updated docs (#70)

Author: ShreyaR

README.md

@@ -15,9 +15,9 @@ _Note: Guardrails is an alpha release, so expect sharp edges and bugs._
 
 Guardrails is a Python package that lets a user add structure, type and quality guarantees to the outputs of large language models (LLMs). Guardrails:
 
-✅ does pydantic-style validation of LLM outputs,  
-✅ takes corrective actions (e.g. reasking LLM) when validation fails,  
-✅ enforces structure and type guarantees (e.g. JSON).
+- does pydantic-style validation of LLM outputs (including semantic validation such as checking for bias in generated text, checking for bugs in generated code, etc.)
+- takes corrective actions (e.g. reasking LLM) when validation fails,
+- enforces structure and type guarantees (e.g. JSON).
 
 
 ## 🚒 Under the hood

docs/guard.md

@@ -1,7 +1,7 @@
 <!-- ::: my_library.my_module.my_class -->
 
 
-::: guardrails.guardrails.Guard
+::: guardrails.guard.Guard
     options:
         members: 
             - "from_rail"

docs/index.md

@@ -6,9 +6,9 @@ _Note: Guardrails is an alpha release, so expect sharp edges and bugs._
 
 Guardrails is a Python package that lets a user add structure, type and quality guarantees to the outputs of large language models (LLMs). Guardrails:
 
-✅ does pydantic-style validation of LLM outputs,
-✅ takes corrective actions (e.g. reasking LLM) when validation fails,  
-✅ enforces structure and type guarantees (e.g. JSON).
+- does pydantic-style validation of LLM outputs. This includes semantic validation such as checking for bias in generated text, checking for bugs in generated code, etc.
+- takes corrective actions (e.g. reasking LLM) when validation fails,
+- enforces structure and type guarantees (e.g. JSON).
 
 ## 🚒 Under the hood
 
@@ -40,12 +40,12 @@ To learn more about the `rail` spec and the design decisions behind it, check ou
 ## 📍 Roadmap
 
 - [ ] Adding more examples, new use cases and domains
-- [ ] Adding integrations with langchain, gpt-index, minichain, manifest
+- [x] Adding integrations with langchain, gpt-index, minichain, manifest
 - [ ] Expanding validators offering
 - [ ] More compilers from `.rail` -> LLM prompt (e.g. `.rail` -> TypeScript)
 - [ ] Informative logging
-- [ ] Improving reasking logic
+- [x] Improving reasking logic
 - [ ] A guardrails.js implementation
 - [ ] VSCode extension for `.rail` files
 - [ ] Next version of `.rail` format
-- [ ] Add more LLM providers
+- [x] Add more LLM providers

docs/integrations/pydantic_validation.ipynb

@@ -8,7 +8,7 @@
     "# Validating LLM Outputs with Pydantic\n",
     "\n",
     "!!! note\n",
-    "    To download this example as a Jupyter notebook, click [here](https://github.com/ShreyaR/guardrails/blob/main/docs/examples/pydantic_validation.ipynb).\n",
+    "    To download this example as a Jupyter notebook, click [here](https://github.com/ShreyaR/guardrails/blob/main/docs/integrations/pydantic_validation.ipynb).\n",
     "\n",
     "In this example, we will use Guardrails with Pydantic.\n",
     "\n",
@@ -38,8 +38,9 @@
     "Ordinarily, we would create an RAIL spec in a separate file. For the purposes of this example, we will create the spec in this notebook as a string following the RAIL syntax. For more information on RAIL, see the [RAIL documentation](../rail/output.md).\n",
     "\n",
     "Here, we define a Pydantic model for a `Person` with the following fields:\n",
-    "- `name`: a string\n",
-    "- `age`: an integer\n",
+    "\n",
+    "- `name`: a string  \n",
+    "- `age`: an integer  \n",
     "- `zip_code`: a string zip code\n",
     "\n",
     "and write very simple validators for the fields as an example. As a way to show how LLM reasking can be used to generate data that is consistent with the Pydantic model, we can define a validator that asks for a zip code in California (including being perversely opposed to the \"90210\" zip code). If this validator fails, the LLM will be sent the error message and will reask the question.\n",

mkdocs.yml

@@ -43,6 +43,7 @@ nav:
       # - 'SFW tutoring system for kids': examples/sfw_tutoring.md
     - 'Integrations':
       - 'LangChain': integrations/langchain.ipynb
+      - 'Pydantic': integrations/pydantic_validation.ipynb
 
 
 theme: