Chat_and_cache (#219)
This commit is contained in:
Gabriel Luiz Freitas Almeida
GitHub
commit
f66c70f3e0
68 changed files with 2696 additions and 1394 deletions
11
Makefile
11
Makefile
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@ -26,8 +26,15 @@ install_frontend:
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run_frontend:
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cd src/frontend && npm start
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run_backend:
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poetry run uvicorn langflow.main:app --port 5003 --reload
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frontend:
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make install_frontend
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make run_frontend
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install_backend:
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poetry install
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backend:
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poetry run uvicorn langflow.main:app --port 7860 --reload --log-level debug
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build_frontend:
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cd src/frontend && CI='' npm run build
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@ -1,11 +0,0 @@
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#! /bin/bash
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cd src/frontend
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docker build -t logspace/frontend_build -f build.Dockerfile .
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cd ../backend
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docker build -t logspace/backend_build -f build.Dockerfile .
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cd ../../
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VERSION=$(toml get --toml-path pyproject.toml tool.poetry.version)
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docker build --build-arg VERSION=$VERSION -t ibiscp/langflow:$VERSION .
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docker push ibiscp/langflow:$VERSION
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6
package-lock.json
generated
Normal file
6
package-lock.json
generated
Normal file
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@ -0,0 +1,6 @@
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{
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"name": "reactFlow",
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"lockfileVersion": 3,
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"requires": true,
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"packages": {}
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}
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297
poetry.lock
generated
297
poetry.lock
generated
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@ -551,77 +551,77 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
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[[package]]
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name = "clickhouse-connect"
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version = "0.5.21"
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version = "0.5.22"
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description = "ClickHouse core driver, SqlAlchemy, and Superset libraries"
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category = "main"
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optional = false
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python-versions = "~=3.7"
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files = [
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[package.dependencies]
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|
@ -703,63 +703,63 @@ typing = ["mypy (>=0.990)"]
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|||
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[[package]]
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name = "coverage"
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version = "7.2.4"
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description = "Code coverage measurement for Python"
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category = "dev"
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optional = false
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python-versions = ">=3.7"
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1573,14 +1573,14 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio"
|
|||
|
||||
[[package]]
|
||||
name = "ipython"
|
||||
version = "8.12.0"
|
||||
version = "8.13.0"
|
||||
description = "IPython: Productive Interactive Computing"
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
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||||
{file = "ipython-8.12.0-py3-none-any.whl", hash = "sha256:1c183bf61b148b00bcebfa5d9b39312733ae97f6dad90d7e9b4d86c8647f498c"},
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1708,14 +1708,14 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
|
|||
|
||||
[[package]]
|
||||
name = "langchain"
|
||||
version = "0.0.151"
|
||||
version = "0.0.152"
|
||||
description = "Building applications with LLMs through composability"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
files = [
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||||
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]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1733,7 +1733,7 @@ tenacity = ">=8.1.0,<9.0.0"
|
|||
tqdm = ">=4.48.0"
|
||||
|
||||
[package.extras]
|
||||
all = ["aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.2.6,<0.3.0)", "arxiv (>=1.4,<2.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "beautifulsoup4 (>=4,<5)", "clickhouse-connect (>=0.5.14,<0.6.0)", "cohere (>=3,<4)", "deeplake (>=3.3.0,<4.0.0)", "duckduckgo-search (>=2.8.6,<3.0.0)", "elasticsearch (>=8,<9)", "faiss-cpu (>=1,<2)", "google-api-python-client (==2.70.0)", "google-search-results (>=2,<3)", "gptcache (>=0.1.7)", "html2text (>=2020.1.16,<2021.0.0)", "huggingface_hub (>=0,<1)", "jina (>=3.14,<4.0)", "jinja2 (>=3,<4)", "lancedb (>=0.1,<0.2)", "lark (>=1.1.5,<2.0.0)", "manifest-ml (>=0.0.1,<0.0.2)", "networkx (>=2.6.3,<3.0.0)", "nlpcloud (>=1,<2)", "nltk (>=3,<4)", "nomic (>=1.0.43,<2.0.0)", "openai (>=0,<1)", "opensearch-py (>=2.0.0,<3.0.0)", "pgvector (>=0.1.6,<0.2.0)", "pinecone-client (>=2,<3)", "pinecone-text (>=0.4.2,<0.5.0)", "psycopg2-binary (>=2.9.5,<3.0.0)", "pyowm (>=3.3.0,<4.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pytesseract (>=0.3.10,<0.4.0)", "qdrant-client (>=1.1.2,<2.0.0)", "redis (>=4,<5)", "sentence-transformers (>=2,<3)", "spacy (>=3,<4)", "tensorflow-text (>=2.11.0,<3.0.0)", "tiktoken (>=0.3.2,<0.4.0)", "torch (>=1,<3)", "transformers (>=4,<5)", "weaviate-client (>=3,<4)", "wikipedia (>=1,<2)", "wolframalpha (==5.0.0)"]
|
||||
all = ["aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.2.6,<0.3.0)", "arxiv (>=1.4,<2.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "beautifulsoup4 (>=4,<5)", "clickhouse-connect (>=0.5.14,<0.6.0)", "cohere (>=3,<4)", "deeplake (>=3.3.0,<4.0.0)", "duckduckgo-search (>=2.8.6,<3.0.0)", "elasticsearch (>=8,<9)", "faiss-cpu (>=1,<2)", "google-api-python-client (==2.70.0)", "google-search-results (>=2,<3)", "gptcache (>=0.1.7)", "html2text (>=2020.1.16,<2021.0.0)", "huggingface_hub (>=0,<1)", "jina (>=3.14,<4.0)", "jinja2 (>=3,<4)", "lancedb (>=0.1,<0.2)", "lark (>=1.1.5,<2.0.0)", "manifest-ml (>=0.0.1,<0.0.2)", "networkx (>=2.6.3,<3.0.0)", "nlpcloud (>=1,<2)", "nltk (>=3,<4)", "nomic (>=1.0.43,<2.0.0)", "openai (>=0,<1)", "opensearch-py (>=2.0.0,<3.0.0)", "pexpect (>=4.8.0,<5.0.0)", "pgvector (>=0.1.6,<0.2.0)", "pinecone-client (>=2,<3)", "pinecone-text (>=0.4.2,<0.5.0)", "psycopg2-binary (>=2.9.5,<3.0.0)", "pyowm (>=3.3.0,<4.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pytesseract (>=0.3.10,<0.4.0)", "qdrant-client (>=1.1.2,<2.0.0)", "redis (>=4,<5)", "sentence-transformers (>=2,<3)", "spacy (>=3,<4)", "tensorflow-text (>=2.11.0,<3.0.0)", "tiktoken (>=0.3.2,<0.4.0)", "torch (>=1,<3)", "transformers (>=4,<5)", "weaviate-client (>=3,<4)", "wikipedia (>=1,<2)", "wolframalpha (==5.0.0)"]
|
||||
azure = ["azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "openai (>=0,<1)"]
|
||||
cohere = ["cohere (>=3,<4)"]
|
||||
embeddings = ["sentence-transformers (>=2,<3)"]
|
||||
|
|
@ -2698,6 +2698,21 @@ pytz = ">=2020.1"
|
|||
[package.extras]
|
||||
test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
|
||||
|
||||
[[package]]
|
||||
name = "pandas-stubs"
|
||||
version = "2.0.0.230412"
|
||||
description = "Type annotations for pandas"
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pandas_stubs-2.0.0.230412-py3-none-any.whl", hash = "sha256:311ab8b42ee574d9fea5061d1f63aeca297e472de6073ba84bf2a017c6cb1b6b"},
|
||||
{file = "pandas_stubs-2.0.0.230412.tar.gz", hash = "sha256:016f567cb9947edd0067ea2665ab00b77fa47e73a65ce1a097de4f499b3485c0"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
types-pytz = ">=2022.1.1"
|
||||
|
||||
[[package]]
|
||||
name = "parso"
|
||||
version = "0.8.3"
|
||||
|
|
@ -4428,6 +4443,30 @@ dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2
|
|||
doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"]
|
||||
test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<13.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "types-pillow"
|
||||
version = "9.5.0.2"
|
||||
description = "Typing stubs for Pillow"
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "types-Pillow-9.5.0.2.tar.gz", hash = "sha256:b3f9f621f259566c19c1deca21901017c8b1e3e200ed2e49e0a2d83c0a5175db"},
|
||||
{file = "types_Pillow-9.5.0.2-py3-none-any.whl", hash = "sha256:58fdebd0ffa2353ecccdd622adde23bce89da5c0c8b96c34f2d1eca7b7e42d0e"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "types-pytz"
|
||||
version = "2023.3.0.0"
|
||||
description = "Typing stubs for pytz"
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "types-pytz-2023.3.0.0.tar.gz", hash = "sha256:ecdc70d543aaf3616a7e48631543a884f74205f284cefd6649ddf44c6a820aac"},
|
||||
{file = "types_pytz-2023.3.0.0-py3-none-any.whl", hash = "sha256:4fc2a7fbbc315f0b6630e0b899fd6c743705abe1094d007b0e612d10da15e0f3"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "types-pyyaml"
|
||||
version = "6.0.12.9"
|
||||
|
|
@ -4442,14 +4481,14 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "types-requests"
|
||||
version = "2.28.11.17"
|
||||
version = "2.29.0.0"
|
||||
description = "Typing stubs for requests"
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "types-requests-2.28.11.17.tar.gz", hash = "sha256:0d580652ce903f643f8c3b494dd01d29367ea57cea0c7ad7f65cf3169092edb0"},
|
||||
{file = "types_requests-2.28.11.17-py3-none-any.whl", hash = "sha256:cc1aba862575019306b2ed134eb1ea994cab1c887a22e18d3383e6dd42e9789b"},
|
||||
{file = "types-requests-2.29.0.0.tar.gz", hash = "sha256:c86f4a955d943d2457120dbe719df24ef0924e11177164d10a0373cf311d7b4d"},
|
||||
{file = "types_requests-2.29.0.0-py3-none-any.whl", hash = "sha256:4cf6e323e856c779fbe8815bb977a5bf5d6c5034713e4c17ff2a9a20610f5b27"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4955,4 +4994,4 @@ cffi = ["cffi (>=1.11)"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.9"
|
||||
content-hash = "49fd166dc3811d79f8344946ee40a62c28215db9fdd41f5f051ab109657d9a62"
|
||||
content-hash = "c8ba5d9e0208fc55dc2f00efffed26d7a2c7d325568dfd697cc6499d210304d9"
|
||||
|
|
|
|||
|
|
@ -47,6 +47,7 @@ fake-useragent = "^1.1.3"
|
|||
docstring-parser = "^0.15"
|
||||
psycopg2-binary = "^2.9.6"
|
||||
pyarrow = "^11.0.0"
|
||||
websockets = "^11.0.2"
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
black = "^23.1.0"
|
||||
|
|
@ -58,6 +59,8 @@ pytest = "^7.2.2"
|
|||
types-requests = "^2.28.11"
|
||||
requests = "^2.28.0"
|
||||
pytest-cov = "^4.0.0"
|
||||
pandas-stubs = "^2.0.0.230412"
|
||||
types-pillow = "^9.5.0.2"
|
||||
|
||||
|
||||
[tool.ruff]
|
||||
|
|
|
|||
|
|
@ -1,52 +0,0 @@
|
|||
# `python-base` sets up all our shared environment variables
|
||||
FROM python:3.10-slim
|
||||
|
||||
# python
|
||||
ENV PYTHONUNBUFFERED=1 \
|
||||
# prevents python creating .pyc files
|
||||
PYTHONDONTWRITEBYTECODE=1 \
|
||||
\
|
||||
# pip
|
||||
PIP_NO_CACHE_DIR=off \
|
||||
PIP_DISABLE_PIP_VERSION_CHECK=on \
|
||||
PIP_DEFAULT_TIMEOUT=100 \
|
||||
\
|
||||
# poetry
|
||||
# https://python-poetry.org/docs/configuration/#using-environment-variables
|
||||
POETRY_VERSION=1.4.0 \
|
||||
# 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"
|
||||
|
||||
RUN apt-get update \
|
||||
&& apt-get install --no-install-recommends -y \
|
||||
# deps for installing poetry
|
||||
curl \
|
||||
# deps for building python deps
|
||||
build-essential libpq-dev
|
||||
|
||||
# install poetry - respects $POETRY_VERSION & $POETRY_HOME
|
||||
RUN curl -sSL https://install.python-poetry.org | python3 -
|
||||
|
||||
# copy project requirement files here to ensure they will be cached.
|
||||
WORKDIR /app
|
||||
COPY poetry.lock pyproject.toml ./
|
||||
COPY langflow/ ./langflow
|
||||
|
||||
# poetry install
|
||||
RUN poetry install --without dev
|
||||
|
||||
# build wheel
|
||||
RUN poetry build -f wheel
|
||||
|
|
@ -1,6 +0,0 @@
|
|||
#! /bin/bash
|
||||
|
||||
docker build -t logspace/backend_build -f build.Dockerfile .
|
||||
VERSION=$(toml get --toml-path pyproject.toml tool.poetry.version)
|
||||
docker build --build-arg VERSION=$VERSION -t ibiscp/langflow:$VERSION .
|
||||
docker push ibiscp/langflow:$VERSION
|
||||
|
|
@ -1 +1,4 @@
|
|||
from langflow.interface.loading import load_flow_from_json # noqa
|
||||
from langflow.interface.loading import load_flow_from_json
|
||||
from langflow.cache import cache_manager
|
||||
|
||||
__all__ = ["load_flow_from_json", "cache_manager"]
|
||||
|
|
|
|||
|
|
@ -3,6 +3,10 @@ from pydantic import BaseModel, validator
|
|||
from langflow.graph.utils import extract_input_variables_from_prompt
|
||||
|
||||
|
||||
class CacheResponse(BaseModel):
|
||||
data: dict
|
||||
|
||||
|
||||
class Code(BaseModel):
|
||||
code: str
|
||||
|
||||
|
|
|
|||
16
src/backend/langflow/api/callback.py
Normal file
16
src/backend/langflow/api/callback.py
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
from typing import Any
|
||||
from langchain.callbacks.base import AsyncCallbackHandler
|
||||
|
||||
from langflow.api.schemas import ChatResponse
|
||||
|
||||
|
||||
# https://github.com/hwchase17/chat-langchain/blob/master/callback.py
|
||||
class StreamingLLMCallbackHandler(AsyncCallbackHandler):
|
||||
"""Callback handler for streaming LLM responses."""
|
||||
|
||||
def __init__(self, websocket):
|
||||
self.websocket = websocket
|
||||
|
||||
async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
|
||||
resp = ChatResponse(message=token, type="stream", intermediate_steps="")
|
||||
await self.websocket.send_json(resp.dict())
|
||||
18
src/backend/langflow/api/chat.py
Normal file
18
src/backend/langflow/api/chat.py
Normal file
|
|
@ -0,0 +1,18 @@
|
|||
from fastapi import APIRouter, WebSocket
|
||||
|
||||
from langflow.api.chat_manager import ChatManager
|
||||
from langflow.utils.logger import logger
|
||||
|
||||
router = APIRouter()
|
||||
chat_manager = ChatManager()
|
||||
|
||||
|
||||
@router.websocket("/chat/{client_id}")
|
||||
async def websocket_endpoint(client_id: str, websocket: WebSocket):
|
||||
"""Websocket endpoint for chat."""
|
||||
try:
|
||||
await chat_manager.handle_websocket(client_id, websocket)
|
||||
except Exception as e:
|
||||
# Log stack trace
|
||||
logger.exception(e)
|
||||
raise e
|
||||
212
src/backend/langflow/api/chat_manager.py
Normal file
212
src/backend/langflow/api/chat_manager.py
Normal file
|
|
@ -0,0 +1,212 @@
|
|||
import asyncio
|
||||
from typing import Dict, List
|
||||
from collections import defaultdict
|
||||
from fastapi import WebSocket
|
||||
import json
|
||||
from langflow.api.schemas import ChatMessage, ChatResponse, FileResponse
|
||||
from langflow.cache.manager import Subject
|
||||
from langflow.interface.run import (
|
||||
get_result_and_steps,
|
||||
load_or_build_langchain_object,
|
||||
)
|
||||
from langflow.interface.utils import pil_to_base64, try_setting_streaming_options
|
||||
from langflow.utils.logger import logger
|
||||
from langflow.cache import cache_manager
|
||||
|
||||
|
||||
class ChatHistory(Subject):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.history: Dict[str, List[ChatMessage]] = defaultdict(list)
|
||||
|
||||
def add_message(self, client_id: str, message: ChatMessage):
|
||||
"""Add a message to the chat history."""
|
||||
|
||||
self.history[client_id].append(message)
|
||||
|
||||
if not isinstance(message, FileResponse):
|
||||
self.notify()
|
||||
|
||||
def get_history(self, client_id: str, filter=True) -> List[ChatMessage]:
|
||||
"""Get the chat history for a client."""
|
||||
if history := self.history.get(client_id, []):
|
||||
if filter:
|
||||
return [msg for msg in history if msg.type not in ["start", "stream"]]
|
||||
return history
|
||||
else:
|
||||
return []
|
||||
|
||||
def empty_history(self, client_id: str):
|
||||
"""Empty the chat history for a client."""
|
||||
self.history[client_id] = []
|
||||
|
||||
|
||||
class ChatManager:
|
||||
def __init__(self):
|
||||
self.active_connections: Dict[str, WebSocket] = {}
|
||||
self.chat_history = ChatHistory()
|
||||
self.chat_history.attach(self.on_chat_history_update)
|
||||
self.cache_manager = cache_manager
|
||||
self.cache_manager.attach(self.update)
|
||||
|
||||
def on_chat_history_update(self):
|
||||
"""Send the last chat message to the client."""
|
||||
client_id = self.cache_manager.current_client_id
|
||||
if client_id in self.active_connections:
|
||||
chat_response = self.chat_history.get_history(client_id, filter=False)[-1]
|
||||
if chat_response.is_bot:
|
||||
# Process FileResponse
|
||||
if isinstance(chat_response, FileResponse):
|
||||
# If data_type is pandas, convert to csv
|
||||
if chat_response.data_type == "pandas":
|
||||
chat_response.data = chat_response.data.to_csv()
|
||||
elif chat_response.data_type == "image":
|
||||
# Base64 encode the image
|
||||
chat_response.data = pil_to_base64(chat_response.data)
|
||||
# get event loop
|
||||
loop = asyncio.get_event_loop()
|
||||
|
||||
coroutine = self.send_json(client_id, chat_response)
|
||||
asyncio.run_coroutine_threadsafe(coroutine, loop)
|
||||
|
||||
def update(self):
|
||||
if self.cache_manager.current_client_id in self.active_connections:
|
||||
self.last_cached_object_dict = self.cache_manager.get_last()
|
||||
# Add a new ChatResponse with the data
|
||||
chat_response = FileResponse(
|
||||
message=None,
|
||||
type="file",
|
||||
data=self.last_cached_object_dict["obj"],
|
||||
data_type=self.last_cached_object_dict["type"],
|
||||
)
|
||||
|
||||
self.chat_history.add_message(
|
||||
self.cache_manager.current_client_id, chat_response
|
||||
)
|
||||
|
||||
async def connect(self, client_id: str, websocket: WebSocket):
|
||||
await websocket.accept()
|
||||
self.active_connections[client_id] = websocket
|
||||
|
||||
def disconnect(self, client_id: str):
|
||||
del self.active_connections[client_id]
|
||||
|
||||
async def send_message(self, client_id: str, message: str):
|
||||
websocket = self.active_connections[client_id]
|
||||
await websocket.send_text(message)
|
||||
|
||||
async def send_json(self, client_id: str, message: ChatMessage):
|
||||
websocket = self.active_connections[client_id]
|
||||
await websocket.send_json(message.dict())
|
||||
|
||||
async def process_message(self, client_id: str, payload: Dict):
|
||||
# Process the graph data and chat message
|
||||
chat_message = payload.pop("message", "")
|
||||
chat_message = ChatMessage(message=chat_message)
|
||||
self.chat_history.add_message(client_id, chat_message)
|
||||
|
||||
graph_data = payload
|
||||
start_resp = ChatResponse(message=None, type="start", intermediate_steps="")
|
||||
self.chat_history.add_message(client_id, start_resp)
|
||||
|
||||
is_first_message = len(self.chat_history.get_history(client_id=client_id)) == 0
|
||||
# Generate result and thought
|
||||
try:
|
||||
logger.debug("Generating result and thought")
|
||||
|
||||
result, intermediate_steps = await process_graph(
|
||||
graph_data=graph_data,
|
||||
is_first_message=is_first_message,
|
||||
chat_message=chat_message,
|
||||
websocket=self.active_connections[client_id],
|
||||
)
|
||||
except Exception as e:
|
||||
# Log stack trace
|
||||
logger.exception(e)
|
||||
self.chat_history.empty_history(client_id)
|
||||
raise e
|
||||
# Send a response back to the frontend, if needed
|
||||
intermediate_steps = intermediate_steps or ""
|
||||
history = self.chat_history.get_history(client_id, filter=False)
|
||||
file_responses = []
|
||||
if history:
|
||||
# Iterate backwards through the history
|
||||
for msg in reversed(history):
|
||||
if isinstance(msg, FileResponse):
|
||||
if msg.data_type == "image":
|
||||
# Base64 encode the image
|
||||
msg.data = pil_to_base64(msg.data)
|
||||
file_responses.append(msg)
|
||||
if msg.type == "start":
|
||||
break
|
||||
|
||||
response = ChatResponse(
|
||||
message=result or "",
|
||||
intermediate_steps=intermediate_steps.strip(),
|
||||
type="end",
|
||||
files=file_responses,
|
||||
)
|
||||
self.chat_history.add_message(client_id, response)
|
||||
|
||||
async def handle_websocket(self, client_id: str, websocket: WebSocket):
|
||||
await self.connect(client_id, websocket)
|
||||
|
||||
try:
|
||||
chat_history = self.chat_history.get_history(client_id)
|
||||
# iterate and make BaseModel into dict
|
||||
chat_history = [chat.dict() for chat in chat_history]
|
||||
await websocket.send_json(chat_history)
|
||||
|
||||
while True:
|
||||
json_payload = await websocket.receive_json()
|
||||
try:
|
||||
payload = json.loads(json_payload)
|
||||
except TypeError:
|
||||
payload = json_payload
|
||||
if "clear_history" in payload:
|
||||
self.chat_history.history[client_id] = []
|
||||
continue
|
||||
|
||||
with self.cache_manager.set_client_id(client_id):
|
||||
await self.process_message(client_id, payload)
|
||||
except Exception as e:
|
||||
# Handle any exceptions that might occur
|
||||
logger.exception(e)
|
||||
# send a message to the client
|
||||
await self.send_message(client_id, str(e))
|
||||
raise e
|
||||
finally:
|
||||
await self.active_connections[client_id].close(
|
||||
code=1000, reason="Client disconnected"
|
||||
)
|
||||
self.disconnect(client_id)
|
||||
|
||||
|
||||
async def process_graph(
|
||||
graph_data: Dict,
|
||||
is_first_message: bool,
|
||||
chat_message: ChatMessage,
|
||||
websocket: WebSocket,
|
||||
):
|
||||
langchain_object = load_or_build_langchain_object(graph_data, is_first_message)
|
||||
langchain_object = try_setting_streaming_options(langchain_object, websocket)
|
||||
logger.debug("Loaded langchain object")
|
||||
|
||||
if langchain_object is None:
|
||||
# Raise user facing error
|
||||
raise ValueError(
|
||||
"There was an error loading the langchain_object. Please, check all the nodes and try again."
|
||||
)
|
||||
|
||||
# Generate result and thought
|
||||
try:
|
||||
logger.debug("Generating result and thought")
|
||||
result, intermediate_steps = get_result_and_steps(
|
||||
langchain_object, chat_message.message or ""
|
||||
)
|
||||
logger.debug("Generated result and intermediate_steps")
|
||||
return result, intermediate_steps
|
||||
except Exception as e:
|
||||
# Log stack trace
|
||||
logger.exception(e)
|
||||
raise e
|
||||
40
src/backend/langflow/api/schemas.py
Normal file
40
src/backend/langflow/api/schemas.py
Normal file
|
|
@ -0,0 +1,40 @@
|
|||
from typing import Any, Union
|
||||
from pydantic import BaseModel, validator
|
||||
|
||||
|
||||
class ChatMessage(BaseModel):
|
||||
"""Chat message schema."""
|
||||
|
||||
is_bot: bool = False
|
||||
message: Union[str, None] = None
|
||||
type: str = "human"
|
||||
|
||||
|
||||
class ChatResponse(ChatMessage):
|
||||
"""Chat response schema."""
|
||||
|
||||
intermediate_steps: str
|
||||
type: str
|
||||
is_bot: bool = True
|
||||
files: list = []
|
||||
|
||||
@validator("type")
|
||||
def validate_message_type(cls, v):
|
||||
if v not in ["start", "stream", "end", "error", "info", "file"]:
|
||||
raise ValueError("type must be start, stream, end, error, info, or file")
|
||||
return v
|
||||
|
||||
|
||||
class FileResponse(ChatMessage):
|
||||
"""File response schema."""
|
||||
|
||||
data: Any
|
||||
data_type: str
|
||||
type: str = "file"
|
||||
is_bot: bool = True
|
||||
|
||||
@validator("data_type")
|
||||
def validate_data_type(cls, v):
|
||||
if v not in ["image", "csv"]:
|
||||
raise ValueError("data_type must be image or csv")
|
||||
return v
|
||||
|
|
@ -7,6 +7,7 @@ from langflow.api.base import (
|
|||
PromptValidationResponse,
|
||||
validate_prompt,
|
||||
)
|
||||
from langflow.interface.run import build_graph
|
||||
from langflow.utils.logger import logger
|
||||
from langflow.utils.validate import validate_code
|
||||
|
||||
|
|
@ -33,3 +34,20 @@ def post_validate_prompt(prompt: Prompt):
|
|||
except Exception as e:
|
||||
logger.exception(e)
|
||||
raise HTTPException(status_code=500, detail=str(e)) from e
|
||||
|
||||
|
||||
# validate node
|
||||
@router.post("/node/{node_id}", status_code=200)
|
||||
def post_validate_node(node_id: str, data: dict):
|
||||
try:
|
||||
# build graph
|
||||
graph = build_graph(data)
|
||||
# validate node
|
||||
node = graph.get_node(node_id)
|
||||
if node is not None:
|
||||
_ = node.build()
|
||||
return str(node.params)
|
||||
raise Exception(f"Node {node_id} not found")
|
||||
except Exception as e:
|
||||
logger.exception(e)
|
||||
raise HTTPException(status_code=500, detail=str(e)) from e
|
||||
|
|
|
|||
1
src/backend/langflow/cache/__init__.py
vendored
1
src/backend/langflow/cache/__init__.py
vendored
|
|
@ -0,0 +1 @@
|
|||
from langflow.cache.manager import cache_manager # noqa
|
||||
152
src/backend/langflow/cache/base.py
vendored
Normal file
152
src/backend/langflow/cache/base.py
vendored
Normal file
|
|
@ -0,0 +1,152 @@
|
|||
import base64
|
||||
import contextlib
|
||||
import functools
|
||||
import hashlib
|
||||
|
||||
import json
|
||||
import os
|
||||
import tempfile
|
||||
from collections import OrderedDict
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict
|
||||
import dill # type: ignore
|
||||
|
||||
CACHE: Dict[str, Any] = {}
|
||||
|
||||
|
||||
def create_cache_folder(func):
|
||||
def wrapper(*args, **kwargs):
|
||||
# Get the destination folder
|
||||
cache_path = Path(tempfile.gettempdir()) / PREFIX
|
||||
|
||||
# Create the destination folder if it doesn't exist
|
||||
os.makedirs(cache_path, exist_ok=True)
|
||||
|
||||
return func(*args, **kwargs)
|
||||
|
||||
return wrapper
|
||||
|
||||
|
||||
def memoize_dict(maxsize=128):
|
||||
cache = OrderedDict()
|
||||
|
||||
def decorator(func):
|
||||
@functools.wraps(func)
|
||||
def wrapper(*args, **kwargs):
|
||||
hashed = compute_dict_hash(args[0])
|
||||
key = (func.__name__, hashed, frozenset(kwargs.items()))
|
||||
if key not in cache:
|
||||
result = func(*args, **kwargs)
|
||||
cache[key] = result
|
||||
if len(cache) > maxsize:
|
||||
cache.popitem(last=False)
|
||||
else:
|
||||
result = cache[key]
|
||||
return result
|
||||
|
||||
def clear_cache():
|
||||
cache.clear()
|
||||
|
||||
wrapper.clear_cache = clear_cache
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
PREFIX = "langflow_cache"
|
||||
|
||||
|
||||
@create_cache_folder
|
||||
def clear_old_cache_files(max_cache_size: int = 3):
|
||||
cache_dir = Path(tempfile.gettempdir()) / PREFIX
|
||||
cache_files = list(cache_dir.glob("*.dill"))
|
||||
|
||||
if len(cache_files) > max_cache_size:
|
||||
cache_files_sorted_by_mtime = sorted(
|
||||
cache_files, key=lambda x: x.stat().st_mtime, reverse=True
|
||||
)
|
||||
|
||||
for cache_file in cache_files_sorted_by_mtime[max_cache_size:]:
|
||||
with contextlib.suppress(OSError):
|
||||
os.remove(cache_file)
|
||||
|
||||
|
||||
def compute_dict_hash(graph_data):
|
||||
graph_data = filter_json(graph_data)
|
||||
|
||||
cleaned_graph_json = json.dumps(graph_data, sort_keys=True)
|
||||
return hashlib.sha256(cleaned_graph_json.encode("utf-8")).hexdigest()
|
||||
|
||||
|
||||
def filter_json(json_data):
|
||||
filtered_data = json_data.copy()
|
||||
|
||||
# Remove 'viewport' and 'chatHistory' keys
|
||||
if "viewport" in filtered_data:
|
||||
del filtered_data["viewport"]
|
||||
if "chatHistory" in filtered_data:
|
||||
del filtered_data["chatHistory"]
|
||||
|
||||
# Filter nodes
|
||||
if "nodes" in filtered_data:
|
||||
for node in filtered_data["nodes"]:
|
||||
if "position" in node:
|
||||
del node["position"]
|
||||
if "positionAbsolute" in node:
|
||||
del node["positionAbsolute"]
|
||||
if "selected" in node:
|
||||
del node["selected"]
|
||||
if "dragging" in node:
|
||||
del node["dragging"]
|
||||
|
||||
return filtered_data
|
||||
|
||||
|
||||
@create_cache_folder
|
||||
def save_binary_file(content: str, file_name: str, accepted_types: list[str]) -> str:
|
||||
"""
|
||||
Save a binary file to the specified folder.
|
||||
|
||||
Args:
|
||||
content: The content of the file as a bytes object.
|
||||
file_name: The name of the file, including its extension.
|
||||
|
||||
Returns:
|
||||
The path to the saved file.
|
||||
"""
|
||||
if not any(file_name.endswith(suffix) for suffix in accepted_types):
|
||||
raise ValueError(f"File {file_name} is not accepted")
|
||||
|
||||
# Get the destination folder
|
||||
cache_path = Path(tempfile.gettempdir()) / PREFIX
|
||||
|
||||
data = content.split(",")[1]
|
||||
decoded_bytes = base64.b64decode(data)
|
||||
|
||||
# Create the full file path
|
||||
file_path = os.path.join(cache_path, file_name)
|
||||
|
||||
# Save the binary content to the file
|
||||
with open(file_path, "wb") as file:
|
||||
file.write(decoded_bytes)
|
||||
|
||||
return file_path
|
||||
|
||||
|
||||
@create_cache_folder
|
||||
def save_cache(hash_val: str, chat_data, clean_old_cache_files: bool):
|
||||
cache_path = Path(tempfile.gettempdir()) / PREFIX / f"{hash_val}.dill"
|
||||
with cache_path.open("wb") as cache_file:
|
||||
dill.dump(chat_data, cache_file)
|
||||
|
||||
if clean_old_cache_files:
|
||||
clear_old_cache_files()
|
||||
|
||||
|
||||
@create_cache_folder
|
||||
def load_cache(hash_val):
|
||||
cache_path = Path(tempfile.gettempdir()) / PREFIX / f"{hash_val}.dill"
|
||||
if cache_path.exists():
|
||||
with cache_path.open("rb") as cache_file:
|
||||
return dill.load(cache_file)
|
||||
return None
|
||||
149
src/backend/langflow/cache/manager.py
vendored
Normal file
149
src/backend/langflow/cache/manager.py
vendored
Normal file
|
|
@ -0,0 +1,149 @@
|
|||
from contextlib import contextmanager
|
||||
from typing import Any, Awaitable, Callable, List, Optional
|
||||
from PIL import Image
|
||||
import pandas as pd
|
||||
|
||||
|
||||
class Subject:
|
||||
"""Base class for implementing the observer pattern."""
|
||||
|
||||
def __init__(self):
|
||||
self.observers: List[Callable[[], None]] = []
|
||||
|
||||
def attach(self, observer: Callable[[], None]):
|
||||
"""Attach an observer to the subject."""
|
||||
self.observers.append(observer)
|
||||
|
||||
def detach(self, observer: Callable[[], None]):
|
||||
"""Detach an observer from the subject."""
|
||||
self.observers.remove(observer)
|
||||
|
||||
def notify(self):
|
||||
"""Notify all observers about an event."""
|
||||
for observer in self.observers:
|
||||
if observer is None:
|
||||
continue
|
||||
observer()
|
||||
|
||||
|
||||
class AsyncSubject:
|
||||
"""Base class for implementing the async observer pattern."""
|
||||
|
||||
def __init__(self):
|
||||
self.observers: List[Callable[[], Awaitable]] = []
|
||||
|
||||
def attach(self, observer: Callable[[], Awaitable]):
|
||||
"""Attach an observer to the subject."""
|
||||
self.observers.append(observer)
|
||||
|
||||
def detach(self, observer: Callable[[], Awaitable]):
|
||||
"""Detach an observer from the subject."""
|
||||
self.observers.remove(observer)
|
||||
|
||||
async def notify(self):
|
||||
"""Notify all observers about an event."""
|
||||
for observer in self.observers:
|
||||
if observer is None:
|
||||
continue
|
||||
await observer()
|
||||
|
||||
|
||||
class CacheManager(Subject):
|
||||
"""Manages cache for different clients and notifies observers on changes."""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.CACHE = {}
|
||||
self.current_client_id = None
|
||||
self.current_cache = {}
|
||||
|
||||
@contextmanager
|
||||
def set_client_id(self, client_id: str):
|
||||
"""
|
||||
Context manager to set the current client_id and associated cache.
|
||||
|
||||
Args:
|
||||
client_id (str): The client identifier.
|
||||
"""
|
||||
previous_client_id = self.current_client_id
|
||||
self.current_client_id = client_id
|
||||
self.current_cache = self.CACHE.setdefault(client_id, {})
|
||||
try:
|
||||
yield
|
||||
finally:
|
||||
self.current_client_id = previous_client_id
|
||||
self.current_cache = self.CACHE.get(self.current_client_id, {})
|
||||
|
||||
def add(self, name: str, obj: Any, obj_type: str, extension: Optional[str] = None):
|
||||
"""
|
||||
Add an object to the current client's cache.
|
||||
|
||||
Args:
|
||||
name (str): The cache key.
|
||||
obj (Any): The object to cache.
|
||||
obj_type (str): The type of the object.
|
||||
"""
|
||||
object_extensions = {
|
||||
"image": "png",
|
||||
"pandas": "csv",
|
||||
}
|
||||
if obj_type in object_extensions:
|
||||
_extension = object_extensions[obj_type]
|
||||
else:
|
||||
_extension = type(obj).__name__.lower()
|
||||
self.current_cache[name] = {
|
||||
"obj": obj,
|
||||
"type": obj_type,
|
||||
"extension": extension or _extension,
|
||||
}
|
||||
self.notify()
|
||||
|
||||
def add_pandas(self, name: str, obj: Any):
|
||||
"""
|
||||
Add a pandas DataFrame or Series to the current client's cache.
|
||||
|
||||
Args:
|
||||
name (str): The cache key.
|
||||
obj (Any): The pandas DataFrame or Series object.
|
||||
"""
|
||||
if isinstance(obj, (pd.DataFrame, pd.Series)):
|
||||
self.add(name, obj.to_csv(), "pandas", extension="csv")
|
||||
else:
|
||||
raise ValueError("Object is not a pandas DataFrame or Series")
|
||||
|
||||
def add_image(self, name: str, obj: Any, extension: str = "png"):
|
||||
"""
|
||||
Add a PIL Image to the current client's cache.
|
||||
|
||||
Args:
|
||||
name (str): The cache key.
|
||||
obj (Any): The PIL Image object.
|
||||
"""
|
||||
if isinstance(obj, Image.Image):
|
||||
self.add(name, obj, "image", extension=extension)
|
||||
else:
|
||||
raise ValueError("Object is not a PIL Image")
|
||||
|
||||
def get(self, name: str):
|
||||
"""
|
||||
Get an object from the current client's cache.
|
||||
|
||||
Args:
|
||||
name (str): The cache key.
|
||||
|
||||
Returns:
|
||||
The cached object associated with the given cache key.
|
||||
"""
|
||||
return self.current_cache[name]
|
||||
|
||||
def get_last(self):
|
||||
"""
|
||||
Get the last added item in the current client's cache.
|
||||
|
||||
Returns:
|
||||
The last added item in the cache.
|
||||
"""
|
||||
return list(self.current_cache.values())[-1]
|
||||
|
||||
|
||||
cache_manager = CacheManager()
|
||||
|
|
@ -4,16 +4,18 @@
|
|||
# - Build each inner agent first, then build the outer agent
|
||||
|
||||
import contextlib
|
||||
import inspect
|
||||
import types
|
||||
import warnings
|
||||
from copy import deepcopy
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from langflow.cache import utils as cache_utils
|
||||
from langflow.cache import base as cache_utils
|
||||
from langflow.graph.constants import DIRECT_TYPES
|
||||
from langflow.interface import loading
|
||||
from langflow.interface.listing import ALL_TYPES_DICT
|
||||
from langflow.utils.logger import logger
|
||||
from langflow.utils.util import sync_to_async
|
||||
|
||||
|
||||
class Node:
|
||||
|
|
@ -158,13 +160,21 @@ class Node:
|
|||
continue
|
||||
result = value.build()
|
||||
# If the key is "func", then we need to use the run method
|
||||
if key == "func" and not isinstance(result, types.FunctionType):
|
||||
# func can be PythonFunction(code='\ndef upper_case(text: str) -> str:\n return text.upper()\n')
|
||||
# so we need to check if there is an attribute called run
|
||||
if hasattr(result, "run"):
|
||||
result = result.run # type: ignore
|
||||
elif hasattr(result, "get_function"):
|
||||
result = result.get_function() # type: ignore
|
||||
if key == "func":
|
||||
if not isinstance(result, types.FunctionType):
|
||||
# func can be
|
||||
# PythonFunction(code='\ndef upper_case(text: str) -> str:\n return text.upper()\n')
|
||||
# so we need to check if there is an attribute called run
|
||||
if hasattr(result, "run"):
|
||||
result = result.run # type: ignore
|
||||
elif hasattr(result, "get_function"):
|
||||
result = result.get_function() # type: ignore
|
||||
elif inspect.iscoroutinefunction(result):
|
||||
self.params["coroutine"] = result
|
||||
else:
|
||||
# turn result which is a function into a coroutine
|
||||
# so that it can be awaited
|
||||
self.params["coroutine"] = sync_to_async(result)
|
||||
|
||||
self.params[key] = result
|
||||
elif isinstance(value, list) and all(
|
||||
|
|
|
|||
|
|
@ -37,7 +37,9 @@ class ChainCreator(LangChainTypeCreator):
|
|||
try:
|
||||
if name in get_custom_nodes(self.type_name).keys():
|
||||
return get_custom_nodes(self.type_name)[name]
|
||||
return build_template_from_class(name, self.type_to_loader_dict)
|
||||
return build_template_from_class(
|
||||
name, self.type_to_loader_dict, add_function=True
|
||||
)
|
||||
except ValueError as exc:
|
||||
raise ValueError("Chain not found") from exc
|
||||
except AttributeError as exc:
|
||||
|
|
|
|||
|
|
@ -20,6 +20,7 @@ from langchain.llms.loading import load_llm_from_config
|
|||
|
||||
from langflow.interface.agents.custom import CUSTOM_AGENTS
|
||||
from langflow.interface.importing.utils import import_by_type
|
||||
from langflow.interface.run import fix_memory_inputs
|
||||
from langflow.interface.toolkits.base import toolkits_creator
|
||||
from langflow.interface.types import get_type_list
|
||||
from langflow.interface.utils import load_file_into_dict
|
||||
|
|
@ -33,6 +34,12 @@ def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
|
|||
return custom_agent.initialize(**params) # type: ignore
|
||||
|
||||
class_object = import_by_type(_type=base_type, name=node_type)
|
||||
# check if it is a class before using issubclass
|
||||
|
||||
# if isinstance(class_object, type) and issubclass(class_object, BaseModel):
|
||||
# # validate params
|
||||
# fields = class_object.__fields__
|
||||
# params = {key: value for key, value in params.items() if key in fields}
|
||||
|
||||
if base_type == "agents":
|
||||
# We need to initialize it differently
|
||||
|
|
@ -65,6 +72,7 @@ def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
|
|||
return load_toolkits_executor(node_type, loaded_toolkit, params)
|
||||
return loaded_toolkit
|
||||
elif base_type == "embeddings":
|
||||
# ? Why remove model from params?
|
||||
params.pop("model")
|
||||
return class_object(**params)
|
||||
elif base_type == "vectorstores":
|
||||
|
|
@ -106,7 +114,19 @@ def load_flow_from_json(path: str, build=True):
|
|||
# Nodes, edges and root node
|
||||
edges = data_graph["edges"]
|
||||
graph = Graph(nodes, edges)
|
||||
return graph.build() if build else graph
|
||||
if build:
|
||||
langchain_object = graph.build()
|
||||
if hasattr(langchain_object, "verbose"):
|
||||
langchain_object.verbose = True
|
||||
|
||||
if hasattr(langchain_object, "return_intermediate_steps"):
|
||||
# https://github.com/hwchase17/langchain/issues/2068
|
||||
# Deactivating until we have a frontend solution
|
||||
# to display intermediate steps
|
||||
langchain_object.return_intermediate_steps = False
|
||||
fix_memory_inputs(langchain_object)
|
||||
return langchain_object
|
||||
return graph
|
||||
|
||||
|
||||
def replace_zero_shot_prompt_with_prompt_template(nodes):
|
||||
|
|
|
|||
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Reference in a new issue