fix: various fixes to patch anthropic integration (#1698)

.github/workflows/test_anthropic.yml

@@ -0,0 +1,37 @@
+name: Endpoint (Anthropic)
+
+env:
+  OPENAI_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    timeout-minutes: 15
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v4
+
+    - name: "Setup Python, Poetry and Dependencies"
+      uses: packetcoders/action-setup-cache-python-poetry@main
+      with:
+        python-version: "3.12"
+        poetry-version: "1.8.2"
+        install-args: "-E dev"
+
+    - name: Initialize credentials
+      env:
+        ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+      run: |
+        poetry run memgpt quickstart --backend anthropic
+
+    - name: Test LLM endpoint
+      env:
+        ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+      run: |
+        poetry run pytest -s -vv tests/test_endpoints.py::test_llm_endpoint_anthropic

configs/llm_model_configs/anthropic.json

@@ -0,0 +1,7 @@
+{
+    "context_window": 200000,
+    "model": "claude-3-opus-20240229",
+    "model_endpoint_type": "anthropic",
+    "model_endpoint": "https://api.anthropic.com/v1",
+    "model_wrapper": null
+}

memgpt/cli/cli.py

@@ -40,6 +40,7 @@ class QuickstartChoice(Enum):
     openai = "openai"
     # azure = "azure"
     memgpt_hosted = "memgpt"
+    anthropic = "anthropic"
 
 
 def str_to_quickstart_choice(choice_str: str) -> QuickstartChoice:
@@ -229,6 +230,26 @@ def quickstart(
                 typer.secho(f"Config file not found at {backup_config_path}", fg=typer.colors.RED)
                 return
 
+    elif backend == QuickstartChoice.anthropic:
+        # Make sure we have an API key
+        api_key = os.getenv("ANTHROPIC_API_KEY")
+        while api_key is None or len(api_key) == 0:
+            # Ask for API key as input
+            api_key = questionary.password("Enter your Anthropic API key:").ask()
+        credentials.anthropic_key = api_key
+        credentials.save()
+
+        script_dir = os.path.dirname(__file__)  # Get the directory where the script is located
+        backup_config_path = os.path.join(script_dir, "..", "configs", "anthropic.json")
+        try:
+            with open(backup_config_path, "r", encoding="utf-8") as file:
+                backup_config = json.load(file)
+            printd("Loaded config file successfully.")
+            new_config, config_was_modified = set_config_with_dict(backup_config)
+        except FileNotFoundError:
+            typer.secho(f"Config file not found at {backup_config_path}", fg=typer.colors.RED)
+            return
+
     else:
         raise NotImplementedError(backend)
 

memgpt/configs/anthropic.json

@@ -0,0 +1,13 @@
+{
+    "context_window": 200000,
+    "model": "claude-3-opus-20240229",
+    "model_endpoint_type": "anthropic",
+    "model_endpoint": "https://api.anthropic.com/v1",
+    "model_wrapper": null,
+    "embedding_endpoint_type": "hugging-face",
+    "embedding_endpoint": "https://embeddings.memgpt.ai",
+    "embedding_model": "BAAI/bge-large-en-v1.5",
+    "embedding_dim": 1024,
+    "embedding_chunk_size": 300
+
+}

memgpt/llm_api/anthropic.py

@@ -1,6 +1,5 @@
 import json
 import re
-import uuid
 from typing import List, Optional, Union
 
 import requests
@@ -256,7 +255,7 @@ def convert_anthropic_response_to_chatcompletion(
                 type="function",
                 function=FunctionCall(
                     name=response_json["content"][1]["name"],
-                    arguments=json_dumps(response_json["content"][1]["input"]),
+                    arguments=json.dumps(response_json["content"][1]["input"], indent=2),
                 ),
             )
         ]
@@ -330,8 +329,14 @@ def anthropic_chat_completions_request(
     data["system"] = data["messages"][0]["content"]
     data["messages"] = data["messages"][1:]
 
+    # set `content` to None if missing
+    for message in data["messages"]:
+        if "content" not in message:
+            message["content"] = None
+
     # Convert to Anthropic format
-    msg_objs = [Message.dict_to_message(user_id=uuid.uuid4(), agent_id=uuid.uuid4(), openai_message_dict=m) for m in data["messages"]]
+
+    msg_objs = [Message.dict_to_message(user_id=None, agent_id=None, openai_message_dict=m) for m in data["messages"]]
     data["messages"] = [m.to_anthropic_dict(inner_thoughts_xml_tag=inner_thoughts_xml_tag) for m in msg_objs]
 
     # Handling Anthropic special requirement for 'user' message in front

memgpt/schemas/message.py

@@ -62,8 +62,8 @@ class Message(BaseMessage):
     id: str = BaseMessage.generate_id_field()
     role: MessageRole = Field(..., description="The role of the participant.")
     text: Optional[str] = Field(None, description="The text of the message.")
-    user_id: str = Field(None, description="The unique identifier of the user.")
-    agent_id: str = Field(None, description="The unique identifier of the agent.")
+    user_id: Optional[str] = Field(None, description="The unique identifier of the user.")
+    agent_id: Optional[str] = Field(None, description="The unique identifier of the agent.")
     model: Optional[str] = Field(None, description="The model used to make the function call.")
     name: Optional[str] = Field(None, description="The name of the participant.")
     created_at: datetime = Field(default_factory=get_utc_time, description="The time the message was created.")
@@ -367,8 +367,8 @@ def add_xml_tag(string: str, xml_tag: Optional[str]):
                         {
                             "type": "tool_use",
                             "id": tool_call.id,
-                            "name": tool_call.function["name"],
-                            "input": json.loads(tool_call.function["arguments"]),
+                            "name": tool_call.function.name,
+                            "input": json.loads(tool_call.function.arguments),
                         }
                     )
 

tests/test_endpoints.py

@@ -103,3 +103,8 @@ def test_llm_endpoint_ollama():
 def test_embedding_endpoint_ollama():
     filename = os.path.join(embedding_config_dir, "ollama.json")
     run_embedding_endpoint(filename)
+
+
+def test_llm_endpoint_anthropic():
+    filename = os.path.join(llm_config_dir, "anthropic.json")
+    run_llm_endpoint(filename)