From 6396a4ad8dc5316caee98179972da7b7b163a0a2 Mon Sep 17 00:00:00 2001 From: liviuasnash1 <112160347+liviuasnash1@users.noreply.github.com> Date: Tue, 2 May 2023 06:58:38 +0300 Subject: [PATCH] Fix documentation typos (#3870) Co-authored-by: Liviu Asnash --- docs/modules/chains/examples/constitutional_chain.ipynb | 4 ++-- docs/modules/chains/examples/openapi.ipynb | 2 +- docs/modules/models/llms/examples/streaming_llm.ipynb | 2 +- docs/use_cases/agent_simulations.md | 4 ++-- docs/use_cases/autonomous_agents.md | 2 +- 5 files changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/modules/chains/examples/constitutional_chain.ipynb b/docs/modules/chains/examples/constitutional_chain.ipynb index 219c07fe..9f7091e4 100644 --- a/docs/modules/chains/examples/constitutional_chain.ipynb +++ b/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", diff --git a/docs/modules/chains/examples/openapi.ipynb b/docs/modules/chains/examples/openapi.ipynb index e43d380a..518fe4f3 100644 --- a/docs/modules/chains/examples/openapi.ipynb +++ b/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." ] }, { diff --git a/docs/modules/models/llms/examples/streaming_llm.ipynb b/docs/modules/models/llms/examples/streaming_llm.ipynb index e10a79d7..b2b5620d 100644 --- a/docs/modules/models/llms/examples/streaming_llm.ipynb +++ b/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`]()." ] }, { diff --git a/docs/use_cases/agent_simulations.md b/docs/use_cases/agent_simulations.md index 1376d458..a6bfd306 100644 --- a/docs/use_cases/agent_simulations.md +++ b/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)). diff --git a/docs/use_cases/autonomous_agents.md b/docs/use_cases/autonomous_agents.md index cebdf3a3..25fcbeb9 100644 --- a/docs/use_cases/autonomous_agents.md +++ b/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))