Merge pull request #2321 from BerriAI/litellm_maintain_Claude2_support

[Fix] Maintain Claude-2, Claude-Instant-1

docs/my-website/docs/providers/anthropic.md

@@ -56,6 +56,7 @@ for chunk in response:
 | claude-2.1  | `completion('claude-2.1', messages)` | `os.environ['ANTHROPIC_API_KEY']`       |
 | claude-2  | `completion('claude-2', messages)` | `os.environ['ANTHROPIC_API_KEY']`       |
 | claude-instant-1.2  | `completion('claude-instant-1.2', messages)` | `os.environ['ANTHROPIC_API_KEY']`       |
+| claude-instant-1  | `completion('claude-instant-1', messages)` | `os.environ['ANTHROPIC_API_KEY']`       |
 
 ## Advanced
 

litellm/__init__.py

@@ -573,6 +573,7 @@ from .utils import (
 )
 from .llms.huggingface_restapi import HuggingfaceConfig
 from .llms.anthropic import AnthropicConfig
+from .llms.anthropic_text import AnthropicTextConfig
 from .llms.replicate import ReplicateConfig
 from .llms.cohere import CohereConfig
 from .llms.ai21 import AI21Config
@@ -594,7 +595,7 @@ from .llms.bedrock import (
     AmazonCohereConfig,
     AmazonLlamaConfig,
     AmazonStabilityConfig,
-    AmazonMistralConfig
+    AmazonMistralConfig,
 )
 from .llms.openai import OpenAIConfig, OpenAITextCompletionConfig
 from .llms.azure import AzureOpenAIConfig, AzureOpenAIError

litellm/llms/anthropic_text.py

@@ -0,0 +1,222 @@
+import os, types
+import json
+from enum import Enum
+import requests
+import time
+from typing import Callable, Optional
+from litellm.utils import ModelResponse, Usage
+import litellm
+from .prompt_templates.factory import prompt_factory, custom_prompt
+import httpx
+
+
+class AnthropicConstants(Enum):
+    HUMAN_PROMPT = "\n\nHuman: "
+    AI_PROMPT = "\n\nAssistant: "
+
+
+class AnthropicError(Exception):
+    def __init__(self, status_code, message):
+        self.status_code = status_code
+        self.message = message
+        self.request = httpx.Request(
+            method="POST", url="https://api.anthropic.com/v1/complete"
+        )
+        self.response = httpx.Response(status_code=status_code, request=self.request)
+        super().__init__(
+            self.message
+        )  # Call the base class constructor with the parameters it needs
+
+
+class AnthropicTextConfig:
+    """
+    Reference: https://docs.anthropic.com/claude/reference/complete_post
+
+    to pass metadata to anthropic, it's {"user_id": "any-relevant-information"}
+    """
+
+    max_tokens_to_sample: Optional[int] = (
+        litellm.max_tokens
+    )  # anthropic requires a default
+    stop_sequences: Optional[list] = None
+    temperature: Optional[int] = None
+    top_p: Optional[int] = None
+    top_k: Optional[int] = None
+    metadata: Optional[dict] = None
+
+    def __init__(
+        self,
+        max_tokens_to_sample: Optional[int] = 256,  # anthropic requires a default
+        stop_sequences: Optional[list] = None,
+        temperature: Optional[int] = None,
+        top_p: Optional[int] = None,
+        top_k: Optional[int] = None,
+        metadata: Optional[dict] = None,
+    ) -> None:
+        locals_ = locals()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+
+# makes headers for API call
+def validate_environment(api_key, user_headers):
+    if api_key is None:
+        raise ValueError(
+            "Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params"
+        )
+    headers = {
+        "accept": "application/json",
+        "anthropic-version": "2023-06-01",
+        "content-type": "application/json",
+        "x-api-key": api_key,
+    }
+    if user_headers is not None and isinstance(user_headers, dict):
+        headers = {**headers, **user_headers}
+    return headers
+
+
+def completion(
+    model: str,
+    messages: list,
+    api_base: str,
+    custom_prompt_dict: dict,
+    model_response: ModelResponse,
+    print_verbose: Callable,
+    encoding,
+    api_key,
+    logging_obj,
+    optional_params=None,
+    litellm_params=None,
+    logger_fn=None,
+    headers={},
+):
+    headers = validate_environment(api_key, headers)
+    if model in custom_prompt_dict:
+        # check if the model has a registered custom prompt
+        model_prompt_details = custom_prompt_dict[model]
+        prompt = custom_prompt(
+            role_dict=model_prompt_details["roles"],
+            initial_prompt_value=model_prompt_details["initial_prompt_value"],
+            final_prompt_value=model_prompt_details["final_prompt_value"],
+            messages=messages,
+        )
+    else:
+        prompt = prompt_factory(
+            model=model, messages=messages, custom_llm_provider="anthropic"
+        )
+
+    ## Load Config
+    config = litellm.AnthropicTextConfig.get_config()
+    for k, v in config.items():
+        if (
+            k not in optional_params
+        ):  # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+            optional_params[k] = v
+
+    data = {
+        "model": model,
+        "prompt": prompt,
+        **optional_params,
+    }
+
+    ## LOGGING
+    logging_obj.pre_call(
+        input=prompt,
+        api_key=api_key,
+        additional_args={
+            "complete_input_dict": data,
+            "api_base": api_base,
+            "headers": headers,
+        },
+    )
+
+    ## COMPLETION CALL
+    if "stream" in optional_params and optional_params["stream"] == True:
+        response = requests.post(
+            api_base,
+            headers=headers,
+            data=json.dumps(data),
+            stream=optional_params["stream"],
+        )
+
+        if response.status_code != 200:
+            raise AnthropicError(
+                status_code=response.status_code, message=response.text
+            )
+
+        return response.iter_lines()
+    else:
+        response = requests.post(api_base, headers=headers, data=json.dumps(data))
+        if response.status_code != 200:
+            raise AnthropicError(
+                status_code=response.status_code, message=response.text
+            )
+
+        ## LOGGING
+        logging_obj.post_call(
+            input=prompt,
+            api_key=api_key,
+            original_response=response.text,
+            additional_args={"complete_input_dict": data},
+        )
+        print_verbose(f"raw model_response: {response.text}")
+        ## RESPONSE OBJECT
+        try:
+            completion_response = response.json()
+        except:
+            raise AnthropicError(
+                message=response.text, status_code=response.status_code
+            )
+        if "error" in completion_response:
+            raise AnthropicError(
+                message=str(completion_response["error"]),
+                status_code=response.status_code,
+            )
+        else:
+            if len(completion_response["completion"]) > 0:
+                model_response["choices"][0]["message"]["content"] = (
+                    completion_response["completion"]
+                )
+            model_response.choices[0].finish_reason = completion_response["stop_reason"]
+
+        ## CALCULATING USAGE
+        prompt_tokens = len(
+            encoding.encode(prompt)
+        )  ##[TODO] use the anthropic tokenizer here
+        completion_tokens = len(
+            encoding.encode(model_response["choices"][0]["message"].get("content", ""))
+        )  ##[TODO] use the anthropic tokenizer here
+
+        model_response["created"] = int(time.time())
+        model_response["model"] = model
+        usage = Usage(
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens,
+        )
+        model_response.usage = usage
+        return model_response
+
+
+def embedding():
+    # logic for parsing in - calling - parsing out model embedding calls
+    pass

litellm/main.py

@@ -39,6 +39,7 @@ from litellm.utils import (
 )
 from .llms import (
     anthropic,
+    anthropic_text,
     together_ai,
     ai21,
     sagemaker,
@@ -1018,28 +1019,55 @@ def completion(
                 or litellm.api_key
                 or os.environ.get("ANTHROPIC_API_KEY")
             )
-            api_base = (
-                api_base
-                or litellm.api_base
-                or get_secret("ANTHROPIC_API_BASE")
-                or "https://api.anthropic.com/v1/messages"
-            )
             custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
-            response = anthropic.completion(
-                model=model,
-                messages=messages,
-                api_base=api_base,
-                custom_prompt_dict=litellm.custom_prompt_dict,
-                model_response=model_response,
-                print_verbose=print_verbose,
-                optional_params=optional_params,
-                litellm_params=litellm_params,
-                logger_fn=logger_fn,
-                encoding=encoding,  # for calculating input/output tokens
-                api_key=api_key,
-                logging_obj=logging,
-                headers=headers,
-            )
+
+            if (model == "claude-2") or (model == "claude-instant-1"):
+                # call anthropic /completion, only use this route for claude-2, claude-instant-1
+                api_base = (
+                    api_base
+                    or litellm.api_base
+                    or get_secret("ANTHROPIC_API_BASE")
+                    or "https://api.anthropic.com/v1/complete"
+                )
+                response = anthropic_text.completion(
+                    model=model,
+                    messages=messages,
+                    api_base=api_base,
+                    custom_prompt_dict=litellm.custom_prompt_dict,
+                    model_response=model_response,
+                    print_verbose=print_verbose,
+                    optional_params=optional_params,
+                    litellm_params=litellm_params,
+                    logger_fn=logger_fn,
+                    encoding=encoding,  # for calculating input/output tokens
+                    api_key=api_key,
+                    logging_obj=logging,
+                    headers=headers,
+                )
+            else:
+                # call /messages
+                # default route for all anthropic models
+                api_base = (
+                    api_base
+                    or litellm.api_base
+                    or get_secret("ANTHROPIC_API_BASE")
+                    or "https://api.anthropic.com/v1/messages"
+                )
+                response = anthropic.completion(
+                    model=model,
+                    messages=messages,
+                    api_base=api_base,
+                    custom_prompt_dict=litellm.custom_prompt_dict,
+                    model_response=model_response,
+                    print_verbose=print_verbose,
+                    optional_params=optional_params,
+                    litellm_params=litellm_params,
+                    logger_fn=logger_fn,
+                    encoding=encoding,  # for calculating input/output tokens
+                    api_key=api_key,
+                    logging_obj=logging,
+                    headers=headers,
+                )
             if "stream" in optional_params and optional_params["stream"] == True:
                 # don't try to access stream object,
                 response = CustomStreamWrapper(

litellm/tests/test_completion.py

@@ -56,7 +56,7 @@ def test_completion_custom_provider_model_name():
 def test_completion_claude():
     litellm.set_verbose = True
     litellm.cache = None
-    litellm.AnthropicConfig(max_tokens=200, metadata={"user_id": "1224"})
+    litellm.AnthropicTextConfig(max_tokens_to_sample=200, metadata={"user_id": "1224"})
     messages = [
         {
             "role": "system",
@@ -67,9 +67,10 @@ def test_completion_claude():
     try:
         # test without max tokens
         response = completion(
-            model="claude-instant-1.2",
+            model="claude-instant-1",
             messages=messages,
             request_timeout=10,
+            max_tokens=10,
         )
         # Add any assertions, here to check response args
         print(response)

litellm/utils.py

@@ -4213,6 +4213,11 @@ def get_optional_params(
         if top_p is not None:
             optional_params["top_p"] = top_p
         if max_tokens is not None:
+            if (model == "claude-2") or (model == "claude-instant-1"):
+                # these models use antropic_text.py which only accepts max_tokens_to_sample
+                optional_params["max_tokens_to_sample"] = max_tokens
+            else:
+                optional_params["max_tokens"] = max_tokens
             optional_params["max_tokens"] = max_tokens
         if tools is not None:
             optional_params["tools"] = tools