feat: add workflow to build + test docker container (letta-ai#1278)

.github/workflows/docker-integration-tests.yaml

@@ -0,0 +1,64 @@
+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: Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.12'
+
+    - name: Set permissions for log directory
+      run: |
+        mkdir -p /home/runner/.memgpt/logs
+        sudo chown -R $USER:$USER /home/runner/.memgpt/logs
+        chmod -R 755 /home/runner/.memgpt/logs
+
+    - name: Build and run docker dev server
+      env:
+        MEMGPT_PG_DB:   memgpt
+        MEMGPT_PG_USER: memgpt
+        MEMGPT_PG_PASSWORD: memgpt
+        MEMGPT_PG_PORT: 8888
+        MEMGPT_SERVER_PASS: test_server_token
+        OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+
+      run: docker compose -f dev-compose.yaml up --build -d
+    #- name: "Setup Python, Poetry and Dependencies"
+    #  uses: packetcoders/[email protected]
+    #  with:
+    #    python-version: "3.12"
+    #    poetry-version: "1.8.2"
+    #    install-args: "--all-extras"
+
+    - name: Wait for service
+      run: bash scripts/wait_for_service.sh http://localhost:8083 -- echo "Service is ready"
+
+    - name: Run tests with pytest
+      env:
+        MEMGPT_PG_DB:   memgpt
+        MEMGPT_PG_USER: memgpt
+        MEMGPT_PG_PASSWORD: memgpt
+        MEMGPT_PG_PORT: 8888
+        MEMGPT_SERVER_PASS: test_server_token
+        MEMGPT_SERVER_URL: http://localhost:8083
+        OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+        PYTHONPATH: ${{ github.workspace }}:${{ env.PYTHONPATH }}
+      run: |
+        pipx install poetry==1.8.2
+        poetry install -E dev
+        poetry run pytest -s tests/test_client.py
+
+    - name: Print docker logs if tests fail
+      if: failure()
+      run: |
+        echo "Printing Docker Logs..."
+        docker compose -f dev-compose.yaml logs

.github/workflows/tests.yml

@@ -25,8 +25,8 @@ jobs:
     - name: "Setup Python, Poetry and Dependencies"
       uses: packetcoders/action-setup-cache-python-poetry@main
       with:
-        python-version: "3.11"
-        poetry-version: "1.7.1"
+        python-version: "3.12"
+        poetry-version: "1.8.2"
         install-args: "--all-extras"
 
     - name: Initialize credentials

compose.yaml

@@ -33,6 +33,7 @@ services:
       - MEMGPT_PG_PASSWORD=${MEMGPT_PG_PASSWORD}
       - MEMGPT_PG_HOST=pgvector_db
       - MEMGPT_PG_PORT=5432
+      - OPENAI_API_KEY=${OPENAI_API_KEY}
     volumes:
       - ./configs/server_config.yaml:/root/.memgpt/config # config file
       - ~/.memgpt/credentials:/root/.memgpt/credentials # credentials file

configs/server_config.yaml

@@ -7,11 +7,13 @@ human = basic
 model = gpt-4
 model_endpoint = https://api.openai.com/v1
 model_endpoint_type = openai
+model_wrapper = null
 context_window = 8192
 
 [embedding]
 embedding_endpoint_type = openai
 embedding_endpoint = https://api.openai.com/v1
+embedding_model = text-embedding-ada-002
 embedding_dim = 1536
 embedding_chunk_size = 300
 

dev-compose.yaml

@@ -11,7 +11,7 @@ services:
       - POSTGRES_PASSWORD=${MEMGPT_PG_PASSWORD}
       - POSTGRES_DB=${MEMGPT_PG_DB}
     volumes:
-      - ./.persist/pgdata:/var/lib/postgresql/data
+      - ./.persist/pgdata-test:/var/lib/postgresql/data
       - ./init.sql:/docker-entrypoint-initdb.d/init.sql
     ports:
       - "5432:5432"
@@ -27,22 +27,14 @@ services:
     ports:
       - "8083:8083"
       - "8283:8283"
-    env_file:
-      - .env
     environment:
       - MEMGPT_SERVER_PASS=${MEMGPT_SERVER_PASS} # memgpt server password
       - MEMGPT_PG_DB=${MEMGPT_PG_DB}
       - MEMGPT_PG_USER=${MEMGPT_PG_USER}
       - MEMGPT_PG_PASSWORD=${MEMGPT_PG_PASSWORD}
       - MEMGPT_PG_HOST=pgvector_db
       - MEMGPT_PG_PORT=5432
+      - OPENAI_API_KEY=${OPENAI_API_KEY}
     volumes:
       - ./configs/server_config.yaml:/root/.memgpt/config # config file
       - ~/.memgpt/credentials:/root/.memgpt/credentials # credentials file
-  memgpt_nginx:
-    hostname: memgpt-nginx
-    image: nginx:stable-alpine3.17-slim
-    volumes:
-      - ./nginx.conf:/etc/nginx/nginx.conf
-    ports:
-      - "80:80"

memgpt/client/client.py

@@ -256,7 +256,7 @@ def create_agent(
         }
         response = requests.post(f"{self.base_url}/api/agents", json=payload, headers=self.headers)
         if response.status_code != 200:
-            raise ValueError(f"Failed to create agent: {response.text}")
+            raise ValueError(f"Status {response.status_code} - Failed to create agent: {response.text}")
         response_obj = CreateAgentResponse(**response.json())
         return self.get_agent_response_to_state(response_obj)
 

memgpt/credentials.py

@@ -16,7 +16,7 @@ class MemGPTCredentials:
 
     # openai config
     openai_auth_type: str = "bearer_token"
-    openai_key: Optional[str] = None
+    openai_key: Optional[str] = os.getenv("OPENAI_API_KEY")
 
     # gemini config
     google_ai_key: Optional[str] = None

memgpt/server/rest_api/agents/index.py

@@ -55,56 +55,56 @@ def create_agent(
         """
         interface.clear()
 
-        try:
-            agent_state = server.create_agent(
-                user_id=user_id,
-                # **request.config
-                # TODO turn into a pydantic model
-                name=request.config["name"],
-                preset=request.config["preset"] if "preset" in request.config else None,
-                persona_name=request.config["persona_name"] if "persona_name" in request.config else None,
-                human_name=request.config["human_name"] if "human_name" in request.config else None,
-                persona=request.config["persona"] if "persona" in request.config else None,
-                human=request.config["human"] if "human" in request.config else None,
-                # llm_config=LLMConfigModel(
-                # model=request.config['model'],
-                # )
-                function_names=request.config["function_names"].split(",") if "function_names" in request.config else None,
-            )
-            llm_config = LLMConfigModel(**vars(agent_state.llm_config))
-            embedding_config = EmbeddingConfigModel(**vars(agent_state.embedding_config))
-
-            # TODO when get_preset returns a PresetModel instead of Preset, we can remove this packing/unpacking line
-            preset = server.ms.get_preset(name=agent_state.preset, user_id=user_id)
-
-            return CreateAgentResponse(
-                agent_state=AgentStateModel(
-                    id=agent_state.id,
-                    name=agent_state.name,
-                    user_id=agent_state.user_id,
-                    preset=agent_state.preset,
-                    persona=agent_state.persona,
-                    human=agent_state.human,
-                    llm_config=llm_config,
-                    embedding_config=embedding_config,
-                    state=agent_state.state,
-                    created_at=int(agent_state.created_at.timestamp()),
-                    functions_schema=agent_state.state["functions"],  # TODO: this is very error prone, jsut lookup the preset instead
-                ),
-                preset=PresetModel(
-                    name=preset.name,
-                    id=preset.id,
-                    user_id=preset.user_id,
-                    description=preset.description,
-                    created_at=preset.created_at,
-                    system=preset.system,
-                    persona=preset.persona,
-                    human=preset.human,
-                    functions_schema=preset.functions_schema,
-                ),
-            )
-        except Exception as e:
-            print(str(e))
-            raise HTTPException(status_code=500, detail=str(e))
+        # try:
+        agent_state = server.create_agent(
+            user_id=user_id,
+            # **request.config
+            # TODO turn into a pydantic model
+            name=request.config["name"],
+            preset=request.config["preset"] if "preset" in request.config else None,
+            persona_name=request.config["persona_name"] if "persona_name" in request.config else None,
+            human_name=request.config["human_name"] if "human_name" in request.config else None,
+            persona=request.config["persona"] if "persona" in request.config else None,
+            human=request.config["human"] if "human" in request.config else None,
+            # llm_config=LLMConfigModel(
+            # model=request.config['model'],
+            # )
+            function_names=request.config["function_names"].split(",") if "function_names" in request.config else None,
+        )
+        llm_config = LLMConfigModel(**vars(agent_state.llm_config))
+        embedding_config = EmbeddingConfigModel(**vars(agent_state.embedding_config))
+
+        # TODO when get_preset returns a PresetModel instead of Preset, we can remove this packing/unpacking line
+        preset = server.ms.get_preset(name=agent_state.preset, user_id=user_id)
+
+        return CreateAgentResponse(
+            agent_state=AgentStateModel(
+                id=agent_state.id,
+                name=agent_state.name,
+                user_id=agent_state.user_id,
+                preset=agent_state.preset,
+                persona=agent_state.persona,
+                human=agent_state.human,
+                llm_config=llm_config,
+                embedding_config=embedding_config,
+                state=agent_state.state,
+                created_at=int(agent_state.created_at.timestamp()),
+                functions_schema=agent_state.state["functions"],  # TODO: this is very error prone, jsut lookup the preset instead
+            ),
+            preset=PresetModel(
+                name=preset.name,
+                id=preset.id,
+                user_id=preset.user_id,
+                description=preset.description,
+                created_at=preset.created_at,
+                system=preset.system,
+                persona=preset.persona,
+                human=preset.human,
+                functions_schema=preset.functions_schema,
+            ),
+        )
+        # except Exception as e:
+        #    print(str(e))
+        #    raise HTTPException(status_code=500, detail=str(e))
 
     return router

memgpt/server/rest_api/server.py

@@ -39,10 +39,6 @@
   cd memgpt/server/rest_api
   poetry run uvicorn server:app --reload
 """
-config = MemGPTConfig.load()
-for memory_type in ("archival", "recall", "metadata"):
-    setattr(config, f"{memory_type}_storage_uri", settings.pg_uri)
-config.save()
 
 interface: QueuingInterface = QueuingInterface()
 server: SyncServer = SyncServer(default_interface=interface)

memgpt/server/server.py

@@ -10,6 +10,7 @@
 
 from fastapi import HTTPException
 
+from memgpt.settings import settings
 import memgpt.constants as constants
 import memgpt.presets.presets as presets
 import memgpt.server.utils as server_utils
@@ -197,9 +198,23 @@ def __init__(
         assert self.config.persona is not None, "Persona must be set in the config"
         assert self.config.human is not None, "Human must be set in the config"
 
+        # Update storage URI to match passed in settings
+        # TODO: very hack, fix in the future
+        for memory_type in ("archival", "recall", "metadata"):
+            setattr(self.config, f"{memory_type}_storage_uri", settings.pg_uri)
+        self.config.save()
+
         # TODO figure out how to handle credentials for the server
         self.credentials = MemGPTCredentials.load()
 
+        # check credentials
+        # TODO: add checks for other providers
+        if (
+            self.config.default_embedding_config.embedding_endpoint_type == "openai"
+            or self.config.default_llm_config.model_endpoint_type == "openai"
+        ):
+            assert self.credentials.openai_key is not None, "OpenAI key must be set in the credentials file"
+
         # Ensure valid database configuration
         # TODO: add back once tests are matched
         # assert (
@@ -665,25 +680,25 @@ def create_agent(
                 preset_override = True
                 preset_obj.human = human
                 # This is a check for a common bug where users were providing filenames instead of values
-                try:
-                    get_human_text(human)
-                    raise ValueError(human)
-                    raise UserWarning(
-                        f"It looks like there is a human file named {human} - did you mean to pass the file contents to the `human` arg?"
-                    )
-                except:
-                    pass
+                # try:
+                #    get_human_text(human)
+                #    raise ValueError(human)
+                #    raise UserWarning(
+                #        f"It looks like there is a human file named {human} - did you mean to pass the file contents to the `human` arg?"
+                #    )
+                # except:
+                #    pass
             if persona is not None:
                 preset_override = True
                 preset_obj.persona = persona
-                try:
-                    get_persona_text(persona)
-                    raise ValueError(persona)
-                    raise UserWarning(
-                        f"It looks like there is a persona file named {persona} - did you mean to pass the file contents to the `persona` arg?"
-                    )
-                except:
-                    pass
+                # try:
+                #    get_persona_text(persona)
+                #    raise ValueError(persona)
+                #    raise UserWarning(
+                #        f"It looks like there is a persona file named {persona} - did you mean to pass the file contents to the `persona` arg?"
+                #    )
+                # except:
+                #    pass
             if human_name is not None and human_name != preset_obj.human_name:
                 preset_override = True
                 preset_obj.human_name = human_name
@@ -721,8 +736,6 @@ def create_agent(
                 # gpt-3.5-turbo tends to omit inner monologue, relax this requirement for now
                 first_message_verify_mono=True if (llm_config.model is not None and "gpt-4" in llm_config.model) else False,
             )
-            save_agent(agent=agent, ms=self.ms)
-
             # FIXME: this is a hacky way to get the system prompts injected into agent into the DB
             # self.ms.update_agent(agent.agent_state)
         except Exception as e: