Merge branch 'dev' into vectorstores/elasticsearch
This commit is contained in:
Gabriel Luiz Freitas Almeida
GitHub
commit
0c592c8f06
23 changed files with 7574 additions and 14831 deletions
|
|
@ -1,2 +1,7 @@
|
|||
.venv/
|
||||
**/aws
|
||||
**/aws
|
||||
# node_modules
|
||||
**/node_modules/
|
||||
dist/
|
||||
**/build/
|
||||
src/backend/langflow/frontend
|
||||
16
.github/workflows/pre-release.yml
vendored
16
.github/workflows/pre-release.yml
vendored
|
|
@ -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 }}
|
||||
|
|
|
|||
18
.github/workflows/release.yml
vendored
18
.github/workflows/release.yml
vendored
|
|
@ -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
|
||||
|
|
|
|||
5
.gitignore
vendored
5
.gitignore
vendored
|
|
@ -17,6 +17,9 @@ qdrant_storage
|
|||
.chroma
|
||||
.ruff_cache
|
||||
|
||||
# PyCharm
|
||||
.idea/
|
||||
|
||||
# Diagnostic reports (https://nodejs.org/api/report.html)
|
||||
report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json
|
||||
|
||||
|
|
@ -256,4 +259,4 @@ langflow.db
|
|||
/tmp/*
|
||||
src/backend/langflow/frontend/
|
||||
.docker
|
||||
scratchpad*
|
||||
scratchpad*
|
||||
|
|
|
|||
|
|
@ -133,7 +133,7 @@ Follow our step-by-step guide to deploy Langflow on Google Cloud Platform (GCP)
|
|||
|
||||
Alternatively, click the **"Open in Cloud Shell"** button below to launch Google Cloud Shell, clone the Langflow repository, and start an **interactive tutorial** that will guide you through the process of setting up the necessary resources and deploying Langflow on your GCP project.
|
||||
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
|
||||
## Deploy on Railway
|
||||
|
||||
|
|
|
|||
79
build_and_push.Dockerfile
Normal file
79
build_and_push.Dockerfile
Normal file
|
|
@ -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"]
|
||||
20173
docs/package-lock.json
generated
20173
docs/package-lock.json
generated
File diff suppressed because it is too large
Load diff
|
|
@ -1,5 +1,5 @@
|
|||
{
|
||||
"name": "docusaurus",
|
||||
"name": "langflow-docs",
|
||||
"version": "0.0.0",
|
||||
"private": true,
|
||||
"scripts": {
|
||||
|
|
@ -36,8 +36,8 @@
|
|||
"path-browserify": "^1.0.1",
|
||||
"postcss": "^8.4.31",
|
||||
"prism-react-renderer": "^1.3.5",
|
||||
"react": "^17.0.2",
|
||||
"react-dom": "^17.0.2",
|
||||
"react": "^18.2.0",
|
||||
"react-dom": "^18.2.0",
|
||||
"react-images": "^0.6.7",
|
||||
"react-medium-image-zoom": "^5.1.6",
|
||||
"react-player": "^2.12.0",
|
||||
|
|
|
|||
1028
poetry.lock
generated
1028
poetry.lock
generated
File diff suppressed because it is too large
Load diff
|
|
@ -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"
|
||||
uvicorn = "^0.23.0"
|
||||
fastapi = "^0.108.0"
|
||||
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"
|
||||
supabase = "^2.0.3"
|
||||
pymongo = "^4.6.0"
|
||||
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-settings = "^2.0.3"
|
||||
pydantic = "^2.5.0"
|
||||
pydantic-settings = "^2.1.0"
|
||||
zep-python = "*"
|
||||
pywin32 = { version = "^306", markers = "sys_platform == 'win32'" }
|
||||
loguru = "^0.7.1"
|
||||
|
|
@ -100,11 +100,13 @@ 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"
|
||||
elasticsearch = "^8.11.1"
|
||||
pytube = "^15.0.0"
|
||||
llama-index = "^0.9.24"
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
pytest-asyncio = "^0.23.1"
|
||||
|
|
|
|||
|
|
@ -1,7 +1,8 @@
|
|||
from typing import Optional
|
||||
from langflow import CustomComponent
|
||||
from langchain.llms.bedrock import Bedrock
|
||||
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langchain.llms.bedrock import Bedrock
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
class AmazonBedrockComponent(CustomComponent):
|
||||
|
|
@ -27,18 +28,32 @@ class AmazonBedrockComponent(CustomComponent):
|
|||
},
|
||||
"credentials_profile_name": {"display_name": "Credentials Profile Name"},
|
||||
"streaming": {"display_name": "Streaming", "field_type": "bool"},
|
||||
"code": {"show": False},
|
||||
"endpoint_url": {"display_name": "Endpoint URL"},
|
||||
"region_name": {"display_name": "Region Name"},
|
||||
"model_kwargs": {"display_name": "Model Kwargs"},
|
||||
"cache": {"display_name": "Cache"},
|
||||
"code": {"advanced": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
model_id: str = "anthropic.claude-instant-v1",
|
||||
credentials_profile_name: Optional[str] = None,
|
||||
region_name: Optional[str] = None,
|
||||
model_kwargs: Optional[dict] = None,
|
||||
endpoint_url: Optional[str] = None,
|
||||
streaming: bool = False,
|
||||
cache: Optional[bool] = None,
|
||||
) -> BaseLLM:
|
||||
try:
|
||||
output = Bedrock(
|
||||
credentials_profile_name=credentials_profile_name,
|
||||
model_id=model_id,
|
||||
region_name=region_name,
|
||||
model_kwargs=model_kwargs,
|
||||
endpoint_url=endpoint_url,
|
||||
streaming=streaming,
|
||||
cache=cache,
|
||||
) # type: ignore
|
||||
except Exception as e:
|
||||
raise ValueError("Could not connect to AmazonBedrock API.") from e
|
||||
|
|
|
|||
252
src/backend/langflow/components/llms/ChatOllamaEndpoint.py
Normal file
252
src/backend/langflow/components/llms/ChatOllamaEndpoint.py
Normal file
|
|
@ -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
|
||||
163
src/backend/langflow/components/llms/OllamaLLM.py
Normal file
163
src/backend/langflow/components/llms/OllamaLLM.py
Normal file
|
|
@ -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
|
||||
|
|
@ -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
|
||||
)
|
||||
|
|
@ -5,14 +5,15 @@ from typing import Any, Dict, List
|
|||
|
||||
import orjson
|
||||
from fastapi import WebSocket, status
|
||||
from loguru import logger
|
||||
from starlette.websockets import WebSocketState
|
||||
|
||||
from langflow.api.v1.schemas import ChatMessage, ChatResponse, FileResponse
|
||||
from langflow.interface.utils import pil_to_base64
|
||||
from langflow.services import ServiceType, service_manager
|
||||
from langflow.services.base import Service
|
||||
from langflow.services.chat.cache import Subject
|
||||
from langflow.services.chat.utils import process_graph
|
||||
from loguru import logger
|
||||
from starlette.websockets import WebSocketState
|
||||
|
||||
from .cache import cache_service
|
||||
|
||||
|
|
@ -117,7 +118,7 @@ class ChatService(Service):
|
|||
if "after sending" in str(exc):
|
||||
logger.error(f"Error closing connection: {exc}")
|
||||
|
||||
async def process_message(self, client_id: str, payload: Dict, langchain_object: Any):
|
||||
async def process_message(self, client_id: str, payload: Dict, build_result: Any):
|
||||
# Process the graph data and chat message
|
||||
chat_inputs = payload.pop("inputs", {})
|
||||
chatkey = payload.pop("chatKey", None)
|
||||
|
|
@ -134,12 +135,12 @@ class ChatService(Service):
|
|||
logger.debug("Generating result and thought")
|
||||
|
||||
result, intermediate_steps, raw_output = await process_graph(
|
||||
langchain_object=langchain_object,
|
||||
build_result=build_result,
|
||||
chat_inputs=chat_inputs,
|
||||
client_id=client_id,
|
||||
session_id=self.connection_ids[client_id],
|
||||
)
|
||||
self.set_cache(client_id, langchain_object)
|
||||
self.set_cache(client_id, build_result)
|
||||
except Exception as e:
|
||||
# Log stack trace
|
||||
logger.exception(e)
|
||||
|
|
@ -205,8 +206,8 @@ class ChatService(Service):
|
|||
continue
|
||||
|
||||
with self.chat_cache.set_client_id(client_id):
|
||||
if langchain_object := self.cache_service.get(client_id).get("result"):
|
||||
await self.process_message(client_id, payload, langchain_object)
|
||||
if build_result := self.cache_service.get(client_id).get("result"):
|
||||
await self.process_message(client_id, payload, build_result)
|
||||
|
||||
else:
|
||||
raise RuntimeError(f"Could not find a build result for client_id {client_id}")
|
||||
|
|
|
|||
|
|
@ -1,20 +1,28 @@
|
|||
from typing import Any
|
||||
|
||||
from langchain.agents import AgentExecutor
|
||||
from langchain.chains.base import Chain
|
||||
from langchain_core.runnables import Runnable
|
||||
from loguru import logger
|
||||
|
||||
from langflow.api.v1.schemas import ChatMessage
|
||||
from langflow.interface.utils import try_setting_streaming_options
|
||||
from langflow.processing.base import get_result_and_steps
|
||||
from langflow.utils.chat import ChatDefinition
|
||||
|
||||
LANGCHAIN_RUNNABLES = (Chain, Runnable, AgentExecutor)
|
||||
|
||||
|
||||
async def process_graph(
|
||||
langchain_object,
|
||||
build_result,
|
||||
chat_inputs: ChatMessage,
|
||||
client_id: str,
|
||||
session_id: str,
|
||||
):
|
||||
langchain_object = try_setting_streaming_options(langchain_object)
|
||||
build_result = try_setting_streaming_options(build_result)
|
||||
logger.debug("Loaded langchain object")
|
||||
|
||||
if langchain_object is None:
|
||||
if build_result is None:
|
||||
# Raise user facing error
|
||||
raise ValueError("There was an error loading the langchain_object. Please, check all the nodes and try again.")
|
||||
|
||||
|
|
@ -25,15 +33,36 @@ async def process_graph(
|
|||
chat_inputs.message = {}
|
||||
|
||||
logger.debug("Generating result and thought")
|
||||
result, intermediate_steps, raw_output = await get_result_and_steps(
|
||||
langchain_object,
|
||||
chat_inputs.message,
|
||||
client_id=client_id,
|
||||
session_id=session_id,
|
||||
)
|
||||
if isinstance(build_result, LANGCHAIN_RUNNABLES):
|
||||
result, intermediate_steps, raw_output = await get_result_and_steps(
|
||||
build_result,
|
||||
chat_inputs.message,
|
||||
client_id=client_id,
|
||||
session_id=session_id,
|
||||
)
|
||||
elif isinstance(build_result, ChatDefinition):
|
||||
raw_output = await run_build_result(
|
||||
build_result,
|
||||
chat_inputs,
|
||||
client_id=client_id,
|
||||
session_id=session_id,
|
||||
)
|
||||
if isinstance(raw_output, dict):
|
||||
if not build_result.output_key:
|
||||
raise ValueError("No output key provided to ChatDefinition when returning a dict.")
|
||||
result = raw_output[build_result.output_key]
|
||||
else:
|
||||
result = raw_output
|
||||
intermediate_steps = []
|
||||
else:
|
||||
raise TypeError(f"Unknown type {type(build_result)}")
|
||||
logger.debug("Generated result and intermediate_steps")
|
||||
return result, intermediate_steps, raw_output
|
||||
except Exception as e:
|
||||
# Log stack trace
|
||||
logger.exception(e)
|
||||
raise e
|
||||
|
||||
|
||||
async def run_build_result(build_result: Any, chat_inputs: ChatMessage, client_id: str, session_id: str):
|
||||
return build_result(inputs=chat_inputs.message)
|
||||
|
|
|
|||
34
src/backend/langflow/utils/chat.py
Normal file
34
src/backend/langflow/utils/chat.py
Normal file
|
|
@ -0,0 +1,34 @@
|
|||
from typing import Any, Callable, Optional, Union
|
||||
|
||||
from langchain_core.prompts import PromptTemplate as LCPromptTemplate
|
||||
from langflow.utils.prompt import GenericPromptTemplate
|
||||
from llama_index.prompts import PromptTemplate as LIPromptTemplate
|
||||
|
||||
PromptTemplate = Union[LCPromptTemplate, LIPromptTemplate]
|
||||
|
||||
|
||||
class ChatDefinition:
|
||||
def __init__(
|
||||
self,
|
||||
func: Callable,
|
||||
inputs: list[str],
|
||||
output_key: Optional[str] = None,
|
||||
prompt_template: Optional[PromptTemplate] = None,
|
||||
):
|
||||
self.func = func
|
||||
self.input_keys = inputs
|
||||
self.output_key = output_key
|
||||
self.prompt_template = prompt_template
|
||||
|
||||
@classmethod
|
||||
def from_prompt_template(cls, prompt_template: PromptTemplate, func: Callable, output_key: Optional[str] = None):
|
||||
prompt = GenericPromptTemplate(prompt_template)
|
||||
return cls(
|
||||
func=func,
|
||||
inputs=prompt.input_keys,
|
||||
output_key=output_key,
|
||||
prompt_template=prompt_template,
|
||||
)
|
||||
|
||||
def __call__(self, inputs: dict, callbacks: Optional[Any] = None) -> dict:
|
||||
return self.func(inputs, callbacks)
|
||||
58
src/backend/langflow/utils/prompt.py
Normal file
58
src/backend/langflow/utils/prompt.py
Normal file
|
|
@ -0,0 +1,58 @@
|
|||
from typing import Any, Union
|
||||
|
||||
from langchain_core.prompts import PromptTemplate as LCPromptTemplate
|
||||
from llama_index.prompts import PromptTemplate as LIPromptTemplate
|
||||
|
||||
PromptTemplateTypes = Union[LCPromptTemplate, LIPromptTemplate]
|
||||
|
||||
|
||||
class GenericPromptTemplate:
|
||||
def __init__(self, prompt_template: PromptTemplateTypes):
|
||||
object.__setattr__(self, "prompt_template", prompt_template)
|
||||
|
||||
@property
|
||||
def input_keys(self):
|
||||
prompt_template = object.__getattribute__(self, "prompt_template")
|
||||
if isinstance(prompt_template, LCPromptTemplate):
|
||||
return prompt_template.input_variables
|
||||
elif isinstance(prompt_template, LIPromptTemplate):
|
||||
return prompt_template.template_vars
|
||||
else:
|
||||
raise TypeError(f"Unknown prompt template type {type(prompt_template)}")
|
||||
|
||||
def to_lc_prompt(self):
|
||||
prompt_template = object.__getattribute__(self, "prompt_template")
|
||||
if isinstance(prompt_template, LCPromptTemplate):
|
||||
return prompt_template
|
||||
elif isinstance(prompt_template, LIPromptTemplate):
|
||||
return LCPromptTemplate.from_template(prompt_template.get_template())
|
||||
else:
|
||||
raise TypeError(f"Unknown prompt template type {type(prompt_template)}")
|
||||
|
||||
def to_li_prompt(self):
|
||||
prompt_template = object.__getattribute__(self, "prompt_template")
|
||||
if isinstance(prompt_template, LIPromptTemplate):
|
||||
return prompt_template
|
||||
elif isinstance(prompt_template, LCPromptTemplate):
|
||||
return LIPromptTemplate(template=prompt_template.template)
|
||||
else:
|
||||
raise TypeError(f"Unknown prompt template type {type(prompt_template)}")
|
||||
|
||||
def __or__(self, other):
|
||||
prompt_template = object.__getattribute__(self, "prompt_template")
|
||||
if isinstance(prompt_template, LIPromptTemplate):
|
||||
return self.to_lc_prompt() | other
|
||||
else:
|
||||
raise TypeError(f"Unknown prompt template type {type(other)}")
|
||||
|
||||
def __getattribute__(self, name: str) -> Any:
|
||||
if name in {
|
||||
"input_keys",
|
||||
"to_lc_prompt",
|
||||
"to_li_prompt",
|
||||
"__or__",
|
||||
"prompt_template",
|
||||
}:
|
||||
return object.__getattribute__(self, name)
|
||||
prompt_template = object.__getattribute__(self, "prompt_template")
|
||||
return getattr(prompt_template, name)
|
||||
|
|
@ -23,107 +23,119 @@ export default function Dropdown({
|
|||
|
||||
return (
|
||||
<>
|
||||
<Listbox
|
||||
value={internalValue}
|
||||
onChange={(value) => {
|
||||
setInternalValue(value);
|
||||
onSelect(value);
|
||||
}}
|
||||
>
|
||||
{({ open }) => (
|
||||
<>
|
||||
<div className={"relative mt-1"}>
|
||||
<Listbox.Button
|
||||
data-test={`${id ?? ""}`}
|
||||
className={
|
||||
editNode
|
||||
? "dropdown-component-outline"
|
||||
: "dropdown-component-false-outline"
|
||||
}
|
||||
>
|
||||
<span
|
||||
className="dropdown-component-display"
|
||||
data-testid={`${id ?? ""}-display`}
|
||||
>
|
||||
{internalValue}
|
||||
</span>
|
||||
<span className={"dropdown-component-arrow"}>
|
||||
<IconComponent
|
||||
name="ChevronsUpDown"
|
||||
className="dropdown-component-arrow-color"
|
||||
aria-hidden="true"
|
||||
/>
|
||||
</span>
|
||||
</Listbox.Button>
|
||||
|
||||
<Transition
|
||||
show={open}
|
||||
as={Fragment}
|
||||
leave="transition ease-in duration-100"
|
||||
leaveFrom="opacity-100"
|
||||
leaveTo="opacity-0"
|
||||
>
|
||||
<Listbox.Options
|
||||
className={classNames(
|
||||
editNode
|
||||
? "dropdown-component-true-options nowheel custom-scroll"
|
||||
: "dropdown-component-false-options nowheel custom-scroll",
|
||||
apiModal ? "mb-2 w-[250px]" : "absolute w-full"
|
||||
)}
|
||||
>
|
||||
{options.map((option, id) => (
|
||||
<Listbox.Option
|
||||
key={id}
|
||||
className={({ active }) =>
|
||||
classNames(
|
||||
active ? " bg-accent" : "",
|
||||
editNode
|
||||
? "dropdown-component-false-option"
|
||||
: "dropdown-component-true-option"
|
||||
)
|
||||
}
|
||||
value={option}
|
||||
{Object.keys(options)?.length > 0 ? (
|
||||
<>
|
||||
<Listbox
|
||||
value={internalValue}
|
||||
onChange={(value) => {
|
||||
setInternalValue(value);
|
||||
onSelect(value);
|
||||
}}
|
||||
>
|
||||
{({ open }) => (
|
||||
<>
|
||||
<div className={"relative mt-1"}>
|
||||
<Listbox.Button
|
||||
data-test={`${id ?? ""}`}
|
||||
className={
|
||||
editNode
|
||||
? "dropdown-component-outline"
|
||||
: "dropdown-component-false-outline"
|
||||
}
|
||||
>
|
||||
<span
|
||||
className="dropdown-component-display"
|
||||
data-testid={`${id ?? ""}-display`}
|
||||
>
|
||||
{({ selected, active }) => (
|
||||
<>
|
||||
<span
|
||||
className={classNames(
|
||||
selected ? "font-semibold" : "font-normal",
|
||||
"block truncate "
|
||||
)}
|
||||
data-testid={`${option}-${id ?? ""}-option`}
|
||||
>
|
||||
{option}
|
||||
</span>
|
||||
{internalValue}
|
||||
</span>
|
||||
<span className={"dropdown-component-arrow"}>
|
||||
<IconComponent
|
||||
name="ChevronsUpDown"
|
||||
className="dropdown-component-arrow-color"
|
||||
aria-hidden="true"
|
||||
/>
|
||||
</span>
|
||||
</Listbox.Button>
|
||||
|
||||
{selected ? (
|
||||
<span
|
||||
className={classNames(
|
||||
active ? "text-background " : "",
|
||||
"dropdown-component-choosal"
|
||||
)}
|
||||
>
|
||||
<IconComponent
|
||||
name="Check"
|
||||
className={
|
||||
active
|
||||
? "dropdown-component-check-icon"
|
||||
: "dropdown-component-check-icon"
|
||||
}
|
||||
aria-hidden="true"
|
||||
/>
|
||||
</span>
|
||||
) : null}
|
||||
</>
|
||||
<Transition
|
||||
show={open}
|
||||
as={Fragment}
|
||||
leave="transition ease-in duration-100"
|
||||
leaveFrom="opacity-100"
|
||||
leaveTo="opacity-0"
|
||||
>
|
||||
<Listbox.Options
|
||||
className={classNames(
|
||||
editNode
|
||||
? "dropdown-component-true-options nowheel custom-scroll"
|
||||
: "dropdown-component-false-options nowheel custom-scroll",
|
||||
apiModal ? "mb-2 w-[250px]" : "absolute w-full"
|
||||
)}
|
||||
</Listbox.Option>
|
||||
))}
|
||||
</Listbox.Options>
|
||||
</Transition>
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
</Listbox>
|
||||
>
|
||||
{options?.map((option, id) => (
|
||||
<Listbox.Option
|
||||
key={id}
|
||||
className={({ active }) =>
|
||||
classNames(
|
||||
active ? " bg-accent" : "",
|
||||
editNode
|
||||
? "dropdown-component-false-option"
|
||||
: "dropdown-component-true-option"
|
||||
)
|
||||
}
|
||||
value={option}
|
||||
>
|
||||
{({ selected, active }) => (
|
||||
<>
|
||||
<span
|
||||
className={classNames(
|
||||
selected ? "font-semibold" : "font-normal",
|
||||
"block truncate "
|
||||
)}
|
||||
data-testid={`${option}-${id ?? ""}-option`}
|
||||
>
|
||||
{option}
|
||||
</span>
|
||||
|
||||
{selected ? (
|
||||
<span
|
||||
className={classNames(
|
||||
active ? "text-background " : "",
|
||||
"dropdown-component-choosal"
|
||||
)}
|
||||
>
|
||||
<IconComponent
|
||||
name="Check"
|
||||
className={
|
||||
active
|
||||
? "dropdown-component-check-icon"
|
||||
: "dropdown-component-check-icon"
|
||||
}
|
||||
aria-hidden="true"
|
||||
/>
|
||||
</span>
|
||||
) : null}
|
||||
</>
|
||||
)}
|
||||
</Listbox.Option>
|
||||
))}
|
||||
</Listbox.Options>
|
||||
</Transition>
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
</Listbox>
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
<div>
|
||||
<span className="text-sm italic">
|
||||
No parameters are available for display.
|
||||
</span>
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
</>
|
||||
);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -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);
|
||||
}}
|
||||
|
|
|
|||
|
|
@ -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";
|
||||
|
|
@ -74,77 +74,71 @@ export default function ChatMessage({
|
|||
<div className="w-full">
|
||||
<div className="w-full dark:text-white">
|
||||
<div className="w-full">
|
||||
{useMemo(
|
||||
() =>
|
||||
chat.message.toString() === "" && lockChat ? (
|
||||
<IconComponent
|
||||
name="MoreHorizontal"
|
||||
className="h-8 w-8 animate-pulse"
|
||||
/>
|
||||
) : (
|
||||
<ReactMarkdown
|
||||
remarkPlugins={[remarkGfm, remarkMath]}
|
||||
rehypePlugins={[rehypeMathjax]}
|
||||
className="markdown prose min-w-full text-primary word-break-break-word
|
||||
{chat.message.toString() === "" && lockChat ? (
|
||||
<IconComponent
|
||||
name="MoreHorizontal"
|
||||
className="h-8 w-8 animate-pulse"
|
||||
/>
|
||||
) : (
|
||||
<ReactMarkdown
|
||||
remarkPlugins={[remarkGfm, remarkMath]}
|
||||
rehypePlugins={[rehypeMathjax]}
|
||||
className="markdown prose min-w-full text-primary word-break-break-word
|
||||
dark:prose-invert"
|
||||
components={{
|
||||
pre({ node, ...props }) {
|
||||
return <>{props.children}</>;
|
||||
},
|
||||
code: ({
|
||||
node,
|
||||
inline,
|
||||
className,
|
||||
children,
|
||||
...props
|
||||
}) => {
|
||||
if (children.length) {
|
||||
if (children[0] === "▍") {
|
||||
return (
|
||||
<span className="form-modal-markdown-span">
|
||||
▍
|
||||
</span>
|
||||
);
|
||||
}
|
||||
|
||||
children[0] = (children[0] as string).replace(
|
||||
"`▍`",
|
||||
"▍"
|
||||
);
|
||||
}
|
||||
|
||||
const match = /language-(\w+)/.exec(
|
||||
className || ""
|
||||
components={{
|
||||
pre({ node, ...props }) {
|
||||
return <>{props.children}</>;
|
||||
},
|
||||
code: ({
|
||||
node,
|
||||
inline,
|
||||
className,
|
||||
children,
|
||||
...props
|
||||
}) => {
|
||||
if (children.length) {
|
||||
if (children[0] === "▍") {
|
||||
return (
|
||||
<span className="form-modal-markdown-span">
|
||||
▍
|
||||
</span>
|
||||
);
|
||||
}
|
||||
|
||||
return !inline ? (
|
||||
<CodeTabsComponent
|
||||
isMessage
|
||||
tabs={[
|
||||
{
|
||||
name: (match && match[1]) || "",
|
||||
mode: (match && match[1]) || "",
|
||||
image:
|
||||
"https://curl.se/logo/curl-symbol-transparent.png",
|
||||
language: (match && match[1]) || "",
|
||||
code: String(children).replace(/\n$/, ""),
|
||||
},
|
||||
]}
|
||||
activeTab={"0"}
|
||||
setActiveTab={() => {}}
|
||||
/>
|
||||
) : (
|
||||
<code className={className} {...props}>
|
||||
{children}
|
||||
</code>
|
||||
);
|
||||
},
|
||||
}}
|
||||
>
|
||||
{chat.message.toString()}
|
||||
</ReactMarkdown>
|
||||
),
|
||||
[chat.message, chat.message.toString()]
|
||||
children[0] = (children[0] as string).replace(
|
||||
"`▍`",
|
||||
"▍"
|
||||
);
|
||||
}
|
||||
|
||||
const match = /language-(\w+)/.exec(className || "");
|
||||
|
||||
return !inline ? (
|
||||
<CodeTabsComponent
|
||||
isMessage
|
||||
tabs={[
|
||||
{
|
||||
name: (match && match[1]) || "",
|
||||
mode: (match && match[1]) || "",
|
||||
image:
|
||||
"https://curl.se/logo/curl-symbol-transparent.png",
|
||||
language: (match && match[1]) || "",
|
||||
code: String(children).replace(/\n$/, ""),
|
||||
},
|
||||
]}
|
||||
activeTab={"0"}
|
||||
setActiveTab={() => {}}
|
||||
/>
|
||||
) : (
|
||||
<code className={className} {...props}>
|
||||
{children}
|
||||
</code>
|
||||
);
|
||||
},
|
||||
}}
|
||||
>
|
||||
{chat.message.toString()}
|
||||
</ReactMarkdown>
|
||||
)}
|
||||
</div>
|
||||
{chat.files && (
|
||||
|
|
|
|||
|
|
@ -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;
|
||||
|
|
|
|||
|
|
@ -122,24 +122,21 @@ export default function GenericModal({
|
|||
}
|
||||
if (apiReturn.data) {
|
||||
let inputVariables = apiReturn.data.input_variables ?? [];
|
||||
if (
|
||||
JSON.stringify(apiReturn.data?.frontend_node) !== JSON.stringify({})
|
||||
) {
|
||||
setNodeClass!(apiReturn.data?.frontend_node, inputValue);
|
||||
setModalOpen(closeModal);
|
||||
setIsEdit(false);
|
||||
}
|
||||
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({})
|
||||
) {
|
||||
setNodeClass!(apiReturn.data?.frontend_node, inputValue);
|
||||
setModalOpen(closeModal);
|
||||
setIsEdit(false);
|
||||
setSuccessData({
|
||||
title: "Prompt is ready",
|
||||
});
|
||||
}
|
||||
setSuccessData({
|
||||
title: "Prompt is ready",
|
||||
});
|
||||
}
|
||||
} else {
|
||||
setIsEdit(true);
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue