Single quotes

examples/agents/biochemist.py

@@ -11,33 +11,33 @@
 
 # Select one of OpenAI or Anthropic models
 # model = "noop"
-model = "gpt-3.5-turbo-16k"
+model = 'gpt-3.5-turbo-16k'
 # model = "claude-1"
 max_turns = 30
 
 llm = None
 summary_model = None
-if "claude" in model:
+if 'claude' in model:
     import anthropic
 
     log10(anthropic)
-    summary_model = "claude-1-100k"
-    llm = Anthropic({"model": model})
-elif model == "noop":
+    summary_model = 'claude-1-100k'
+    llm = Anthropic({'model': model})
+elif model == 'noop':
     summary_model = model
     llm = NoopLLM()
 else:
     import openai
 
     log10(openai)
-    summary_model = "gpt-3.5-turbo-16k"
-    llm = OpenAI({"model": model})
+    summary_model = 'gpt-3.5-turbo-16k'
+    llm = OpenAI({'model': model})
 
 # 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",
+    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',
     summary_model=summary_model,
     max_turns=max_turns,
     llm=llm,

examples/agents/code_optimizer.py

@@ -8,38 +8,37 @@
 from log10.tools import code_extractor
 
 # Select one of OpenAI or Anthropic models
-model = "gpt-3.5-turbo-16k"
+model = 'gpt-3.5-turbo-16k'
 # model = "claude-1"
 # model = "noop"
 max_turns = 10
 
 llm = None
 summary_model = None
 extraction_model = None
-if "claude" in model:
+if 'claude' in model:
     import anthropic
 
     log10(anthropic)
-    summary_model = "claude-1-100k"
-    extraction_model = "claude-1-100k"
-    llm = Anthropic({"model": model})
-elif model == "noop":
+    summary_model = 'claude-1-100k'
+    extraction_model = 'claude-1-100k'
+    llm = Anthropic({'model': model})
+elif model == 'noop':
     summary_model = model
     extraction_model = model
     llm = NoopLLM()
 else:
     import openai
 
     log10(openai)
-    summary_model = "gpt-3.5-turbo-16k"
-    extraction_model = "gpt-4"
-    llm = OpenAI({"model": model})
-
+    summary_model = 'gpt-3.5-turbo-16k'
+    extraction_model = 'gpt-4'
+    llm = OpenAI({'model': model})
 
 # example calls from playground (select 1)
 user_messages, assistant_messages = camel_agent(
-    user_role="C developer",
-    assistant_role="Cybersecurity expert",
+    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}',
     summary_model=summary_model,
     max_turns=max_turns,
@@ -49,13 +48,13 @@
 full_response = assistant_messages[-1].content
 
 # Next extract just the C code
-code = code_extractor(full_response, "C", extraction_model, llm=llm)
-print(f"Extracted code\n###\n{code}")
+code = code_extractor(full_response, 'C', extraction_model, llm=llm)
+print(f'Extracted code\n###\n{code}')
 
 # Evaluate if the code compiles
 result = compile(code)
 if result is True:
-    print("Compilation successful")
+    print('Compilation successful')
 else:
-    print("Compilation failed with error:")
+    print('Compilation failed with error:')
     print(result[1])

examples/agents/coder.py

@@ -10,34 +10,34 @@
 load_dotenv()
 
 # Select one of OpenAI or Anthropic models
-model = "gpt-3.5-turbo-16k"
+model = 'gpt-3.5-turbo-16k'
 # model = "claude-1"
 # model = "noop"
 max_turns = 30
 
 llm = None
 summary_model = None
-if "claude" in model:
+if 'claude' in model:
     import anthropic
 
     log10(anthropic)
-    summary_model = "claude-1-100k"
-    llm = Anthropic({"model": model})
-elif model == "noop":
+    summary_model = 'claude-1-100k'
+    llm = Anthropic({'model': model})
+elif model == 'noop':
     summary_model = model
     llm = NoopLLM()
 else:
     import openai
 
     log10(openai)
-    summary_model = "gpt-3.5-turbo-16k"
-    llm = OpenAI({"model": model})
+    summary_model = 'gpt-3.5-turbo-16k'
+    llm = OpenAI({'model': model})
 
 # example calls from playground (select 1)
 camel_agent(
-    user_role="Stock Trader",
-    assistant_role="Python Programmer",
-    task_prompt="Develop a trading bot for the stock market",
+    user_role='Stock Trader',
+    assistant_role='Python Programmer',
+    task_prompt='Develop a trading bot for the stock market',
     summary_model=summary_model,
     max_turns=max_turns,
     llm=llm,

examples/agents/cybersecurity_expert.py

@@ -10,33 +10,33 @@
 load_dotenv()
 
 # Select one of OpenAI or Anthropic models
-model = "gpt-3.5-turbo-16k"
+model = 'gpt-3.5-turbo-16k'
 # model = "claude-1"
 # model = "noop"
 max_turns = 30
 
 llm = None
 summary_model = None
-if "claude" in model:
+if 'claude' in model:
     import anthropic
 
     log10(anthropic)
-    summary_model = "claude-1-100k"
-    llm = Anthropic({"model": model})
-elif model == "noop":
+    summary_model = 'claude-1-100k'
+    llm = Anthropic({'model': model})
+elif model == 'noop':
     summary_model = model
     llm = NoopLLM()
 else:
     import openai
 
     log10(openai)
-    summary_model = "gpt-3.5-turbo-16k"
-    llm = OpenAI({"model": model})
+    summary_model = 'gpt-3.5-turbo-16k'
+    llm = OpenAI({'model': model})
 
 # example calls from playground (select 1)
 camel_agent(
-    user_role="C developer",
-    assistant_role="Cybersecurity expert",
+    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}',
     summary_model=summary_model,
     max_turns=max_turns,

examples/agents/email_generator.py

@@ -10,34 +10,34 @@
 load_dotenv()
 
 # Select one of OpenAI or Anthropic models
-model = "gpt-3.5-turbo-16k"
+model = 'gpt-3.5-turbo-16k'
 # model = "claude-1"
 # model = "noop"
 max_turns = 30
 
 llm = None
 summary_model = None
-if "claude" in model:
+if 'claude' in model:
     import anthropic
 
     log10(anthropic)
-    summary_model = "claude-1-100k"
-    llm = Anthropic({"model": model})
-elif model == "noop":
+    summary_model = 'claude-1-100k'
+    llm = Anthropic({'model': model})
+elif model == 'noop':
     summary_model = model
     llm = NoopLLM()
 else:
     import openai
 
     log10(openai)
-    summary_model = "gpt-3.5-turbo-16k"
-    llm = OpenAI({"model": model})
+    summary_model = 'gpt-3.5-turbo-16k'
+    llm = OpenAI({'model': model})
 
 # example calls from playground (select 1)
 camel_agent(
-    user_role="Sales email copyeditor",
-    assistant_role="Sales email copywriter",
-    task_prompt="Write a sales email to Pfizer about a new healthcare CRM",
+    user_role='Sales email copyeditor',
+    assistant_role='Sales email copywriter',
+    task_prompt='Write a sales email to Pfizer about a new healthcare CRM',
     summary_model=summary_model,
     max_turns=max_turns,
     llm=llm,

examples/agents/scrape_summarizer.py

@@ -5,25 +5,24 @@
 from log10.load import log10
 from log10.openai import OpenAI
 
-
 # Select one of OpenAI or Anthropic models
-model = "gpt-3.5-turbo-16k"
+model = 'gpt-3.5-turbo-16k'
 # model = "claude-1"
 # model = "noop"
 
 llm = None
-if "claude" in model:
+if 'claude' in model:
     import anthropic
 
     log10(anthropic)
-    llm = Anthropic({"model": model})
-elif model == "noop":
+    llm = Anthropic({'model': model})
+elif model == 'noop':
     llm = NoopLLM()
 else:
     import openai
 
     log10(openai)
-    llm = OpenAI({"model": model})
+    llm = OpenAI({'model': model})
 
-url = "https://nytimes.com"
+url = 'https://nytimes.com'
 print(scrape_summarizer(url, llm))

examples/agents/translator.py

@@ -10,35 +10,35 @@
 load_dotenv()
 
 # Select one of OpenAI or Anthropic models
-model = "gpt-3.5-turbo-16k"
+model = 'gpt-3.5-turbo-16k'
 # model = "claude-1"
 # model = "noop"
 
 max_turns = 30
 
 llm = None
 summary_model = None
-if "claude" in model:
+if 'claude' in model:
     import anthropic
 
     log10(anthropic)
-    summary_model = "claude-1-100k"
-    llm = Anthropic({"model": model})
-elif model == "noop":
+    summary_model = 'claude-1-100k'
+    llm = Anthropic({'model': model})
+elif model == 'noop':
     summary_model = model
     llm = NoopLLM()
 else:
     import openai
 
     log10(openai)
-    summary_model = "gpt-3.5-turbo-16k"
-    llm = OpenAI({"model": model})
+    summary_model = 'gpt-3.5-turbo-16k'
+    llm = OpenAI({'model': model})
 
 # example calls from playground (select 1)
 camel_agent(
-    user_role="Web3 guru",
-    assistant_role="Hindi translator",
-    task_prompt="Write a blog post about web3 in Hindi",
+    user_role='Web3 guru',
+    assistant_role='Hindi translator',
+    task_prompt='Write a blog post about web3 in Hindi',
     summary_model=summary_model,
     max_turns=max_turns,
     llm=llm,

examples/evals/basic_eval.py

@@ -4,49 +4,43 @@
 from log10.openai import OpenAI
 
 # Choose provider
-provider = "openai"  # "anthropic"
+provider = 'openai'  # "anthropic"
 
 # TODO: Replace with LLM abstraction.
 llm = None
-if provider == "openai":
+if provider == 'openai':
     llm = OpenAI(
         {
-            "model": "gpt-3.5-turbo",
-            "temperature": 0,
-            "max_tokens": 1024,
-            "top_p": 1,
-            "frequency_penalty": 0,
-            "presence_penalty": 0,
-        }
-    )
-elif provider == "anthropic":
-    llm = Anthropic(
-        {
-            "model": "claude-1",
-            "temperature": 0,
-            "max_tokens_to_sample": 1024,
+            'model': 'gpt-3.5-turbo',
+            'temperature': 0,
+            'max_tokens': 1024,
+            'top_p': 1,
+            'frequency_penalty': 0,
+            'presence_penalty': 0,
         }
     )
+elif provider == 'anthropic':
+    llm = Anthropic({'model': 'claude-1', 'temperature': 0, 'max_tokens_to_sample': 1024,})
 else:
     print(
         f"Unsupported provider option: {provider}. Supported providers are 'openai' or 'anthropic'."
     )
 
 # Ground truth dataset to use for evaluation
 eval_dataset = (
-    "match_data.csv",
-    {"input": "my_input_column", "ideal": "my_output_column"},
+    'match_data.csv',
+    {'input': 'my_input_column', 'ideal': 'my_output_column'},
 )
 
 # Specify which metrics to use. Options are:
 # 'match': model_output.startswith(ideal)
 # 'includes': ideal.lower() in model_output.lower()
 # 'fuzzy_match': similar to includes but remove punctuation, articles and extra whitespace and compare both ways
-eval_metric = "includes"
+eval_metric = 'includes'
 
 # Path to output file to store the metrics
 # Example from: https://github.com/openai/evals/blob/a24f20a357ecb3cc5eec8323097aeade9585796c/evals/elsuite/test/match.py
-out_file_path = "match_output.csv"
+out_file_path = 'match_output.csv'
 
 # Get back and id and url for the summary of results and status
 # todo: get back path to logfile; eval_id, eval_url =