test

Refactored `requests.py`. The same as
https://github.com/langchain-ai/langchain/pull/7961 #8098 #8099
requests.py is in the root code folder. This creates the
`langchain.requests: Requests` group on the API Reference navigation
ToC, on the same level as Chains and Agents which is incorrect.

Refactoring:

- copied requests.py content into utils/requests.py
- I added the backwards compatibility ref in the original requests.py. 
- updated imports to requests objects

@hwchase17, @baskaryan

docs/docs_skeleton/docs/get_started/introduction.mdx

@@ -51,7 +51,7 @@ Walkthroughs and best-practices for common end-to-end use cases, like:
 Learn best practices for developing with LangChain.
 
 ### [Ecosystem](/docs/ecosystem/)
-LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/ecosystem/integrations/) and [dependent repos](/docs/ecosystem/dependents.html).
+LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/integrations/) and [dependent repos](/docs/ecosystem/dependents).
 
 ### [Additional resources](/docs/additional_resources/)
 Our community is full of prolific developers, creative builders, and fantastic teachers. Check out [YouTube tutorials](/docs/additional_resources/youtube.html) for great tutorials from folks in the community, and [Gallery](https://github.com/kyrolabs/awesome-langchain) for a list of awesome LangChain projects, compiled by the folks at [KyroLabs](https://kyrolabs.com).

docs/docs_skeleton/docs/get_started/quickstart.mdx

@@ -22,28 +22,74 @@ import OpenAISetup from "@snippets/get_started/quickstart/openai_setup.mdx"
 
 ## Building an application
 
-Now we can start building our language model application. LangChain provides many modules that can be used to build language model applications. Modules can be used as stand-alones in simple applications and they can be combined for more complex use cases.
+Now we can start building our language model application. LangChain provides many modules that can be used to build language model applications.
+Modules can be used as stand-alones in simple applications and they can be combined for more complex use cases.
+
+The core building block of LangChain applications is the LLMChain.
+This combines three things:
+- LLM: The language model is the core reasoning engine here. In order to work with LangChain, you need to understand the different types of language models and how to work with them.
+- Prompt Templates: This provides instructions to the language model. This controls what the language model outputs, so understanding how to construct prompts and different prompting strategies is crucial.
+- Output Parsers: These translate the raw response from the LLM to a more workable format, making it easy to use the output downstream.
+
+In this getting started guide we will cover those three components by themselves, and then cover the LLMChain which combines all of them.
+Understanding these concepts will set you up well for being able to use and customize LangChain applications.
+Most LangChain applications allow you to configure the LLM and/or the prompt used, so knowing how to take advantage of this will be a big enabler.
 
 ## LLMs
-#### Get predictions from a language model
 
-The basic building block of LangChain is the LLM, which takes in text and generates more text.
+There are two types of language models, which in LangChain are called:
 
-As an example, suppose we're building an application that generates a company name based on a company description. In order to do this, we need to initialize an OpenAI model wrapper. In this case, since we want the outputs to be MORE random, we'll initialize our model with a HIGH temperature.
+- LLMs: this is a language model which takes a string as input and returns a string
+- ChatModels: this is a language model which takes a list of messages as input and returns a message
 
-import LLM from "@snippets/get_started/quickstart/llm.mdx"
+The input/output for LLMs is simple and easy to understand - a string.
+But what about ChatModels? The input there is a list of `ChatMessage`s, and the output is a single `ChatMessage`.
+A `ChatMessage` has two required components:
 
-<LLM/>
+- `content`: This is the content of the message.
+- `role`: This is the role of the entity from which the `ChatMessage` is coming from.
 
-## Chat models
+LangChain provides several objects to easily distinguish between different roles:
 
-Chat models are a variation on language models. While chat models use language models under the hood, the interface they expose is a bit different: rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs.
+- `HumanMessage`: A `ChatMessage` coming from a human/user.
+- `AIMessage`: A `ChatMessage` coming from an AI/assistant.
+- `SystemMessage`: A `ChatMessage` coming from the system.
+- `FunctionMessage`: A `ChatMessage` coming from a function call.
 
-You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`.
+If none of those roles sound right, there is also a `ChatMessage` class where you can specify the role manually.
+For more information on how to use these different messages most effectively, see our prompting guide.
 
-import ChatModel from "@snippets/get_started/quickstart/chat_model.mdx"
+LangChain exposes a standard interface for both, but it's useful to understand this difference in order to construct prompts for a given language model.
+The standard interface that LangChain exposes has two methods:
+- `predict`: Takes in a string, returns a string
+- `predict_messages`: Takes in a list of messages, returns a message.
+
+Let's see how to work with these different types of models and these different types of inputs.
+First, let's import an LLM and a ChatModel.
+
+import ImportLLMs from "@snippets/get_started/quickstart/import_llms.mdx"
+
+<ImportLLMs/>
+
+The `OpenAI` and `ChatOpenAI` objects are basically just configuration objects.
+You can initialize them with parameters like `temperature` and others, and pass them around.
+
+Next, let's use the `predict` method to run over a string input.
+
+import InputString from "@snippets/get_started/quickstart/input_string.mdx"
+
+<InputString/>
+
+Finally, let's use the `predict_messages` method to run over a list of messages.
+
+import InputMessages from "@snippets/get_started/quickstart/input_messages.mdx"
+
+<InputMessages/>
+
+For both these methods, you can also pass in parameters as key word arguments.
+For example, you could pass in `temperature=0` to adjust the temperature that is used from what the object was configured with.
+Whatever values are passed in during run time will always override what the object was configured with.
 
-<ChatModel/>
 
 ## Prompt templates
 
@@ -51,108 +97,66 @@ Most LLM applications do not pass user input directly into an LLM. Usually they
 
 In the previous example, the text we passed to the model contained instructions to generate a company name. For our application, it'd be great if the user only had to provide the description of a company/product, without having to worry about giving the model instructions.
 
+PromptTemplates help with exactly this!
+They bundle up all the logic for going from user input into a fully formatted prompt.
+This can start off very simple - for example, a prompt to produce the above string would just be:
+
 import PromptTemplateLLM from "@snippets/get_started/quickstart/prompt_templates_llms.mdx"
 import PromptTemplateChatModel from "@snippets/get_started/quickstart/prompt_templates_chat_models.mdx"
 
-<Tabs>
-    <TabItem value="llms" label="LLMs" default>
-
-With PromptTemplates this is easy! In this case our template would be very simple:
-
 <PromptTemplateLLM/>
-</TabItem>
-<TabItem value="chat_models" label="Chat models">
 
-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_messages` method to generate the formatted messages.
+However, the advantages of using these over raw string formatting are several.
+You can "partial" out variables - eg you can format only some of the variables at a time.
+You can compose them together, easily combining different templates into a single prompt.
+For explanations of these functionalities, see the [section on prompts](/docs/modules/model_io/prompts) for more detail.
 
-Because this is generating a list of messages, it is slightly more complex than the normal prompt template which is generating only a string. Please see the detailed guides on prompts to understand more options available to you here.
+PromptTemplates can also be used to produce a list of messages.
+In this case, the prompt not only contains information about the content, but also each message (its role, its position in the list, etc)
+Here, what happens most often is a ChatPromptTemplate is a list of ChatMessageTemplates.
+Each ChatMessageTemplate contains instructions for how to format that ChatMessage - its role, and then also its content.
+Let's take a look at this below:
 
 <PromptTemplateChatModel/>
-    </TabItem>
-</Tabs>
 
-## Chains
+ChatPromptTemplates can also include other things besides ChatMessageTemplates - see the [section on prompts](/docs/modules/model_io/prompts) for more detail.
 
-Now that we've got a model and a prompt template, we'll want to combine the two. Chains give us a way to link (or chain) together multiple primitives, like models, prompts, and other chains.
+## Output Parsers
 
-import ChainLLM from "@snippets/get_started/quickstart/chains_llms.mdx"
-import ChainChatModel from "@snippets/get_started/quickstart/chains_chat_models.mdx"
+OutputParsers convert the raw output of an LLM into a format that can be used downstream.
+There are few main type of OutputParsers, including:
 
-<Tabs>
-<TabItem value="llms" label="LLMs" default>
+- Convert text from LLM -> structured information (eg JSON)
+- Convert a ChatMessage into just a string
+- Convert the extra information returned from a call besides the message (like OpenAI function invocation) into a string.
 
-The simplest and most common type of chain is an LLMChain, which passes an input first to a PromptTemplate and then to an LLM. We can construct an LLM chain from our existing model and prompt template.
+For full information on this, see the [section on output parsers](/docs/modules/model_io/output_parsers)
 
-<ChainLLM/>
+In this getting started guide, we will write our own output parser - one that converts a comma separated list into a list.
 
-There we go, our first chain! Understanding how this simple chain works will set you up well for working with more complex chains.
+import OutputParser from "@snippets/get_started/quickstart/output_parser.mdx"
 
-</TabItem>
-<TabItem value="chat_models" label="Chat models">
+<OutputParser/>
 
-The `LLMChain` can be used with chat models as well:
+## LLMChain
 
-<ChainChatModel/>
-</TabItem>
-</Tabs>
+We can now combine all these into one chain.
+This chain will take input variables, pass those to a prompt template to create a prompt, pass the prompt to an LLM, and then pass the output through an (optional) output parser.
+This is a convenient way to bundle up a modular piece of logic.
+Let's see it in action!
 
-## Agents
+import LLMChain from "@snippets/get_started/quickstart/llm_chain.mdx"
 
-import AgentLLM from "@snippets/get_started/quickstart/agents_llms.mdx"
-import AgentChatModel from "@snippets/get_started/quickstart/agents_chat_models.mdx"
+<LLMChain/>
 
-Our first chain ran a pre-determined sequence of steps. To handle complex workflows, we need to be able to dynamically choose actions based on inputs.
+## Next Steps
 
-Agents do just this: they use a language model to determine which actions to take and in what order. Agents are given access to tools, and they repeatedly choose a tool, run the tool, and observe the output until they come up with a final answer.
+This is it!
+We've now gone over how to create the core building block of LangChain applications - the LLMChains.
+There is a lot more nuance in all these components (LLMs, prompts, output parsers) and a lot more different components to learn about as well.
+To continue on your journey:
 
-To load an agent, you need to choose a(n):
-- LLM/Chat model: The language model powering the agent.
-- Tool(s): A function that performs a specific duty. This can be things like: Google Search, Database lookup, Python REPL, other chains. For a list of predefined tools and their specifications, see the [Tools documentation](/docs/modules/agents/tools/).
-- Agent name: A string that references a supported agent class. An agent class is largely parameterized by the prompt the language model uses to determine which action to take. 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 [here](/docs/modules/agents/how_to/custom_agent.html). For a list of supported agents and their specifications, see [here](/docs/modules/agents/agent_types/).
-
-For this example, we'll be using SerpAPI to query a search engine.
-
-You'll need to install the SerpAPI Python package:
-
-```bash
-pip install google-search-results
-```
-
-And set the `SERPAPI_API_KEY` environment variable.
-
-<Tabs>
-<TabItem value="llms" label="LLMs" default>
-<AgentLLM/>
-</TabItem>
-<TabItem value="chat_models" label="Chat models">
-
-Agents can also be used with chat models, you can initialize one using `AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION` as the agent type.
-
-<AgentChatModel/>
-</TabItem>
-</Tabs>
-
-## Memory
-
-The chains and agents we've looked at so far have been stateless, but for many applications it's necessary to reference past interactions. This is clearly the case with a chatbot for example, where you want it to understand new messages in the context of past messages.
-
-The Memory module gives you a way to maintain application state. The base Memory interface is simple: it lets you update state given the latest run inputs and outputs and it lets you modify (or contextualize) the next input using the stored state.
-
-There are a number of built-in memory systems. The simplest of these is a buffer memory which just prepends the last few inputs/outputs to the current input - we will use this in the example below.
-
-import MemoryLLM from "@snippets/get_started/quickstart/memory_llms.mdx"
-import MemoryChatModel from "@snippets/get_started/quickstart/memory_chat_models.mdx"
-
-<Tabs>
-<TabItem value="llms" label="LLMs" default>
-
-<MemoryLLM/>
-</TabItem>
-<TabItem value="chat_models" label="Chat models">
-
-You can use Memory with chains and agents initialized with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object.
-
-<MemoryChatModel/>
-
-</TabItem>
-</Tabs>
+- [Dive deeper](/docs/modules/model_io) into LLMs, prompts, and output parsers
+- Learn the other [key components](/docs/modules)
+- Check out our [helpful guides](/docs/guides) for detailed walkthroughs on particular topics
+- Explore [end-to-end use cases](/docs/use_cases)

docs/docs_skeleton/docs/guides/evaluation/index.mdx

@@ -6,7 +6,7 @@ import DocCardList from "@theme/DocCardList";
 
 # Evaluation
 
-Language models can be unpredictable. This makes it challenging to ship reliable applications to production, where repeatable, useful outcomes across diverse inputs are a minimum requirement. Tests help demonstrate each component in an LLM application can produce the required or expected functionality. These tests also safeguard against regressions while you improve interconnected pieces of an integrated system. However, measuring the quality of generated text can be challenging. It can be hard to agree on the right set of metrics for your application, and it can be difficult to translate those into better performance. Furthermore, it's common to lack sufficient evaluation data adequately test the range of inputs and expected outputs for each component when you're just getting started. The LangChain community is building open source tools and guides to help address these challenges.
+Language models can be unpredictable. This makes it challenging to ship reliable applications to production, where repeatable, useful outcomes across diverse inputs are a minimum requirement. Tests help demonstrate each component in an LLM application can produce the required or expected functionality. These tests also safeguard against regressions while you improve interconnected pieces of an integrated system. However, measuring the quality of generated text can be challenging. It can be hard to agree on the right set of metrics for your application, and it can be difficult to translate those into better performance. Furthermore, it's common to lack sufficient evaluation data to adequately test the range of inputs and expected outputs for each component when you're just getting started. The LangChain community is building open source tools and guides to help address these challenges.
 
 LangChain exposes different types of evaluators for common types of evaluation. Each type has off-the-shelf implementations you can use to get started, as well as an
  extensible API so you can create your own or contribute improvements for everyone to use. The following sections have example notebooks for you to get started.

docs/docs_skeleton/docs/modules/agents/index.mdx

@@ -3,46 +3,80 @@ sidebar_position: 4
 ---
 # Agents
 
-Some applications require a flexible chain of calls to LLMs and other tools based on user input. The **Agent** interface provides the flexibility for such applications. An agent has access to a suite of tools, and determines which ones to use depending on the user input. Agents can use multiple tools, and use the output of one tool as the input to the next.
+The core idea of agents is to use an LLM to choose a sequence of actions to take.
+In chains, a sequence of actions is hardcoded (in code).
+In agents, a language model is used as a reasoning engine to determine which actions to take and in which order.
 
-There are two main types of agents:
+There are several key components here:
 
-- **Action agents**: at each timestep, decide on the next action using the outputs of all previous actions
-- **Plan-and-execute agents**: decide on the full sequence of actions up front, then execute them all without updating the plan
+## Agent
 
-Action agents are suitable for small tasks, while plan-and-execute agents are better for complex or long-running tasks that require maintaining long-term objectives and focus. Often the best approach is to combine the dynamism of an action agent with the planning abilities of a plan-and-execute agent by letting the plan-and-execute agent use action agents to execute plans.
+This is the class responsible for deciding what step to take next.
+This is powered by a language model and a prompt.
+This prompt can include things like:
 
-For a full list of agent types see [agent types](/docs/modules/agents/agent_types/). Additional abstractions involved in agents are:
-- [**Tools**](/docs/modules/agents/tools/): the actions an agent can take. What tools you give an agent highly depend on what you want the agent to do
-- [**Toolkits**](/docs/modules/agents/toolkits/): wrappers around collections of tools that can be used together a specific use case. For example, in order for an agent to
-  interact with a SQL database it will likely need one tool to execute queries and another to inspect tables
+1. The personality of the agent (useful for having it respond in a certain way)
+2. Background context for the agent (useful for giving it more context on the types of tasks it's being asked to do)
+3. Prompting strategies to invoke better reasoning (the most famous/widely used being [ReAct](https://arxiv.org/abs/2210.03629))
 
-## Action agents
+LangChain provides a few different types of agents to get started.
+Even then, you will likely want to customize those agents with parts (1) and (2).
+For a full list of agent types see [agent types](/docs/modules/agents/agent_types/)
 
-At a high-level an action agent:
-1. Receives user input
-2. Decides which tool, if any, to use and the tool input
-3. Calls the tool and records the output (also known as an "observation")
-4. Decides the next step using the history of tools, tool inputs, and observations
-5. Repeats 3-4 until it determines it can respond directly to the user
+## Tools
 
-Action agents are wrapped in **agent executors**, which are responsible for calling the agent, getting back an action and action input, calling the tool that the action references with the generated input, getting the output of the tool, and then passing all that information back into the agent to get the next action it should take.
+Tools are functions that an agent calls.
+There are two important considerations here:
 
-Although an agent can be constructed in many ways, it typically involves these components:
+1. Giving the agent access to the right tools
+2. Describing the tools in a way that is most helpful to the agent
 
-- **Prompt template**: Responsible for taking the user input and previous steps and constructing a prompt
-  to send to the language model
-- **Language model**: Takes the prompt with use input and action history and decides what to do next
-- **Output parser**: Takes the output of the language model and parses it into the next action or a final answer
+Without both, the agent you are trying to build will not work.
+If you don't give the agent access to a correct set of tools, it will never be able to accomplish the objective.
+If you don't describe the tools properly, the agent won't know how to properly use them.
 
-## Plan-and-execute agents
+LangChain provides a wide set of tools to get started, but also makes it easy to define your own (including custom descriptions).
+For a full list of tools, see [here](/docs/modules/agents/tools/)
 
-At a high-level a plan-and-execute agent:
-1. Receives user input
-2. Plans the full sequence of steps to take
-3. Executes the steps in order, passing the outputs of past steps as inputs to future steps
+## Toolkits
 
-The most typical implementation is to have the planner be a language model, and the executor be an action agent. Read more [here](/docs/modules/agents/agent_types/plan_and_execute.html).
+Often the set of tools an agent has access to is more important than a single tool.
+For this LangChain provides the concept of toolkits - groups of tools needed to accomplish specific objectives.
+There are generally around 3-5 tools in a toolkit.
+
+LangChain provides a wide set of toolkits to get started.
+For a full list of toolkits, see [here](/docs/modules/agents/toolkits/)
+
+## AgentExecutor
+
+The agent executor is the runtime for an agent.
+This is what actually calls the agent and executes the actions it chooses.
+Pseudocode for this runtime is below:
+
+```python
+next_action = agent.get_action(...)
+while next_action != AgentFinish:
+    observation = run(next_action)
+    next_action = agent.get_action(..., next_action, observation)
+return next_action
+```
+
+While this may seem simple, there are several complexities this runtime handles for you, including:
+
+1. Handling cases where the agent selects a non-existent tool
+2. Handling cases where the tool errors
+3. Handling cases where the agent produces output that cannot be parsed into a tool invocation
+4. Logging and observability at all levels (agent decisions, tool calls) either to stdout or [LangSmith](https://smith.langchain.com).
+
+## Other types of agent runtimes
+
+The `AgentExecutor` class is the main agent runtime supported by LangChain.
+However, there are other, more experimental runtimes we also support.
+These include:
+
+- [Plan-and-execute Agent](/docs/modules/agents/agent_types/plan_and_execute.html)
+- [Baby AGI](/docs/use_cases/autonomous_agents/baby_agi.html)
+- [Auto GPT](/docs/use_cases/autonomous_agents/autogpt.html)
 
 ## Get started
 

docs/docs_skeleton/docs/modules/agents/toolkits/index.mdx

@@ -3,8 +3,8 @@ sidebar_position: 3
 ---
 # Toolkits
 
+:::info
+Head to [Integrations](/docs/integrations/toolkits/) for documentation on built-in toolkit integrations.
+:::
+
 Toolkits are collections of tools that are designed to be used together for specific tasks and have convenience loading methods.
-
-import DocCardList from "@theme/DocCardList";
-
-<DocCardList />

docs/docs_skeleton/docs/modules/agents/tools/how_to/_category_.yml

@@ -1,2 +0,0 @@
-label: 'How-to'
-position: 0

docs/docs_skeleton/docs/modules/agents/tools/index.mdx

@@ -3,6 +3,10 @@ sidebar_position: 2
 ---
 # Tools
 
+:::info
+Head to [Integrations](/docs/integrations/tools/) for documentation on built-in tool integrations.
+:::
+
 Tools are interfaces that an agent can use to interact with the world.
 
 ## Get started

docs/docs_skeleton/docs/modules/agents/tools/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/docs_skeleton/docs/modules/callbacks/how_to/_category_.yml

@@ -1,2 +0,0 @@
-label: 'How-to'
-position: 0

docs/docs_skeleton/docs/modules/callbacks/index.mdx

@@ -3,6 +3,10 @@ sidebar_position: 5
 ---
 # Callbacks
 
+:::info
+Head to [Integrations](/docs/integrations/callbacks/) for documentation on built-in callbacks integrations with 3rd-party tools.
+:::
+
 LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
 
 import GetStarted from "@snippets/modules/callbacks/get_started.mdx"

docs/docs_skeleton/docs/modules/callbacks/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/docs_skeleton/docs/modules/data_connection/document_loaders/how_to/_category_.yml

@@ -1,2 +0,0 @@
-label: 'How-to'
-position: 0

docs/docs_skeleton/docs/modules/data_connection/document_loaders/index.mdx

@@ -3,6 +3,10 @@ sidebar_position: 0
 ---
 # Document loaders
 
+:::info
+Head to [Integrations](/docs/integrations/document_loaders/) for documentation on built-in document loader integrations with 3rd-party tools.
+:::
+
 Use document loaders to load data from a source as `Document`'s. A `Document` is a piece of text
 and associated metadata. For example, there are document loaders for loading a simple `.txt` file, for loading the text
 contents of any web page, or even for loading a transcript of a YouTube video.

docs/docs_skeleton/docs/modules/data_connection/document_loaders/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/docs_skeleton/docs/modules/data_connection/document_transformers/index.mdx

@@ -3,6 +3,10 @@ sidebar_position: 1
 ---
 # Document transformers
 
+:::info
+Head to [Integrations](/docs/integrations/document_transformers/) for documentation on built-in document transformer integrations with 3rd-party tools.
+:::
+
 Once you've loaded documents, you'll often want to transform them to better suit your application. The simplest example
 is you may want to split a long document into smaller chunks that can fit into your model's context window. LangChain
 has a number of built-in document transformers that make it easy to split, combine, filter, and otherwise manipulate documents.

docs/docs_skeleton/docs/modules/data_connection/retrievers/how_to/_category_.yml

@@ -1,2 +0,0 @@
-label: 'How-to'
-position: 0

docs/docs_skeleton/docs/modules/data_connection/retrievers/index.mdx

@@ -3,6 +3,10 @@ sidebar_position: 4
 ---
 # Retrievers
 
+:::info
+Head to [Integrations](/docs/integrations/retrievers/) for documentation on built-in retriever integrations with 3rd-party tools.
+:::
+
 A retriever is an interface that returns documents given an unstructured query. It is more general than a vector store.
 A retriever does not need to be able to store documents, only to return (or retrieve) it. Vector stores can be used
 as the backbone of a retriever, but there are other types of retrievers as well.

docs/docs_skeleton/docs/modules/data_connection/text_embedding/index.mdx

@@ -3,6 +3,10 @@ sidebar_position: 2
 ---
 # Text embedding models
 
+:::info
+Head to [Integrations](/docs/integrations/text_embedding/) for documentation on built-in integrations with text embedding model providers.
+:::
+
 The Embeddings class is a class designed for interfacing with text embedding models. There are lots of embedding model providers (OpenAI, Cohere, Hugging Face, etc) - this class is designed to provide a standard interface for all of them.
 
 Embeddings create a vector representation of a piece of text. This is useful because it means we can think about text in the vector space, and do things like semantic search where we look for pieces of text that are most similar in the vector space.

docs/docs_skeleton/docs/modules/data_connection/text_embedding/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/docs_skeleton/docs/modules/data_connection/vectorstores/index.mdx

@@ -3,6 +3,10 @@ sidebar_position: 3
 ---
 # Vector stores
 
+:::info
+Head to [Integrations](/docs/integrations/vectorstores/) for documentation on built-in integrations with 3rd-party vector stores.
+:::
+
 One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding
 vectors, and then at query time to embed the unstructured query and retrieve the embedding vectors that are
 'most similar' to the embedded query. A vector store takes care of storing embedded data and performing vector search

docs/docs_skeleton/docs/modules/data_connection/vectorstores/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/docs_skeleton/docs/modules/memory/how_to/_category_.yml

@@ -1,2 +0,0 @@
-label: 'How-to'
-position: 0

docs/docs_skeleton/docs/modules/memory/index.mdx

@@ -6,6 +6,10 @@ sidebar_position: 3
 
 🚧 _Docs under construction_ 🚧
 
+:::info
+Head to [Integrations](/docs/integrations/memory/) for documentation on built-in memory integrations with 3rd-party tools.
+:::
+
 By default, Chains and Agents are stateless,
 meaning that they treat each incoming query independently (like the underlying LLMs and chat models themselves).
 In some applications, like chatbots, it is essential

docs/docs_skeleton/docs/modules/memory/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/docs_skeleton/docs/modules/model_io/models/chat/how_to/_category_.yml

@@ -1,2 +0,0 @@
-label: 'How-to'
-position: 0

docs/docs_skeleton/docs/modules/model_io/models/chat/index.mdx

@@ -3,18 +3,16 @@ sidebar_position: 1
 ---
 # Chat models
 
+:::info
+Head to [Integrations](/docs/integrations/chat/) for documentation on built-in integrations with chat model providers.
+:::
+
 Chat models are a variation on language models.
 While chat models use language models under the hood, the interface they expose is a bit different.
 Rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs.
 
 Chat model APIs are fairly new, so we are still figuring out the correct abstractions.
 
-The following sections of documentation are provided:
-
-- **How-to guides**: Walkthroughs of core functionality, like streaming, creating chat prompts, etc.
-
-- **Integrations**: How to use different chat model providers (OpenAI, Anthropic, etc).
-
 ## Get started
 
 import GetStarted from "@snippets/modules/model_io/models/chat/get_started.mdx"

docs/docs_skeleton/docs/modules/model_io/models/chat/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/docs_skeleton/docs/modules/model_io/models/llms/how_to/_category_.yml

@@ -1,2 +0,0 @@
-label: 'How-to'
-position: 0

docs/docs_skeleton/docs/modules/model_io/models/llms/index.mdx

@@ -3,14 +3,12 @@ sidebar_position: 0
 ---
 # LLMs
 
+:::info
+Head to [Integrations](/docs/integrations/llms/) for documentation on built-in integrations with LLM providers.
+:::
+
 Large Language Models (LLMs) are a core component of LangChain.
-LangChain does not serve it's own LLMs, but rather provides a standard interface for interacting with many different LLMs.
-
-For more detailed documentation check out our:
-
-- **How-to guides**: Walkthroughs of core functionality, like streaming, async, etc.
-
-- **Integrations**: How to use different LLM providers (OpenAI, Anthropic, etc.)
+LangChain does not serve its own LLMs, but rather provides a standard interface for interacting with many different LLMs.
 
 ## Get started
 

docs/docs_skeleton/docs/modules/model_io/models/llms/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/extras/_templates/integration.mdx

@@ -31,7 +31,7 @@ There isn't any special setup for it.
 
 ## LLM
 
-See a [usage example](/docs/modules/model_io/models/llms/integrations/INCLUDE_REAL_NAME.html).
+See a [usage example](/docs/integrations/llms/INCLUDE_REAL_NAME).
 
 ```python
 from langchain.llms import integration_class_REPLACE_ME
@@ -40,7 +40,7 @@ from langchain.llms import integration_class_REPLACE_ME
 
 ## Text Embedding Models
 
-See a [usage example](/docs/modules/data_connection/text_embedding/integrations/INCLUDE_REAL_NAME.html)
+See a [usage example](/docs/integrations/text_embedding/INCLUDE_REAL_NAME)
 
 ```python
 from langchain.embeddings import integration_class_REPLACE_ME
@@ -49,7 +49,7 @@ from langchain.embeddings import integration_class_REPLACE_ME
 
 ## Chat Models
 
-See a [usage example](/docs/modules/model_io/models/chat/integrations/INCLUDE_REAL_NAME.html)
+See a [usage example](/docs/integrations/chat/INCLUDE_REAL_NAME)
 
 ```python
 from langchain.chat_models import integration_class_REPLACE_ME
@@ -57,7 +57,7 @@ from langchain.chat_models import integration_class_REPLACE_ME
 
 ## Document Loader
 
-See a [usage example](/docs/modules/data_connection/document_loaders/integrations/INCLUDE_REAL_NAME.html).
+See a [usage example](/docs/integrations/document_loaders/INCLUDE_REAL_NAME).
 
 ```python
 from langchain.document_loaders import integration_class_REPLACE_ME

docs/extras/integrations/callbacks/index.mdx

@@ -0,0 +1,9 @@
+---
+sidebar_position: 0
+---
+
+# Callbacks
+
+import DocCardList from "@theme/DocCardList";
+
+<DocCardList />

docs/extras/integrations/callbacks/promptlayer.ipynb

@@ -11,7 +11,7 @@
     "\n",
     "[PromptLayer](https://promptlayer.com) is a an LLM observability platform that lets you visualize requests, version prompts, and track usage. In this guide we will go over how to setup the `PromptLayerCallbackHandler`. \n",
     "\n",
-    "While PromptLayer does have LLMs that integrate directly with LangChain (eg [`PromptLayerOpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/promptlayer_openai)), this callback is the recommended way to integrate PromptLayer with LangChain.\n",
+    "While PromptLayer does have LLMs that integrate directly with LangChain (eg [`PromptLayerOpenAI`](https://python.langchain.com/docs/integrations/llms/promptlayer_openai)), this callback is the recommended way to integrate PromptLayer with LangChain.\n",
     "\n",
     "See [our docs](https://docs.promptlayer.com/languages/langchain) for more information."
    ]

docs/extras/integrations/chat/index.mdx

@@ -0,0 +1,9 @@
+---
+sidebar_position: 0
+---
+
+# Chat models
+
+import DocCardList from "@theme/DocCardList";
+
+<DocCardList />

docs/extras/integrations/chat/llama_api.ipynb

@@ -0,0 +1,134 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "90a1faf2",
+   "metadata": {},
+   "source": [
+    "# Llama API\n",
+    "\n",
+    "This notebook shows how to use LangChain with [LlamaAPI](https://llama-api.com/) - a hosted version of Llama2 that adds in support for function calling."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5b652cf",
+   "metadata": {},
+   "source": [
+    "!pip install -U llamaapi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bfd385fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llamaapi import LlamaAPI\n",
+    "\n",
+    "# Replace 'Your_API_Token' with your actual API token\n",
+    "llama = LlamaAPI('Your_API_Token')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "632eb3e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.12) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_experimental.llms import ChatLlamaAPI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6f850e82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ChatLlamaAPI(client=llama)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "975c2bf4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import create_tagging_chain\n",
+    "\n",
+    "schema = {\n",
+    "    \"properties\": {\n",
+    "        \"sentiment\": {\"type\": \"string\", 'description': 'the sentiment encountered in the passage'},\n",
+    "        \"aggressiveness\": {\"type\": \"integer\", 'description': 'a 0-10 score of how aggressive the passage is'},\n",
+    "        \"language\": {\"type\": \"string\", 'description': 'the language of the passage'},\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "chain = create_tagging_chain(schema, model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef9638c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'sentiment': 'aggressive', 'aggressiveness': 8}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"give me your money\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "238b4f62",
+   "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/extras/integrations/document_loaders/Etherscan.ipynb

@@ -0,0 +1,220 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1ab83660",
+   "metadata": {},
+   "source": [
+    "# Etherscan Loader\n",
+    "## Overview\n",
+    "\n",
+    "The Etherscan loader use etherscan api to load transacactions histories under specific account on Ethereum Mainnet.\n",
+    "\n",
+    "You will need a Etherscan api key to proceed. The free api key has 5 calls per seconds quota.\n",
+    "\n",
+    "The loader supports the following six functinalities:\n",
+    "* Retrieve normal transactions under specifc account on Ethereum Mainet\n",
+    "* Retrieve internal transactions under specifc account on Ethereum Mainet\n",
+    "* Retrieve erc20 transactions under specifc account on Ethereum Mainet\n",
+    "* Retrieve erc721 transactions under specifc account on Ethereum Mainet\n",
+    "* Retrieve erc1155 transactions under specifc account on Ethereum Mainet\n",
+    "* Retrieve ethereum balance in wei under specifc account on Ethereum Mainet\n",
+    "\n",
+    "\n",
+    "If the account does not have corresponding transactions, the loader will a list with one document. The content of document is ''.\n",
+    "\n",
+    "You can pass differnt filters to loader to access different functionalities we mentioned above:\n",
+    "* \"normal_transaction\"\n",
+    "* \"internal_transaction\"\n",
+    "* \"erc20_transaction\"\n",
+    "* \"eth_balance\"\n",
+    "* \"erc721_transaction\"\n",
+    "* \"erc1155_transaction\"\n",
+    "The filter is default to normal_transaction\n",
+    "\n",
+    "If you have any questions, you can access [Etherscan API Doc](https://etherscan.io/tx/0x0ffa32c787b1398f44303f731cb06678e086e4f82ce07cebf75e99bb7c079c77) or contact me via i@inevitable.tech.\n",
+    "\n",
+    "All functions related to transactions histories are restricted 1000 histories maximum because of Etherscan limit. You can use the following parameters to find the transaction histories you need:\n",
+    "* offset: default to 20. Shows 20 transactions for one time\n",
+    "* page: default to 1. This controls pagenation.\n",
+    "* start_block: Default to 0. The transaction histories starts from 0 block.\n",
+    "* end_block: Default to 99999999. The transaction histories starts from 99999999 block\n",
+    "* sort: \"desc\" or \"asc\". Set default to \"desc\" to get latest transactions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d72d4e22",
+   "metadata": {},
+   "source": [
+    "# Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2911e51e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install langchain -q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "208e2fbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import EtherscanLoader\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5d24b650",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"ETHERSCAN_API_KEY\"] = etherscanAPIKey"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bcbb63e",
+   "metadata": {},
+   "source": [
+    "# Create a ERC20 transaction loader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d525e6c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'blockNumber': '13242975',\n",
+       " 'timeStamp': '1631878751',\n",
+       " 'hash': '0x366dda325b1a6570928873665b6b418874a7dedf7fee9426158fa3536b621788',\n",
+       " 'nonce': '28',\n",
+       " 'blockHash': '0x5469dba1b1e1372962cf2be27ab2640701f88c00640c4d26b8cc2ae9ac256fb6',\n",
+       " 'from': '0x2ceee24f8d03fc25648c68c8e6569aa0512f6ac3',\n",
+       " 'contractAddress': '0x2ceee24f8d03fc25648c68c8e6569aa0512f6ac3',\n",
+       " 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b',\n",
+       " 'value': '298131000000000',\n",
+       " 'tokenName': 'ABCHANGE.io',\n",
+       " 'tokenSymbol': 'XCH',\n",
+       " 'tokenDecimal': '9',\n",
+       " 'transactionIndex': '71',\n",
+       " 'gas': '15000000',\n",
+       " 'gasPrice': '48614996176',\n",
+       " 'gasUsed': '5712724',\n",
+       " 'cumulativeGasUsed': '11507920',\n",
+       " 'input': 'deprecated',\n",
+       " 'confirmations': '4492277'}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "account_address = \"0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b\"\n",
+    "loader = EtherscanLoader(account_address, filter=\"erc20_transaction\")\n",
+    "result = loader.load()\n",
+    "eval(result[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a1ecce0",
+   "metadata": {},
+   "source": [
+    "# Create a normal transaction loader with customized parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "07aa2b6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content=\"{'blockNumber': '1723771', 'timeStamp': '1466213371', 'hash': '0xe00abf5fa83a4b23ee1cc7f07f9dda04ab5fa5efe358b315df8b76699a83efc4', 'nonce': '3155', 'blockHash': '0xc2c2207bcaf341eed07f984c9a90b3f8e8bdbdbd2ac6562f8c2f5bfa4b51299d', 'transactionIndex': '5', 'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '13149213761000000000', 'gas': '90000', 'gasPrice': '22655598156', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '126000', 'gasUsed': '21000', 'confirmations': '16011481', 'methodId': '0x', 'functionName': ''}\", metadata={'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'tx_hash': '0xe00abf5fa83a4b23ee1cc7f07f9dda04ab5fa5efe358b315df8b76699a83efc4', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}),\n",
+       " Document(page_content=\"{'blockNumber': '1727090', 'timeStamp': '1466262018', 'hash': '0xd5a779346d499aa722f72ffe7cd3c8594a9ddd91eb7e439e8ba92ceb7bc86928', 'nonce': '3267', 'blockHash': '0xc0cff378c3446b9b22d217c2c5f54b1c85b89a632c69c55b76cdffe88d2b9f4d', 'transactionIndex': '20', 'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '11521979886000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '3806725', 'gasUsed': '21000', 'confirmations': '16008162', 'methodId': '0x', 'functionName': ''}\", metadata={'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'tx_hash': '0xd5a779346d499aa722f72ffe7cd3c8594a9ddd91eb7e439e8ba92ceb7bc86928', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}),\n",
+       " Document(page_content=\"{'blockNumber': '1730337', 'timeStamp': '1466308222', 'hash': '0xceaffdb3766d2741057d402738eb41e1d1941939d9d438c102fb981fd47a87a4', 'nonce': '3344', 'blockHash': '0x3a52d28b8587d55c621144a161a0ad5c37dd9f7d63b629ab31da04fa410b2cfa', 'transactionIndex': '1', 'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '9783400526000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '60788', 'gasUsed': '21000', 'confirmations': '16004915', 'methodId': '0x', 'functionName': ''}\", metadata={'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'tx_hash': '0xceaffdb3766d2741057d402738eb41e1d1941939d9d438c102fb981fd47a87a4', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}),\n",
+       " Document(page_content=\"{'blockNumber': '1733479', 'timeStamp': '1466352351', 'hash': '0x720d79bf78775f82b40280aae5abfc347643c5f6708d4bf4ec24d65cd01c7121', 'nonce': '3367', 'blockHash': '0x9928661e7ae125b3ae0bcf5e076555a3ee44c52ae31bd6864c9c93a6ebb3f43e', 'transactionIndex': '0', 'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '1570706444000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '21000', 'gasUsed': '21000', 'confirmations': '16001773', 'methodId': '0x', 'functionName': ''}\", metadata={'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'tx_hash': '0x720d79bf78775f82b40280aae5abfc347643c5f6708d4bf4ec24d65cd01c7121', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}),\n",
+       " Document(page_content=\"{'blockNumber': '1734172', 'timeStamp': '1466362463', 'hash': '0x7a062d25b83bafc9fe6b22bc6f5718bca333908b148676e1ac66c0adeccef647', 'nonce': '1016', 'blockHash': '0x8a8afe2b446713db88218553cfb5dd202422928e5e0bc00475ed2f37d95649de', 'transactionIndex': '4', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '6322276709000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '105333', 'gasUsed': '21000', 'confirmations': '16001080', 'methodId': '0x', 'functionName': ''}\", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'tx_hash': '0x7a062d25b83bafc9fe6b22bc6f5718bca333908b148676e1ac66c0adeccef647', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}),\n",
+       " Document(page_content=\"{'blockNumber': '1737276', 'timeStamp': '1466406037', 'hash': '0xa4e89bfaf075abbf48f96700979e6c7e11a776b9040113ba64ef9c29ac62b19b', 'nonce': '1024', 'blockHash': '0xe117cad73752bb485c3bef24556e45b7766b283229180fcabc9711f3524b9f79', 'transactionIndex': '35', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '9976891868000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '3187163', 'gasUsed': '21000', 'confirmations': '15997976', 'methodId': '0x', 'functionName': ''}\", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'tx_hash': '0xa4e89bfaf075abbf48f96700979e6c7e11a776b9040113ba64ef9c29ac62b19b', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}),\n",
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+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loader = EtherscanLoader(\n",
+    "    account_address,\n",
+    "    page=2,\n",
+    "    offset=20,\n",
+    "    start_block=10000,\n",
+    "    end_block=8888888888,\n",
+    "    sort=\"asc\",\n",
+    ")\n",
+    "result = loader.load()\n",
+    "result"
+   ]
+  }
+ ],
+ "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": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/apify_dataset.ipynb

@@ -13,7 +13,7 @@
     "\n",
     "## Prerequisites\n",
     "\n",
-    "You need to have an existing dataset on the Apify platform. If you don't have one, please first check out [this notebook](/docs/modules/agents/tools/integrations/apify.html) on how to use Apify to extract content from documentation, knowledge bases, help centers, or blogs."
+    "You need to have an existing dataset on the Apify platform. If you don't have one, please first check out [this notebook](/docs/integrations/tools/apify.html) on how to use Apify to extract content from documentation, knowledge bases, help centers, or blogs."
    ]
   },
   {