Merge pull request #42 from microsoft/0.1.3.6

Update to v0.1.3.6

README.md

@@ -274,17 +274,38 @@ with TraceGraph coming soon).
 ## LLM API Setup
 
 Currently we rely on [LiteLLM](https://github.com/BerriAI/litellm) or [AutoGen v0.2](https://github.com/microsoft/autogen/tree/0.2) for LLM caching and API-Key management.
-By default, LiteLLM is used. To use it, set the keys as the environment variables, e.g.
+
+By default, LiteLLM is used. To change the default backend, set the environment variable `TRACE_DEFAULT_LLM_BACKEND` on terminal
+```bash
+export TRACE_DEFAULT_LLM_BACKEND="<your LLM backend here>"  # 'LiteLLM' or 'AutoGen`
+```
+or in python before importing `opto`
+```python
+import os
+os.environ["TRACE_DEFAULT_LLM_BACKEND"] = "<your LLM backend here>"  # 'LiteLLM' or 'AutoGen`
+import opto
+```
+
+
+
+### Using LiteLLM as Backend
+
+Set the keys as the environment variables, following the [documentation of LiteLLM](https://docs.litellm.ai/docs/providers). For example,
 
 ```python
 import os
-os.environ["OPENAI_API_KEY"] = "your-openai-key"
-os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key"
+os.environ["OPENAI_API_KEY"] = "<your OpenAI API key here>"
+os.environ["ANTHROPIC_API_KEY"] = "<your Anthropic API key here>"
+```
+In Trace, we add another environment variable `TRACE_LITELLM_MODEL` to set the default model name used by LiteLLM for convenience, e.g.,
+```bash
+export TRACE_LITELLM_MODEL='gpt-4o'
 ```
+will set all LLM instances in Trace to use `gpt-4o` by default.
 
-Please see the [documentation of LiteLLM](https://docs.litellm.ai/docs/providers) for more details on setting keys and end-point urls.
 
-On the other hand, to use AutoGen, install Trace with autogen flag, `pip install trace-opt[autogen]`. AutoGen relies on `OAI_CONFIG_LIST`, which is a file you put in your working directory. It has the format of:
+### Using AutoGen as Backend
+First install Trace with autogen flag, `pip install trace-opt[autogen]`. AutoGen relies on `OAI_CONFIG_LIST`, which is a file you put in your working directory. It has the format of:
 
 ```json lines
 [
@@ -298,7 +319,8 @@ On the other hand, to use AutoGen, install Trace with autogen flag, `pip install
     }
 ]
 ```
-You switch between different LLM models by changing the `model` field in this configuration file.
+You can switch between different LLM models by changing the `model` field in this configuration file.
+Note AutoGen by default will use the first model available in this config file.
 
 You can also set an `os.environ` variable `OAI_CONFIG_LIST` to point to the location of this file or directly set a JSON string as the value of this variable.
 

opto/trace/iterators.py

@@ -4,14 +4,14 @@
 from opto.trace.bundle import bundle
 import opto.trace.operators as ops
 from opto.trace.errors import ExecutionError
-
+import numpy as np
 
 # List[Nodes], Node[List]
 def iterate(x: Any):
     """Return an iterator object for node of list, tuple, set, or dict."""
     if not isinstance(x, Node):
         x = node(x)
-    if issubclass(x.type, list) or issubclass(x.type, tuple) or issubclass(x.type, str):
+    if issubclass(x.type, list) or issubclass(x.type, tuple) or issubclass(x.type, str) or issubclass(x.type, np.ndarray):
         return SeqIterable(x)
     elif issubclass(x.type, set):
         converted_list = ops.to_list(x)

opto/utils/llm.py

@@ -5,6 +5,7 @@
 import litellm
 import os
 import openai
+import warnings
 
 try:
     import autogen  # We import autogen here to avoid the need of installing autogen
@@ -153,15 +154,19 @@ class LiteLLM(AbstractModel):
 
     To use this, set the credentials through the environment variable as
     instructed in the LiteLLM documentation. For convenience, you can set the
-    default model name through the environment variable DEFAULT_LITELLM_MODEL.
+    default model name through the environment variable TRACE_LITELLM_MODEL.
     When using Azure models via token provider, you can set the Azure token
     provider scope through the environment variable AZURE_TOKEN_PROVIDER_SCOPE.
     """
 
     def __init__(self, model: Union[str, None] = None, reset_freq: Union[int, None] = None,
                  cache=True) -> None:
         if model is None:
-            model = os.environ.get('DEFAULT_LITELLM_MODEL', 'gpt-4o')
+            model = os.environ.get('TRACE_LITELLM_MODEL')
+            if model is None:
+                warnings.warn("TRACE_LITELLM_MODEL environment variable is not found when loading the default model for LiteLLM. Attempt to load the default model from DEFAULT_LITELLM_MODEL environment variable. The usage of DEFAULT_LITELLM_MODEL will be deprecated. Please use the environment variable TRACE_LITELLM_MODEL for setting the default model name for LiteLLM.")
+                model = os.environ.get('DEFAULT_LITELLM_MODEL', 'gpt-4o')
+
         self.model_name = model
         self.cache = cache
         factory = lambda: self._factory(self.model_name)  # an LLM instance uses a fixed model
@@ -198,9 +203,9 @@ class CustomLLM(AbstractModel):
     def __init__(self, model: Union[str, None] = None, reset_freq: Union[int, None] = None,
                  cache=True) -> None:
         if model is None:
-            model = os.environ.get('DEFAULT_LITELLM_CUSTOM_MODEL', 'gpt-4o')
-            base_url = os.environ.get('DEFAULT_LITELLM_CUSTOM_URL', 'http://xx.xx.xxx.xx:4000')
-            server_api_key = os.environ.get('DEFAULT_LITELLM_CUSTOM_API',
+            model = os.environ.get('TRACE_CUSTOMLLM_MODEL', 'gpt-4o')
+            base_url = os.environ.get('TRACE_CUSTOMLLM_URL', 'http://xx.xx.xxx.xx:4000')
+            server_api_key = os.environ.get('TRACE_CUSTOMLLM_API_KEY',
                                             'sk-Xhg...')  # we assume the server has an API key
             # the server API is set through `master_key` in `config.yaml` for LiteLLM proxy server
 
@@ -224,5 +229,16 @@ def create(self, **config: Any):
         return self._model.chat.completions.create(**config)
 
 
-# Set Default LLM class
-LLM = LiteLLM  # synonym
+
+TRACE_DEFAULT_LLM_BACKEND = os.getenv('TRACE_DEFAULT_LLM_BACKEND', 'LiteLLM')
+if TRACE_DEFAULT_LLM_BACKEND == 'AutoGen':
+    print("Using AutoGen as the default LLM backend.")
+    LLM = AutoGenLLM
+elif TRACE_DEFAULT_LLM_BACKEND == 'CustomLLM':
+    print("Using CustomLLM as the default LLM backend.")
+    LLM = CustomLLM
+elif TRACE_DEFAULT_LLM_BACKEND == 'LiteLLM':
+    print("Using LiteLLM as the default LLM backend.")
+    LLM = LiteLLM
+else:
+    raise ValueError(f"Unknown LLM backend: {TRACE_DEFAULT_LLM_BACKEND}")

tests/unit_tests/test_llm.py

@@ -2,7 +2,7 @@
 from opto.optimizers.utils import print_color
 import os
 
-if os.path.exists("OAI_CONFIG_LIST") or os.environ.get("DEFAULT_LITELLM_MODEL") or os.environ.get("OPENAI_API_KEY"):
+if os.path.exists("OAI_CONFIG_LIST") or os.environ.get("TRACE_LITELLM_MODEL") or os.environ.get("OPENAI_API_KEY"):
     llm = LLM()
     system_prompt = 'You are a helpful assistant.'
     user_prompt = "Hello world."

tests/unit_tests/test_nodes.py

@@ -2,7 +2,7 @@
 from opto.trace import node
 from opto.trace import operators as ops
 from opto.trace.utils import contain
-
+import numpy as np
 
 # Sum of str
 x = node("NodeX")
@@ -151,4 +151,11 @@ def fun(x):
 assert x.description == "[ParameterNode] x"
 
 x = node(1, trainable=True)
-assert x.description == "[ParameterNode] This is a ParameterNode in a computational graph."
+assert x.description == "[ParameterNode] This is a ParameterNode in a computational graph."
+
+
+# Test iterating numpy array
+x = node(np.array([1, 2, 3]))
+for i, v in enumerate(x):
+   assert isinstance(v, type(x))
+   assert v.data == x.data[i]

tests/unit_tests/test_optimizer.py

@@ -34,7 +34,7 @@ def user(x):
     else:
         return "Success."
 
-if os.path.exists("OAI_CONFIG_LIST") or os.environ.get("DEFAULT_LITELLM_MODEL") or os.environ.get("OPENAI_API_KEY"):
+if os.path.exists("OAI_CONFIG_LIST") or os.environ.get("TRACE_LITELLM_MODEL") or os.environ.get("OPENAI_API_KEY"):
     # One-step optimization example
     x = node(-1.0, trainable=True)
     optimizer = OptoPrime([x])
@@ -124,7 +124,7 @@ def foobar_text(x):
 GRAPH.clear()
 x = node("negative point one", trainable=True)
 
-if os.path.exists("OAI_CONFIG_LIST") or os.environ.get("DEFAULT_LITELLM_MODEL") or os.environ.get("OPENAI_API_KEY"):
+if os.path.exists("OAI_CONFIG_LIST") or os.environ.get("TRACE_LITELLM_MODEL") or os.environ.get("OPENAI_API_KEY"):
     optimizer = OptoPrime([x])
     output = foobar_text(x)
     feedback = user(output.data)