Merge remote-tracking branch 'origin/main' into dev

docs/docs/components/text-splitters.mdx

@@ -1,11 +1,13 @@
-import Admonition from '@theme/Admonition';
+import Admonition from "@theme/Admonition";
 
 # Text Splitters
 
 <Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
-    <p>
-        We appreciate your understanding as we polish our documentation – it may contain some rough edges. Share your feedback or report issues to help us improve! 🛠️📝
-    </p>
+  <p>
+    We appreciate your understanding as we polish our documentation – it may
+    contain some rough edges. Share your feedback or report issues to help us
+    improve! 🛠️📝
+  </p>
 </Admonition>
 
 A text splitter is a tool that divides a document or text into smaller chunks or segments. It is used to break down large texts into more manageable pieces for analysis or processing.
@@ -22,13 +24,13 @@ The `CharacterTextSplitter` is used to split a long text into smaller chunks bas
 
 - **chunk_overlap:** Determines the number of characters that overlap between consecutive chunks when splitting text. It specifies how much of the previous chunk should be included in the next chunk.
 
-    For example, if the `chunk_overlap` is set to 20 and the `chunk_size` is set to 100, the splitter will create chunks of 100 characters each, but the last 20 characters of each chunk will overlap with the first 20 characters of the next chunk. This allows for a smoother transition between chunks and ensures that no information is lost – defaults to `200`.
+  For example, if the `chunk_overlap` is set to 20 and the `chunk_size` is set to 100, the splitter will create chunks of 100 characters each, but the last 20 characters of each chunk will overlap with the first 20 characters of the next chunk. This allows for a smoother transition between chunks and ensures that no information is lost – defaults to `200`.
 
 - **chunk_size:** Determines the maximum number of characters in each chunk when splitting a text. It specifies the size or length of each chunk.
 
-    For example, if the chunk_size is set to 100, the splitter will create chunks of 100 characters each. If the text is longer than 100 characters, it will be divided into multiple chunks of equal size, except for the last chunk, which may be smaller if there are remaining characters –defaults to `1000`.
+  For example, if the chunk_size is set to 100, the splitter will create chunks of 100 characters each. If the text is longer than 100 characters, it will be divided into multiple chunks of equal size, except for the last chunk, which may be smaller if there are remaining characters –defaults to `1000`.
 
-- **separator:** Specifies the character that will be used to split the text into chunks – defaults to `.` 
+- **separator:** Specifies the character that will be used to split the text into chunks – defaults to `.`
 
 ---
 
@@ -44,6 +46,18 @@ The `RecursiveCharacterTextSplitter` splits the text by trying to keep paragra
 
 - **chunk_size:** Determines the maximum number of characters in each chunk when splitting a text. It specifies the size or length of each chunk.
 
-- **separator_type:** The parameter allows the user to split the code with multiple language support. It supports various languages such as Text, Ruby, Python, Solidity, Java, and more. Defaults to `Text`.
+- **separators:** The `separators` in RecursiveCharacterTextSplitter are the characters used to split the text into chunks. The text splitter tries to create chunks based on splitting on the first character in the list of `separators`. If any chunks are too large, it moves on to the next character in the list and continues splitting. Defaults to ["\n\n", "\n", " ", ""].
 
-- **separators:** The `separators` in RecursiveCharacterTextSplitter are the characters used to split the text into chunks. The text splitter tries to create chunks based on splitting on the first character in the list of `separators`. If any chunks are too large, it moves on to the next character in the list and continues splitting. Defaults to `.`
+### LanguageRecursiveTextSplitter
+
+The `LanguageRecursiveTextSplitter` is a text splitter that splits the text into smaller chunks based on the (programming) language of the text.
+
+**Params**
+
+- **Documents:** Input documents to split.
+
+- **chunk_overlap:** Determines the number of characters that overlap between consecutive chunks when splitting text. It specifies how much of the previous chunk should be included in the next chunk.
+
+- **chunk_size:** Determines the maximum number of characters in each chunk when splitting a text. It specifies the size or length of each chunk.
+
+- **separator_type:** The parameter allows the user to split the code with multiple language support. It supports various languages such as Ruby, Python, Solidity, Java, and more. Defaults to `Python`.

docs/docs/guides/langfuse_integration.mdx

@@ -0,0 +1,49 @@
+# Integrating Langfuse with Langflow
+
+## Introduction
+
+Langfuse is an open-source tracing and analytics tool designed for LLM applications. Integrating Langfuse with Langflow provides detailed production traces and granular insights into quality, cost, and latency. This integration allows you to monitor and debug your Langflow's chat or APIs easily.
+
+## Step-by-Step Instructions
+
+### Step 1: Create a Langfuse account
+
+1. Go to [Langfuse](https://langfuse.com) and click on the "Sign In" button in the top right corner.
+2. Click on the "Sign Up" button and create an account.
+3. Once logged in, click on "Settings" and then on "Create new API keys."
+4. Copy the Public key and the Secret Key and save them somewhere safe.
+   {/* Add these keys to your environment variables in the following step. */}
+
+### Step 2: Set up Langfuse in Langflow
+
+1. **Export the Environment Variables**: You'll need to export the environment variables `LANGFLOW_LANGFUSE_SECRET_KEY` and `LANGFLOW_LANGFUSE_PUBLIC_KEY` with the values obtained in Step 1.
+
+   You can do this by executing the following commands in your terminal:
+
+   ```bash
+   export LANGFLOW_LANGFUSE_SECRET_KEY=<your secret key>
+   export LANGFLOW_LANGFUSE_PUBLIC_KEY=<your public key>
+   ```
+
+   Alternatively, you can run the Langflow CLI command:
+
+   ```bash
+   LANGFLOW_LANGFUSE_SECRET_KEY=<your secret key> LANGFLOW_LANGFUSE_PUBLIC_KEY=<your public key> langflow
+   ```
+
+   If you are self-hosting Langfuse, you can also set the environment variable `LANGFLOW_LANGFUSE_HOST` to point to your Langfuse instance. By default, Langfuse points to the cloud instance at `https://cloud.langfuse.com`.
+
+2. **Verify Integration**: Ensure that the environment variables are set correctly by checking their existence in your environment, for example by running:
+
+   ```bash
+   echo $LANGFLOW_LANGFUSE_SECRET_KEY
+   echo $LANGFLOW_LANGFUSE_PUBLIC_KEY
+   ```
+
+3. **Monitor Langflow**: Now, whenever you use Langflow's chat or API, you will be able to see the tracing of your conversations in Langfuse.
+
+That's it! You have successfully integrated Langfuse with Langflow, enhancing observability and debugging capabilities for your LLM application.
+
+---
+
+Note: For more details or customized configurations, please refer to the official [Langfuse documentation](https://langfuse.com/docs/integrations/langchain).

docs/sidebars.js

@@ -51,7 +51,11 @@ module.exports = {
       type: "category",
       label: "Step-by-Step Guides",
       collapsed: false,
-      items: ["guides/loading_document", "guides/chatprompttemplate_guide"],
+      items: [
+        "guides/loading_document",
+        "guides/chatprompttemplate_guide",
+        "guides/langfuse_integration",
+      ],
     },
     // {
     //   type: 'category',

pyproject.toml

@@ -32,17 +32,17 @@ beautifulsoup4 = "^4.12.2"
 google-search-results = "^2.4.1"
 google-api-python-client = "^2.79.0"
 typer = "^0.9.0"
-gunicorn = "^21.1.0"
+gunicorn = "^21.2.0"
 langchain = "^0.0.274"
 openai = "^0.27.8"
 pandas = "^2.0.0"
-chromadb = "^0.3.21"
+chromadb = "^0.3.0"
 huggingface-hub = { version = "^0.16.0", extras = ["inference"] }
-rich = "^13.4.2"
+rich = "^13.5.0"
 llama-cpp-python = { version = "~0.1.0", optional = true }
 networkx = "^3.1"
-unstructured = "^0.7.0"
-pypdf = "^3.11.0"
+unstructured = "^0.10.0"
+pypdf = "^3.15.0"
 lxml = "^4.9.2"
 pysrt = "^1.1.2"
 fake-useragent = "^1.2.1"
@@ -52,13 +52,13 @@ pyarrow = "^12.0.0"
 tiktoken = "~0.4.0"
 wikipedia = "^1.4.0"
 langchain-serve = { version = ">0.0.51", optional = true }
-qdrant-client = "^1.3.0"
+qdrant-client = "^1.4.0"
 websockets = "^10.3"
-weaviate-client = "^3.21.0"
+weaviate-client = "^3.23.0"
 jina = "3.15.2"
 sentence-transformers = { version = "^2.2.2", optional = true }
 ctransformers = { version = "^0.2.10", optional = true }
-cohere = "^4.11.0"
+cohere = "^4.21.0"
 python-multipart = "^0.0.6"
 sqlmodel = "^0.0.8"
 faiss-cpu = "^1.7.4"
@@ -82,9 +82,12 @@ passlib = "^1.7.4"
 bcrypt = "^4.0.1"
 python-jose = "^3.3.0"
 metaphor-python = "^0.1.11"
-markupsafe = "^2.1.3"
 pywin32 = { version = "^306", markers = "sys_platform == 'win32'" }
 loguru = "^0.7.1"
+langfuse = "^1.0.13"
+pillow = "^10.0.0"
+metal-sdk = "^2.0.2"
+markupsafe = "^2.1.3"
 
 [tool.poetry.group.dev.dependencies]
 black = "^23.1.0"
@@ -105,6 +108,7 @@ types-passlib = "^1.7.7.13"
 pytest-mock = "^3.11.1"
 pytest-xdist = "^3.3.1"
 types-pywin32 = "^306.0.0.4"
+types-google-cloud-ndb = "^2.2.0.0"
 
 
 [tool.poetry.extras]

src/backend/langflow/components/agents/OpenAIConversationalAgent.py

@@ -42,8 +42,8 @@ class ConversationalAgent(CustomComponent):
         self,
         model_name: str,
         openai_api_key: str,
-        openai_api_base: str,
         tools: Tool,
+        openai_api_base: Optional[str] = None,
         memory: Optional[BaseMemory] = None,
         system_message: Optional[SystemMessagePromptTemplate] = None,
         max_token_limit: int = 2000,

src/backend/langflow/components/llms/HuggingFaceEndpoints.py

@@ -0,0 +1,42 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.llms import HuggingFaceEndpoint
+from langchain.llms.base import BaseLLM
+
+
+class HuggingFaceEndpointsComponent(CustomComponent):
+    display_name: str = "Hugging Face Inference API"
+    description: str = "LLM model from Hugging Face Inference API."
+
+    def build_config(self):
+        return {
+            "endpoint_url": {"display_name": "Endpoint URL", "password": True},
+            "task": {
+                "display_name": "Task",
+                "type": "select",
+                "options": ["text2text-generation", "text-generation", "summarization"],
+            },
+            "huggingfacehub_api_token": {"display_name": "API token", "password": True},
+            "model_kwargs": {
+                "display_name": "Model Keyword Arguments",
+                "field_type": "code",
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        endpoint_url: str,
+        task="text2text-generation",
+        huggingfacehub_api_token: Optional[str] = None,
+        model_kwargs: Optional[dict] = None,
+    ) -> BaseLLM:
+        try:
+            output = HuggingFaceEndpoint(
+                endpoint_url=endpoint_url,
+                task=task,
+                huggingfacehub_api_token=huggingfacehub_api_token,
+            )
+        except Exception as e:
+            raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
+        return output

src/backend/langflow/components/retrievers/MetalRetriever.py

@@ -0,0 +1,28 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.retrievers import MetalRetriever
+from langchain.schema import BaseRetriever
+from metal_sdk.metal import Metal  # type: ignore
+
+
+class MetalRetrieverComponent(CustomComponent):
+    display_name: str = "Metal Retriever"
+    description: str = "Retriever that uses the Metal API."
+
+    def build_config(self):
+        return {
+            "api_key": {"display_name": "API Key", "password": True},
+            "client_id": {"display_name": "Client ID", "password": True},
+            "index_id": {"display_name": "Index ID"},
+            "params": {"display_name": "Parameters", "field_type": "code"},
+            "code": {"show": False},
+        }
+
+    def build(
+        self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None
+    ) -> BaseRetriever:
+        try:
+            metal = Metal(api_key=api_key, client_id=client_id, index_id=index_id)
+        except Exception as e:
+            raise ValueError("Could not connect to Metal API.") from e
+        return MetalRetriever(client=metal, params=params or {})

src/backend/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py

@@ -0,0 +1,82 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.text_splitter import Language
+from langchain.schema import Document
+from langflow.utils.util import build_loader_repr_from_documents
+
+
+class LanguageRecursiveTextSplitterComponent(CustomComponent):
+    display_name: str = "Language Recursive Text Splitter"
+    description: str = "Split text into chunks of a specified length based on language."
+    documentation: str = "https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter"
+
+    def build_config(self):
+        options = [x.value for x in Language]
+        return {
+            "documents": {
+                "display_name": "Documents",
+                "info": "The documents to split.",
+            },
+            "separator_type": {
+                "display_name": "Separator Type",
+                "info": "The type of separator to use.",
+                "field_type": "str",
+                "options": options,
+                "value": "Python",
+            },
+            "separators": {
+                "display_name": "Separators",
+                "info": "The characters to split on.",
+                "is_list": True,
+            },
+            "chunk_size": {
+                "display_name": "Chunk Size",
+                "info": "The maximum length of each chunk.",
+                "field_type": "int",
+                "value": 1000,
+            },
+            "chunk_overlap": {
+                "display_name": "Chunk Overlap",
+                "info": "The amount of overlap between chunks.",
+                "field_type": "int",
+                "value": 200,
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        documents: list[Document],
+        chunk_size: Optional[int] = 1000,
+        chunk_overlap: Optional[int] = 200,
+        separator_type: Optional[str] = "Python",
+    ) -> list[Document]:
+        """
+        Split text into chunks of a specified length.
+
+        Args:
+            separators (list[str]): The characters to split on.
+            chunk_size (int): The maximum length of each chunk.
+            chunk_overlap (int): The amount of overlap between chunks.
+            length_function (function): The function to use to calculate the length of the text.
+
+        Returns:
+            list[str]: The chunks of text.
+        """
+        from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+        # Make sure chunk_size and chunk_overlap are ints
+        if isinstance(chunk_size, str):
+            chunk_size = int(chunk_size)
+        if isinstance(chunk_overlap, str):
+            chunk_overlap = int(chunk_overlap)
+
+        splitter = RecursiveCharacterTextSplitter.from_language(
+            language=Language(separator_type),
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+        )
+
+        docs = splitter.split_documents(documents)
+        self.repr_value = build_loader_repr_from_documents(docs)
+        return docs

src/backend/langflow/components/textsplitters/RecursiveCharacterTextSplitter.py

@@ -0,0 +1,79 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.schema import Document
+
+
+class RecursiveCharacterTextSplitterComponent(CustomComponent):
+    display_name: str = "Recursive Character Text Splitter"
+    description: str = "Split text into chunks of a specified length."
+    documentation: str = "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter"
+
+    def build_config(self):
+        return {
+            "documents": {
+                "display_name": "Documents",
+                "info": "The documents to split.",
+            },
+            "separators": {
+                "display_name": "Separators",
+                "info": 'The characters to split on.\nIf left empty defaults to ["\\n\\n", "\\n", " ", ""].',
+                "is_list": True,
+            },
+            "chunk_size": {
+                "display_name": "Chunk Size",
+                "info": "The maximum length of each chunk.",
+                "field_type": "int",
+                "value": 1000,
+            },
+            "chunk_overlap": {
+                "display_name": "Chunk Overlap",
+                "info": "The amount of overlap between chunks.",
+                "field_type": "int",
+                "value": 200,
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        documents: list[Document],
+        separators: Optional[list[str]] = None,
+        chunk_size: Optional[int] = 1000,
+        chunk_overlap: Optional[int] = 200,
+    ) -> list[Document]:
+        """
+        Split text into chunks of a specified length.
+
+        Args:
+            separators (list[str]): The characters to split on.
+            chunk_size (int): The maximum length of each chunk.
+            chunk_overlap (int): The amount of overlap between chunks.
+            length_function (function): The function to use to calculate the length of the text.
+
+        Returns:
+            list[str]: The chunks of text.
+        """
+        from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+        if separators == "":
+            separators = None
+        elif separators:
+            # check if the separators list has escaped characters
+            # if there are escaped characters, unescape them
+            separators = [x.encode().decode("unicode-escape") for x in separators]
+
+        # Make sure chunk_size and chunk_overlap are ints
+        if isinstance(chunk_size, str):
+            chunk_size = int(chunk_size)
+        if isinstance(chunk_overlap, str):
+            chunk_overlap = int(chunk_overlap)
+        splitter = RecursiveCharacterTextSplitter(
+            separators=separators,
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+        )
+
+        docs = splitter.split_documents(documents)
+        # self.repr_value = build_loader_repr_from_documents(docs)
+        self.repr_value = separators
+        return docs

src/backend/langflow/config.yaml

@@ -171,8 +171,6 @@ prompts:
 textsplitters:
   CharacterTextSplitter:
     documentation: "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter"
-  RecursiveCharacterTextSplitter:
-    documentation: "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter"
 toolkits:
   OpenAPIToolkit:
     documentation: ""

src/backend/langflow/interface/types.py

@@ -5,6 +5,7 @@ from langflow.api.utils import merge_nested_dicts_with_renaming
 from langflow.interface.agents.base import agent_creator
 from langflow.interface.chains.base import chain_creator
 from langflow.interface.custom.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
+from langflow.interface.custom.utils import extract_inner_type
 from langflow.interface.document_loaders.base import documentloader_creator
 from langflow.interface.embeddings.base import embedding_creator
 from langflow.interface.importing.utils import get_function_custom
@@ -84,6 +85,8 @@ def build_langchain_types_dict():  # sourcery skip: dict-assign-update-to-union
 
 
 def process_type(field_type: str):
+    if field_type.startswith("list") or field_type.startswith("List"):
+        return extract_inner_type(field_type)
     return "prompt" if field_type == "Prompt" else field_type
 
 
@@ -100,6 +103,7 @@ def add_new_custom_field(
     # if it is, update the value
     display_name = field_config.pop("display_name", field_name)
     field_type = field_config.pop("field_type", field_type)
+    field_contains_list = "list" in field_type.lower()
     field_type = process_type(field_type)
     field_value = field_config.pop("value", field_value)
     field_advanced = field_config.pop("advanced", False)
@@ -110,7 +114,9 @@ def add_new_custom_field(
     # If options is a list, then it's a dropdown
     # If options is None, then it's a list of strings
     is_list = isinstance(field_config.get("options"), list)
-    field_config["is_list"] = is_list or field_config.get("is_list", False)
+    field_config["is_list"] = (
+        is_list or field_config.get("is_list", False) or field_contains_list
+    )
 
     if "name" in field_config:
         warnings.warn(
@@ -172,7 +178,7 @@ def extract_type_from_optional(field_type):
     Returns:
     str: The extracted type, or an empty string if no type was found.
     """
-    match = re.search(r"\[(.*?)\]", field_type)
+    match = re.search(r"\[(.*?)\]$", field_type)
     return match[1] if match else None
 
 

src/backend/langflow/main.py

@@ -11,6 +11,7 @@ from langflow.api import router
 from langflow.interface.utils import setup_llm_caching
 from langflow.services.database.utils import initialize_database
 from langflow.services.manager import initialize_services, teardown_services
+from langflow.services.plugins.langfuse import LangfuseInstance
 from langflow.utils.logger import configure
 
 
@@ -41,6 +42,8 @@ def create_app():
     app.on_event("startup")(initialize_database)
     app.on_event("startup")(setup_llm_caching)
     app.on_event("shutdown")(teardown_services)
+    app.on_event("startup")(LangfuseInstance.update)
+    app.on_event("shutdown")(LangfuseInstance.teardown)
     return app
 
 

src/backend/langflow/processing/base.py

@@ -1,4 +1,4 @@
-from typing import Union
+from typing import List, Union, TYPE_CHECKING
 from langflow.api.v1.callback import (
     AsyncStreamingLLMCallbackHandler,
     StreamingLLMCallbackHandler,
@@ -6,6 +6,52 @@ from langflow.api.v1.callback import (
 from langflow.processing.process import fix_memory_inputs, format_actions
 from loguru import logger
 from langchain.agents.agent import AgentExecutor
+from langchain.callbacks.base import BaseCallbackHandler
+
+if TYPE_CHECKING:
+    from langfuse.callback import CallbackHandler  # type: ignore
+
+
+def setup_callbacks(sync, trace_id, **kwargs):
+    """Setup callbacks for langchain object"""
+    callbacks = []
+    if sync:
+        callbacks.append(StreamingLLMCallbackHandler(**kwargs))
+    else:
+        callbacks.append(AsyncStreamingLLMCallbackHandler(**kwargs))
+
+    if langfuse_callback := get_langfuse_callback(trace_id=trace_id):
+        logger.debug("Langfuse callback loaded")
+        callbacks.append(langfuse_callback)
+    return callbacks
+
+
+def get_langfuse_callback(trace_id):
+    from langflow.services.plugins.langfuse import LangfuseInstance
+    from langfuse.callback import CreateTrace
+
+    logger.debug("Initializing langfuse callback")
+    if langfuse := LangfuseInstance.get():
+        logger.debug("Langfuse credentials found")
+        try:
+            trace = langfuse.trace(CreateTrace(id=trace_id))
+            return trace.getNewHandler()
+        except Exception as exc:
+            logger.error(f"Error initializing langfuse callback: {exc}")
+
+    return None
+
+
+def flush_langfuse_callback_if_present(
+    callbacks: List[Union[BaseCallbackHandler, "CallbackHandler"]]
+):
+    """
+    If langfuse callback is present, run callback.langfuse.flush()
+    """
+    for callback in callbacks:
+        if hasattr(callback, "langfuse"):
+            callback.langfuse.flush()
+            break
 
 
 async def get_result_and_steps(langchain_object, inputs: Union[dict, str], **kwargs):
@@ -27,13 +73,18 @@ async def get_result_and_steps(langchain_object, inputs: Union[dict, str], **kwa
             logger.error(f"Error fixing memory inputs: {exc}")
 
         try:
-            async_callbacks = [AsyncStreamingLLMCallbackHandler(**kwargs)]
-            output = await langchain_object.acall(inputs, callbacks=async_callbacks)
+            trace_id = kwargs.pop("session_id", None)
+            callbacks = setup_callbacks(sync=False, trace_id=trace_id, **kwargs)
+            output = await langchain_object.acall(inputs, callbacks=callbacks)
         except Exception as exc:
             # make the error message more informative
             logger.debug(f"Error: {str(exc)}")
-            sync_callbacks = [StreamingLLMCallbackHandler(**kwargs)]
-            output = langchain_object(inputs, callbacks=sync_callbacks)
+            trace_id = kwargs.pop("session_id", None)
+            callbacks = setup_callbacks(sync=True, trace_id=trace_id, **kwargs)
+            output = langchain_object(inputs, callbacks=callbacks)
+
+        # if langfuse callback is present, run callback.langfuse.flush()
+        flush_langfuse_callback_if_present(callbacks)
 
         intermediate_steps = (
             output.get("intermediate_steps", []) if isinstance(output, dict) else []

src/backend/langflow/processing/process.py

@@ -11,6 +11,7 @@ from langflow.graph import Graph
 from langchain.chains.base import Chain
 from langchain.vectorstores.base import VectorStore
 from typing import Any, Dict, List, Optional, Tuple, Union
+from langchain.schema import Document
 
 
 def fix_memory_inputs(langchain_object):
@@ -142,6 +143,8 @@ def generate_result(langchain_object: Union[Chain, VectorStore], inputs: dict):
         logger.debug("Generated result and thought")
     elif isinstance(langchain_object, VectorStore):
         result = langchain_object.search(**inputs)
+    elif isinstance(langchain_object, Document):
+        result = langchain_object.dict()
     else:
         raise ValueError(
             f"Unknown langchain_object type: {type(langchain_object).__name__}"

src/backend/langflow/services/chat/manager.py

@@ -1,4 +1,5 @@
 from collections import defaultdict
+import uuid
 from fastapi import WebSocket, status
 from langflow.api.v1.schemas import ChatMessage, ChatResponse, FileResponse
 from langflow.services.base import Service
@@ -49,6 +50,7 @@ class ChatManager(Service):
 
     def __init__(self):
         self.active_connections: Dict[str, WebSocket] = {}
+        self.connection_ids: Dict[str, str] = {}
         self.chat_history = ChatHistory()
         self.cache_manager = service_manager.get(ServiceType.CACHE_MANAGER)
         self.cache_manager.attach(self.update)
@@ -93,9 +95,13 @@ class ChatManager(Service):
 
     async def connect(self, client_id: str, websocket: WebSocket):
         self.active_connections[client_id] = websocket
+        # This is to avoid having multiple clients with the same id
+        #! Temporary solution
+        self.connection_ids[client_id] = f"{client_id}-{uuid.uuid4()}"
 
     def disconnect(self, client_id: str):
         self.active_connections.pop(client_id, None)
+        self.connection_ids.pop(client_id, None)
 
     async def send_message(self, client_id: str, message: str):
         websocket = self.active_connections[client_id]
@@ -137,6 +143,7 @@ class ChatManager(Service):
                 langchain_object=langchain_object,
                 chat_inputs=chat_inputs,
                 websocket=self.active_connections[client_id],
+                session_id=self.connection_ids[client_id],
             )
         except Exception as e:
             # Log stack trace

src/backend/langflow/services/chat/utils.py

@@ -9,6 +9,7 @@ async def process_graph(
     langchain_object,
     chat_inputs: ChatMessage,
     websocket: WebSocket,
+    session_id: str,
 ):
     langchain_object = try_setting_streaming_options(langchain_object, websocket)
     logger.debug("Loaded langchain object")
@@ -27,7 +28,10 @@ async def process_graph(
 
         logger.debug("Generating result and thought")
         result, intermediate_steps = await get_result_and_steps(
-            langchain_object, chat_inputs.message, websocket=websocket
+            langchain_object,
+            chat_inputs.message,
+            websocket=websocket,
+            session_id=session_id,
         )
         logger.debug("Generated result and intermediate_steps")
         return result, intermediate_steps

src/backend/langflow/services/plugins/langfuse.py

@@ -0,0 +1,44 @@
+from langflow.utils.logger import logger
+
+### Temporary implementation
+# This will be replaced by a plugin system once merged into 0.5.0
+
+
+class LangfuseInstance:
+    _instance = None
+
+    @classmethod
+    def get(cls):
+        logger.debug("Getting Langfuse instance")
+        if cls._instance is None:
+            cls.create()
+        return cls._instance
+
+    @classmethod
+    def create(cls):
+        logger.debug("Creating Langfuse instance")
+        from langflow.settings import settings
+        from langfuse import Langfuse  # type: ignore
+
+        if settings.LANGFUSE_PUBLIC_KEY and settings.LANGFUSE_SECRET_KEY:
+            logger.debug("Langfuse credentials found")
+            cls._instance = Langfuse(
+                public_key=settings.LANGFUSE_PUBLIC_KEY,
+                secret_key=settings.LANGFUSE_SECRET_KEY,
+            )
+        else:
+            logger.debug("No Langfuse credentials found")
+            cls._instance = None
+
+    @classmethod
+    def update(cls):
+        logger.debug("Updating Langfuse instance")
+        cls._instance = None
+        cls.create()
+
+    @classmethod
+    def teardown(cls):
+        logger.debug("Tearing down Langfuse instance")
+        if cls._instance is not None:
+            cls._instance.flush()
+        cls._instance = None

src/backend/langflow/services/settings/base.py

@@ -41,6 +41,10 @@ class Settings(BaseSettings):
     REMOVE_API_KEYS: bool = False
     COMPONENTS_PATH: List[str] = []
 
+    LANGFUSE_SECRET_KEY: Optional[str] = None
+    LANGFUSE_PUBLIC_KEY: Optional[str] = None
+    LANGFUSE_HOST: Optional[str] = None
+
     @validator("CONFIG_DIR", pre=True, allow_reuse=True)
     def set_langflow_dir(cls, value):
         if not value:

src/frontend/src/components/EditFlowSettingsComponent/index.tsx

@@ -47,21 +47,9 @@ export const EditFlowSettings: React.FC<InputProps> = ({
       setInvalidName!(true);
     }
     setName(value);
-    setCurrentName(value);
   };
 
-  const [currentName, setCurrentName] = useState(name);
-
-  const [currentDescription, setCurrentDescription] = useState(description);
-
-  useEffect(() => {
-    setCurrentName(name);
-    setCurrentDescription(description);
-  }, [name, description]);
-
   const handleDescriptionChange = (event: ChangeEvent<HTMLTextAreaElement>) => {
-    flows.find((f) => f.id === tabId).description = event.target.value;
-    setCurrentDescription(flows.find((f) => f.id === tabId).description);
     setDescription(event.target.value);
   };
 
@@ -82,7 +70,7 @@ export const EditFlowSettings: React.FC<InputProps> = ({
           onChange={handleNameChange}
           type="text"
           name="name"
-          value={currentName ?? ""}
+          value={name ?? ""}
           placeholder="File name"
           id="name"
           maxLength={maxLength}
@@ -97,7 +85,7 @@ export const EditFlowSettings: React.FC<InputProps> = ({
           name="description"
           id="description"
           onChange={handleDescriptionChange}
-          value={currentDescription}
+          value={description}
           placeholder="Flow description"
           className="mt-2 max-h-[100px] font-normal"
           rows={3}

src/frontend/src/components/inputListComponent/index.tsx

@@ -18,6 +18,9 @@ export default function InputListComponent({
     }
   }, [disabled]);
 
+  // @TODO Recursive Character Text Splitter - the value might be in string format, whereas the InputListComponent specifically requires an array format. To ensure smooth operation and prevent potential errors, it's crucial that we handle the conversion from a string to an array with the string as its element.
+  typeof value === "string" ? (value = [value]) : (value = value);
+
   return (
     <div
       className={classNames(