Release 0.6.4 (#1268)

This PR:
- Updates Vectara component by @JAtharva22
- Adds Ollama components by @yamonkjd 
- Fixes bugs in the canvas by @Cristhianzl
- adds Docker image builds on release.

.dockerignore

@@ -1,2 +1,7 @@
 .venv/
 **/aws
+# node_modules
+**/node_modules/
+dist/
+**/build/
+src/backend/langflow/frontend

.github/workflows/pre-release.yml

@@ -47,3 +47,19 @@ jobs:
           POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
         run: |
           poetry publish
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+      - name: Build and push
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          push: true
+          file: ./build_and_push.Dockerfile
+          tags: logspace/langflow:${{ steps.check-version.outputs.version }}

.github/workflows/release.yml

@@ -45,3 +45,21 @@ jobs:
           POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
         run: |
           poetry publish
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+      - name: Build and push
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          push: true
+          file: ./build_and_push.Dockerfile
+          tags: |
+            logspace/langflow:${{ steps.check-version.outputs.version }}
+            logspace/langflow:latest

build_and_push.Dockerfile

@@ -0,0 +1,79 @@
+
+
+# syntax=docker/dockerfile:1
+# Keep this syntax directive! It's used to enable Docker BuildKit
+
+# Based on https://github.com/python-poetry/poetry/discussions/1879?sort=top#discussioncomment-216865
+# but I try to keep it updated (see history)
+
+################################
+# PYTHON-BASE
+# Sets up all our shared environment variables
+################################
+FROM python:3.10-slim as python-base
+
+# python
+ENV PYTHONUNBUFFERED=1 \
+    # prevents python creating .pyc files
+    PYTHONDONTWRITEBYTECODE=1 \
+    \
+    # pip
+    PIP_DISABLE_PIP_VERSION_CHECK=on \
+    PIP_DEFAULT_TIMEOUT=100 \
+    \
+    # poetry
+    # https://python-poetry.org/docs/configuration/#using-environment-variables
+    POETRY_VERSION=1.7.1 \
+    # make poetry install to this location
+    POETRY_HOME="/opt/poetry" \
+    # make poetry create the virtual environment in the project's root
+    # it gets named `.venv`
+    POETRY_VIRTUALENVS_IN_PROJECT=true \
+    # do not ask any interactive question
+    POETRY_NO_INTERACTION=1 \
+    \
+    # paths
+    # this is where our requirements + virtual environment will live
+    PYSETUP_PATH="/opt/pysetup" \
+    VENV_PATH="/opt/pysetup/.venv"
+
+
+# prepend poetry and venv to path
+ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH"
+
+
+################################
+# BUILDER-BASE
+# Used to build deps + create our virtual environment
+################################
+FROM python-base as builder-base
+RUN
+RUN apt-get update \
+    && apt-get install --no-install-recommends -y \
+    # deps for installing poetry
+    curl \
+    # deps for building python deps
+    build-essential \
+    # npm
+    npm
+
+
+# Now we need to copy the entire project into the image
+WORKDIR /app
+COPY pyproject.toml poetry.lock ./
+COPY src ./src
+COPY Makefile ./
+COPY README.md ./
+RUN curl -sSL https://install.python-poetry.org | python3 - && make build
+
+# Final stage for the application
+FROM python-base as final
+
+# Copy virtual environment and built .tar.gz from builder base
+COPY --from=builder-base /app/dist/*.tar.gz ./
+
+# Install the package from the .tar.gz
+RUN pip install *.tar.gz
+
+WORKDIR /app
+CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"]

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langflow"
-version = "0.6.3"
+version = "0.6.4"
 description = "A Python package with a built-in web application"
 authors = ["Logspace <contact@logspace.ai>"]
 maintainers = [
@@ -28,15 +28,15 @@ langflow = "langflow.__main__:main"
 
 
 python = ">=3.9,<3.11"
-fastapi = "^0.104.0"
+fastapi = "^0.108.0"
-uvicorn = "^0.23.0"
+uvicorn = "^0.25.0"
 beautifulsoup4 = "^4.12.2"
 google-search-results = "^2.4.1"
 google-api-python-client = "^2.79.0"
 typer = "^0.9.0"
 gunicorn = "^21.2.0"
 langchain = "~0.0.345"
-openai = "^1.3.6"
+openai = "^1.6.1"
 pandas = "2.0.3"
 chromadb = "^0.4.0"
 huggingface-hub = { version = "^0.19.0", extras = ["inference"] }
@@ -47,31 +47,31 @@ unstructured = "^0.11.0"
 pypdf = "^3.17.0"
 lxml = "^4.9.2"
 pysrt = "^1.1.2"
-fake-useragent = "^1.3.0"
+fake-useragent = "^1.4.0"
 docstring-parser = "^0.15"
 psycopg2-binary = "^2.9.6"
 pyarrow = "^14.0.0"
 tiktoken = "~0.5.0"
 wikipedia = "^1.4.0"
-qdrant-client = "^1.4.0"
+qdrant-client = "^1.7.0"
 websockets = "^10.3"
-weaviate-client = "^3.23.0"
+weaviate-client = "^3.26.0"
 jina = "*"
 sentence-transformers = { version = "^2.2.2", optional = true }
 ctransformers = { version = "^0.2.10", optional = true }
-cohere = "^4.37.0"
+cohere = "^4.39.0"
 python-multipart = "^0.0.6"
 sqlmodel = "^0.0.14"
 faiss-cpu = "^1.7.4"
-anthropic = "^0.7.0"
+anthropic = "^0.8.0"
 orjson = "3.9.3"
 multiprocess = "^0.70.14"
 cachetools = "^5.3.1"
 types-cachetools = "^5.3.0.5"
 platformdirs = "^4.1.0"
 pinecone-client = "^2.2.2"
-pymongo = "^4.5.0"
+pymongo = "^4.6.0"
-supabase = "^2.0.3"
+supabase = "^2.3.0"
 certifi = "^2023.11.17"
 google-cloud-aiplatform = "^1.36.0"
 psycopg = "^3.1.9"
@@ -81,13 +81,13 @@ langchain-experimental = "*"
 celery = { extras = ["redis"], version = "^5.3.6", optional = true }
 redis = { version = "^4.6.0", optional = true }
 flower = { version = "^2.0.0", optional = true }
-alembic = "^1.12.0"
+alembic = "^1.13.0"
 passlib = "^1.7.4"
 bcrypt = "4.0.1"
 python-jose = "^3.3.0"
 metaphor-python = "^0.1.11"
-pydantic = "^2.0.0"
+pydantic = "^2.5.0"
-pydantic-settings = "^2.0.3"
+pydantic-settings = "^2.1.0"
 zep-python = "*"
 pywin32 = { version = "^306", markers = "sys_platform == 'win32'" }
 loguru = "^0.7.1"
@@ -100,10 +100,11 @@ extract-msg = "^0.45.0"
 jq = { version = "^1.6.0", markers = "sys_platform != 'win32'" }
 boto3 = "^1.28.63"
 numexpr = "^2.8.6"
-qianfan = "0.0.5"
+qianfan = "0.2.0"
 pgvector = "^0.2.3"
 pyautogen = "^0.2.0"
 langchain-google-genai = "^0.0.2"
+pytube = "^15.0.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest-asyncio = "^0.23.1"

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

@@ -0,0 +1,252 @@
+from typing import Any, Dict, List, Optional
+
+# from langchain_community.chat_models import ChatOllama
+from langchain.chat_models import ChatOllama
+from langchain.chat_models.base import BaseChatModel
+
+# from langchain.chat_models import ChatOllama
+from langflow import CustomComponent
+
+# whe When a callback component is added to Langflow, the comment must be uncommented.
+# from langchain.callbacks.manager import CallbackManager
+
+
+class ChatOllamaComponent(CustomComponent):
+    display_name = "ChatOllama"
+    description = "Local LLM for chat with Ollama."
+
+    def build_config(self) -> dict:
+        return {
+            "base_url": {
+                "display_name": "Base URL",
+                "info": "Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.",
+            },
+            "model": {
+                "display_name": "Model Name",
+                "value": "llama2",
+                "info": "Refer to https://ollama.ai/library for more models.",
+            },
+            "temperature": {
+                "display_name": "Temperature",
+                "field_type": "float",
+                "value": 0.8,
+                "info": "Controls the creativity of model responses.",
+            },
+            "cache": {
+                "display_name": "Cache",
+                "field_type": "bool",
+                "info": "Enable or disable caching.",
+                "advanced": True,
+                "value": False,
+            },
+            ### When a callback component is added to Langflow, the comment must be uncommented. ###
+            # "callback_manager": {
+            #     "display_name": "Callback Manager",
+            #     "info": "Optional callback manager for additional functionality.",
+            #     "advanced": True,
+            # },
+            # "callbacks": {
+            #     "display_name": "Callbacks",
+            #     "info": "Callbacks to execute during model runtime.",
+            #     "advanced": True,
+            # },
+            ########################################################################################
+            "format": {
+                "display_name": "Format",
+                "field_type": "str",
+                "info": "Specify the format of the output (e.g., json).",
+                "advanced": True,
+            },
+            "metadata": {
+                "display_name": "Metadata",
+                "info": "Metadata to add to the run trace.",
+                "advanced": True,
+            },
+            "mirostat": {
+                "display_name": "Mirostat",
+                "options": ["Disabled", "Mirostat", "Mirostat 2.0"],
+                "info": "Enable/disable Mirostat sampling for controlling perplexity.",
+                "value": "Disabled",
+                "advanced": True,
+            },
+            "mirostat_eta": {
+                "display_name": "Mirostat Eta",
+                "field_type": "float",
+                "info": "Learning rate for Mirostat algorithm. (Default: 0.1)",
+                "advanced": True,
+            },
+            "mirostat_tau": {
+                "display_name": "Mirostat Tau",
+                "field_type": "float",
+                "info": "Controls the balance between coherence and diversity of the output. (Default: 5.0)",
+                "advanced": True,
+            },
+            "num_ctx": {
+                "display_name": "Context Window Size",
+                "field_type": "int",
+                "info": "Size of the context window for generating tokens. (Default: 2048)",
+                "advanced": True,
+            },
+            "num_gpu": {
+                "display_name": "Number of GPUs",
+                "field_type": "int",
+                "info": "Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)",
+                "advanced": True,
+            },
+            "num_thread": {
+                "display_name": "Number of Threads",
+                "field_type": "int",
+                "info": "Number of threads to use during computation. (Default: detected for optimal performance)",
+                "advanced": True,
+            },
+            "repeat_last_n": {
+                "display_name": "Repeat Last N",
+                "field_type": "int",
+                "info": "How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)",
+                "advanced": True,
+            },
+            "repeat_penalty": {
+                "display_name": "Repeat Penalty",
+                "field_type": "float",
+                "info": "Penalty for repetitions in generated text. (Default: 1.1)",
+                "advanced": True,
+            },
+            "tfs_z": {
+                "display_name": "TFS Z",
+                "field_type": "float",
+                "info": "Tail free sampling value. (Default: 1)",
+                "advanced": True,
+            },
+            "timeout": {
+                "display_name": "Timeout",
+                "field_type": "int",
+                "info": "Timeout for the request stream.",
+                "advanced": True,
+            },
+            "top_k": {
+                "display_name": "Top K",
+                "field_type": "int",
+                "info": "Limits token selection to top K. (Default: 40)",
+                "advanced": True,
+            },
+            "top_p": {
+                "display_name": "Top P",
+                "field_type": "float",
+                "info": "Works together with top-k. (Default: 0.9)",
+                "advanced": True,
+            },
+            "verbose": {
+                "display_name": "Verbose",
+                "field_type": "bool",
+                "info": "Whether to print out response text.",
+            },
+            "tags": {
+                "display_name": "Tags",
+                "field_type": "list",
+                "info": "Tags to add to the run trace.",
+                "advanced": True,
+            },
+            "stop": {
+                "display_name": "Stop Tokens",
+                "field_type": "list",
+                "info": "List of tokens to signal the model to stop generating text.",
+                "advanced": True,
+            },
+            "system": {
+                "display_name": "System",
+                "field_type": "str",
+                "info": "System to use for generating text.",
+                "advanced": True,
+            },
+            "template": {
+                "display_name": "Template",
+                "field_type": "str",
+                "info": "Template to use for generating text.",
+                "advanced": True,
+            },
+        }
+
+    def build(
+        self,
+        base_url: Optional[str],
+        model: str,
+        mirostat: Optional[str],
+        mirostat_eta: Optional[float] = None,
+        mirostat_tau: Optional[float] = None,
+        ### When a callback component is added to Langflow, the comment must be uncommented.###
+        # callback_manager: Optional[CallbackManager] = None,
+        # callbacks: Optional[List[Callbacks]] = None,
+        #######################################################################################
+        repeat_last_n: Optional[int] = None,
+        verbose: Optional[bool] = None,
+        cache: Optional[bool] = None,
+        num_ctx: Optional[int] = None,
+        num_gpu: Optional[int] = None,
+        format: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+        num_thread: Optional[int] = None,
+        repeat_penalty: Optional[float] = None,
+        stop: Optional[List[str]] = None,
+        system: Optional[str] = None,
+        tags: Optional[List[str]] = None,
+        temperature: Optional[float] = None,
+        template: Optional[str] = None,
+        tfs_z: Optional[float] = None,
+        timeout: Optional[int] = None,
+        top_k: Optional[int] = None,
+        top_p: Optional[int] = None,
+    ) -> BaseChatModel:
+        if not base_url:
+            base_url = "http://localhost:11434"
+
+        # Mapping mirostat settings to their corresponding values
+        mirostat_options = {"Mirostat": 1, "Mirostat 2.0": 2}
+
+        # Default to 0 for 'Disabled'
+        mirostat_value = mirostat_options.get(mirostat, 0)  # type: ignore
+
+        # Set mirostat_eta and mirostat_tau to None if mirostat is disabled
+        if mirostat_value == 0:
+            mirostat_eta = None
+            mirostat_tau = None
+
+        # Mapping system settings to their corresponding values
+        llm_params = {
+            "base_url": base_url,
+            "cache": cache,
+            "model": model,
+            "mirostat": mirostat_value,
+            "format": format,
+            "metadata": metadata,
+            "tags": tags,
+            ## When a callback component is added to Langflow, the comment must be uncommented.##
+            # "callback_manager": callback_manager,
+            # "callbacks": callbacks,
+            #####################################################################################
+            "mirostat_eta": mirostat_eta,
+            "mirostat_tau": mirostat_tau,
+            "num_ctx": num_ctx,
+            "num_gpu": num_gpu,
+            "num_thread": num_thread,
+            "repeat_last_n": repeat_last_n,
+            "repeat_penalty": repeat_penalty,
+            "temperature": temperature,
+            "stop": stop,
+            "system": system,
+            "template": template,
+            "tfs_z": tfs_z,
+            "timeout": timeout,
+            "top_k": top_k,
+            "top_p": top_p,
+            "verbose": verbose,
+        }
+
+        # None Value remove
+        llm_params = {k: v for k, v in llm_params.items() if v is not None}
+
+        try:
+            output = ChatOllama(**llm_params)  # type: ignore
+        except Exception as e:
+            raise ValueError("Could not initialize Ollama LLM.") from e
+
+        return output  # type: ignore

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

@@ -0,0 +1,163 @@
+from typing import Optional, List
+
+from langchain.llms import Ollama
+from langchain.llms.base import BaseLLM
+
+from langflow import CustomComponent
+
+
+class OllamaLLM(CustomComponent):
+    display_name = "Ollama"
+    description = "Local LLM with Ollama."
+
+    def build_config(self) -> dict:
+        return {
+            "base_url": {
+                "display_name": "Base URL",
+                "info": "Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.",
+            },
+            "model": {
+                "display_name": "Model Name",
+                "value": "llama2",
+                "info": "Refer to https://ollama.ai/library for more models.",
+            },
+            "temperature": {
+                "display_name": "Temperature",
+                "field_type": "float",
+                "value": 0.8,
+                "info": "Controls the creativity of model responses.",
+            },
+            "mirostat": {
+                "display_name": "Mirostat",
+                "options": ["Disabled", "Mirostat", "Mirostat 2.0"],
+                "info": "Enable/disable Mirostat sampling for controlling perplexity.",
+                "value": "Disabled",
+                "advanced": True,
+            },
+            "mirostat_eta": {
+                "display_name": "Mirostat Eta",
+                "field_type": "float",
+                "info": "Learning rate influencing the algorithm's response to feedback.",
+                "advanced": True,
+            },
+            "mirostat_tau": {
+                "display_name": "Mirostat Tau",
+                "field_type": "float",
+                "info": "Controls balance between coherence and diversity.",
+                "advanced": True,
+            },
+            "num_ctx": {
+                "display_name": "Context Window Size",
+                "field_type": "int",
+                "info": "Size of the context window for generating the next token.",
+                "advanced": True,
+            },
+            "num_gpu": {
+                "display_name": "Number of GPUs",
+                "field_type": "int",
+                "info": "Number of GPUs to use for computation.",
+                "advanced": True,
+            },
+            "num_thread": {
+                "display_name": "Number of Threads",
+                "field_type": "int",
+                "info": "Number of threads to use during computation.",
+                "advanced": True,
+            },
+            "repeat_last_n": {
+                "display_name": "Repeat Last N",
+                "field_type": "int",
+                "info": "Sets how far back the model looks to prevent repetition.",
+                "advanced": True,
+            },
+            "repeat_penalty": {
+                "display_name": "Repeat Penalty",
+                "field_type": "float",
+                "info": "Penalty for repetitions in generated text.",
+                "advanced": True,
+            },
+            "stop": {
+                "display_name": "Stop Tokens",
+                "info": "List of tokens to signal the model to stop generating text.",
+                "advanced": True,
+            },
+            "tfs_z": {
+                "display_name": "TFS Z",
+                "field_type": "float",
+                "info": "Tail free sampling to reduce impact of less probable tokens.",
+                "advanced": True,
+            },
+            "top_k": {
+                "display_name": "Top K",
+                "field_type": "int",
+                "info": "Limits token selection to top K for reducing nonsense generation.",
+                "advanced": True,
+            },
+            "top_p": {
+                "display_name": "Top P",
+                "field_type": "int",
+                "info": "Works with top-k to control diversity of generated text.",
+                "advanced": True,
+            },
+        }
+
+    def build(
+        self,
+        base_url: Optional[str],
+        model: str,
+        temperature: Optional[float],
+        mirostat: Optional[str],
+        mirostat_eta: Optional[float] = None,
+        mirostat_tau: Optional[float] = None,
+        num_ctx: Optional[int] = None,
+        num_gpu: Optional[int] = None,
+        num_thread: Optional[int] = None,
+        repeat_last_n: Optional[int] = None,
+        repeat_penalty: Optional[float] = None,
+        stop: Optional[List[str]] = None,
+        tfs_z: Optional[float] = None,
+        top_k: Optional[int] = None,
+        top_p: Optional[int] = None,
+    ) -> BaseLLM:
+        if not base_url:
+            base_url = "http://localhost:11434"
+
+        # Mapping mirostat settings to their corresponding values
+        mirostat_options = {"Mirostat": 1, "Mirostat 2.0": 2}
+
+        # Default to 0 for 'Disabled'
+        mirostat_value = mirostat_options.get(mirostat, 0)  # type: ignore
+
+        # Set mirostat_eta and mirostat_tau to None if mirostat is disabled
+        if mirostat_value == 0:
+            mirostat_eta = None
+            mirostat_tau = None
+
+        llm_params = {
+            "base_url": base_url,
+            "model": model,
+            "mirostat": mirostat_value,
+            "mirostat_eta": mirostat_eta,
+            "mirostat_tau": mirostat_tau,
+            "num_ctx": num_ctx,
+            "num_gpu": num_gpu,
+            "num_thread": num_thread,
+            "repeat_last_n": repeat_last_n,
+            "repeat_penalty": repeat_penalty,
+            "temperature": temperature,
+            "stop": stop,
+            "tfs_z": tfs_z,
+            "top_k": top_k,
+            "top_p": top_p,
+        }
+        
+                # None Value remove
+        llm_params = {k: v for k, v in llm_params.items() if v is not None}
+
+
+        try:
+            llm = Ollama(**llm_params)
+        except Exception as e:
+            raise ValueError("Could not connect to Ollama.") from e
+
+        return llm

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

@@ -0,0 +1,70 @@
+from typing import List
+from langflow import CustomComponent
+import json
+from langchain.schema import BaseRetriever
+from langchain.schema.vectorstore import VectorStore
+from langchain.base_language import BaseLanguageModel
+from langchain.retrievers.self_query.base import SelfQueryRetriever
+from langchain.chains.query_constructor.base import AttributeInfo
+
+
+class VectaraSelfQueryRetriverComponent(CustomComponent):
+    """
+    A custom component for implementing Vectara Self Query Retriever using a vector store.
+    """
+
+    display_name: str = "Vectara Self Query Retriever for Vectara Vector Store"
+    description: str = "Implementation of Vectara Self Query Retriever"
+    documentation = (
+        "https://python.langchain.com/docs/integrations/retrievers/self_query/vectara_self_query"
+    )
+    beta = True
+
+    field_config = {
+        "code": {"show": True},
+        "vectorstore": {
+            "display_name": "Vector Store", 
+            "info": "Input Vectara Vectore Store"
+            },
+        "llm": {
+            "display_name": "LLM", 
+            "info": "For self query retriever"
+            },
+        "document_content_description":{
+            "display_name": "Document Content Description", 
+            "info": "For self query retriever",
+            },
+        "metadata_field_info": {
+            "display_name": "Metadata Field Info", 
+            "info": "Each metadata field info is a string in the form of key value pair dictionary containing additional search metadata.\nExample input: {\"name\":\"speech\",\"description\":\"what name of the speech\",\"type\":\"string or list[string]\"}.\nThe keys should remain constant(name, description, type)",
+            },
+    }
+
+    def build(
+        self,
+        vectorstore: VectorStore,
+        document_content_description: str,
+        llm: BaseLanguageModel,
+        metadata_field_info: List[str],
+    ) -> BaseRetriever:
+        
+        metadata_field_obj = []
+
+        for meta in metadata_field_info:
+            meta_obj = json.loads(meta)
+            if 'name' not in meta_obj or 'description' not in meta_obj or 'type' not in meta_obj :
+                raise Exception('Incorrect metadata field info format.')
+            attribute_info = AttributeInfo(
+                name = meta_obj['name'],
+                description = meta_obj['description'],
+                type = meta_obj['type'],
+            )
+            metadata_field_obj.append(attribute_info)
+
+        return SelfQueryRetriever.from_llm(
+            llm,
+            vectorstore, 
+            document_content_description, 
+            metadata_field_obj, 
+            verbose=True
+        )

src/backend/langflow/components/vectorstores/Vectara.py

@@ -1,10 +1,14 @@
-from typing import Optional, Union
+from typing import Optional, Union, List
-
-from langchain.schema import BaseRetriever, Document
-from langchain.vectorstores import Vectara
-from langchain.vectorstores.base import VectorStore
-
 from langflow import CustomComponent
+import tempfile
+import urllib.request
+import urllib
+
+from langchain.vectorstores import Vectara
+from langchain.schema import Document
+from langchain.vectorstores.base import VectorStore
+from langchain.schema import BaseRetriever
+from langchain.embeddings import FakeEmbeddings
 
 
 class VectaraComponent(CustomComponent):
@@ -12,13 +16,29 @@ class VectaraComponent(CustomComponent):
     description: str = "Implementation of Vector Store using Vectara"
     documentation = "https://python.langchain.com/docs/integrations/vectorstores/vectara"
     beta = True
-    # api key should be password = True
     field_config = {
-        "vectara_customer_id": {"display_name": "Vectara Customer ID"},
+        "vectara_customer_id": {
-        "vectara_corpus_id": {"display_name": "Vectara Corpus ID"},
+            "display_name": "Vectara Customer ID",
-        "vectara_api_key": {"display_name": "Vectara API Key", "password": True},
+            "required": True,
+        },
+        "vectara_corpus_id": {
+            "display_name": "Vectara Corpus ID",
+            "required": True,
+        },
+        "vectara_api_key": {
+            "display_name": "Vectara API Key",
+            "password": True,
+            "required": True,
+        },
         "code": {"show": False},
-        "documents": {"display_name": "Documents"},
+        "documents": {
+            "display_name": "Documents",
+            "info": "Pass in either for Self Query Retriever or for making a Vectara Object",
+        },
+        "files_url": {
+            "display_name": "Files Url",
+            "info": "Make vectara object using url of files(documents not needed)",
+        },
     }
 
     def build(
@@ -26,21 +46,35 @@ class VectaraComponent(CustomComponent):
         vectara_customer_id: str,
         vectara_corpus_id: str,
         vectara_api_key: str,
+        files_url: Optional[List[str]] = None,
         documents: Optional[Document] = None,
     ) -> Union[VectorStore, BaseRetriever]:
-        # If documents, then we need to create a Vectara instance using .from_documents
         if documents is not None:
             return Vectara.from_documents(
-                documents=documents,  # type: ignore
+                documents=documents,
+                embedding=FakeEmbeddings(size=768),
+                vectara_customer_id=vectara_customer_id,
+                vectara_corpus_id=vectara_corpus_id,
+                vectara_api_key=vectara_api_key,
+            )
+
+        if files_url is not None:
+            files_list = []
+            for url in files_url:
+                name = tempfile.NamedTemporaryFile().name
+                urllib.request.urlretrieve(url, name)
+                files_list.append(name)
+
+            return Vectara.from_files(
+                files=files_list,
+                embedding=FakeEmbeddings(size=768),
                 vectara_customer_id=vectara_customer_id,
                 vectara_corpus_id=vectara_corpus_id,
                 vectara_api_key=vectara_api_key,
-                source="langflow",
             )
 
         return Vectara(
             vectara_customer_id=vectara_customer_id,
             vectara_corpus_id=vectara_corpus_id,
             vectara_api_key=vectara_api_key,
-            source="langflow",
         )

src/backend/langflow/graph/vertex/base.py

@@ -3,13 +3,12 @@ import inspect
 import types
 from typing import TYPE_CHECKING, Any, Coroutine, Dict, List, Optional
 
-from loguru import logger
-
 from langflow.graph.utils import UnbuiltObject
 from langflow.interface.initialize import loading
 from langflow.interface.listing import lazy_load_dict
 from langflow.utils.constants import DIRECT_TYPES
 from langflow.utils.util import sync_to_async
+from loguru import logger
 
 if TYPE_CHECKING:
     from langflow.graph.edge.base import Edge
@@ -140,7 +139,11 @@ class Vertex:
             if not hasattr(edge, "target_param"):
                 continue
             param_key = edge.target_param
-            if param_key in template_dict:
+
+            # If the param_key is in the template_dict and the edge.target_id is the current node
+            # We check this to make sure params with the same name but different target_id
+            # don't get overwritten
+            if param_key in template_dict and edge.target_id == self.id:
                 if template_dict[param_key]["list"]:
                     if param_key not in params:
                         params[param_key] = []

src/frontend/src/components/dropdownComponent/index.tsx

@@ -22,6 +22,8 @@ export default function Dropdown({
   }, [value]);
 
   return (
+    <>
+      {Object.keys(options)?.length > 0 ? (
         <>
           <Listbox
             value={internalValue}
@@ -71,7 +73,7 @@ export default function Dropdown({
                         apiModal ? "mb-2 w-[250px]" : "absolute w-full"
                       )}
                     >
-                  {options.map((option, id) => (
+                      {options?.map((option, id) => (
                         <Listbox.Option
                           key={id}
                           className={({ active }) =>
@@ -125,5 +127,15 @@ export default function Dropdown({
             )}
           </Listbox>
         </>
+      ) : (
+        <>
+          <div>
+            <span className="text-sm italic">
+              No parameters are available for display.
+            </span>
+          </div>
+        </>
+      )}
+    </>
   );
 }

src/frontend/src/modals/formModal/chatInput/index.tsx

@@ -46,7 +46,14 @@ export default function ChatInput({
               : "hidden"
           }`,
         }}
-        value={lockChat ? "Thinking..." : chatValue}
+        value={
+          lockChat
+            ? "Thinking..."
+            : typeof chatValue === "object" &&
+              Object.keys(chatValue)?.length === 0
+            ? "No chat input variables found. Click to run your flow."
+            : chatValue
+        }
         onChange={(event): void => {
           setChatValue(event.target.value);
         }}

src/frontend/src/modals/formModal/chatMessage/index.tsx

@@ -1,5 +1,5 @@
 import Convert from "ansi-to-html";
-import { useMemo, useState } from "react";
+import { useState } from "react";
 import ReactMarkdown from "react-markdown";
 import rehypeMathjax from "rehype-mathjax";
 import remarkGfm from "remark-gfm";
@@ -74,9 +74,7 @@ export default function ChatMessage({
             <div className="w-full">
               <div className="w-full dark:text-white">
                 <div className="w-full">
-                  {useMemo(
+                  {chat.message.toString() === "" && lockChat ? (
-                    () =>
-                      chat.message.toString() === "" && lockChat ? (
                     <IconComponent
                       name="MoreHorizontal"
                       className="h-8 w-8 animate-pulse"
@@ -113,9 +111,7 @@ export default function ChatMessage({
                             );
                           }
 
-                              const match = /language-(\w+)/.exec(
+                          const match = /language-(\w+)/.exec(className || "");
-                                className || ""
-                              );
 
                           return !inline ? (
                             <CodeTabsComponent
@@ -143,8 +139,6 @@ export default function ChatMessage({
                     >
                       {chat.message.toString()}
                     </ReactMarkdown>
-                      ),
-                    [chat.message, chat.message.toString()]
                   )}
                 </div>
                 {chat.files && (

src/frontend/src/modals/formModal/index.tsx

@@ -146,13 +146,14 @@ export default function FormModal({
             newChat[newChat.length - 1].message + str;
         }
       }
-      if (thought) {
+
+      if (thought && newChat[newChat.length - 1]?.thought) {
         newChat[newChat.length - 1].thought = thought;
       }
-      if (files) {
+      if (files && newChat[newChat.length - 1]?.files) {
         newChat[newChat.length - 1].files = files;
       }
-      if (prompt) {
+      if (prompt && newChat[newChat.length - 2]?.template) {
         newChat[newChat.length - 2].template = prompt;
       }
       return newChat;

src/frontend/src/modals/genericModal/index.tsx

@@ -122,25 +122,22 @@ export default function GenericModal({
         }
         if (apiReturn.data) {
           let inputVariables = apiReturn.data.input_variables ?? [];
-          if (!inputVariables || inputVariables.length === 0) {
-            setIsEdit(true);
-            setNoticeData({
-              title: "Your template does not have any variables.",
-            });
-            setModalOpen(false);
-          } else {
           if (
-              JSON.stringify(apiReturn.data?.frontend_node) !==
+            JSON.stringify(apiReturn.data?.frontend_node) !== JSON.stringify({})
-              JSON.stringify({})
           ) {
             setNodeClass!(apiReturn.data?.frontend_node, inputValue);
             setModalOpen(closeModal);
             setIsEdit(false);
+          }
+          if (!inputVariables || inputVariables.length === 0) {
+            setNoticeData({
+              title: "Your template does not have any variables.",
+            });
+          } else {
             setSuccessData({
               title: "Prompt is ready",
             });
           }
-          }
         } else {
           setIsEdit(true);
           setErrorData({