Fix documentation typos (#3870)

Co-authored-by: Liviu Asnash <liviua@maximallearning.com>

docs/modules/chains/examples/constitutional_chain.ipynb

@@ -164,7 +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",
@@ -172,7 +172,7 @@
     "\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",

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/models/llms/examples/streaming_llm.ipynb

@@ -7,7 +7,7 @@
    "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`]()."
    ]
   },
   {

docs/use_cases/agent_simulations.md

@@ -1,12 +1,12 @@
 # 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)).

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))