Add READMEs for 5 utility chains (#27)

README.md

@@ -56,8 +56,8 @@ For more detailed information on how chains are organized in the Hub, and how be
 
 ## 🤖 Agents
 
-At a high level, chains are organized by use case inside the `chains` directory.
-To load a chain in LangChain, you should use the following code snippet:
+At a high level, agents are organized by use case inside the `agents` directory.
+To load an agent in LangChain, you should use the following code snippet:
 
 ```python
 from langchain.agents import initialize_agent
@@ -76,4 +76,4 @@ For more detailed information on how agents are organized in the Hub, and how be
 
 ## 👷 Agent Executors
 
-Coming soon!
+Coming soon!

chains/llm-bash/README.md

@@ -0,0 +1,13 @@
+# BashChain
+
+## Description
+
+The LLMBashChain takes in a task to perform and generates a series of bash commands that will perform the task.
+
+## Chain type
+
+LLMBashChain
+
+## Input Variables
+
+question: a question about how to perform a task in bash.

chains/llm-checker/README.md

@@ -0,0 +1,13 @@
+# LLMCheckerChain
+
+## Description
+
+The LLMCheckerChain is designed to generate better answers to factual questions. The chain works by first generating a draft answer based on the question. The model is then asked to list its assumptions for this statement. The model is then asked to determine whether each assertion is true or false, and explain why if it is false. Finally, the model is prompted to revise their answer based on the above checks and assertions.
+
+## Chain type
+
+LLMCheckerChain
+
+## Input Variables
+
+question: the question to answer

chains/llm-math/README.md

@@ -0,0 +1,14 @@
+# LLM Math
+
+
+## Description
+
+A LLMMathChain that uses LLMs and the Python REPL to do complex word math problems.
+
+## Chain type
+
+LLMMathChain
+
+## Input Variables
+
+question: math problem to be solved.

chains/llm-requests/README.md

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+# LLMRequestsChain
+
+## Description
+
+The LLMRequestsChain extracts answers from HTML results from a URL. Given a URL, and a query to extract information from the results, the chain will extract the answer to the query from the text of the results or return "not found" if the information is not contained in the results text.
+
+## Chain type
+
+LLMRequestsChain
+
+## Input Variables
+
+url: URL
+query: query to extract information from search results.

chains/pal/math/README.md

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+# PAL Math Chain
+
+## Description
+
+The PALChain mplements Program-Aided Language Models, as in https://arxiv.org/pdf/2211.10435.pdf. Gives the model examples of math questions sent as text and answers written in Python, then provides the input question to do the same.
+
+## Chain type
+
+PALChain
+
+## Input Variables
+
+question: a math problem in plain text