Initial lint run

.github/workflows/ci.yaml

@@ -0,0 +1,26 @@
+name: Continuous Integration
+
+on:
+  push:
+
+
+env:
+  PYTHON_VERSION: 3.11.4
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    name: Lint
+    steps:
+      - uses: actions/checkout@v3
+      - name: Install poetry
+        run: pipx install poetry
+      - uses: actions/setup-python@v4
+        with:
+          python-version: ${{env.PYTHON_VERSION}}
+          cache: 'poetry'
+          architecture: 'x64'
+      - name: Install
+        run: poetry install
+      - name: Lint
+        run: make lint-ci-cd
+

Makefile

@@ -1,4 +1,10 @@
-.PHONY: examples agents evals logging
+POETRY := poetry
+FOLDERS=log10 examples
+BLUE=\033[0;34m
+NC=\033[0m # No Color
+
+.PHONY: examples agents evals logging  lint lint-ci-cd lint-flake8 lint-mypy check-lock lint-ci-cd
+
 
 agents:
 	LOG10_EXAMPLES_MODEL=noop python examples/agents/biochemist.py
@@ -47,4 +53,24 @@ evals:
 	(cd examples/evals && python compile.py)
 	(cd examples/evals && python fuzzy.py)
 
-examples: agents evals logging
+examples: agents evals logging
+
+autolint:
+	${POETRY} run black .
+	${POETRY} run ruff --fix .
+	${POETRY} run isort ${FOLDERS}
+
+lint-ci-cd: check-lock lint-ruff lint-pyright
+
+lint: autolint check-lock lint-ruff lint-pyright
+
+lint-pyright:
+	@echo "\n${BLUE}Running mypy...${NC}\n"
+	${POETRY} run pyright ${FOLDERS}
+
+lint-ruff: ## Run the flake8 linter
+	@echo "\n${BLUE}Running ruff...${NC}\n"
+	${POETRY} run ruff .
+
+check-lock:
+	${POETRY} lock --check

examples/agents/biochemist.py

@@ -1,19 +1,21 @@
 import os
-from log10.anthropic import Anthropic
-from log10.llm import NoopLLM
-from log10.agents.camel import camel_agent
+from typing import Any, Optional
+
 from dotenv import load_dotenv
 
-from log10.openai import OpenAI
+from log10.agents.camel import camel_agent
+from log10.anthropic import Anthropic
+from log10.llm import NoopLLM
 from log10.load import log10
+from log10.openai import OpenAI
 
 load_dotenv()
 
 # Select one of OpenAI or Anthropic models
 model = os.environ.get("LOG10_EXAMPLES_MODEL", "gpt-3.5-turbo-16k")
 max_turns = 30
 
-llm = None
+llm: Optional[Any] = None
 summary_model = None
 if "claude" in model:
     import anthropic
@@ -31,11 +33,15 @@
     summary_model = "gpt-3.5-turbo-16k"
     llm = OpenAI({"model": model})
 
+task_prompt = """Perform a molecular dynamics solution of a molecule: CN1CCC[C@H]1c2cccnc2.
+Design and conduct a 100 ns molecular dynamics simulation of the molecule CN1CCC[C@H]1c2cccnc2
+in an explicit solvent environment using the CHARMM force field and analyze the conformational
+changes and hydrogen bonding patterns over time"""
 # example calls from playground (select 1)
 camel_agent(
     user_role="Poor PhD Student",
     assistant_role="Experienced Computational Chemist",
-    task_prompt="Perform a molecular dynamics solution of a molecule: CN1CCC[C@H]1c2cccnc2. Design and conduct a 100 ns molecular dynamics simulation of the molecule CN1CCC[C@H]1c2cccnc2 in an explicit solvent environment using the CHARMM force field and analyze the conformational changes and hydrogen bonding patterns over time",
+    task_prompt=task_prompt,
     summary_model=summary_model,
     max_turns=max_turns,
     llm=llm,

examples/agents/code_optimizer.py

@@ -1,17 +1,23 @@
+import logging
 import os
+from typing import Any, Optional
+
+from log10.agents.camel import camel_agent
 from log10.anthropic import Anthropic
+from log10.evals import compile
 from log10.llm import NoopLLM
 from log10.load import log10
-from log10.evals import compile
-from log10.agents.camel import camel_agent
 from log10.openai import OpenAI
 from log10.tools import code_extractor
 
+_logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
+
 # Select one of OpenAI or Anthropic models
 model = os.environ.get("LOG10_EXAMPLES_MODEL", "gpt-3.5-turbo-16k")
 max_turns = 10
 
-llm = None
+llm: Optional[Any] = None
 summary_model = None
 extraction_model = None
 if "claude" in model:
@@ -33,12 +39,32 @@
     extraction_model = "gpt-4"
     llm = OpenAI({"model": model})
 
+task_prompt = """Correct the following code.
+```
+#include <stdio.h>
+#include <string.h>
+int main() {
+    char password[8];
+    int granted = 0;
+    printf("Enter password: ");
+    scanf("%s", password);
+    if (strcmp(password, "password") == 0){
+        granted = 1;
+    }
+    if (granted) {
+        printf("Access granted.\\n");
+    } else {
+        printf("Access denied.\\n");
+    }
+    return 0;
+}```
+"""
 
 # example calls from playground (select 1)
 user_messages, assistant_messages = camel_agent(
     user_role="C developer",
     assistant_role="Cybersecurity expert",
-    task_prompt='Correct the following code.\n\n#include <stdio.h>\n#include <string.h>\n\nint main() {\n    char password[8];\n    int granted = 0;\n\n    printf("Enter password: ");\n    scanf("%s", password);\n\n    if (strcmp(password, "password") == 0) {\n        granted = 1;\n    }\n\n    if (granted) {\n        printf("Access granted.\\n");\n    } else {\n        printf("Access denied.\\n");\n    }\n\n    return 0;\n}',
+    task_prompt="Correct the following code.\n\n",
     summary_model=summary_model,
     max_turns=max_turns,
     llm=llm,
@@ -48,12 +74,11 @@
 
 # Next extract just the C code
 code = code_extractor(full_response, "C", extraction_model, llm=llm)
-print(f"Extracted code\n###\n{code}")
+_logger.info(f"Extracted code\n###\n{code}")
 
 # Evaluate if the code compiles
 result = compile(code)
 if result is True:
-    print("Compilation successful")
+    _logger.info("Compilation successful")
 else:
-    print("Compilation failed with error:")
-    print(result[1])
+    _logger.error(f"Compilation failed with error: {result[1]}")

examples/agents/coder.py

@@ -1,10 +1,12 @@
 import os
+from typing import Any, Optional
+
+from dotenv import load_dotenv
+
+from log10.agents.camel import camel_agent
 from log10.anthropic import Anthropic
 from log10.llm import NoopLLM
 from log10.load import log10
-from log10.agents.camel import camel_agent
-from dotenv import load_dotenv
-
 from log10.openai import OpenAI
 
 load_dotenv()
@@ -13,7 +15,7 @@
 model = os.environ.get("LOG10_EXAMPLES_MODEL", "gpt-3.5-turbo-16k")
 max_turns = 30
 
-llm = None
+llm: Optional[Any] = None
 summary_model = None
 if "claude" in model:
     import anthropic

examples/agents/cybersecurity_expert.py

@@ -1,10 +1,12 @@
 import os
+from typing import Any, Optional
+
+from dotenv import load_dotenv
+
+from log10.agents.camel import camel_agent
 from log10.anthropic import Anthropic
 from log10.llm import NoopLLM
 from log10.load import log10
-from log10.agents.camel import camel_agent
-from dotenv import load_dotenv
-
 from log10.openai import OpenAI
 
 load_dotenv()
@@ -13,7 +15,7 @@
 model = os.environ.get("LOG10_EXAMPLES_MODEL", "gpt-3.5-turbo-16k")
 max_turns = 30
 
-llm = None
+llm: Optional[Any] = None
 summary_model = None
 if "claude" in model:
     import anthropic
@@ -31,11 +33,31 @@
     summary_model = "gpt-3.5-turbo-16k"
     llm = OpenAI({"model": model})
 
+task_prompt = """Correct the following code.
+```
+#include <stdio.h>
+#include <string.h>
+int main() {
+    i
+    char password[8];
+    int granted = 0;
+    printf("Enter password: ");
+    scanf("%s", password);
+    if (strcmp(password, "password") == 0) {
+        granted = 1;
+    }
+    if (granted) {
+        printf("Access granted.\\n");
+    } else {
+        printf("Access denied.\\n");
+    }
+    return 0;
+}```"""
 # example calls from playground (select 1)
 camel_agent(
     user_role="C developer",
     assistant_role="Cybersecurity expert",
-    task_prompt='Correct the following code.\n\n#include <stdio.h>\n#include <string.h>\n\nint main() {\n    char password[8];\n    int granted = 0;\n\n    printf("Enter password: ");\n    scanf("%s", password);\n\n    if (strcmp(password, "password") == 0) {\n        granted = 1;\n    }\n\n    if (granted) {\n        printf("Access granted.\\n");\n    } else {\n        printf("Access denied.\\n");\n    }\n\n    return 0;\n}',
+    task_prompt=task_prompt,
     summary_model=summary_model,
     max_turns=max_turns,
     llm=llm,

examples/agents/email_generator.py

@@ -1,10 +1,12 @@
 import os
+from typing import Any, Optional
+
+from dotenv import load_dotenv
+
+from log10.agents.camel import camel_agent
 from log10.anthropic import Anthropic
 from log10.llm import NoopLLM
 from log10.load import log10
-from log10.agents.camel import camel_agent
-from dotenv import load_dotenv
-
 from log10.openai import OpenAI
 
 load_dotenv()
@@ -13,7 +15,7 @@
 model = os.environ.get("LOG10_EXAMPLES_MODEL", "gpt-3.5-turbo-16k")
 max_turns = 30
 
-llm = None
+llm: Optional[Any] = None
 summary_model = None
 if "claude" in model:
     import anthropic

examples/agents/scrape_summarizer.py

@@ -1,15 +1,16 @@
 import os
+from typing import Any, Optional
+
 from log10.agents.scrape_summarizer import scrape_summarizer
 from log10.anthropic import Anthropic
 from log10.llm import NoopLLM
 from log10.load import log10
 from log10.openai import OpenAI
 
-
 # Select one of OpenAI or Anthropic models
 model = os.environ.get("LOG10_EXAMPLES_MODEL", "gpt-3.5-turbo-16k")
 
-llm = None
+llm: Optional[Any] = None
 if "claude" in model:
     import anthropic
 

examples/agents/translator.py

@@ -1,10 +1,12 @@
 import os
+from typing import Any, Optional
+
+from dotenv import load_dotenv
+
+from log10.agents.camel import camel_agent
 from log10.anthropic import Anthropic
 from log10.llm import NoopLLM
 from log10.load import log10
-from log10.agents.camel import camel_agent
-from dotenv import load_dotenv
-
 from log10.openai import OpenAI
 
 load_dotenv()
@@ -14,7 +16,7 @@
 
 max_turns = 30
 
-llm = None
+llm: Optional[Any] = None
 summary_model = None
 if "claude" in model:
     import anthropic

examples/evals/basic_eval.py

@@ -1,4 +1,5 @@
-import os
+from typing import Any, Optional
+
 from log10.anthropic import Anthropic
 from log10.evals import eval
 from log10.openai import OpenAI
@@ -7,7 +8,7 @@
 provider = "openai"  # "anthropic"
 
 # TODO: Replace with LLM abstraction.
-llm = None
+llm: Optional[Any] = None
 if provider == "openai":
     llm = OpenAI(
         {

examples/evals/compile.py

@@ -1,7 +1,8 @@
+from typing import Any, Optional
+
 from log10.anthropic import Anthropic
-from log10.llm import Message, NoopLLM
-from log10.load import log10
 from log10.evals import compile
+from log10.llm import Message, NoopLLM, Role
 from log10.openai import OpenAI
 from log10.tools import code_extractor
 
@@ -10,7 +11,7 @@
 # model = "claude-1"
 
 
-llm = None
+llm: Optional[Any] = None
 if "claude" in model:
     llm = Anthropic({"model": model})
     extraction_model = "claude-1-100k"
@@ -24,7 +25,7 @@
 
 # First, write a hello world program
 messages = [
-    Message(role="system", content="You are an expert C programmer."),
+    Message(role=Role.system, content="You are an expert C programmer."),
     Message(
         role="user",
         content="Write a hello world program. Insert a null character after the hello world",

examples/evals/fuzzy.py

@@ -1,11 +1,12 @@
-import os
-from log10.openai import OpenAI
+from typing import Any, Optional
+
 from log10.anthropic import Anthropic
 from log10.evals import eval
+from log10.openai import OpenAI
 
 # Choose provider
 provider = "anthropic"  # "anthropic"
-llm = None
+llm: Optional[Any] = None
 if provider == "openai":
     llm = OpenAI(
         {