docs in progress

docs/modules/utils/key_concepts.md

@@ -1,38 +1,50 @@
 # Key Concepts
 
 ## Text Splitter
+
 This class is responsible for splitting long pieces of text into smaller components.
 It contains different ways for splitting text (on characters, using Spacy, etc)
 as well as different ways for measuring length (token based, character based, etc).
 
 ## Embeddings
-These classes are very similar to the LLM classes in that they are wrappers around models, 
-but rather than return a string they return an embedding (list of floats). These are particularly useful when 
+
+These classes are very similar to the LLM classes in that they are wrappers around models,
+but rather than return a string they return an embedding (list of floats). These are particularly useful when
 implementing semantic search functionality. They expose separate methods for embedding queries versus embedding documents.
 
 ## Vectorstores
+
 These are datastores that store embeddings of documents in vector form.
 They expose a method for passing in a string and finding similar documents.
 
 ## Python REPL
-Sometimes, for complex calculations, rather than have an LLM generate the answer directly, 
-it can be better to have the LLM generate code to calculate the answer, and then run that code to get the answer. 
+
+Sometimes, for complex calculations, rather than have an LLM generate the answer directly,
+it can be better to have the LLM generate code to calculate the answer, and then run that code to get the answer.
 In order to easily do that, we provide a simple Python REPL to execute commands in.
-This interface will only return things that are printed - 
+This interface will only return things that are printed -
 therefore, if you want to use it to calculate an answer, make sure to have it print out the answer.
 
 ## Bash
-It can often be useful to have an LLM generate bash commands, and then run them. 
-A common use case this is for letting it interact with your local file system. 
+
+It can often be useful to have an LLM generate bash commands, and then run them.
+A common use case this is for letting it interact with your local file system.
 We provide an easy component to execute bash commands.
 
 ## Requests Wrapper
-The web contains a lot of information that LLMs do not have access to. 
-In order to easily let LLMs interact with that information, 
+
+The web contains a lot of information that LLMs do not have access to.
+In order to easily let LLMs interact with that information,
 we provide a wrapper around the Python Requests module that takes in a URL and fetches data from that URL.
 
 ## Google Search
+
 This uses the official Google Search API to look up information on the web.
 
 ## SerpAPI
+
 This uses SerpAPI, a third party search API engine, to interact with Google Search.
+
+## Guards
+
+The `@guard` directive can be used to prevent your chain or agent from outputting something you would rather avoid like a racist or off topic response.