🚀 feat(langflow): add new chains to config.yaml and custom chains to interface/chains/custom.py

✨ feat(langflow): add new chains to config.yaml and custom chains to interface/chains/custom.py
The following chains were added to the config.yaml file: RetrievalQA, RetrievalQAWithSourcesChain, QAWithSourcesChain, ConversationalRetrievalChain, and CombineDocsChain. These chains were added to improve the functionality of the application and provide more options for users.

In addition, custom chains were added to the interface/chains/custom.py file. The CombineDocsChain was added to allow users to combine multiple documents into a single document for use in the question answering chains. The QA_CHAIN_TYPES constant was also added to the frontend_node/constants.py file to provide a list of available question answering chain types.

src/backend/langflow/config.yaml

@@ -16,6 +16,11 @@ chains:
   - MidJourneyPromptChain
   - TimeTravelGuideChain
   - SQLDatabaseChain
+  - RetrievalQA
+  - RetrievalQAWithSourcesChain
+  - QAWithSourcesChain
+  - ConversationalRetrievalChain
+  - CombineDocsChain
 documentloaders:
   - AirbyteJSONLoader
   - CoNLLULoader

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

@@ -1,4 +1,3 @@
-from abc import ABC
 from typing import Any, List, Optional
 
 from langchain import LLMChain
@@ -33,27 +32,10 @@ from langchain.memory.chat_memory import BaseChatMemory
 from langchain.sql_database import SQLDatabase
 from langchain.tools.python.tool import PythonAstREPLTool
 from langchain.tools.sql_database.prompt import QUERY_CHECKER
+from langflow.interface.base import CustomChain
 
 
-class CustomAgentExecutor(AgentExecutor, ABC):
-    """Custom agent executor"""
-
-    @staticmethod
-    def function_name():
-        return "CustomAgentExecutor"
-
-    @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)
-
-
-class JsonAgent(CustomAgentExecutor):
+class JsonAgent(CustomChain):
     """Json agent"""
 
     @staticmethod
@@ -89,7 +71,7 @@ class JsonAgent(CustomAgentExecutor):
         return super().run(*args, **kwargs)
 
 
-class CSVAgent(CustomAgentExecutor):
+class CSVAgent(CustomChain):
     """CSV agent"""
 
     @staticmethod
@@ -137,7 +119,7 @@ class CSVAgent(CustomAgentExecutor):
         return super().run(*args, **kwargs)
 
 
-class VectorStoreAgent(CustomAgentExecutor):
+class VectorStoreAgent(CustomChain):
     """Vector Store agent"""
 
     @staticmethod
@@ -175,7 +157,7 @@ class VectorStoreAgent(CustomAgentExecutor):
         return super().run(*args, **kwargs)
 
 
-class SQLAgent(CustomAgentExecutor):
+class SQLAgent(CustomChain):
     """SQL agent"""
 
     @staticmethod
@@ -247,7 +229,7 @@ class SQLAgent(CustomAgentExecutor):
         return super().run(*args, **kwargs)
 
 
-class VectorStoreRouterAgent(CustomAgentExecutor):
+class VectorStoreRouterAgent(CustomChain):
     """Vector Store Router Agent"""
 
     @staticmethod
@@ -286,7 +268,7 @@ class VectorStoreRouterAgent(CustomAgentExecutor):
         return super().run(*args, **kwargs)
 
 
-class InitializeAgent(CustomAgentExecutor):
+class InitializeAgent(CustomChain):
     """Implementation of initialize_agent function"""
 
     @staticmethod

src/backend/langflow/interface/chains/base.py

@@ -1,12 +1,13 @@
-from typing import Dict, List, Optional, Type
+from typing import Any, Dict, List, Optional, Type
 
 from langflow.custom.customs import get_custom_nodes
 from langflow.interface.base import LangChainTypeCreator
-from langflow.interface.custom_lists import chain_type_to_cls_dict
+from langflow.interface.importing.utils import import_class
 from langflow.settings import settings
 from langflow.template.frontend_node.chains import ChainFrontendNode
 from langflow.utils.logger import logger
-from langflow.utils.util import build_template_from_class
+from langflow.utils.util import build_template_from_class, build_template_from_method
+from langchain import chains
 
 # Assuming necessary imports for Field, Template, and FrontendNode classes
 
@@ -18,10 +19,16 @@ class ChainCreator(LangChainTypeCreator):
     def frontend_node_class(self) -> Type[ChainFrontendNode]:
         return ChainFrontendNode
 
+    #! We need to find a better solution for this
+    from_method_nodes = {"ConversationalRetrievalChain": "from_llm"}
+
     @property
     def type_to_loader_dict(self) -> Dict:
         if self.type_dict is None:
-            self.type_dict = chain_type_to_cls_dict
+            self.type_dict: dict[str, Any] = {
+                chain_name: import_class(f"langchain.chains.{chain_name}")
+                for chain_name in chains.__all__
+            }
             from langflow.interface.chains.custom import CUSTOM_CHAINS
 
             self.type_dict.update(CUSTOM_CHAINS)
@@ -37,20 +44,38 @@ class ChainCreator(LangChainTypeCreator):
         try:
             if name in get_custom_nodes(self.type_name).keys():
                 return get_custom_nodes(self.type_name)[name]
+            elif name in self.from_method_nodes.keys():
+                return build_template_from_method(
+                    name,
+                    type_to_cls_dict=self.type_to_loader_dict,
+                    method_name=self.from_method_nodes[name],
+                    add_function=True,
+                )
             return build_template_from_class(
                 name, self.type_to_loader_dict, add_function=True
             )
         except ValueError as exc:
-            raise ValueError("Chain not found") from exc
+            raise ValueError(f"Chain {name} not found: {exc}") from exc
         except AttributeError as exc:
             logger.error(f"Chain {name} not loaded: {exc}")
             return None
 
-    def to_list(self) -> List[str]:
-        custom_chains = list(get_custom_nodes("chains").keys())
-        default_chains = list(self.type_to_loader_dict.keys())
+    # def to_list(self) -> List[str]:
+    #     custom_chains = list(get_custom_nodes("chains").keys())
+    #     default_chains = list(self.type_to_loader_dict.keys())
 
-        return default_chains + custom_chains
+    #     return default_chains + custom_chains
+
+    def to_list(self) -> List[str]:
+        names = []
+        for _, chain in self.type_to_loader_dict.items():
+            chain_name = (
+                chain.function_name()
+                if hasattr(chain, "function_name")
+                else chain.__name__
+            )
+            names.append(chain_name)
+        return names
 
 
 chain_creator = ChainCreator()

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

@@ -1,11 +1,13 @@
-from typing import Dict, Optional, Type
+from typing import Dict, Optional, Type, Union
 
 from langchain.chains import ConversationChain
 from langchain.memory.buffer import ConversationBufferMemory
 from langchain.schema import BaseMemory
+from langflow.interface.base import CustomChain
 from pydantic import Field, root_validator
-
+from langchain.chains.question_answering import load_qa_chain
 from langflow.interface.utils import extract_input_variables_from_prompt
+from langchain.base_language import BaseLanguageModel
 
 DEFAULT_SUFFIX = """"
 Current conversation:
@@ -14,7 +16,7 @@ Human: {input}
 {ai_prefix}"""
 
 
-class BaseCustomChain(ConversationChain):
+class BaseCustomConversationChain(ConversationChain):
     """BaseCustomChain is a chain you can use to have a conversation with a custom character."""
 
     template: Optional[str]
@@ -47,7 +49,7 @@ class BaseCustomChain(ConversationChain):
         return values
 
 
-class SeriesCharacterChain(BaseCustomChain):
+class SeriesCharacterChain(BaseCustomConversationChain):
     """SeriesCharacterChain is a chain you can use to have a conversation with a character from a series."""
 
     character: str
@@ -66,7 +68,7 @@ Human: {input}
     """Default memory store."""
 
 
-class MidJourneyPromptChain(BaseCustomChain):
+class MidJourneyPromptChain(BaseCustomConversationChain):
     """MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts."""
 
     template: Optional[
@@ -84,7 +86,7 @@ class MidJourneyPromptChain(BaseCustomChain):
     AI:"""  # noqa: E501
 
 
-class TimeTravelGuideChain(BaseCustomChain):
+class TimeTravelGuideChain(BaseCustomConversationChain):
     template: Optional[
         str
     ] = """I want you to act as my time travel guide. You are helpful and creative. I will provide you with the historical period or future time I want to visit and you will suggest the best events, sights, or people to experience. Provide the suggestions and any necessary information.
@@ -94,7 +96,26 @@ class TimeTravelGuideChain(BaseCustomChain):
     AI:"""  # noqa: E501
 
 
-CUSTOM_CHAINS: Dict[str, Type[ConversationChain]] = {
+class CombineDocsChain(CustomChain):
+    """Implementation of initialize_agent function"""
+
+    @staticmethod
+    def function_name():
+        return "load_qa_chain"
+
+    @classmethod
+    def initialize(cls, llm: BaseLanguageModel, chain_type: str):
+        return load_qa_chain(llm=llm, chain_type=chain_type)
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def run(self, *args, **kwargs):
+        return super().run(*args, **kwargs)
+
+
+CUSTOM_CHAINS: Dict[str, Type[Union[ConversationChain, CustomChain]]] = {
+    "CombineDocsChain": CombineDocsChain,
     "SeriesCharacterChain": SeriesCharacterChain,
     "MidJourneyPromptChain": MidJourneyPromptChain,
     "TimeTravelGuideChain": TimeTravelGuideChain,

src/backend/langflow/template/frontend_node/constants.py

@@ -30,3 +30,5 @@ You are a good listener and you can talk about anything.
 """
 
 HUMAN_PROMPT = "{input}"
+
+QA_CHAIN_TYPES = ["stuff", "map_reduce", "map_rerank", "refine"]