Merge pull request #110 from logspace-ai/memory_fixes

Makefile

@@ -41,13 +41,13 @@ build:
 
 dev:
 	make install_frontend
-	ifeq ($(build),1)
+ifeq ($(build),1)
 		@echo 'Running docker compose up with build'
 		docker compose up $(if $(debug),-f docker-compose.debug.yml) --build
-	else
+else
 		@echo 'Running docker compose up without build'
 		docker compose up $(if $(debug),-f docker-compose.debug.yml)
-	endif
+endif
 
 publish:
 	make build

src/backend/langflow/config.yaml

@@ -46,6 +46,7 @@ toolkits:
 memories:
   - ConversationBufferMemory
   - ConversationSummaryMemory
+  - ConversationKGMemory
 
 embeddings: []
 

src/backend/langflow/graph/graph.py

@@ -6,6 +6,7 @@ from langflow.graph.nodes import (
     ChainNode,
     FileToolNode,
     LLMNode,
+    MemoryNode,
     PromptNode,
     ToolkitNode,
     ToolNode,
@@ -20,6 +21,7 @@ from langflow.interface.tools.base import tool_creator
 from langflow.interface.tools.constants import FILE_TOOLS
 from langflow.interface.tools.util import get_tools_dict
 from langflow.interface.wrappers.base import wrapper_creator
+from langflow.interface.memories.base import memory_creator
 from langflow.utils import payload
 
 
@@ -65,6 +67,8 @@ class Graph:
     def build(self) -> List[Node]:
         # Get root node
         root_node = payload.get_root_node(self)
+        if root_node is None:
+            raise ValueError("No root node found")
         return root_node.build()
 
     def get_node_neighbors(self, node: Node) -> Dict[Node, int]:
@@ -130,6 +134,11 @@ class Graph:
                 or node_lc_type in llm_creator.to_list()
             ):
                 nodes.append(LLMNode(node))
+            elif (
+                node_type in memory_creator.to_list()
+                or node_lc_type in memory_creator.to_list()
+            ):
+                nodes.append(MemoryNode(node))
             else:
                 nodes.append(Node(node))
         return nodes

src/backend/langflow/graph/nodes.py

@@ -144,3 +144,13 @@ class WrapperNode(Node):
                 self.params["headers"] = eval(self.params["headers"])
             self._build()
         return deepcopy(self._built_object)
+
+
+class MemoryNode(Node):
+    def __init__(self, data: Dict):
+        super().__init__(data, base_type="memory")
+
+    def build(self, force: bool = False) -> Any:
+        if not self._built or force:
+            self._build()
+        return deepcopy(self._built_object)

src/backend/langflow/graph/utils.py

@@ -26,7 +26,14 @@ def load_file(file_name, file_content, accepted_types) -> Any:
         return json.loads(decoded_string)
     elif suffix in ["yaml", "yml"]:
         # Return the yaml content
-        return yaml.safe_load(decoded_string)
+        loaded_yaml = yaml.load(decoded_string, Loader=yaml.FullLoader)
+        try:
+            from langchain.agents.agent_toolkits.openapi.spec import reduce_openapi_spec  # type: ignore
+
+            return reduce_openapi_spec(loaded_yaml)
+        except ImportError:
+            return loaded_yaml
+
     elif suffix == "csv":
         # Load the csv content
         csv_reader = csv.DictReader(io.StringIO(decoded_string))

src/backend/langflow/interface/importing/utils.py

@@ -29,6 +29,8 @@ def import_module(module_path: str) -> Any:
 
 def import_by_type(_type: str, name: str) -> Any:
     """Import class by type and name"""
+    if _type is None:
+        raise ValueError(f"Type cannot be None. Check if {name} is in the config file.")
     func_dict = {
         "agents": import_agent,
         "prompts": import_prompt,

src/backend/langflow/interface/run.py

@@ -72,41 +72,56 @@ def process_graph(data_graph: Dict[str, Any]):
     return {"result": str(result), "thought": thought.strip()}
 
 
-def fix_memory_inputs(langchain_object):
+def get_memory_key(langchain_object):
     """
-    Fix memory inputs by replacing the memory key with the input key.
+    Given a LangChain object, this function retrieves the current memory key from the object's memory attribute.
+    It then checks if the key exists in a dictionary of known memory keys and returns the corresponding key,
+    or None if the current key is not recognized.
     """
-    # Possible memory keys
-    # "chat_history", "history"
-    # if memory_key is "chat_history" and input_keys has "history"
-    # we need to replace "chat_history" with "history"
     mem_key_dict = {
         "chat_history": "history",
         "history": "chat_history",
     }
     memory_key = langchain_object.memory.memory_key
-    possible_new_mem_key = mem_key_dict.get(memory_key)
+    return mem_key_dict.get(memory_key)
-    if possible_new_mem_key is not None:
-        # get input_key
-        input_key = [
-            key
-            for key in langchain_object.input_keys
-            if key not in [memory_key, possible_new_mem_key]
-        ][0]
 
-        # get output_key
-        output_key = [
-            key
-            for key in langchain_object.output_keys
-            if key not in [memory_key, possible_new_mem_key]
-        ][0]
 
-        # set input_key and output_key in memory
+def update_memory_keys(langchain_object, possible_new_mem_key):
-        langchain_object.memory.input_key = input_key
+    """
-        langchain_object.memory.output_key = output_key
+    Given a LangChain object and a possible new memory key, this function updates the input and output keys in the
-        for input_key in langchain_object.input_keys:
+    object's memory attribute to exclude the current memory key and the possible new key. It then sets the memory key
-            if input_key == possible_new_mem_key:
+    to the possible new key.
-                langchain_object.memory.memory_key = possible_new_mem_key
+    """
+    input_key = [
+        key
+        for key in langchain_object.input_keys
+        if key not in [langchain_object.memory.memory_key, possible_new_mem_key]
+    ][0]
+
+    output_key = [
+        key
+        for key in langchain_object.output_keys
+        if key not in [langchain_object.memory.memory_key, possible_new_mem_key]
+    ][0]
+
+    langchain_object.memory.input_key = input_key
+    langchain_object.memory.output_key = output_key
+    langchain_object.memory.memory_key = possible_new_mem_key
+
+
+def fix_memory_inputs(langchain_object):
+    """
+    Given a LangChain object, this function checks if it has a memory attribute and if that memory key exists in the
+    object's input variables. If so, it does nothing. Otherwise, it gets a possible new memory key using the
+    get_memory_key function and updates the memory keys using the update_memory_keys function.
+    """
+    if hasattr(langchain_object, "memory") and langchain_object.memory is not None:
+        if langchain_object.memory.memory_key in langchain_object.input_variables:
+            return
+
+        possible_new_mem_key = get_memory_key(langchain_object)
+        if possible_new_mem_key is not None:
+            update_memory_keys(langchain_object, possible_new_mem_key)
 
 
 def get_result_and_thought_using_graph(langchain_object, message: str):
@@ -114,27 +129,24 @@ def get_result_and_thought_using_graph(langchain_object, message: str):
     try:
         if hasattr(langchain_object, "verbose"):
             langchain_object.verbose = True
+        chat_input = None
+        memory_key = ""
+        if hasattr(langchain_object, "memory") and langchain_object.memory is not None:
+            memory_key = langchain_object.memory.memory_key
+
+        for key in langchain_object.input_keys:
+            if key not in [memory_key, "chat_history"]:
+                chat_input = {key: message}
+
+        if hasattr(langchain_object, "return_intermediate_steps"):
+            # https://github.com/hwchase17/langchain/issues/2068
+            # Deactivating until we have a frontend solution
+            # to display intermediate steps
+            langchain_object.return_intermediate_steps = False
+
+        fix_memory_inputs(langchain_object)
+
         with io.StringIO() as output_buffer, contextlib.redirect_stdout(output_buffer):
-            chat_input = None
-            memory_key = ""
-            if (
-                hasattr(langchain_object, "memory")
-                and langchain_object.memory is not None
-            ):
-                memory_key = langchain_object.memory.memory_key
-
-            for key in langchain_object.input_keys:
-                if key not in [memory_key, "chat_history"]:
-                    chat_input = {key: message}
-
-            if hasattr(langchain_object, "return_intermediate_steps"):
-                # https://github.com/hwchase17/langchain/issues/2068
-                # Deactivating until we have a frontend solution
-                # to display intermediate steps
-                langchain_object.return_intermediate_steps = False
-
-            fix_memory_inputs(langchain_object)
-
             try:
                 output = langchain_object(chat_input)
             except ValueError as exc: