Merge branch 'tthoraldson-feature/claude' into 0.3.0

Author: zsimjee

docs/llm_api_wrappers.md

@@ -71,6 +71,35 @@ raw_llm_output, guardrail_output, *rest = guard(
 )
 ```
 
+## Anthropic
+
+### Completion
+
+```python
+from anthropic import Anthropic
+import guardrails as gd
+
+# Create a Guard class
+guard = gd.Guard.from_rail(...)
+
+# Create an Anthropic client
+anthropic_client = Anthropic(api_key="my_api_key")
+
+# Wrap Anthropic API call
+raw_llm_output, guardrail_output = guard(
+    anthropic_client.completions.create,
+    prompt_params={
+        "prompt_param_1": "value_1", 
+        "prompt_param_2": "value_2",
+        ...
+    },
+    model="claude-2",
+    max_tokens_to_sample=100,
+    ...
+)
+```
+
+
 ## Using Manifest
 [Manifest](https://github.com/HazyResearch/manifest) is a wrapper around most model APIs and supports hosting local models. It can be used as a LLM API.
 

guardrails/llm_providers.py

@@ -240,6 +240,53 @@ def _invoke_llm(
         )
 
 
+class AnthropicCallable(PromptCallableBase):
+    def _invoke_llm(
+        self,
+        prompt: str,
+        client_callable: Any,
+        model: str = "claude-instant-1",
+        max_tokens_to_sample: int = 100,
+        *args,
+        **kwargs,
+    ) -> LLMResponse:
+        """Wrapper for Anthropic Completions.
+
+        To use Anthropic for guardrails, do
+        ```
+        client = anthropic.Anthropic(api_key=...)
+
+        raw_llm_response, validated_response = guard(
+            client,
+            model="claude-2",
+            max_tokens_to_sample=200,
+            prompt_params={...},
+            ...
+        ```
+        """
+        try:
+            import anthropic
+        except ImportError:
+            raise PromptCallableException(
+                "The `anthropic` package is not installed. "
+                "Install with `pip install anthropic`"
+            )
+
+        if "instructions" in kwargs:
+            prompt = kwargs.pop("instructions") + "\n\n" + prompt
+
+        anthropic_prompt = f"{anthropic.HUMAN_PROMPT} {prompt} {anthropic.AI_PROMPT}"
+
+        anthropic_response = client_callable(
+            model=model,
+            prompt=anthropic_prompt,
+            max_tokens_to_sample=max_tokens_to_sample,
+            *args,
+            **kwargs,
+        )
+        return LLMResponse(output=anthropic_response.completion)
+
+
 class ArbitraryCallable(PromptCallableBase):
     def __init__(self, llm_api: Callable, *args, **kwargs):
         self.llm_api = llm_api
@@ -314,6 +361,17 @@ def get_llm_ask(llm_api: Callable, *args, **kwargs) -> PromptCallableBase:
     except ImportError:
         pass
 
+    try:
+        import anthropic.resources  # noqa: F401 # type: ignore
+
+        if isinstance(
+            getattr(llm_api, "__self__", None),
+            anthropic.resources.completions.Completions,
+        ):
+            return AnthropicCallable(*args, client_callable=llm_api, **kwargs)
+    except ImportError:
+        pass
+
     # Let the user pass in an arbitrary callable.
     return ArbitraryCallable(*args, llm_api=llm_api, **kwargs)
 

poetry.lock

@@ -45,6 +45,7 @@ torch = {version = "^2.1.0", optional = true, source = "torch"}
 presidio_analyzer = {version = "^2.2.33", optional = true}
 presidio_anonymizer = {version = "^2.2.33", optional = true}
 spacy = {version = "^3.7.2", optional = true}
+anthropic = {version = "^0.7.2", optional = true}
 
 [tool.poetry.extras]
 sql = ["sqlvalidator", "sqlalchemy", "sqlglot"]
@@ -57,6 +58,7 @@ critique = ["inspiredco"]
 toxic_language = ["transformers", "torch"]
 pii = ["presidio_analyzer", "presidio_anonymizer"]
 competitor-check = ["spacy"]
+anthropic = ["anthropic"]
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.4.3"