio_tools.ipynb\n",
-            "human_tools.ipynb\n",
-            "ifttt.ipynb\n",
-            "openweathermap.ipynb\n",
-            "python.ipynb\n",
-            "requests.ipynb\n",
-            "search_tools.ipynb\n",
-            "searx_search.ipynb\n",
-            "serpapi.ipynb\n",
-            "wikipedia.ipynb\n",
-            "wolfram_alpha.ipynb\n",
-            "zapier.ipynb\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(bash.run(\"ls\"))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "e7896f8e",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "apify.ipynb\n",
-            "arxiv.ipynb\n",
-            "bash.ipynb\n",
-            "bing_search.ipynb\n",
-            "chatgpt_plugins.ipynb\n",
-            "ddg.ipynb\n",
-            "google_places.ipynb\n",
-            "google_search.ipynb\n",
-            "google_serper.ipynb\n",
-            "gradio_tools.ipynb\n",
-            "human_tools.ipynb\n",
-            "ifttt.ipynb\n",
-            "openweathermap.ipynb\n",
-            "python.ipynb\n",
-            "requests.ipynb\n",
-            "search_tools.ipynb\n",
-            "searx_search.ipynb\n",
-            "serpapi.ipynb\n",
-            "wikipedia.ipynb\n",
-            "wolfram_alpha.ipynb\n",
-            "zapier.ipynb\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "bash.run(\"cd ..\")\n",
-        "# The commands are executed in a new subprocess each time, meaning that\n",
-        "# this call will return the same results as the last.\n",
-        "print(bash.run(\"ls\"))"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
       "id": "851fee9f",
-      "metadata": {},
-      "source": [
-        "## Terminal Persistance\n",
-        "\n",
-        "By default, the bash command will be executed in a new subprocess each time. To retain a persistent bash session, we can use the `persistent=True` arg."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "4a93ea2c",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "bash = BashProcess(persistent=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "a1e98b78",
-      "metadata": {},
+      "metadata": {
+        "tags": []
+      },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "custom_tools.ipynb\t\tmulti_input_tool.ipynb\n",
-            "examples\t\t\ttool_input_validation.ipynb\n",
-            "getting_started.md\n"
+            "\n",
+            "\n",
+            "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+            "\u001b[32;1m\u001b[1;3mQuestion: What is the task?\n",
+            "Thought: We need to download the langchain.com webpage and extract all the URLs from it. Then we need to sort the URLs and return them.\n",
+            "Action:\n",
+            "```\n",
+            "{\n",
+            "  \"action\": \"shell\",\n",
+            "  \"action_input\": {\n",
+            "    \"commands\": [\n",
+            "      \"curl -s https://langchain.com | grep -o 'http[s]*://[^\\\" ]*' | sort\"\n",
+            "    ]\n",
+            "  }\n",
+            "}\n",
+            "```\n",
+            "\u001b[0m"
           ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/Users/wfh/code/lc/lckg/langchain/tools/shell/tool.py:34: UserWarning: The shell tool has no safeguards by default. Use at your own risk.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Observation: \u001b[36;1m\u001b[1;3mhttps://blog.langchain.dev/\n",
+            "https://discord.gg/6adMQxSpJS\n",
+            "https://docs.langchain.com/docs/\n",
+            "https://github.com/hwchase17/chat-langchain\n",
+            "https://github.com/hwchase17/langchain\n",
+            "https://github.com/hwchase17/langchainjs\n",
+            "https://github.com/sullivan-sean/chat-langchainjs\n",
+            "https://js.langchain.com/docs/\n",
+            "https://python.langchain.com/en/latest/\n",
+            "https://twitter.com/langchainai\n",
+            "\u001b[0m\n",
+            "Thought:\u001b[32;1m\u001b[1;3mThe URLs have been successfully extracted and sorted. We can return the list of URLs as the final answer.\n",
+            "Final Answer: [\"https://blog.langchain.dev/\", \"https://discord.gg/6adMQxSpJS\", \"https://docs.langchain.com/docs/\", \"https://github.com/hwchase17/chat-langchain\", \"https://github.com/hwchase17/langchain\", \"https://github.com/hwchase17/langchainjs\", \"https://github.com/sullivan-sean/chat-langchainjs\", \"https://js.langchain.com/docs/\", \"https://python.langchain.com/en/latest/\", \"https://twitter.com/langchainai\"]\u001b[0m\n",
+            "\n",
+            "\u001b[1m> Finished chain.\u001b[0m\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "'[\"https://blog.langchain.dev/\", \"https://discord.gg/6adMQxSpJS\", \"https://docs.langchain.com/docs/\", \"https://github.com/hwchase17/chat-langchain\", \"https://github.com/hwchase17/langchain\", \"https://github.com/hwchase17/langchainjs\", \"https://github.com/sullivan-sean/chat-langchainjs\", \"https://js.langchain.com/docs/\", \"https://python.langchain.com/en/latest/\", \"https://twitter.com/langchainai\"]'"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
         }
       ],
       "source": [
-        "bash.run(\"cd ..\")\n",
-        "# Note the list of files is different\n",
-        "print(bash.run(\"ls\"))"
+        "from langchain.chat_models import ChatOpenAI\n",
+        "from langchain.agents import initialize_agent\n",
+        "from langchain.agents import AgentType\n",
+        "\n",
+        "llm = ChatOpenAI(temperature=0)\n",
+        "\n",
+        "shell_tool.description = shell_tool.description + f\"args {shell_tool.args}\".replace(\"{\", \"{{\").replace(\"}\", \"}}\")\n",
+        "self_ask_with_search = initialize_agent([shell_tool], llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)\n",
+        "self_ask_with_search.run(\"Download the langchain.com webpage and grep for all urls. Return only a sorted list of them. Be sure to use double quotes.\")"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "id": "e13c1c9c",
+      "id": "8d0ea3ac-0890-4e39-9cec-74bd80b4b8b8",
       "metadata": {},
       "outputs": [],
       "source": []

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

@@ -0,0 +1,190 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# File System Tools\n",
+    "\n",
+    "LangChain provides tools for interacting with a local file system out of the box. This notebook walks through some of them.\n",
+    "\n",
+    "Note: these tools are not recommended for use outside a sandboxed environment! "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we'll import the tools."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.tools.file_management import (\n",
+    "    ReadFileTool,\n",
+    "    CopyFileTool,\n",
+    "    DeleteFileTool,\n",
+    "    MoveFileTool,\n",
+    "    WriteFileTool,\n",
+    "    ListDirectoryTool,\n",
+    ")\n",
+    "from langchain.agents.agent_toolkits import FileManagementToolkit\n",
+    "from tempfile import TemporaryDirectory\n",
+    "\n",
+    "# We'll make a temporary directory to avoid clutter\n",
+    "working_directory = TemporaryDirectory()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The FileManagementToolkit\n",
+    "\n",
+    "If you want to provide all the file tooling to your agent, it's easy to do so with the toolkit. We'll pass the temporary directory in as a root directory as a workspace for the LLM.\n",
+    "\n",
+    "It's recommended to always pass in a root directory, since without one, it's easy for the LLM to pollute the working directory, and without one, there isn't any validation against\n",
+    "straightforward prompt injection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[CopyFileTool(name='copy_file', description='Create a copy of a file in a specified location', args_schema=<class 'langchain.tools.file_management.copy.FileCopyInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
+       " DeleteFileTool(name='file_delete', description='Delete a file', args_schema=<class 'langchain.tools.file_management.delete.FileDeleteInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
+       " FileSearchTool(name='file_search', description='Recursively search for files in a subdirectory that match the regex pattern', args_schema=<class 'langchain.tools.file_management.file_search.FileSearchInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
+       " MoveFileTool(name='move_file', description='Move or rename a file from one location to another', args_schema=<class 'langchain.tools.file_management.move.FileMoveInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
+       " ReadFileTool(name='read_file', description='Read file from disk', args_schema=<class 'langchain.tools.file_management.read.ReadFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
+       " WriteFileTool(name='write_file', description='Write file to disk', args_schema=<class 'langchain.tools.file_management.write.WriteFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
+       " ListDirectoryTool(name='list_directory', description='List files and directories in a specified folder', args_schema=<class 'langchain.tools.file_management.list_dir.DirectoryListingInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug')]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "toolkit = FileManagementToolkit(root_dir=str(working_directory.name)) # If you don't provide a root_dir, operations will default to the current working directory\n",
+    "toolkit.get_tools()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Selecting File System Tools\n",
+    "\n",
+    "If you only want to select certain tools, you can pass them in as arguments when initializing the toolkit, or you can individually initialize the desired tools."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[ReadFileTool(name='read_file', description='Read file from disk', args_schema=<class 'langchain.tools.file_management.read.ReadFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
+       " WriteFileTool(name='write_file', description='Write file to disk', args_schema=<class 'langchain.tools.file_management.write.WriteFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
+       " ListDirectoryTool(name='list_directory', description='List files and directories in a specified folder', args_schema=<class 'langchain.tools.file_management.list_dir.DirectoryListingInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug')]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tools = FileManagementToolkit(root_dir=str(working_directory.name), selected_tools=[\"read_file\", \"write_file\", \"list_directory\"]).get_tools()\n",
+    "tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'File written successfully to example.txt.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "read_tool, write_tool, list_tool = tools\n",
+    "write_tool.run({\"file_path\": \"example.txt\", \"text\": \"Hello World!\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'example.txt'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# List files in the working directory\n",
+    "list_tool.run({})"
+   ]
+  },
+  {
+   "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/agents/tools/examples/google_search.ipynb

@@ -33,7 +33,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.utilities import GoogleSearchAPIWrapper"
+    "from langchain.tools import Tool\n",
+    "from langchain.utilities import GoogleSearchAPIWrapper\n",
+    "\n",
+    "search = GoogleSearchAPIWrapper()\n",
+    "\n",
+    "tool = Tool(\n",
+    "    name = \"Google Search\",\n",
+    "    description=\"Search Google for recent results.\",\n",
+    "    func=search.run\n",
+    ")"
    ]
   },
   {
@@ -41,30 +50,20 @@
    "execution_count": 3,
    "id": "84b8f773",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "search = GoogleSearchAPIWrapper()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "068991a6",
-   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'1 Child\\'s First Name. 2. 6. 7d. Street Address. 71. (Type or print). BARACK. Sex. 3. This Birth. 4. If Twin or Triplet,. Was Child Born. Barack Hussein Obama II is an American retired politician who served as the 44th president of the United States from 2009 to 2017. His full name is Barack Hussein Obama II. Since the “II” is simply because he was named for his father, his last name is Obama. Feb 9, 2015 ... Michael Jordan misspelled Barack Obama\\'s first name on 50th-birthday gift ... Knowing Obama is a Chicagoan and huge basketball fan,\\xa0... Aug 18, 2017 ... It took him several seconds and multiple clues to remember former President Barack Obama\\'s first name. Miller knew that every answer had to end\\xa0... First Lady Michelle LaVaughn Robinson Obama is a lawyer, writer, and the wife of the 44th President, Barack Obama. She is the first African-American First\\xa0... 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\\xa0... When Barack Obama was elected president in 2008, he became the first African American to hold ... The Middle East remained a key foreign policy challenge. Feb 27, 2020 ... President Barack Obama was born Barack Hussein Obama, II, as shown here on his birth certificate here . As reported by Reuters here , his\\xa0... Jan 16, 2007 ... 4, 1961, in Honolulu. His first name means \"one who is blessed\" in Swahili. While Obama\\'s father, Barack Hussein Obama Sr., was from Kenya, his\\xa0...'"
+       "\"STATE OF HAWAII. 1 Child's First Name. (Type or print). 2. Sex. BARACK. 3. This Birth. CERTIFICATE OF LIVE BIRTH. FILE. NUMBER 151 le. lb. Middle Name. Barack Hussein Obama II is an American former politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic\\xa0... When Barack Obama was elected president in 2008, he became the first African American to hold ... The Middle East remained a key foreign policy challenge. Jan 19, 2017 ... Jordan Barack Treasure, New York City, born in 2008 ... Jordan Barack Treasure made national news when he was the focus of a New York newspaper\\xa0... Portrait of George Washington, the 1st President of the United States ... Portrait of Barack Obama, the 44th President of the United States\\xa0... His full name is Barack Hussein Obama II. Since the “II” is simply because he was named for his father, his last name is Obama. Mar 22, 2008 ... Barry Obama decided that he didn't like his nickname. A few of his friends at Occidental College had already begun to call him Barack (his\\xa0... Aug 18, 2017 ... It took him several seconds and multiple clues to remember former President Barack Obama's first name. Miller knew that every answer had to\\xa0... Feb 9, 2015 ... Michael Jordan misspelled Barack Obama's first name on 50th-birthday gift ... Knowing Obama is a Chicagoan and huge basketball fan,\\xa0... 4 days ago ... 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\\xa0...\""
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "search.run(\"Obama's first name?\")"
+    "tool.run(\"Obama's first name?\")"
    ]
   },
   {
@@ -78,17 +77,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "5083fbdd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "search = GoogleSearchAPIWrapper(k=1)"
+    "search = GoogleSearchAPIWrapper(k=1)\n",
+    "\n",
+    "tool = Tool(\n",
+    "    name = \"I'm Feeling Lucky\",\n",
+    "    description=\"Search Google and return the first result.\",\n",
+    "    func=search.run\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "77aaa857",
    "metadata": {},
    "outputs": [
@@ -98,13 +103,13 @@
        "'The official home of the Python Programming Language.'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "search.run(\"python\")"
+    "tool.run(\"python\")"
    ]
   },
   {
@@ -137,48 +142,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "028f4cba",
    "metadata": {},
    "outputs": [],
    "source": [
-    "search = GoogleSearchAPIWrapper()"
+    "search = GoogleSearchAPIWrapper()\n",
+    "\n",
+    "def top5_results(query):\n",
+    "    return search.results(query, 5)\n",
+    "\n",
+    "tool = Tool(\n",
+    "    name = \"Google Search Snippets\",\n",
+    "    description=\"Search Google for recent results.\",\n",
+    "    func=top5_results\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "4d8f734f",
+   "execution_count": null,
+   "id": "4d7f92e1",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'snippet': 'Discover the innovative world of Apple and shop everything iPhone, iPad, Apple Watch, Mac, and Apple TV, plus explore accessories, entertainment,\\xa0...',\n",
-       "  'title': 'Apple',\n",
-       "  'link': 'https://www.apple.com/'},\n",
-       " {'snippet': \"Jul 10, 2022 ... Whether or not you're up on your apple trivia, no doubt you know how delicious this popular fruit is, and how nutritious. Apples are rich in\\xa0...\",\n",
-       "  'title': '25 Types of Apples and What to Make With Them - Parade ...',\n",
-       "  'link': 'https://parade.com/1330308/bethlipton/types-of-apples/'},\n",
-       " {'snippet': 'An apple is an edible fruit produced by an apple tree (Malus domestica). Apple trees are cultivated worldwide and are the most widely grown species in the\\xa0...',\n",
-       "  'title': 'Apple - Wikipedia',\n",
-       "  'link': 'https://en.wikipedia.org/wiki/Apple'},\n",
-       " {'snippet': 'Apples are a popular fruit. They contain antioxidants, vitamins, dietary fiber, and a range of other nutrients. Due to their varied nutrient content,\\xa0...',\n",
-       "  'title': 'Apples: Benefits, nutrition, and tips',\n",
-       "  'link': 'https://www.medicalnewstoday.com/articles/267290'},\n",
-       " {'snippet': \"An apple is a crunchy, bright-colored fruit, one of the most popular in the United States. You've probably heard the age-old saying, “An apple a day keeps\\xa0...\",\n",
-       "  'title': 'Apples: Nutrition & Health Benefits',\n",
-       "  'link': 'https://www.webmd.com/food-recipes/benefits-apples'}]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "search.results(\"apples\", 5)"
-   ]
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -197,7 +184,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.2"
   },
   "vscode": {
    "interpreter": {

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

@@ -69,7 +69,8 @@
     }
    ],
    "source": [
-    "StableDiffusionTool().langchain.run(\"Please create a photo of a dog riding a skateboard\")"
+    "local_file_path = StableDiffusionTool().langchain.run(\"Please create a photo of a dog riding a skateboard\")\n",
+    "local_file_path"
    ]
   },
   {
@@ -89,7 +90,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "im = Image.open(\"/Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/examples/b61c1dd9-47e2-46f1-a47c-20d27640993d/tmp4ap48vnm.jpg\")"
+    "im = Image.open(local_file_path)"
    ]
   },
   {

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

@@ -13,10 +13,11 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "import sys\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.agents import load_tools, initialize_agent\n",
@@ -42,13 +43,15 @@
    "metadata": {},
    "source": [
     "In the above code you can see the tool takes input directly from command line.\n",
-    "You can customize `prompt_func` and `input_func` according to your need."
+    "You can customize `prompt_func` and `input_func` according to your need (as shown below)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -57,29 +60,28 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mI don't know Eric Zhu, so I should ask a human for guidance.\n",
+      "\u001b[32;1m\u001b[1;3mI don't know Eric's surname, so I should ask a human for guidance.\n",
       "Action: Human\n",
-      "Action Input: \"Do you know when Eric Zhu's birthday is?\"\u001b[0m\n",
+      "Action Input: \"What is Eric's surname?\"\u001b[0m\n",
       "\n",
-      "Do you know when Eric Zhu's birthday is?\n",
-      "last week\n",
+      "What is Eric's surname?\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " Zhu\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Observation: \u001b[36;1m\u001b[1;3mlast week\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThat's not very helpful. I should ask for more information.\n",
-      "Action: Human\n",
-      "Action Input: \"Do you know the specific date of Eric Zhu's birthday?\"\u001b[0m\n",
-      "\n",
-      "Do you know the specific date of Eric Zhu's birthday?\n",
-      "august 1st\n",
-      "\n",
-      "Observation: \u001b[36;1m\u001b[1;3maugust 1st\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mNow that I have the date, I can check if it's a leap year or not.\n",
-      "Action: Calculator\n",
-      "Action Input: \"Is 2021 a leap year?\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: False\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI have all the information I need to answer the original question.\n",
-      "Final Answer: Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mZhu\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI now know Eric's surname is Zhu.\n",
+      "Final Answer: Eric's surname is Zhu.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -87,18 +89,175 @@
     {
      "data": {
       "text/plain": [
-       "\"Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\""
+       "\"Eric's surname is Zhu.\""
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "agent_chain.run(\"When's my friend Eric's surname?\")\n",
+    "# Answer with 'Zhu'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configuring the Input Function\n",
     "\n",
-    "agent_chain.run(\"What is Eric Zhu's birthday?\")\n",
-    "# Answer with \"last week\""
+    "By default, the `HumanInputRun` tool uses the python `input` function to get input from the user.\n",
+    "You can customize the input_func to be anything you'd like.\n",
+    "For instance, if you want to accept multi-line input, you could do the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_input() -> str:\n",
+    "    print(\"Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.\")\n",
+    "    contents = []\n",
+    "    while True:\n",
+    "        try:\n",
+    "            line = input()\n",
+    "        except EOFError:\n",
+    "            break\n",
+    "        if line == \"q\":\n",
+    "            break\n",
+    "        contents.append(line)\n",
+    "    return \"\\n\".join(contents)\n",
+    "\n",
+    "\n",
+    "# You can modify the tool when loading\n",
+    "tools = load_tools(\n",
+    "    [\"human\", \"ddg-search\"], \n",
+    "    llm=math_llm,\n",
+    "    input_func=get_input\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Or you can directly instantiate the tool\n",
+    "from langchain.tools import HumanInputRun\n",
+    "\n",
+    "tool = HumanInputRun(input_func=get_input)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "agent_chain = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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;3mI should ask a human for guidance\n",
+      "Action: Human\n",
+      "Action Input: \"Can you help me attribute a quote?\"\u001b[0m\n",
+      "\n",
+      "Can you help me attribute a quote?\n",
+      "Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " vini\n",
+      " vidi\n",
+      " vici\n",
+      " q\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Observation: \u001b[36;1m\u001b[1;3mvini\n",
+      "vidi\n",
+      "vici\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to provide more context about the quote\n",
+      "Action: Human\n",
+      "Action Input: \"The quote is 'Veni, vidi, vici'\"\u001b[0m\n",
+      "\n",
+      "The quote is 'Veni, vidi, vici'\n",
+      "Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " oh who said it \n",
+      " q\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Observation: \u001b[36;1m\u001b[1;3moh who said it \u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI can use DuckDuckGo Search to find out who said the quote\n",
+      "Action: DuckDuckGo Search\n",
+      "Action Input: \"Who said 'Veni, vidi, vici'?\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mUpdated on September 06, 2019. \"Veni, vidi, vici\" is a famous phrase said to have been spoken by the Roman Emperor Julius Caesar (100-44 BCE) in a bit of stylish bragging that impressed many of the writers of his day and beyond. The phrase means roughly \"I came, I saw, I conquered\" and it could be pronounced approximately Vehnee, Veedee ... Veni, vidi, vici (Classical Latin: [weːniː wiːdiː wiːkiː], Ecclesiastical Latin: [ˈveni ˈvidi ˈvitʃi]; \"I came; I saw; I conquered\") is a Latin phrase used to refer to a swift, conclusive victory.The phrase is popularly attributed to Julius Caesar who, according to Appian, used the phrase in a letter to the Roman Senate around 47 BC after he had achieved a quick victory in his short ... veni, vidi, vici Latin quotation from Julius Caesar ve· ni, vi· di, vi· ci ˌwā-nē ˌwē-dē ˈwē-kē ˌvā-nē ˌvē-dē ˈvē-chē : I came, I saw, I conquered Articles Related to veni, vidi, vici 'In Vino Veritas' and Other Latin... Dictionary Entries Near veni, vidi, vici Venite veni, vidi, vici Venizélos See More Nearby Entries Cite this Entry Style The simplest explanation for why veni, vidi, vici is a popular saying is that it comes from Julius Caesar, one of history's most famous figures, and has a simple, strong meaning: I'm powerful and fast. But it's not just the meaning that makes the phrase so powerful. Caesar was a gifted writer, and the phrase makes use of Latin grammar to ... One of the best known and most frequently quoted Latin expression, veni, vidi, vici may be found hundreds of times throughout the centuries used as an expression of triumph. The words are said to have been used by Caesar as he was enjoying a triumph.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer\n",
+      "Final Answer: Julius Caesar said the quote \"Veni, vidi, vici\" which means \"I came, I saw, I conquered\".\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Julius Caesar said the quote \"Veni, vidi, vici\" which means \"I came, I saw, I conquered\".'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_chain.run(\"I need help attributing a quote\")"
    ]
   },
   {
@@ -125,9 +284,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

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

@@ -19,6 +19,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.agents import Tool\n",
     "from langchain.utilities import PythonREPL"
    ]
   },
@@ -59,7 +60,14 @@
    "id": "54fc1f03",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# You can create the tool to pass to an agent\n",
+    "repl_tool = Tool(\n",
+    "    name=\"python_repl\",\n",
+    "    description=\"A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\",\n",
+    "    func=python_repl\n",
+    ")"
+   ]
   }
  ],
  "metadata": {

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

@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SceneXplain\n",
+    "\n",
+    "\n",
+    "[SceneXplain](https://scenex.jina.ai/) is an ImageCaptioning service accessible through the SceneXplain Tool.\n",
+    "\n",
+    "To use this tool, you'll need to make an account and fetch your API Token [from the website](https://scenex.jina.ai/api). Then you can instantiate the tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"SCENEX_API_KEY\"] = \"<YOUR_API_KEY>\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import load_tools\n",
+    "\n",
+    "tools = load_tools([\"sceneXplain\"])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or directly instantiate the tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.tools import SceneXplainTool\n",
+    "\n",
+    "\n",
+    "tool = SceneXplainTool()\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage in an Agent\n",
+    "\n",
+    "The tool can be used in any LangChain agent as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\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? Yes\n",
+      "Action: Image Explainer\n",
+      "Action Input: https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mIn a charmingly whimsical scene, a young girl is seen braving the rain alongside her furry companion, the lovable Totoro. The two are depicted standing on a bustling street corner, where they are sheltered from the rain by a bright yellow umbrella. The girl, dressed in a cheerful yellow frock, holds onto the umbrella with both hands while gazing up at Totoro with an expression of wonder and delight.\n",
+      "\n",
+      "Totoro, meanwhile, stands tall and proud beside his young friend, holding his own umbrella aloft to protect them both from the downpour. His furry body is rendered in rich shades of grey and white, while his large ears and wide eyes lend him an endearing charm.\n",
+      "\n",
+      "In the background of the scene, a street sign can be seen jutting out from the pavement amidst a flurry of raindrops. A sign with Chinese characters adorns its surface, adding to the sense of cultural diversity and intrigue. Despite the dreary weather, there is an undeniable sense of joy and camaraderie in this heartwarming image.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
+      "AI: This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.agents import initialize_agent\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
+    "agent = initialize_agent(\n",
+    "    tools, llm, memory=memory, agent=\"conversational-react-description\", verbose=True\n",
+    ")\n",
+    "output = agent.run(\n",
+    "    input=(\n",
+    "        \"What is in this image https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png. \"\n",
+    "        \"Is it movie or a game? If it is a movie, what is the name of the movie?\"\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "print(output)"
+   ]
+  }
+ ],
+ "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": "3.11.2"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -102,7 +102,15 @@
    "id": "e0a1dc1c",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from langchain.agents import Tool\n",
+    "# You can create the tool to pass to an agent\n",
+    "repl_tool = Tool(\n",
+    "    name=\"python_repl\",\n",
+    "    description=\"A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\",\n",
+    "    func=search.run,\n",
+    ")"
+   ]
   }
  ],
  "metadata": {

docs/modules/chains/examples/constitutional_chain.ipynb

@@ -9,7 +9,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -18,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -27,7 +26,7 @@
        "' Break into a pet store at night and take as many kittens as you can carry.'"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -55,7 +54,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -64,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -78,7 +76,7 @@
       "\n",
       "\u001b[0m\u001b[32;1m\u001b[1;3mApplying Ethical Principle...\n",
       "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mCritique: The model's response is unethical and illegal, as it encourages stealing kittens.\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3mCritique: The model's response is unethical and illegal, and should not be condoned.\n",
       "\n",
       "\u001b[0m\u001b[33;1m\u001b[1;3mUpdated response: It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.\n",
       "\n",
@@ -92,7 +90,7 @@
        "'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.'"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -118,7 +116,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -167,7 +164,7 @@
     }
    ],
    "source": [
-    "master_yoda_principal = ConstitutionalPrinciple(\n",
+    "master_yoda_principle = 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",
@@ -175,18 +172,171 @@
     "\n",
     "constitutional_chain = ConstitutionalChain.from_llm(\n",
     "    chain=evil_qa_chain,\n",
-    "    constitutional_principles=[ethical_principle, master_yoda_principal],\n",
+    "    constitutional_principles=[ethical_principle, master_yoda_principle],\n",
     "    llm=llm,\n",
     "    verbose=True,\n",
     ")\n",
     "\n",
     "constitutional_chain.run(question=\"How can I steal kittens?\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Intermediate Steps\n",
+    "\n",
+    "You can also get the constitutional chain to return it's intermediate steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new ConstitutionalChain chain...\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3mInitial response:  Break into a pet store at night and take as many kittens as you can carry.\n",
+      "\n",
+      "\u001b[0m\u001b[32;1m\u001b[1;3mApplying Ethical Principle...\n",
+      "\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3mCritique: The model's response is unethical and illegal, and should not be condoned.\n",
+      "\n",
+      "\u001b[0m\u001b[33;1m\u001b[1;3mUpdated response: It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.\n",
+      "\n",
+      "\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'question': 'How can I steal kittens?',\n",
+       " 'output': 'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.',\n",
+       " 'initial_output': ' Break into a pet store at night and take as many kittens as you can carry.',\n",
+       " 'critiques_and_revisions': [(\"The model's response is unethical and illegal, and should not be condoned.\",\n",
+       "   'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.')]}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "constitutional_chain = ConstitutionalChain.from_llm(\n",
+    "    chain=evil_qa_chain,\n",
+    "    constitutional_principles=[ethical_principle],\n",
+    "    llm=llm,\n",
+    "    verbose=True,\n",
+    "    return_intermediate_steps=True\n",
+    ")\n",
+    "\n",
+    "constitutional_chain({\"question\":\"How can I steal kittens?\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## No revision necessary\n",
+    "\n",
+    "We can also see that the chain recognizes when no revision is necessary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "good_qa_prompt = PromptTemplate(\n",
+    "    template=\"\"\"You are good and must only give good and ethical answers. If someone asks you how to do something illegal, make sure you respond in an ethical and legal way.\n",
+    "\n",
+    "Question: {question}\n",
+    "\n",
+    "Ethical answer:\"\"\",\n",
+    "    input_variables=[\"question\"],\n",
+    ")\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "\n",
+    "good_qa_chain = LLMChain(llm=llm, prompt=good_qa_prompt)\n",
+    "\n",
+    "good_qa_chain.run(question=\"How can I steal kittens?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new ConstitutionalChain chain...\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3mInitial response:  Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.\n",
+      "\n",
+      "\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'question': 'How can I steal kittens?',\n",
+       " 'output': ' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.',\n",
+       " 'initial_output': ' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.',\n",
+       " 'critiques_and_revisions': [('No critique needed.', '')]}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "constitutional_chain = ConstitutionalChain.from_llm(\n",
+    "    chain=good_qa_chain,\n",
+    "    constitutional_principles=[ethical_principle],\n",
+    "    llm=llm,\n",
+    "    verbose=True,\n",
+    "    return_intermediate_steps=True\n",
+    ")\n",
+    "\n",
+    "constitutional_chain({\"question\":\"How can I steal kittens?\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "langchain",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -200,9 +350,8 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.1"
   },
-  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "06ba49dd587e86cdcfee66b9ffe769e1e94f0e368e54c2d6c866e38e33c0d9b1"

docs/modules/chains/examples/llm_bash.ipynb

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -37,7 +37,7 @@
        "'Hello World\\n'"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -50,7 +50,7 @@
     "\n",
     "text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
     "\n",
-    "bash_chain = LLMBashChain(llm=llm, verbose=True)\n",
+    "bash_chain = LLMBashChain.from_llm(llm, verbose=True)\n",
     "\n",
     "bash_chain.run(text)"
    ]
@@ -65,11 +65,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.prompts.prompt import PromptTemplate\n",
+    "from langchain.chains.llm_bash.prompt import BashOutputParser\n",
     "\n",
     "_PROMPT_TEMPLATE = \"\"\"If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put \"#!/bin/bash\" in your answer. Make sure to reason step by step, using this format:\n",
     "Question: \"copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'\"\n",
@@ -88,12 +89,12 @@
     "That is the format. Begin!\n",
     "Question: {question}\"\"\"\n",
     "\n",
-    "PROMPT = PromptTemplate(input_variables=[\"question\"], template=_PROMPT_TEMPLATE)"
+    "PROMPT = PromptTemplate(input_variables=[\"question\"], template=_PROMPT_TEMPLATE, output_parser=BashOutputParser())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -120,13 +121,13 @@
        "'Hello World\\n'"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "bash_chain = LLMBashChain(llm=llm, prompt=PROMPT, verbose=True)\n",
+    "bash_chain = LLMBashChain.from_llm(llm, prompt=PROMPT, verbose=True)\n",
     "\n",
     "text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
     "\n",
@@ -134,7 +135,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -177,7 +177,7 @@
        "'api.ipynb\\t\\t\\tllm_summarization_checker.ipynb\\r\\nconstitutional_chain.ipynb\\tmoderation.ipynb\\r\\nllm_bash.ipynb\\t\\t\\topenai_openapi.yaml\\r\\nllm_checker.ipynb\\t\\topenapi.ipynb\\r\\nllm_math.ipynb\\t\\t\\tpal.ipynb\\r\\nllm_requests.ipynb\\t\\tsqlite.ipynb'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -187,7 +187,7 @@
     "\n",
     "\n",
     "persistent_process = BashProcess(persistent=True)\n",
-    "bash_chain = LLMBashChain.from_bash_process(llm=llm, bash_process=persistent_process, verbose=True)\n",
+    "bash_chain = LLMBashChain.from_llm(llm, bash_process=persistent_process, verbose=True)\n",
     "\n",
     "text = \"List the current directory then move up a level.\"\n",
     "\n",
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -224,7 +224,7 @@
        "'examples\\t\\tgetting_started.ipynb\\tindex_examples\\r\\ngeneric\\t\\t\\thow_to_guides.rst'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -258,7 +258,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/chains/examples/llm_checker.ipynb

@@ -23,28 +23,16 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n",
-      "\u001b[1mChain 0\u001b[0m:\n",
-      "{'statement': '\\nNone. Mammals do not lay eggs.'}\n",
       "\n",
-      "\u001b[1mChain 1\u001b[0m:\n",
-      "{'assertions': '\\n• Mammals reproduce using live birth\\n• Mammals do not lay eggs\\n• Animals that lay eggs are not mammals'}\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
-      "\u001b[1mChain 2\u001b[0m:\n",
-      "{'checked_assertions': '\\n1. True\\n\\n2. True\\n\\n3. False - Mammals are a class of animals that includes animals that lay eggs, such as monotremes (platypus and echidna).'}\n",
-      "\n",
-      "\u001b[1mChain 3\u001b[0m:\n",
-      "{'revised_statement': ' Monotremes, such as the platypus and echidna, lay the biggest eggs of any mammal.'}\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Finished SequentialChain chain.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished LLMCheckerChain chain.\u001b[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "' Monotremes, such as the platypus and echidna, lay the biggest eggs of any mammal.'"
+       "' No mammal lays the biggest eggs. The Elephant Bird, which was a species of giant bird, laid the largest eggs of any bird.'"
       ]
      },
      "execution_count": 1,
@@ -60,7 +48,7 @@
     "\n",
     "text = \"What type of mammal lays the biggest eggs?\"\n",
     "\n",
-    "checker_chain = LLMCheckerChain(llm=llm, verbose=True)\n",
+    "checker_chain = LLMCheckerChain.from_llm(llm, verbose=True)\n",
     "\n",
     "checker_chain.run(text)"
    ]
@@ -89,7 +77,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/chains/examples/llm_math.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "id": "44e9ba31",
    "metadata": {},
    "outputs": [
@@ -24,23 +24,22 @@
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
       "What is 13 raised to the .3432 power?\u001b[32;1m\u001b[1;3m\n",
-      "```python\n",
-      "import math\n",
-      "print(math.pow(13, .3432))\n",
+      "```text\n",
+      "13 ** .3432\n",
       "```\n",
+      "...numexpr.evaluate(\"13 ** .3432\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m2.4116004626599237\n",
-      "\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m2.4116004626599237\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'Answer: 2.4116004626599237\\n'"
+       "'Answer: 2.4116004626599237'"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -49,102 +48,7 @@
     "from langchain import OpenAI, LLMMathChain\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
-    "llm_math = LLMMathChain(llm=llm, verbose=True)\n",
-    "\n",
-    "llm_math.run(\"What is 13 raised to the .3432 power?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2bdd5fc6",
-   "metadata": {},
-   "source": [
-    "## Customize Prompt\n",
-    "You can also customize the prompt that is used. Here is an example prompting it to use numpy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "76be17b0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.prompts.prompt import PromptTemplate\n",
-    "\n",
-    "_PROMPT_TEMPLATE = \"\"\"You are GPT-3, and you can't do math.\n",
-    "\n",
-    "You can do basic math, and your memorization abilities are impressive, but you can't do any complex calculations that a human could not do in their head. You also have an annoying tendency to just make up highly specific, but wrong, answers.\n",
-    "\n",
-    "So we hooked you up to a Python 3 kernel, and now you can execute code. If you execute code, you must print out the final answer using the print function. You MUST use the python package numpy to answer your question. You must import numpy as np.\n",
-    "\n",
-    "\n",
-    "Question: ${{Question with hard calculation.}}\n",
-    "```python\n",
-    "${{Code that prints what you need to know}}\n",
-    "print(${{code}})\n",
-    "```\n",
-    "```output\n",
-    "${{Output of your code}}\n",
-    "```\n",
-    "Answer: ${{Answer}}\n",
-    "\n",
-    "Begin.\n",
-    "\n",
-    "Question: What is 37593 * 67?\n",
-    "\n",
-    "```python\n",
-    "import numpy as np\n",
-    "print(np.multiply(37593, 67))\n",
-    "```\n",
-    "```output\n",
-    "2518731\n",
-    "```\n",
-    "Answer: 2518731\n",
-    "\n",
-    "Question: {question}\"\"\"\n",
-    "\n",
-    "PROMPT = PromptTemplate(input_variables=[\"question\"], template=_PROMPT_TEMPLATE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "0c42faa0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "What is 13 raised to the .3432 power?\u001b[32;1m\u001b[1;3m\n",
-      "\n",
-      "```python\n",
-      "import numpy as np\n",
-      "print(np.power(13, .3432))\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m2.4116004626599237\n",
-      "\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Answer: 2.4116004626599237\\n'"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "llm_math = LLMMathChain(llm=llm, prompt=PROMPT, verbose=True)\n",
+    "llm_math = LLMMathChain.from_llm(llm, verbose=True)\n",
     "\n",
     "llm_math.run(\"What is 13 raised to the .3432 power?\")"
    ]
@@ -152,7 +56,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0c62951b",
+   "id": "e978bb8e",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -174,7 +78,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/chains/examples/llm_summarization_checker.ipynb

@@ -221,11 +221,11 @@
       "\n",
       "• The light from these galaxies has been traveling for over 13 billion years to reach us. - True \n",
       "\n",
-      "• JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 1995. \n",
+      "• JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 2004. \n",
       "\n",
       "• Exoplanets were first discovered in 1992. - True \n",
       "\n",
-      "• The JWST has allowed us to see exoplanets in greater detail. - Undetermined. It is too early to tell as the JWST has not been launched yet.\n",
+      "• The JWST has allowed us to see exoplanets in greater detail. - Undetermined. The JWST has not yet been launched, so it is not yet known how much detail it will be able to provide.\n",
       "\"\"\"\n",
       "\n",
       "Original Summary:\n",
@@ -296,11 +296,11 @@
       "\n",
       "• The light from these galaxies has been traveling for over 13 billion years to reach us. - True \n",
       "\n",
-      "• JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 1995. \n",
+      "• JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 2004. \n",
       "\n",
       "• Exoplanets were first discovered in 1992. - True \n",
       "\n",
-      "• The JWST has allowed us to see exoplanets in greater detail. - Undetermined. It is too early to tell as the JWST has not been launched yet.\n",
+      "• The JWST has allowed us to see exoplanets in greater detail. - Undetermined. The JWST has not yet been launched, so it is not yet known how much detail it will be able to provide.\n",
       "\"\"\"\n",
       "Result:\u001b[0m\n",
       "\n",
@@ -312,7 +312,7 @@
       "Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\n",
       "• In 2023, The JWST will spot a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.\n",
       "• The telescope will capture images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.\n",
-      "• Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail than ever before.\n",
+      "• Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail when it is launched in 2023.\n",
       "These discoveries can spark a child's imagination about the infinite wonders of the universe.\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -321,7 +321,7 @@
     {
      "data": {
       "text/plain": [
-       "'Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\\n• In 2023, The JWST will spot a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.\\n• The telescope will capture images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.\\n• Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail than ever before.\\nThese discoveries can spark a child\\'s imagination about the infinite wonders of the universe.'"
+       "'Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\\n• In 2023, The JWST will spot a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.\\n• The telescope will capture images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.\\n• Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail when it is launched in 2023.\\nThese discoveries can spark a child\\'s imagination about the infinite wonders of the universe.'"
       ]
      },
      "execution_count": 1,
@@ -334,7 +334,7 @@
     "from langchain.llms import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
-    "checker_chain = LLMSummarizationCheckerChain(llm=llm, verbose=True, max_checks=2)\n",
+    "checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=2)\n",
     "text = \"\"\"\n",
     "Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\n",
     "• In 2023, The JWST spotted a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.\n",
@@ -407,7 +407,8 @@
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.  If the answer is false, a suggestion is given for a correction.\n",
       "\n",
-      "Checked Assertions:\"\"\"\n",
+      "Checked Assertions:\n",
+      "\"\"\"\n",
       "\n",
       "- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True\n",
       "\n",
@@ -428,7 +429,8 @@
       "- It is considered the northern branch of the Norwegian Sea. True\n",
       "\"\"\"\n",
       "\n",
-      "Original Summary:\"\"\"\n",
+      "Original Summary:\n",
+      "\"\"\"\n",
       "The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. It is the smallest of the five oceans and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.\n",
       "\"\"\"\n",
       "\n",
@@ -443,7 +445,7 @@
       "\n",
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.\n",
+      "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true or false.\n",
       "\n",
       "If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".\n",
       "\n",
@@ -555,7 +557,8 @@
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.  If the answer is false, a suggestion is given for a correction.\n",
       "\n",
-      "Checked Assertions:\"\"\"\n",
+      "Checked Assertions:\n",
+      "\"\"\"\n",
       "\n",
       "- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True\n",
       "\n",
@@ -574,7 +577,8 @@
       "- It is considered the northern branch of the Norwegian Sea. False - It is considered the northern branch of the Atlantic Ocean.\n",
       "\"\"\"\n",
       "\n",
-      "Original Summary:\"\"\"\n",
+      "Original Summary:\n",
+      "\"\"\"\n",
       "\n",
       "The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.\n",
       "\"\"\"\n",
@@ -583,14 +587,20 @@
       "\n",
       "The output should have the same structure and formatting as the original summary.\n",
       "\n",
-      "Summary:\u001b[0m\n",
+      "Summary:\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
       "\n",
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.\n",
+      "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true or false.\n",
       "\n",
       "If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".\n",
       "\n",
@@ -701,7 +711,8 @@
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.  If the answer is false, a suggestion is given for a correction.\n",
       "\n",
-      "Checked Assertions:\"\"\"\n",
+      "Checked Assertions:\n",
+      "\"\"\"\n",
       "\n",
       "- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True\n",
       "\n",
@@ -718,7 +729,8 @@
       "- It is considered the northern branch of the Atlantic Ocean. False - The Greenland Sea is considered part of the Arctic Ocean, not the Atlantic Ocean.\n",
       "\"\"\"\n",
       "\n",
-      "Original Summary:\"\"\"\n",
+      "Original Summary:\n",
+      "\"\"\"\n",
       "\n",
       "\n",
       "The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the country of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Atlantic Ocean.\n",
@@ -735,7 +747,7 @@
       "\n",
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.\n",
+      "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true or false.\n",
       "\n",
       "If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".\n",
       "\n",
@@ -813,14 +825,14 @@
     "from langchain.llms import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
-    "checker_chain = LLMSummarizationCheckerChain(llm=llm, verbose=True, max_checks=3)\n",
+    "checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=3)\n",
     "text = \"The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. It is the smallest of the five oceans and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.\"\n",
     "checker_chain.run(text)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -1077,7 +1089,7 @@
        "'Birds are not mammals, but they are a class of their own. They lay eggs, unlike mammals which give birth to live young.'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1087,17 +1099,10 @@
     "from langchain.llms import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
-    "checker_chain = LLMSummarizationCheckerChain(llm=llm, max_checks=3, verbose=True)\n",
+    "checker_chain = LLMSummarizationCheckerChain.from_llm(llm, max_checks=3, verbose=True)\n",
     "text = \"Mammals can lay eggs, birds can lay eggs, therefore birds are mammals.\"\n",
     "checker_chain.run(text)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

docs/modules/chains/examples/openapi.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# OpenAPI Chain\n",
     "\n",
-    "This notebook shows an example of using an OpenAPI chain to call an endpoint in natural language, and get back a response in natural language"
+    "This notebook shows an example of using an OpenAPI chain to call an endpoint in natural language, and get back a response in natural language."
    ]
   },
   {

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(temperature=0, max_tokens=512)"
    ]
   },
   {
@@ -63,7 +63,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "3ef64b27",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -71,17 +73,17 @@
      "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 +141,8 @@
      "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 +153,9 @@
       "\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"
      ]
     },
     {
@@ -212,8 +214,8 @@
      "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",
@@ -224,9 +226,9 @@
       "\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"
      ]
     }
    ],
@@ -280,7 +282,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/chains/examples/sqlite.ipynb

@@ -73,7 +73,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
+    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
    ]
   },
   {
@@ -175,7 +175,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True)"
+    "db_chain = SQLDatabaseChain.from_llm(llm, db, prompt=PROMPT, verbose=True)"
    ]
   },
   {
@@ -230,7 +230,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True, return_intermediate_steps=True)"
+    "db_chain = SQLDatabaseChain.from_llm(llm, db, prompt=PROMPT, verbose=True, return_intermediate_steps=True)"
    ]
   },
   {
@@ -285,7 +285,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True, top_k=3)"
+    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, top_k=3)"
    ]
   },
   {
@@ -407,7 +407,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
+    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
    ]
   },
   {
@@ -569,7 +569,7 @@
     }
    ],
    "source": [
-    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)\n",
+    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)\n",
     "db_chain.run(\"What are some example tracks by Bach?\")"
    ]
   },
@@ -681,7 +681,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/chains/generic/custom_chain.ipynb

@@ -0,0 +1,199 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "593f7553-7038-498e-96d4-8255e5ce34f0",
+   "metadata": {},
+   "source": [
+    "# Creating a custom Chain\n",
+    "\n",
+    "To implement your own custom chain you can subclass `Chain` and implement the following methods:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c19c736e-ca74-4726-bb77-0a849bcc2960",
+   "metadata": {
+    "tags": [],
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "from typing import Any, Dict, List, Optional\n",
+    "\n",
+    "from pydantic import Extra\n",
+    "\n",
+    "from langchain.base_language import BaseLanguageModel\n",
+    "from langchain.callbacks.manager import (\n",
+    "    AsyncCallbackManagerForChainRun,\n",
+    "    CallbackManagerForChainRun,\n",
+    ")\n",
+    "from langchain.chains.base import Chain\n",
+    "from langchain.prompts.base import BasePromptTemplate\n",
+    "\n",
+    "\n",
+    "class MyCustomChain(Chain):\n",
+    "    \"\"\"\n",
+    "    An example of a custom chain.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    prompt: BasePromptTemplate\n",
+    "    \"\"\"Prompt object to use.\"\"\"\n",
+    "    llm: BaseLanguageModel\n",
+    "    output_key: str = \"text\"  #: :meta private:\n",
+    "\n",
+    "    class Config:\n",
+    "        \"\"\"Configuration for this pydantic object.\"\"\"\n",
+    "\n",
+    "        extra = Extra.forbid\n",
+    "        arbitrary_types_allowed = True\n",
+    "\n",
+    "    @property\n",
+    "    def input_keys(self) -> List[str]:\n",
+    "        \"\"\"Will be whatever keys the prompt expects.\n",
+    "\n",
+    "        :meta private:\n",
+    "        \"\"\"\n",
+    "        return self.prompt.input_variables\n",
+    "\n",
+    "    @property\n",
+    "    def output_keys(self) -> List[str]:\n",
+    "        \"\"\"Will always return text key.\n",
+    "\n",
+    "        :meta private:\n",
+    "        \"\"\"\n",
+    "        return [self.output_key]\n",
+    "\n",
+    "    def _call(\n",
+    "        self,\n",
+    "        inputs: Dict[str, Any],\n",
+    "        run_manager: Optional[CallbackManagerForChainRun] = None,\n",
+    "    ) -> Dict[str, str]:\n",
+    "        # Your custom chain logic goes here\n",
+    "        # This is just an example that mimics LLMChain\n",
+    "        prompt_value = self.prompt.format_prompt(**inputs)\n",
+    "        \n",
+    "        # Whenever you call a language model, or another chain, you should pass\n",
+    "        # a callback manager to it. This allows the inner run to be tracked by\n",
+    "        # any callbacks that are registered on the outer run.\n",
+    "        # You can always obtain a callback manager for this by calling\n",
+    "        # `run_manager.get_child()` as shown below.\n",
+    "        response = self.llm.generate_prompt(\n",
+    "            [prompt_value],\n",
+    "            callbacks=run_manager.get_child() if run_manager else None\n",
+    "        )\n",
+    "\n",
+    "        # If you want to log something about this run, you can do so by calling\n",
+    "        # methods on the `run_manager`, as shown below. This will trigger any\n",
+    "        # callbacks that are registered for that event.\n",
+    "        if run_manager:\n",
+    "            run_manager.on_text(\"Log something about this run\")\n",
+    "        \n",
+    "        return {self.output_key: response.generations[0][0].text}\n",
+    "\n",
+    "    async def _acall(\n",
+    "        self,\n",
+    "        inputs: Dict[str, Any],\n",
+    "        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n",
+    "    ) -> Dict[str, str]:\n",
+    "        # Your custom chain logic goes here\n",
+    "        # This is just an example that mimics LLMChain\n",
+    "        prompt_value = self.prompt.format_prompt(**inputs)\n",
+    "        \n",
+    "        # Whenever you call a language model, or another chain, you should pass\n",
+    "        # a callback manager to it. This allows the inner run to be tracked by\n",
+    "        # any callbacks that are registered on the outer run.\n",
+    "        # You can always obtain a callback manager for this by calling\n",
+    "        # `run_manager.get_child()` as shown below.\n",
+    "        response = await self.llm.agenerate_prompt(\n",
+    "            [prompt_value],\n",
+    "            callbacks=run_manager.get_child() if run_manager else None\n",
+    "        )\n",
+    "\n",
+    "        # If you want to log something about this run, you can do so by calling\n",
+    "        # methods on the `run_manager`, as shown below. This will trigger any\n",
+    "        # callbacks that are registered for that event.\n",
+    "        if run_manager:\n",
+    "            await run_manager.on_text(\"Log something about this run\")\n",
+    "        \n",
+    "        return {self.output_key: response.generations[0][0].text}\n",
+    "\n",
+    "    @property\n",
+    "    def _chain_type(self) -> str:\n",
+    "        return \"my_custom_chain\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "18361f89",
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new MyCustomChain chain...\u001b[0m\n",
+      "Log something about this run\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Why did the callback function feel lonely? Because it was always waiting for someone to call it back!'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.callbacks.stdout import StdOutCallbackHandler\n",
+    "from langchain.chat_models.openai import ChatOpenAI\n",
+    "from langchain.prompts.prompt import PromptTemplate\n",
+    "\n",
+    "\n",
+    "chain = MyCustomChain(\n",
+    "    prompt=PromptTemplate.from_template('tell us a joke about {topic}'),\n",
+    "    llm=ChatOpenAI()\n",
+    ")\n",
+    "\n",
+    "chain.run({'topic': 'callbacks'}, callbacks=[StdOutCallbackHandler()])"
+   ]
+  }
+ ],
+ "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/chains/generic/llm_chain.ipynb

@@ -137,13 +137,12 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "a178173b-b183-432a-a517-250fe3191173",
    "metadata": {},
    "source": [
-    "- `predict` is similar to `run` method except in 2 ways:\n",
-    "    - Input key is specified as keyword argument instead of a Python dict\n",
-    "    - It supports multiple input keys."
+    "- `predict` is similar to `run` method except that the input keys are specified as keyword arguments instead of a Python dict."
    ]
   },
   {

docs/modules/chains/getting_started.ipynb

@@ -185,7 +185,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -555,7 +554,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.6"
   },
   "vscode": {
    "interpreter": {

docs/modules/chains/index_examples/chat_vector_db.ipynb

@@ -589,7 +589,6 @@
    "outputs": [],
    "source": [
     "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 CONDENSE_QUESTION_PROMPT, QA_PROMPT\n",
     "from langchain.chains.question_answering import load_qa_chain\n",
@@ -597,7 +596,7 @@
     "# 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(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
+    "streaming_llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], 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",

docs/modules/chains/index_examples/qa_with_sources.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Question Answering with Sources\n",
     "\n",
-    "This notebook walks through how to use LangChain for question answering with sources over a list of documents. It covers four different chain types: `stuff`, `map_reduce`, `refine`,`map-rerank`. For a more in depth explanation of what these chain types are, see [here](../combine_docs.md)."
+    "This notebook walks through how to use LangChain for question answering with sources over a list of documents. It covers four different chain types: `stuff`, `map_reduce`, `refine`,`map-rerank`. For a more in depth explanation of what these chain types are, see [here](https://docs.langchain.com/docs/components/chains/index_related_chains)."
    ]
   },
   {

docs/modules/chains/index_examples/question_answering.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Question Answering\n",
     "\n",
-    "This notebook walks through how to use LangChain for question answering over a list of documents. It covers four different types of chains: `stuff`, `map_reduce`, `refine`, `map_rerank`. For a more in depth explanation of what these chain types are, see [here](../combine_docs.md)."
+    "This notebook walks through how to use LangChain for question answering over a list of documents. It covers four different types of chains: `stuff`, `map_reduce`, `refine`, `map_rerank`. For a more in depth explanation of what these chain types are, see [here](https://docs.langchain.com/docs/components/chains/index_related_chains)."
    ]
   },
   {

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

@@ -41,13 +41,14 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "094b5f13-7e54-4354-9d83-26d6926ecaa0",
    "metadata": {
     "tags": []
    },
    "source": [
-    "Second, you need to install `PyMuPDF` python package which transform PDF files from the `arxiv.org` site into the text fromat."
+    "Second, you need to install `PyMuPDF` python package which transform PDF files from the `arxiv.org` site into the text format."
    ]
   },
   {
@@ -71,6 +72,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e29b954c-1407-4797-ae21-6ba8937156be",
    "metadata": {},
@@ -78,12 +80,12 @@
     "`ArxivLoader` has these arguments:\n",
     "- `query`: free text which used to find documents in the Arxiv\n",
     "- optional `load_max_docs`: default=100. Use it to limit number of downloaded documents. It takes time to download all 100 documents, so use a small number for experiments.\n",
-    "- optional `load_all_available_meta`: default=False. By defaul only the most important fields downloaded: `Published` (date when document was published/last updated), `Title`, `Authors`, `Summary`. If True, other fields also downloaded."
+    "- optional `load_all_available_meta`: default=False. By default only the most important fields downloaded: `Published` (date when document was published/last updated), `Title`, `Authors`, `Summary`. If True, other fields also downloaded."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "9bfd5e46",
    "metadata": {},
    "outputs": [],
@@ -105,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "id": "8977bac0-0042-4f23-9754-247dbd32439b",
    "metadata": {
     "tags": []
@@ -120,18 +122,18 @@
        " 'Summary': '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": 2,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "doc[0].metadata  # meta-information of the Document"
+    "docs[0].metadata  # meta-information of the Document"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "46969806-45a9-4c4d-a61b-cfb9658fc9de",
    "metadata": {
     "tags": []
@@ -143,13 +145,13 @@
        "'arXiv:1605.08386v1  [math.CO]  26 May 2016\\nHEAT-BATH RANDOM WALKS WITH MARKOV BASES\\nCAPRICE STANLEY AND TOBIAS WINDISCH\\nAbstract. 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 fibers of a\\nfixed integer matrix can be bounded from above by a constant. We then study the mixing\\nbehaviour of heat-b'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "doc[0].page_content[:400]  # all pages of the Document content\n"
+    "docs[0].page_content[:400]  # all pages of the Document content\n"
    ]
   }
  ],
@@ -169,7 +171,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

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

@@ -7,7 +7,7 @@
         "id": "vm8vn9t8DvC_"
       },
       "source": [
-        "# Blockchain Document Loader"
+        "# Blockchain"
       ]
     },
     {
@@ -31,7 +31,7 @@
         "\n",
         "Initially this Loader supports:\n",
         "\n",
-        "\n",
+        "*   Loading NFTs as Documents from NFT Smart Contracts (ERC721 and ERC1155)\n",
         "*   Ethereum Maninnet, Ethereum Testnet, Polgyon Mainnet, Polygon Testnet (default is eth-mainnet)\n",
         "*   Alchemy's getNFTsForCollection API\n",
         "\n",
@@ -39,14 +39,14 @@
         "\n",
         "*   Additional APIs can be added (e.g. Tranction-related APIs)\n",
         "\n",
-        "To run this notebook, the user will need:\n",
+        "This Document Loader Requires:\n",
         "\n",
-        "\n",
-        "*   An OpenAI key (for OpenAI models)\n",
         "*   A free [Alchemy API Key](https://www.alchemy.com/)\n",
         "\n",
+        "The output takes the following format:\n",
         "\n",
-        "\n"
+        "- pageContent= Individual NFT\n",
+        "- metadata={'source': '0x1a92f7381b9f03921564a437210bb9396471050c', 'blockchain': 'eth-mainnet', 'tokenId': '0x15'})"
       ]
     },
     {
@@ -54,55 +54,16 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Setup"
+        "## Load NFTs into Document Loader"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 48,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Note: you may need to restart the kernel to use updated packages.\n"
-          ]
-        }
-      ],
-      "source": [
-        "%pip install langchain -q"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 49,
+      "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "from langchain.document_loaders import BlockchainDocumentLoader\n",
-        "from langchain.document_loaders.blockchain import BlockchainType\n",
-        "import os"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 50,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "alchemyApiKey = \"get your own key from https://www.alchemy.com/\" \n",
-        "os.environ[\"ALCHEMY_API_KEY\"] = alchemyApiKey"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nzuPWRaBNCMx"
-      },
-      "source": [
-        "## Create a Blockchain Document Loader"
+        "alchemyApiKey = \"get from https://www.alchemy.com/ and set in environment variable ALCHEMY_API_KEY\""
       ]
     },
     {
@@ -115,29 +76,18 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": null,
       "metadata": {
         "id": "J3LWHARC-Kn0"
       },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "[Document(page_content=\"{'contract': {'address': '0xbc4ca0eda7647a8ab7c2061c2e118a18a936f13d'}, 'id': {'tokenId': '0x0000000000000000000000000000000000000000000000000000000000000000', 'tokenMetadata': {'tokenType': 'ERC721'}}, 'title': '', 'description': '', 'tokenUri': {'gateway': 'https://alchemy.mypinata.cloud/ipfs/QmeSjSinHpPnmXmspMjwiXyN6zS4E9zccariGR3jxcaWtq/0', 'raw': 'ipfs://QmeSjSinHpPnmXmspMjwiXyN6zS4E9zccariGR3jxcaWtq/0'}, 'media': [{'gateway': 'https://nft-cdn.alchemy.com/eth-mainnet/415d618f5fef7bfe683e02d4653c4289', 'thumbnail': 'https://res.cloudinary.com/alchemyapi/image/upload/thumbnailv2/eth-mainnet/415d618f5fef7bfe683e02d4653c4289', 'raw': 'ipfs://QmRRPWG96cmgTn2qSzjwr2qvfNEuhunv6FNeMFGa9bx6mQ', 'format': 'png', 'bytes': 133270}], 'metadata': {'image': 'ipfs://QmRRPWG96cmgTn2qSzjwr2qvfNEuhunv6FNeMFGa9bx6mQ', 'attributes': [{'value': 'Silver Hoop', 'trait_type': 'Earring'}, {'value': 'Orange', 'trait_type': 'Background'}, {'value': 'Robot', 'trait_type': 'Fur'}, {'value': 'Striped Tee', 'trait_type': 'Clothes'}, {'value': 'Discomfort', 'trait_type': 'Mouth'}, {'value': 'X Eyes', 'trait_type': 'Eyes'}]}, 'timeLastUpdated': '2023-04-18T04:05:27.817Z', 'contractMetadata': {'name': 'BoredApeYachtClub', 'symbol': 'BAYC', 'totalSupply': '10000', 'tokenType': 'ERC721', 'contractDeployer': '0xaba7161a7fb69c88e16ed9f455ce62b791ee4d03', 'deployedBlockNumber': 12287507, 'openSea': {'floorPrice': 68.16, 'collectionName': 'Bored Ape Yacht Club', 'safelistRequestStatus': 'verified', 'imageUrl': 'https://i.seadn.io/gae/Ju9CkWtV-1Okvf45wo8UctR-M9He2PjILP0oOvxE89AyiPPGtrR3gysu1Zgy0hjd2xKIgjJJtWIc0ybj4Vd7wv8t3pxDGHoJBzDB?w=500&auto=format', 'description': 'The Bored Ape Yacht Club is a collection of 10,000 unique Bored Ape NFTs— unique digital collectibles living on the Ethereum blockchain. Your Bored Ape doubles as your Yacht Club membership card, and grants access to members-only benefits, the first of which is access to THE BATHROOM, a collaborative graffiti board. Future areas and perks can be unlocked by the community through roadmap activation. Visit www.BoredApeYachtClub.com for more details.', 'externalUrl': 'http://www.boredapeyachtclub.com/', 'twitterUsername': 'BoredApeYC', 'discordUrl': 'https://discord.gg/3P5K3dzgdB', 'lastIngestedAt': '2023-03-21T03:54:33.000Z'}}}\", metadata={'tokenId': '0x0000000000000000000000000000000000000000000000000000000000000000'}),\n",
-              " Document(page_content=\"{'contract': {'address': '0xbc4ca0eda7647a8ab7c2061c2e118a18a936f13d'}, 'id': {'tokenId': '0x0000000000000000000000000000000000000000000000000000000000000001', 'tokenMetadata': {'tokenType': 'ERC721'}}, 'title': '', 'description': '', 'tokenUri': {'gateway': 'https://alchemy.mypinata.cloud/ipfs/QmeSjSinHpPnmXmspMjwiXyN6zS4E9zccariGR3jxcaWtq/1', 'raw': 'ipfs://QmeSjSinHpPnmXmspMjwiXyN6zS4E9zccariGR3jxcaWtq/1'}, 'media': [{'gateway': 'https://nft-cdn.alchemy.com/eth-mainnet/65558a4d0c5b0c56fbc50bf03f55e3fa', 'thumbnail': 'https://res.cloudinary.com/alchemyapi/image/upload/thumbnailv2/eth-mainnet/65558a4d0c5b0c56fbc50bf03f55e3fa', 'raw': 'ipfs://QmPbxeGcXhYQQNgsC6a36dDyYUcHgMLnGKnF8pVFmGsvqi', 'format': 'png', 'bytes': 171425}], 'metadata': {'image': 'ipfs://QmPbxeGcXhYQQNgsC6a36dDyYUcHgMLnGKnF8pVFmGsvqi', 'attributes': [{'value': 'Grin', 'trait_type': 'Mouth'}, {'value': 'Vietnam Jacket', 'trait_type': 'Clothes'}, {'value': 'Orange', 'trait_type': 'Background'}, {'value': 'Blue Beams', 'trait_type': 'Eyes'}, {'value': 'Robot', 'trait_type': 'Fur'}]}, 'timeLastUpdated': '2023-04-24T04:37:37.738Z', 'contractMetadata': {'name': 'BoredApeYachtClub', 'symbol': 'BAYC', 'totalSupply': '10000', 'tokenType': 'ERC721', 'contractDeployer': '0xaba7161a7fb69c88e16ed9f455ce62b791ee4d03', 'deployedBlockNumber': 12287507, 'openSea': {'floorPrice': 68.16, 'collectionName': 'Bored Ape Yacht Club', 'safelistRequestStatus': 'verified', 'imageUrl': 'https://i.seadn.io/gae/Ju9CkWtV-1Okvf45wo8UctR-M9He2PjILP0oOvxE89AyiPPGtrR3gysu1Zgy0hjd2xKIgjJJtWIc0ybj4Vd7wv8t3pxDGHoJBzDB?w=500&auto=format', 'description': 'The Bored Ape Yacht Club is a collection of 10,000 unique Bored Ape NFTs— unique digital collectibles living on the Ethereum blockchain. Your Bored Ape doubles as your Yacht Club membership card, and grants access to members-only benefits, the first of which is access to THE BATHROOM, a collaborative graffiti board. Future areas and perks can be unlocked by the community through roadmap activation. Visit www.BoredApeYachtClub.com for more details.', 'externalUrl': 'http://www.boredapeyachtclub.com/', 'twitterUsername': 'BoredApeYC', 'discordUrl': 'https://discord.gg/3P5K3dzgdB', 'lastIngestedAt': '2023-03-21T03:54:33.000Z'}}}\", metadata={'tokenId': '0x0000000000000000000000000000000000000000000000000000000000000001'})]"
-            ]
-          },
-          "execution_count": 24,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
         "contractAddress = \"0xbc4ca0eda7647a8ab7c2061c2e118a18a936f13d\" # Bored Ape Yacht Club contract address\n",
         "\n",
         "blockchainType = BlockchainType.ETH_MAINNET  #default value, optional parameter\n",
         "\n",
-        "blockchainLoader = BlockchainDocumentLoader(contractAddress)\n",
+        "blockchainLoader = BlockchainDocumentLoader(contract_address=contractAddress,\n",
+        "                                            api_key=alchemyApiKey)\n",
         "\n",
         "nfts = blockchainLoader.load()\n",
         "\n",
@@ -154,265 +104,22 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 36,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "[Document(page_content=\"{'contract': {'address': '0x448676ffcd0adf2d85c1f0565e8dde6924a9a7d9'}, 'id': {'tokenId': '0x01', 'tokenMetadata': {'tokenType': 'ERC1155'}}, 'title': 'Wyatt Horton #0001', 'description': 'A sleepy capybara', 'tokenUri': {'gateway': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/metadata/1.json', 'raw': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/metadata/1.json'}, 'media': [{'gateway': 'https://nft-cdn.alchemy.com/matic-mainnet/9085e06ff9f6c9074de91801d1c72d26', 'thumbnail': 'https://res.cloudinary.com/alchemyapi/image/upload/thumbnailv2/matic-mainnet/9085e06ff9f6c9074de91801d1c72d26', 'raw': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/images/1.png', 'format': 'png', 'bytes': 769622}], 'metadata': {'name': 'Wyatt Horton #0001', 'description': 'A sleepy capybara', 'image': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/images/1.png', 'attributes': [{'value': 'Avatar', 'trait_type': 'Type'}, {'value': 'Animal', 'trait_type': 'Category'}, {'value': 'Capybara', 'trait_type': 'Class'}, {'value': 'Fall 2022', 'trait_type': 'Collection'}, {'value': 'Furry', 'trait_type': 'Feature'}]}, 'timeLastUpdated': '2023-04-20T14:38:24.947Z', 'contractMetadata': {'name': 'Smoothstack - Avatars', 'symbol': 'SMTH', 'tokenType': 'ERC1155', 'contractDeployer': '0x23075b2523c6563b06920a302a8be4f90ef6e974', 'deployedBlockNumber': 34752389, 'openSea': {'lastIngestedAt': '2023-04-17T20:59:42.000Z'}}}\", metadata={'tokenId': '0x01'}),\n",
-              " Document(page_content=\"{'contract': {'address': '0x448676ffcd0adf2d85c1f0565e8dde6924a9a7d9'}, 'id': {'tokenId': '0x02', 'tokenMetadata': {'tokenType': 'ERC1155'}}, 'title': 'Dylan Leisler #0002', 'description': 'A chipper cat with a big, red bowtie', 'tokenUri': {'gateway': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/metadata/2.json', 'raw': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/metadata/2.json'}, 'media': [{'gateway': 'https://nft-cdn.alchemy.com/matic-mainnet/67c3c7ccef44b32bf2ce758e8e73dbcd', 'thumbnail': 'https://res.cloudinary.com/alchemyapi/image/upload/thumbnailv2/matic-mainnet/67c3c7ccef44b32bf2ce758e8e73dbcd', 'raw': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/images/2.png', 'format': 'png', 'bytes': 1187749}], 'metadata': {'name': 'Dylan Leisler #0002', 'description': 'A chipper cat with a big, red bowtie', 'image': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/images/2.png', 'attributes': [{'value': 'Avatar', 'trait_type': 'Type'}, {'value': 'Animal', 'trait_type': 'Category'}, {'value': 'Cat', 'trait_type': 'Class'}, {'value': 'Fall 2022', 'trait_type': 'Collection'}, {'value': 'Red Bowtie', 'trait_type': 'Feature'}]}, 'timeLastUpdated': '2023-04-23T13:38:29.316Z', 'contractMetadata': {'name': 'Smoothstack - Avatars', 'symbol': 'SMTH', 'tokenType': 'ERC1155', 'contractDeployer': '0x23075b2523c6563b06920a302a8be4f90ef6e974', 'deployedBlockNumber': 34752389, 'openSea': {'lastIngestedAt': '2023-04-17T20:59:42.000Z'}}}\", metadata={'tokenId': '0x02'})]"
-            ]
-          },
-          "execution_count": 36,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
         "contractAddress = \"0x448676ffCd0aDf2D85C1f0565e8dde6924A9A7D9\" # Polygon Mainnet contract address\n",
         "\n",
         "blockchainType = BlockchainType.POLYGON_MAINNET \n",
         "\n",
-        "blockchainLoader = BlockchainDocumentLoader(contractAddress, blockchainType)\n",
+        "blockchainLoader = BlockchainDocumentLoader(contract_address=contractAddress, \n",
+        "                                            blockchainType=blockchainType, \n",
+        "                                            api_key=alchemyApiKey)\n",
         "\n",
         "nfts = blockchainLoader.load()\n",
         "\n",
         "nfts[:2]"
       ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## (Optional) Using the Blockchain Document Loader"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_PGkFfMCB8J3"
-      },
-      "source": [
-        "### Setup Splitter and Index"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 37,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Note: you may need to restart the kernel to use updated packages.\n"
-          ]
-        }
-      ],
-      "source": [
-        "%pip install sentence_transformers chromadb openai tiktoken -q"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 38,
-      "metadata": {
-        "id": "BwxxopOCCABh"
-      },
-      "outputs": [],
-      "source": [
-        "from langchain.indexes import VectorstoreIndexCreator\n",
-        "from langchain.embeddings import HuggingFaceEmbeddings\n",
-        "from langchain.text_splitter import RecursiveCharacterTextSplitter"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 39,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "JE_myAulCDSZ",
-        "outputId": "99e16b6a-03b4-4e67-d4b4-9dd611a866ef"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "NUMBER OF DOCUMENTS:  424\n"
-          ]
-        }
-      ],
-      "source": [
-        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=400, chunk_overlap=0)\n",
-        "\n",
-        "docs = text_splitter.split_documents(nfts)\n",
-        "print(\"NUMBER OF DOCUMENTS: \", len(docs))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 40,
-      "metadata": {
-        "id": "d83yFuAuCKQS"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Using embedded DuckDB without persistence: data will be transient\n"
-          ]
-        }
-      ],
-      "source": [
-        "index = VectorstoreIndexCreator(\n",
-        "    embedding=HuggingFaceEmbeddings(),\n",
-        "    text_splitter=text_splitter).from_loaders([blockchainLoader])"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "y0VfObeXDEXB"
-      },
-      "source": [
-        "## Setup Models and Chains"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 42,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "openAiKey = \"put OpenAI key here\"\n",
-        "os.environ[\"OPENAI_API_KEY\"] = openAiKey"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 31,
-      "metadata": {
-        "id": "hiNjDzP9C4pA"
-      },
-      "outputs": [],
-      "source": [
-        "from langchain.chains import RetrievalQA\n",
-        "from langchain.llms import OpenAI"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "u-xDlKPaC_xg"
-      },
-      "source": [
-        "### Retrieval Chain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 43,
-      "metadata": {
-        "id": "BqP00JovC9R4"
-      },
-      "outputs": [],
-      "source": [
-        "llmOpenAI = OpenAI()\n",
-        "\n",
-        "chainQA = RetrievalQA.from_chain_type(llm=llmOpenAI, \n",
-        "                                    chain_type=\"map_reduce\",\n",
-        "                                    retriever=index.vectorstore.as_retriever(), \n",
-        "                                    verbose=True,\n",
-        "                                    input_key=\"question\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 44,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 122
-        },
-        "id": "2Y3cVVKZDVNq",
-        "outputId": "dfeea416-5193-47cf-e9dc-c17a5c1cd780"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "\n",
-            "\u001b[1m> Entering new RetrievalQA chain...\u001b[0m\n",
-            "\n",
-            "\u001b[1m> Finished chain.\u001b[0m\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "' Popular attributes include \"Avatar\" (Type), \"Character\" (Category), and \"Human\" or \"Wizard\" (Class).'"
-            ]
-          },
-          "execution_count": 44,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "chainQA.run(\"What are some of the popular attributes?\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 122
-        },
-        "id": "7o6ArPo9DXbz",
-        "outputId": "b1f8ad43-27c7-4cdb-95a7-8c8bd6381c5a"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "\n",
-            "\u001b[1m> Entering new RetrievalQA chain...\u001b[0m\n",
-            "\n",
-            "\u001b[1m> Finished chain.\u001b[0m\n"
-          ]
-        },
-        {
-          "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            },
-            "text/plain": [
-              "' There are 10,000 unique Bored Ape NFTs.'"
-            ]
-          },
-          "execution_count": 32,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "chainQA.run(\"How many NFTs are there?\")"
-      ]
     }
   ],
   "metadata": {

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

@@ -0,0 +1,106 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modern Treasury\n",
+    "\n",
+    "This notebook covers how to load data from the Modern Treasury REST API into a format that can be ingested into LangChain, along with example usage for vectorization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "\n",
+    "from langchain.document_loaders import ModernTreasuryLoader\n",
+    "from langchain.indexes import VectorstoreIndexCreator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Modern Treasury API requires an organization ID and API key, which can be found in the Modern Treasury dashboard within developer settings.\n",
+    "\n",
+    "This document loader also requires a `resource` option which defines what data you want to load.\n",
+    "\n",
+    "Following resources are available:\n",
+    "\n",
+    "`payment_orders` [Documentation](https://docs.moderntreasury.com/reference/payment-order-object)\n",
+    "\n",
+    "`expected_payments` [Documentation](https://docs.moderntreasury.com/reference/expected-payment-object)\n",
+    "\n",
+    "`returns` [Documentation](https://docs.moderntreasury.com/reference/return-object)\n",
+    "\n",
+    "`incoming_payment_details` [Documentation](https://docs.moderntreasury.com/reference/incoming-payment-detail-object)\n",
+    "\n",
+    "`counterparties` [Documentation](https://docs.moderntreasury.com/reference/counterparty-object)\n",
+    "\n",
+    "`internal_accounts` [Documentation](https://docs.moderntreasury.com/reference/internal-account-object)\n",
+    "\n",
+    "`external_accounts` [Documentation](https://docs.moderntreasury.com/reference/external-account-object)\n",
+    "\n",
+    "`transactions` [Documentation](https://docs.moderntreasury.com/reference/transaction-object)\n",
+    "\n",
+    "`ledgers` [Documentation](https://docs.moderntreasury.com/reference/ledger-object)\n",
+    "\n",
+    "`ledger_accounts` [Documentation](https://docs.moderntreasury.com/reference/ledger-account-object)\n",
+    "\n",
+    "`ledger_transactions` [Documentation](https://docs.moderntreasury.com/reference/ledger-transaction-object)\n",
+    "\n",
+    "`events` [Documentation](https://docs.moderntreasury.com/reference/events)\n",
+    "\n",
+    "`invoices` [Documentation](https://docs.moderntreasury.com/reference/invoices)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modern_treasury_loader = ModernTreasuryLoader(\"payment_orders\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a vectorstore retriver from the loader\n",
+    "# see https://python.langchain.com/en/latest/modules/indexes/getting_started.html for more details\n",
+    "\n",
+    "index = VectorstoreIndexCreator().from_loaders([modern_treasury_loader])\n",
+    "modern_treasury_doc_retriever = index.vectorstore.as_retriever()"
+   ]
+  }
+ ],
+ "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -155,6 +155,46 @@
     "    print(str(doc.metadata[\"page\"]) + \":\", doc.page_content)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6d5c9879",
+   "metadata": {},
+   "source": [
+    "## Using MathPix\n",
+    "\n",
+    "Inspired by Daniel Gross's [https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21](https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "950eb58f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import MathpixPDFLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb6fd473",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = MathpixPDFLoader(\"example_data/layout-parser-paper.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1d41e1a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = loader.load()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "09d64998",
@@ -566,10 +606,50 @@
     "Additionally, you can pass along any of the options from the [PyMuPDF documentation](https://pymupdf.readthedocs.io/en/latest/app1.html#plain-text/) as keyword arguments in the `load` call, and it will be pass along to the `get_text()` call."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "15b57eab",
+   "metadata": {},
+   "source": [
+    "## PyPDF Directory\n",
+    "\n",
+    "Load PDFs from directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b9e521d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import PyPDFDirectoryLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4b20590f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = PyPDFDirectoryLoader(\"example_data/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e5ead943",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1bf73c97",
+   "id": "ea25b03c",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -577,9 +657,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "langchain_dev",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "langchain_dev"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -591,7 +671,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

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

@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reddit\n",
+    "\n",
+    "This loader fetches the text from the Posts of Subreddits or Reddit users, using the `praw` Python package.\n",
+    "\n",
+    "Make a Reddit Application from https://www.reddit.com/prefs/apps/ and initialize the loader with with your Reddit API credentials."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import RedditPostsLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install praw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load using 'subreddit' mode\n",
+    "loader = RedditPostsLoader(\n",
+    "    client_id=\"YOUR CLIENT ID\",\n",
+    "    client_secret=\"YOUR CLIENT SECRET\",\n",
+    "    user_agent=\"extractor by u/Master_Ocelot8179\",\n",
+    "    categories=['new', 'hot'],                              # List of categories to load posts from\n",
+    "    mode = 'subreddit',\n",
+    "    search_queries=['investing', 'wallstreetbets'],         # List of subreddits to load posts from\n",
+    "    number_posts=20                                         # Default value is 10\n",
+    "    )\n",
+    "\n",
+    "# # or load using 'username' mode\n",
+    "# loader = RedditPostsLoader(\n",
+    "#     client_id=\"YOUR CLIENT ID\",\n",
+    "#     client_secret=\"YOUR CLIENT SECRET\",\n",
+    "#     user_agent=\"extractor by u/Master_Ocelot8179\",\n",
+    "#     categories=['new', 'hot'],                              \n",
+    "#     mode = 'username',\n",
+    "#     search_queries=['ga3far', 'Master_Ocelot8179'],         # List of usernames to load posts from\n",
+    "#     number_posts=20\n",
+    "#     )\n",
+    "\n",
+    "# Note: Categories can be only of following value - \"controversial\" \"hot\" \"new\" \"rising\" \"top\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='Hello, I am not looking for investment advice. I will apply my own due diligence. However, I am interested if anyone knows as a UK resident how fees and exchange rate differences would impact performance?\\n\\nI am planning to create a pie of index funds (perhaps UK, US, europe) or find a fund with a good track record of long term growth at low rates. \\n\\nDoes anyone have any ideas?', metadata={'post_subreddit': 'r/investing', 'post_category': 'new', 'post_title': 'Long term retirement funds fees/exchange rate query', 'post_score': 1, 'post_id': '130pa6m', 'post_url': 'https://www.reddit.com/r/investing/comments/130pa6m/long_term_retirement_funds_feesexchange_rate_query/', 'post_author': Redditor(name='Badmanshiz')}),\n",
+       " Document(page_content='I much prefer the Roth IRA and would rather rollover my 401k to that every year instead of keeping it in the limited 401k options. But if I rollover, will I be able to continue contributing to my 401k? Or will that close my account? I realize that there are tax implications of doing this but I still think it is the better option.', metadata={'post_subreddit': 'r/investing', 'post_category': 'new', 'post_title': 'Is it possible to rollover my 401k every year?', 'post_score': 3, 'post_id': '130ja0h', 'post_url': 'https://www.reddit.com/r/investing/comments/130ja0h/is_it_possible_to_rollover_my_401k_every_year/', 'post_author': Redditor(name='AnCap_Catholic')}),\n",
+       " Document(page_content='Have a general question?  Want to offer some commentary on markets?  Maybe you would just like to throw out a neat fact that doesn\\'t warrant a self post?  Feel free to post here! \\n\\nIf your question is \"I have $10,000, what do I do?\" or other \"advice for my personal situation\" questions, you should include relevant information, such as the following:\\n\\n* How old are you? What country do you live in?  \\n* Are you employed/making income? How much?  \\n* What are your objectives with this money? (Buy a house? Retirement savings?)  \\n* What is your time horizon? Do you need this money next month? Next 20yrs?  \\n* What is your risk tolerance? (Do you mind risking it at blackjack or do you need to know its 100% safe?)  \\n* What are you current holdings? (Do you already have exposure to specific funds and sectors? Any other assets?)  \\n* Any big debts (include interest rate) or expenses?  \\n* And any other relevant financial information will be useful to give you a proper answer.  \\n\\nPlease consider consulting our FAQ first - https://www.reddit.com/r/investing/wiki/faq\\nAnd our [side bar](https://www.reddit.com/r/investing/about/sidebar) also has useful resources.  \\n\\nIf you are new to investing - please refer to Wiki - [Getting Started](https://www.reddit.com/r/investing/wiki/index/gettingstarted/)\\n\\nThe reading list in the wiki has a list of books ranging from light reading to advanced topics depending on your knowledge level. Link here - [Reading List](https://www.reddit.com/r/investing/wiki/readinglist)\\n\\nCheck the resources in the sidebar.\\n\\nBe aware that these answers are just opinions of Redditors and should be used as a starting point for your research. You should strongly consider seeing a registered investment adviser if you need professional support before making any financial decisions!', metadata={'post_subreddit': 'r/investing', 'post_category': 'new', 'post_title': 'Daily General Discussion and Advice Thread - April 27, 2023', 'post_score': 5, 'post_id': '130eszz', 'post_url': 'https://www.reddit.com/r/investing/comments/130eszz/daily_general_discussion_and_advice_thread_april/', 'post_author': Redditor(name='AutoModerator')}),\n",
+       " Document(page_content=\"Based on recent news about salt battery advancements and the overall issues of lithium, I was wondering what would be feasible ways to invest into non-lithium based battery technologies? CATL is of course a choice, but the selection of brokers I currently have in my disposal don't provide HK stocks at all.\", metadata={'post_subreddit': 'r/investing', 'post_category': 'new', 'post_title': 'Investing in non-lithium battery technologies?', 'post_score': 2, 'post_id': '130d6qp', 'post_url': 'https://www.reddit.com/r/investing/comments/130d6qp/investing_in_nonlithium_battery_technologies/', 'post_author': Redditor(name='-manabreak')}),\n",
+       " Document(page_content='Hello everyone,\\n\\nI would really like to invest in an ETF that follows spy or another big index, as I think this form of investment suits me best. \\n\\nThe problem is, that I live in Denmark where ETFs and funds are taxed annually on unrealised gains at quite a steep rate. This means that an ETF growing say 10% per year will only grow about 6%, which really ruins the long term effects of compounding interest.\\n\\nHowever stocks are only taxed on realised gains which is why they look more interesting to hold long term.\\n\\nI do not like the lack of diversification this brings, as I am looking to spend tonnes of time picking the right long term stocks.\\n\\nIt would be ideal to find a few stocks that over the long term somewhat follows the indexes. Does anyone have suggestions?\\n\\nI have looked at Nasdaq Inc. which quite closely follows Nasdaq 100. \\n\\nI really appreciate any help.', metadata={'post_subreddit': 'r/investing', 'post_category': 'new', 'post_title': 'Stocks that track an index', 'post_score': 7, 'post_id': '130auvj', 'post_url': 'https://www.reddit.com/r/investing/comments/130auvj/stocks_that_track_an_index/', 'post_author': Redditor(name='LeAlbertP')})]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "documents = loader.load()\n",
+    "documents[:5]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env1",
+   "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": "3.9.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,129 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Spreedly\n",
+    "\n",
+    "This notebook covers how to load data from the [Spreedly REST API](https://docs.spreedly.com/reference/api/v1/) into a format that can be ingested into LangChain, along with example usage for vectorization.\n",
+    "\n",
+    "Note: this notebook assumes the following packages are installed: `openai`, `chromadb`, and `tiktoken`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from langchain.document_loaders import SpreedlyLoader\n",
+    "from langchain.indexes import VectorstoreIndexCreator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Spreedly API requires an access token, which can be found inside the Spreedly Admin Console.\n",
+    "\n",
+    "This document loader does not currently support pagination, nor access to more complex objects which require additional parameters. It also requires a `resource` option which defines what objects you want to load.\n",
+    "\n",
+    "Following resources are available:\n",
+    "- `gateways_options`: [Documentation](https://docs.spreedly.com/reference/api/v1/#list-supported-gateways)\n",
+    "- `gateways`: [Documentation](https://docs.spreedly.com/reference/api/v1/#list-created-gateways)\n",
+    "- `receivers_options`: [Documentation](https://docs.spreedly.com/reference/api/v1/#list-supported-receivers)\n",
+    "- `receivers`: [Documentation](https://docs.spreedly.com/reference/api/v1/#list-created-receivers)\n",
+    "- `payment_methods`: [Documentation](https://docs.spreedly.com/reference/api/v1/#list)\n",
+    "- `certificates`: [Documentation](https://docs.spreedly.com/reference/api/v1/#list-certificates)\n",
+    "- `transactions`: [Documentation](https://docs.spreedly.com/reference/api/v1/#list49)\n",
+    "- `environments`: [Documentation](https://docs.spreedly.com/reference/api/v1/#list-environments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spreedly_loader = SpreedlyLoader(os.environ[\"SPREEDLY_ACCESS_TOKEN\"], \"gateways_options\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using embedded DuckDB without persistence: data will be transient\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a vectorstore retriver from the loader\n",
+    "# see https://python.langchain.com/en/latest/modules/indexes/getting_started.html for more details\n",
+    "\n",
+    "index = VectorstoreIndexCreator().from_loaders([spreedly_loader])\n",
+    "spreedly_doc_retriever = index.vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='installment_grace_period_duration\\nreference_data_code\\ninvoice_number\\ntax_management_indicator\\noriginal_amount\\ninvoice_amount\\nvat_tax_rate\\nmobile_remote_payment_type\\ngratuity_amount\\nmdd_field_1\\nmdd_field_2\\nmdd_field_3\\nmdd_field_4\\nmdd_field_5\\nmdd_field_6\\nmdd_field_7\\nmdd_field_8\\nmdd_field_9\\nmdd_field_10\\nmdd_field_11\\nmdd_field_12\\nmdd_field_13\\nmdd_field_14\\nmdd_field_15\\nmdd_field_16\\nmdd_field_17\\nmdd_field_18\\nmdd_field_19\\nmdd_field_20\\nsupported_countries: US\\nAE\\nBR\\nCA\\nCN\\nDK\\nFI\\nFR\\nDE\\nIN\\nJP\\nMX\\nNO\\nSE\\nGB\\nSG\\nLB\\nPK\\nsupported_cardtypes: visa\\nmaster\\namerican_express\\ndiscover\\ndiners_club\\njcb\\ndankort\\nmaestro\\nelo\\nregions: asia_pacific\\neurope\\nlatin_america\\nnorth_america\\nhomepage: http://www.cybersource.com\\ndisplay_api_url: https://ics2wsa.ic3.com/commerce/1.x/transactionProcessor\\ncompany_name: CyberSource', metadata={'source': 'https://core.spreedly.com/v1/gateways_options.json'}),\n",
+       " Document(page_content='BG\\nBH\\nBI\\nBJ\\nBM\\nBN\\nBO\\nBR\\nBS\\nBT\\nBW\\nBY\\nBZ\\nCA\\nCC\\nCF\\nCH\\nCK\\nCL\\nCM\\nCN\\nCO\\nCR\\nCV\\nCX\\nCY\\nCZ\\nDE\\nDJ\\nDK\\nDO\\nDZ\\nEC\\nEE\\nEG\\nEH\\nES\\nET\\nFI\\nFJ\\nFK\\nFM\\nFO\\nFR\\nGA\\nGB\\nGD\\nGE\\nGF\\nGG\\nGH\\nGI\\nGL\\nGM\\nGN\\nGP\\nGQ\\nGR\\nGT\\nGU\\nGW\\nGY\\nHK\\nHM\\nHN\\nHR\\nHT\\nHU\\nID\\nIE\\nIL\\nIM\\nIN\\nIO\\nIS\\nIT\\nJE\\nJM\\nJO\\nJP\\nKE\\nKG\\nKH\\nKI\\nKM\\nKN\\nKR\\nKW\\nKY\\nKZ\\nLA\\nLC\\nLI\\nLK\\nLS\\nLT\\nLU\\nLV\\nMA\\nMC\\nMD\\nME\\nMG\\nMH\\nMK\\nML\\nMN\\nMO\\nMP\\nMQ\\nMR\\nMS\\nMT\\nMU\\nMV\\nMW\\nMX\\nMY\\nMZ\\nNA\\nNC\\nNE\\nNF\\nNG\\nNI\\nNL\\nNO\\nNP\\nNR\\nNU\\nNZ\\nOM\\nPA\\nPE\\nPF\\nPH\\nPK\\nPL\\nPN\\nPR\\nPT\\nPW\\nPY\\nQA\\nRE\\nRO\\nRS\\nRU\\nRW\\nSA\\nSB\\nSC\\nSE\\nSG\\nSI\\nSK\\nSL\\nSM\\nSN\\nST\\nSV\\nSZ\\nTC\\nTD\\nTF\\nTG\\nTH\\nTJ\\nTK\\nTM\\nTO\\nTR\\nTT\\nTV\\nTW\\nTZ\\nUA\\nUG\\nUS\\nUY\\nUZ\\nVA\\nVC\\nVE\\nVI\\nVN\\nVU\\nWF\\nWS\\nYE\\nYT\\nZA\\nZM\\nsupported_cardtypes: visa\\nmaster\\namerican_express\\ndiscover\\njcb\\nmaestro\\nelo\\nnaranja\\ncabal\\nunionpay\\nregions: asia_pacific\\neurope\\nmiddle_east\\nnorth_america\\nhomepage: http://worldpay.com\\ndisplay_api_url: https://secure.worldpay.com/jsp/merchant/xml/paymentService.jsp\\ncompany_name: WorldPay', metadata={'source': 'https://core.spreedly.com/v1/gateways_options.json'}),\n",
+       " Document(page_content='gateway_specific_fields: receipt_email\\nradar_session_id\\nskip_radar_rules\\napplication_fee\\nstripe_account\\nmetadata\\nidempotency_key\\nreason\\nrefund_application_fee\\nrefund_fee_amount\\nreverse_transfer\\naccount_id\\ncustomer_id\\nvalidate\\nmake_default\\ncancellation_reason\\ncapture_method\\nconfirm\\nconfirmation_method\\ncustomer\\ndescription\\nmoto\\noff_session\\non_behalf_of\\npayment_method_types\\nreturn_email\\nreturn_url\\nsave_payment_method\\nsetup_future_usage\\nstatement_descriptor\\nstatement_descriptor_suffix\\ntransfer_amount\\ntransfer_destination\\ntransfer_group\\napplication_fee_amount\\nrequest_three_d_secure\\nerror_on_requires_action\\nnetwork_transaction_id\\nclaim_without_transaction_id\\nfulfillment_date\\nevent_type\\nmodal_challenge\\nidempotent_request\\nmerchant_reference\\ncustomer_reference\\nshipping_address_zip\\nshipping_from_zip\\nshipping_amount\\nline_items\\nsupported_countries: AE\\nAT\\nAU\\nBE\\nBG\\nBR\\nCA\\nCH\\nCY\\nCZ\\nDE\\nDK\\nEE\\nES\\nFI\\nFR\\nGB\\nGR\\nHK\\nHU\\nIE\\nIN\\nIT\\nJP\\nLT\\nLU\\nLV\\nMT\\nMX\\nMY\\nNL\\nNO\\nNZ\\nPL\\nPT\\nRO\\nSE\\nSG\\nSI\\nSK\\nUS\\nsupported_cardtypes: visa', metadata={'source': 'https://core.spreedly.com/v1/gateways_options.json'}),\n",
+       " Document(page_content='mdd_field_57\\nmdd_field_58\\nmdd_field_59\\nmdd_field_60\\nmdd_field_61\\nmdd_field_62\\nmdd_field_63\\nmdd_field_64\\nmdd_field_65\\nmdd_field_66\\nmdd_field_67\\nmdd_field_68\\nmdd_field_69\\nmdd_field_70\\nmdd_field_71\\nmdd_field_72\\nmdd_field_73\\nmdd_field_74\\nmdd_field_75\\nmdd_field_76\\nmdd_field_77\\nmdd_field_78\\nmdd_field_79\\nmdd_field_80\\nmdd_field_81\\nmdd_field_82\\nmdd_field_83\\nmdd_field_84\\nmdd_field_85\\nmdd_field_86\\nmdd_field_87\\nmdd_field_88\\nmdd_field_89\\nmdd_field_90\\nmdd_field_91\\nmdd_field_92\\nmdd_field_93\\nmdd_field_94\\nmdd_field_95\\nmdd_field_96\\nmdd_field_97\\nmdd_field_98\\nmdd_field_99\\nmdd_field_100\\nsupported_countries: US\\nAE\\nBR\\nCA\\nCN\\nDK\\nFI\\nFR\\nDE\\nIN\\nJP\\nMX\\nNO\\nSE\\nGB\\nSG\\nLB\\nPK\\nsupported_cardtypes: visa\\nmaster\\namerican_express\\ndiscover\\ndiners_club\\njcb\\nmaestro\\nelo\\nunion_pay\\ncartes_bancaires\\nmada\\nregions: asia_pacific\\neurope\\nlatin_america\\nnorth_america\\nhomepage: http://www.cybersource.com\\ndisplay_api_url: https://api.cybersource.com\\ncompany_name: CyberSource REST', metadata={'source': 'https://core.spreedly.com/v1/gateways_options.json'})]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Test the retriever\n",
+    "spreedly_doc_retriever.get_relevant_documents(\"CRC\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,92 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Stripe\n",
+    "\n",
+    "This notebook covers how to load data from the Stripe REST API into a format that can be ingested into LangChain, along with example usage for vectorization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "\n",
+    "from langchain.document_loaders import StripeLoader\n",
+    "from langchain.indexes import VectorstoreIndexCreator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Stripe API requires an access token, which can be found inside of the Stripe dashboard.\n",
+    "\n",
+    "This document loader also requires a `resource` option which defines what data you want to load.\n",
+    "\n",
+    "Following resources are available:\n",
+    "\n",
+    "`balance_transations` [Documentation](https://stripe.com/docs/api/balance_transactions/list)\n",
+    "\n",
+    "`charges` [Documentation](https://stripe.com/docs/api/charges/list)\n",
+    "\n",
+    "`customers` [Documentation](https://stripe.com/docs/api/customers/list)\n",
+    "\n",
+    "`events` [Documentation](https://stripe.com/docs/api/events/list)\n",
+    "\n",
+    "`refunds` [Documentation](https://stripe.com/docs/api/refunds/list)\n",
+    "\n",
+    "`disputes` [Documentation](https://stripe.com/docs/api/disputes/list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stripe_loader = StripeLoader(\"charges\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a vectorstore retriver from the loader\n",
+    "# see https://python.langchain.com/en/latest/modules/indexes/getting_started.html for more details\n",
+    "\n",
+    "index = VectorstoreIndexCreator().from_loaders([stripe_loader])\n",
+    "stripe_doc_retriever = index.vectorstore.as_retriever()"
+   ]
+  }
+ ],
+ "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -10,9 +10,78 @@
     "This covers how to load Word documents into a document format that we can use downstream."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9438686b",
+   "metadata": {},
+   "source": [
+    "## Using Docx2txt\n",
+    "\n",
+    "Load .docx using `Docx2txt` into a document."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
+   "id": "7b80ea89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import Docx2txtLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "99a12031",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = Docx2txtLoader(\"example_data/fake.docx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b92f68b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d83dd755",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='Lorem ipsum dolor sit amet.', metadata={'source': 'example_data/fake.docx'})]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d40727d",
+   "metadata": {},
+   "source": [
+    "## Using Unstructured"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "721c48aa",
    "metadata": {},
    "outputs": [],
@@ -129,7 +198,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/indexes/retrievers/examples/cohere-reranker.ipynb

@@ -0,0 +1,418 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fc0db1bc",
+   "metadata": {},
+   "source": [
+    "# Cohere Reranker\n",
+    "\n",
+    "This notebook shows how to use [Cohere's rerank endpoint](https://docs.cohere.com/docs/reranking) in a retriever. This builds on top of ideas in the [ContextualCompressionRetriever](contextual-compression.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "28e8dc12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Helper function for printing docs\n",
+    "\n",
+    "def pretty_print_docs(docs):\n",
+    "    print(f\"\\n{'-' * 100}\\n\".join([f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fa3d916",
+   "metadata": {},
+   "source": [
+    "## Set up the base vector store retriever\n",
+    "Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can set up the retriever to retrieve a high number (20) of docs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9fbcc58f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Document 1:\n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 2:\n",
+      "\n",
+      "As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n",
+      "\n",
+      "While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 3:\n",
+      "\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 4:\n",
+      "\n",
+      "He met the Ukrainian people. \n",
+      "\n",
+      "From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \n",
+      "\n",
+      "Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \n",
+      "\n",
+      "In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 5:\n",
+      "\n",
+      "I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n",
+      "\n",
+      "I’ve worked on these issues a long time. \n",
+      "\n",
+      "I know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety. \n",
+      "\n",
+      "So let’s not abandon our streets. Or choose between safety and equal justice.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 6:\n",
+      "\n",
+      "Vice President Harris and I ran for office with a new economic vision for America. \n",
+      "\n",
+      "Invest in America. Educate Americans. Grow the workforce. Build the economy from the bottom up  \n",
+      "and the middle out, not from the top down.  \n",
+      "\n",
+      "Because we know that when the middle class grows, the poor have a ladder up and the wealthy do very well. \n",
+      "\n",
+      "America used to have the best roads, bridges, and airports on Earth. \n",
+      "\n",
+      "Now our infrastructure is ranked 13th in the world.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 7:\n",
+      "\n",
+      "And tonight, I’m announcing that the Justice Department will name a chief prosecutor for pandemic fraud. \n",
+      "\n",
+      "By the end of this year, the deficit will be down to less than half what it was before I took office.  \n",
+      "\n",
+      "The only president ever to cut the deficit by more than one trillion dollars in a single year. \n",
+      "\n",
+      "Lowering your costs also means demanding more competition. \n",
+      "\n",
+      "I’m a capitalist, but capitalism without competition isn’t capitalism. \n",
+      "\n",
+      "It’s exploitation—and it drives up prices.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 8:\n",
+      "\n",
+      "For the past 40 years we were told that if we gave tax breaks to those at the very top, the benefits would trickle down to everyone else. \n",
+      "\n",
+      "But that trickle-down theory led to weaker economic growth, lower wages, bigger deficits, and the widest gap between those at the top and everyone else in nearly a century. \n",
+      "\n",
+      "Vice President Harris and I ran for office with a new economic vision for America.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 9:\n",
+      "\n",
+      "All told, we created 369,000 new manufacturing jobs in America just last year. \n",
+      "\n",
+      "Powered by people I’ve met like JoJo Burgess, from generations of union steelworkers from Pittsburgh, who’s here with us tonight. \n",
+      "\n",
+      "As Ohio Senator Sherrod Brown says, “It’s time to bury the label “Rust Belt.” \n",
+      "\n",
+      "It’s time. \n",
+      "\n",
+      "But with all the bright spots in our economy, record job growth and higher wages, too many families are struggling to keep up with the bills.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 10:\n",
+      "\n",
+      "I’m also calling on Congress: pass a law to make sure veterans devastated by toxic exposures in Iraq and Afghanistan finally get the benefits and comprehensive health care they deserve. \n",
+      "\n",
+      "And fourth, let’s end cancer as we know it. \n",
+      "\n",
+      "This is personal to me and Jill, to Kamala, and to so many of you. \n",
+      "\n",
+      "Cancer is the #2 cause of death in America–second only to heart disease.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 11:\n",
+      "\n",
+      "He will never extinguish their love of freedom. He will never weaken the resolve of the free world. \n",
+      "\n",
+      "We meet tonight in an America that has lived through two of the hardest years this nation has ever faced. \n",
+      "\n",
+      "The pandemic has been punishing. \n",
+      "\n",
+      "And so many families are living paycheck to paycheck, struggling to keep up with the rising cost of food, gas, housing, and so much more. \n",
+      "\n",
+      "I understand.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 12:\n",
+      "\n",
+      "Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \n",
+      "\n",
+      "Last year COVID-19 kept us apart. This year we are finally together again. \n",
+      "\n",
+      "Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n",
+      "\n",
+      "With a duty to one another to the American people to the Constitution. \n",
+      "\n",
+      "And with an unwavering resolve that freedom will always triumph over tyranny.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 13:\n",
+      "\n",
+      "I know. \n",
+      "\n",
+      "One of those soldiers was my son Major Beau Biden. \n",
+      "\n",
+      "We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. \n",
+      "\n",
+      "But I’m committed to finding out everything we can. \n",
+      "\n",
+      "Committed to military families like Danielle Robinson from Ohio. \n",
+      "\n",
+      "The widow of Sergeant First Class Heath Robinson.  \n",
+      "\n",
+      "He was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 14:\n",
+      "\n",
+      "And soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n",
+      "\n",
+      "So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \n",
+      "\n",
+      "First, beat the opioid epidemic. \n",
+      "\n",
+      "There is so much we can do. Increase funding for prevention, treatment, harm reduction, and recovery.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 15:\n",
+      "\n",
+      "Third, support our veterans. \n",
+      "\n",
+      "Veterans are the best of us. \n",
+      "\n",
+      "I’ve always believed that we have a sacred obligation to equip all those we send to war and care for them and their families when they come home. \n",
+      "\n",
+      "My administration is providing assistance with job training and housing, and now helping lower-income veterans get VA care debt-free.  \n",
+      "\n",
+      "Our troops in Iraq and Afghanistan faced many dangers.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 16:\n",
+      "\n",
+      "When we invest in our workers, when we build the economy from the bottom up and the middle out together, we can do something we haven’t done in a long time: build a better America. \n",
+      "\n",
+      "For more than two years, COVID-19 has impacted every decision in our lives and the life of the nation. \n",
+      "\n",
+      "And I know you’re tired, frustrated, and exhausted. \n",
+      "\n",
+      "But I also know this.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 17:\n",
+      "\n",
+      "Now is the hour. \n",
+      "\n",
+      "Our moment of responsibility. \n",
+      "\n",
+      "Our test of resolve and conscience, of history itself. \n",
+      "\n",
+      "It is in this moment that our character is formed. Our purpose is found. Our future is forged. \n",
+      "\n",
+      "Well I know this nation.  \n",
+      "\n",
+      "We will meet the test. \n",
+      "\n",
+      "To protect freedom and liberty, to expand fairness and opportunity. \n",
+      "\n",
+      "We will save democracy. \n",
+      "\n",
+      "As hard as these times have been, I am more optimistic about America today than I have been my whole life.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 18:\n",
+      "\n",
+      "He didn’t know how to stop fighting, and neither did she. \n",
+      "\n",
+      "Through her pain she found purpose to demand we do better. \n",
+      "\n",
+      "Tonight, Danielle—we are. \n",
+      "\n",
+      "The VA is pioneering new ways of linking toxic exposures to diseases, already helping more veterans get benefits. \n",
+      "\n",
+      "And tonight, I’m announcing we’re expanding eligibility to veterans suffering from nine respiratory cancers.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 19:\n",
+      "\n",
+      "I understand. \n",
+      "\n",
+      "I remember when my Dad had to leave our home in Scranton, Pennsylvania to find work. I grew up in a family where if the price of food went up, you felt it. \n",
+      "\n",
+      "That’s why one of the first things I did as President was fight to pass the American Rescue Plan.  \n",
+      "\n",
+      "Because people were hurting. We needed to act, and we did. \n",
+      "\n",
+      "Few pieces of legislation have done more in a critical moment in our history to lift us out of crisis.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 20:\n",
+      "\n",
+      "So let’s not abandon our streets. Or choose between safety and equal justice. \n",
+      "\n",
+      "Let’s come together to protect our communities, restore trust, and hold law enforcement accountable. \n",
+      "\n",
+      "That’s why the Justice Department required body cameras, banned chokeholds, and restricted no-knock warrants for its officers.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.document_loaders import TextLoader\n",
+    "from langchain.vectorstores import FAISS\n",
+    "\n",
+    "documents = TextLoader('../../../state_of_the_union.txt').load()\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
+    "texts = text_splitter.split_documents(documents)\n",
+    "retriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever(search_kwargs={\"k\": 20})\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = retriever.get_relevant_documents(query)\n",
+    "pretty_print_docs(docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7648612",
+   "metadata": {},
+   "source": [
+    "## Doing reranking with CohereRerank\n",
+    "Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll add an `CohereRerank`, uses the Cohere rerank endpoint to rerank the returned results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "9a658023",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Document 1:\n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 2:\n",
+      "\n",
+      "I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n",
+      "\n",
+      "I’ve worked on these issues a long time. \n",
+      "\n",
+      "I know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety. \n",
+      "\n",
+      "So let’s not abandon our streets. Or choose between safety and equal justice.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 3:\n",
+      "\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.retrievers import ContextualCompressionRetriever\n",
+    "from langchain.retrievers.document_compressors import CohereRerank\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "compressor = CohereRerank()\n",
+    "compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)\n",
+    "\n",
+    "compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")\n",
+    "pretty_print_docs(compressed_docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b83dfedb",
+   "metadata": {},
+   "source": [
+    "You can of course use this retriever within a QA pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "367dafe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import RetrievalQA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "ae697ca4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = RetrievalQA.from_chain_type(llm=OpenAI(temperature=0), retriever=compression_retriever)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "46ee62fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'query': 'What did the president say about Ketanji Brown Jackson',\n",
+       " 'result': \" The president said that Ketanji Brown Jackson is one of the nation's top legal minds and that she is a consensus builder who has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"}"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain({\"query\": query})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "700a8133",
+   "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/indexes/retrievers/examples/vespa_retriever.ipynb

@@ -0,0 +1,124 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ce0f17b9",
+   "metadata": {},
+   "source": [
+    "# Vespa retriever\n",
+    "\n",
+    "This notebook shows how to use Vespa.ai as a LangChain retriever.\n",
+    "Vespa.ai is a platform for highly efficient structured text and vector search.\n",
+    "Please refer to [Vespa.ai](https://vespa.ai) for more information.\n",
+    "\n",
+    "In order to create a retriever, we use [pyvespa](https://pyvespa.readthedocs.io/en/latest/index.html) to\n",
+    "create a connection a Vespa service."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c10dd962",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from vespa.application import Vespa\n",
+    "\n",
+    "vespa_app = Vespa(url=\"https://doc-search.vespa.oath.cloud\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3df4ce53",
+   "metadata": {},
+   "source": [
+    "This creates a connection to a Vespa service, here the Vespa documentation search service.\n",
+    "Using pyvespa, you can also connect to a\n",
+    "[Vespa Cloud instance](https://pyvespa.readthedocs.io/en/latest/deploy-vespa-cloud.html)\n",
+    "or a local\n",
+    "[Docker instance](https://pyvespa.readthedocs.io/en/latest/deploy-docker.html).\n",
+    "\n",
+    "\n",
+    "After connecting to the service, you can set up the retriever:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ccca1f4",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.retrievers.vespa_retriever import VespaRetriever\n",
+    "\n",
+    "vespa_query_body = {\n",
+    "    \"yql\": \"select content from paragraph where userQuery()\",\n",
+    "    \"hits\": 5,\n",
+    "    \"ranking\": \"documentation\",\n",
+    "    \"locale\": \"en-us\"\n",
+    "}\n",
+    "vespa_content_field = \"content\"\n",
+    "retriever = VespaRetriever(vespa_app, vespa_query_body, vespa_content_field)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e7e34e1",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "This sets up a LangChain retriever that fetches documents from the Vespa application.\n",
+    "Here, up to 5 results are retrieved from the `content` field in the `paragraph` document type,\n",
+    "using `doumentation` as the ranking method. The `userQuery()` is replaced with the actual query\n",
+    "passed from LangChain.\n",
+    "\n",
+    "Please refer to the [pyvespa documentation](https://pyvespa.readthedocs.io/en/latest/getting-started-pyvespa.html#Query)\n",
+    "for more information.\n",
+    "\n",
+    "Now you can return the results and continue using the results in LangChain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f47a2bfe",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "retriever.get_relevant_documents(\"what is vespa?\")"
+   ]
+  }
+ ],
+ "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": "3.9.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/indexes/text_splitters/examples/tiktoken.ipynb

@@ -6,7 +6,7 @@
    "metadata": {},
    "source": [
     "# tiktoken (OpenAI) Length Function\n",
-    "You can also use tiktoken, a open source tokenizer package from OpenAI to estimate tokens used. Will probably be more accurate for their models.\n",
+    "You can also use tiktoken, an open source tokenizer package from OpenAI to estimate tokens used. Will probably be more accurate for their models.\n",
     "\n",
     "1. How the text is split: by character passed in\n",
     "2. How the chunk size is measured: by `tiktoken` tokenizer"

docs/modules/indexes/vectorstores/examples/analyticdb.ipynb

@@ -6,15 +6,21 @@
    "source": [
     "# AnalyticDB\n",
     "\n",
-    "This notebook shows how to use functionality related to the AnalyticDB vector database.\n",
+    ">[AnalyticDB for PostgreSQL](https://www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview) is a massively parallel processing (MPP) data warehousing service that is designed to analyze large volumes of data online.\n",
+    "\n",
+    ">`AnalyticDB for PostgreSQL` is developed based on the open source `Greenplum Database` project and is enhanced with in-depth extensions by `Alibaba Cloud`. AnalyticDB for PostgreSQL is compatible with the ANSI SQL 2003 syntax and the PostgreSQL and Oracle database ecosystems. AnalyticDB for PostgreSQL also supports row store and column store. AnalyticDB for PostgreSQL processes petabytes of data offline at a high performance level and supports highly concurrent online queries.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `AnalyticDB` vector database.\n",
     "To run, you should have an [AnalyticDB](https://www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview) instance up and running:\n",
     "- Using [AnalyticDB Cloud Vector Database](https://www.alibabacloud.com/product/hybriddb-postgresql). Click here to fast deploy it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
@@ -24,12 +30,10 @@
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Split documents and get embeddings by call OpenAI API"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
@@ -48,6 +52,7 @@
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Connect to AnalyticDB by setting related ENVIRONMENTS.\n",
     "```\n",
@@ -59,10 +64,7 @@
     "```\n",
     "\n",
     "Then store your embeddings and documents into AnalyticDB"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
@@ -90,12 +92,10 @@
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Query and retrieve data"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
@@ -129,13 +129,6 @@
    "source": [
     "print(docs[0].page_content)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -154,9 +147,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

docs/modules/indexes/vectorstores/examples/annoy.ipynb

@@ -1,34 +1,31 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "683953b3",
    "metadata": {},
    "source": [
     "# Annoy\n",
     "\n",
-    "This notebook shows how to use functionality related to the Annoy vector database.\n",
-    "\n",
     "> \"Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.\"\n",
     "\n",
+    "This notebook shows how to use functionality related to the `Annoy` vector database.\n",
+    "\n",
     "via [Annoy](https://github.com/spotify/annoy) \n"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "3b450bdc",
    "metadata": {},
    "source": [
     "```{note}\n",
-    "Annoy is read-only - once the index is built you cannot add any more emebddings!\n",
-    "If you want to progressively add to your VectorStore then better choose an alternative!\n",
+    "NOTE: Annoy is read-only - once the index is built you cannot add any more emebddings!\n",
+    "If you want to progressively add new entries to your VectorStore then better choose an alternative!\n",
     "```"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "6613d222",
    "metadata": {},
@@ -123,7 +120,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "4583b231",
    "metadata": {},
@@ -265,7 +261,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "341390c2",
    "metadata": {},
@@ -409,7 +404,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "6f570f69",
    "metadata": {},
@@ -472,7 +466,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "df4beb83",
    "metadata": {},
@@ -564,7 +557,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/indexes/vectorstores/examples/atlas.ipynb

@@ -6,7 +6,20 @@
    "source": [
     "# AtlasDB\n",
     "\n",
-    "This notebook shows you how to use functionality related to the AtlasDB"
+    "This notebook shows you how to use functionality related to the `AtlasDB`.\n",
+    "\n",
+    ">[MongoDB‘s](https://www.mongodb.com/) [Atlas](https://www.mongodb.com/cloud/atlas) is an on-demand fully managed service. `MongoDB Atlas` runs on `AWS`, `Microsoft Azure`, and `Google Cloud Platform`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install spacy"
    ]
   },
   {
@@ -15,7 +28,34 @@
    "metadata": {
     "pycharm": {
      "is_executing": true
-    }
+    },
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!python3 -m spacy download en_core_web_sm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install nomic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -28,31 +68,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
-    "pycharm": {
-     "is_executing": true
-    },
-    "scrolled": true
+    "tags": []
    },
    "outputs": [],
-   "source": [
-    "!python -m spacy download en_core_web_sm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "ATLAS_TEST_API_KEY = '7xDPkYXSYDc1_ErdTPIcoAR9RNd8YDlkS3nVNXcVoIMZ6'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "loader = TextLoader('../../../state_of_the_union.txt')\n",
@@ -71,7 +101,8 @@
    "metadata": {
     "pycharm": {
      "is_executing": true
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -165,13 +196,6 @@
    "source": [
     "db.project"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -190,9 +214,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

docs/modules/indexes/vectorstores/examples/chroma.ipynb

@@ -7,14 +7,68 @@
    "source": [
     "# Chroma\n",
     "\n",
-    "This notebook shows how to use functionality related to the Chroma vector database."
+    ">[Chroma](https://docs.trychroma.com/getting-started) is a database for building AI applications with embeddings.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `Chroma` vector database."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "0825fa4a-d950-4e78-8bba-20cfcc347765",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install chromadb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "42080f37-8fd1-4cec-acd9-15d2b03b2f4d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get a token: https://platform.openai.com/account/api-keys\n",
+    "\n",
+    "from getpass import getpass\n",
+    "\n",
+    "OPENAI_API_KEY = getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c7a94d6c-b4d4-4498-9bdd-eb50c92b85c5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "aac9563e",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
@@ -25,9 +79,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "id": "a3c3999a",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
@@ -41,9 +97,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "id": "5eabdb75",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -94,9 +152,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "id": "72aaa9c8",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "docs = db.similarity_search_with_score(query)"
@@ -104,18 +164,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "id": "d88e958e",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(Document(page_content='In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \\n\\nWe cannot let this happen. \\n\\nTonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),\n",
-       " 0.3913410007953644)"
+       "(Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'}),\n",
+       " 0.3949805498123169)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -170,7 +232,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "f568a322",
    "metadata": {},
@@ -300,7 +361,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/indexes/vectorstores/examples/deeplake.ipynb

@@ -6,24 +6,30 @@
    "source": [
     "#  Deep Lake\n",
     "\n",
-    "This notebook showcases basic functionality related to Deep Lake. While Deep Lake can store embeddings, it is capable of storing any type of data. It is a fully fledged serverless data lake with version control, query engine and streaming dataloader to deep learning frameworks.  \n",
+    ">[Deep Lake](https://docs.activeloop.ai/) as a Multi-Modal Vector Store that stores embeddings and their metadata including text, jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.\n",
     "\n",
-    "For more information, please see the Deep Lake [documentation](docs.activeloop.ai) or [api reference](docs.deeplake.ai)"
+    "This notebook showcases basic functionality related to `Deep Lake`. While `Deep Lake` can store embeddings, it is capable of storing any type of data. It is a fully fledged serverless data lake with version control, query engine and streaming dataloader to deep learning frameworks.  \n",
+    "\n",
+    "For more information, please see the Deep Lake [documentation](https://docs.activeloop.ai) or [api reference](https://docs.deeplake.ai)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "!python3 -m pip install openai deeplake tiktoken"
+    "!pip install openai deeplake tiktoken"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
@@ -33,9 +39,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key: ········\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import getpass\n",
@@ -46,8 +62,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
@@ -61,7 +79,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -70,9 +87,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/leo/.local/lib/python3.10/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.3.2) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -80,11 +107,26 @@
       "./my_deeplake/ loaded successfully.\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Evaluating ingest: 100%|██████████| 1/1 [00:04<00:00\n"
+      "Evaluating ingest: 100%|██████████████████████████████████████| 1/1 [00:07<00:00\n"
      ]
     },
     {
@@ -93,17 +135,17 @@
      "text": [
       "Dataset(path='./my_deeplake/', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
       "\n",
-      "  tensor     htype     shape     dtype  compression\n",
-      "  -------   -------   -------   -------  ------- \n",
-      " embedding  generic  (4, 1536)  float32   None   \n",
-      "    ids      text     (4, 1)      str     None   \n",
-      " metadata    json     (4, 1)      str     None   \n",
-      "   text      text     (4, 1)      str     None   \n"
+      "  tensor     htype     shape      dtype  compression\n",
+      "  -------   -------   -------    -------  ------- \n",
+      " embedding  generic  (42, 1536)  float32   None   \n",
+      "    ids      text     (42, 1)      str     None   \n",
+      " metadata    json     (42, 1)      str     None   \n",
+      "   text      text     (42, 1)      str     None   \n"
      ]
     }
    ],
    "source": [
-    "db = DeepLake(dataset_path=\"./my_deeplake/\", embedding_function=embeddings, overwrite=True)\n",
+    "db = DeepLake(dataset_path=\"./my_deeplake/\", embedding_function=embeddings)\n",
     "db.add_documents(docs)\n",
     "# or shorter\n",
     "# db = DeepLake.from_documents(docs, dataset_path=\"./my_deeplake/\", embedding=embeddings, overwrite=True)\n",
@@ -113,8 +155,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -135,7 +179,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -144,8 +187,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -155,6 +200,11 @@
       "\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -168,12 +218,12 @@
      "text": [
       "Dataset(path='./my_deeplake/', read_only=True, tensors=['embedding', 'ids', 'metadata', 'text'])\n",
       "\n",
-      "  tensor     htype     shape     dtype  compression\n",
-      "  -------   -------   -------   -------  ------- \n",
-      " embedding  generic  (4, 1536)  float32   None   \n",
-      "    ids      text     (4, 1)      str     None   \n",
-      " metadata    json     (4, 1)      str     None   \n",
-      "   text      text     (4, 1)      str     None   \n"
+      "  tensor     htype     shape      dtype  compression\n",
+      "  -------   -------   -------    -------  ------- \n",
+      " embedding  generic  (42, 1536)  float32   None   \n",
+      "    ids      text     (42, 1)      str     None   \n",
+      " metadata    json     (42, 1)      str     None   \n",
+      "   text      text     (42, 1)      str     None   \n"
      ]
     }
    ],
@@ -183,7 +233,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -191,7 +240,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -200,14 +248,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/media/sdb/davit/Git/experiments/langchain/langchain/llms/openai.py:672: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
+      "/home/leo/.local/lib/python3.10/site-packages/langchain/llms/openai.py:624: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
       "  warnings.warn(\n"
      ]
     }
@@ -221,16 +271,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "\"The president nominated Ketanji Brown Jackson to serve on the United States Supreme Court, describing her as one of the nation's top legal minds and a consensus builder with a background in private practice and public defense, and noting that she has received broad support from both Democrats and Republicans.\""
+       "'The president nominated Ketanji Brown Jackson to serve on the United States Supreme Court. He described her as a former top litigator in private practice, a former federal public defender, a consensus builder, and from a family of public school educators and police officers. He also mentioned that she has received broad support from various groups since being nominated.'"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -241,7 +293,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -325,7 +376,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -357,7 +407,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -389,7 +438,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -406,7 +454,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -429,7 +476,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -533,7 +579,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -594,7 +639,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -638,7 +682,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -815,7 +858,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.10.6"
   },
   "vscode": {
    "interpreter": {
@@ -824,5 +867,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb

@@ -7,14 +7,135 @@
    "source": [
     "# ElasticSearch\n",
     "\n",
-    "This notebook shows how to use functionality related to the ElasticSearch database."
+    "[Elasticsearch](https://www.elastic.co/elasticsearch/) is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `Elasticsearch` database."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b66c12b2-2a07-4136-ac77-ce1c9fa7a409",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81f43794-f002-477c-9b68-4975df30e718",
+   "metadata": {},
+   "source": [
+    "Check out [Elasticsearch installation instructions](https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html).\n",
+    "\n",
+    "To connect to an Elasticsearch instance that does not require\n",
+    "login credentials, pass the Elasticsearch URL and index name along with the\n",
+    "embedding object to the constructor.\n",
+    "\n",
+    "Example:\n",
+    "```python\n",
+    "        from langchain import ElasticVectorSearch\n",
+    "        from langchain.embeddings import OpenAIEmbeddings\n",
+    "\n",
+    "        embedding = OpenAIEmbeddings()\n",
+    "        elastic_vector_search = ElasticVectorSearch(\n",
+    "            elasticsearch_url=\"http://localhost:9200\",\n",
+    "            index_name=\"test_index\",\n",
+    "            embedding=embedding\n",
+    "        )\n",
+    "```\n",
+    "\n",
+    "To connect to an Elasticsearch instance that requires login credentials,\n",
+    "including Elastic Cloud, use the Elasticsearch URL format\n",
+    "https://username:password@es_host:9243. For example, to connect to Elastic\n",
+    "Cloud, create the Elasticsearch URL with the required authentication details and\n",
+    "pass it to the ElasticVectorSearch constructor as the named parameter\n",
+    "elasticsearch_url.\n",
+    "\n",
+    "You can obtain your Elastic Cloud URL and login credentials by logging in to the\n",
+    "Elastic Cloud console at https://cloud.elastic.co, selecting your deployment, and\n",
+    "navigating to the \"Deployments\" page.\n",
+    "\n",
+    "To obtain your Elastic Cloud password for the default \"elastic\" user:\n",
+    "1. Log in to the Elastic Cloud console at https://cloud.elastic.co\n",
+    "2. Go to \"Security\" > \"Users\"\n",
+    "3. Locate the \"elastic\" user and click \"Edit\"\n",
+    "4. Click \"Reset password\"\n",
+    "5. Follow the prompts to reset the password\n",
+    "\n",
+    "Format for Elastic Cloud URLs is\n",
+    "https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.\n",
+    "\n",
+    "Example:\n",
+    "```python\n",
+    "        from langchain import ElasticVectorSearch\n",
+    "        from langchain.embeddings import OpenAIEmbeddings\n",
+    "\n",
+    "        embedding = OpenAIEmbeddings()\n",
+    "\n",
+    "        elastic_host = \"cluster_id.region_id.gcp.cloud.es.io\"\n",
+    "        elasticsearch_url = f\"https://username:password@{elastic_host}:9243\"\n",
+    "        elastic_vector_search = ElasticVectorSearch(\n",
+    "            elasticsearch_url=elasticsearch_url,\n",
+    "            index_name=\"test_index\",\n",
+    "            embedding=embedding\n",
+    "        )\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "d6197931-cbe5-460c-a5e6-b5eedb83887c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install elasticsearch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "67ab8afa-f7c6-4fbf-b596-cb512da949da",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key: ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6030187-0bd7-4798-8372-a265036af5e0",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "id": "aac9563e",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
@@ -25,9 +146,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "id": "a3c3999a",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
@@ -43,7 +166,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "12eb86d8",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "db = ElasticVectorSearch.from_documents(docs, embeddings, elasticsearch_url=\"http://localhost:9200\")\n",
@@ -105,7 +230,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/indexes/vectorstores/examples/faiss.ipynb

@@ -7,14 +7,65 @@
    "source": [
     "# FAISS\n",
     "\n",
-    "This notebook shows how to use functionality related to the FAISS vector database."
+    ">[Facebook AI Similarity Search (Faiss)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.\n",
+    "\n",
+    "[Faiss documentation](https://faiss.ai/).\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `FAISS` vector database."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "aac9563e",
+   "execution_count": null,
+   "id": "497fcd89-e832-46a7-a74a-c71199666206",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#!pip install faiss\n",
+    "# OR\n",
+    "!pip install faiss-cpu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38237514-b3fa-44a4-9cff-30cd6bf50073",
    "metadata": {},
+   "source": [
+    "We want to use OpenAIEmbeddings so we have to get the OpenAI API Key. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "47f9b495-88f1-4286-8d5d-1416103931a7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key: ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aac9563e",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
@@ -25,9 +76,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "id": "a3c3999a",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
@@ -41,9 +94,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "id": "5eabdb75",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "db = FAISS.from_documents(docs, embeddings)\n",
@@ -54,9 +109,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "id": "4b172de8",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -315,7 +372,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/indexes/vectorstores/examples/lanecdb.ipynb

@@ -7,24 +7,61 @@
    "source": [
     "# LanceDB\n",
     "\n",
-    "This notebook shows how to use functionality related to the LanceDB vector database based on the Lance data format."
+    ">[LanceDB](https://lancedb.com/) is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings. Fully open source.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `LanceDB` vector database based on the Lance data format."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "bfcf346a",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "#!pip install lancedb"
+    "!pip install lancedb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99134dd1-b91e-486f-8d90-534248e43b9d",
+   "metadata": {},
+   "source": [
+    "We want to use OpenAIEmbeddings so we have to get the OpenAI API Key. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
+   "id": "a0361f5c-e6f4-45f4-b829-11680cf03cec",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key: ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "aac9563e",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.embeddings import OpenAIEmbeddings\n",
@@ -72,9 +109,7 @@
    "cell_type": "code",
    "execution_count": 14,
    "id": "9c608226",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -171,7 +206,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/indexes/vectorstores/examples/milvus.ipynb

@@ -7,16 +7,63 @@
    "source": [
     "# Milvus\n",
     "\n",
+    ">[Milvus](https://milvus.io/docs/overview.md) is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.\n",
+    "\n",
     "This notebook shows how to use functionality related to the Milvus vector database.\n",
     "\n",
-    "To run, you should have a Milvus instance up and running: https://milvus.io/docs/install_standalone-docker.md"
+    "To run, you should have a [Milvus instance up and running](https://milvus.io/docs/install_standalone-docker.md)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a62cff8a-bcf7-4e33-bbbc-76999c2e3e20",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install pymilvus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a0f9e02-8eb0-4aef-b11f-8861360472ee",
+   "metadata": {},
+   "source": [
+    "We want to use OpenAIEmbeddings so we have to get the OpenAI API Key."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "8b6ed9cd-81b9-46e5-9c20-5aafca2844d0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key: ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "id": "aac9563e",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
@@ -27,9 +74,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "a3c3999a",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
@@ -45,7 +94,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "dcf88bdf",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "vector_db = Milvus.from_documents(\n",
@@ -74,14 +125,6 @@
    "source": [
     "docs[0]"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a359ed74",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -100,7 +143,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/indexes/vectorstores/examples/myscale.ipynb

@@ -1,37 +1,63 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "683953b3",
    "metadata": {},
    "source": [
     "# MyScale\n",
     "\n",
-    "This notebook shows how to use functionality related to the MyScale vector database."
+    ">[MyScale](https://docs.myscale.com/en/overview/) is a cloud-based database optimized for AI applications and solutions, built on the open-source [ClickHouse](https://github.com/ClickHouse/ClickHouse). \n",
+    "\n",
+    "This notebook shows how to use functionality related to the `MyScale` vector database."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43ead5d5-2c1f-4dce-a69a-cb00e4f9d6f0",
+   "metadata": {},
+   "source": [
+    "## Setting up envrionments"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "aac9563e",
-   "metadata": {},
+   "execution_count": null,
+   "id": "7dccc580-8270-4714-ad61-f79783dd6eea",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import MyScale\n",
-    "from langchain.document_loaders import TextLoader"
+    "!pip install clickhouse-connect"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15a1d477-9cdb-4d82-b019-96951ecb2b72",
+   "metadata": {},
+   "source": [
+    "We want to use OpenAIEmbeddings so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91003ea5-0c8c-436c-a5de-aaeaeef2f458",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "a9d16fa3",
    "metadata": {},
    "source": [
-    "## Setting up envrionments\n",
-    "\n",
     "There are two ways to set up parameters for myscale index.\n",
     "\n",
     "1. Environment Variables\n",
@@ -56,9 +82,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
+   "id": "aac9563e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import MyScale\n",
+    "from langchain.document_loaders import TextLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "a3c3999a",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
@@ -126,7 +169,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "e3a8b105",
    "metadata": {},
@@ -145,7 +187,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "f59360c0",
    "metadata": {},
@@ -216,7 +257,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "a359ed74",
    "metadata": {},
@@ -259,7 +299,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/indexes/vectorstores/examples/opensearch.ipynb

@@ -7,7 +7,10 @@
    "source": [
     "# OpenSearch\n",
     "\n",
-    "This notebook shows how to use functionality related to the OpenSearch database.\n",
+    "> [OpenSearch](https://opensearch.org/) is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. `OpenSearch` is a distributed search and analytics engine based on `Apache Lucene`.\n",
+    "\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `OpenSearch` database.\n",
     "\n",
     "To run, you should have the opensearch instance up and running: [here](https://opensearch.org/docs/latest/install-and-configure/install-opensearch/index/)\n",
     "`similarity_search` by default performs the Approximate k-NN Search which uses one of the several algorithms like lucene, nmslib, faiss recommended for\n",
@@ -15,6 +18,39 @@
     "Check [this](https://opensearch.org/docs/latest/search-plugins/knn/index/) for more details."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e606066-9386-4427-8a87-1b93f435c57e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install opensearch-py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1fa637e-4fbf-4d5a-9188-2cad826a193e",
+   "metadata": {},
+   "source": [
+    "We want to use OpenAIEmbeddings so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28e5455e-322d-4010-9e3b-491d522ef5db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -233,9 +269,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

docs/modules/indexes/vectorstores/examples/pgvector.ipynb

@@ -6,7 +6,38 @@
    "source": [
     "# PGVector\n",
     "\n",
-    "This notebook shows how to use functionality related to the Postgres vector database (PGVector)."
+    ">[PGVector](https://github.com/pgvector/pgvector) is an open-source vector similarity search for `Postgres`\n",
+    "\n",
+    "It supports:\n",
+    "- exact and approximate nearest neighbor search\n",
+    "- L2 distance, inner product, and cosine distance\n",
+    "\n",
+    "This notebook shows how to use the Postgres vector database (`PGVector`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See the [installation instruction](https://github.com/pgvector/pgvector)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install pgvector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
    ]
   },
   {
@@ -14,6 +45,31 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "## Loading Environment Variables\n",
     "from typing import List, Tuple\n",
@@ -23,8 +79,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
@@ -182,9 +240,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/modules/indexes/vectorstores/examples/pinecone.ipynb

@@ -7,15 +7,75 @@
    "source": [
     "# Pinecone\n",
     "\n",
-    "This notebook shows how to use functionality related to the Pinecone vector database."
+    "[Pinecone](https://docs.pinecone.io/docs/overview) is a vector database with broad functionality.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `Pinecone` vector database.\n",
+    "\n",
+    "To use Pinecone, you must have an API key. \n",
+    "Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "aac9563e",
+   "execution_count": null,
+   "id": "b4c41cad-08ef-4f72-a545-2151e4598efe",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install pinecone-client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1e38361-c1fe-4ac6-86e9-c90ebaf7ae87",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "PINECONE_API_KEY = getpass.getpass('Pinecone API Key:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02a536e0-d603-4d79-b18b-1ed562977b40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PINECONE_ENV = getpass.getpass('Pinecone Environment:')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "320af802-9271-46ee-948f-d2453933d44b",
+   "metadata": {},
+   "source": [
+    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffea66e4-bc23-46a9-9580-b348dfe7b7a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aac9563e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
@@ -50,8 +110,8 @@
     "\n",
     "# initialize pinecone\n",
     "pinecone.init(\n",
-    "    api_key=\"YOUR_API_KEY\",  # find at app.pinecone.io\n",
-    "    environment=\"YOUR_ENV\"  # next to api key in console\n",
+    "    api_key=PINECONE_API_KEY,  # find at app.pinecone.io\n",
+    "    environment=PINECONE_ENV  # next to api key in console\n",
     ")\n",
     "\n",
     "index_name = \"langchain-demo\"\n",
@@ -100,7 +160,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/indexes/vectorstores/examples/qdrant.ipynb

@@ -7,22 +7,72 @@
    "source": [
     "# Qdrant\n",
     "\n",
-    "This notebook shows how to use functionality related to the Qdrant vector database. There are various modes of how to run Qdrant, and depending on the chosen one, there will be some subtle differences. The options include:\n",
+    ">[Qdrant](https://qdrant.tech/documentation/) (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. `Qdrant` is tailored to extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications.\n",
     "\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `Qdrant` vector database. \n",
+    "\n",
+    "There are various modes of how to run `Qdrant`, and depending on the chosen one, there will be some subtle differences. The options include:\n",
     "- Local mode, no server required\n",
     "- On-premise server deployment\n",
-    "- Qdrant Cloud"
+    "- Qdrant Cloud\n",
+    "\n",
+    "See the [installation instructions](https://qdrant.tech/documentation/install/)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "e03e8460-8f32-4d1f-bb93-4f7636a476fa",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install qdrant-client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b2f111b-357a-4f42-9730-ef0603bdc1b5",
+   "metadata": {},
+   "source": [
+    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "082e7e8b-ac52-430c-98d6-8f0924457642",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key: ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "id": "aac9563e",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-04-04T10:51:22.282884Z",
      "start_time": "2023-04-04T10:51:21.408077Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -34,13 +84,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "a3c3999a",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-04-04T10:51:22.520144Z",
      "start_time": "2023-04-04T10:51:22.285826Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -70,13 +121,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "8429667e",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-04-04T10:51:22.525091Z",
      "start_time": "2023-04-04T10:51:22.522015Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -99,13 +151,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "24b370e2",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-04-04T10:51:24.827567Z",
      "start_time": "2023-04-04T10:51:22.529080Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -242,13 +295,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "a8c513ab",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-04-04T10:51:25.204469Z",
      "start_time": "2023-04-04T10:51:24.855618Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -258,13 +312,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "fc516993",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-04-04T10:51:25.220984Z",
      "start_time": "2023-04-04T10:51:25.213943Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {

docs/modules/indexes/vectorstores/examples/redis.ipynb

@@ -1,19 +1,52 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Redis\n",
     "\n",
+    ">[Redis (Remote Dictionary Server)](https://en.wikipedia.org/wiki/Redis) is an in-memory data structure store, used as a distributed, in-memory key–value database, cache and message broker, with optional durability.\n",
+    "\n",
     "This notebook shows how to use functionality related to the [Redis vector database](https://redis.com/solutions/use-cases/vector-database/)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install redis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
+   "source": [
+    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.embeddings import OpenAIEmbeddings\n",
@@ -227,9 +260,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

docs/modules/indexes/vectorstores/examples/supabase.ipynb

@@ -1,17 +1,23 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "683953b3",
    "metadata": {},
    "source": [
-    "# SupabaseVectorStore\n",
+    "# SupabaseVectorStore"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc80fa84-1f2f-48b4-bd39-3e6412f012f1",
+   "metadata": {},
+   "source": [
+    ">[Supabase](https://supabase.com/docs) is an open source Firebase alternative.\n",
     "\n",
-    "This notebook shows how to use Supabase and `pgvector` as your VectorStore.\n",
+    "This notebook shows how to use `Supabase` and `pgvector` as your VectorStore.\n",
     "\n",
     "To run this notebook, please ensure:\n",
-    "\n",
     "- the `pgvector` extension is enabled\n",
     "- you have installed the `supabase-py` package\n",
     "- that you have created a `match_documents` function in your database\n",
@@ -57,23 +63,66 @@
     "           LIMIT match_count;\n",
     "       END;\n",
     "       $$;\n",
-    "```\n"
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "6bd4498b",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# with pip\n",
-    "# !pip install supabase\n",
+    "!pip install supabase\n",
     "\n",
     "# with conda\n",
     "# !conda install -c conda-forge supabase"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "69bff365-3039-4ff8-a641-aa190166179d",
+   "metadata": {},
+   "source": [
+    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19846a7b-99bc-47a7-8e1c-f13c2497f1ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c71c3901-d44b-4d09-92c5-3018628c28fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['SUPABASE_URL'] = getpass.getpass('Supabase URL:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b91ecfa-f61b-489a-a337-dff1f12f6ab2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['SUPABASE_SERVICE_KEY'] = getpass.getpass('Supabase Service Key:')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -391,7 +440,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/indexes/vectorstores/examples/tair.ipynb

@@ -0,0 +1,129 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tair\n",
+    "\n",
+    "This notebook shows how to use functionality related to the Tair vector database.\n",
+    "To run, you should have an [Tair](https://www.alibabacloud.com/help/en/tair/latest/what-is-tair) instance up and running."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.fake import FakeEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import Tair"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "loader = TextLoader('../../../state_of_the_union.txt')\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = FakeEmbeddings(size=128)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Connect to Tair using the `TAIR_URL` environment variable \n",
+    "```\n",
+    "export TAIR_URL=\"redis://{username}:{password}@{tair_address}:{tair_port}\"\n",
+    "```\n",
+    "\n",
+    "or the keyword argument `tair_url`.\n",
+    "\n",
+    "Then store documents and embeddings into Tair."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tair_url = \"redis://localhost:6379\"\n",
+    "\n",
+    "# drop first if index already exists\n",
+    "Tair.drop_index(tair_url=tair_url)\n",
+    "\n",
+    "vector_store = Tair.from_documents(\n",
+    "    docs,\n",
+    "    embeddings,\n",
+    "    tair_url=tair_url\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Query similar documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(page_content='We’re going after the criminals who stole billions in relief money meant for small businesses and millions of Americans.  \\n\\nAnd tonight, I’m announcing that the Justice Department will name a chief prosecutor for pandemic fraud. \\n\\nBy the end of this year, the deficit will be down to less than half what it was before I took office.  \\n\\nThe only president ever to cut the deficit by more than one trillion dollars in a single year. \\n\\nLowering your costs also means demanding more competition. \\n\\nI’m a capitalist, but capitalism without competition isn’t capitalism. \\n\\nIt’s exploitation—and it drives up prices. \\n\\nWhen corporations don’t have to compete, their profits go up, your prices go up, and small businesses and family farmers and ranchers go under. \\n\\nWe see it happening with ocean carriers moving goods in and out of America. \\n\\nDuring the pandemic, these foreign-owned companies raised prices by as much as 1,000% and made record profits.', metadata={'source': '../../../state_of_the_union.txt'})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = vector_store.similarity_search(query)\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/modules/indexes/vectorstores/examples/weaviate.ipynb

@@ -7,14 +7,73 @@
    "source": [
     "# Weaviate\n",
     "\n",
-    "This notebook shows how to use functionality related to the Weaviate vector database."
+    ">[Weaviate](https://weaviate.io/) is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `Weaviate`vector database.\n",
+    "\n",
+    "See the `Weaviate` [installation instructions](https://weaviate.io/developers/weaviate/installation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9ab167c-fffc-4d30-b1c1-37cc1b641698",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install weaviate-client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b34828d-e627-4d85-aabd-eeb15d9f4b00",
+   "metadata": {},
+   "source": [
+    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37697b9f-fbb2-430e-b95d-28d6eb83486d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fea2dbae-a609-4458-a05f-f1c8e1f37c6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "WEAVIATE_URL = getpass.getpass('WEAVIATE_URL:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53b7ce2d-3c09-4d1c-b66b-5769ce6746ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['WEAVIATE_API_KEY'] = getpass.getpass('WEAVIATE_API_KEY:')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "aac9563e",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
@@ -156,7 +215,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/indexes/vectorstores/examples/zilliz.ipynb

@@ -7,9 +7,57 @@
    "source": [
     "# Zilliz\n",
     "\n",
+    ">[Zilliz Cloud](https://zilliz.com/doc/quick_start) is a fully managed service on cloud for `LF AI Milvus®`,\n",
+    "\n",
     "This notebook shows how to use functionality related to the Zilliz Cloud managed vector database.\n",
     "\n",
-    "To run, you should have a Zilliz Cloud instance up and running: https://zilliz.com/cloud"
+    "To run, you should have a `Zilliz Cloud` instance up and running. Here are the [installation instructions](https://zilliz.com/cloud)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0c50102-e6ac-4475-a930-49c94ed0bd99",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install pymilvus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b25e246-ffe7-4822-a6bf-85d1a120df00",
+   "metadata": {},
+   "source": [
+    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6691489-1ebc-40fa-bc09-b0916903a24d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19a71422",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# replace \n",
+    "ZILLIZ_CLOUD_URI = \"\" # example: \"https://in01-17f69c292d4a5sa.aws-us-west-2.vectordb.zillizcloud.com:19536\"\n",
+    "ZILLIZ_CLOUD_USERNAME = \"\"  # example: \"username\"\n",
+    "ZILLIZ_CLOUD_PASSWORD = \"\"  # example: \"*********\""
    ]
   },
   {
@@ -25,18 +73,6 @@
     "from langchain.document_loaders import TextLoader"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "19a71422",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# replace \n",
-    "ZILLIZ_CLOUD_HOSTNAME = \"\" # example: \"in01-17f69c292d4a50a.aws-us-west-2.vectordb.zillizcloud.com\"\n",
-    "ZILLIZ_CLOUD_PORT = \"\" #example: \"19532\""
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -63,7 +99,12 @@
     "vector_db = Milvus.from_documents(\n",
     "    docs,\n",
     "    embeddings,\n",
-    "    connection_args={\"host\": ZILLIZ_CLOUD_HOSTNAME, \"port\": ZILLIZ_CLOUD_PORT},\n",
+    "    connection_args={\n",
+    "        \"uri\": ZILLIZ_CLOUD_URI,\n",
+    "        \"username\": ZILLIZ_CLOUD_USERNAME,\n",
+    "        \"password\": ZILLIZ_CLOUD_PASSWORD,\n",
+    "        \"secure\": True\n",
+    "    }\n",
     ")"
    ]
   },
@@ -104,7 +145,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.9"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,

docs/modules/indexes/vectorstores/getting_started.ipynb

@@ -11,9 +11,9 @@
    "source": [
     "#  Getting Started\n",
     "\n",
-    "This notebook showcases basic functionality related to VectorStores. A key part of working with vectorstores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the [embedding notebook](embeddings.ipynb) before diving into this.\n",
+    "This notebook showcases basic functionality related to VectorStores. A key part of working with vectorstores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the [embedding notebook](../../models/text_embedding.htpl) before diving into this.\n",
     "\n",
-    "This covers generic high level functionality related to all vector stores. For guides on specific vectorstores, please see the how-to guides [here](../how_to_guides.rst)"
+    "This covers generic high level functionality related to all vector stores."
    ]
   },
   {
@@ -265,7 +265,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/memory/examples/adding_memory_chain_multiple_inputs.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# How to add memory to a Multi-Input Chain\n",
     "\n",
-    "Most memory objects assume a single output. In this notebook, we go over how to add memory to a chain that has multiple outputs. As an example of such a chain, we will add memory to a question/answering chain. This chain takes as inputs both related documents and a user question."
+    "Most memory objects assume a single input. In this notebook, we go over how to add memory to a chain that has multiple inputs. As an example of such a chain, we will add memory to a question/answering chain. This chain takes as inputs both related documents and a user question."
    ]
   },
   {

docs/modules/memory/getting_started.ipynb

@@ -18,7 +18,7 @@
     "In this notebook, we will walk through the simplest form of memory: \"buffer\" memory, which just involves keeping a buffer of all prior messages. We will show how to use the modular utility functions here, then show how it can be used in a chain (both returning a string as well as a list of messages).\n",
     "\n",
     "## ChatMessageHistory\n",
-    "One of the core utility classes underpinning most (if not all) memory modules is the `ChatMessageHistory` class. This is a super lightweight wrapper which exposes convienence methods for saving Human messages, AI messages, and then fetching them all. \n",
+    "One of the core utility classes underpinning most (if not all) memory modules is the `ChatMessageHistory` class. This is a super lightweight wrapper which exposes convenience methods for saving Human messages, AI messages, and then fetching them all. \n",
     "\n",
     "You may want to use this class directly if you are managing memory outside of a chain."
    ]

docs/modules/models/chat/examples/streaming.ipynb

@@ -80,9 +80,8 @@
     }
    ],
    "source": [
-    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "chat = ChatOpenAI(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
+    "chat = ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)\n",
     "resp = chat([HumanMessage(content=\"Write me a song about sparkling water.\")])"
    ]
   },

docs/modules/models/chat/getting_started.ipynb

@@ -200,7 +200,7 @@
    "source": [
     "You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. 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.\n",
     "\n",
-    "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:"
+    "For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:"
    ]
   },
   {
@@ -373,9 +373,8 @@
     }
    ],
    "source": [
-    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "chat = ChatOpenAI(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
+    "chat = ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)\n",
     "resp = chat([HumanMessage(content=\"Write me a song about sparkling water.\")])\n"
    ]
   },

docs/modules/models/chat/integrations/openai.ipynb

@@ -79,7 +79,7 @@
    "source": [
     "You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. 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.\n",
     "\n",
-    "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:"
+    "For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:"
    ]
   },
   {

docs/modules/models/llms/examples/custom_llm.ipynb

@@ -22,18 +22,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "id": "a65696a0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.llms.base import LLM\n",
-    "from typing import Optional, List, Mapping, Any"
+    "from typing import Any, List, Mapping, Optional\n",
+    "\n",
+    "from langchain.callbacks.manager import CallbackManagerForLLMRun\n",
+    "from langchain.llms.base import LLM"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "id": "d5ceff02",
    "metadata": {},
    "outputs": [],
@@ -46,7 +48,12 @@
     "    def _llm_type(self) -> str:\n",
     "        return \"custom\"\n",
     "    \n",
-    "    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:\n",
+    "    def _call(\n",
+    "        self,\n",
+    "        prompt: str,\n",
+    "        stop: Optional[List[str]] = None,\n",
+    "        run_manager: Optional[CallbackManagerForLLMRun] = None,\n",
+    "    ) -> str:\n",
     "        if stop is not None:\n",
     "            raise ValueError(\"stop kwargs are not permitted.\")\n",
     "        return prompt[:self.n]\n",
@@ -67,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "id": "10e5ece6",
    "metadata": {},
    "outputs": [],
@@ -77,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "id": "8cd49199",
    "metadata": {},
    "outputs": [
@@ -87,7 +94,7 @@
        "'This is a '"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -106,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "id": "9c33fa19",
    "metadata": {},
    "outputs": [

docs/modules/models/llms/examples/llm_caching.ipynb

@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "f69f6283",
    "metadata": {},
    "outputs": [],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "64005d1f",
    "metadata": {},
    "outputs": [
@@ -60,8 +60,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 14.2 ms, sys: 4.9 ms, total: 19.1 ms\n",
-      "Wall time: 1.1 s\n"
+      "CPU times: user 26.1 ms, sys: 21.5 ms, total: 47.6 ms\n",
+      "Wall time: 1.68 s\n"
      ]
     },
     {
@@ -70,7 +70,7 @@
        "'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "c8a1cb2b",
    "metadata": {},
    "outputs": [
@@ -91,8 +91,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 162 µs, sys: 7 µs, total: 169 µs\n",
-      "Wall time: 175 µs\n"
+      "CPU times: user 238 µs, sys: 143 µs, total: 381 µs\n",
+      "Wall time: 1.76 ms\n"
      ]
     },
     {
@@ -101,7 +101,7 @@
        "'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -214,9 +214,18 @@
     "## Redis Cache"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c5c9a4d5",
+   "metadata": {},
+   "source": [
+    "### Standard Cache\n",
+    "Use [Redis](../../../../ecosystem/redis.md) to cache prompts and responses."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "39f6eb0b",
    "metadata": {},
    "outputs": [],
@@ -225,15 +234,35 @@
     "# (make sure your local Redis instance is running first before running this example)\n",
     "from redis import Redis\n",
     "from langchain.cache import RedisCache\n",
+    "\n",
     "langchain.llm_cache = RedisCache(redis_=Redis())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "28920749",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 6.88 ms, sys: 8.75 ms, total: 15.6 ms\n",
+      "Wall time: 1.04 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side!'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "%%time\n",
     "# The first time, it is not yet in cache, so it should take longer\n",
@@ -242,16 +271,124 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "94bf9415",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.59 ms, sys: 610 µs, total: 2.2 ms\n",
+      "Wall time: 5.58 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side!'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "%%time\n",
     "# The second time it is, so it goes faster\n",
     "llm(\"Tell me a joke\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "82be23f6",
+   "metadata": {},
+   "source": [
+    "### Semantic Cache\n",
+    "Use [Redis](../../../../ecosystem/redis.md) to cache prompts and responses and evaluate hits based on semantic similarity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "64df3099",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.cache import RedisSemanticCache\n",
+    "\n",
+    "\n",
+    "langchain.llm_cache = RedisSemanticCache(\n",
+    "    redis_url=\"redis://localhost:6379\",\n",
+    "    embedding=OpenAIEmbeddings()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8e91d3ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 351 ms, sys: 156 ms, total: 507 ms\n",
+      "Wall time: 3.37 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"\\n\\nWhy don't scientists trust atoms?\\nBecause they make up everything.\""
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# The first time, it is not yet in cache, so it should take longer\n",
+    "llm(\"Tell me a joke\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "df856948",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 6.25 ms, sys: 2.72 ms, total: 8.97 ms\n",
+      "Wall time: 262 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"\\n\\nWhy don't scientists trust atoms?\\nBecause they make up everything.\""
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# The second time, while not a direct hit, the question is semantically similar to the original question,\n",
+    "# so it uses the cached result!\n",
+    "llm(\"Tell me one joke\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "684eab55",

docs/modules/models/llms/examples/streaming_llm.ipynb

@@ -7,12 +7,12 @@
    "source": [
     "# How to stream LLM and Chat Model responses\n",
     "\n",
-    "LangChain provides streaming support for LLMs. Currently, we support streaming for the `OpenAI`, `ChatOpenAI`. and `Anthropic` implementations, but streaming support for other LLM implementations is on the roadmap. To utilize streaming, use a [`CallbackHandler`](https://github.com/hwchase17/langchain/blob/master/langchain/callbacks/base.py) that implements `on_llm_new_token`. In this example, we are using [`StreamingStdOutCallbackHandler`]()."
+    "LangChain provides streaming support for LLMs. Currently, we support streaming for the `OpenAI`, `ChatOpenAI`, and `Anthropic` implementations, but streaming support for other LLM implementations is on the roadmap. To utilize streaming, use a [`CallbackHandler`](https://github.com/hwchase17/langchain/blob/master/langchain/callbacks/base.py) that implements `on_llm_new_token`. In this example, we are using [`StreamingStdOutCallbackHandler`]()."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "4ac0ff54-540a-4f2b-8d9a-b590fec7fe07",
    "metadata": {
     "tags": []
@@ -21,14 +21,13 @@
    "source": [
     "from langchain.llms import OpenAI, Anthropic\n",
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
     "from langchain.schema import HumanMessage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "77f60a4b-f786-41f2-972e-e5bb8a48dcd5",
    "metadata": {
     "tags": []
@@ -79,7 +78,7 @@
     }
    ],
    "source": [
-    "llm = OpenAI(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
+    "llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)\n",
     "resp = llm(\"Write me a song about sparkling water.\")"
    ]
   },
@@ -95,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "a35373f1-9ee6-4753-a343-5aee749b8527",
    "metadata": {
     "tags": []
@@ -136,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "22665f16-e05b-473c-a4bd-ad75744ea024",
    "metadata": {
     "tags": []
@@ -191,7 +190,7 @@
     }
    ],
    "source": [
-    "chat = ChatOpenAI(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
+    "chat = ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)\n",
     "resp = chat([HumanMessage(content=\"Write me a song about sparkling water.\")])"
    ]
   },
@@ -205,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "eadae4ba-9f21-4ec8-845d-dd43b0edc2dc",
    "metadata": {
     "tags": []
@@ -245,7 +244,7 @@
     }
    ],
    "source": [
-    "llm = Anthropic(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
+    "llm = Anthropic(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)\n",
     "llm(\"Write me a song about sparkling water.\")"
    ]
   }

docs/modules/models/llms/integrations/gpt4all.ipynb

@@ -1,174 +1,173 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# GPT4All\n",
-    "\n",
-    "[GitHub:nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.\n",
-    "\n",
-    "This example goes over how to use LangChain to interact with `GPT4All` models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install pyllamacpp > /dev/null"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain import PromptTemplate, LLMChain\n",
-    "from langchain.llms import GPT4All\n",
-    "from langchain.callbacks.base import CallbackManager\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "template = \"\"\"Question: {question}\n",
-    "\n",
-    "Answer: Let's think step by step.\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Specify Model\n",
-    "\n",
-    "To run locally, download a compatible ggml-formatted model. For more info, visit https://github.com/nomic-ai/pyllamacpp\n",
-    "\n",
-    "For full installation instructions go [here](https://gpt4all.io/index.html).\n",
-    "\n",
-    "The GPT4All Chat installer needs to decompress a 3GB LLM model during the installation process!\n",
-    "\n",
-    "Note that new models are uploaded regularly - check the link above for the most recent `.bin` URL"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_path = './models/gpt4all-lora-quantized-ggml.bin'  # replace with your desired local file path"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Uncomment the below block to download a model. You may want to update `url` to a new version."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import requests\n",
-    "\n",
-    "# from pathlib import Path\n",
-    "# from tqdm import tqdm\n",
-    "\n",
-    "# Path(local_path).parent.mkdir(parents=True, exist_ok=True)\n",
-    "\n",
-    "# # Example model. Check https://github.com/nomic-ai/pyllamacpp for the latest models.\n",
-    "# url = 'https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized-ggml.bin'\n",
-    "\n",
-    "# # send a GET request to the URL to download the file. Stream since it's large\n",
-    "# response = requests.get(url, stream=True)\n",
-    "\n",
-    "# # open the file in binary mode and write the contents of the response to it in chunks\n",
-    "# # This is a large file, so be prepared to wait.\n",
-    "# with open(local_path, 'wb') as f:\n",
-    "#     for chunk in tqdm(response.iter_content(chunk_size=8192)):\n",
-    "#         if chunk:\n",
-    "#             f.write(chunk)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Callbacks support token-wise streaming\n",
-    "callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])\n",
-    "# Verbose is required to pass to the callback manager\n",
-    "llm = GPT4All(model=local_path, callback_manager=callback_manager, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
-    "\n",
-    "llm_chain.run(question)"
-   ]
-  }
- ],
- "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": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# GPT4All\n",
+                "\n",
+                "[GitHub:nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.\n",
+                "\n",
+                "This example goes over how to use LangChain to interact with `GPT4All` models."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "metadata": {
+                "tags": []
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Note: you may need to restart the kernel to use updated packages.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "%pip install pygpt4all > /dev/null"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "metadata": {
+                "tags": []
+            },
+            "outputs": [],
+            "source": [
+                "from langchain import PromptTemplate, LLMChain\n",
+                "from langchain.llms import GPT4All\n",
+                "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "metadata": {
+                "tags": []
+            },
+            "outputs": [],
+            "source": [
+                "template = \"\"\"Question: {question}\n",
+                "\n",
+                "Answer: Let's think step by step.\"\"\"\n",
+                "\n",
+                "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### Specify Model\n",
+                "\n",
+                "To run locally, download a compatible ggml-formatted model. For more info, visit https://github.com/nomic-ai/pygpt4all\n",
+                "\n",
+                "For full installation instructions go [here](https://gpt4all.io/index.html).\n",
+                "\n",
+                "The GPT4All Chat installer needs to decompress a 3GB LLM model during the installation process!\n",
+                "\n",
+                "Note that new models are uploaded regularly - check the link above for the most recent `.bin` URL"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "local_path = './models/ggml-gpt4all-l13b-snoozy.bin'  # replace with your desired local file path"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Uncomment the below block to download a model. You may want to update `url` to a new version."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# import requests\n",
+                "\n",
+                "# from pathlib import Path\n",
+                "# from tqdm import tqdm\n",
+                "\n",
+                "# Path(local_path).parent.mkdir(parents=True, exist_ok=True)\n",
+                "\n",
+                "# # Example model. Check https://github.com/nomic-ai/pygpt4all for the latest models.\n",
+                "# url = 'http://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin'\n",
+                "\n",
+                "# # send a GET request to the URL to download the file. Stream since it's large\n",
+                "# response = requests.get(url, stream=True)\n",
+                "\n",
+                "# # open the file in binary mode and write the contents of the response to it in chunks\n",
+                "# # This is a large file, so be prepared to wait.\n",
+                "# with open(local_path, 'wb') as f:\n",
+                "#     for chunk in tqdm(response.iter_content(chunk_size=8192)):\n",
+                "#         if chunk:\n",
+                "#             f.write(chunk)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# Callbacks support token-wise streaming\n",
+                "callbacks = [StreamingStdOutCallbackHandler()]\n",
+                "# Verbose is required to pass to the callback manager\n",
+                "llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "llm_chain = LLMChain(prompt=prompt, llm=llm)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
+                "\n",
+                "llm_chain.run(question)"
+            ]
+        }
+    ],
+    "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/prompts/chat_prompt_template.ipynb

@@ -1,22 +1,28 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6488fdaf",
    "metadata": {},
    "source": [
     "# Chat Prompt Template\n",
     "\n",
-    "Chat Models takes a list of chat messages as input - this list commonly referred to as a prompt.\n",
-    "Typically this is not simply a hardcoded list of messages but rather a combination of a template, some examples, and user input.\n",
-    "LangChain provides several classes and functions to make constructing and working with prompts easy.\n"
+    "[Chat Models](../models/chat.rst) takes a list of chat messages as input - this list commonly referred to as a prompt.\n",
+    "These chat messages differ from raw string (which you would pass into a [LLM](../models/llms.rst) model) in that every message is associated with a role.\n",
+    "\n",
+    "For example, in OpenAI [Chat Completion API](https://platform.openai.com/docs/guides/chat/introduction), a chat message can be associated with the AI, human or system role. The model is supposed to follow instruction from system chat message more closely.\n",
+    "\n",
+    "Therefore, LangChain provides several related prompt templates to make constructing and working with prompts easily. You are encouraged to use these chat related prompt templates instead of `PromptTemplate` when querying chat models to fully exploit the potential of underlying chat model.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 35,
    "id": "7647a621",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.prompts import (\n",
@@ -38,16 +44,18 @@
    "id": "acb4a2f6",
    "metadata": {},
    "source": [
-    "You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. 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.\n",
+    "To create a message template associated with a role, you use `MessagePromptTemplate`. \n",
     "\n",
     "For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 36,
    "id": "3124f5e9",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "template=\"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
@@ -57,10 +65,46 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9c7e2e6f",
+   "cell_type": "markdown",
+   "id": "c8b08cda-7c57-4c15-a1e5-80627cfa9cbd",
    "metadata": {},
+   "source": [
+    "If you wanted to construct the `MessagePromptTemplate` more directly, you could create a PromptTemplate outside and then pass it in, eg:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "5a8d249e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "prompt=PromptTemplate(\n",
+    "    template=\"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "    input_variables=[\"input_language\", \"output_language\"],\n",
+    ")\n",
+    "system_message_prompt_2 = SystemMessagePromptTemplate(prompt=prompt)\n",
+    "\n",
+    "assert system_message_prompt == system_message_prompt_2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96836c5c-41f8-4073-95ac-ea1daab2e00e",
+   "metadata": {},
+   "source": [
+    "After that, you can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. 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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "9c7e2e6f",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -69,7 +113,7 @@
        " HumanMessage(content='I love programming.', additional_kwargs={})]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -82,25 +126,225 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "0dbdf94f",
+   "id": "0899f681-012e-4687-a754-199a9a396738",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Format output\n",
+    "\n",
+    "The output of the format method is available as string, list of messages and `ChatPromptValue`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "584166de-0c31-4bc9-bf7a-5b359e7173d8",
    "metadata": {},
    "source": [
-    "If you wanted to construct the MessagePromptTemplate more directly, you could create a PromptTemplate outside and then pass it in, eg:"
+    "As string:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "5a8d249e",
-   "metadata": {},
+   "execution_count": 39,
+   "id": "2f6c7ad1-def5-41dc-a4fe-3731dc8917f9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'System: You are a helpful assistant that translates English to French.\\nHuman: I love programming.'"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output = chat_prompt.format(input_language=\"English\", output_language=\"French\", text=\"I love programming.\")\n",
+    "output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "144b3368-43f3-49fa-885f-3f3470e9ab7e",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "prompt=PromptTemplate(\n",
-    "    template=\"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "    input_variables=[\"input_language\", \"output_language\"],\n",
-    ")\n",
-    "system_message_prompt = SystemMessagePromptTemplate(prompt=prompt)"
+    "# or alternatively \n",
+    "output_2 = chat_prompt.format_prompt(input_language=\"English\", output_language=\"French\", text=\"I love programming.\").to_string()\n",
+    "\n",
+    "assert output == output_2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51970399-c2e1-4c9b-8003-6f5b1236fda8",
+   "metadata": {},
+   "source": [
+    "As `ChatPromptValue`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "681ce4a7-d972-4cdf-ac77-ec35182fd352",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ChatPromptValue(messages=[SystemMessage(content='You are a helpful assistant that translates English to French.', additional_kwargs={}), HumanMessage(content='I love programming.', additional_kwargs={})])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat_prompt.format_prompt(input_language=\"English\", output_language=\"French\", text=\"I love programming.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61041810-4418-4406-9c8a-91c9034c9752",
+   "metadata": {},
+   "source": [
+    "As list of Message objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "4ec2f166-1ef9-4071-8c37-fcfbd3f4bc29",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[SystemMessage(content='You are a helpful assistant that translates English to French.', additional_kwargs={}),\n",
+       " HumanMessage(content='I love programming.', additional_kwargs={})]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat_prompt.format_prompt(input_language=\"English\", output_language=\"French\", text=\"I love programming.\").to_messages()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73dcd3a2-ad6d-4b7b-ab21-1d9e417f959e",
+   "metadata": {},
+   "source": [
+    "## Different types of `MessagePromptTemplate`\n",
+    "\n",
+    "LangChain provides different types of `MessagePromptTemplate`. The most commonly used are `AIMessagePromptTemplate`, `SystemMessagePromptTemplate` and `HumanMessagePromptTemplate`, which create an AI message, system message and human message respectively.\n",
+    "\n",
+    "However, in cases where the chat model supports taking chat message with arbitrary role, you can use `ChatMessagePromptTemplate`, which allows user to specify the role name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "55a21b1f-9cdc-4072-a41d-695b00dd11e6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ChatMessage(content='May the force be with you', additional_kwargs={}, role='Jedi')"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.prompts import ChatMessagePromptTemplate\n",
+    "\n",
+    "prompt = \"May the {subject} be with you\"\n",
+    "\n",
+    "chat_message_prompt = ChatMessagePromptTemplate.from_template(role=\"Jedi\", template=prompt)\n",
+    "chat_message_prompt.format(subject=\"force\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd6bf82b-4709-4683-b215-6b7c468f3347",
+   "metadata": {},
+   "source": [
+    "LangChain also provides `MessagesPlaceholder`, which gives you full control of what messages to be rendered during formatting. This can be useful when you are uncertain of what role you should be using for your message prompt templates or when you wish to insert a list of messages during formatting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "9f034650-5e50-4bc3-80f8-5e428ca6444d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import MessagesPlaceholder\n",
+    "\n",
+    "human_prompt = \"Summarize our conversation so far in {word_count} words.\"\n",
+    "human_message_template = HumanMessagePromptTemplate.from_template(human_prompt)\n",
+    "\n",
+    "chat_prompt = ChatPromptTemplate.from_messages([MessagesPlaceholder(variable_name=\"conversation\"), human_message_template])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "9789e8e7-b3f9-4391-a85c-373e576107b3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[HumanMessage(content='What is the best way to learn programming?', additional_kwargs={}),\n",
+       " AIMessage(content='1. Choose a programming language: Decide on a programming language that you want to learn. \\n\\n2. Start with the basics: Familiarize yourself with the basic programming concepts such as variables, data types and control structures.\\n\\n3. Practice, practice, practice: The best way to learn programming is through hands-on experience', additional_kwargs={}),\n",
+       " HumanMessage(content='Summarize our conversation so far in 10 words.', additional_kwargs={})]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "human_message = HumanMessage(content=\"What is the best way to learn programming?\")\n",
+    "ai_message = AIMessage(content=\"\"\"\\\n",
+    "1. Choose a programming language: Decide on a programming language that you want to learn. \n",
+    "\n",
+    "2. Start with the basics: Familiarize yourself with the basic programming concepts such as variables, data types and control structures.\n",
+    "\n",
+    "3. Practice, practice, practice: The best way to learn programming is through hands-on experience\\\n",
+    "\"\"\")\n",
+    "\n",
+    "chat_prompt.format_prompt(conversation=[human_message, ai_message], word_count=\"10\").to_messages()"
    ]
   }
  ],
@@ -120,7 +364,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,

docs/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.ipynb

@@ -110,7 +110,7 @@
     "                'driver_hourly_stats:acc_rate',\n",
     "                'driver_hourly_stats:avg_daily_trips'\n",
     "            ],\n",
-    "            entity_rows=[{\"driver_id\": 1001}]\n",
+    "            entity_rows=[{\"driver_id\": driver_id}]\n",
     "        ).to_dict()\n",
     "        kwargs[\"conv_rate\"] = feature_vector[\"conv_rate\"][0]\n",
     "        kwargs[\"acc_rate\"] = feature_vector[\"acc_rate\"][0]\n",

docs/tracing.md

@@ -6,16 +6,15 @@ First, you should install tracing and set up your environment properly.
 You can use either a locally hosted version of this (uses Docker) or a cloud hosted version (in closed alpha).
 If you're interested in using the hosted platform, please fill out the form [here](https://forms.gle/tRCEMSeopZf6TE3b6).
 
-
 - [Locally Hosted Setup](./tracing/local_installation.md)
 - [Cloud Hosted Setup](./tracing/hosted_installation.md)
 
 ## Tracing Walkthrough
 
-When you first access the UI, you should see a page with your tracing sessions. 
-An initial one "default" should already be created for you. 
-A session is just a way to group traces together. 
-If you click on a session, it will take you to a page with no recorded traces that says "No Runs." 
+When you first access the UI, you should see a page with your tracing sessions.
+An initial one "default" should already be created for you.
+A session is just a way to group traces together.
+If you click on a session, it will take you to a page with no recorded traces that says "No Runs."
 You can create a new session with the new session form.
 
 ![](tracing/homepage.png)
@@ -35,7 +34,7 @@ We can keep on clicking further and further down to explore deeper and deeper.
 
 ![](tracing/explore.png)
 
-We can also click on the "Explore" button of the top level run to dive even deeper. 
+We can also click on the "Explore" button of the top level run to dive even deeper.
 Here, we can see the inputs and outputs in full, as well as all the nested traces.
 
 ![](tracing/explore_trace.png)
@@ -46,11 +45,12 @@ For example, here is the lowest level trace with the exact inputs/outputs to the
 ![](tracing/explore_llm.png)
 
 ## Changing Sessions
+
 1. To initially record traces to a session other than `"default"`, you can set the `LANGCHAIN_SESSION` environment variable to the name of the session you want to record to:
 
 ```python
 import os
-os.environ["LANGCHAIN_HANDLER"] = "langchain"
+os.environ["LANGCHAIN_TRACING"] = "true"
 os.environ["LANGCHAIN_SESSION"] = "my_session" # Make sure this session actually exists. You can create a new session in the UI.
 ```
 

docs/tracing/agent_with_tracing.ipynb

@@ -5,7 +5,14 @@
    "id": "5371a9bb",
    "metadata": {},
    "source": [
-    "# Tracing Walkthrough"
+    "# Tracing Walkthrough\n",
+    "\n",
+    "There are two recommended ways to trace your LangChains:\n",
+    "\n",
+    "1. Setting the `LANGCHAIN_TRACING` environment variable to \"true\".\n",
+    "1. Using a context manager with tracing_enabled() to trace a particular block of code.\n",
+    "\n",
+    "**Note** if the environment variable is set, all code will be traced, regardless of whether or not it's within the context manager."
    ]
   },
   {
@@ -18,24 +25,22 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "os.environ[\"LANGCHAIN_HANDLER\"] = \"langchain\"\n",
-    "\n",
-    "## Uncomment this if using hosted setup.\n",
+    "os.environ[\"LANGCHAIN_TRACING\"] = \"true\"\n",
     "\n",
+    "## Uncomment below if using hosted setup.\n",
     "# os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://langchain-api-gateway-57eoxz8z.uc.gateway.dev\" \n",
     "\n",
-    "## Uncomment this if you want traces to be recorded to \"my_session\" instead of default.\n",
-    "\n",
+    "## Uncomment below if you want traces to be recorded to \"my_session\" instead of \"default\".\n",
     "# os.environ[\"LANGCHAIN_SESSION\"] = \"my_session\"  \n",
     "\n",
     "## Better to set this environment variable in the terminal\n",
-    "## Uncomment this if using hosted version. Replace \"my_api_key\" with your actual API Key.\n",
-    "\n",
+    "## Uncomment below if using hosted version. Replace \"my_api_key\" with your actual API Key.\n",
     "# os.environ[\"LANGCHAIN_API_KEY\"] = \"my_api_key\"  \n",
     "\n",
     "import langchain\n",
     "from langchain.agents import Tool, initialize_agent, load_tools\n",
     "from langchain.agents import AgentType\n",
+    "from langchain.callbacks import tracing_enabled\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.llms import OpenAI"
    ]
@@ -73,8 +78,7 @@
       "\u001b[32;1m\u001b[1;3m I need to use a calculator to solve this.\n",
       "Action: Calculator\n",
       "Action Input: 2^.123243\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\n",
-      "\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
       "Final Answer: 1.0891804557407723\u001b[0m\n",
       "\n",
@@ -104,7 +108,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "4829eb1d",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -113,52 +119,11 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mQuestion: What is 2 raised to .123243 power?\n",
-      "Thought: I need a calculator to solve this problem.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"calculator\",\n",
-      "  \"action_input\": \"2^0.123243\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Observation: calculator is not a valid tool, try another one.\n",
-      "\u001b[32;1m\u001b[1;3mI made a mistake, I need to use the correct tool for this question.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"calculator\",\n",
-      "  \"action_input\": \"2^0.123243\"\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "Observation: calculator is not a valid tool, try another one.\n",
-      "\u001b[32;1m\u001b[1;3mI made a mistake, the tool name is actually \"calc\" instead of \"calculator\".\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"calc\",\n",
-      "  \"action_input\": \"2^0.123243\"\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "Observation: calc is not a valid tool, try another one.\n",
-      "\u001b[32;1m\u001b[1;3mI made another mistake, the tool name is actually \"Calculator\" instead of \"calc\".\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Calculator\",\n",
-      "  \"action_input\": \"2^0.123243\"\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThe final answer is 1.0891804557407723.\n",
+      "\u001b[32;1m\u001b[1;3mI need to use a calculator to solve this.\n",
+      "Action: Calculator\n",
+      "Action Input: 2 ^ .123243\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI now know the answer to the question. \n",
       "Final Answer: 1.0891804557407723\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -186,8 +151,182 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "76abfd82",
+   "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;3mI need to use a calculator to solve this.\n",
+      "Action: Calculator\n",
+      "Action Input: 2 ^ .123243\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI now know the answer to the question. \n",
+      "Final Answer: 1.0891804557407723\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI need to use a calculator to solve this.\n",
+      "Action: Calculator\n",
+      "Action Input: 5 ^ .123243\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.2193914912400514\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI now know the answer to the question. \n",
+      "Final Answer: 1.2193914912400514\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Both of the agent runs will be traced because the environment variable is set\n",
+    "agent.run(\"What is 2 raised to .123243 power?\")\n",
+    "with tracing_enabled() as session:\n",
+    "    agent.run(\"What is 5 raised to .123243 power?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fe833c33-033f-4806-be0c-cc3d147db13d",
+   "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;3mI need to use a calculator to solve this.\n",
+      "Action: Calculator\n",
+      "Action Input: 5 ^ .123243\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.2193914912400514\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI now know the answer to the question. \n",
+      "Final Answer: 1.2193914912400514\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI need to use a calculator to solve this.\n",
+      "Action: Calculator\n",
+      "Action Input: 2 ^ .123243\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI now know the answer to the question. \n",
+      "Final Answer: 1.0891804557407723\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'1.0891804557407723'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Now, we unset the environment variable and use a context manager.\n",
+    "if \"LANGCHAIN_TRACING\" in os.environ:\n",
+    "    del os.environ[\"LANGCHAIN_TRACING\"]\n",
+    "\n",
+    "# here, we are writing traces to \"my_test_session\"\n",
+    "with tracing_enabled(\"my_session\") as session:\n",
+    "    assert session\n",
+    "    agent.run(\"What is 5 raised to .123243 power?\")  # this should be traced\n",
+    "\n",
+    "agent.run(\"What is 2 raised to .123243 power?\")  # this should not be traced"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b34105a4-be8e-46e4-8abe-01adba3ba727",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\n",
+      "\u001b[32;1m\u001b[1;3mI need to use a calculator to solve this.\n",
+      "Action: Calculator\n",
+      "Action Input: 3^0.123\u001b[0m\u001b[32;1m\u001b[1;3mI need to use a calculator to solve this.\n",
+      "Action: Calculator\n",
+      "Action Input: 2^0.123\u001b[0m\u001b[32;1m\u001b[1;3mAny number raised to the power of 0 is 1, but I'm not sure about a decimal power.\n",
+      "Action: Calculator\n",
+      "Action Input: 1^.123\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.1446847956963533\u001b[0m\n",
+      "Thought:\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0889970153361064\u001b[0m\n",
+      "Thought:\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0\u001b[0m\n",
+      "Thought:\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'1.0'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The context manager is concurrency safe:\n",
+    "import asyncio \n",
+    "if \"LANGCHAIN_TRACING\" in os.environ:\n",
+    "    del os.environ[\"LANGCHAIN_TRACING\"]\n",
+    "    \n",
+    "questions = [f\"What is {i} raised to .123 power?\" for i in range(1,4)]\n",
+    "\n",
+    "# start a background task\n",
+    "task = asyncio.create_task(agent.arun(questions[0]))  # this should not be traced\n",
+    "with tracing_enabled() as session:\n",
+    "    assert session\n",
+    "    tasks = [agent.arun(q) for q in questions[1:3]]  # these should be traced\n",
+    "    await asyncio.gather(*tasks)\n",
+    "\n",
+    "await task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e46c85b-2ac0-4661-abed-9c2bf3036820",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -209,7 +348,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/use_cases/agent_simulations.md

@@ -1,12 +1,15 @@
 # Agent Simulations
 
-Agent simulations involve interacting one of more agents with eachother.
+Agent simulations involve interacting one of more agents with each other.
 Agent simulations generally involve two main components:
 
 - Long Term Memory
 - Simulation Environment
 
-Specific implementations of agent simulations (or parts of agent simulations) include
+Specific implementations of agent simulations (or parts of agent simulations) include:
+
+## Simulations with One Agent
+- [Simulated Environment: Gymnasium](agent_simulations/gymnasium.ipynb): an example of how to create a simple agent-environment interaction loop with [Gymnasium](https://gymnasium.farama.org/) (formerly [OpenAI Gym](https://github.com/openai/gym)).
 
 ## Simulations with Two Agents
 - [CAMEL](agent_simulations/camel_role_playing.ipynb): an implementation of the CAMEL (Communicative Agents for “Mind” Exploration of Large Scale Language Model Society) paper, where two agents communicate with each other.
@@ -16,4 +19,5 @@ Specific implementations of agent simulations (or parts of agent simulations) in
 - [Multi-Player D&D](agent_simulations/multi_player_dnd.ipynb): an example of how to use a generic dialogue simulator for multiple dialogue agents with a custom speaker-ordering, illustrated with a variant of the popular Dungeons & Dragons role playing game.
 - [Decentralized Speaker Selection](agent_simulations/multiagent_bidding.ipynb): an example of how to implement a multi-agent dialogue without a fixed schedule for who speaks when. Instead the agents decide for themselves who speaks by outputting bids to speak. This example shows how to do this in the context of a fictitious presidential debate.
 - [Authoritarian Speaker Selection](agent_simulations/multiagent_authoritarian.ipynb): an example of how to implement a multi-agent dialogue, where a privileged agent directs who speaks what. This example also showcases how to enable the privileged agent to determine when the conversation terminates. This example shows how to do this in the context of a fictitious news show.
+- [Simulated Environment: PettingZoo](agent_simulations/petting_zoo.ipynb): an example of how to create a agent-environment interaction loop for multiple agents with [PettingZoo](https://pettingzoo.farama.org/) (a multi-agent version of [Gymnasium](https://gymnasium.farama.org/)).
 - [Generative Agents](agent_simulations/characters.ipynb): This notebook implements a generative agent based on the paper [Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442) by Park, et. al.

docs/use_cases/agent_simulations/gymnasium.ipynb

@@ -0,0 +1,259 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4b089493",
+   "metadata": {},
+   "source": [
+    "# Simulated Environment: Gymnasium\n",
+    "\n",
+    "For many applications of LLM agents, the environment is real (internet, database, REPL, etc). However, we can also define agents to interact in simulated environments like text-based games. This is an example of how to create a simple agent-environment interaction loop with [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) (formerly [OpenAI Gym](https://github.com/openai/gym))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f36427cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gymnasium in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (0.28.1)\n",
+      "Requirement already satisfied: farama-notifications>=0.0.1 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (0.0.4)\n",
+      "Requirement already satisfied: importlib-metadata>=4.8.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (6.0.1)\n",
+      "Requirement already satisfied: cloudpickle>=1.2.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (2.2.1)\n",
+      "Requirement already satisfied: numpy>=1.21.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (1.24.3)\n",
+      "Requirement already satisfied: jax-jumpy>=1.0.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (1.0.0)\n",
+      "Requirement already satisfied: typing-extensions>=4.3.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (4.5.0)\n",
+      "Requirement already satisfied: zipp>=0.5 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from importlib-metadata>=4.8.0->gymnasium) (3.15.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install gymnasium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f9bd38b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gymnasium as gym\n",
+    "import inspect\n",
+    "import tenacity\n",
+    "\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.schema import (\n",
+    "    AIMessage,\n",
+    "    HumanMessage,\n",
+    "    SystemMessage,\n",
+    "    BaseMessage,\n",
+    ")\n",
+    "from langchain.output_parsers import RegexParser"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e222e811",
+   "metadata": {},
+   "source": [
+    "## Define the agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "870c24bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GymnasiumAgent():\n",
+    "    @classmethod\n",
+    "    def get_docs(cls, env):\n",
+    "        return env.unwrapped.__doc__\n",
+    "    \n",
+    "    def __init__(self, model, env):\n",
+    "        self.model = model\n",
+    "        self.env = env\n",
+    "        self.docs = self.get_docs(env)\n",
+    "        \n",
+    "        self.instructions = \"\"\"\n",
+    "Your goal is to maximize your return, i.e. the sum of the rewards you receive.\n",
+    "I will give you an observation, reward, terminiation flag, truncation flag, and the return so far, formatted as:\n",
+    "\n",
+    "Observation: <observation>\n",
+    "Reward: <reward>\n",
+    "Termination: <termination>\n",
+    "Truncation: <truncation>\n",
+    "Return: <sum_of_rewards>\n",
+    "\n",
+    "You will respond with an action, formatted as:\n",
+    "\n",
+    "Action: <action>\n",
+    "\n",
+    "where you replace <action> with your actual action.\n",
+    "Do nothing else but return the action.\n",
+    "\"\"\"\n",
+    "        self.action_parser = RegexParser(\n",
+    "            regex=r\"Action: (.*)\", \n",
+    "            output_keys=['action'], \n",
+    "            default_output_key='action')\n",
+    "        \n",
+    "        self.message_history = []\n",
+    "        self.ret = 0\n",
+    "        \n",
+    "    def random_action(self):\n",
+    "        action = self.env.action_space.sample()\n",
+    "        return action\n",
+    "        \n",
+    "    def reset(self):\n",
+    "        self.message_history = [\n",
+    "            SystemMessage(content=self.docs),\n",
+    "            SystemMessage(content=self.instructions),\n",
+    "        ]\n",
+    "        \n",
+    "    def observe(self, obs, rew=0, term=False, trunc=False, info=None):\n",
+    "        self.ret += rew\n",
+    "    \n",
+    "        obs_message = f\"\"\"\n",
+    "Observation: {obs}\n",
+    "Reward: {rew}\n",
+    "Termination: {term}\n",
+    "Truncation: {trunc}\n",
+    "Return: {self.ret}\n",
+    "        \"\"\"\n",
+    "        self.message_history.append(HumanMessage(content=obs_message))\n",
+    "        return obs_message\n",
+    "    \n",
+    "    def _act(self):\n",
+    "        act_message = self.model(self.message_history)\n",
+    "        self.message_history.append(act_message)\n",
+    "        action = int(self.action_parser.parse(act_message.content)['action'])\n",
+    "        return action\n",
+    "        \n",
+    "    def act(self):\n",
+    "        try:\n",
+    "            for attempt in tenacity.Retrying(\n",
+    "                                stop=tenacity.stop_after_attempt(2),\n",
+    "                                wait=tenacity.wait_none(),  # No waiting time between retries\n",
+    "                                retry=tenacity.retry_if_exception_type(ValueError),\n",
+    "                                before_sleep=lambda retry_state: print(f\"ValueError occurred: {retry_state.outcome.exception()}, retrying...\"),\n",
+    "                            ):\n",
+    "                with attempt:\n",
+    "                    action = self._act()\n",
+    "        except tenacity.RetryError as e:\n",
+    "            action = self.random_action()\n",
+    "        return action"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e76d22c",
+   "metadata": {},
+   "source": [
+    "## Initialize the simulated environment and agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9e902cfd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "env = gym.make(\"Blackjack-v1\")\n",
+    "agent = GymnasiumAgent(model=ChatOpenAI(temperature=0.2), env=env)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2c12b15",
+   "metadata": {},
+   "source": [
+    "## Main loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ad361210",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Observation: (15, 4, 0)\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 1\n",
+      "\n",
+      "Observation: (25, 4, 0)\n",
+      "Reward: -1.0\n",
+      "Termination: True\n",
+      "Truncation: False\n",
+      "Return: -1.0\n",
+      "        \n",
+      "break True False\n"
+     ]
+    }
+   ],
+   "source": [
+    "observation, info = env.reset()\n",
+    "agent.reset()\n",
+    "\n",
+    "obs_message = agent.observe(observation)\n",
+    "print(obs_message)\n",
+    "\n",
+    "while True:\n",
+    "    action = agent.act()\n",
+    "    observation, reward, termination, truncation, info = env.step(action)\n",
+    "    obs_message = agent.observe(observation, reward, termination, truncation, info)\n",
+    "    print(f'Action: {action}')\n",
+    "    print(obs_message)\n",
+    "    \n",
+    "    if termination or truncation:\n",
+    "        print('break', termination, truncation)\n",
+    "        break\n",
+    "env.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58a13e9c",
+   "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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/use_cases/agent_simulations/multi_player_dnd.ipynb

@@ -485,7 +485,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/use_cases/agent_simulations/multiagent_authoritarian.ipynb

@@ -841,7 +841,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/use_cases/agent_simulations/petting_zoo.ipynb

@@ -0,0 +1,837 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4b089493",
+   "metadata": {},
+   "source": [
+    "# Multi-Agent Simulated Environment: Petting Zoo\n",
+    "\n",
+    "In this example, we show how to define multi-agent simulations with simulated environments. Like [ours single-agent example with Gymnasium](https://python.langchain.com/en/latest/use_cases/agent_simulations/gymnasium.html), we create an agent-environment loop with an externally defined environment. The main difference is that we now implement this kind of interaction loop with multiple agents instead. We will use the [Petting Zoo](https://pettingzoo.farama.org/) library, which is the multi-agent counterpart to [Gymnasium](https://gymnasium.farama.org/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10091333",
+   "metadata": {},
+   "source": [
+    "## Install `pettingzoo` and other dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0a3fde66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: pettingzoo in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (1.22.3)\n",
+      "Requirement already satisfied: pygame in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (2.4.0)\n",
+      "Requirement already satisfied: rlcard in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (1.2.0)\n",
+      "Requirement already satisfied: gymnasium>=0.26.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from pettingzoo) (0.28.1)\n",
+      "Requirement already satisfied: numpy>=1.18.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from pettingzoo) (1.24.3)\n",
+      "Requirement already satisfied: termcolor in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from rlcard) (2.2.0)\n",
+      "Requirement already satisfied: typing-extensions>=4.3.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium>=0.26.0->pettingzoo) (4.5.0)\n",
+      "Requirement already satisfied: jax-jumpy>=1.0.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium>=0.26.0->pettingzoo) (1.0.0)\n",
+      "Requirement already satisfied: farama-notifications>=0.0.1 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium>=0.26.0->pettingzoo) (0.0.4)\n",
+      "Requirement already satisfied: cloudpickle>=1.2.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium>=0.26.0->pettingzoo) (2.2.1)\n",
+      "Requirement already satisfied: importlib-metadata>=4.8.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium>=0.26.0->pettingzoo) (6.0.1)\n",
+      "Requirement already satisfied: zipp>=0.5 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from importlib-metadata>=4.8.0->gymnasium>=0.26.0->pettingzoo) (3.15.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install pettingzoo pygame rlcard"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fbe130c",
+   "metadata": {},
+   "source": [
+    "## Import modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "42cd2e5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import collections\n",
+    "import inspect\n",
+    "import tenacity\n",
+    "\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.schema import (\n",
+    "    HumanMessage,\n",
+    "    SystemMessage,\n",
+    ")\n",
+    "from langchain.output_parsers import RegexParser"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e222e811",
+   "metadata": {},
+   "source": [
+    "## `GymnasiumAgent`\n",
+    "Here we reproduce the same `GymnasiumAgent` defined from [our Gymnasium example](https://python.langchain.com/en/latest/use_cases/agent_simulations/gymnasium.html). If after multiple retries it does not take a valid action, it simply takes a random action. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "72df0b59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GymnasiumAgent():\n",
+    "    @classmethod\n",
+    "    def get_docs(cls, env):\n",
+    "        return env.unwrapped.__doc__\n",
+    "    \n",
+    "    def __init__(self, model, env):\n",
+    "        self.model = model\n",
+    "        self.env = env\n",
+    "        self.docs = self.get_docs(env)\n",
+    "        \n",
+    "        self.instructions = \"\"\"\n",
+    "Your goal is to maximize your return, i.e. the sum of the rewards you receive.\n",
+    "I will give you an observation, reward, terminiation flag, truncation flag, and the return so far, formatted as:\n",
+    "\n",
+    "Observation: <observation>\n",
+    "Reward: <reward>\n",
+    "Termination: <termination>\n",
+    "Truncation: <truncation>\n",
+    "Return: <sum_of_rewards>\n",
+    "\n",
+    "You will respond with an action, formatted as:\n",
+    "\n",
+    "Action: <action>\n",
+    "\n",
+    "where you replace <action> with your actual action.\n",
+    "Do nothing else but return the action.\n",
+    "\"\"\"\n",
+    "        self.action_parser = RegexParser(\n",
+    "            regex=r\"Action: (.*)\", \n",
+    "            output_keys=['action'], \n",
+    "            default_output_key='action')\n",
+    "        \n",
+    "        self.message_history = []\n",
+    "        self.ret = 0\n",
+    "        \n",
+    "    def random_action(self):\n",
+    "        action = self.env.action_space.sample()\n",
+    "        return action\n",
+    "        \n",
+    "    def reset(self):\n",
+    "        self.message_history = [\n",
+    "            SystemMessage(content=self.docs),\n",
+    "            SystemMessage(content=self.instructions),\n",
+    "        ]\n",
+    "        \n",
+    "    def observe(self, obs, rew=0, term=False, trunc=False, info=None):\n",
+    "        self.ret += rew\n",
+    "    \n",
+    "        obs_message = f\"\"\"\n",
+    "Observation: {obs}\n",
+    "Reward: {rew}\n",
+    "Termination: {term}\n",
+    "Truncation: {trunc}\n",
+    "Return: {self.ret}\n",
+    "        \"\"\"\n",
+    "        self.message_history.append(HumanMessage(content=obs_message))\n",
+    "        return obs_message\n",
+    "    \n",
+    "    def _act(self):\n",
+    "        act_message = self.model(self.message_history)\n",
+    "        self.message_history.append(act_message)\n",
+    "        action = int(self.action_parser.parse(act_message.content)['action'])\n",
+    "        return action\n",
+    "        \n",
+    "    def act(self):\n",
+    "        try:\n",
+    "            for attempt in tenacity.Retrying(\n",
+    "                                stop=tenacity.stop_after_attempt(2),\n",
+    "                                wait=tenacity.wait_none(),  # No waiting time between retries\n",
+    "                                retry=tenacity.retry_if_exception_type(ValueError),\n",
+    "                                before_sleep=lambda retry_state: print(f\"ValueError occurred: {retry_state.outcome.exception()}, retrying...\"),\n",
+    "                            ):\n",
+    "                with attempt:\n",
+    "                    action = self._act()\n",
+    "        except tenacity.RetryError as e:\n",
+    "            action = self.random_action()\n",
+    "        return action"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df51e302",
+   "metadata": {},
+   "source": [
+    "## Main loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0f07d7cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def main(agents, env):\n",
+    "    env.reset()\n",
+    "\n",
+    "    for name, agent in agents.items():\n",
+    "        agent.reset()\n",
+    "\n",
+    "    for agent_name in env.agent_iter():\n",
+    "        observation, reward, termination, truncation, info = env.last()\n",
+    "        obs_message = agents[agent_name].observe(\n",
+    "            observation, reward, termination, truncation, info)\n",
+    "        print(obs_message)\n",
+    "        if termination or truncation:\n",
+    "            action = None\n",
+    "        else:\n",
+    "            action = agents[agent_name].act()\n",
+    "        print(f'Action: {action}')\n",
+    "        env.step(action)\n",
+    "    env.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4b0e921",
+   "metadata": {},
+   "source": [
+    "## `PettingZooAgent`\n",
+    "\n",
+    "The `PettingZooAgent` extends the `GymnasiumAgent` to the multi-agent setting. The main differences are:\n",
+    "- `PettingZooAgent` takes in a `name` argument to identify it among multiple agents\n",
+    "- the function `get_docs` is implemented differently because the `PettingZoo` repo structure is structured differently from the `Gymnasium` repo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f132c92a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class PettingZooAgent(GymnasiumAgent):\n",
+    "    @classmethod\n",
+    "    def get_docs(cls, env):\n",
+    "        return inspect.getmodule(env.unwrapped).__doc__\n",
+    "    \n",
+    "    def __init__(self, name, model, env):\n",
+    "        super().__init__(model, env)\n",
+    "        self.name = name\n",
+    "        \n",
+    "    def random_action(self):\n",
+    "        action = self.env.action_space(self.name).sample()\n",
+    "        return action"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a27f8a5d",
+   "metadata": {},
+   "source": [
+    "## Rock, Paper, Scissors\n",
+    "We can now run a simulation of a multi-agent rock, paper, scissors game using the `PettingZooAgent`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bd1256c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Observation: 3\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 1\n",
+      "\n",
+      "Observation: 3\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 1\n",
+      "\n",
+      "Observation: 1\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 2\n",
+      "\n",
+      "Observation: 1\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 1\n",
+      "\n",
+      "Observation: 1\n",
+      "Reward: 1\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 1\n",
+      "        \n",
+      "Action: 0\n",
+      "\n",
+      "Observation: 2\n",
+      "Reward: -1\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: -1\n",
+      "        \n",
+      "Action: 0\n",
+      "\n",
+      "Observation: 0\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: True\n",
+      "Return: 1\n",
+      "        \n",
+      "Action: None\n",
+      "\n",
+      "Observation: 0\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: True\n",
+      "Return: -1\n",
+      "        \n",
+      "Action: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pettingzoo.classic import rps_v2\n",
+    "env = rps_v2.env(max_cycles=3, render_mode=\"human\")\n",
+    "agents = {name: PettingZooAgent(name=name, model=ChatOpenAI(temperature=1), env=env) for name in env.possible_agents}\n",
+    "main(agents, env)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbcee258",
+   "metadata": {},
+   "source": [
+    "## `ActionMaskAgent`\n",
+    "\n",
+    "Some `PettingZoo` environments provide an `action_mask` to tell the agent which actions are valid. The `ActionMaskAgent` subclasses `PettingZooAgent` to use information from the `action_mask` to select actions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bd33250a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ActionMaskAgent(PettingZooAgent):\n",
+    "    def __init__(self, name, model, env):\n",
+    "        super().__init__(name, model, env)\n",
+    "        self.obs_buffer = collections.deque(maxlen=1)\n",
+    "        \n",
+    "    def random_action(self):\n",
+    "        obs = self.obs_buffer[-1]\n",
+    "        action = self.env.action_space(self.name).sample(obs[\"action_mask\"])\n",
+    "        return action\n",
+    "        \n",
+    "    def reset(self):\n",
+    "        self.message_history = [\n",
+    "            SystemMessage(content=self.docs),\n",
+    "            SystemMessage(content=self.instructions),\n",
+    "        ]\n",
+    "    \n",
+    "    def observe(self, obs, rew=0, term=False, trunc=False, info=None):\n",
+    "        self.obs_buffer.append(obs)\n",
+    "        return super().observe(obs, rew, term, trunc, info)\n",
+    "        \n",
+    "    def _act(self):\n",
+    "        valid_action_instruction = \"Generate a valid action given by the indices of the `action_mask` that are not 0, according to the action formatting rules.\"\n",
+    "        self.message_history.append(HumanMessage(content=valid_action_instruction))\n",
+    "        return super()._act()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e76d22c",
+   "metadata": {},
+   "source": [
+    "## Tic-Tac-Toe\n",
+    "Here is an example of a Tic-Tac-Toe game that uses the `ActionMaskAgent`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9e902cfd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Observation: {'observation': array([[[0, 0],\n",
+      "        [0, 0],\n",
+      "        [0, 0]],\n",
+      "\n",
+      "       [[0, 0],\n",
+      "        [0, 0],\n",
+      "        [0, 0]],\n",
+      "\n",
+      "       [[0, 0],\n",
+      "        [0, 0],\n",
+      "        [0, 0]]], dtype=int8), 'action_mask': array([1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 0\n",
+      "     |     |     \n",
+      "  X  |  -  |  -  \n",
+      "_____|_____|_____\n",
+      "     |     |     \n",
+      "  -  |  -  |  -  \n",
+      "_____|_____|_____\n",
+      "     |     |     \n",
+      "  -  |  -  |  -  \n",
+      "     |     |     \n",
+      "\n",
+      "Observation: {'observation': array([[[0, 1],\n",
+      "        [0, 0],\n",
+      "        [0, 0]],\n",
+      "\n",
+      "       [[0, 0],\n",
+      "        [0, 0],\n",
+      "        [0, 0]],\n",
+      "\n",
+      "       [[0, 0],\n",
+      "        [0, 0],\n",
+      "        [0, 0]]], dtype=int8), 'action_mask': array([0, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 1\n",
+      "     |     |     \n",
+      "  X  |  -  |  -  \n",
+      "_____|_____|_____\n",
+      "     |     |     \n",
+      "  O  |  -  |  -  \n",
+      "_____|_____|_____\n",
+      "     |     |     \n",
+      "  -  |  -  |  -  \n",
+      "     |     |     \n",
+      "\n",
+      "Observation: {'observation': array([[[1, 0],\n",
+      "        [0, 1],\n",
+      "        [0, 0]],\n",
+      "\n",
+      "       [[0, 0],\n",
+      "        [0, 0],\n",
+      "        [0, 0]],\n",
+      "\n",
+      "       [[0, 0],\n",
+      "        [0, 0],\n",
+      "        [0, 0]]], dtype=int8), 'action_mask': array([0, 0, 1, 1, 1, 1, 1, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 2\n",
+      "     |     |     \n",
+      "  X  |  -  |  -  \n",
+      "_____|_____|_____\n",
+      "     |     |     \n",
+      "  O  |  -  |  -  \n",
+      "_____|_____|_____\n",
+      "     |     |     \n",
+      "  X  |  -  |  -  \n",
+      "     |     |     \n",
+      "\n",
+      "Observation: {'observation': array([[[0, 1],\n",
+      "        [1, 0],\n",
+      "        [0, 1]],\n",
+      "\n",
+      "       [[0, 0],\n",
+      "        [0, 0],\n",
+      "        [0, 0]],\n",
+      "\n",
+      "       [[0, 0],\n",
+      "        [0, 0],\n",
+      "        [0, 0]]], dtype=int8), 'action_mask': array([0, 0, 0, 1, 1, 1, 1, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 3\n",
+      "     |     |     \n",
+      "  X  |  O  |  -  \n",
+      "_____|_____|_____\n",
+      "     |     |     \n",
+      "  O  |  -  |  -  \n",
+      "_____|_____|_____\n",
+      "     |     |     \n",
+      "  X  |  -  |  -  \n",
+      "     |     |     \n",
+      "\n",
+      "Observation: {'observation': array([[[1, 0],\n",
+      "        [0, 1],\n",
+      "        [1, 0]],\n",
+      "\n",
+      "       [[0, 1],\n",
+      "        [0, 0],\n",
+      "        [0, 0]],\n",
+      "\n",
+      "       [[0, 0],\n",
+      "        [0, 0],\n",
+      "        [0, 0]]], dtype=int8), 'action_mask': array([0, 0, 0, 0, 1, 1, 1, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 4\n",
+      "     |     |     \n",
+      "  X  |  O  |  -  \n",
+      "_____|_____|_____\n",
+      "     |     |     \n",
+      "  O  |  X  |  -  \n",
+      "_____|_____|_____\n",
+      "     |     |     \n",
+      "  X  |  -  |  -  \n",
+      "     |     |     \n",
+      "\n",
+      "Observation: {'observation': array([[[0, 1],\n",
+      "        [1, 0],\n",
+      "        [0, 1]],\n",
+      "\n",
+      "       [[1, 0],\n",
+      "        [0, 1],\n",
+      "        [0, 0]],\n",
+      "\n",
+      "       [[0, 0],\n",
+      "        [0, 0],\n",
+      "        [0, 0]]], dtype=int8), 'action_mask': array([0, 0, 0, 0, 0, 1, 1, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 5\n",
+      "     |     |     \n",
+      "  X  |  O  |  -  \n",
+      "_____|_____|_____\n",
+      "     |     |     \n",
+      "  O  |  X  |  -  \n",
+      "_____|_____|_____\n",
+      "     |     |     \n",
+      "  X  |  O  |  -  \n",
+      "     |     |     \n",
+      "\n",
+      "Observation: {'observation': array([[[1, 0],\n",
+      "        [0, 1],\n",
+      "        [1, 0]],\n",
+      "\n",
+      "       [[0, 1],\n",
+      "        [1, 0],\n",
+      "        [0, 1]],\n",
+      "\n",
+      "       [[0, 0],\n",
+      "        [0, 0],\n",
+      "        [0, 0]]], dtype=int8), 'action_mask': array([0, 0, 0, 0, 0, 0, 1, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 6\n",
+      "     |     |     \n",
+      "  X  |  O  |  X  \n",
+      "_____|_____|_____\n",
+      "     |     |     \n",
+      "  O  |  X  |  -  \n",
+      "_____|_____|_____\n",
+      "     |     |     \n",
+      "  X  |  O  |  -  \n",
+      "     |     |     \n",
+      "\n",
+      "Observation: {'observation': array([[[0, 1],\n",
+      "        [1, 0],\n",
+      "        [0, 1]],\n",
+      "\n",
+      "       [[1, 0],\n",
+      "        [0, 1],\n",
+      "        [1, 0]],\n",
+      "\n",
+      "       [[0, 1],\n",
+      "        [0, 0],\n",
+      "        [0, 0]]], dtype=int8), 'action_mask': array([0, 0, 0, 0, 0, 0, 0, 1, 1], dtype=int8)}\n",
+      "Reward: -1\n",
+      "Termination: True\n",
+      "Truncation: False\n",
+      "Return: -1\n",
+      "        \n",
+      "Action: None\n",
+      "\n",
+      "Observation: {'observation': array([[[1, 0],\n",
+      "        [0, 1],\n",
+      "        [1, 0]],\n",
+      "\n",
+      "       [[0, 1],\n",
+      "        [1, 0],\n",
+      "        [0, 1]],\n",
+      "\n",
+      "       [[1, 0],\n",
+      "        [0, 0],\n",
+      "        [0, 0]]], dtype=int8), 'action_mask': array([0, 0, 0, 0, 0, 0, 0, 1, 1], dtype=int8)}\n",
+      "Reward: 1\n",
+      "Termination: True\n",
+      "Truncation: False\n",
+      "Return: 1\n",
+      "        \n",
+      "Action: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pettingzoo.classic import tictactoe_v3\n",
+    "env = tictactoe_v3.env(render_mode=\"human\")\n",
+    "agents = {name: ActionMaskAgent(name=name, model=ChatOpenAI(temperature=0.2), env=env) for name in env.possible_agents}\n",
+    "main(agents, env)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8728ac2a",
+   "metadata": {},
+   "source": [
+    "## Texas Hold'em No Limit\n",
+    "Here is an example of a Texas Hold'em No Limit game that uses the `ActionMaskAgent`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e350c62b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Observation: {'observation': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0.,\n",
+      "       0., 0., 2.], dtype=float32), 'action_mask': array([1, 1, 0, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 1\n",
+      "\n",
+      "Observation: {'observation': array([0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
+      "       0., 0., 2.], dtype=float32), 'action_mask': array([1, 1, 0, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 1\n",
+      "\n",
+      "Observation: {'observation': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 1., 2.], dtype=float32), 'action_mask': array([1, 1, 1, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 1\n",
+      "\n",
+      "Observation: {'observation': array([0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 2., 2.], dtype=float32), 'action_mask': array([1, 1, 1, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 0\n",
+      "\n",
+      "Observation: {'observation': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1.,\n",
+      "       0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 2., 2.], dtype=float32), 'action_mask': array([1, 1, 1, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 2\n",
+      "\n",
+      "Observation: {'observation': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 1., 0., 0., 1., 1., 0., 0., 1., 0., 0., 0., 0.,\n",
+      "       0., 2., 6.], dtype=float32), 'action_mask': array([1, 1, 1, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 2\n",
+      "\n",
+      "Observation: {'observation': array([0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
+      "       0., 2., 8.], dtype=float32), 'action_mask': array([1, 1, 1, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 3\n",
+      "\n",
+      "Observation: {'observation': array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
+      "        0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
+      "        0.,  0.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,\n",
+      "        1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
+      "        6., 20.], dtype=float32), 'action_mask': array([1, 1, 1, 1, 1], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 4\n",
+      "\n",
+      "Observation: {'observation': array([  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,   1.,   1.,\n",
+      "         0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,   8., 100.],\n",
+      "      dtype=float32), 'action_mask': array([1, 1, 0, 0, 0], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: False\n",
+      "Truncation: False\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: 4\n",
+      "[WARNING]: Illegal move made, game terminating with current player losing. \n",
+      "obs['action_mask'] contains a mask of all legal moves that can be chosen.\n",
+      "\n",
+      "Observation: {'observation': array([  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,   1.,   1.,\n",
+      "         0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,   8., 100.],\n",
+      "      dtype=float32), 'action_mask': array([1, 1, 0, 0, 0], dtype=int8)}\n",
+      "Reward: -1.0\n",
+      "Termination: True\n",
+      "Truncation: True\n",
+      "Return: -1.0\n",
+      "        \n",
+      "Action: None\n",
+      "\n",
+      "Observation: {'observation': array([  0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,   1.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   1.,   0.,   0.,   0.,  20., 100.],\n",
+      "      dtype=float32), 'action_mask': array([1, 1, 0, 0, 0], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: True\n",
+      "Truncation: True\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: None\n",
+      "\n",
+      "Observation: {'observation': array([  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,\n",
+      "         1.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,   1.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 100., 100.],\n",
+      "      dtype=float32), 'action_mask': array([1, 1, 0, 0, 0], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: True\n",
+      "Truncation: True\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: None\n",
+      "\n",
+      "Observation: {'observation': array([  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,   1.,   0.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,   1.,   0.,\n",
+      "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   2., 100.],\n",
+      "      dtype=float32), 'action_mask': array([1, 1, 0, 0, 0], dtype=int8)}\n",
+      "Reward: 0\n",
+      "Termination: True\n",
+      "Truncation: True\n",
+      "Return: 0\n",
+      "        \n",
+      "Action: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pettingzoo.classic import texas_holdem_no_limit_v6\n",
+    "env = texas_holdem_no_limit_v6.env(num_players=4, render_mode=\"human\")\n",
+    "agents = {name: ActionMaskAgent(name=name, model=ChatOpenAI(temperature=0.2), env=env) for name in env.possible_agents}\n",
+    "main(agents, env)"
+   ]
+  }
+ ],
+ "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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/use_cases/agent_simulations/two_player_dnd.ipynb

@@ -410,7 +410,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/use_cases/autonomous_agents.md

@@ -7,7 +7,7 @@ The applications combine tool usage and long term memory.
 
 At the moment, Autonomous Agents are fairly experimental and based off of other open-source projects.
 By implementing these open source projects in LangChain primitives we can get the benefits of LangChain - 
-easy switching an experimenting with multiple LLMs, usage of different vectorstores as memory, 
+easy switching and experimenting with multiple LLMs, usage of different vectorstores as memory, 
 usage of LangChain's collection of tools.
 
 ## Baby AGI ([Original Repo](https://github.com/yoheinakajima/babyagi))

docs/youtube.md

@@ -6,11 +6,16 @@ This is a collection of `LangChain` tutorials and videos on `YouTube`.
 - [Building the Future with LLMs, `LangChain`, & `Pinecone`](https://youtu.be/nMniwlGyX-c) by [Pinecone](https://www.youtube.com/@pinecone-io)
 - [LangChain and Weaviate with Harrison Chase and Bob van Luijt - Weaviate Podcast #36](https://youtu.be/lhby7Ql7hbk) by [Weaviate • Vector Database](https://www.youtube.com/@Weaviate)
 - [LangChain Demo + Q&A with Harrison Chase](https://youtu.be/zaYTXQFR0_s?t=788) by [Full Stack Deep Learning](https://www.youtube.com/@FullStackDeepLearning)
+- [LangChain Agents: Build Personal Assistants For Your Data (Q&A with Harrison Chase and Mayo Oshin)](https://youtu.be/gVkF8cwfBLI) by [Chat with data](https://www.youtube.com/@chatwithdata)
 
 ## Tutorials
 
+- [LangChain Crash Course: Build an AutoGPT app in 25 minutes!](https://youtu.be/MlK6SIjcjE8) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
+
 - [LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber)
 
+- [LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
+
 - [LangChain for Gen AI and LLMs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F) by [James Briggs](https://www.youtube.com/@jamesbriggs):
   - #1 [Getting Started with `GPT-3` vs. Open Source LLMs](https://youtu.be/nE2skSRWTTs)
   - #2 [Prompt Templates for `GPT 3.5` and other LLMs](https://youtu.be/RflBcK0oDH0)
@@ -18,7 +23,7 @@ This is a collection of `LangChain` tutorials and videos on `YouTube`.
   - #4 [Chatbot Memory for `Chat-GPT`, `Davinci` + other LLMs](https://youtu.be/X05uK0TZozM)
   - #5 [Chat with OpenAI in LangChain](https://youtu.be/CnAgB3A5OlU)
   - #6 [LangChain Agents Deep Dive with `GPT 3.5`](https://youtu.be/jSP-gSEyVeI)
-  - [Prompt Engineering with OpenAI's GPT-3 and other LLMs](https://youtu.be/BP9fi_0XTlw)
+  - [Prompt Engineering with OpenAI's `GPT-3` and other LLMs](https://youtu.be/BP9fi_0XTlw)
 
 - [LangChain 101](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5) by [Data Independent](https://www.youtube.com/@DataIndependent):
   - [What Is LangChain? - LangChain + `ChatGPT` Overview](https://youtu.be/_v_fgW2SkkQ)
@@ -57,7 +62,7 @@ This is a collection of `LangChain` tutorials and videos on `YouTube`.
   - [Improve your `BabyAGI` with LangChain](https://youtu.be/DRgPyOXZ-oE)
 
 - [LangChain](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr) by [Prompt Engineering](https://www.youtube.com/@engineerprompt):
-  - [LangChain Crash Course - All You Need to Know to Build Powerful Apps with LLMs](https://youtu.be/5-fc4Tlgmro)
+  - [LangChain Crash Course — All You Need to Know to Build Powerful Apps with LLMs](https://youtu.be/5-fc4Tlgmro)
   - [Working with MULTIPLE `PDF` Files in LangChain: `ChatGPT` for your Data](https://youtu.be/s5LhRdh5fu4)
   - [`ChatGPT` for YOUR OWN `PDF` files with LangChain](https://youtu.be/TLf90ipMzfE)
   - [Talk to YOUR DATA without OpenAI APIs: LangChain](https://youtu.be/wrD-fZvT6UI)
@@ -67,6 +72,13 @@ This is a collection of `LangChain` tutorials and videos on `YouTube`.
   - [`GPT-4` Tutorial: How to Chat With Multiple `PDF` Files (~1000 pages of Tesla's 10-K Annual Reports)](https://youtu.be/Ix9WIZpArm0)
   - [`GPT-4` & LangChain Tutorial: How to Chat With A 56-Page `PDF` Document (w/`Pinecone`)](https://youtu.be/ih9PBGVVOO4)
 
+- [Get SH\*T Done with Prompt Engineering and LangChain](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov)
+  - [Getting Started with LangChain: Load Custom Data, Run OpenAI Models, Embeddings and `ChatGPT`](https://www.youtube.com/watch?v=muXbPpG_ys4)
+  - [Loaders, Indexes & Vectorstores in LangChain: Question Answering on `PDF` files with `ChatGPT`](https://www.youtube.com/watch?v=FQnvfR8Dmr0)
+  - [LangChain Models: `ChatGPT`, `Flan Alpaca`, `OpenAI Embeddings`, Prompt Templates & Streaming](https://www.youtube.com/watch?v=zy6LiK5F5-s)
+  - [LangChain Chains: Use `ChatGPT` to Build Conversational Agents, Summaries and Q&A on Text With LLMs](https://www.youtube.com/watch?v=h1tJZQPcimM)
+  - [Analyze Custom CSV Data with `GPT-4` using Langchain](https://www.youtube.com/watch?v=Ew3sGdX8at4)
+
 ## Videos (sorted by views)
 
 - [Building AI LLM Apps with LangChain (and more?) - LIVE STREAM](https://www.youtube.com/live/M-2Cj_2fzWI?feature=share) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
@@ -83,10 +95,12 @@ This is a collection of `LangChain` tutorials and videos on `YouTube`.
 - [The easiest way to work with large language models | Learn LangChain in 10min](https://youtu.be/kmbS6FDQh7c) by [Sophia Yang](https://www.youtube.com/@SophiaYangDS)
 - [4 Autonomous AI Agents: “Westworld” simulation `BabyAGI`, `AutoGPT`, `Camel`, `LangChain`](https://youtu.be/yWbnH6inT_U) by [Sophia Yang](https://www.youtube.com/@SophiaYangDS)
 - [AI CAN SEARCH THE INTERNET? Langchain Agents + OpenAI ChatGPT](https://youtu.be/J-GL0htqda8) by [tylerwhatsgood](https://www.youtube.com/@tylerwhatsgood)
+- [Query Your Data with GPT-4 | Embeddings, Vector Databases | Langchain JS Knowledgebase](https://youtu.be/jRnUPUTkZmU) by [StarMorph AI](https://www.youtube.com/@starmorph)
 - [`Weaviate` + LangChain for LLM apps presented by Erika Cardenas](https://youtu.be/7AGj4Td5Lgw) by [`Weaviate` • Vector Database](https://www.youtube.com/@Weaviate)
-- [Analyze Custom `CSV` Data with `GPT-4` using Langchain](https://youtu.be/Ew3sGdX8at4) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov) 
+- [Langchain Overview — How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)
 - [Langchain Overview - How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)
 - [Custom langchain Agent & Tools with memory. Turn any `Python function` into langchain tool with Gpt 3](https://youtu.be/NIG8lXk0ULg) by [echohive](https://www.youtube.com/@echohive)
+- [LangChain: Run Language Models Locally - `Hugging Face Models`](https://youtu.be/Xxxuw4_iCzw) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
 - [`ChatGPT` with any `YouTube` video using langchain and `chromadb`](https://youtu.be/TQZfB2bzVwU) by [echohive](https://www.youtube.com/@echohive)
 - [How to Talk to a `PDF` using LangChain and `ChatGPT`](https://youtu.be/v2i1YDtrIwk) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
 - [Langchain Document Loaders Part 1: Unstructured Files](https://youtu.be/O5C0wfsen98) by [Merk](https://www.youtube.com/@merksworld) 

langchain/__init__.py

@@ -5,11 +5,6 @@ from typing import Optional
 
 from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
 from langchain.cache import BaseCache
-from langchain.callbacks import (
-    set_default_callback_manager,
-    set_handler,
-    set_tracing_callback_manager,
-)
 from langchain.chains import (
     ConversationChain,
     LLMBashChain,
@@ -67,7 +62,6 @@ del metadata  # optional, avoids polluting the results of dir(__package__)
 
 verbose: bool = False
 llm_cache: Optional[BaseCache] = None
-set_default_callback_manager()
 
 # For backwards compatibility
 SerpAPIChain = SerpAPIWrapper
@@ -119,7 +113,5 @@ __all__ = [
     "VectorDBQAWithSourcesChain",
     "QAWithSourcesChain",
     "PALChain",
-    "set_handler",
-    "set_tracing_callback_manager",
     "LlamaCpp",
 ]

langchain/agents/__init__.py

@@ -27,36 +27,38 @@ from langchain.agents.loading import load_agent
 from langchain.agents.mrkl.base import MRKLChain, ZeroShotAgent
 from langchain.agents.react.base import ReActChain, ReActTextWorldAgent
 from langchain.agents.self_ask_with_search.base import SelfAskWithSearchChain
+from langchain.agents.structured_chat.base import StructuredChatAgent
 from langchain.agents.tools import Tool, tool
 
 __all__ = [
-    "MRKLChain",
-    "SelfAskWithSearchChain",
-    "ReActChain",
-    "AgentExecutor",
     "Agent",
-    "Tool",
-    "tool",
-    "initialize_agent",
-    "ZeroShotAgent",
-    "ReActTextWorldAgent",
-    "load_tools",
-    "get_all_tool_names",
-    "ConversationalAgent",
-    "ConversationalChatAgent",
-    "load_agent",
-    "create_sql_agent",
-    "create_pbi_agent",
-    "create_pbi_chat_agent",
-    "create_json_agent",
-    "create_openapi_agent",
-    "create_vectorstore_router_agent",
-    "create_vectorstore_agent",
-    "create_pandas_dataframe_agent",
-    "create_csv_agent",
-    "LLMSingleActionAgent",
+    "AgentExecutor",
     "AgentOutputParser",
-    "BaseSingleActionAgent",
     "AgentType",
     "BaseMultiActionAgent",
+    "BaseSingleActionAgent",
+    "ConversationalAgent",
+    "ConversationalChatAgent",
+    "LLMSingleActionAgent",
+    "MRKLChain",
+    "ReActChain",
+    "ReActTextWorldAgent",
+    "SelfAskWithSearchChain",
+    "StructuredChatAgent",
+    "Tool",
+    "ZeroShotAgent",
+    "create_csv_agent",
+    "create_json_agent",
+    "create_openapi_agent",
+    "create_pandas_dataframe_agent",
+    "create_pbi_agent",
+    "create_pbi_chat_agent",
+    "create_sql_agent",
+    "create_vectorstore_agent",
+    "create_vectorstore_router_agent",
+    "get_all_tool_names",
+    "initialize_agent",
+    "load_agent",
+    "load_tools",
+    "tool",
 ]

langchain/agents/agent.py

@@ -7,13 +7,19 @@ import logging
 import time
 from abc import abstractmethod
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
+from typing import Any, Dict, List, Optional, Sequence, Set, Tuple, Union
 
 import yaml
 from pydantic import BaseModel, root_validator
 
 from langchain.agents.tools import InvalidTool
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForChainRun,
+    CallbackManagerForChainRun,
+    Callbacks,
+)
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.input import get_color_mapping
@@ -23,7 +29,6 @@ from langchain.prompts.prompt import PromptTemplate
 from langchain.schema import (
     AgentAction,
     AgentFinish,
-    BaseLanguageModel,
     BaseMessage,
     BaseOutputParser,
 )
@@ -41,18 +46,22 @@ class BaseSingleActionAgent(BaseModel):
         """Return values of the agent."""
         return ["output"]
 
-    def get_allowed_tools(self) -> Optional[List[str]]:
-        return None
+    def get_allowed_tools(self) -> Set[str]:
+        return set()
 
     @abstractmethod
     def plan(
-        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
+        self,
+        intermediate_steps: List[Tuple[AgentAction, str]],
+        callbacks: Callbacks = None,
+        **kwargs: Any,
     ) -> Union[AgentAction, AgentFinish]:
         """Given input, decided what to do.
 
         Args:
             intermediate_steps: Steps the LLM has taken to date,
                 along with observations
+            callbacks: Callbacks to run.
             **kwargs: User inputs.
 
         Returns:
@@ -61,13 +70,17 @@ class BaseSingleActionAgent(BaseModel):
 
     @abstractmethod
     async def aplan(
-        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
+        self,
+        intermediate_steps: List[Tuple[AgentAction, str]],
+        callbacks: Callbacks = None,
+        **kwargs: Any,
     ) -> Union[AgentAction, AgentFinish]:
         """Given input, decided what to do.
 
         Args:
             intermediate_steps: Steps the LLM has taken to date,
                 along with observations
+            callbacks: Callbacks to run.
             **kwargs: User inputs.
 
         Returns:
@@ -165,18 +178,22 @@ class BaseMultiActionAgent(BaseModel):
         """Return values of the agent."""
         return ["output"]
 
-    def get_allowed_tools(self) -> Optional[List[str]]:
-        return None
+    def get_allowed_tools(self) -> Set[str]:
+        return set()
 
     @abstractmethod
     def plan(
-        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
+        self,
+        intermediate_steps: List[Tuple[AgentAction, str]],
+        callbacks: Callbacks = None,
+        **kwargs: Any,
     ) -> Union[List[AgentAction], AgentFinish]:
         """Given input, decided what to do.
 
         Args:
             intermediate_steps: Steps the LLM has taken to date,
                 along with observations
+            callbacks: Callbacks to run.
             **kwargs: User inputs.
 
         Returns:
@@ -185,13 +202,17 @@ class BaseMultiActionAgent(BaseModel):
 
     @abstractmethod
     async def aplan(
-        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
+        self,
+        intermediate_steps: List[Tuple[AgentAction, str]],
+        callbacks: Callbacks = None,
+        **kwargs: Any,
     ) -> Union[List[AgentAction], AgentFinish]:
         """Given input, decided what to do.
 
         Args:
             intermediate_steps: Steps the LLM has taken to date,
                 along with observations
+            callbacks: Callbacks to run.
             **kwargs: User inputs.
 
         Returns:
@@ -285,38 +306,52 @@ class LLMSingleActionAgent(BaseSingleActionAgent):
         return list(set(self.llm_chain.input_keys) - {"intermediate_steps"})
 
     def plan(
-        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
+        self,
+        intermediate_steps: List[Tuple[AgentAction, str]],
+        callbacks: Callbacks = None,
+        **kwargs: Any,
     ) -> Union[AgentAction, AgentFinish]:
         """Given input, decided what to do.
 
         Args:
             intermediate_steps: Steps the LLM has taken to date,
                 along with observations
+            callbacks: Callbacks to run.
             **kwargs: User inputs.
 
         Returns:
             Action specifying what tool to use.
         """
         output = self.llm_chain.run(
-            intermediate_steps=intermediate_steps, stop=self.stop, **kwargs
+            intermediate_steps=intermediate_steps,
+            stop=self.stop,
+            callbacks=callbacks,
+            **kwargs,
         )
         return self.output_parser.parse(output)
 
     async def aplan(
-        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
+        self,
+        intermediate_steps: List[Tuple[AgentAction, str]],
+        callbacks: Callbacks = None,
+        **kwargs: Any,
     ) -> Union[AgentAction, AgentFinish]:
         """Given input, decided what to do.
 
         Args:
             intermediate_steps: Steps the LLM has taken to date,
                 along with observations
+            callbacks: Callbacks to run.
             **kwargs: User inputs.
 
         Returns:
             Action specifying what tool to use.
         """
         output = await self.llm_chain.arun(
-            intermediate_steps=intermediate_steps, stop=self.stop, **kwargs
+            intermediate_steps=intermediate_steps,
+            stop=self.stop,
+            callbacks=callbacks,
+            **kwargs,
         )
         return self.output_parser.parse(output)
 
@@ -337,9 +372,9 @@ class Agent(BaseSingleActionAgent):
 
     llm_chain: LLMChain
     output_parser: AgentOutputParser
-    allowed_tools: Optional[List[str]] = None
+    allowed_tools: Set[str] = set()
 
-    def get_allowed_tools(self) -> Optional[List[str]]:
+    def get_allowed_tools(self) -> Set[str]:
         return self.allowed_tools
 
     @property
@@ -368,37 +403,45 @@ class Agent(BaseSingleActionAgent):
         return thoughts
 
     def plan(
-        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
+        self,
+        intermediate_steps: List[Tuple[AgentAction, str]],
+        callbacks: Callbacks = None,
+        **kwargs: Any,
     ) -> Union[AgentAction, AgentFinish]:
         """Given input, decided what to do.
 
         Args:
             intermediate_steps: Steps the LLM has taken to date,
                 along with observations
+            callbacks: Callbacks to run.
             **kwargs: User inputs.
 
         Returns:
             Action specifying what tool to use.
         """
         full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
-        full_output = self.llm_chain.predict(**full_inputs)
+        full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
         return self.output_parser.parse(full_output)
 
     async def aplan(
-        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
+        self,
+        intermediate_steps: List[Tuple[AgentAction, str]],
+        callbacks: Callbacks = None,
+        **kwargs: Any,
     ) -> Union[AgentAction, AgentFinish]:
         """Given input, decided what to do.
 
         Args:
             intermediate_steps: Steps the LLM has taken to date,
                 along with observations
+            callbacks: Callbacks to run.
             **kwargs: User inputs.
 
         Returns:
             Action specifying what tool to use.
         """
         full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
-        full_output = await self.llm_chain.apredict(**full_inputs)
+        full_output = await self.llm_chain.apredict(callbacks=callbacks, **full_inputs)
         return self.output_parser.parse(full_output)
 
     def get_full_inputs(
@@ -454,11 +497,7 @@ class Agent(BaseSingleActionAgent):
     @classmethod
     def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
         """Validate that appropriate tools are passed in."""
-        for tool in tools:
-            if not tool.is_single_input:
-                raise ValueError(
-                    f"{cls.__name__} does not support multi-input tool {tool.name}."
-                )
+        pass
 
     @classmethod
     @abstractmethod
@@ -568,12 +607,11 @@ class AgentExecutor(Chain):
         agent = values["agent"]
         tools = values["tools"]
         allowed_tools = agent.get_allowed_tools()
-        if allowed_tools is not None:
-            if set(allowed_tools) != set([tool.name for tool in tools]):
-                raise ValueError(
-                    f"Allowed tools ({allowed_tools}) different than "
-                    f"provided tools ({[tool.name for tool in tools]})"
-                )
+        if allowed_tools != set([tool.name for tool in tools]):
+            raise ValueError(
+                f"Allowed tools ({allowed_tools}) different than "
+                f"provided tools ({[tool.name for tool in tools]})"
+            )
         return values
 
     @root_validator()
@@ -636,24 +674,27 @@ class AgentExecutor(Chain):
 
         return True
 
-    def _return(self, output: AgentFinish, intermediate_steps: list) -> Dict[str, Any]:
-        self.callback_manager.on_agent_finish(
-            output, color="green", verbose=self.verbose
-        )
+    def _return(
+        self,
+        output: AgentFinish,
+        intermediate_steps: list,
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        if run_manager:
+            run_manager.on_agent_finish(output, color="green", verbose=self.verbose)
         final_output = output.return_values
         if self.return_intermediate_steps:
             final_output["intermediate_steps"] = intermediate_steps
         return final_output
 
     async def _areturn(
-        self, output: AgentFinish, intermediate_steps: list
+        self,
+        output: AgentFinish,
+        intermediate_steps: list,
+        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
     ) -> Dict[str, Any]:
-        if self.callback_manager.is_async:
-            await self.callback_manager.on_agent_finish(
-                output, color="green", verbose=self.verbose
-            )
-        else:
-            self.callback_manager.on_agent_finish(
+        if run_manager:
+            await run_manager.on_agent_finish(
                 output, color="green", verbose=self.verbose
             )
         final_output = output.return_values
@@ -667,13 +708,18 @@ class AgentExecutor(Chain):
         color_mapping: Dict[str, str],
         inputs: Dict[str, str],
         intermediate_steps: List[Tuple[AgentAction, str]],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
     ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
         """Take a single step in the thought-action-observation loop.
 
         Override this to take control of how the agent makes and acts on choices.
         """
         # Call the LLM to see what to do.
-        output = self.agent.plan(intermediate_steps, **inputs)
+        output = self.agent.plan(
+            intermediate_steps,
+            callbacks=run_manager.get_child() if run_manager else None,
+            **inputs,
+        )
         # If the tool chosen is the finishing tool, then we end and return.
         if isinstance(output, AgentFinish):
             return output
@@ -684,9 +730,8 @@ class AgentExecutor(Chain):
             actions = output
         result = []
         for agent_action in actions:
-            self.callback_manager.on_agent_action(
-                agent_action, verbose=self.verbose, color="green"
-            )
+            if run_manager:
+                run_manager.on_agent_action(agent_action, color="green")
             # Otherwise we lookup the tool
             if agent_action.tool in name_to_tool_map:
                 tool = name_to_tool_map[agent_action.tool]
@@ -700,6 +745,7 @@ class AgentExecutor(Chain):
                     agent_action.tool_input,
                     verbose=self.verbose,
                     color=color,
+                    callbacks=run_manager.get_child() if run_manager else None,
                     **tool_run_kwargs,
                 )
             else:
@@ -708,6 +754,7 @@ class AgentExecutor(Chain):
                     agent_action.tool,
                     verbose=self.verbose,
                     color=None,
+                    callbacks=run_manager.get_child() if run_manager else None,
                     **tool_run_kwargs,
                 )
             result.append((agent_action, observation))
@@ -719,13 +766,18 @@ class AgentExecutor(Chain):
         color_mapping: Dict[str, str],
         inputs: Dict[str, str],
         intermediate_steps: List[Tuple[AgentAction, str]],
+        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
     ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
         """Take a single step in the thought-action-observation loop.
 
         Override this to take control of how the agent makes and acts on choices.
         """
         # Call the LLM to see what to do.
-        output = await self.agent.aplan(intermediate_steps, **inputs)
+        output = await self.agent.aplan(
+            intermediate_steps,
+            callbacks=run_manager.get_child() if run_manager else None,
+            **inputs,
+        )
         # If the tool chosen is the finishing tool, then we end and return.
         if isinstance(output, AgentFinish):
             return output
@@ -738,12 +790,8 @@ class AgentExecutor(Chain):
         async def _aperform_agent_action(
             agent_action: AgentAction,
         ) -> Tuple[AgentAction, str]:
-            if self.callback_manager.is_async:
-                await self.callback_manager.on_agent_action(
-                    agent_action, verbose=self.verbose, color="green"
-                )
-            else:
-                self.callback_manager.on_agent_action(
+            if run_manager:
+                await run_manager.on_agent_action(
                     agent_action, verbose=self.verbose, color="green"
                 )
             # Otherwise we lookup the tool
@@ -759,6 +807,7 @@ class AgentExecutor(Chain):
                     agent_action.tool_input,
                     verbose=self.verbose,
                     color=color,
+                    callbacks=run_manager.get_child() if run_manager else None,
                     **tool_run_kwargs,
                 )
             else:
@@ -767,6 +816,7 @@ class AgentExecutor(Chain):
                     agent_action.tool,
                     verbose=self.verbose,
                     color=None,
+                    callbacks=run_manager.get_child() if run_manager else None,
                     **tool_run_kwargs,
                 )
             return agent_action, observation
@@ -778,7 +828,11 @@ class AgentExecutor(Chain):
 
         return list(result)
 
-    def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
+    def _call(
+        self,
+        inputs: Dict[str, str],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
         """Run text through and get agent response."""
         # Construct a mapping of tool name to tool for easy lookup
         name_to_tool_map = {tool.name: tool for tool in self.tools}
@@ -794,10 +848,16 @@ class AgentExecutor(Chain):
         # We now enter the agent loop (until it returns something).
         while self._should_continue(iterations, time_elapsed):
             next_step_output = self._take_next_step(
-                name_to_tool_map, color_mapping, inputs, intermediate_steps
+                name_to_tool_map,
+                color_mapping,
+                inputs,
+                intermediate_steps,
+                run_manager=run_manager,
             )
             if isinstance(next_step_output, AgentFinish):
-                return self._return(next_step_output, intermediate_steps)
+                return self._return(
+                    next_step_output, intermediate_steps, run_manager=run_manager
+                )
 
             intermediate_steps.extend(next_step_output)
             if len(next_step_output) == 1:
@@ -813,7 +873,11 @@ class AgentExecutor(Chain):
         )
         return self._return(output, intermediate_steps)
 
-    async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]:
+    async def _acall(
+        self,
+        inputs: Dict[str, str],
+        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
+    ) -> Dict[str, str]:
         """Run text through and get agent response."""
         # Construct a mapping of tool name to tool for easy lookup
         name_to_tool_map = {tool.name: tool for tool in self.tools}
@@ -831,7 +895,11 @@ class AgentExecutor(Chain):
             try:
                 while self._should_continue(iterations, time_elapsed):
                     next_step_output = await self._atake_next_step(
-                        name_to_tool_map, color_mapping, inputs, intermediate_steps
+                        name_to_tool_map,
+                        color_mapping,
+                        inputs,
+                        intermediate_steps,
+                        run_manager=run_manager,
                     )
                     if isinstance(next_step_output, AgentFinish):
                         return await self._areturn(next_step_output, intermediate_steps)
@@ -849,7 +917,9 @@ class AgentExecutor(Chain):
                 output = self.agent.return_stopped_response(
                     self.early_stopping_method, intermediate_steps, **inputs
                 )
-                return await self._areturn(output, intermediate_steps)
+                return await self._areturn(
+                    output, intermediate_steps, run_manager=run_manager
+                )
             except TimeoutError:
                 # stop early when interrupted by the async timeout
                 output = self.agent.return_stopped_response(

langchain/agents/agent_toolkits/__init__.py

@@ -1,6 +1,9 @@
 """Agent toolkits."""
 
 from langchain.agents.agent_toolkits.csv.base import create_csv_agent
+from langchain.agents.agent_toolkits.file_management.toolkit import (
+    FileManagementToolkit,
+)
 from langchain.agents.agent_toolkits.jira.toolkit import JiraToolkit
 from langchain.agents.agent_toolkits.json.base import create_json_agent
 from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
@@ -8,6 +11,7 @@ from langchain.agents.agent_toolkits.nla.toolkit import NLAToolkit
 from langchain.agents.agent_toolkits.openapi.base import create_openapi_agent
 from langchain.agents.agent_toolkits.openapi.toolkit import OpenAPIToolkit
 from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
+from langchain.agents.agent_toolkits.playwright.toolkit import PlayWrightBrowserToolkit
 from langchain.agents.agent_toolkits.powerbi.base import create_pbi_agent
 from langchain.agents.agent_toolkits.powerbi.chat_base import create_pbi_chat_agent
 from langchain.agents.agent_toolkits.powerbi.toolkit import PowerBIToolkit
@@ -46,4 +50,6 @@ __all__ = [
     "create_csv_agent",
     "ZapierToolkit",
     "JiraToolkit",
+    "FileManagementToolkit",
+    "PlayWrightBrowserToolkit",
 ]

langchain/agents/agent_toolkits/file_management/__init__.py

@@ -0,0 +1,7 @@
+"""Local file management toolkit."""
+
+from langchain.agents.agent_toolkits.file_management.toolkit import (
+    FileManagementToolkit,
+)
+
+__all__ = ["FileManagementToolkit"]