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Merge branch 'dev' into cz/bug/state/zustand

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cristhianzl 2024-01-18 09:35:09 -03:00
commit 06bc86142a
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5
.gitignore vendored
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@ -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*

2
README.md
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@ -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.
[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](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

20
docs/docs/components/embeddings.mdx
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@ -1,11 +1,13 @@
import Admonition from '@theme/Admonition';
import Admonition from "@theme/Admonition";
# Embeddings
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
<p>
We appreciate your understanding as we polish our documentation – it may contain some rough edges. Share your feedback or report issues to help us improve! 🛠️📝
</p>
<p>
We appreciate your understanding as we polish our documentation – it may
contain some rough edges. Share your feedback or report issues to help us
improve! 🛠️📝
</p>
</Admonition>
Embeddings are vector representations of text that capture the semantic meaning of the text. They are created using text embedding models and allow us to think about the text in a vector space, enabling us to perform tasks like semantic search, where we look for pieces of text that are most similar in the vector space.
@ -110,4 +112,12 @@ Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP).
- **top_k:** How the model selects tokens for output, the next token is selected from – defaults to `40`.
- **top_p:** Tokens are selected from most probable to least until the sum of their – defaults to `0.95`.
- **tuned_model_name:** The name of a tuned model. If provided, model_name is ignored.
- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output – defaults to `False`.
- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output – defaults to `False`.
### OllamaEmbeddings
Used to load [Ollama’s](https://ollama.ai/) embedding models. Wrapper around LangChain's [Ollama API](https://python.langchain.com/docs/integrations/text_embedding/ollama).
- **model** The name of the Ollama model to use – defaults to `llama2`.
- **base_url** The base URL for the Ollama API – defaults to `http://localhost:11434`.
- **temperature** Tunes the degree of randomness in text generations. Should be a non-negative value – defaults to `0`.

20173
docs/package-lock.json generated
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6
docs/package.json
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@ -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",

1858
poetry.lock generated
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6
pyproject.toml
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@ -1,6 +1,6 @@
[tool.poetry]
name = "langflow"
version = "0.6.4"
version = "0.6.5a8"
description = "A Python package with a built-in web application"
authors = ["Logspace <contact@logspace.ai>"]
maintainers = [
@ -55,7 +55,7 @@ tiktoken = "~0.5.0"
wikipedia = "^1.4.0"
qdrant-client = "^1.7.0"
websockets = "^10.3"
weaviate-client = "^3.26.0"
weaviate-client = { version = "^4.4b6", allow-prereleases = true }
jina = "*"
sentence-transformers = { version = "^2.2.2", optional = true }
ctransformers = { version = "^0.2.10", optional = true }
@ -104,7 +104,9 @@ 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"

35
src/backend/langflow/__main__.py
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@ -1,15 +1,12 @@
import platform
import socket
import sys
import time
import webbrowser
from pathlib import Path
from typing import Optional
import httpx
import typer
from dotenv import load_dotenv
from multiprocess import Process, cpu_count # type: ignore
from multiprocess import cpu_count # type: ignore
from rich import box
from rich import print as rprint
from rich.console import Console
@ -212,23 +209,12 @@ def run(
run_on_windows(host, port, log_level, options, app)
else:
# Run using gunicorn on Linux
run_on_mac_or_linux(host, port, log_level, options, app, open_browser)
run_on_mac_or_linux(host, port, log_level, options, app)
def run_on_mac_or_linux(host, port, log_level, options, app, open_browser=True):
webapp_process = Process(target=run_langflow, args=(host, port, log_level, options, app))
webapp_process.start()
status_code = 0
while status_code != 200:
try:
status_code = httpx.get(f"http://{host}:{port}/health").status_code
except Exception:
time.sleep(1)
def run_on_mac_or_linux(host, port, log_level, options, app):
print_banner(host, port)
if open_browser:
webbrowser.open(f"http://{host}:{port}")
run_langflow(host, port, log_level, options, app)
def run_on_windows(host, port, log_level, options, app):
@ -303,19 +289,26 @@ def run_langflow(host, port, log_level, options, app):
Run Langflow server on localhost
"""
try:
if platform.system() in ["Windows"]:
if platform.system() in ["Windows", "Darwin"]:
# Run using uvicorn on MacOS and Windows
# Windows doesn't support gunicorn
# MacOS requires an env variable to be set to use gunicorn
import uvicorn
uvicorn.run(app, host=host, port=port, log_level=log_level)
uvicorn.run(
app,
host=host,
port=port,
log_level=log_level,
)
else:
from langflow.server import LangflowApplication
LangflowApplication(app, options).run()
except KeyboardInterrupt:
pass
logger.info("Shutting down server")
sys.exit(0)
except Exception as e:
logger.exception(e)
sys.exit(1)

6
src/backend/langflow/alembic/versions/006b3990db50_add_unique_constraints.py
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@ -27,7 +27,8 @@ def upgrade() -> None:
with op.batch_alter_table('user', schema=None) as batch_op:
batch_op.create_unique_constraint('uq_user_id', ['id'])
except Exception:
except Exception as e:
print(e)
pass
# ### end Alembic commands ###
@ -44,6 +45,7 @@ def downgrade() -> None:
with op.batch_alter_table('apikey', schema=None) as batch_op:
batch_op.drop_constraint('uq_apikey_id', type_='unique')
except Exception:
except Exception as e:
print(e)
pass
# ### end Alembic commands ###

71
src/backend/langflow/alembic/versions/0b8757876a7c_.py Normal file
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@ -0,0 +1,71 @@
"""empty message
Revision ID: 0b8757876a7c
Revises: 006b3990db50
Create Date: 2024-01-17 10:32:56.686287
"""
from typing import Sequence, Union
from alembic import op
# revision identifiers, used by Alembic.
revision: str = '0b8757876a7c'
down_revision: Union[str, None] = '006b3990db50'
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
try:
with op.batch_alter_table('apikey', schema=None) as batch_op:
batch_op.create_index(batch_op.f('ix_apikey_api_key'), ['api_key'], unique=True)
batch_op.create_index(batch_op.f('ix_apikey_name'), ['name'], unique=False)
batch_op.create_index(batch_op.f('ix_apikey_user_id'), ['user_id'], unique=False)
except Exception as e:
print(e)
pass
try:
with op.batch_alter_table('flow', schema=None) as batch_op:
batch_op.create_index(batch_op.f('ix_flow_description'), ['description'], unique=False)
batch_op.create_index(batch_op.f('ix_flow_name'), ['name'], unique=False)
batch_op.create_index(batch_op.f('ix_flow_user_id'), ['user_id'], unique=False)
except Exception as e:
print(e)
pass
try:
with op.batch_alter_table('user', schema=None) as batch_op:
batch_op.create_index(batch_op.f('ix_user_username'), ['username'], unique=True)
except Exception as e:
print(e)
pass
# ### end Alembic commands ###
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
try:
with op.batch_alter_table('user', schema=None) as batch_op:
batch_op.drop_index(batch_op.f('ix_user_username'))
except Exception as e:
print(e)
pass
try:
with op.batch_alter_table('flow', schema=None) as batch_op:
batch_op.drop_index(batch_op.f('ix_flow_user_id'))
batch_op.drop_index(batch_op.f('ix_flow_name'))
batch_op.drop_index(batch_op.f('ix_flow_description'))
except Exception as e:
print(e)
pass
try:
with op.batch_alter_table('apikey', schema=None) as batch_op:
batch_op.drop_index(batch_op.f('ix_apikey_user_id'))
batch_op.drop_index(batch_op.f('ix_apikey_name'))
batch_op.drop_index(batch_op.f('ix_apikey_api_key'))
except Exception as e:
print(e)
pass
# ### end Alembic commands ###

14
src/backend/langflow/alembic/versions/260dbcc8b680_adds_tables.py
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@ -60,8 +60,8 @@ def upgrade() -> None:
sa.Column("create_at", sa.DateTime(), nullable=False),
sa.Column("updated_at", sa.DateTime(), nullable=False),
sa.Column("last_login_at", sa.DateTime(), nullable=True),
sa.PrimaryKeyConstraint("id"),
sa.UniqueConstraint("id"),
sa.PrimaryKeyConstraint("id", name="pk_user"),
sa.UniqueConstraint("id", name="uq_user_id"),
)
with op.batch_alter_table("user", schema=None) as batch_op:
batch_op.create_index(
@ -83,8 +83,8 @@ def upgrade() -> None:
["user_id"],
["user.id"],
),
sa.PrimaryKeyConstraint("id"),
sa.UniqueConstraint("id"),
sa.PrimaryKeyConstraint("id", name="pk_apikey"),
sa.UniqueConstraint("id", name="uq_apikey_id"),
)
with op.batch_alter_table("apikey", schema=None) as batch_op:
batch_op.create_index(
@ -106,8 +106,8 @@ def upgrade() -> None:
["user_id"],
["user.id"],
),
sa.PrimaryKeyConstraint("id"),
sa.UniqueConstraint("id"),
sa.PrimaryKeyConstraint("id", name="pk_flow"),
sa.UniqueConstraint("id", name="uq_flow_id"),
)
# Conditionally create indices for 'flow' table
# if _alembic_tmp_flow exists, then we need to drop it first
@ -145,7 +145,7 @@ def upgrade() -> None:
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
conn = op.get_bind()
conn = op.get_bind()
inspector = Inspector.from_engine(conn) # type: ignore
# List existing tables
existing_tables = inspector.get_table_names()

8
src/backend/langflow/alembic/versions/2ac71eb9c3ae_adds_credential_table.py
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@ -29,9 +29,10 @@ def upgrade() -> None:
sa.Column('id', sqlmodel.sql.sqltypes.GUID(), nullable=False),
sa.Column('created_at', sa.DateTime(), nullable=False),
sa.Column('updated_at', sa.DateTime(), nullable=True),
sa.PrimaryKeyConstraint('id')
sa.PrimaryKeyConstraint('id'),
)
except Exception:
except Exception as e:
print(e)
pass
# ### end Alembic commands ###
@ -40,6 +41,7 @@ def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
try:
op.drop_table('credential')
except Exception:
except Exception as e:
print(e)
pass
# ### end Alembic commands ###

3
src/backend/langflow/alembic/versions/7843803a87b5_store_updates.py
View file

@ -45,6 +45,7 @@ def downgrade() -> None:
with op.batch_alter_table("flow", schema=None) as batch_op:
batch_op.drop_column("is_component")
except Exception:
except Exception as e:
print(e)
pass
# ### end Alembic commands ###

1
src/backend/langflow/alembic/versions/7d2162acc8b2_adds_updated_at_and_folder_cols.py
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@ -37,7 +37,6 @@ def upgrade() -> None:
with op.batch_alter_table('flow', schema=None) as batch_op:
batch_op.add_column(sa.Column('updated_at', sa.DateTime(), nullable=True))
batch_op.add_column(sa.Column('folder', sqlmodel.sql.sqltypes.AutoString(), nullable=True))
# ### end Alembic commands ###

18
src/backend/langflow/alembic/versions/eb5866d51fd2_change_columns_to_be_nullable.py
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@ -29,7 +29,8 @@ def upgrade() -> None:
except exc.SQLAlchemyError:
# connection.execute(text("ROLLBACK"))
pass
except Exception:
except Exception as e:
print(e)
pass
try:
@ -37,7 +38,8 @@ def upgrade() -> None:
except exc.SQLAlchemyError:
# connection.execute(text("ROLLBACK"))
pass
except Exception:
except Exception as e:
print(e)
pass
# ### end Alembic commands ###
@ -57,14 +59,15 @@ def downgrade() -> None:
sa.Column("is_read_only", sa.BOOLEAN(), nullable=False),
sa.Column("create_at", sa.DATETIME(), nullable=False),
sa.Column("update_at", sa.DATETIME(), nullable=False),
sa.PrimaryKeyConstraint("id"),
sa.PrimaryKeyConstraint("id", name="pk_component"),
)
with op.batch_alter_table("component", schema=None) as batch_op:
batch_op.create_index("ix_component_name", ["name"], unique=False)
batch_op.create_index(
"ix_component_frontend_node_id", ["frontend_node_id"], unique=False
)
except Exception:
except Exception as e:
print(e)
pass
try:
@ -78,9 +81,10 @@ def downgrade() -> None:
["flow_id"],
["flow.id"],
),
sa.PrimaryKeyConstraint("id"),
sa.UniqueConstraint("id"),
sa.PrimaryKeyConstraint("id", name="pk_flowstyle"),
sa.UniqueConstraint("id", name="uq_flowstyle_id"),
)
except Exception:
except Exception as e:
print(e)
pass
# ### end Alembic commands ###

10
src/backend/langflow/alembic/versions/f5ee9749d1a6_user_id_can_be_null_in_flow.py
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@ -7,10 +7,8 @@ Create Date: 2023-10-18 23:12:27.297016
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
import sqlmodel
from alembic import op
# revision identifiers, used by Alembic.
revision: str = "f5ee9749d1a6"
@ -26,7 +24,8 @@ def upgrade() -> None:
batch_op.alter_column(
"user_id", existing_type=sa.CHAR(length=32), nullable=True
)
except Exception:
except Exception as e:
print(e)
pass
# ### end Alembic commands ###
@ -39,7 +38,8 @@ def downgrade() -> None:
batch_op.alter_column(
"user_id", existing_type=sa.CHAR(length=32), nullable=False
)
except Exception:
except Exception as e:
print(e)
pass
# ### end Alembic commands ###

6
src/backend/langflow/alembic/versions/fd531f8868b1_fix_credential_table.py
View file

@ -21,7 +21,8 @@ def upgrade() -> None:
try:
with op.batch_alter_table('credential', schema=None) as batch_op:
batch_op.create_foreign_key("fk_credential_user_id", 'user', ['user_id'], ['id'])
except Exception:
except Exception as e:
print(e)
pass
# ### end Alembic commands ###
@ -32,7 +33,8 @@ def downgrade() -> None:
try:
with op.batch_alter_table('credential', schema=None) as batch_op:
batch_op.drop_constraint("fk_credential_user_id", type_='foreignkey')
except Exception:
except Exception as e:
print(e)
pass
# ### end Alembic commands ###

142
src/backend/langflow/api/v1/endpoints.py
View file

@ -3,12 +3,10 @@ from typing import Annotated, Any, List, Optional, Union
import sqlalchemy as sa
from fastapi import APIRouter, Body, Depends, HTTPException, UploadFile, status
from loguru import logger
from sqlmodel import select
from langflow.api.utils import update_frontend_node_with_template_values
from langflow.api.v1.schemas import (
CustomComponentCode,
PreloadResponse,
ProcessResponse,
TaskResponse,
TaskStatusResponse,
@ -17,12 +15,15 @@ from langflow.api.v1.schemas import (
from langflow.interface.custom.custom_component import CustomComponent
from langflow.interface.custom.directory_reader import DirectoryReader
from langflow.interface.custom.utils import build_custom_component_template
from langflow.processing.process import process_graph_cached, process_tweaks
from langflow.processing.process import build_graph_and_generate_result, process_graph_cached, process_tweaks
from langflow.services.auth.utils import api_key_security, get_current_active_user
from langflow.services.cache.utils import save_uploaded_file
from langflow.services.database.models.flow import Flow
from langflow.services.database.models.user.model import User
from langflow.services.deps import get_session, get_session_service, get_settings_service, get_task_service
from langflow.services.session.service import SessionService
from loguru import logger
from sqlmodel import select
try:
from langflow.worker import process_graph_cached_task
@ -32,9 +33,8 @@ except ImportError:
raise NotImplementedError("Celery is not installed")
from sqlmodel import Session
from langflow.services.task.service import TaskService
from sqlmodel import Session
# build router
router = APIRouter(tags=["Base"])
@ -148,6 +148,55 @@ async def process_json(
raise HTTPException(status_code=500, detail=str(exc)) from exc
# Endpoint to preload a graph
@router.post("/process/preload/{flow_id}", response_model=PreloadResponse)
async def preload_flow(
session: Annotated[Session, Depends(get_session)],
flow_id: str,
session_id: Optional[str] = None,
session_service: SessionService = Depends(get_session_service),
api_key_user: User = Depends(api_key_security),
clear_session: Annotated[bool, Body(embed=True)] = False, # noqa: F821
):
try:
# Get the flow that matches the flow_id and belongs to the user
# flow = session.query(Flow).filter(Flow.id == flow_id).filter(Flow.user_id == api_key_user.id).first()
if clear_session:
session_service.clear_session(session_id)
# Check if the session exists
session_data = await session_service.load_session(session_id)
# Session data is a tuple of (graph, artifacts)
# or (None, None) if the session is empty
if isinstance(session_data, tuple):
graph, artifacts = session_data
is_clear = graph is None and artifacts is None
else:
is_clear = session_data is None
return PreloadResponse(session_id=session_id, is_clear=is_clear)
else:
if session_id is None:
session_id = flow_id
flow = session.exec(select(Flow).where(Flow.id == flow_id).where(Flow.user_id == api_key_user.id)).first()
if flow is None:
raise ValueError(f"Flow {flow_id} not found")
if flow.data is None:
raise ValueError(f"Flow {flow_id} has no data")
graph_data = flow.data
session_service.clear_session(session_id)
# Load the graph using SessionService
session_data = await session_service.load_session(session_id, graph_data)
graph, artifacts = session_data if session_data else (None, None)
if not graph:
raise ValueError("Graph not found in the session")
_ = await graph.build()
session_service.update_session(session_id, (graph, artifacts))
return PreloadResponse(session_id=session_id)
except Exception as exc:
logger.exception(exc)
raise HTTPException(status_code=500, detail=str(exc)) from exc
@router.post(
"/predict/{flow_id}",
response_model=ProcessResponse,
@ -167,36 +216,75 @@ async def process(
task_service: "TaskService" = Depends(get_task_service),
api_key_user: User = Depends(api_key_security),
sync: Annotated[bool, Body(embed=True)] = True, # noqa: F821
session_service: SessionService = Depends(get_session_service),
):
"""
Endpoint to process an input with a given flow_id.
"""
try:
if api_key_user is None:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Invalid API Key",
if session_id:
session_data = await session_service.load_session(session_id)
graph, artifacts = session_data if session_data else (None, None)
task_result: Any = None
task_status = None
task_id = None
if not graph:
raise ValueError("Graph not found in the session")
result = await build_graph_and_generate_result(
graph=graph,
inputs=inputs,
artifacts=artifacts,
session_id=session_id,
session_service=session_service,
)
task_id = str(id(result))
if isinstance(result, dict) and "result" in result:
task_result = result["result"]
session_id = result["session_id"]
elif hasattr(result, "result") and hasattr(result, "session_id"):
task_result = result.result
session_id = result.session_id
else:
task_result = result
if task_id:
task_response = TaskResponse(id=task_id, href=f"api/v1/task/{task_id}")
else:
task_response = None
return ProcessResponse(
result=task_result,
status=task_status,
task=task_response,
session_id=session_id,
backend=task_service.backend_name,
)
# Get the flow that matches the flow_id and belongs to the user
# flow = session.query(Flow).filter(Flow.id == flow_id).filter(Flow.user_id == api_key_user.id).first()
flow = session.exec(select(Flow).where(Flow.id == flow_id).where(Flow.user_id == api_key_user.id)).first()
if flow is None:
raise ValueError(f"Flow {flow_id} not found")
else:
if api_key_user is None:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Invalid API Key",
)
if flow.data is None:
raise ValueError(f"Flow {flow_id} has no data")
graph_data = flow.data
return await process_graph_data(
graph_data=graph_data,
inputs=inputs,
tweaks=tweaks,
clear_cache=clear_cache,
session_id=session_id,
task_service=task_service,
sync=sync,
)
# Get the flow that matches the flow_id and belongs to the user
# flow = session.query(Flow).filter(Flow.id == flow_id).filter(Flow.user_id == api_key_user.id).first()
flow = session.exec(select(Flow).where(Flow.id == flow_id).where(Flow.user_id == api_key_user.id)).first()
if flow is None:
raise ValueError(f"Flow {flow_id} not found")
if flow.data is None:
raise ValueError(f"Flow {flow_id} has no data")
graph_data = flow.data
return await process_graph_data(
graph_data=graph_data,
inputs=inputs,
tweaks=tweaks,
clear_cache=clear_cache,
session_id=session_id,
task_service=task_service,
sync=sync,
)
except sa.exc.StatementError as exc:
# StatementError('(builtins.ValueError) badly formed hexadecimal UUID string')
if "badly formed hexadecimal UUID string" in str(exc):

7
src/backend/langflow/api/v1/schemas.py
View file

@ -64,6 +64,13 @@ class ProcessResponse(BaseModel):
backend: Optional[str] = None
class PreloadResponse(BaseModel):
"""Preload response schema."""
session_id: Optional[str] = None
is_clear: Optional[bool] = None
# TaskStatusResponse(
# status=task.status, result=task.result if task.ready() else None
# )

64
src/backend/langflow/components/embeddings/AzureOpenAIEmbeddings.py Normal file
View file

@ -0,0 +1,64 @@
from langflow import CustomComponent
from langchain.embeddings.base import Embeddings
from langchain_community.embeddings import AzureOpenAIEmbeddings
class AzureOpenAIEmbeddingsComponent(CustomComponent):
display_name: str = "AzureOpenAIEmbeddings"
description: str = "Embeddings model from Azure OpenAI."
documentation: str = "https://python.langchain.com/docs/integrations/text_embedding/azureopenai"
beta = False
API_VERSION_OPTIONS = [
"2022-12-01",
"2023-03-15-preview",
"2023-05-15",
"2023-06-01-preview",
"2023-07-01-preview",
"2023-08-01-preview"
]
def build_config(self):
return {
"azure_endpoint": {
"display_name": "Azure Endpoint",
"required": True,
"info": "Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`",
},
"azure_deployment": {
"display_name": "Deployment Name",
"required": True,
},
"api_version": {
"display_name": "API Version",
"options": self.API_VERSION_OPTIONS,
"value": self.API_VERSION_OPTIONS[-1],
"advanced": True,
},
"api_key": {
"display_name": "API Key",
"required": True,
"password": True,
},
"code": {
"show": False
},
}
def build(
self,
azure_endpoint: str,
azure_deployment: str,
api_version: str,
api_key: str,
) -> Embeddings:
try:
embeddings = AzureOpenAIEmbeddings(
azure_endpoint = azure_endpoint,
deployment = azure_deployment,
openai_api_version = api_version,
openai_api_key = api_key,
)
except Exception as e:
raise ValueError("Could not connect to AzureOpenAIEmbeddings API.") from e
return embeddings

41
src/backend/langflow/components/embeddings/OllamaEmbeddings.py Normal file
View file

@ -0,0 +1,41 @@
from typing import Optional
from langflow import CustomComponent
from langchain.embeddings.base import Embeddings
from langchain_community.embeddings import OllamaEmbeddings
class OllamaEmbeddingsComponent(CustomComponent):
"""
A custom component for implementing an Embeddings Model using Ollama.
"""
display_name: str = "Ollama Embeddings"
description: str = "Embeddings model from Ollama."
documentation = "https://python.langchain.com/docs/integrations/text_embedding/ollama"
beta = True
def build_config(self):
return {
"model": {
"display_name": "Ollama Model",
},
"base_url": {"display_name": "Ollama Base URL"},
"temperature": {"display_name": "Model Temperature"},
"code": {"show": False},
}
def build(
self,
model: str = "llama2",
base_url: str = "http://localhost:11434",
temperature: Optional[float] = None,
) -> Embeddings:
try:
output = OllamaEmbeddings(
model=model,
base_url=base_url,
temperature=temperature
) # type: ignore
except Exception as e:
raise ValueError("Could not connect to Ollama API.") from e
return output

21
src/backend/langflow/components/llms/AmazonBedrock.py
View file

@ -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

51
src/backend/langflow/components/llms/AzureChatOpenAI.py
View file

@ -8,6 +8,7 @@ class AzureChatOpenAIComponent(CustomComponent):
display_name: str = "AzureChatOpenAI"
description: str = "LLM model from Azure OpenAI."
documentation: str = "https://python.langchain.com/docs/integrations/llms/azure_openai"
beta = False
AZURE_OPENAI_MODELS = [
"gpt-35-turbo",
@ -18,11 +19,20 @@ class AzureChatOpenAIComponent(CustomComponent):
"gpt-4-vision",
]
AZURE_OPENAI_API_VERSIONS = [
"2023-03-15-preview",
"2023-05-15",
"2023-06-01-preview",
"2023-07-01-preview",
"2023-08-01-preview",
"2023-12-01-preview"
]
def build_config(self):
return {
"model": {
"display_name": "Model Name",
"value": "gpt-35-turbo",
"value": self.AZURE_OPENAI_MODELS[0],
"options": self.AZURE_OPENAI_MODELS,
"required": True,
},
@ -37,11 +47,16 @@ class AzureChatOpenAIComponent(CustomComponent):
},
"api_version": {
"display_name": "API Version",
"value": "2023-05-15",
"options": self.AZURE_OPENAI_API_VERSIONS,
"value": self.AZURE_OPENAI_API_VERSIONS[-1],
"required": True,
"advanced": True,
},
"api_key": {"display_name": "API Key", "required": True, "password": True},
"api_key": {
"display_name": "API Key",
"required": True,
"password": True
},
"temperature": {
"display_name": "Temperature",
"value": 0.7,
@ -54,26 +69,32 @@ class AzureChatOpenAIComponent(CustomComponent):
"required": False,
"field_type": "int",
"advanced": True,
"info": "Maximum number of tokens to generate.",
},
"code": {
"show": False
},
"code": {"show": False},
}
def build(
self,
model: str,
azure_endpoint: str,
azure_deployment: str,
api_key: str,
api_version: str = "2023-05-15",
api_version: str,
temperature: float = 0.7,
max_tokens: Optional[int] = 1000,
) -> BaseLanguageModel:
return AzureChatOpenAI(
model=model,
azure_endpoint=azure_endpoint,
azure_deployment=azure_deployment,
api_version=api_version,
api_key=api_key,
temperature=temperature,
max_tokens=max_tokens,
)
try:
llm = AzureChatOpenAI(
model=model,
azure_endpoint=azure_endpoint,
azure_deployment=azure_deployment,
api_version=api_version,
api_key=api_key,
temperature=temperature,
max_tokens=max_tokens,
)
except Exception as e:
raise ValueError("Could not connect to AzureOpenAI API.") from e
return llm

4
src/backend/langflow/config.yaml
View file

@ -106,6 +106,8 @@ embeddings:
documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/google_vertex_ai_palm"
AmazonBedrockEmbeddings:
documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock"
OllamaEmbeddings:
documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/ollama"
llms:
OpenAI:
@ -274,6 +276,8 @@ vectorstores:
documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss"
Pinecone:
documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pinecone"
ElasticsearchStore:
documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/elasticsearch"
SupabaseVectorStore:
documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/supabase"
MongoDBAtlasVectorSearch:

24
src/backend/langflow/interface/initialize/vector_store.py
View file

@ -1,6 +1,7 @@
from typing import Any, Callable, Dict, Type
from langchain.vectorstores import (
Pinecone,
ElasticsearchStore,
Qdrant,
Chroma,
FAISS,
@ -226,11 +227,34 @@ def initialize_qdrant(class_object: Type[Qdrant], params: dict):
return class_object.from_documents(**params)
def initialize_elasticsearch(class_object: Type[ElasticsearchStore], params: dict):
"""Initialize elastic and return the class object"""
if "index_name" not in params:
raise ValueError("Elasticsearch Index must be provided in the params")
if "es_url" not in params:
raise ValueError("Elasticsearch URL must be provided in the params")
if not docs_in_params(params):
existing_index_params = {
"embedding": params.pop("embedding"),
}
if "index_name" in params:
existing_index_params["index_name"] = params.pop("index_name")
if "es_url" in params:
existing_index_params["es_url"] = params.pop("es_url")
return class_object.from_existing_index(**existing_index_params)
# If there are docs in the params, create a new index
if "texts" in params:
params["documents"] = params.pop("texts")
return class_object.from_documents(**params)
vecstore_initializer: Dict[str, Callable[[Type[Any], dict], Any]] = {
"Pinecone": initialize_pinecone,
"Chroma": initialize_chroma,
"Qdrant": initialize_qdrant,
"Weaviate": initialize_weaviate,
"ElasticsearchStore": initialize_elasticsearch,
"FAISS": initialize_faiss,
"SupabaseVectorStore": initialize_supabase,
"MongoDBAtlasVectorSearch": initialize_mongodb,

4
src/backend/langflow/main.py
View file

@ -7,6 +7,7 @@ from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse
from fastapi.staticfiles import StaticFiles
from langflow.api import router
from langflow.interface.utils import setup_llm_caching
from langflow.services.plugins.langfuse_plugin import LangfuseInstance
@ -102,11 +103,12 @@ def setup_app(static_files_dir: Optional[Path] = None, backend_only: bool = Fals
if __name__ == "__main__":
import uvicorn
from langflow.__main__ import get_number_of_workers
configure()
uvicorn.run(
create_app,
"langflow.main:create_app",
host="127.0.0.1",
port=7860,
workers=get_number_of_workers(),

22
src/backend/langflow/processing/process.py
View file

@ -7,9 +7,11 @@ from langchain.schema import AgentAction, Document
from langchain.vectorstores.base import VectorStore
from langchain_core.messages import AIMessage
from langchain_core.runnables.base import Runnable
from langflow.graph.graph.base import Graph
from langflow.interface.custom.custom_component import CustomComponent
from langflow.interface.run import build_sorted_vertices, get_memory_key, update_memory_keys
from langflow.services.deps import get_session_service
from langflow.services.session.service import SessionService
from loguru import logger
from pydantic import BaseModel
@ -220,13 +222,29 @@ async def process_graph_cached(
graph, artifacts = session if session else (None, None)
if not graph:
raise ValueError("Graph not found in the session")
result = await build_graph_and_generate_result(
graph=graph, session_id=session_id, inputs=inputs, artifacts=artifacts, session_service=session_service
)
return result
async def build_graph_and_generate_result(
graph: "Graph",
session_id: str,
inputs: Optional[Union[dict, List[dict]]] = None,
artifacts: Optional[Dict[str, Any]] = None,
session_service: Optional[SessionService] = None,
):
"""Build the graph and generate the result"""
built_object = await graph.build()
processed_inputs = process_inputs(inputs, artifacts or {})
result = await generate_result(built_object, processed_inputs)
# langchain_object is now updated with the new memory
# we need to update the cache with the updated langchain_object
session_service.update_session(session_id, (graph, artifacts))
if session_id and session_service:
session_service.update_session(session_id, (graph, artifacts))
return Result(result=result, session_id=session_id)

15
src/backend/langflow/services/chat/service.py
View file

@ -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}")

47
src/backend/langflow/services/chat/utils.py
View file

@ -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)

11
src/backend/langflow/services/database/service.py
View file

@ -5,17 +5,16 @@ from typing import TYPE_CHECKING
import sqlalchemy as sa
from alembic import command, util
from alembic.config import Config
from loguru import logger
from sqlalchemy import inspect
from sqlalchemy.exc import OperationalError
from sqlmodel import Session, SQLModel, create_engine, select, text
from langflow.services.base import Service
from langflow.services.database import models # noqa
from langflow.services.database.models.user.crud import get_user_by_username
from langflow.services.database.utils import Result, TableResults
from langflow.services.deps import get_settings_service
from langflow.services.utils import teardown_superuser
from loguru import logger
from sqlalchemy import inspect
from sqlalchemy.exc import OperationalError
from sqlmodel import Session, SQLModel, create_engine, select, text
if TYPE_CHECKING:
from sqlalchemy.engine import Engine
@ -40,7 +39,7 @@ class DatabaseService(Service):
connect_args = {"check_same_thread": False}
else:
connect_args = {}
return create_engine(self.database_url, connect_args=connect_args)
return create_engine(self.database_url, connect_args=connect_args, max_overflow=-1)
def __enter__(self):
self._session = Session(self.engine)

7
src/backend/langflow/services/session/service.py
View file

@ -1,4 +1,4 @@
from typing import TYPE_CHECKING
from typing import TYPE_CHECKING, Optional
from langflow.interface.run import build_sorted_vertices
from langflow.services.base import Service
@ -14,14 +14,15 @@ class SessionService(Service):
def __init__(self, cache_service):
self.cache_service: "BaseCacheService" = cache_service
async def load_session(self, key, data_graph):
async def load_session(self, key, data_graph: Optional[dict] = None):
# Check if the data is cached
if key in self.cache_service:
return self.cache_service.get(key)
if key is None:
key = self.generate_key(session_id=None, data_graph=data_graph)
if data_graph is None:
return (None, None)
# If not cached, build the graph and cache it
graph, artifacts = await build_sorted_vertices(data_graph)

28
src/backend/langflow/template/frontend_node/vectorstores.py
View file

@ -11,6 +11,7 @@ BASIC_FIELDS = [
"persist_directory",
"persist",
"weaviate_url",
"es_url",
"index_name",
"namespace",
"folder_path",
@ -170,6 +171,33 @@ class VectorStoreFrontendNode(FrontendNode):
value="",
)
extra_fields.extend((extra_field, extra_field2))
elif self.template.type_name == "ElasticsearchStore":
# add elastic and elastic credentials
extra_field = TemplateField(
name="es_url",
field_type="str",
required=True,
placeholder="http://localhost:9200",
show=True,
advanced=False,
multiline=False,
value="http://localhost:9200",
display_name="Elasticsearch URL",
)
extra_field2 = TemplateField(
name="index_name",
field_type="str",
required=True,
placeholder="test-index",
show=True,
advanced=False,
multiline=False,
value="test-index",
display_name="Index Name",
)
extra_fields.extend((extra_field, extra_field2))
elif self.template.type_name == "FAISS":
extra_field = TemplateField(
name="folder_path",

34
src/backend/langflow/utils/chat.py Normal file
View 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
View 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)

19
src/frontend/src/icons/ElasticsearchStore/ElasticsearchLogo.jsx Normal file
View file

@ -0,0 +1,19 @@
const SvgElasticsearchLogo = (props) => (
<svg
xmlns="http://www.w3.org/2000/svg"
width="24"
height="24"
fill="none"
stroke="currentColor"
strokeLinecap="round"
strokeLinejoin="round"
strokeWidth="2"
className="icon icon-tabler icon-tabler-brand-elastic"
viewBox="0 0 24 24"
>
<path stroke="none" d="M0 0h24v24H0z"></path>
<path d="M14 2a5 5 0 015 5c0 .712-.232 1.387-.5 2 1.894.042 3.5 1.595 3.5 3.5 0 1.869-1.656 3.4-3.5 3.5.333.625.5 1.125.5 1.5a2.5 2.5 0 01-2.5 2.5c-.787 0-1.542-.432-2-1-.786 1.73-2.476 3-4.5 3a5 5 0 01-4.583-7 3.5 3.5 0 01-.11-6.992h.195a2.5 2.5 0 012-4c.787 0 1.542.432 2 1 .786-1.73 2.476-3 4.5-3zM8.5 9l-3-1"></path>
<path d="M9.5 5l-1 4 1 2 5 2 4-4M18.499 16l-3-.5-1-2.5M14.5 19l1-3.5M5.417 15L9.5 11"></path>
</svg>
);
export default SvgElasticsearchLogo;

9
src/frontend/src/icons/ElasticsearchStore/ElasticsearchLogo.svg Normal file
View file

@ -0,0 +1,9 @@
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-brand-elastic" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
<path stroke="none" d="M0 0h24v24H0z" fill="none" />
<path d="M14 2a5 5 0 0 1 5 5c0 .712 -.232 1.387 -.5 2c1.894 .042 3.5 1.595 3.5 3.5c0 1.869 -1.656 3.4 -3.5 3.5c.333 .625 .5 1.125 .5 1.5a2.5 2.5 0 0 1 -2.5 2.5c-.787 0 -1.542 -.432 -2 -1c-.786 1.73 -2.476 3 -4.5 3a5 5 0 0 1 -4.583 -7a3.5 3.5 0 0 1 -.11 -6.992l.195 0a2.5 2.5 0 0 1 2 -4c.787 0 1.542 .432 2 1c.786 -1.73 2.476 -3 4.5 -3z" />
<path d="M8.5 9l-3 -1" />
<path d="M9.5 5l-1 4l1 2l5 2l4 -4" />
<path d="M18.499 16l-3 -.5l-1 -2.5" />
<path d="M14.5 19l1 -3.5" />
<path d="M5.417 15l4.083 -4" />
</svg>
Side by side

After

Width:  |  Height:  |  Size: 810 B

9
src/frontend/src/icons/ElasticsearchStore/index.tsx Normal file
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@ -0,0 +1,9 @@
import React, { forwardRef } from "react";
import SvgElasticsearchLogo from "./ElasticsearchLogo";
export const ElasticsearchIcon = forwardRef<
SVGSVGElement,
React.PropsWithChildren<{}>
>((props, ref) => {
return <SvgElasticsearchLogo ref={ref} {...props} />;
});

2
src/frontend/src/utils/styleUtils.ts
View file

@ -111,6 +111,7 @@ import { AnthropicIcon } from "../icons/Anthropic";
import { BingIcon } from "../icons/Bing";
import { ChromaIcon } from "../icons/ChromaIcon";
import { CohereIcon } from "../icons/Cohere";
import { ElasticsearchIcon } from "../icons/ElasticsearchStore";
import { EvernoteIcon } from "../icons/Evernote";
import { FBIcon } from "../icons/FacebookMessenger";
import { GitBookIcon } from "../icons/GitBook";
@ -256,6 +257,7 @@ export const nodeIconsLucide: iconsType = {
OpenAIEmbeddings: OpenAiIcon,
Pinecone: PineconeIcon,
Qdrant: QDrantIcon,
ElasticsearchStore: ElasticsearchIcon,
Weaviate: WeaviateIcon,
Searx: SearxIcon,
SlackDirectoryLoader: SvgSlackIcon,