Merge branch 'main' into fix-multimodal

Author: fm1320

.github/workflows/python-test.yml

@@ -14,7 +14,7 @@ jobs:
         python-version: ['3.9', '3.10', '3.11', '3.12']
 
     steps:
-      - uses: actions/checkout@v3 # Updated to the latest version
+      - uses: actions/checkout@v4 # Updated to the latest version
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v4 # Updated to the latest version
         with:
@@ -37,7 +37,7 @@ jobs:
           poetry run pytest
 
       - name: Upload pytest results as an artifact (optional)
-        uses: actions/upload-artifact@v3 # Updated to the latest version
+        uses: actions/upload-artifact@v4 # Updated to the latest version
         if: always() # Always run this step to ensure test results are saved even if previous steps fail
         with:
           name: pytest-results

adalflow/CHANGELOG.md

@@ -1,16 +1,23 @@
-## [0.2.7] - 2024-09-23
+## [0.2.7] - 2025-01-16
 
-### Improved
-- Better diagnose report for `Trainer.diagnose`.
-- Multi-hop RAG with handling of Cycle.
-
-## [0.2.7] - TO Be Released
 ### Added
 - `Memory` is completed with `call` and `add_dialog_turn` methods.
 - Integrated `LanceDB` in the `Retriever`
+- Multi-modal (image input and generation) in `OpenAIClient` along with tests.
+- `ComponentList` to support a list of components registered in a component. Added `test_componentlist` to test the `ComponentList`.
+
 ### Improved
+- Better diagnose report for `Trainer.diagnose`.
 - `BedrockAPIClient` added more details on setup, yet it is still in experimental stage.
 - `AzureAPIClient` added more details on setup, yet it is still in experimental stage.
+-  `Retriever` class:
+  - Support data id (field).
+- `GradComponent`: Support pass-through gradient for the `forward` method.
+
+Optimization
+- Aggregated all backward engine prompts in `backward_engine_prompt`.
+- Added `TGDData` for the optimizer to support reasoning at proposing new prompt.
+- Added `sequential_order` in the `Trainer` to support the sequential training order. Reorganized the trainer code.
 ## [0.2.6] - 2024-11-25
 ### Improved
 - Add default `max_tokens=512` to the `AnthropicAPIClient` to avoid the error when the user does not provide the `max_tokens` in the prompt.

adalflow/adalflow/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.2.6"
+__version__ = "0.2.7"
 
 from adalflow.core.component import Component, fun_to_component
 from adalflow.core.container import Sequential, ComponentList

adalflow/adalflow/components/model_client/bedrock_client.py

@@ -1,7 +1,8 @@
 """AWS Bedrock ModelClient integration."""
 
+import json
 import os
-from typing import Dict, Optional, Any, Callable
+from typing import Dict, Optional, Any, Callable, Generator as GeneratorType
 import backoff
 import logging
 
@@ -26,7 +27,6 @@ def get_first_message_content(completion: Dict) -> str:
     r"""When we only need the content of the first message.
     It is the default parser for chat completion."""
     return completion["output"]["message"]["content"][0]["text"]
-    return completion["output"]["message"]["content"][0]["text"]
 
 
 __all__ = [
@@ -117,6 +117,7 @@ def __init__(
         self._aws_connection_timeout = aws_connection_timeout
         self._aws_read_timeout = aws_read_timeout
 
+        self._client = None
         self.session = None
         self.sync_client = self.init_sync_client()
         self.chat_completion_parser = (
@@ -158,16 +159,51 @@ def init_sync_client(self):
     def init_async_client(self):
         raise NotImplementedError("Async call not implemented yet.")
 
-    def parse_chat_completion(self, completion):
-        log.debug(f"completion: {completion}")
+    def handle_stream_response(self, stream: dict) -> GeneratorType:
+        r"""Handle the stream response from bedrock. Yield the chunks.
+
+        Args:
+            stream (dict): The stream response generator from bedrock.
+
+        Returns:
+            GeneratorType: A generator that yields the chunks from bedrock stream.
+        """
+        try:
+            stream: GeneratorType = stream["stream"]
+            for chunk in stream:
+                log.debug(f"Raw chunk: {chunk}")
+                yield chunk
+        except Exception as e:
+            log.debug(f"Error in handle_stream_response: {e}")  # Debug print
+            raise
+
+    def parse_chat_completion(self, completion: dict) -> "GeneratorOutput":
+        r"""Parse the completion, and assign it into the raw_response attribute.
+
+        If the completion is a stream, it will be handled by the handle_stream_response
+        method that returns a Generator. Otherwise, the completion will be parsed using
+        the get_first_message_content method.
+
+        Args:
+            completion (dict): The completion response from bedrock API call.
+
+        Returns:
+            GeneratorOutput: A generator output object with the parsed completion. May
+                return a generator if the completion is a stream.
+        """
         try:
-            data = completion["output"]["message"]["content"][0]["text"]
-            usage = self.track_completion_usage(completion)
-            return GeneratorOutput(data=None, usage=usage, raw_response=data)
+            usage = None
+            data = self.chat_completion_parser(completion)
+            if not isinstance(data, GeneratorType):
+                # Streaming completion usage tracking is not implemented.
+                usage = self.track_completion_usage(completion)
+            return GeneratorOutput(
+                data=None, error=None, raw_response=data, usage=usage
+            )
         except Exception as e:
-            log.error(f"Error parsing completion: {e}")
+            log.error(f"Error parsing the completion: {e}")
             return GeneratorOutput(
-                data=None, error=str(e), raw_response=str(completion)
+                data=None, error=str(e), raw_response=json.dumps(completion)
             )
 
     def track_completion_usage(self, completion: Dict) -> CompletionUsage:
@@ -191,6 +227,7 @@ def list_models(self):
                 print(f"  Description: {model['description']}")
                 print(f"  Provider: {model['provider']}")
                 print("")
+
         except Exception as e:
             print(f"Error listing models: {e}")
 
@@ -222,14 +259,27 @@ def convert_inputs_to_api_kwargs(
             bedrock_runtime_exceptions.ModelErrorException,
             bedrock_runtime_exceptions.ValidationException,
         ),
-        max_time=5,
+        max_time=2,
     )
-    def call(self, api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED):
+    def call(
+        self,
+        api_kwargs: Dict = {},
+        model_type: ModelType = ModelType.UNDEFINED,
+    ) -> dict:
         """
         kwargs is the combined input and model_kwargs
         """
         if model_type == ModelType.LLM:
-            return self.sync_client.converse(**api_kwargs)
+            if "stream" in api_kwargs and api_kwargs.get("stream", False):
+                log.debug("Streaming call")
+                api_kwargs.pop(
+                    "stream", None
+                )  # stream is not a valid parameter for bedrock
+                self.chat_completion_parser = self.handle_stream_response
+                return self.sync_client.converse_stream(**api_kwargs)
+            else:
+                api_kwargs.pop("stream", None)
+                return self.sync_client.converse(**api_kwargs)
         else:
             raise ValueError(f"model_type {model_type} is not supported")
 

adalflow/adalflow/components/model_client/openai_client.py

@@ -361,6 +361,7 @@ def convert_inputs_to_api_kwargs(
 
             else:
                 raise ValueError(f"Invalid operation: {operation}")
+
         else:
             raise ValueError(f"model_type {self.model_type} is not supported")
         return final_model_kwargs
@@ -471,6 +472,7 @@ async def acall(
                     api_kwargs["image"].close()
                 if "mask" in api_kwargs and hasattr(api_kwargs["mask"], "close"):
                     api_kwargs["mask"].close()
+
         else:
             raise ValueError(f"model_type {model_type} is not supported")