Merge pull request #30 from log10-io/llm-abstraction

Llm abstraction

.gitignore

@@ -8,4 +8,6 @@ dist
 users.db
 
 # Environments
-.env
+.env
+
+.vscode/

examples/agents/biochemist.py

@@ -1,31 +1,42 @@
 import os
-from log10.load import log10
+from log10.anthropic import Anthropic
+from log10.llm import NoopLLM
 from log10.agents.camel import camel_agent
 from dotenv import load_dotenv
+
+from log10.openai import OpenAI
+from log10.load import log10
+
 load_dotenv()
 
 # Select one of OpenAI or Anthropic models
+# model = "noop"
 model = "gpt-3.5-turbo-16k"
 # model = "claude-1"
-maxTurns = 30
+max_turns = 30
 
-if 'claude' in model:
+llm = None
+summary_model = None
+if "claude" in model:
     import anthropic
     log10(anthropic)
-    anthropicClient = anthropic.Client(os.environ["ANTHROPIC_API_KEY"])
-    module = anthropicClient
-    hparams = {'max_tokens_to_sample': 1024}
     summary_model = "claude-1-100k"
-else:  # openai
+    llm = Anthropic({"model": model})
+elif model == "noop":
+    summary_model = model
+    llm = NoopLLM()
+else:
     import openai
     log10(openai)
-    openai.api_key = os.getenv("OPENAI_API_KEY")
-    hparams = {}
-    module = openai
     summary_model = "gpt-3.5-turbo-16k"
+    llm = OpenAI({"model": model})
 
 # example calls from playground (select 1)
-camel_agent(userRole='Poor PhD Student', assistantRole='Experienced Computational Chemist',
-            taskPrompt='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',
-            model=model, summary_model=summary_model, maxTurns=maxTurns,
-            module=module, hparams=hparams)
+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",
+    summary_model=summary_model,
+    max_turns=max_turns,
+    llm=llm,
+)

examples/agents/code_optimizer.py

@@ -1,43 +1,53 @@
 import os
+from log10.anthropic import Anthropic
+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
 
 # Select one of OpenAI or Anthropic models
-#model = "gpt-3.5-turbo-16k"
-model = "claude-1"
-maxTurns = 10
+model = "gpt-3.5-turbo-16k"
+# model = "claude-1"
+# model = "noop"
+max_turns = 10
 
-if 'claude' in model:
+llm = None
+summary_model = None
+extraction_model = None
+if "claude" in model:
     import anthropic
     log10(anthropic)
-    anthropicClient = anthropic.Client(os.environ["ANTHROPIC_API_KEY"])
-    module = anthropicClient
-    hparams = {'max_tokens_to_sample': 1024}
     summary_model = "claude-1-100k"
     extraction_model = "claude-1-100k"
-    completion_func = anthropicClient.completion
-else:  # openai
+    llm = Anthropic({"model": model})
+elif model == "noop":
+    summary_model = model
+    extraction_model = model
+    llm = NoopLLM()
+else:
     import openai
     log10(openai)
-    openai.api_key = os.getenv("OPENAI_API_KEY")
-    hparams = {}
-    module = openai
     summary_model = "gpt-3.5-turbo-16k"
     extraction_model = "gpt-4"
-    completion_func = openai.ChatCompletion.create
+    llm = OpenAI({"model": model})
+
 
 # example calls from playground (select 1)
-user_messages, assistant_messages = camel_agent(userRole='C developer', assistantRole='Cybersecurity expert',
-                                                taskPrompt="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}",
-                                                model=model, summary_model=summary_model, maxTurns=maxTurns,
-                                                module=module, hparams=hparams)
+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}',
+    summary_model=summary_model,
+    max_turns=max_turns,
+    llm=llm,
+)
 
-full_response = assistant_messages[-1]['content']
+full_response = assistant_messages[-1].content
 
 # Next extract just the C code
-code = code_extractor(full_response, "C", completion_func, extraction_model, hparams)
+code = code_extractor(full_response, "C", extraction_model, llm=llm)
 print(f"Extracted code\n###\n{code}")
 
 # Evaluate if the code compiles

examples/agents/coder.py

@@ -1,31 +1,42 @@
 import os
+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()
 
 # Select one of OpenAI or Anthropic models
 model = "gpt-3.5-turbo-16k"
 # model = "claude-1"
-maxTurns = 30
+# model = "noop"
+max_turns = 30
 
-if 'claude' in model:
+llm = None
+summary_model = None
+if "claude" in model:
     import anthropic
     log10(anthropic)
-    anthropicClient = anthropic.Client(os.environ["ANTHROPIC_API_KEY"])
-    module = anthropicClient
-    hparams = {'max_tokens_to_sample': 1024}
     summary_model = "claude-1-100k"
-else:  # openai
+    llm = Anthropic({"model": model})
+elif model == "noop":
+    summary_model = model
+    llm = NoopLLM()
+else:
     import openai
     log10(openai)
-    openai.api_key = os.getenv("OPENAI_API_KEY")
-    hparams = {}
-    module = openai
     summary_model = "gpt-3.5-turbo-16k"
+    llm = OpenAI({"model": model})
 
 # example calls from playground (select 1)
-camel_agent(userRole='Stock Trader', assistantRole='Python Programmer',
-            taskPrompt='Develop a trading bot for the stock market',
-            model=model, summary_model=summary_model, maxTurns=maxTurns,
-            module=module, hparams=hparams)
+camel_agent(
+    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

@@ -1,31 +1,42 @@
 import os
+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()
 
 # Select one of OpenAI or Anthropic models
 model = "gpt-3.5-turbo-16k"
 # model = "claude-1"
-maxTurns = 30
+# model = "noop"
+max_turns = 30
 
-if 'claude' in model:
+llm = None
+summary_model = None
+if "claude" in model:
     import anthropic
     log10(anthropic)
-    anthropicClient = anthropic.Client(os.environ["ANTHROPIC_API_KEY"])
-    module = anthropicClient
-    hparams = {'max_tokens_to_sample': 1024}
     summary_model = "claude-1-100k"
-else:  # openai
+    llm = Anthropic({"model": model})
+elif model == "noop":
+    summary_model = model
+    llm = NoopLLM()
+else:
     import openai
     log10(openai)
-    openai.api_key = os.getenv("OPENAI_API_KEY")
-    hparams = {}
-    module = openai
     summary_model = "gpt-3.5-turbo-16k"
+    llm = OpenAI({"model": model})
 
 # example calls from playground (select 1)
-camel_agent(userRole='C developer', assistantRole='Cybersecurity expert',
-            taskPrompt="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}",
-            model=model, summary_model=summary_model, maxTurns=maxTurns,
-            module=module, hparams=hparams)
+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}',
+    summary_model=summary_model,
+    max_turns=max_turns,
+    llm=llm,
+)

examples/agents/email_generator.py

@@ -1,31 +1,42 @@
 import os
+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()
 
 # Select one of OpenAI or Anthropic models
 model = "gpt-3.5-turbo-16k"
 # model = "claude-1"
-maxTurns = 30
+# model = "noop"
+max_turns = 30
 
-if 'claude' in model:
+llm = None
+summary_model = None
+if "claude" in model:
     import anthropic
     log10(anthropic)
-    anthropicClient = anthropic.Client(os.environ["ANTHROPIC_API_KEY"])
-    module = anthropicClient
-    hparams = {'max_tokens_to_sample': 1024}
     summary_model = "claude-1-100k"
-else:  # openai
+    llm = Anthropic({"model": model})
+elif model == "noop":
+    summary_model = model
+    llm = NoopLLM()
+else:
     import openai
     log10(openai)
-    openai.api_key = os.getenv("OPENAI_API_KEY")
-    hparams = {}
-    module = openai
     summary_model = "gpt-3.5-turbo-16k"
+    llm = OpenAI({"model": model})
 
 # example calls from playground (select 1)
-camel_agent(userRole='Sales email copyeditor', assistantRole='Sales email copywriter',
-            taskPrompt='Write a sales email to Pfizer about a new healthcare CRM',
-            model=model, summary_model=summary_model, maxTurns=maxTurns,
-            module=module, hparams=hparams)
+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",
+    summary_model=summary_model,
+    max_turns=max_turns,
+    llm=llm,
+)

examples/agents/scrape_summarizer.py

@@ -1,27 +1,27 @@
-
 import os
 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 = "gpt-3.5-turbo-16k"
 # model = "claude-1"
+# model = "noop"
 
-if 'claude' in model:
+llm = None
+if "claude" in model:
     import anthropic
     log10(anthropic)
-    anthropicClient = anthropic.Client(os.environ["ANTHROPIC_API_KEY"])
-    module = anthropicClient
-    hparams = {'max_tokens_to_sample': 1024}
-    summary_model = "claude-1-100k"
-else:  # openai
+    llm = Anthropic({"model": model})
+elif model == "noop":
+    llm = NoopLLM()
+else:
     import openai
     log10(openai)
-    openai.api_key = os.getenv("OPENAI_API_KEY")
-    hparams = {}
-    module = openai
-    summary_model = "gpt-3.5-turbo-16k"
+    llm = OpenAI({"model": model})
 
 url = "https://nytimes.com"
-print(scrape_summarizer(url, model, module, hparams))
+print(scrape_summarizer(url, llm))