Documentation: Minor typo fixes (#1327)

Fixing a few minor typos in the documentation (and likely introducing
other
ones in the process).

docs/modules/agents.rst

@@ -2,7 +2,7 @@ Agents
 ==========================
 
 Some applications will require not just a predetermined chain of calls to LLMs/other tools,
-but potentially an unknown chain that depends on the user input.
+but potentially an unknown chain that depends on the user's input.
 In these types of chains, there is a “agent” which has access to a suite of tools.
 Depending on the user input, the agent can then decide which, if any, of these tools to call.
 
@@ -12,7 +12,7 @@ The following sections of documentation are provided:
 
 - `Key Concepts <./agents/key_concepts.html>`_: A conceptual guide going over the various concepts related to agents.
 
-- `How-To Guides <./agents/how_to_guides.html>`_: A collection of how-to guides. These highlight how to integrate various types of tools, how to work with different types of agent, and how to customize agents.
+- `How-To Guides <./agents/how_to_guides.html>`_: A collection of how-to guides. These highlight how to integrate various types of tools, how to work with different types of agents, and how to customize agents.
 
 - `Reference <../reference/modules/agents.html>`_: API reference documentation for all Agent classes.
 

docs/modules/agents/agents.md

@@ -1,7 +1,7 @@
 # Agents
 
 Agents use an LLM to determine which actions to take and in what order.
-An action can either be using a tool and observing its output, or returning to the user.
+An action can either be using a tool and observing its output, or returning a response to the user.
 For a list of easily loadable tools, see [here](tools.md).
 Here are the agents available in LangChain.
 

docs/modules/chains.rst

@@ -2,8 +2,8 @@ Chains
 ==========================
 
 Using an LLM in isolation is fine for some simple applications,
-but many more complex ones require chaining LLMs - either with eachother or with other experts.
-LangChain provides a standard interface for Chains, as well as some common implementations of chains for easy use.
+but many more complex ones require chaining LLMs - either with each other or with other experts.
+LangChain provides a standard interface for Chains, as well as some common implementations of chains for ease of use.
 
 The following sections of documentation are provided:
 

docs/modules/chains/getting_started.ipynb

@@ -9,13 +9,13 @@
     "In this tutorial, we will learn about creating simple chains in LangChain. We will learn how to create a chain, add components to it, and run it.\n",
     "\n",
     "In this tutorial, we will cover:\n",
-    "- Using the simple LLM chain\n",
+    "- Using a simple LLM chain\n",
     "- Creating sequential chains\n",
     "- Creating a custom chain\n",
     "\n",
     "## Why do we need chains?\n",
     "\n",
-    "Chains allow us to combine multiple components together to create a single, coherent application. For example, we can create a chain that takes user input, format it with a PromptTemplate, and then passes the formatted response to an LLM. We can build more complex chains by combining multiple chains together, or by combining chains with other components.\n"
+    "Chains allow us to combine multiple components together to create a single, coherent application. For example, we can create a chain that takes user input, formats it with a PromptTemplate, and then passes the formatted response to an LLM. We can build more complex chains by combining multiple chains together, or by combining chains with other components.\n"
    ]
   },
   {
@@ -88,7 +88,7 @@
    "source": [
     "## Combine chains with the `SequentialChain`\n",
     "\n",
-    "The next step after calling a language model is make a series of calls to a language model. We can do this using sequential chains, which are chains that execute their links in a predefined order. Specifically, we will use the `SimpleSequentialChain`. This is the simplest form of sequential chains, where each step has a singular input/output, and the output of one step is the input to the next.\n",
+    "The next step after calling a language model is to make a series of calls to a language model. We can do this using sequential chains, which are chains that execute their links in a predefined order. Specifically, we will use the `SimpleSequentialChain`. This is the simplest type of a sequential chain, where each step has a single input/output, and the output of one step is the input to the next.\n",
     "\n",
     "In this tutorial, our sequential chain will:\n",
     "1. First, create a company name for a product. We will reuse the `LLMChain` we'd previously initialized to create this company name.\n",
@@ -156,7 +156,7 @@
    "source": [
     "## Create a custom chain with the `Chain` class\n",
     "\n",
-    "LangChain provides many chains out of the box, but sometimes you may want to create a custom chains for your specific use case. For this example, we will create a custom chain that concatenates the outputs of 2 `LLMChain`s.\n",
+    "LangChain provides many chains out of the box, but sometimes you may want to create a custom chain for your specific use case. For this example, we will create a custom chain that concatenates the outputs of 2 `LLMChain`s.\n",
     "\n",
     "In order to create a custom chain:\n",
     "1. Start by subclassing the `Chain` class,\n",