Merge branch 'dev' into 146-improve-conversationchain-node

dev.Dockerfile

@@ -3,7 +3,7 @@ FROM python:3.10-slim
 WORKDIR /app
 
 # Install Poetry
-RUN apt-get update && apt-get install gcc g++ curl -y
+RUN apt-get update && apt-get install gcc g++ curl build-essential -y
 RUN curl -sSL https://install.python-poetry.org | python3 -
 # # Add Poetry to PATH
 ENV PATH="${PATH}:/root/.local/bin"

pyproject.toml

@@ -34,6 +34,7 @@ openai = "^0.27.2"
 types-pyyaml = "^6.0.12.8"
 dill = "^0.3.6"
 pandas = "^1.5.3"
+chromadb = "^0.3.21"
 huggingface-hub = "^0.13.3"
 rich = "^13.3.3"
 llama-cpp-python = "0.1.23"

src/backend/langflow/api/endpoints.py

@@ -3,7 +3,7 @@ from typing import Any, Dict
 
 from fastapi import APIRouter, HTTPException
 
-from langflow.interface.run import process_graph
+from langflow.interface.run import process_graph_cached
 from langflow.interface.types import build_langchain_types_dict
 
 # build router
@@ -19,7 +19,7 @@ def get_all():
 @router.post("/predict")
 def get_load(data: Dict[str, Any]):
     try:
-        return process_graph(data)
+        return process_graph_cached(data)
     except Exception as e:
         # Log stack trace
         logger.exception(e)

src/backend/langflow/cache/utils.py

@@ -1,12 +1,41 @@
 import contextlib
+import functools
 import hashlib
 import json
 import os
 import tempfile
+from collections import OrderedDict
 from pathlib import Path
 
 import dill  # type: ignore
 
+
+def memoize_dict(maxsize=128):
+    cache = OrderedDict()
+
+    def decorator(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            hashed = compute_dict_hash(args[0])
+            key = (func.__name__, hashed, frozenset(kwargs.items()))
+            if key not in cache:
+                result = func(*args, **kwargs)
+                cache[key] = result
+                if len(cache) > maxsize:
+                    cache.popitem(last=False)
+            else:
+                result = cache[key]
+            return result
+
+        def clear_cache():
+            cache.clear()
+
+        wrapper.clear_cache = clear_cache
+        return wrapper
+
+    return decorator
+
+
 PREFIX = "langflow_cache"
 
 
@@ -24,6 +53,13 @@ def clear_old_cache_files(max_cache_size: int = 3):
                 os.remove(cache_file)
 
 
+def compute_dict_hash(graph_data):
+    graph_data = filter_json(graph_data)
+
+    cleaned_graph_json = json.dumps(graph_data, sort_keys=True)
+    return hashlib.sha256(cleaned_graph_json.encode("utf-8")).hexdigest()
+
+
 def filter_json(json_data):
     filtered_data = json_data.copy()
 
@@ -48,13 +84,6 @@ def filter_json(json_data):
     return filtered_data
 
 
-def compute_hash(graph_data):
-    graph_data = filter_json(graph_data)
-
-    cleaned_graph_json = json.dumps(graph_data, sort_keys=True)
-    return hashlib.sha256(cleaned_graph_json.encode("utf-8")).hexdigest()
-
-
 def save_cache(hash_val: str, chat_data, clean_old_cache_files: bool):
     cache_path = Path(tempfile.gettempdir()) / f"{PREFIX}_{hash_val}.dill"
     with cache_path.open("wb") as cache_file:

src/backend/langflow/config.yaml

@@ -12,6 +12,8 @@ agents:
   - JsonAgent
   - CSVAgent
   - initialize_agent
+  - VectorStoreAgent
+  - VectorStoreRouterAgent
 
 prompts:
   - PromptTemplate
@@ -44,16 +46,25 @@ wrappers:
 toolkits:
   - OpenAPIToolkit
   - JsonToolkit
+  - VectorStoreInfo
+  - VectorStoreRouterToolkit
 
 memories:
   - ConversationBufferMemory
   - ConversationSummaryMemory
   - ConversationKGMemory
 
-embeddings: []
+embeddings:
+  - OpenAIEmbeddings
 
-vectorstores: []
+vectorstores:
+  - Chroma
 
-documentloaders: []
+documentloaders:
+  - TextLoader
+  - WebBaseLoader
+
+textsplitters:
+  - CharacterTextSplitter
 
 dev: false

src/backend/langflow/custom/customs.py

@@ -8,6 +8,8 @@ CUSTOM_NODES = {
         "JsonAgent": nodes.JsonAgentNode(),
         "CSVAgent": nodes.CSVAgentNode(),
         "initialize_agent": nodes.InitializeAgentNode(),
+        "VectorStoreAgent": nodes.VectorStoreAgentNode(),
+        "VectorStoreRouterAgent": nodes.VectorStoreRouterAgentNode(),
     },
 }
 

src/backend/langflow/graph/base.py

@@ -160,6 +160,7 @@ class Node:
                         result = result.run  # type: ignore
                     elif hasattr(result, "get_function"):
                         result = result.get_function()  # type: ignore
+
                 self.params[key] = result
             elif isinstance(value, list) and all(
                 isinstance(node, Node) for node in value
@@ -189,6 +190,15 @@ class Node:
     def build(self, force: bool = False) -> Any:
         if not self._built or force:
             self._build()
+
+        #! Deepcopy is breaking for vectorstores
+        if self.base_type in [
+            "vectorstores",
+            "VectorStoreRouterAgent",
+            "VectorStoreAgent",
+            "VectorStoreInfo",
+        ] or self.node_type in ["VectorStoreInfo", "VectorStoreRouterToolkit"]:
+            return self._built_object
         return deepcopy(self._built_object)
 
     def add_edge(self, edge: "Edge") -> None:

src/backend/langflow/graph/graph.py

@@ -4,22 +4,30 @@ from langflow.graph.base import Edge, Node
 from langflow.graph.nodes import (
     AgentNode,
     ChainNode,
+    DocumentLoaderNode,
+    EmbeddingNode,
     FileToolNode,
     LLMNode,
     MemoryNode,
     PromptNode,
+    TextSplitterNode,
     ToolkitNode,
     ToolNode,
+    VectorStoreNode,
     WrapperNode,
 )
 from langflow.interface.agents.base import agent_creator
 from langflow.interface.chains.base import chain_creator
+from langflow.interface.documentLoaders.base import documentloader_creator
+from langflow.interface.embeddings.base import embedding_creator
 from langflow.interface.llms.base import llm_creator
 from langflow.interface.memories.base import memory_creator
 from langflow.interface.prompts.base import prompt_creator
+from langflow.interface.textSplitters.base import textsplitter_creator
 from langflow.interface.toolkits.base import toolkits_creator
 from langflow.interface.tools.base import tool_creator
 from langflow.interface.tools.constants import FILE_TOOLS
+from langflow.interface.vectorStore.base import vectorstore_creator
 from langflow.interface.wrappers.base import wrapper_creator
 from langflow.utils import payload
 
@@ -117,6 +125,10 @@ class Graph:
             **{t: WrapperNode for t in wrapper_creator.to_list()},
             **{t: LLMNode for t in llm_creator.to_list()},
             **{t: MemoryNode for t in memory_creator.to_list()},
+            **{t: EmbeddingNode for t in embedding_creator.to_list()},
+            **{t: VectorStoreNode for t in vectorstore_creator.to_list()},
+            **{t: DocumentLoaderNode for t in documentloader_creator.to_list()},
+            **{t: TextSplitterNode for t in textsplitter_creator.to_list()},
         }
 
         if node_type in FILE_TOOLS:

src/backend/langflow/graph/nodes.py

@@ -32,6 +32,10 @@ class AgentNode(Node):
                 chain_node.build(tools=self.tools)
 
             self._build()
+
+        #! Cannot deepcopy VectorStore
+        if self.node_type in ["VectorStoreAgent", "VectorStoreRouterAgent"]:
+            return self._built_object
         return deepcopy(self._built_object)
 
 
@@ -39,11 +43,6 @@ class ToolNode(Node):
     def __init__(self, data: Dict):
         super().__init__(data, base_type="tools")
 
-    def build(self, force: bool = False) -> Any:
-        if not self._built or force:
-            self._build()
-        return deepcopy(self._built_object)
-
 
 class PromptNode(Node):
     def __init__(self, data: Dict):
@@ -109,32 +108,16 @@ class LLMNode(Node):
     def __init__(self, data: Dict):
         super().__init__(data, base_type="llms")
 
-    def build(self, force: bool = False) -> Any:
-        if not self._built or force:
-            self._build()
-        return deepcopy(self._built_object)
-
 
 class ToolkitNode(Node):
     def __init__(self, data: Dict):
         super().__init__(data, base_type="toolkits")
 
-    def build(self, force: bool = False) -> Any:
-        if not self._built or force:
-            self._build()
-
-        return deepcopy(self._built_object)
-
 
 class FileToolNode(ToolNode):
     def __init__(self, data: Dict):
         super().__init__(data)
 
-    def build(self, force: bool = False) -> Any:
-        if not self._built or force:
-            self._build()
-        return deepcopy(self._built_object)
-
 
 class WrapperNode(Node):
     def __init__(self, data: Dict):
@@ -148,11 +131,26 @@ class WrapperNode(Node):
         return deepcopy(self._built_object)
 
 
+class DocumentLoaderNode(Node):
+    def __init__(self, data: Dict):
+        super().__init__(data, base_type="documentloaders")
+
+
+class EmbeddingNode(Node):
+    def __init__(self, data: Dict):
+        super().__init__(data, base_type="embeddings")
+
+
+class VectorStoreNode(Node):
+    def __init__(self, data: Dict):
+        super().__init__(data, base_type="vectorstores")
+
+
 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:
+class TextSplitterNode(Node):
-            self._build()
+    def __init__(self, data: Dict):
-        return deepcopy(self._built_object)
+        super().__init__(data, base_type="textsplitters")

src/backend/langflow/graph/utils.py

@@ -38,6 +38,9 @@ def load_file(file_name, file_content, accepted_types) -> Any:
         # Load the csv content
         csv_reader = csv.DictReader(io.StringIO(decoded_string))
         return list(csv_reader)
+    elif suffix == "txt":
+        # Return the text content
+        return decoded_string
     else:
         raise ValueError(f"File {file_name} is not accepted")
 

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

@@ -2,10 +2,21 @@ from typing import Any, List, Optional
 
 from langchain import LLMChain
 from langchain.agents import AgentExecutor, Tool, ZeroShotAgent, initialize_agent
+from langchain.agents.agent_toolkits import (
+    VectorStoreInfo,
+    VectorStoreRouterToolkit,
+    VectorStoreToolkit,
+)
 from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
 from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
 from langchain.agents.agent_toolkits.pandas.prompt import PREFIX as PANDAS_PREFIX
 from langchain.agents.agent_toolkits.pandas.prompt import SUFFIX as PANDAS_SUFFIX
+from langchain.agents.agent_toolkits.vectorstore.prompt import (
+    PREFIX as VECTORSTORE_PREFIX,
+)
+from langchain.agents.agent_toolkits.vectorstore.prompt import (
+    ROUTER_PREFIX as VECTORSTORE_ROUTER_PREFIX,
+)
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
 from langchain.llms.base import BaseLLM
 from langchain.memory.chat_memory import BaseChatMemory
@@ -97,6 +108,83 @@ class CSVAgent(AgentExecutor):
         return super().run(*args, **kwargs)
 
 
+class VectorStoreAgent(AgentExecutor):
+    """Vector Store agent"""
+
+    @staticmethod
+    def function_name():
+        return "VectorStoreAgent"
+
+    @classmethod
+    def initialize(cls, *args, **kwargs):
+        return cls.from_toolkit_and_llm(*args, **kwargs)
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    @classmethod
+    def from_toolkit_and_llm(
+        cls, llm: BaseLLM, vectorstoreinfo: VectorStoreInfo, **kwargs: Any
+    ):
+        """Construct a vectorstore agent from an LLM and tools."""
+
+        toolkit = VectorStoreToolkit(vectorstore_info=vectorstoreinfo, llm=llm)
+
+        tools = toolkit.get_tools()
+        prompt = ZeroShotAgent.create_prompt(tools, prefix=VECTORSTORE_PREFIX)
+        llm_chain = LLMChain(
+            llm=llm,
+            prompt=prompt,
+        )
+        tool_names = [tool.name for tool in tools]
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+        return AgentExecutor.from_agent_and_tools(
+            agent=agent, tools=tools, verbose=True
+        )
+
+    def run(self, *args, **kwargs):
+        return super().run(*args, **kwargs)
+
+
+class VectorStoreRouterAgent(AgentExecutor):
+    """Vector Store Router Agent"""
+
+    @staticmethod
+    def function_name():
+        return "VectorStoreRouterAgent"
+
+    @classmethod
+    def initialize(cls, *args, **kwargs):
+        return cls.from_toolkit_and_llm(*args, **kwargs)
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    @classmethod
+    def from_toolkit_and_llm(
+        cls,
+        llm: BaseLanguageModel,
+        vectorstoreroutertoolkit: VectorStoreRouterToolkit,
+        **kwargs: Any
+    ):
+        """Construct a vector store router agent from an LLM and tools."""
+
+        tools = vectorstoreroutertoolkit.get_tools()
+        prompt = ZeroShotAgent.create_prompt(tools, prefix=VECTORSTORE_ROUTER_PREFIX)
+        llm_chain = LLMChain(
+            llm=llm,
+            prompt=prompt,
+        )
+        tool_names = [tool.name for tool in tools]
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+        return AgentExecutor.from_agent_and_tools(
+            agent=agent, tools=tools, verbose=True
+        )
+
+    def run(self, *args, **kwargs):
+        return super().run(*args, **kwargs)
+
+
 class InitializeAgent(AgentExecutor):
     """Implementation of initialize_agent function"""
 
@@ -128,4 +216,6 @@ CUSTOM_AGENTS = {
     "JsonAgent": JsonAgent,
     "CSVAgent": CSVAgent,
     "initialize_agent": InitializeAgent,
+    "VectorStoreAgent": VectorStoreAgent,
+    "VectorStoreRouterAgent": VectorStoreRouterAgent,
 }

src/backend/langflow/interface/custom_lists.py

@@ -1,6 +1,6 @@
+import inspect
 from typing import Any
 
-## LLM
 from langchain import (
     chains,
     document_loaders,
@@ -8,6 +8,7 @@ from langchain import (
     llms,
     memory,
     requests,
+    text_splitter,
     vectorstores,
 )
 from langchain.agents import agent_toolkits
@@ -15,16 +16,17 @@ from langchain.chat_models import ChatOpenAI
 
 from langflow.interface.importing.utils import import_class
 
-## LLM
+## LLMs
 llm_type_to_cls_dict = llms.type_to_cls_dict
 llm_type_to_cls_dict["openai-chat"] = ChatOpenAI  # type: ignore
 
-## Chain
+## 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] = {
     toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}")
     # if toolkit_name is lower case it is a loader
@@ -69,3 +71,8 @@ documentloaders_type_to_cls_dict: dict[str, Any] = {
     )
     for documentloader_name in document_loaders.__all__
 }
+
+## Text Splitters
+textsplitter_type_to_cls_dict: dict[str, Any] = dict(
+    inspect.getmembers(text_splitter, inspect.isclass)
+)

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

@@ -2,21 +2,54 @@ from typing import Dict, List, Optional
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.custom_lists import documentloaders_type_to_cls_dict
+from langflow.interface.documentLoaders.custom import CUSTOM_DOCUMENTLOADERS
 from langflow.settings import settings
 from langflow.utils.util import build_template_from_class
 
 
 class DocumentLoaderCreator(LangChainTypeCreator):
-    type_name: str = "documentloader"
+    type_name: str = "documentloaders"
 
     @property
     def type_to_loader_dict(self) -> Dict:
-        return documentloaders_type_to_cls_dict
+        types = documentloaders_type_to_cls_dict
+
+        # Drop some types that are reimplemented with the same name
+        types.pop("TextLoader")
+
+        for name, documentloader in CUSTOM_DOCUMENTLOADERS.items():
+            types[name] = documentloader
+
+        return types
 
     def get_signature(self, name: str) -> Optional[Dict]:
         """Get the signature of a document loader."""
         try:
-            return build_template_from_class(name, documentloaders_type_to_cls_dict)
+            signature = build_template_from_class(
+                name, documentloaders_type_to_cls_dict
+            )
+
+            if name == "TextLoader":
+                signature["template"]["file"] = {
+                    "type": "file",
+                    "required": True,
+                    "show": True,
+                    "name": "path",
+                    "value": "",
+                    "suffixes": [".txt"],
+                    "fileTypes": ["txt"],
+                }
+            elif name == "WebBaseLoader":
+                signature["template"]["web_path"] = {
+                    "type": "str",
+                    "required": True,
+                    "show": True,
+                    "name": "web_path",
+                    "value": "",
+                    "display_name": "Web Path",
+                }
+
+            return signature
         except ValueError as exc:
             raise ValueError(f"Documment Loader {name} not found") from exc
 

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

@@ -0,0 +1,22 @@
+"""Load text files."""
+from typing import List
+
+from langchain.docstore.document import Document
+from langchain.document_loaders.base import BaseLoader
+
+
+class TextLoader(BaseLoader):
+    """Load Text files."""
+
+    def __init__(self, file: str):
+        """Initialize with file path."""
+        self.file = file
+
+    def load(self) -> List[Document]:
+        """Load from file path."""
+        return [Document(page_content=self.file, metadata={"source": "loaded"})]
+
+
+CUSTOM_DOCUMENTLOADERS = {
+    "TextLoader": TextLoader,
+}

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

@@ -10,6 +10,7 @@ from langchain.chat_models.base import BaseChatModel
 from langchain.llms.base import BaseLLM
 from langchain.tools import BaseTool
 
+from langflow.interface.documentLoaders.custom import CUSTOM_DOCUMENTLOADERS
 from langflow.interface.tools.util import get_tool_by_name
 
 
@@ -40,6 +41,10 @@ def import_by_type(_type: str, name: str) -> Any:
         "toolkits": import_toolkit,
         "wrappers": import_wrapper,
         "memory": import_memory,
+        "embeddings": import_embedding,
+        "vectorstores": import_vectorstore,
+        "documentloaders": import_documentloader,
+        "textsplitters": import_textsplitter,
     }
     if _type == "llms":
         key = "chat" if "chat" in name.lower() else "llm"
@@ -113,3 +118,26 @@ def import_chain(chain: str) -> Type[Chain]:
     if chain in CUSTOM_CHAINS:
         return CUSTOM_CHAINS[chain]
     return import_class(f"langchain.chains.{chain}")
+
+
+def import_embedding(embedding: str) -> Any:
+    """Import embedding from embedding name"""
+    return import_class(f"langchain.embeddings.{embedding}")
+
+
+def import_vectorstore(vectorstore: str) -> Any:
+    """Import vectorstore from vectorstore name"""
+    return import_class(f"langchain.vectorstores.{vectorstore}")
+
+
+def import_documentloader(documentloader: str) -> Any:
+    """Import documentloader from documentloader name"""
+    if documentloader in CUSTOM_DOCUMENTLOADERS:
+        return CUSTOM_DOCUMENTLOADERS[documentloader]
+
+    return import_class(f"langchain.document_loaders.{documentloader}")
+
+
+def import_textsplitter(textsplitter: str) -> Any:
+    """Import textsplitter from textsplitter name"""
+    return import_class(f"langchain.text_splitter.{textsplitter}")

src/backend/langflow/interface/listing.py

@@ -1,10 +1,14 @@
 from langflow.interface.agents.base import agent_creator
 from langflow.interface.chains.base import chain_creator
+from langflow.interface.documentLoaders.base import documentloader_creator
+from langflow.interface.embeddings.base import embedding_creator
 from langflow.interface.llms.base import llm_creator
 from langflow.interface.memories.base import memory_creator
 from langflow.interface.prompts.base import prompt_creator
+from langflow.interface.textSplitters.base import textsplitter_creator
 from langflow.interface.toolkits.base import toolkits_creator
 from langflow.interface.tools.base import tool_creator
+from langflow.interface.vectorStore.base import vectorstore_creator
 from langflow.interface.wrappers.base import wrapper_creator
 
 
@@ -18,6 +22,10 @@ def get_type_dict():
         "memory": memory_creator.to_list(),
         "toolkits": toolkits_creator.to_list(),
         "wrappers": wrapper_creator.to_list(),
+        "documentLoaders": documentloader_creator.to_list(),
+        "vectorStore": vectorstore_creator.to_list(),
+        "embeddings": embedding_creator.to_list(),
+        "textSplitters": textsplitter_creator.to_list(),
     }
 
 

src/backend/langflow/interface/loading.py

@@ -57,6 +57,17 @@ def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
         if toolkits_creator.has_create_function(node_type):
             return load_toolkits_executor(node_type, loaded_toolkit, params)
         return loaded_toolkit
+    elif base_type == "embeddings":
+        params.pop("model")
+        return class_object(**params)
+    elif base_type == "vectorstores":
+        return class_object.from_documents(**params)
+    elif base_type == "documentloaders":
+        return class_object(**params).load()
+    elif base_type == "textsplitters":
+        documents = params.pop("documents")
+        text_splitter = class_object(**params)
+        return text_splitter.split_documents(documents)
     else:
         return class_object(**params)
 

src/backend/langflow/interface/run.py

@@ -2,7 +2,7 @@ import contextlib
 import io
 from typing import Any, Dict
 
-from langflow.cache.utils import compute_hash, load_cache, save_cache
+from langflow.cache.utils import compute_dict_hash, load_cache, memoize_dict
 from langflow.graph.graph import Graph
 from langflow.interface import loading
 from langflow.utils.logger import logger
@@ -12,7 +12,7 @@ def load_langchain_object(data_graph, is_first_message=False):
     """
     Load langchain object from cache if it exists, otherwise build it.
     """
-    computed_hash = compute_hash(data_graph)
+    computed_hash = compute_dict_hash(data_graph)
     if is_first_message:
         langchain_object = build_langchain_object(data_graph)
     else:
@@ -22,6 +22,32 @@ def load_langchain_object(data_graph, is_first_message=False):
     return computed_hash, langchain_object
 
 
+def load_or_build_langchain_object(data_graph, is_first_message=False):
+    """
+    Load langchain object from cache if it exists, otherwise build it.
+    """
+    if is_first_message:
+        build_langchain_object_with_caching.clear_cache()
+    return build_langchain_object_with_caching(data_graph)
+
+
+@memoize_dict(maxsize=1)
+def build_langchain_object_with_caching(data_graph):
+    """
+    Build langchain object from data_graph.
+    """
+
+    logger.debug("Building langchain object")
+    nodes = data_graph["nodes"]
+    # Add input variables
+    # nodes = payload.extract_input_variables(nodes)
+    # Nodes, edges and root node
+    edges = data_graph["edges"]
+    graph = Graph(nodes, edges)
+
+    return graph.build()
+
+
 def build_langchain_object(data_graph):
     """
     Build langchain object from data_graph.
@@ -67,11 +93,35 @@ def process_graph(data_graph: Dict[str, Any]):
     # We have to save it here because if the
     # memory is updated we need to keep the new values
     logger.debug("Saving langchain object to cache")
-    save_cache(computed_hash, langchain_object, is_first_message)
+    # save_cache(computed_hash, langchain_object, is_first_message)
     logger.debug("Saved langchain object to cache")
     return {"result": str(result), "thought": thought.strip()}
 
 
+def process_graph_cached(data_graph: Dict[str, Any]):
+    """
+    Process graph by extracting input variables and replacing ZeroShotPrompt
+    with PromptTemplate,then run the graph and return the result and thought.
+    """
+    # Load langchain object
+    message = data_graph.pop("message", "")
+    is_first_message = len(data_graph.get("chatHistory", [])) == 0
+    langchain_object = load_or_build_langchain_object(data_graph, is_first_message)
+    logger.debug("Loaded langchain object")
+
+    if langchain_object is None:
+        # Raise user facing error
+        raise ValueError(
+            "There was an error loading the langchain_object. Please, check all the nodes and try again."
+        )
+
+    # Generate result and thought
+    logger.debug("Generating result and thought")
+    result, thought = get_result_and_thought_using_graph(langchain_object, message)
+    logger.debug("Generated result and thought")
+    return {"result": str(result), "thought": thought.strip()}
+
+
 def get_memory_key(langchain_object):
     """
     Given a LangChain object, this function retrieves the current memory key from the object's memory attribute.

src/backend/langflow/interface/textSplitters/__init__.py

@@ -0,0 +1,3 @@
+from langflow.interface.textSplitters.base import TextSplitterCreator
+
+__all__ = ["TextSplitterCreator"]

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

@@ -0,0 +1,40 @@
+from typing import Dict, List, Optional
+
+from langflow.interface.base import LangChainTypeCreator
+from langflow.interface.custom_lists import textsplitter_type_to_cls_dict
+from langflow.settings import settings
+from langflow.utils.util import build_template_from_class
+
+
+class TextSplitterCreator(LangChainTypeCreator):
+    type_name: str = "textsplitters"
+
+    @property
+    def type_to_loader_dict(self) -> Dict:
+        return textsplitter_type_to_cls_dict
+
+    def get_signature(self, name: str) -> Optional[Dict]:
+        """Get the signature of a text splitter."""
+        try:
+            signature = build_template_from_class(name, textsplitter_type_to_cls_dict)
+
+            signature["template"]["documents"] = {
+                "type": "BaseLoader",
+                "required": True,
+                "show": True,
+                "name": "documents",
+            }
+
+            return signature
+        except ValueError as exc:
+            raise ValueError(f"Text Splitter {name} not found") from exc
+
+    def to_list(self) -> List[str]:
+        return [
+            textsplitter.__name__
+            for textsplitter in self.type_to_loader_dict.values()
+            if textsplitter.__name__ in settings.textsplitters or settings.dev
+        ]
+
+
+textsplitter_creator = TextSplitterCreator()

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

@@ -7,7 +7,7 @@ from langchain.agents.load_tools import (
 )
 from langchain.tools.json.tool import JsonSpec
 
-from langflow.interface.custom.types import PythonFunction
+from langflow.interface.tools.custom import PythonFunction
 
 FILE_TOOLS = {"JsonSpec": JsonSpec}
 CUSTOM_TOOLS = {"Tool": Tool, "PythonFunction": PythonFunction}

src/backend/langflow/interface/types.py

@@ -5,6 +5,7 @@ from langflow.interface.embeddings.base import embedding_creator
 from langflow.interface.llms.base import llm_creator
 from langflow.interface.memories.base import memory_creator
 from langflow.interface.prompts.base import prompt_creator
+from langflow.interface.textSplitters.base import textsplitter_creator
 from langflow.interface.toolkits.base import toolkits_creator
 from langflow.interface.tools.base import tool_creator
 from langflow.interface.vectorStore.base import vectorstore_creator
@@ -40,6 +41,7 @@ def build_langchain_types_dict():  # sourcery skip: dict-assign-update-to-union
         embedding_creator,
         vectorstore_creator,
         documentloader_creator,
+        textsplitter_creator,
     ]
 
     all_types = {}

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

@@ -7,7 +7,7 @@ from langflow.utils.util import build_template_from_class
 
 
 class VectorstoreCreator(LangChainTypeCreator):
-    type_name: str = "vectorstore"
+    type_name: str = "vectorstores"
 
     @property
     def type_to_loader_dict(self) -> Dict:
@@ -16,7 +16,27 @@ class VectorstoreCreator(LangChainTypeCreator):
     def get_signature(self, name: str) -> Optional[Dict]:
         """Get the signature of an embedding."""
         try:
-            return build_template_from_class(name, vectorstores_type_to_cls_dict)
+            signature = build_template_from_class(name, vectorstores_type_to_cls_dict)
+
+            # TODO: Use FrontendendNode class to build the signature
+            signature["template"] = {
+                "documents": {
+                    "type": "TextSplitter",
+                    "required": True,
+                    "show": True,
+                    "name": "documents",
+                    "display_name": "Text Splitter",
+                },
+                "embedding": {
+                    "type": "Embeddings",
+                    "required": True,
+                    "show": True,
+                    "name": "embedding",
+                    "display_name": "Embedding",
+                },
+            }
+            return signature
+
         except ValueError as exc:
             raise ValueError(f"Vector Store {name} not found") from exc
 

src/backend/langflow/settings.py

@@ -17,6 +17,7 @@ class Settings(BaseSettings):
     documentloaders: List[str] = []
     wrappers: List[str] = []
     toolkits: List[str] = []
+    textsplitters: List[str] = []
     dev: bool = False
 
     class Config:
@@ -40,6 +41,7 @@ class Settings(BaseSettings):
         self.memories = new_settings.memories or []
         self.wrappers = new_settings.wrappers or []
         self.toolkits = new_settings.toolkits or []
+        self.textsplitters = new_settings.textsplitters or []
         self.dev = new_settings.dev or False
 
 

src/backend/langflow/template/nodes.py

@@ -248,6 +248,62 @@ class CSVAgentNode(FrontendNode):
         return super().to_dict()
 
 
+class VectorStoreAgentNode(FrontendNode):
+    name: str = "VectorStoreAgent"
+    template: Template = Template(
+        type_name="vectorstore_agent",
+        fields=[
+            TemplateField(
+                field_type="VectorStoreInfo",
+                required=True,
+                show=True,
+                name="vectorstoreinfo",
+                display_name="Vector Store Info",
+            ),
+            TemplateField(
+                field_type="BaseLanguageModel",
+                required=True,
+                show=True,
+                name="llm",
+                display_name="LLM",
+            ),
+        ],
+    )
+    description: str = """Construct an agent from a Vector Store."""
+    base_classes: list[str] = ["AgentExecutor"]
+
+    def to_dict(self):
+        return super().to_dict()
+
+
+class VectorStoreRouterAgentNode(FrontendNode):
+    name: str = "VectorStoreRouterAgent"
+    template: Template = Template(
+        type_name="vectorstorerouter_agent",
+        fields=[
+            TemplateField(
+                field_type="VectorStoreRouterToolkit",
+                required=True,
+                show=True,
+                name="vectorstoreroutertoolkit",
+                display_name="Vector Store Router Toolkit",
+            ),
+            TemplateField(
+                field_type="BaseLanguageModel",
+                required=True,
+                show=True,
+                name="llm",
+                display_name="LLM",
+            ),
+        ],
+    )
+    description: str = """Construct an agent from a Vector Store Router."""
+    base_classes: list[str] = ["AgentExecutor"]
+
+    def to_dict(self):
+        return super().to_dict()
+
+
 class PromptFrontendNode(FrontendNode):
     @staticmethod
     def format_field(field: TemplateField, name: Optional[str] = None) -> None:

src/frontend/src/pages/FlowPage/index.tsx

@@ -6,9 +6,10 @@ import ReactFlow, {
 	useEdgesState,
 	useNodesState,
 	useReactFlow,
-	ControlButton,
+	updateEdge,
 	EdgeChange,
 	Connection,
+	Edge,
 } from "reactflow";
 import { locationContext } from "../../contexts/locationContext";
 import ExtraSidebar from "./components/extraSidebarComponent";
@@ -20,6 +21,7 @@ import { typesContext } from "../../contexts/typesContext";
 import ConnectionLineComponent from "./components/ConnectionLineComponent";
 import { FlowType, NodeType } from "../../types/flow";
 import { APIClassType } from "../../types/api";
+import { isValidConnection } from "../../utils";
 
 const nodeTypes = {
 	genericNode: GenericNode,
@@ -28,7 +30,7 @@ const nodeTypes = {
 var _ = require("lodash");
 
 export default function FlowPage({ flow }:{flow:FlowType}) {
-	let { updateFlow, incrementNodeId, downloadFlow, uploadFlow } =
+	let { updateFlow, incrementNodeId} =
 		useContext(TabsContext);
 	const { types, reactFlowInstance, setReactFlowInstance } =
 		useContext(typesContext);
@@ -43,6 +45,7 @@ export default function FlowPage({ flow }:{flow:FlowType}) {
 		flow.data?.edges ?? []
 	);
 	const { setViewport } = useReactFlow();
+	const edgeUpdateSuccessful = useRef(true)
 
 	useEffect(() => {
 		if (reactFlowInstance && flow) {
@@ -155,6 +158,26 @@ export default function FlowPage({ flow }:{flow:FlowType}) {
 		setEdges(edges.filter((ns) => !nodes.some((n) => ns.source === n.id || ns.target === n.id)));
 	}
 
+	const onEdgeUpdateStart = useCallback(() => {
+		edgeUpdateSuccessful.current = false;
+	  }, []);	
+
+	
+	  const onEdgeUpdate = useCallback((oldEdge:Edge, newConnection:Connection) => {
+		if(isValidConnection(newConnection,reactFlowInstance)){
+			edgeUpdateSuccessful.current = true;
+			setEdges((els) => updateEdge(oldEdge, newConnection, els));
+		}
+	}, []);
+
+	const onEdgeUpdateEnd = useCallback((_, edge) => {
+		if (!edgeUpdateSuccessful.current) {
+		  setEdges((eds) => eds.filter((e) => e.id !== edge.id));
+		}
+	
+		edgeUpdateSuccessful.current = true;
+	  }, []);
+	  
 	return (
 		<div className="w-full h-full" ref={reactFlowWrapper}>
 			{Object.keys(types).length > 0 ? (
@@ -171,6 +194,9 @@ export default function FlowPage({ flow }:{flow:FlowType}) {
 						onLoad={setReactFlowInstance}
 						onInit={setReactFlowInstance}
 						nodeTypes={nodeTypes}
+						onEdgeUpdate={onEdgeUpdate}
+						onEdgeUpdateStart={onEdgeUpdateStart}
+						onEdgeUpdateEnd={onEdgeUpdateEnd}
 						connectionLineComponent={ConnectionLineComponent}
 						onDragOver={onDragOver}
 						onDrop={onDrop}

src/frontend/src/utils.ts

@@ -9,7 +9,11 @@ import {
   ComputerDesktopIcon,
   Bars3CenterLeftIcon,
   GiftIcon,
+  PaperClipIcon,
   QuestionMarkCircleIcon,
+  FingerPrintIcon,
+  ScissorsIcon,
+  CircleStackIcon
 } from "@heroicons/react/24/outline";
 import { Connection, Edge, Node, ReactFlowInstance } from "reactflow";
 import { FlowType } from "./types/flow";
@@ -71,11 +75,14 @@ export const nodeColors: {[char: string]: string} = {
   chains: "#FE7500",
   agents: "#903BBE",
   tools: "#FF3434",
-  memories: "#FF9135",
+  memories: "#F5B85A",
   advanced: "#000000",
   chat: "#454173",
   thought:"#272541",
-  docloaders:"#FF9135",
+  embeddings:"#42BAA7",
+  documentloaders:"#7AAE42",
+  vectorstores: "#AA8742",
+  textsplitters: "#B47CB5",
   toolkits:"#DB2C2C",
   wrappers:"#E6277A",
   unknown:"#9CA3AF"
@@ -90,9 +97,12 @@ export const nodeNames:{[char: string]: string} = {
   memories: "Memories",
   advanced: "Advanced",
   chat: "Chat",
-  docloaders:"Document Loader",
+  embeddings: "Embeddings",
+  documentloaders: "Document Loaders",
+  vectorstores: "Vector Stores",
   toolkits:"Toolkits",
   wrappers:"Wrappers",
+  textsplitters: "Text Splitters",
   unknown:"Unknown"
 };
 
@@ -105,8 +115,11 @@ export const nodeIcons:{[char: string]: React.ForwardRefExoticComponent<React.SV
   tools: WrenchIcon,
   advanced: ComputerDesktopIcon,
   chat: Bars3CenterLeftIcon,
-  docloaders:Bars3CenterLeftIcon,
+  embeddings:FingerPrintIcon,
+  documentloaders:PaperClipIcon,
+  vectorstores: CircleStackIcon,
   toolkits:WrenchScrewdriverIcon,
+  textsplitters:ScissorsIcon,
   wrappers:GiftIcon,
   unknown:QuestionMarkCircleIcon
 };

tests/test_custom_types.py

@@ -1,6 +1,6 @@
 # Test this:
 import pytest
-from langflow.interface.custom.types import PythonFunction
+from langflow.interface.tools.custom import PythonFunction
 from langflow.utils import constants
 
 

tests/test_graph.py

@@ -3,6 +3,7 @@ from typing import Type, Union
 
 import pytest
 from langchain.agents import AgentExecutor
+from langchain.llms.fake import FakeListLLM
 from langflow.graph import Edge, Graph, Node
 from langflow.graph.nodes import (
     AgentNode,
@@ -14,9 +15,8 @@ from langflow.graph.nodes import (
     ToolNode,
     WrapperNode,
 )
-from langflow.utils.payload import build_json, get_root_node
 from langflow.interface.run import get_result_and_thought_using_graph
-from langchain.llms.fake import FakeListLLM
+from langflow.utils.payload import build_json, get_root_node
 
 # Test cases for the graph module
 

tests/test_vectorstore_template.py

@@ -0,0 +1,12 @@
+from fastapi.testclient import TestClient
+from langflow.settings import settings
+
+
+# check that all agents are in settings.agents
+# are in json_response["agents"]
+def test_vectorstores_settings(client: TestClient):
+    response = client.get("/all")
+    assert response.status_code == 200
+    json_response = response.json()
+    vectorstores = json_response["vectorstores"]
+    assert set(vectorstores.keys()) == set(settings.vectorstores)