Merge branch 'dev' into minimizeNode

README.md

@@ -245,7 +245,7 @@ print(run_flow("Your message", flow_id=FLOW_ID, tweaks=TWEAKS))
 
 ## Deploy on Railway
 
-[![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/Emy2sU?referralCode=MnPSdg)
+[![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/JMXEWp?referralCode=MnPSdg)
 
 ## Deploy on Render
 

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/guidelines/custom-component.mdx

@@ -387,7 +387,7 @@ Your structure should look something like this:
 The recommended way to load custom components is to set the _`LANGFLOW_COMPONENTS_PATH`_ environment variable to the path of your custom components directory. Then, run the Langflow CLI as usual.
 
 ```bash
-export LANGFLOW_COMPONENTS_PATH=/path/to/components
+export LANGFLOW_COMPONENTS_PATH='["/path/to/components"]'
 langflow
 ```
 

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]

render.yaml

@@ -3,9 +3,14 @@ services:
   - type: web
     name: langflow
     runtime: docker
-    plan: free
     dockerfilePath: ./Dockerfile
     repo: https://github.com/logspace-ai/langflow
     branch: main
     healthCheckPath: /health
     autoDeploy: false
+    envVars:
+      - key: LANGFLOW_DATABASE_URL
+        value: sqlite:////home/user/.cache/langflow/langflow.db
+    disk:
+      name: langflow-data
+      mountPath: /home/user/.cache/langflow

src/backend/langflow/api/v1/users.py

@@ -88,7 +88,7 @@ def read_all_users(
 def patch_user(
     user_id: UUID,
     user_update: UserUpdate,
-    user: Session = Depends(get_current_active_user),
+    user: User = Depends(get_current_active_user),
     session: Session = Depends(get_session),
 ) -> User:
     """
@@ -113,7 +113,7 @@ def patch_user(
 def reset_password(
     user_id: UUID,
     user_update: UserUpdate,
-    user: Session = Depends(get_current_active_user),
+    user: User = Depends(get_current_active_user),
     session: Session = Depends(get_session),
 ) -> User:
     """

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 +1,80 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.text_splitter import Language
+from langchain.schema import Document
+
+
+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)
+        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
+from langflow.utils.util import build_loader_repr_from_documents
+
+
+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)
+        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