Fixes a bug in fix_memory_keys, Adds OpenAI ConversationalAgent (#753)

poetry.lock

@@ -253,13 +253,13 @@ test = ["astroid", "pytest"]
 
 [[package]]
 name = "async-timeout"
-version = "4.0.2"
+version = "4.0.3"
 description = "Timeout context manager for asyncio programs"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "async-timeout-4.0.2.tar.gz", hash = "sha256:2163e1640ddb52b7a8c80d0a67a08587e5d245cc9c553a74a847056bc2976b15"},
+    {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"},
-    {file = "async_timeout-4.0.2-py3-none-any.whl", hash = "sha256:8ca1e4fcf50d07413d66d1a5e416e42cfdf5851c981d679a09851a6853383b3c"},
+    {file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"},
 ]
 
 [[package]]
@@ -739,13 +739,13 @@ sqlalchemy = ["sqlalchemy (>1.3.21,<2.0)"]
 
 [[package]]
 name = "cohere"
-version = "4.19.2"
+version = "4.19.3"
 description = ""
 optional = false
 python-versions = ">=3.7,<4.0"
 files = [
-    {file = "cohere-4.19.2-py3-none-any.whl", hash = "sha256:0b6a4fe04380a481a8e975ebcc9bb6433febe4d3eb583b6d6e04342a5e998345"},
+    {file = "cohere-4.19.3-py3-none-any.whl", hash = "sha256:6c98f1e58b93b6316c824385c1d2032ed352280e9efa5695ba98306258abf84f"},
-    {file = "cohere-4.19.2.tar.gz", hash = "sha256:a0b0fa698b3d3983fb328bb90d68fcf08faaa2268f3772ebc6bfea6ba55acf27"},
+    {file = "cohere-4.19.3.tar.gz", hash = "sha256:c3aaa716c4da7d7a8ed68705fcdc92f1b1a2260b737cee6bd27af5c347f31496"},
 ]
 
 [package.dependencies]
@@ -1644,13 +1644,13 @@ six = "*"
 
 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.29.0"
+version = "1.30.0"
 description = "Vertex AI API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-aiplatform-1.29.0.tar.gz", hash = "sha256:fceabb924d2d26057e3c8c5c2e251929389aa6d553361377bc402781150c0db3"},
+    {file = "google-cloud-aiplatform-1.30.0.tar.gz", hash = "sha256:26c16069553d177c3277a4371279871c38e31eab1134906cf83ea905e4203ec4"},
-    {file = "google_cloud_aiplatform-1.29.0-py2.py3-none-any.whl", hash = "sha256:cf81c1d93c61ccf3df60a65e3a5a1e465e044059d36b6fc1202b940c46c4c1e1"},
+    {file = "google_cloud_aiplatform-1.30.0-py2.py3-none-any.whl", hash = "sha256:8a5d47378babb491cf4737e4f1951289b33b9254bed1e3d8c07be4896cae83ce"},
 ]
 
 [package.dependencies]

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langflow"
-version = "0.4.7"
+version = "0.4.8"
 description = "A Python package with a built-in web application"
 authors = ["Logspace <contact@logspace.ai>"]
 maintainers = [

src/backend/langflow/components/agents/OpenAIConversationalAgent.py

@@ -0,0 +1,76 @@
+from langflow import CustomComponent
+from typing import Optional
+from langchain.prompts import SystemMessagePromptTemplate
+from langchain.tools import Tool
+from langchain.schema.memory import BaseMemory
+from langchain.chat_models import ChatOpenAI
+
+from langchain.agents.agent import AgentExecutor
+from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
+from langchain.memory.token_buffer import ConversationTokenBufferMemory
+from langchain.prompts.chat import MessagesPlaceholder
+from langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import (
+    _get_default_system_message,
+)
+
+
+class ConversationalAgent(CustomComponent):
+    display_name: str = "OpenaAI Conversational Agent"
+    description: str = "Conversational Agent that can use OpenAI's function calling API"
+
+    def build_config(self):
+        openai_function_models = [
+            "gpt-3.5-turbo-0613",
+            "gpt-3.5-turbo-16k-0613",
+            "gpt-4-0613",
+            "gpt-4-32k-0613",
+        ]
+        return {
+            "tools": {"is_list": True, "display_name": "Tools"},
+            "memory": {"display_name": "Memory"},
+            "system_message": {"display_name": "System Message"},
+            "max_token_limit": {"display_name": "Max Token Limit"},
+            "model_name": {
+                "display_name": "Model Name",
+                "options": openai_function_models,
+                "value": openai_function_models[0],
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        model_name: str,
+        tools: Tool,
+        memory: Optional[BaseMemory] = None,
+        system_message: Optional[SystemMessagePromptTemplate] = None,
+        max_token_limit: int = 2000,
+    ) -> AgentExecutor:
+        llm = ChatOpenAI(model=model_name)
+        if not memory:
+            memory_key = "chat_history"
+            memory = ConversationTokenBufferMemory(
+                memory_key=memory_key,
+                return_messages=True,
+                output_key="output",
+                llm=llm,
+                max_token_limit=max_token_limit,
+            )
+        else:
+            memory_key = memory.memory_key  # type: ignore
+
+        _system_message = system_message or _get_default_system_message()
+        prompt = OpenAIFunctionsAgent.create_prompt(
+            system_message=_system_message,  # type: ignore
+            extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],
+        )
+        agent = OpenAIFunctionsAgent(
+            llm=llm, tools=tools, prompt=prompt  # type: ignore
+        )
+        return AgentExecutor(
+            agent=agent,
+            tools=tools,  # type: ignore
+            memory=memory,
+            verbose=True,
+            return_intermediate_steps=True,
+        )

src/backend/langflow/graph/utils.py

@@ -3,6 +3,10 @@ from typing import Any, Union
 from langflow.interface.utils import extract_input_variables_from_prompt
 
 
+class UnbuiltObject:
+    pass
+
+
 def validate_prompt(prompt: str):
     """Validate prompt."""
     if extract_input_variables_from_prompt(prompt):

src/backend/langflow/graph/vertex/base.py

@@ -1,4 +1,5 @@
 import ast
+from langflow.graph.utils import UnbuiltObject
 from langflow.interface.initialize import loading
 from langflow.interface.listing import ALL_TYPES_DICT
 from langflow.utils.constants import DIRECT_TYPES
@@ -22,7 +23,7 @@ class Vertex:
         self.edges: List["Edge"] = []
         self.base_type: Optional[str] = base_type
         self._parse_data()
-        self._built_object = None
+        self._built_object = UnbuiltObject()
         self._built = False
         self.artifacts: Dict[str, Any] = {}
 
@@ -245,8 +246,14 @@ class Vertex:
         """
         Checks if the built object is None and raises a ValueError if so.
         """
-        if self._built_object is None:
+        if isinstance(self._built_object, UnbuiltObject):
-            raise ValueError(f"Node type {self.vertex_type} not found")
+            raise ValueError(f"{self.vertex_type}: {self._built_object_repr()}")
+        elif self._built_object is None:
+            message = f"{self.vertex_type} returned None."
+            if self.base_type == "custom_components":
+                message += " Make sure your build method returns a component."
+
+            raise ValueError(message)
 
     def build(self, force: bool = False) -> Any:
         if not self._built or force:

src/backend/langflow/interface/custom/constants.py

@@ -8,10 +8,13 @@ from langchain.text_splitter import TextSplitter
 from langchain.tools import Tool
 from langchain.vectorstores.base import VectorStore
 from langchain.schema import BaseOutputParser
-
+from langchain.schema.memory import BaseMemory
+from langchain.memory.chat_memory import BaseChatMemory
+from langchain.agents.agent import AgentExecutor
 
 LANGCHAIN_BASE_TYPES = {
     "Chain": Chain,
+    "AgentExecutor": AgentExecutor,
     "Tool": Tool,
     "BaseLLM": BaseLLM,
     "PromptTemplate": PromptTemplate,
@@ -22,6 +25,8 @@ LANGCHAIN_BASE_TYPES = {
     "Embeddings": Embeddings,
     "BaseRetriever": BaseRetriever,
     "BaseOutputParser": BaseOutputParser,
+    "BaseMemory": BaseMemory,
+    "BaseChatMemory": BaseChatMemory,
 }
 
 # Langchain base types plus Python base types

src/backend/langflow/interface/custom/custom_component.py

@@ -50,8 +50,8 @@ class CustomComponent(Component, extra=Extra.allow):
 
         for type_hint in TYPE_HINT_LIST:
             if reader._is_type_hint_used_in_args(
-                "Optional", code
+                type_hint, code
-            ) and not reader._is_type_hint_imported("Optional", code):
+            ) and not reader._is_type_hint_imported(type_hint, code):
                 error_detail = {
                     "error": "Type hint Error",
                     "traceback": f"Type hint '{type_hint}' is used but not imported in the code.",

src/backend/langflow/processing/base.py

@@ -5,6 +5,7 @@ from langflow.api.v1.callback import (
 )
 from langflow.processing.process import fix_memory_inputs, format_actions
 from langflow.utils.logger import logger
+from langchain.agents.agent import AgentExecutor
 
 
 async def get_result_and_steps(langchain_object, inputs: Union[dict, str], **kwargs):
@@ -20,7 +21,8 @@ async def get_result_and_steps(langchain_object, inputs: Union[dict, str], **kwa
             # to display intermediate steps
             langchain_object.return_intermediate_steps = True
         try:
-            fix_memory_inputs(langchain_object)
+            if not isinstance(langchain_object, AgentExecutor):
+                fix_memory_inputs(langchain_object)
         except Exception as exc:
             logger.error(f"Error fixing memory inputs: {exc}")
 

src/frontend/src/components/ui/popover.tsx

@@ -27,4 +27,4 @@ const PopoverContent = React.forwardRef<
 ));
 PopoverContent.displayName = PopoverPrimitive.Content.displayName;
 
-export { Popover, PopoverTrigger, PopoverContent };
+export { Popover, PopoverContent, PopoverTrigger };