🔀 refactor(customs.py): add load_qa_chain to CUSTOM_NODES

🔀 refactor(custom_lists.py): merge CUSTOM_AGENTS and CUSTOM_CHAINS into CUSTOM_NODES
🔀 refactor(loading.py): add instantiate_chains function to instantiate chains
🔀 refactor(base.py): add CustomChain class
The changes add support for a new chain called load_qa_chain to CUSTOM_NODES. CUSTOM_AGENTS and CUSTOM_CHAINS are merged into CUSTOM_NODES. A new function called instantiate_chains is added to instantiate chains. A new class called CustomChain is added to the base.py file. This class is used to define a custom chain.

src/backend/langflow/custom/customs.py

@@ -2,7 +2,9 @@ from langflow.template import frontend_node
 
 # These should always be instantiated
 CUSTOM_NODES = {
-    "prompts": {"ZeroShotPrompt": frontend_node.prompts.ZeroShotPromptNode()},
+    "prompts": {
+        "ZeroShotPrompt": frontend_node.prompts.ZeroShotPromptNode(),
+    },
     "tools": {
         "PythonFunctionTool": frontend_node.tools.PythonFunctionToolNode(),
         "PythonFunction": frontend_node.tools.PythonFunctionNode(),
@@ -23,6 +25,7 @@ CUSTOM_NODES = {
         "SeriesCharacterChain": frontend_node.chains.SeriesCharacterChainNode(),
         "TimeTravelGuideChain": frontend_node.chains.TimeTravelGuideChainNode(),
         "MidJourneyPromptChain": frontend_node.chains.MidJourneyPromptChainNode(),
+        "load_qa_chain": frontend_node.chains.CombineDocsChainNode(),
     },
 }
 

src/backend/langflow/interface/base.py

@@ -1,5 +1,6 @@
 from abc import ABC, abstractmethod
 from typing import Any, Dict, List, Optional, Type, Union
+from langchain.chains.base import Chain
 
 from pydantic import BaseModel
 
@@ -81,5 +82,24 @@ class LangChainTypeCreator(BaseModel, ABC):
             )
 
         signature.add_extra_fields()
+        signature.add_extra_base_classes()
 
         return signature
+
+
+class CustomChain(Chain, ABC):
+    """Custom chain"""
+
+    @staticmethod
+    def function_name():
+        return "CustomChain"
+
+    @classmethod
+    def initialize(cls, *args, **kwargs):
+        pass
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def run(self, *args, **kwargs):
+        return super().run(*args, **kwargs)

src/backend/langflow/interface/custom_lists.py

@@ -2,7 +2,6 @@ import inspect
 from typing import Any
 
 from langchain import (
-    chains,
     document_loaders,
     embeddings,
     llms,
@@ -15,6 +14,8 @@ from langchain.chat_models import AzureChatOpenAI, ChatOpenAI
 from langchain.chat_models import ChatAnthropic
 
 from langflow.interface.importing.utils import import_class
+from langflow.interface.agents.custom import CUSTOM_AGENTS
+from langflow.interface.chains.custom import CUSTOM_CHAINS
 
 ## LLMs
 llm_type_to_cls_dict = llms.type_to_cls_dict
@@ -22,11 +23,6 @@ llm_type_to_cls_dict["anthropic-chat"] = ChatAnthropic  # type: ignore
 llm_type_to_cls_dict["azure-chat"] = AzureChatOpenAI  # type: ignore
 llm_type_to_cls_dict["openai-chat"] = ChatOpenAI  # type: ignore
 
-## Chains
-chain_type_to_cls_dict: dict[str, Any] = {
-    chain_name: import_class(f"langchain.chains.{chain_name}")
-    for chain_name in chains.__all__
-}
 
 ## Toolkits
 toolkit_type_to_loader_dict: dict[str, Any] = {
@@ -73,3 +69,6 @@ documentloaders_type_to_cls_dict: dict[str, Any] = {
 textsplitter_type_to_cls_dict: dict[str, Any] = dict(
     inspect.getmembers(text_splitter, inspect.isclass)
 )
+
+# merge CUSTOM_AGENTS and CUSTOM_CHAINS
+CUSTOM_NODES = {**CUSTOM_AGENTS, **CUSTOM_CHAINS}

src/backend/langflow/interface/loading.py

@@ -19,9 +19,10 @@ from langchain.chains.loading import load_chain_from_config
 from langchain.llms.loading import load_llm_from_config
 from pydantic import ValidationError
 
-from langflow.interface.agents.custom import CUSTOM_AGENTS
+from langflow.interface.custom_lists import CUSTOM_NODES
 from langflow.interface.importing.utils import get_function, import_by_type
 from langflow.interface.toolkits.base import toolkits_creator
+from langflow.interface.chains.base import chain_creator
 from langflow.interface.types import get_type_list
 from langflow.interface.utils import load_file_into_dict
 from langflow.utils import util, validate
@@ -31,8 +32,8 @@ def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
     """Instantiate class from module type and key, and params"""
     params = convert_params_to_sets(params)
     params = convert_kwargs(params)
-    if node_type in CUSTOM_AGENTS:
-        if custom_agent := CUSTOM_AGENTS.get(node_type):
+    if node_type in CUSTOM_NODES:
+        if custom_agent := CUSTOM_NODES.get(node_type):
             return custom_agent.initialize(**params)
 
     class_object = import_by_type(_type=base_type, name=node_type)
@@ -77,10 +78,24 @@ def instantiate_based_on_type(class_object, base_type, node_type, params):
         return instantiate_textsplitter(class_object, params)
     elif base_type == "utilities":
         return instantiate_utility(node_type, class_object, params)
+    elif base_type == "chains":
+        return instantiate_chains(node_type, class_object, params)
     else:
         return class_object(**params)
 
 
+def instantiate_chains(node_type, class_object, params):
+    if "retriever" in params and hasattr(params["retriever"], "as_retriever"):
+        params["retriever"] = params["retriever"].as_retriever()
+    if node_type in chain_creator.from_method_nodes:
+        method = chain_creator.from_method_nodes[node_type]
+        if class_method := getattr(class_object, method, None):
+            return class_method(**params)
+        raise ValueError(f"Method {method} not found in {class_object}")
+
+    return class_object(**params)
+
+
 def instantiate_agent(class_object, params):
     return load_agent_executor(class_object, params)
 
@@ -141,6 +156,14 @@ def instantiate_vectorstore(class_object, params):
             "The source you provided did not load correctly or was empty."
             "This may cause an error in the vectorstore."
         )
+    # Chroma requires all metadata values to not be None
+    if class_object.__name__ == "Chroma":
+        for doc in params["documents"]:
+            if doc.metadata is None:
+                doc.metadata = {}
+            for key, value in doc.metadata.items():
+                if value is None:
+                    doc.metadata[key] = ""
     return class_object.from_documents(**params)