Merge branch 'main' into distributed

.gitignore

@@ -312,5 +312,7 @@ dist
 static/
 *.db-shm
 *.db-wal
+
+logdir/
 .aider*
 examples/logdir

docs/ramblings/0.1.0/chat.md

@@ -154,4 +154,155 @@ class MyAgent():
         pass
 
 
+
+
+from functools import wraps
+from typing import Generator, Any
+# Precanned AI responses for demo purposes
+ai_responses = [
+    "The capital of France is Paris.",
+    "Population: 2200000",
+]
+
+
+def lmp(func):
+    """
+    Decorator that simulates multi-step calls to an LLM API.
+    Prints each step and collects all yields from a generator.
+    Returns the collected values.
+    """
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        system_prompt = func.__doc__
+        print(f"\033[94mSystem: {system_prompt}\033[0m")
+        generator = func(*args, **kwargs)
+        message_history = []
+        step = 1
+
+        try:
+            user_prompt = next(generator)
+            while True:
+                print(f"\033[92mUser: {user_prompt}\033[0m")
+                message_history.append({"role": "user", "content": user_prompt})
+
+                # Use precanned AI response
+                ai_response = ai_responses[step - 1] if step <= len(ai_responses) else f"AI response for step {step}"
+                print(f"\033[93mAssistant: {ai_response}\033[0m")
+
+                message_history.append({"role": "assistant", "content": ai_response})
+                step += 1
+
+                # Send AI response back to the generator
+                user_prompt = generator.send(ai_response)
+
+        except StopIteration as e:
+            return e.value
+    return wrapper
+
+@lmp
+def multistep_prompt():
+    """You are a helpful assistant."""
+    assistant_response = yield "What is the capital of France?"
+    print("City!", assistant_response)
+    assistant_response_2 = yield "What is the population of that city?"
+
+    # This is allowed in a generator
+    return int(assistant_response_2.split("Population: ")[-1])
+
+# Execute the multi-step prompt
+result = multistep_prompt()
+print(f"{result}")
+
+
+import asyncio
+from functools import wraps
+
+async def async_lmp(func):
+    """
+    Async decorator that simulates multi-step calls to an LLM API.
+    Prints each step and collects all yields from an async generator.
+    Returns the collected values.
+    """
+    @wraps(func)
+    async def wrapper(*args, **kwargs):
+        system_prompt = func.__doc__
+        print(f"\033[94mSystem: {system_prompt}\033[0m")
+        generator = func(*args, **kwargs)
+        message_history = []
+        step = 1
+
+        try:
+            user_prompt = await anext(generator)
+            while True:
+                print(f"\033[92mUser: {user_prompt}\033[0m")
+                message_history.append({"role": "user", "content": user_prompt})
+
+                # Use precanned AI response
+                ai_response = ai_responses[step - 1] if step <= len(ai_responses) else f"AI response for step {step}"
+                print(f"\033[93mAssistant: {ai_response}\033[0m")
+
+                message_history.append({"role": "assistant", "content": ai_response})
+                step += 1
+
+                # Send AI response back to the generator
+                user_prompt = await generator.asend(ai_response)
+
+        except StopAsyncIteration as e:
+            return e.value
+    return wrapper
+
+@async_generator
+async def async_multistep_prompt():
+    """You are a helpful assistant."""
+    resp = await yield_("What is the capital of France?")
+    resp = await yield_("What is the population of that city?")
+    return int(resp.split("Population: ")[-1])
+
+async def main():
+    result = await async_multistep_prompt()
+    print(f"{result}")
+
+asyncio.run(main())
+
+
+# so in some sense this is the most natural interface for ell which is just the fucking api iterface with an lmp context for multistep, the yield statment feels just right for multistep though. it's so unclear to me why async generators do not have a return value though.
+@ell.lmp(model="gpt-4o", temperature=0.0, api_params={"max_tokens": 1000})
+def my_prompt():
+    resp = yield ell.user("What is the capital of France?")
+
+@ell.lmp(model="gpt-4o", temperature=0.0, api_params={"max_tokens": 1000})
+def my_prompt():
+    resp = yield [ell.user("What is the capital of France?")]
+
+@ell.lmp(model="gpt-4o", temperature=0.0, api_params={"max_tokens": 1000})
+def my_prompt():
+    resp = yield ell.Call(messages=[ell.user("What is the capital of France?")], api_params={"max_tokens": 10})
+
+@ell.lmp(model="gpt-4o", temperature=0.0, api_params={"max_tokens": 1000})
+def my_prompt():
+    resp = yield [ell.user("What is the capital of France?")], {"max_tokens": 10}
+
+
+# This is unacceptable.
+
+@ell.lmp(model="gpt-4o", temperature=0.0, api_params={"max_tokens": 1000})
+def my_prompt():
+    claude_says = yield "What is the capital of France?", {'model': 'claude'}
+    gpt_says = yield "What is the capital of France?"
+
+-->
+
+def normal_prompt():
+    anthropic_client = anthropic.Anthropic()
+    openai_client = openai.OpenAI()
+
+    claude_says = anthropic_client.messages.create(model="claude-3-opus", messages=[{"role": "user", "content": "What is the capital of France?"}])
+    gpt_says = openai_client.chat.completions.create(model="gpt-4o", messages=[
+        {"role": "user", "content": "What is the capital of France?"},
+        { "role": "assistant", "content": claude_says.content},
+        {"role": "user", "content": "What is the capital of France?"}
+    ])
+
+    return None
+
 ```