feat(anthropic.py): adds tool calling support

litellm/llms/anthropic.py

@@ -6,7 +6,11 @@ import time
 from typing import Callable, Optional
 from litellm.utils import ModelResponse, Usage
 import litellm
-from .prompt_templates.factory import prompt_factory, custom_prompt
+from .prompt_templates.factory import (
+    prompt_factory,
+    custom_prompt,
+    construct_tool_use_system_prompt,
+)
 import httpx
 
 
@@ -142,6 +146,15 @@ def completion(
         ):  # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
             optional_params[k] = v
 
+    ## Handle Tool Calling
+    if "tools" in optional_params:
+        tool_calling_system_prompt = construct_tool_use_system_prompt(
+            tools=optional_params["tools"]
+        )
+        optional_params["system"] = (
+            optional_params("system", "\n") + tool_calling_system_prompt
+        )  # add the anthropic tool calling prompt to the system prompt
+
     data = {
         "model": model,
         "messages": messages,

litellm/llms/prompt_templates/factory.py

@@ -390,7 +390,7 @@ def format_prompt_togetherai(messages, prompt_format, chat_template):
     return prompt
 
 
-###
+### ANTHROPIC ###
 
 
 def anthropic_pt(
@@ -424,6 +424,62 @@ def anthropic_pt(
     return prompt
 
 
+def construct_format_parameters_prompt(parameters: dict):
+    parameter_str = "<parameter>\n"
+    for k, v in parameters.items():
+        parameter_str += f"<{k}>"
+        parameter_str += f"{v}"
+        parameter_str += f"</{k}>"
+    parameter_str += "\n</parameter>"
+    return parameter_str
+
+
+def construct_format_tool_for_claude_prompt(name, description, parameters):
+    constructed_prompt = (
+        "<tool_description>\n"
+        f"<tool_name>{name}</tool_name>\n"
+        "<description>\n"
+        f"{description}\n"
+        "</description>\n"
+        "<parameters>\n"
+        f"{construct_format_parameters_prompt(parameters)}\n"
+        "</parameters>\n"
+        "</tool_description>"
+    )
+    return constructed_prompt
+
+
+def construct_tool_use_system_prompt(
+    tools,
+):  # from https://github.com/anthropics/anthropic-cookbook/blob/main/function_calling/function_calling.ipynb
+    tool_str_list = []
+    for tool in tools:
+        tool_str = construct_format_tool_for_claude_prompt(
+            tool["function"]["name"],
+            tool["function"].get("description", ""),
+            tool["function"].get("parameters", {}),
+        )
+        tool_str_list.append(tool_str)
+    tool_use_system_prompt = (
+        "In this environment you have access to a set of tools you can use to answer the user's question.\n"
+        "\n"
+        "You may call them like this:\n"
+        "<function_calls>\n"
+        "<invoke>\n"
+        "<tool_name>$TOOL_NAME</tool_name>\n"
+        "<parameters>\n"
+        "<$PARAMETER_NAME>$PARAMETER_VALUE</$PARAMETER_NAME>\n"
+        "...\n"
+        "</parameters>\n"
+        "</invoke>\n"
+        "</function_calls>\n"
+        "\n"
+        "Here are the tools available:\n"
+        "<tools>\n" + "\n".join([tool_str for tool_str in tool_str_list]) + "\n</tools>"
+    )
+    return tool_use_system_prompt
+
+
 def anthropic_messages_pt(messages: list):
     """
     format messages for anthropic
@@ -464,6 +520,9 @@ def anthropic_messages_pt(messages: list):
     return new_messages
 
 
+###
+
+
 def amazon_titan_pt(
     messages: list,
 ):  # format - https://github.com/BerriAI/litellm/issues/1896
@@ -690,6 +749,8 @@ def prompt_factory(
     if custom_llm_provider == "ollama":
         return ollama_pt(model=model, messages=messages)
     elif custom_llm_provider == "anthropic":
+        if model == "claude-instant-1" or model == "claude-2.1":
+            return anthropic_pt(messages=messages)
         return anthropic_messages_pt(messages=messages)
     elif custom_llm_provider == "together_ai":
         prompt_format, chat_template = get_model_info(token=api_key, model=model)

litellm/utils.py

@@ -4106,6 +4106,7 @@ def get_optional_params(
             and custom_llm_provider != "anyscale"
             and custom_llm_provider != "together_ai"
             and custom_llm_provider != "mistral"
+            and custom_llm_provider != "anthropic"
         ):
             if custom_llm_provider == "ollama" or custom_llm_provider == "ollama_chat":
                 # ollama actually supports json output
@@ -4186,7 +4187,15 @@ def get_optional_params(
     ## raise exception if provider doesn't support passed in param
     if custom_llm_provider == "anthropic":
         ## check if unsupported param passed in
-        supported_params = ["stream", "stop", "temperature", "top_p", "max_tokens"]
+        supported_params = [
+            "stream",
+            "stop",
+            "temperature",
+            "top_p",
+            "max_tokens",
+            "tools",
+            "tool_choice",
+        ]
         _check_valid_arg(supported_params=supported_params)
         # handle anthropic params
         if stream:
@@ -4201,6 +4210,8 @@ def get_optional_params(
             optional_params["top_p"] = top_p
         if max_tokens is not None:
             optional_params["max_tokens"] = max_tokens
+        if tools is not None:
+            optional_params["tools"] = tools
     elif custom_llm_provider == "cohere":
         ## check if unsupported param passed in
         supported_params = [
@@ -9704,4 +9715,4 @@ def _get_base_model_from_metadata(model_call_details=None):
                 base_model = model_info.get("base_model", None)
                 if base_model is not None:
                     return base_model
-    return None
+    return None