anthropic working

application/api/answer/routes.py

@@ -29,6 +29,8 @@ answer = Blueprint('answer', __name__)
 
 if settings.LLM_NAME == "gpt4":
     gpt_model = 'gpt-4'
+elif settings.LLM_NAME == "anthropic":
+    gpt_model = 'claude-2'
 else:
     gpt_model = 'gpt-3.5-turbo'
 

application/llm/anthropic.py

@@ -0,0 +1,40 @@
+from application.llm.base import BaseLLM
+from application.core.settings import settings
+
+class AnthropicLLM(BaseLLM):
+
+    def __init__(self, api_key=None):
+        from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
+        self.api_key = api_key or settings.ANTHROPIC_API_KEY  # If not provided, use a default from settings
+        self.anthropic = Anthropic(api_key=self.api_key)
+        self.HUMAN_PROMPT = HUMAN_PROMPT
+        self.AI_PROMPT = AI_PROMPT
+
+    def gen(self, model, messages, engine=None, max_tokens=300, stream=False, **kwargs):
+        context = messages[0]['content']
+        user_question = messages[-1]['content']
+        prompt = f"### Context \n {context} \n ### Question \n {user_question}"
+        if stream:
+            return self.gen_stream(model, prompt, max_tokens, **kwargs)
+
+        completion = self.anthropic.completions.create(
+            model=model,
+            max_tokens_to_sample=max_tokens,
+            stream=stream,
+            prompt=f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT}",
+        )
+        return completion.completion
+
+    def gen_stream(self, model, messages, engine=None, max_tokens=300, **kwargs):
+        context = messages[0]['content']
+        user_question = messages[-1]['content']
+        prompt = f"### Context \n {context} \n ### Question \n {user_question}"
+        stream_response = self.anthropic.completions.create(
+            model=model,
+            prompt=f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT}",
+            max_tokens_to_sample=max_tokens,
+            stream=True,
+        )
+
+        for completion in stream_response:
+            yield completion.completion

application/llm/llm_creator.py

@@ -2,6 +2,7 @@ from application.llm.openai import OpenAILLM, AzureOpenAILLM
 from application.llm.sagemaker import SagemakerAPILLM
 from application.llm.huggingface import HuggingFaceLLM
 from application.llm.llama_cpp import LlamaCpp
+from application.llm.anthropic import AnthropicLLM
 
 
 
@@ -11,7 +12,8 @@ class LLMCreator:
         'azure_openai': AzureOpenAILLM,
         'sagemaker': SagemakerAPILLM,
         'huggingface': HuggingFaceLLM,
-        'llama.cpp': LlamaCpp
+        'llama.cpp': LlamaCpp,
+        'anthropic': AnthropicLLM
     }
 
     @classmethod

application/requirements.txt

@@ -4,6 +4,7 @@ aiohttp-retry==2.8.3
 aiosignal==1.3.1
 aleph-alpha-client==2.16.1
 amqp==5.1.1
+anthropic==0.5.0
 async-timeout==4.0.2
 attrs==22.2.0
 billiard==3.6.4.0

docker-compose.yaml

@@ -16,6 +16,7 @@ services:
     environment:
       - API_KEY=$API_KEY
       - EMBEDDINGS_KEY=$API_KEY
+      - LLM_NAME=$LLM_NAME
       - CELERY_BROKER_URL=redis://redis:6379/0
       - CELERY_RESULT_BACKEND=redis://redis:6379/1
       - MONGO_URI=mongodb://mongo:27017/docsgpt
@@ -35,6 +36,7 @@ services:
     environment:
       - API_KEY=$API_KEY
       - EMBEDDINGS_KEY=$API_KEY
+      - LLM_NAME=$LLM_NAME
       - CELERY_BROKER_URL=redis://redis:6379/0
       - CELERY_RESULT_BACKEND=redis://redis:6379/1
       - MONGO_URI=mongodb://mongo:27017/docsgpt

tests/llm/test_anthropic.py

@@ -0,0 +1,57 @@
+import unittest
+from unittest.mock import patch, Mock
+from application.llm.anthropic import AnthropicLLM
+
+class TestAnthropicLLM(unittest.TestCase):
+
+    def setUp(self):
+        self.api_key = "TEST_API_KEY"
+        self.llm = AnthropicLLM(api_key=self.api_key)
+
+    @patch("application.llm.anthropic.settings")
+    def test_init_default_api_key(self, mock_settings):
+        mock_settings.ANTHROPIC_API_KEY = "DEFAULT_API_KEY"
+        llm = AnthropicLLM()
+        self.assertEqual(llm.api_key, "DEFAULT_API_KEY")
+
+    def test_gen(self):
+        messages = [
+            {"content": "context"},
+            {"content": "question"}
+        ]
+        mock_response = Mock()
+        mock_response.completion = "test completion"
+
+        with patch.object(self.llm.anthropic.completions, "create", return_value=mock_response) as mock_create:
+            response = self.llm.gen("test_model", messages)
+            self.assertEqual(response, "test completion")
+
+            prompt_expected = "### Context \n context \n ### Question \n question"
+            mock_create.assert_called_with(
+                model="test_model",
+                max_tokens_to_sample=300,
+                stream=False,
+                prompt=f"{self.llm.HUMAN_PROMPT} {prompt_expected}{self.llm.AI_PROMPT}"
+            )
+
+    def test_gen_stream(self):
+        messages = [
+            {"content": "context"},
+            {"content": "question"}
+        ]
+        mock_responses = [Mock(completion="response_1"), Mock(completion="response_2")]
+
+        with patch.object(self.llm.anthropic.completions, "create", return_value=iter(mock_responses)) as mock_create:
+            responses = list(self.llm.gen_stream("test_model", messages))
+            self.assertListEqual(responses, ["response_1", "response_2"])
+
+            prompt_expected = "### Context \n context \n ### Question \n question"
+            mock_create.assert_called_with(
+                model="test_model",
+                prompt=f"{self.llm.HUMAN_PROMPT} {prompt_expected}{self.llm.AI_PROMPT}",
+                max_tokens_to_sample=300,
+                stream=True
+            )
+
+if __name__ == "__main__":
+    unittest.main()