Add New Chains: RetrievalQA, RetrievalQAWithSourcesChain, Conversatio… (#488)

2023-06-15 09:46:28 -03:00 · 2023-06-15 09:46:28 -03:00 · 327ad83994
commit 327ad83994
parent 5e99ba0e99 cfbcedcf5f
22 changed files with 1015 additions and 293 deletions
--- a/poetry.lock
+++ b/poetry.lock
@ -614,77 +614,77 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 [[package]]
 name = "clickhouse-connect"
-version = "0.6.1"
+version = "0.6.2"
 description = "ClickHouse Database Core Driver for Python, Pandas, and Superset"
 category = "main"
 optional = false
 python-versions = "~=3.7"
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 [[package]]
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+version = "0.0.44"
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+    {file = "SQLAlchemy-2.0.16-cp39-cp39-win_amd64.whl", hash = "sha256:43e69c8c1cea0188b7094e22fb93ae1a1890aac748628b7e925024a206f75368"},
-    {file = "SQLAlchemy-2.0.15-py3-none-any.whl", hash = "sha256:933d30273861fe61f014ce2a7e3c364915f5efe9ed250ec1066ca6ea5942c0bd"},
+    {file = "SQLAlchemy-2.0.16-py3-none-any.whl", hash = "sha256:53081c6fce0d49bb36d05f12dc87e008c9b0df58a163b792c5fc4ac638925f98"},
-    {file = "SQLAlchemy-2.0.15.tar.gz", hash = "sha256:2e940a8659ef870ae10e0d9e2a6d5aaddf0ff6e91f7d0d7732afc9e8c4be9bbc"},
+    {file = "SQLAlchemy-2.0.16.tar.gz", hash = "sha256:1e2caba78e7d1f5003e88817b7a1754d4e58f4a8f956dc423bf8e304c568ab09"},
 ]
 [package.dependencies]
@ -5092,6 +5099,7 @@ postgresql-pg8000 = ["pg8000 (>=1.29.1)"]
 postgresql-psycopg = ["psycopg (>=3.0.7)"]
 postgresql-psycopg2binary = ["psycopg2-binary"]
 postgresql-psycopg2cffi = ["psycopg2cffi"]
 postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
 sqlcipher = ["sqlcipher3-binary"]
@ -5860,19 +5868,19 @@ files = [
 [[package]]
 name = "weaviate-client"
-version = "3.19.2"
+version = "3.20.0"
-description = "A python native weaviate client"
+description = "A python native Weaviate client"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "weaviate-client-3.19.2.tar.gz", hash = "sha256:662cb2a5f6dacc2c9cdf6db2df70e9a3ac9d18b404d0c2ff971d9cb85d84ebed"},
+    {file = "weaviate-client-3.20.0.tar.gz", hash = "sha256:27c724596e9d3a4609dc7b7e9e362fc97dcfd9ba25f931c95a177bc60789be8e"},
-    {file = "weaviate_client-3.19.2-py3-none-any.whl", hash = "sha256:f4bbfb868907089f57fdfb836c4d00cf8a6fc5e296fa08879681ba1d2273cd40"},
+    {file = "weaviate_client-3.20.0-py3-none-any.whl", hash = "sha256:7559cb1500a5a22019a32cb55b162e204620179792cdfdd98b81e258c6ca7d96"},
 ]
 [package.dependencies]
 authlib = ">=1.1.0"
-requests = ">=2.28.0,<2.29.0"
+requests = ">=2.28.0,<=2.31.0"
 tqdm = ">=4.59.0,<5.0.0"
 validators = ">=0.18.2,<=0.21.0"
@ -5881,14 +5889,14 @@ grpc = ["grpcio", "grpcio-tools"]
 [[package]]
 name = "websocket-client"
-version = "1.5.2"
+version = "1.5.3"
 description = "WebSocket client for Python with low level API options"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "websocket-client-1.5.2.tar.gz", hash = "sha256:c7d67c13b928645f259d9b847ab5b57fd2d127213ca41ebd880de1f553b7c23b"},
+    {file = "websocket-client-1.5.3.tar.gz", hash = "sha256:b96f3bce3e54e3486ebe6504bc22bd4c140392bd2eb71764db29be8f2639aa65"},
-    {file = "websocket_client-1.5.2-py3-none-any.whl", hash = "sha256:f8c64e28cd700e7ba1f04350d66422b6833b82a796b525a51e740b8cc8dab4b1"},
+    {file = "websocket_client-1.5.3-py3-none-any.whl", hash = "sha256:3566f8467cd350874c4913816355642a4942f6c1ed1e9406e3d42fae6d6c072a"},
 ]
 [package.extras]
--- a/pyproject.toml
+++ b/pyproject.toml
@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langflow"
-version = "0.0.87"
+version = "0.0.89"
 description = "A Python package with a built-in web application"
 authors = ["Logspace <contact@logspace.ai>"]
 maintainers = [
--- a/src/backend/langflow/init.py
+++ b/src/backend/langflow/init.py
@ -1,4 +1,4 @@
 from langflow.cache import cache_manager
-from langflow.interface.loading import load_flow_from_json
+from langflow.processing.process import load_flow_from_json
 __all__ = ["load_flow_from_json", "cache_manager"]
--- a/src/backend/langflow/config.yaml
+++ b/src/backend/langflow/config.yaml
@ -3,7 +3,7 @@ agents:
  - ZeroShotAgent
  - JsonAgent
  - CSVAgent
-  - initialize_agent
+  - AgentInitializer
  - VectorStoreAgent
  - VectorStoreRouterAgent
  - SQLAgent
@ -16,6 +16,10 @@ chains:
  - MidJourneyPromptChain
  - TimeTravelGuideChain
  - SQLDatabaseChain
  - RetrievalQA
  - RetrievalQAWithSourcesChain
  - ConversationalRetrievalChain
  - CombineDocsChain
 documentloaders:
  - AirbyteJSONLoader
  - CoNLLULoader
--- a/src/backend/langflow/custom/customs.py
+++ b/src/backend/langflow/custom/customs.py
@ -10,7 +10,7 @@ CUSTOM_NODES = {
    "agents": {
        "JsonAgent": frontend_node.agents.JsonAgentNode(),
        "CSVAgent": frontend_node.agents.CSVAgentNode(),
-        "initialize_agent": frontend_node.agents.InitializeAgentNode(),
+        "AgentInitializer": frontend_node.agents.InitializeAgentNode(),
        "VectorStoreAgent": frontend_node.agents.VectorStoreAgentNode(),
        "VectorStoreRouterAgent": frontend_node.agents.VectorStoreRouterAgentNode(),
        "SQLAgent": frontend_node.agents.SQLAgentNode(),
@ -22,6 +22,7 @@ CUSTOM_NODES = {
        "SeriesCharacterChain": frontend_node.chains.SeriesCharacterChainNode(),
        "TimeTravelGuideChain": frontend_node.chains.TimeTravelGuideChainNode(),
        "MidJourneyPromptChain": frontend_node.chains.MidJourneyPromptChainNode(),
        "load_qa_chain": frontend_node.chains.CombineDocsChainNode(),
    },
 }
--- a/src/backend/langflow/interface/agents/custom.py
+++ b/src/backend/langflow/interface/agents/custom.py
@ -82,7 +82,9 @@ class JsonAgent(CustomAgentExecutor):
            llm=llm,
            prompt=prompt,
        )
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)  # type: ignore
+        agent = ZeroShotAgent(
            llm_chain=llm_chain, allowed_tools=tool_names  # type: ignore
        )
        return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
    def run(self, *args, **kwargs):
@ -129,7 +131,9 @@ class CSVAgent(CustomAgentExecutor):
            prompt=partial_prompt,
        )
        tool_names = {tool.name for tool in tools}
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)  # type: ignore
+        agent = ZeroShotAgent(
            llm_chain=llm_chain, allowed_tools=tool_names, **kwargs  # type: ignore
        )
        return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
@ -166,7 +170,9 @@ class VectorStoreAgent(CustomAgentExecutor):
            prompt=prompt,
        )
        tool_names = {tool.name for tool in tools}
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)  # type: ignore
+        agent = ZeroShotAgent(
            llm_chain=llm_chain, allowed_tools=tool_names, **kwargs  # type: ignore
        )
        return AgentExecutor.from_agent_and_tools(
            agent=agent, tools=tools, verbose=True
        )
@ -234,7 +240,9 @@ class SQLAgent(CustomAgentExecutor):
            prompt=prompt,
        )
        tool_names = {tool.name for tool in tools}  # type: ignore
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)  # type: ignore
+        agent = ZeroShotAgent(
            llm_chain=llm_chain, allowed_tools=tool_names, **kwargs  # type: ignore
        )
        return AgentExecutor.from_agent_and_tools(
            agent=agent,
            tools=tools,  # type: ignore
@ -277,7 +285,9 @@ class VectorStoreRouterAgent(CustomAgentExecutor):
            prompt=prompt,
        )
        tool_names = {tool.name for tool in tools}
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)  # type: ignore
+        agent = ZeroShotAgent(
            llm_chain=llm_chain, allowed_tools=tool_names, **kwargs  # type: ignore
        )
        return AgentExecutor.from_agent_and_tools(
            agent=agent, tools=tools, verbose=True
        )
@ -287,11 +297,11 @@ class VectorStoreRouterAgent(CustomAgentExecutor):
 class InitializeAgent(CustomAgentExecutor):
-    """Implementation of initialize_agent function"""
+    """Implementation of AgentInitializer function"""
    @staticmethod
    def function_name():
-        return "initialize_agent"
+        return "AgentInitializer"
    @classmethod
    def initialize(
@ -320,7 +330,7 @@ class InitializeAgent(CustomAgentExecutor):
 CUSTOM_AGENTS = {
    "JsonAgent": JsonAgent,
    "CSVAgent": CSVAgent,
-    "initialize_agent": InitializeAgent,
+    "AgentInitializer": InitializeAgent,
    "VectorStoreAgent": VectorStoreAgent,
    "VectorStoreRouterAgent": VectorStoreRouterAgent,
    "SQLAgent": SQLAgent,
--- a/src/backend/langflow/interface/base.py
+++ b/src/backend/langflow/interface/base.py
@ -1,6 +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
 from langflow.template.field.base import TemplateField
@ -81,5 +81,24 @@ 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)
--- a/src/backend/langflow/interface/chains/base.py
+++ b/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 +44,32 @@ 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())
+        names = []
-        default_chains = list(self.type_to_loader_dict.keys())
+        for _, chain in self.type_to_loader_dict.items():
-
+            chain_name = (
-        return default_chains + custom_chains
+                chain.function_name()
                if hasattr(chain, "function_name")
                else chain.__name__
            )
            names.append(chain_name)
        return names
 chain_creator = ChainCreator()
--- a/src/backend/langflow/interface/chains/custom.py
+++ b/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.graph.utils import extract_input_variables_from_prompt
+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 load_qa_chain 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,
--- a/src/backend/langflow/interface/custom_lists.py
+++ b/src/backend/langflow/interface/custom_lists.py
@ -14,18 +14,20 @@ from langchain.agents import agent_toolkits
 from langchain.chat_models import ChatOpenAI
 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
+# LLMs
 llm_type_to_cls_dict = llms.type_to_cls_dict
 llm_type_to_cls_dict["openai-chat"] = ChatOpenAI  # type: ignore
-## Chains
+# 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
+# Toolkits
 toolkit_type_to_loader_dict: dict[str, Any] = {
    toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}")
    # if toolkit_name is lower case it is a loader
@ -40,25 +42,25 @@ toolkit_type_to_cls_dict: dict[str, Any] = {
    if not toolkit_name.islower()
 }
-## Memories
+# Memories
 memory_type_to_cls_dict: dict[str, Any] = {
    memory_name: import_class(f"langchain.memory.{memory_name}")
    for memory_name in memory.__all__
 }
-## Wrappers
+# Wrappers
 wrapper_type_to_cls_dict: dict[str, Any] = {
    wrapper.__name__: wrapper for wrapper in [requests.RequestsWrapper]
 }
-## Embeddings
+# Embeddings
 embedding_type_to_cls_dict: dict[str, Any] = {
    embedding_name: import_class(f"langchain.embeddings.{embedding_name}")
    for embedding_name in embeddings.__all__
 }
-## Document Loaders
+# Document Loaders
 documentloaders_type_to_cls_dict: dict[str, Any] = {
    documentloader_name: import_class(
        f"langchain.document_loaders.{documentloader_name}"
@ -66,7 +68,10 @@ documentloaders_type_to_cls_dict: dict[str, Any] = {
    for documentloader_name in document_loaders.__all__
 }
-## Text Splitters
+# Text Splitters
 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}  # type: ignore
--- a/src/backend/langflow/interface/importing/utils.py
+++ b/src/backend/langflow/interface/importing/utils.py
@ -9,6 +9,7 @@ from langchain.base_language import BaseLanguageModel
 from langchain.chains.base import Chain
 from langchain.chat_models.base import BaseChatModel
 from langchain.tools import BaseTool
 from langflow.utils import validate
 def import_module(module_path: str) -> Any:
@ -147,3 +148,10 @@ def import_utility(utility: str) -> Any:
    if utility == "SQLDatabase":
        return import_class(f"langchain.sql_database.{utility}")
    return import_class(f"langchain.utilities.{utility}")
 def get_function(code):
    """Get the function"""
    function_name = validate.extract_function_name(code)
    return validate.create_function(code, function_name)
--- a/src/backend/langflow/interface/loading.py
+++ b/src/backend/langflow/interface/loading.py
@ -19,10 +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 import_by_type
 from langflow.interface.run import fix_memory_inputs
 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
@ -32,10 +32,11 @@ 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 node_type in CUSTOM_NODES:
-        custom_agent = CUSTOM_AGENTS.get(node_type)
+        if custom_node := CUSTOM_NODES.get(node_type):
-        if custom_agent:
+            if hasattr(custom_node, "initialize"):
-            return custom_agent.initialize(**params)
+                return custom_node.initialize(**params)
            return custom_node(**params)
    class_object = import_by_type(_type=base_type, name=node_type)
    return instantiate_based_on_type(class_object, base_type, node_type, params)
@ -79,10 +80,24 @@ 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)
@ -136,6 +151,15 @@ 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)
@ -161,38 +185,6 @@ def instantiate_utility(node_type, class_object, params):
    return class_object(**params)
 def load_flow_from_json(path: str, build=True):
    """Load flow from json file"""
    # This is done to avoid circular imports
    from langflow.graph import Graph
    with open(path, "r", encoding="utf-8") as f:
        flow_graph = json.load(f)
    data_graph = flow_graph["data"]
    nodes = data_graph["nodes"]
    # Substitute ZeroShotPrompt with PromptTemplate
    # nodes = replace_zero_shot_prompt_with_prompt_template(nodes)
    # Add input variables
    # nodes = payload.extract_input_variables(nodes)
    # Nodes, edges and root node
    edges = data_graph["edges"]
    graph = Graph(nodes, edges)
    if build:
        langchain_object = graph.build()
        if hasattr(langchain_object, "verbose"):
            langchain_object.verbose = True
        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)
        return langchain_object
    return graph
 def replace_zero_shot_prompt_with_prompt_template(nodes):
    """Replace ZeroShotPrompt with PromptTemplate"""
    for node in nodes:
--- a/src/backend/langflow/interface/utils.py
+++ b/src/backend/langflow/interface/utils.py
@ -2,6 +2,7 @@ import base64
 import json
 import os
 from io import BytesIO
 import re
 import yaml
 from langchain.base_language import BaseLanguageModel
@ -52,3 +53,8 @@ def try_setting_streaming_options(langchain_object, websocket):
            llm.stream = True
    return langchain_object
 def extract_input_variables_from_prompt(prompt: str) -> list[str]:
    """Extract input variables from prompt."""
    return re.findall(r"{(.*?)}", prompt)
--- a/src/backend/langflow/processing/init.py
+++ b/src/backend/langflow/processing/init.py
--- a/src/backend/langflow/processing/process.py
+++ b/src/backend/langflow/processing/process.py
@ -0,0 +1,61 @@
 import json
 from langflow.interface.run import (
    get_memory_key,
    update_memory_keys,
 )
 from langflow.graph import Graph
 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:
        try:
            if langchain_object.memory.memory_key in langchain_object.input_variables:
                return
        except AttributeError:
            input_variables = (
                langchain_object.prompt.input_variables
                if hasattr(langchain_object, "prompt")
                else langchain_object.input_keys
            )
            if langchain_object.memory.memory_key in 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 load_flow_from_json(path: str, build=True):
    """Load flow from json file"""
    # This is done to avoid circular imports
    with open(path, "r", encoding="utf-8") as f:
        flow_graph = json.load(f)
    data_graph = flow_graph["data"]
    nodes = data_graph["nodes"]
    # Substitute ZeroShotPrompt with PromptTemplate
    # nodes = replace_zero_shot_prompt_with_prompt_template(nodes)
    # Add input variables
    # nodes = payload.extract_input_variables(nodes)
    # Nodes, edges and root node
    edges = data_graph["edges"]
    graph = Graph(nodes, edges)
    if build:
        langchain_object = graph.build()
        if hasattr(langchain_object, "verbose"):
            langchain_object.verbose = True
        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)
        return langchain_object
    return graph
--- a/src/backend/langflow/template/frontend_node/agents.py
+++ b/src/backend/langflow/template/frontend_node/agents.py
@ -154,9 +154,9 @@ class CSVAgentNode(FrontendNode):
 class InitializeAgentNode(FrontendNode):
-    name: str = "initialize_agent"
+    name: str = "AgentInitializer"
    template: Template = Template(
-        type_name="initailize_agent",
+        type_name="AgentInitializer",
        fields=[
            TemplateField(
                field_type="str",
--- a/src/backend/langflow/template/frontend_node/base.py
+++ b/src/backend/langflow/template/frontend_node/base.py
@ -27,6 +27,9 @@ class FrontendNode(BaseModel):
    def add_extra_fields(self) -> None:
        pass
    def add_extra_base_classes(self) -> None:
        pass
    @staticmethod
    def format_field(field: TemplateField, name: Optional[str] = None) -> None:
        """Formats a given field based on its attributes and value."""
--- a/src/backend/langflow/template/frontend_node/chains.py
+++ b/src/backend/langflow/template/frontend_node/chains.py
@ -2,10 +2,24 @@ from typing import Optional
 from langflow.template.field.base import TemplateField
 from langflow.template.frontend_node.base import FrontendNode
 from langflow.template.frontend_node.constants import QA_CHAIN_TYPES
 from langflow.template.template.base import Template
 class ChainFrontendNode(FrontendNode):
    def add_extra_fields(self) -> None:
        if self.template.type_name == "ConversationalRetrievalChain":
            # add memory
            self.template.add_field(
                TemplateField(
                    field_type="BaseChatMemory",
                    required=False,
                    show=True,
                    name="memory",
                    advanced=False,
                )
            )
    @staticmethod
    def format_field(field: TemplateField, name: Optional[str] = None) -> None:
        FrontendNode.format_field(field, name)
@ -155,3 +169,41 @@ class MidJourneyPromptChainNode(FrontendNode):
        "ConversationChain",
        "MidJourneyPromptChain",
    ]
 class CombineDocsChainNode(FrontendNode):
    name: str = "CombineDocsChain"
    template: Template = Template(
        type_name="load_qa_chain",
        fields=[
            TemplateField(
                field_type="str",
                required=True,
                is_list=True,
                show=True,
                multiline=False,
                options=QA_CHAIN_TYPES,
                value=QA_CHAIN_TYPES[0],
                name="chain_type",
                advanced=False,
            ),
            TemplateField(
                field_type="BaseLanguageModel",
                required=True,
                show=True,
                name="llm",
                display_name="LLM",
                advanced=False,
            ),
        ],
    )
    description: str = """Construct a zero shot agent from an LLM and tools."""
    base_classes: list[str] = ["BaseCombineDocumentsChain", "function"]
    def to_dict(self):
        return super().to_dict()
    @staticmethod
    def format_field(field: TemplateField, name: Optional[str] = None) -> None:
        # do nothing and don't return anything
        pass
--- a/src/backend/langflow/template/frontend_node/constants.py
+++ b/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"]
--- a/src/backend/langflow/template/frontend_node/vectorstores.py
+++ b/src/backend/langflow/template/frontend_node/vectorstores.py
@ -20,6 +20,9 @@ class VectorStoreFrontendNode(FrontendNode):
            self.template.add_field(extra_field)
    def add_extra_base_classes(self) -> None:
        self.base_classes.append("BaseRetriever")
    @staticmethod
    def format_field(field: TemplateField, name: Optional[str] = None) -> None:
        FrontendNode.format_field(field, name)
--- a/tests/data/basic_example.json
+++ b/tests/data/basic_example.json
--- a/tests/test_agents_template.py
+++ b/tests/test_agents_template.py
@ -131,7 +131,7 @@ def test_initialize_agent(client: TestClient):
    json_response = response.json()
    agents = json_response["agents"]
-    initialize_agent = agents["initialize_agent"]
+    initialize_agent = agents["AgentInitializer"]
    assert initialize_agent["base_classes"] == ["AgentExecutor", "function"]
    template = initialize_agent["template"]