Merge branch 'main' into dev
This commit is contained in:
Gabriel Luiz Freitas Almeida
GitHub
commit
7b5225f857
239 changed files with 28526 additions and 15389 deletions
5
.gitignore
vendored
5
.gitignore
vendored
|
|
@ -17,9 +17,6 @@ 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
|
||||
|
||||
|
|
@ -259,4 +256,4 @@ langflow.db
|
|||
/tmp/*
|
||||
src/backend/langflow/frontend/
|
||||
.docker
|
||||
scratchpad*
|
||||
scratchpad*
|
||||
7
Makefile
7
Makefile
|
|
@ -45,13 +45,6 @@ run_frontend:
|
|||
@-kill -9 `lsof -t -i:3000`
|
||||
cd src/frontend && npm start
|
||||
|
||||
tests_frontend:
|
||||
ifeq ($(UI), true)
|
||||
cd src/frontend && ./run-tests.sh --ui
|
||||
else
|
||||
cd src/frontend && ./run-tests.sh
|
||||
endif
|
||||
|
||||
run_cli:
|
||||
poetry run langflow run --path src/frontend/build
|
||||
|
||||
|
|
|
|||
|
|
@ -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/gcp&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
|
||||
## Deploy on Railway
|
||||
|
||||
|
|
|
|||
|
|
@ -1,13 +1,11 @@
|
|||
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.
|
||||
|
|
@ -112,12 +110,4 @@ 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`.
|
||||
|
||||
### 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`.
|
||||
- **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`.
|
||||
|
|
@ -21,7 +21,7 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
|
|||
|
||||
<Admonition type="note" title="LangChain Components 🦜🔗">
|
||||
|
||||
- [`ConversationBufferMemory`](https://python.langchain.com/docs/modules/memory/types/buffer)
|
||||
- [`ConversationBufferMemory`](https://python.langchain.com/docs/modules/memory/how_to/buffer)
|
||||
- [`ConversationChain`](https://python.langchain.com/docs/modules/chains/)
|
||||
- [`ChatOpenAI`](https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai)
|
||||
|
||||
|
|
|
|||
20117
docs/package-lock.json
generated
20117
docs/package-lock.json
generated
File diff suppressed because it is too large
Load diff
|
|
@ -1,5 +1,5 @@
|
|||
{
|
||||
"name": "langflow-docs",
|
||||
"name": "docusaurus",
|
||||
"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": "^18.2.0",
|
||||
"react-dom": "^18.2.0",
|
||||
"react": "^17.0.2",
|
||||
"react-dom": "^17.0.2",
|
||||
"react-images": "^0.6.7",
|
||||
"react-medium-image-zoom": "^5.1.6",
|
||||
"react-player": "^2.12.0",
|
||||
|
|
|
|||
3312
poetry.lock
generated
3312
poetry.lock
generated
File diff suppressed because it is too large
Load diff
|
|
@ -25,6 +25,8 @@ documentation = "https://docs.langflow.org"
|
|||
langflow = "langflow.__main__:main"
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
|
||||
|
||||
python = ">=3.9,<3.11"
|
||||
fastapi = "^0.108.0"
|
||||
uvicorn = "^0.25.0"
|
||||
|
|
@ -33,8 +35,8 @@ google-search-results = "^2.4.1"
|
|||
google-api-python-client = "^2.79.0"
|
||||
typer = "^0.9.0"
|
||||
gunicorn = "^21.2.0"
|
||||
langchain = "~0.1.0"
|
||||
openai = "^1.10.0"
|
||||
langchain = "~0.0.345"
|
||||
openai = "^1.6.1"
|
||||
pandas = "2.0.3"
|
||||
chromadb = "^0.4.0"
|
||||
huggingface-hub = { version = "^0.19.0", extras = ["inference"] }
|
||||
|
|
@ -53,7 +55,7 @@ tiktoken = "~0.5.0"
|
|||
wikipedia = "^1.4.0"
|
||||
qdrant-client = "^1.7.0"
|
||||
websockets = "^10.3"
|
||||
weaviate-client = "*"
|
||||
weaviate-client = "^3.26.0"
|
||||
jina = "*"
|
||||
sentence-transformers = { version = "^2.2.2", optional = true }
|
||||
ctransformers = { version = "^0.2.10", optional = true }
|
||||
|
|
@ -61,7 +63,7 @@ cohere = "^4.39.0"
|
|||
python-multipart = "^0.0.6"
|
||||
sqlmodel = "^0.0.14"
|
||||
faiss-cpu = "^1.7.4"
|
||||
anthropic = "^0.13.0"
|
||||
anthropic = "^0.8.0"
|
||||
orjson = "3.9.3"
|
||||
multiprocess = "^0.70.14"
|
||||
cachetools = "^5.3.1"
|
||||
|
|
@ -96,22 +98,19 @@ markupsafe = "^2.1.3"
|
|||
extract-msg = "^0.45.0"
|
||||
# jq is not available for windows
|
||||
jq = { version = "^1.6.0", markers = "sys_platform != 'win32'" }
|
||||
boto3 = "^1.34.0"
|
||||
boto3 = "^1.28.63"
|
||||
numexpr = "^2.8.6"
|
||||
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"
|
||||
langchain-openai = "^0.0.2"
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
pytest-asyncio = "^0.23.1"
|
||||
types-redis = "^4.6.0.5"
|
||||
ipykernel = "^6.27.0"
|
||||
mypy = "^1.8.0"
|
||||
mypy = "^1.7.1"
|
||||
ruff = "^0.1.5"
|
||||
httpx = "*"
|
||||
pytest = "^7.4.2"
|
||||
|
|
@ -153,8 +152,7 @@ exclude = ["src/backend/langflow/alembic/*"]
|
|||
line-length = 120
|
||||
|
||||
[tool.mypy]
|
||||
plugins = ["pydantic.mypy"]
|
||||
follow_imports = "silent"
|
||||
plugins = "pydantic.mypy"
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
|
|
|
|||
|
|
@ -1,12 +1,15 @@
|
|||
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 cpu_count # type: ignore
|
||||
from multiprocess import Process, cpu_count # type: ignore
|
||||
from rich import box
|
||||
from rich import print as rprint
|
||||
from rich.console import Console
|
||||
|
|
@ -209,12 +212,23 @@ 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)
|
||||
run_on_mac_or_linux(host, port, log_level, options, app, open_browser)
|
||||
|
||||
|
||||
def 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)
|
||||
|
||||
print_banner(host, port)
|
||||
run_langflow(host, port, log_level, options, app)
|
||||
if open_browser:
|
||||
webbrowser.open(f"http://{host}:{port}")
|
||||
|
||||
|
||||
def run_on_windows(host, port, log_level, options, app):
|
||||
|
|
@ -289,26 +303,19 @@ def run_langflow(host, port, log_level, options, app):
|
|||
Run Langflow server on localhost
|
||||
"""
|
||||
try:
|
||||
if platform.system() in ["Windows", "Darwin"]:
|
||||
if platform.system() in ["Windows"]:
|
||||
# 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:
|
||||
logger.info("Shutting down server")
|
||||
sys.exit(0)
|
||||
pass
|
||||
except Exception as e:
|
||||
logger.exception(e)
|
||||
sys.exit(1)
|
||||
|
|
|
|||
|
|
@ -27,8 +27,7 @@ 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 as e:
|
||||
print(e)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
|
@ -45,7 +44,6 @@ 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 as e:
|
||||
print(e)
|
||||
except Exception:
|
||||
pass
|
||||
# ### end Alembic commands ###
|
||||
|
|
|
|||
|
|
@ -1,71 +0,0 @@
|
|||
"""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 ###
|
||||
|
|
@ -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", name="pk_user"),
|
||||
sa.UniqueConstraint("id", name="uq_user_id"),
|
||||
sa.PrimaryKeyConstraint("id"),
|
||||
sa.UniqueConstraint("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", name="pk_apikey"),
|
||||
sa.UniqueConstraint("id", name="uq_apikey_id"),
|
||||
sa.PrimaryKeyConstraint("id"),
|
||||
sa.UniqueConstraint("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", name="pk_flow"),
|
||||
sa.UniqueConstraint("id", name="uq_flow_id"),
|
||||
sa.PrimaryKeyConstraint("id"),
|
||||
sa.UniqueConstraint("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()
|
||||
|
|
|
|||
|
|
@ -29,10 +29,9 @@ 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 as e:
|
||||
print(e)
|
||||
except Exception:
|
||||
pass
|
||||
# ### end Alembic commands ###
|
||||
|
||||
|
|
@ -41,7 +40,6 @@ def downgrade() -> None:
|
|||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
try:
|
||||
op.drop_table('credential')
|
||||
except Exception as e:
|
||||
print(e)
|
||||
except Exception:
|
||||
pass
|
||||
# ### end Alembic commands ###
|
||||
|
|
|
|||
|
|
@ -45,7 +45,6 @@ def downgrade() -> None:
|
|||
|
||||
with op.batch_alter_table("flow", schema=None) as batch_op:
|
||||
batch_op.drop_column("is_component")
|
||||
except Exception as e:
|
||||
print(e)
|
||||
except Exception:
|
||||
pass
|
||||
# ### end Alembic commands ###
|
||||
|
|
|
|||
|
|
@ -37,6 +37,7 @@ 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 ###
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,59 +0,0 @@
|
|||
"""Add unique constraints
|
||||
|
||||
Revision ID: b2fa308044b5
|
||||
Revises: 0b8757876a7c
|
||||
Create Date: 2024-01-26 13:31:14.797548
|
||||
|
||||
"""
|
||||
from typing import Sequence, Union
|
||||
|
||||
import sqlalchemy as sa
|
||||
import sqlmodel
|
||||
from alembic import op
|
||||
|
||||
# revision identifiers, used by Alembic.
|
||||
revision: str = 'b2fa308044b5'
|
||||
down_revision: Union[str, None] = '0b8757876a7c'
|
||||
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:
|
||||
op.drop_table('flowstyle')
|
||||
with op.batch_alter_table('flow', schema=None) as batch_op:
|
||||
batch_op.add_column(sa.Column('is_component', sa.Boolean(), nullable=True))
|
||||
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))
|
||||
batch_op.add_column(sa.Column('user_id', sqlmodel.sql.sqltypes.GUID(), nullable=True))
|
||||
batch_op.create_index(batch_op.f('ix_flow_user_id'), ['user_id'], unique=False)
|
||||
batch_op.create_foreign_key('fk_flow_user_id_user', 'user', ['user_id'], ['id'])
|
||||
except Exception:
|
||||
pass
|
||||
# ### end Alembic commands ###
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
try:
|
||||
with op.batch_alter_table('flow', schema=None) as batch_op:
|
||||
batch_op.drop_constraint('fk_flow_user_id_user', type_='foreignkey')
|
||||
batch_op.drop_index(batch_op.f('ix_flow_user_id'))
|
||||
batch_op.drop_column('user_id')
|
||||
batch_op.drop_column('folder')
|
||||
batch_op.drop_column('updated_at')
|
||||
batch_op.drop_column('is_component')
|
||||
|
||||
op.create_table('flowstyle',
|
||||
sa.Column('color', sa.VARCHAR(), nullable=False),
|
||||
sa.Column('emoji', sa.VARCHAR(), nullable=False),
|
||||
sa.Column('flow_id', sa.CHAR(length=32), nullable=True),
|
||||
sa.Column('id', sa.CHAR(length=32), nullable=False),
|
||||
sa.ForeignKeyConstraint(['flow_id'], ['flow.id'], ),
|
||||
sa.PrimaryKeyConstraint('id'),
|
||||
sa.UniqueConstraint('id')
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
# ### end Alembic commands ###
|
||||
|
|
@ -1,50 +0,0 @@
|
|||
"""New fixes
|
||||
|
||||
Revision ID: bc2f01c40e4a
|
||||
Revises: b2fa308044b5
|
||||
Create Date: 2024-01-26 13:34:14.496769
|
||||
|
||||
"""
|
||||
from typing import Sequence, Union
|
||||
|
||||
import sqlalchemy as sa
|
||||
import sqlmodel
|
||||
from alembic import op
|
||||
|
||||
# revision identifiers, used by Alembic.
|
||||
revision: str = 'bc2f01c40e4a'
|
||||
down_revision: Union[str, None] = 'b2fa308044b5'
|
||||
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('flow', schema=None) as batch_op:
|
||||
batch_op.add_column(sa.Column('is_component', sa.Boolean(), nullable=True))
|
||||
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))
|
||||
batch_op.add_column(sa.Column('user_id', sqlmodel.sql.sqltypes.GUID(), nullable=True))
|
||||
batch_op.create_index(batch_op.f('ix_flow_user_id'), ['user_id'], unique=False)
|
||||
batch_op.create_foreign_key('flow_user_id_fkey'
|
||||
, 'user', ['user_id'], ['id'])
|
||||
except Exception:
|
||||
pass
|
||||
# ### end Alembic commands ###
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
try:
|
||||
with op.batch_alter_table('flow', schema=None) as batch_op:
|
||||
batch_op.drop_constraint('flow_user_id_fkey', type_='foreignkey')
|
||||
batch_op.drop_index(batch_op.f('ix_flow_user_id'))
|
||||
batch_op.drop_column('user_id')
|
||||
batch_op.drop_column('folder')
|
||||
batch_op.drop_column('updated_at')
|
||||
batch_op.drop_column('is_component')
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
|
@ -29,8 +29,7 @@ def upgrade() -> None:
|
|||
except exc.SQLAlchemyError:
|
||||
# connection.execute(text("ROLLBACK"))
|
||||
pass
|
||||
except Exception as e:
|
||||
print(e)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
try:
|
||||
|
|
@ -38,8 +37,7 @@ def upgrade() -> None:
|
|||
except exc.SQLAlchemyError:
|
||||
# connection.execute(text("ROLLBACK"))
|
||||
pass
|
||||
except Exception as e:
|
||||
print(e)
|
||||
except Exception:
|
||||
pass
|
||||
# ### end Alembic commands ###
|
||||
|
||||
|
|
@ -59,15 +57,14 @@ 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", name="pk_component"),
|
||||
sa.PrimaryKeyConstraint("id"),
|
||||
)
|
||||
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 as e:
|
||||
print(e)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
try:
|
||||
|
|
@ -81,10 +78,9 @@ def downgrade() -> None:
|
|||
["flow_id"],
|
||||
["flow.id"],
|
||||
),
|
||||
sa.PrimaryKeyConstraint("id", name="pk_flowstyle"),
|
||||
sa.UniqueConstraint("id", name="uq_flowstyle_id"),
|
||||
sa.PrimaryKeyConstraint("id"),
|
||||
sa.UniqueConstraint("id"),
|
||||
)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
except Exception:
|
||||
pass
|
||||
# ### end Alembic commands ###
|
||||
|
|
|
|||
|
|
@ -7,8 +7,10 @@ Create Date: 2023-10-18 23:12:27.297016
|
|||
"""
|
||||
from typing import Sequence, Union
|
||||
|
||||
import sqlalchemy as sa
|
||||
from alembic import op
|
||||
import sqlalchemy as sa
|
||||
import sqlmodel
|
||||
|
||||
|
||||
# revision identifiers, used by Alembic.
|
||||
revision: str = "f5ee9749d1a6"
|
||||
|
|
@ -24,8 +26,7 @@ def upgrade() -> None:
|
|||
batch_op.alter_column(
|
||||
"user_id", existing_type=sa.CHAR(length=32), nullable=True
|
||||
)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
|
@ -38,8 +39,7 @@ def downgrade() -> None:
|
|||
batch_op.alter_column(
|
||||
"user_id", existing_type=sa.CHAR(length=32), nullable=False
|
||||
)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
|
|
|||
|
|
@ -21,8 +21,7 @@ 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 as e:
|
||||
print(e)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
|
@ -33,8 +32,7 @@ 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 as e:
|
||||
print(e)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
|
|
|||
|
|
@ -1,11 +1,11 @@
|
|||
import time
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, WebSocket, WebSocketException, status
|
||||
from fastapi import APIRouter, Depends, HTTPException, Query, WebSocket, WebSocketException, status
|
||||
from fastapi.responses import StreamingResponse
|
||||
from langflow.api.utils import build_input_keys_response, format_elapsed_time
|
||||
from langflow.api.v1.schemas import BuildStatus, BuiltResponse, InitResponse, StreamData
|
||||
from langflow.graph.graph.base import Graph
|
||||
from langflow.services.auth.utils import get_current_active_user, get_current_user_for_websocket
|
||||
from langflow.services.auth.utils import get_current_active_user, get_current_user_by_jwt
|
||||
from langflow.services.cache.service import BaseCacheService
|
||||
from langflow.services.cache.utils import update_build_status
|
||||
from langflow.services.chat.service import ChatService
|
||||
|
|
@ -20,16 +20,17 @@ router = APIRouter(tags=["Chat"])
|
|||
async def chat(
|
||||
client_id: str,
|
||||
websocket: WebSocket,
|
||||
token: str = Query(...),
|
||||
db: Session = Depends(get_session),
|
||||
chat_service: "ChatService" = Depends(get_chat_service),
|
||||
):
|
||||
"""Websocket endpoint for chat."""
|
||||
try:
|
||||
user = await get_current_user_for_websocket(websocket, db)
|
||||
user = await get_current_user_by_jwt(token, db)
|
||||
await websocket.accept()
|
||||
if not user:
|
||||
await websocket.close(code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized")
|
||||
elif not user.is_active:
|
||||
if not user.is_active:
|
||||
await websocket.close(code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized")
|
||||
|
||||
if client_id in chat_service.cache_service:
|
||||
|
|
|
|||
|
|
@ -3,10 +3,12 @@ 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,
|
||||
|
|
@ -15,15 +17,12 @@ 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 build_graph_and_generate_result, process_graph_cached, process_tweaks
|
||||
from langflow.processing.process import 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
|
||||
|
|
@ -33,9 +32,10 @@ except ImportError:
|
|||
raise NotImplementedError("Celery is not installed")
|
||||
|
||||
|
||||
from langflow.services.task.service import TaskService
|
||||
from sqlmodel import Session
|
||||
|
||||
from langflow.services.task.service import TaskService
|
||||
|
||||
# build router
|
||||
router = APIRouter(tags=["Base"])
|
||||
|
||||
|
|
@ -148,55 +148,6 @@ 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,
|
||||
|
|
@ -216,75 +167,36 @@ 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 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,
|
||||
if api_key_user is None:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Invalid API Key",
|
||||
)
|
||||
|
||||
else:
|
||||
if api_key_user is None:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Invalid API Key",
|
||||
)
|
||||
# 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")
|
||||
|
||||
# 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,
|
||||
)
|
||||
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):
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from fastapi import APIRouter, Depends, HTTPException, Request, Response, status
|
||||
from fastapi import APIRouter, Depends, HTTPException, status
|
||||
from fastapi.security import OAuth2PasswordRequestForm
|
||||
from sqlmodel import Session
|
||||
|
||||
|
|
@ -16,7 +16,6 @@ router = APIRouter(tags=["Login"])
|
|||
|
||||
@router.post("/login", response_model=Token)
|
||||
async def login_to_get_access_token(
|
||||
response: Response,
|
||||
form_data: OAuth2PasswordRequestForm = Depends(),
|
||||
db: Session = Depends(get_session),
|
||||
# _: Session = Depends(get_current_active_user)
|
||||
|
|
@ -32,10 +31,7 @@ async def login_to_get_access_token(
|
|||
) from exc
|
||||
|
||||
if user:
|
||||
tokens = create_user_tokens(user_id=user.id, db=db, update_last_login=True)
|
||||
response.set_cookie("refresh_token_lf", tokens["refresh_token"], httponly=True)
|
||||
response.set_cookie("access_token_lf", tokens["access_token"], httponly=False)
|
||||
return tokens
|
||||
return create_user_tokens(user_id=user.id, db=db, update_last_login=True)
|
||||
else:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
|
|
@ -45,13 +41,9 @@ async def login_to_get_access_token(
|
|||
|
||||
|
||||
@router.get("/auto_login")
|
||||
async def auto_login(
|
||||
response: Response, db: Session = Depends(get_session), settings_service=Depends(get_settings_service)
|
||||
):
|
||||
async def auto_login(db: Session = Depends(get_session), settings_service=Depends(get_settings_service)):
|
||||
if settings_service.auth_settings.AUTO_LOGIN:
|
||||
tokens = create_user_longterm_token(db)
|
||||
response.set_cookie("access_token_lf", tokens["access_token"], httponly=False)
|
||||
return tokens
|
||||
return create_user_longterm_token(db)
|
||||
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
|
|
@ -63,23 +55,12 @@ async def auto_login(
|
|||
|
||||
|
||||
@router.post("/refresh")
|
||||
async def refresh_token(request: Request, response: Response):
|
||||
token = request.cookies.get("refresh_token_lf")
|
||||
async def refresh_token(token: str):
|
||||
if token:
|
||||
tokens = create_refresh_token(token)
|
||||
response.set_cookie("refresh_token_lf", tokens["refresh_token"], httponly=True)
|
||||
response.set_cookie("access_token_lf", tokens["access_token"], httponly=False)
|
||||
return tokens
|
||||
return create_refresh_token(token)
|
||||
else:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Invalid refresh token",
|
||||
headers={"WWW-Authenticate": "Bearer"},
|
||||
)
|
||||
|
||||
|
||||
@router.post("/logout")
|
||||
async def logout(response: Response):
|
||||
response.delete_cookie("refresh_token_lf")
|
||||
response.delete_cookie("access_token_lf")
|
||||
return {"message": "Logout successful"}
|
||||
|
|
|
|||
|
|
@ -64,13 +64,6 @@ 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
|
||||
# )
|
||||
|
|
|
|||
|
|
@ -41,11 +41,12 @@ class AgentInitializerComponent(CustomComponent):
|
|||
handle_parsing_errors=True,
|
||||
max_iterations=max_iterations,
|
||||
)
|
||||
return initialize_agent(
|
||||
tools=tools,
|
||||
llm=llm,
|
||||
agent=agent,
|
||||
return_intermediate_steps=True,
|
||||
handle_parsing_errors=True,
|
||||
max_iterations=max_iterations,
|
||||
)
|
||||
else:
|
||||
return initialize_agent(
|
||||
tools=tools,
|
||||
llm=llm,
|
||||
agent=agent,
|
||||
return_intermediate_steps=True,
|
||||
handle_parsing_errors=True,
|
||||
max_iterations=max_iterations,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,23 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel, AgentExecutor
|
||||
from langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent
|
||||
|
||||
|
||||
class CSVAgentComponent(CustomComponent):
|
||||
display_name = "CSVAgent"
|
||||
description = "Construct a CSV agent from a CSV and tools."
|
||||
documentation = "https://python.langchain.com/docs/modules/agents/toolkits/csv"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"llm": {"display_name": "LLM", "type": BaseLanguageModel},
|
||||
"path": {"display_name": "Path", "field_type": "file", "suffixes": [".csv"], "file_types": [".csv"]},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
llm: BaseLanguageModel,
|
||||
path: str,
|
||||
) -> AgentExecutor:
|
||||
# Instantiate and return the CSV agent class with the provided llm and path
|
||||
return create_csv_agent(llm=llm, path=path)
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from langchain.agents import AgentExecutor, create_json_agent
|
||||
from langflow.field_typing import (
|
||||
BaseLanguageModel,
|
||||
)
|
||||
from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
|
||||
|
||||
|
||||
class JsonAgentComponent(CustomComponent):
|
||||
display_name = "JsonAgent"
|
||||
description = "Construct a json agent from an LLM and tools."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"llm": {"display_name": "LLM"},
|
||||
"toolkit": {"display_name": "Toolkit"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
llm: BaseLanguageModel,
|
||||
toolkit: JsonToolkit,
|
||||
) -> AgentExecutor:
|
||||
return create_json_agent(llm=llm, toolkit=toolkit)
|
||||
|
|
@ -3,12 +3,13 @@ from typing import List, Optional
|
|||
from langchain.agents.agent import AgentExecutor
|
||||
from langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import _get_default_system_message
|
||||
from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
|
||||
from langchain.chat_models import ChatOpenAI
|
||||
from langchain.memory.token_buffer import ConversationTokenBufferMemory
|
||||
from langchain.prompts import SystemMessagePromptTemplate
|
||||
from langchain.prompts.chat import MessagesPlaceholder
|
||||
from langchain.schema.memory import BaseMemory
|
||||
from langchain.tools import Tool
|
||||
from langchain_community.chat_models import ChatOpenAI
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing.range_spec import RangeSpec
|
||||
|
||||
|
|
@ -19,12 +20,11 @@ class ConversationalAgent(CustomComponent):
|
|||
|
||||
def build_config(self):
|
||||
openai_function_models = [
|
||||
"gpt-4-turbo-preview",
|
||||
"gpt-4-0125-preview",
|
||||
"gpt-4-1106-preview",
|
||||
"gpt-4-vision-preview",
|
||||
"gpt-3.5-turbo-0125",
|
||||
"gpt-3.5-turbo-1106",
|
||||
"gpt-3.5-turbo",
|
||||
"gpt-3.5-turbo-16k",
|
||||
"gpt-4",
|
||||
"gpt-4-32k",
|
||||
]
|
||||
return {
|
||||
"tools": {"display_name": "Tools"},
|
||||
|
|
|
|||
|
|
@ -1,29 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from typing import Union, Callable
|
||||
from langchain.agents import AgentExecutor
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
from langchain_community.agent_toolkits.sql.base import create_sql_agent
|
||||
from langchain.sql_database import SQLDatabase
|
||||
from langchain_community.agent_toolkits import SQLDatabaseToolkit
|
||||
|
||||
|
||||
class SQLAgentComponent(CustomComponent):
|
||||
display_name = "SQLAgent"
|
||||
description = "Construct an SQL agent from an LLM and tools."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"llm": {"display_name": "LLM"},
|
||||
"database_uri": {"display_name": "Database URI"},
|
||||
"verbose": {"display_name": "Verbose", "value": False, "advanced": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
llm: BaseLanguageModel,
|
||||
database_uri: str,
|
||||
verbose: bool = False,
|
||||
) -> Union[AgentExecutor, Callable]:
|
||||
db = SQLDatabase.from_uri(database_uri)
|
||||
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
|
||||
return create_sql_agent(llm=llm, toolkit=toolkit)
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from langchain.agents import AgentExecutor, create_vectorstore_agent
|
||||
from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit
|
||||
from typing import Union, Callable
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
|
||||
|
||||
class VectorStoreAgentComponent(CustomComponent):
|
||||
display_name = "VectorStoreAgent"
|
||||
description = "Construct an agent from a Vector Store."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"llm": {"display_name": "LLM"},
|
||||
"vector_store_toolkit": {"display_name": "Vector Store Info"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
llm: BaseLanguageModel,
|
||||
vector_store_toolkit: VectorStoreToolkit,
|
||||
) -> Union[AgentExecutor, Callable]:
|
||||
return create_vectorstore_agent(llm=llm, toolkit=vector_store_toolkit)
|
||||
|
|
@ -1,19 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from langchain_core.language_models.base import BaseLanguageModel
|
||||
from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit
|
||||
from langchain.agents import create_vectorstore_router_agent
|
||||
from typing import Callable
|
||||
|
||||
|
||||
class VectorStoreRouterAgentComponent(CustomComponent):
|
||||
display_name = "VectorStoreRouterAgent"
|
||||
description = "Construct an agent from a Vector Store Router."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"llm": {"display_name": "LLM"},
|
||||
"vectorstoreroutertoolkit": {"display_name": "Vector Store Router Toolkit"},
|
||||
}
|
||||
|
||||
def build(self, llm: BaseLanguageModel, vectorstoreroutertoolkit: VectorStoreRouterToolkit) -> Callable:
|
||||
return create_vectorstore_router_agent(llm=llm, toolkit=vectorstoreroutertoolkit)
|
||||
|
|
@ -28,5 +28,5 @@ class LLMChainComponent(CustomComponent):
|
|||
prompt: BasePromptTemplate,
|
||||
llm: BaseLanguageModel,
|
||||
memory: Optional[BaseMemory] = None,
|
||||
) -> Union[Chain, Callable, LLMChain]:
|
||||
) -> Union[Chain, Callable]:
|
||||
return LLMChain(prompt=prompt, llm=llm, memory=memory)
|
||||
|
|
|
|||
|
|
@ -1,24 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from langchain.chains import LLMCheckerChain
|
||||
from typing import Union, Callable
|
||||
from langflow.field_typing import (
|
||||
BaseLanguageModel,
|
||||
Chain,
|
||||
)
|
||||
|
||||
|
||||
class LLMCheckerChainComponent(CustomComponent):
|
||||
display_name = "LLMCheckerChain"
|
||||
description = ""
|
||||
documentation = "https://python.langchain.com/docs/modules/chains/additional/llm_checker"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"llm": {"display_name": "LLM"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
llm: BaseLanguageModel,
|
||||
) -> Union[Chain, Callable]:
|
||||
return LLMCheckerChain(llm=llm)
|
||||
|
|
@ -1,31 +0,0 @@
|
|||
from typing import Callable, Optional, Union
|
||||
|
||||
from langchain.chains import LLMChain, LLMMathChain
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel, BaseMemory, Chain
|
||||
|
||||
|
||||
class LLMMathChainComponent(CustomComponent):
|
||||
display_name = "LLMMathChain"
|
||||
description = "Chain that interprets a prompt and executes python code to do math."
|
||||
documentation = "https://python.langchain.com/docs/modules/chains/additional/llm_math"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"llm": {"display_name": "LLM"},
|
||||
"llm_chain": {"display_name": "LLM Chain"},
|
||||
"memory": {"display_name": "Memory"},
|
||||
"input_key": {"display_name": "Input Key"},
|
||||
"output_key": {"display_name": "Output Key"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
llm: BaseLanguageModel,
|
||||
llm_chain: LLMChain,
|
||||
input_key: str = "question",
|
||||
output_key: str = "answer",
|
||||
memory: Optional[BaseMemory] = None,
|
||||
) -> Union[LLMMathChain, Callable, Chain]:
|
||||
return LLMMathChain(llm=llm, llm_chain=llm_chain, input_key=input_key, output_key=output_key, memory=memory)
|
||||
|
|
@ -1,8 +1,8 @@
|
|||
from langflow import CustomComponent
|
||||
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langchain.prompts import PromptTemplate
|
||||
from langchain_core.documents import Document
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langchain.schema import Document
|
||||
|
||||
|
||||
class PromptRunner(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -1,39 +0,0 @@
|
|||
from typing import Callable, Optional, Union
|
||||
|
||||
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
|
||||
from langchain.chains.retrieval_qa.base import BaseRetrievalQA, RetrievalQA
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import BaseMemory, BaseRetriever
|
||||
|
||||
|
||||
class RetrievalQAComponent(CustomComponent):
|
||||
display_name = "RetrievalQA"
|
||||
description = "Chain for question-answering against an index."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"combine_documents_chain": {"display_name": "Combine Documents Chain"},
|
||||
"retriever": {"display_name": "Retriever"},
|
||||
"memory": {"display_name": "Memory", "required": False},
|
||||
"input_key": {"display_name": "Input Key", "advanced": True},
|
||||
"output_key": {"display_name": "Output Key", "advanced": True},
|
||||
"return_source_documents": {"display_name": "Return Source Documents"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
combine_documents_chain: BaseCombineDocumentsChain,
|
||||
retriever: BaseRetriever,
|
||||
memory: Optional[BaseMemory] = None,
|
||||
input_key: str = "query",
|
||||
output_key: str = "result",
|
||||
return_source_documents: bool = True,
|
||||
) -> Union[BaseRetrievalQA, Callable]:
|
||||
return RetrievalQA(
|
||||
combine_documents_chain=combine_documents_chain,
|
||||
retriever=retriever,
|
||||
memory=memory,
|
||||
input_key=input_key,
|
||||
output_key=output_key,
|
||||
return_source_documents=return_source_documents,
|
||||
)
|
||||
|
|
@ -1,42 +0,0 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain.chains import RetrievalQAWithSourcesChain
|
||||
from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
|
||||
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever
|
||||
|
||||
|
||||
class RetrievalQAWithSourcesChainComponent(CustomComponent):
|
||||
display_name = "RetrievalQAWithSourcesChain"
|
||||
description = "Question-answering with sources over an index."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"llm": {"display_name": "LLM"},
|
||||
"chain_type": {
|
||||
"display_name": "Chain Type",
|
||||
"options": ["stuff", "map_reduce", "map_rerank", "refine"],
|
||||
},
|
||||
"memory": {"display_name": "Memory"},
|
||||
"return_source_documents": {"display_name": "Return Source Documents"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
retriever: BaseRetriever,
|
||||
llm: BaseLanguageModel,
|
||||
combine_documents_chain: BaseCombineDocumentsChain,
|
||||
chain_type: str,
|
||||
memory: Optional[BaseMemory] = None,
|
||||
return_source_documents: Optional[bool] = True,
|
||||
) -> BaseQAWithSourcesChain:
|
||||
return RetrievalQAWithSourcesChain.from_chain_type(
|
||||
llm=llm,
|
||||
chain_type=chain_type,
|
||||
combine_documents_chain=combine_documents_chain,
|
||||
memory=memory,
|
||||
return_source_documents=return_source_documents,
|
||||
retriever=retriever,
|
||||
)
|
||||
|
|
@ -1,25 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from typing import Callable, Union
|
||||
from langflow.field_typing import BasePromptTemplate, BaseLanguageModel, Chain
|
||||
from langchain_community.utilities.sql_database import SQLDatabase
|
||||
from langchain_experimental.sql.base import SQLDatabaseChain
|
||||
|
||||
|
||||
class SQLDatabaseChainComponent(CustomComponent):
|
||||
display_name = "SQLDatabaseChain"
|
||||
description = ""
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"db": {"display_name": "Database"},
|
||||
"llm": {"display_name": "LLM"},
|
||||
"prompt": {"display_name": "Prompt"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
db: SQLDatabase,
|
||||
llm: BaseLanguageModel,
|
||||
prompt: BasePromptTemplate,
|
||||
) -> Union[Chain, Callable, SQLDatabaseChain]:
|
||||
return SQLDatabaseChain.from_llm(llm=llm, db=db, prompt=prompt)
|
||||
|
|
@ -1,42 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from langchain.docstore.document import Document
|
||||
from typing import Optional, Dict, Any
|
||||
|
||||
|
||||
class DirectoryLoaderComponent(CustomComponent):
|
||||
display_name = "DirectoryLoader"
|
||||
description = "Load from a directory."
|
||||
|
||||
def build_config(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"glob": {"display_name": "Glob Pattern", "value": "**/*.txt"},
|
||||
"load_hidden": {"display_name": "Load Hidden Files", "value": False, "advanced": True},
|
||||
"max_concurrency": {"display_name": "Max Concurrency", "value": 10, "advanced": True},
|
||||
"metadata": {"display_name": "Metadata", "value": {}},
|
||||
"path": {"display_name": "Local Directory"},
|
||||
"recursive": {"display_name": "Recursive", "value": True, "advanced": True},
|
||||
"silent_errors": {"display_name": "Silent Errors", "value": False, "advanced": True},
|
||||
"use_multithreading": {"display_name": "Use Multithreading", "value": True, "advanced": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
glob: str,
|
||||
path: str,
|
||||
load_hidden: Optional[bool] = False,
|
||||
max_concurrency: Optional[int] = 10,
|
||||
metadata: Optional[dict] = {},
|
||||
recursive: Optional[bool] = True,
|
||||
silent_errors: Optional[bool] = False,
|
||||
use_multithreading: Optional[bool] = True,
|
||||
) -> Document:
|
||||
return Document(
|
||||
glob=glob,
|
||||
path=path,
|
||||
load_hidden=load_hidden,
|
||||
max_concurrency=max_concurrency,
|
||||
metadata=metadata,
|
||||
recursive=recursive,
|
||||
silent_errors=silent_errors,
|
||||
use_multithreading=use_multithreading,
|
||||
)
|
||||
|
|
@ -1,4 +1,5 @@
|
|||
from langchain_core.documents import Document
|
||||
from langchain.schema import Document
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langflow.utils.constants import LOADERS_INFO
|
||||
|
||||
|
|
|
|||
|
|
@ -1,8 +1,7 @@
|
|||
from typing import List
|
||||
|
||||
from langchain import document_loaders
|
||||
from langchain_core.documents import Document
|
||||
|
||||
from langchain.schema import Document
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,65 +0,0 @@
|
|||
from langchain.embeddings.base import Embeddings
|
||||
from langchain_community.embeddings import AzureOpenAIEmbeddings
|
||||
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
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,
|
||||
azure_deployment=azure_deployment,
|
||||
api_version=api_version,
|
||||
api_key=api_key,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
raise ValueError("Could not connect to AzureOpenAIEmbeddings API.") from e
|
||||
|
||||
return embeddings
|
||||
|
|
@ -1,36 +0,0 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_community.embeddings.cohere import CohereEmbeddings
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
class CohereEmbeddingsComponent(CustomComponent):
|
||||
display_name = "CohereEmbeddings"
|
||||
description = "Cohere embedding models."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"cohere_api_key": {"display_name": "Cohere API Key", "password": True},
|
||||
"model": {"display_name": "Model", "default": "embed-english-v2.0", "advanced": True},
|
||||
"truncate": {"display_name": "Truncate", "advanced": True},
|
||||
"max_retries": {"display_name": "Max Retries", "advanced": True},
|
||||
"user_agent": {"display_name": "User Agent", "advanced": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
request_timeout: Optional[float] = None,
|
||||
cohere_api_key: str = "",
|
||||
max_retries: Optional[int] = None,
|
||||
model: str = "embed-english-v2.0",
|
||||
truncate: Optional[str] = None,
|
||||
user_agent: str = "langchain",
|
||||
) -> CohereEmbeddings:
|
||||
return CohereEmbeddings( # type: ignore
|
||||
max_retries=max_retries,
|
||||
user_agent=user_agent,
|
||||
request_timeout=request_timeout,
|
||||
cohere_api_key=cohere_api_key,
|
||||
model=model,
|
||||
truncate=truncate,
|
||||
)
|
||||
|
|
@ -1,36 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from typing import Optional, Dict
|
||||
from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
|
||||
|
||||
class HuggingFaceEmbeddingsComponent(CustomComponent):
|
||||
display_name = "HuggingFaceEmbeddings"
|
||||
description = "HuggingFace sentence_transformers embedding models."
|
||||
documentation = (
|
||||
"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers"
|
||||
)
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"cache_folder": {"display_name": "Cache Folder", "advanced": True},
|
||||
"encode_kwargs": {"display_name": "Encode Kwargs", "advanced": True, "field_type": "dict"},
|
||||
"model_kwargs": {"display_name": "Model Kwargs", "field_type": "dict", "advanced": True},
|
||||
"model_name": {"display_name": "Model Name"},
|
||||
"multi_process": {"display_name": "Multi Process", "advanced": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
cache_folder: Optional[str] = None,
|
||||
encode_kwargs: Optional[Dict] = {},
|
||||
model_kwargs: Optional[Dict] = {},
|
||||
model_name: str = "sentence-transformers/all-mpnet-base-v2",
|
||||
multi_process: bool = False,
|
||||
) -> HuggingFaceEmbeddings:
|
||||
return HuggingFaceEmbeddings(
|
||||
cache_folder=cache_folder,
|
||||
encode_kwargs=encode_kwargs,
|
||||
model_kwargs=model_kwargs,
|
||||
model_name=model_name,
|
||||
multi_process=multi_process,
|
||||
)
|
||||
|
|
@ -1,38 +0,0 @@
|
|||
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
|
||||
|
|
@ -1,121 +0,0 @@
|
|||
from typing import Any, Callable, Dict, List, Optional, Union
|
||||
|
||||
from langchain_openai.embeddings.base import OpenAIEmbeddings
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import NestedDict
|
||||
|
||||
|
||||
class OpenAIEmbeddingsComponent(CustomComponent):
|
||||
display_name = "OpenAIEmbeddings"
|
||||
description = "OpenAI embedding models"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"allowed_special": {
|
||||
"display_name": "Allowed Special",
|
||||
"advanced": True,
|
||||
"field_type": "str",
|
||||
"is_list": True,
|
||||
},
|
||||
"default_headers": {
|
||||
"display_name": "Default Headers",
|
||||
"advanced": True,
|
||||
"field_type": "dict",
|
||||
},
|
||||
"default_query": {
|
||||
"display_name": "Default Query",
|
||||
"advanced": True,
|
||||
"field_type": "NestedDict",
|
||||
},
|
||||
"disallowed_special": {
|
||||
"display_name": "Disallowed Special",
|
||||
"advanced": True,
|
||||
"field_type": "str",
|
||||
"is_list": True,
|
||||
},
|
||||
"chunk_size": {"display_name": "Chunk Size", "advanced": True},
|
||||
"client": {"display_name": "Client", "advanced": True},
|
||||
"deployment": {"display_name": "Deployment", "advanced": True},
|
||||
"embedding_ctx_length": {
|
||||
"display_name": "Embedding Context Length",
|
||||
"advanced": True,
|
||||
},
|
||||
"max_retries": {"display_name": "Max Retries", "advanced": True},
|
||||
"model": {
|
||||
"display_name": "Model",
|
||||
"advanced": False,
|
||||
"options": ["text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002"],
|
||||
},
|
||||
"model_kwargs": {"display_name": "Model Kwargs", "advanced": True},
|
||||
"openai_api_base": {"display_name": "OpenAI API Base", "password": True, "advanced": True},
|
||||
"openai_api_key": {"display_name": "OpenAI API Key", "password": True},
|
||||
"openai_api_type": {"display_name": "OpenAI API Type", "advanced": True, "password": True},
|
||||
"openai_api_version": {
|
||||
"display_name": "OpenAI API Version",
|
||||
"advanced": True,
|
||||
},
|
||||
"openai_organization": {
|
||||
"display_name": "OpenAI Organization",
|
||||
"advanced": True,
|
||||
},
|
||||
"openai_proxy": {"display_name": "OpenAI Proxy", "advanced": True},
|
||||
"request_timeout": {"display_name": "Request Timeout", "advanced": True},
|
||||
"show_progress_bar": {
|
||||
"display_name": "Show Progress Bar",
|
||||
"advanced": True,
|
||||
},
|
||||
"skip_empty": {"display_name": "Skip Empty", "advanced": True},
|
||||
"tiktoken_model_name": {"display_name": "TikToken Model Name"},
|
||||
"tikToken_enable": {"display_name": "TikToken Enable"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
default_headers: Optional[Dict[str, str]] = None,
|
||||
default_query: Optional[NestedDict] = {},
|
||||
allowed_special: List[str] = [],
|
||||
disallowed_special: List[str] = ["all"],
|
||||
chunk_size: int = 1000,
|
||||
client: Optional[Any] = None,
|
||||
deployment: str = "text-embedding-3-small",
|
||||
embedding_ctx_length: int = 8191,
|
||||
max_retries: int = 6,
|
||||
model: str = "text-embedding-3-small",
|
||||
model_kwargs: NestedDict = {},
|
||||
openai_api_base: Optional[str] = None,
|
||||
openai_api_key: Optional[str] = "",
|
||||
openai_api_type: Optional[str] = None,
|
||||
openai_api_version: Optional[str] = None,
|
||||
openai_organization: Optional[str] = None,
|
||||
openai_proxy: Optional[str] = None,
|
||||
request_timeout: Optional[float] = None,
|
||||
show_progress_bar: bool = False,
|
||||
skip_empty: bool = False,
|
||||
tikToken_enable: bool = True,
|
||||
tiktoken_model_name: Optional[str] = None,
|
||||
) -> Union[OpenAIEmbeddings, Callable]:
|
||||
return OpenAIEmbeddings(
|
||||
tiktoken_enabled=tikToken_enable,
|
||||
default_headers=default_headers,
|
||||
default_query=default_query,
|
||||
allowed_special=set(allowed_special),
|
||||
disallowed_special=set(disallowed_special),
|
||||
chunk_size=chunk_size,
|
||||
client=client,
|
||||
deployment=deployment,
|
||||
embedding_ctx_length=embedding_ctx_length,
|
||||
max_retries=max_retries,
|
||||
model=model,
|
||||
model_kwargs=model_kwargs,
|
||||
base_url=openai_api_base,
|
||||
api_key=openai_api_key,
|
||||
openai_api_type=openai_api_type,
|
||||
api_version=openai_api_version,
|
||||
organization=openai_organization,
|
||||
openai_proxy=openai_proxy,
|
||||
timeout=request_timeout,
|
||||
show_progress_bar=show_progress_bar,
|
||||
skip_empty=skip_empty,
|
||||
tiktoken_model_name=tiktoken_model_name,
|
||||
)
|
||||
|
|
@ -1,60 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from langchain.embeddings import VertexAIEmbeddings
|
||||
from typing import Optional, List
|
||||
|
||||
|
||||
class VertexAIEmbeddingsComponent(CustomComponent):
|
||||
display_name = "VertexAIEmbeddings"
|
||||
description = "Google Cloud VertexAI embedding models."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"credentials": {"display_name": "Credentials", "value": "", "file_types": [".json"], "field_type": "file"},
|
||||
"instance": {"display_name": "instance", "advanced": True, "field_type": "dict"},
|
||||
"location": {"display_name": "Location", "value": "us-central1", "advanced": True},
|
||||
"max_output_tokens": {"display_name": "Max Output Tokens", "value": 128},
|
||||
"max_retries": {"display_name": "Max Retries", "value": 6, "advanced": True},
|
||||
"model_name": {"display_name": "Model Name", "value": "textembedding-gecko"},
|
||||
"n": {"display_name": "N", "value": 1, "advanced": True},
|
||||
"project": {"display_name": "Project", "advanced": True},
|
||||
"request_parallelism": {"display_name": "Request Parallelism", "value": 5, "advanced": True},
|
||||
"stop": {"display_name": "Stop", "advanced": True},
|
||||
"streaming": {"display_name": "Streaming", "value": False, "advanced": True},
|
||||
"temperature": {"display_name": "Temperature", "value": 0.0},
|
||||
"top_k": {"display_name": "Top K", "value": 40, "advanced": True},
|
||||
"top_p": {"display_name": "Top P", "value": 0.95, "advanced": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
instance: Optional[str] = None,
|
||||
credentials: Optional[str] = None,
|
||||
location: str = "us-central1",
|
||||
max_output_tokens: int = 128,
|
||||
max_retries: int = 6,
|
||||
model_name: str = "textembedding-gecko",
|
||||
n: int = 1,
|
||||
project: Optional[str] = None,
|
||||
request_parallelism: int = 5,
|
||||
stop: Optional[List[str]] = None,
|
||||
streaming: bool = False,
|
||||
temperature: float = 0.0,
|
||||
top_k: int = 40,
|
||||
top_p: float = 0.95,
|
||||
) -> VertexAIEmbeddings:
|
||||
return VertexAIEmbeddings(
|
||||
instance=instance,
|
||||
credentials=credentials,
|
||||
location=location,
|
||||
max_output_tokens=max_output_tokens,
|
||||
max_retries=max_retries,
|
||||
model_name=model_name,
|
||||
n=n,
|
||||
project=project,
|
||||
request_parallelism=request_parallelism,
|
||||
stop=stop,
|
||||
streaming=streaming,
|
||||
temperature=temperature,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
)
|
||||
|
|
@ -1,8 +1,7 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langchain.llms.bedrock import Bedrock
|
||||
from langflow import CustomComponent
|
||||
from langchain.llms.bedrock import Bedrock
|
||||
from langchain.llms.base import BaseLLM
|
||||
|
||||
|
||||
class AmazonBedrockComponent(CustomComponent):
|
||||
|
|
@ -28,32 +27,18 @@ class AmazonBedrockComponent(CustomComponent):
|
|||
},
|
||||
"credentials_profile_name": {"display_name": "Credentials Profile Name"},
|
||||
"streaming": {"display_name": "Streaming", "field_type": "bool"},
|
||||
"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},
|
||||
"code": {"show": False},
|
||||
}
|
||||
|
||||
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
|
||||
|
|
|
|||
|
|
@ -1,48 +0,0 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_community.llms.anthropic import Anthropic
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel, NestedDict
|
||||
|
||||
|
||||
class AnthropicComponent(CustomComponent):
|
||||
display_name = "Anthropic"
|
||||
description = "Anthropic large language models."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"anthropic_api_key": {
|
||||
"display_name": "Anthropic API Key",
|
||||
"type": str,
|
||||
"password": True,
|
||||
},
|
||||
"anthropic_api_url": {
|
||||
"display_name": "Anthropic API URL",
|
||||
"type": str,
|
||||
},
|
||||
"model_kwargs": {
|
||||
"display_name": "Model Kwargs",
|
||||
"field_type": "NestedDict",
|
||||
"advanced": True,
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
anthropic_api_key: str,
|
||||
anthropic_api_url: str,
|
||||
model_kwargs: NestedDict = {},
|
||||
temperature: Optional[float] = None,
|
||||
) -> BaseLanguageModel:
|
||||
return Anthropic(
|
||||
anthropic_api_key=SecretStr(anthropic_api_key),
|
||||
anthropic_api_url=anthropic_api_url,
|
||||
model_kwargs=model_kwargs,
|
||||
temperature=temperature,
|
||||
)
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_community.chat_models.anthropic import ChatAnthropic
|
||||
from langchain.chat_models.anthropic import ChatAnthropic
|
||||
from langchain.llms.base import BaseLanguageModel
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
|
|
|
|||
|
|
@ -1,14 +1,13 @@
|
|||
from typing import Optional
|
||||
from langflow import CustomComponent
|
||||
from langchain.llms.base import BaseLanguageModel
|
||||
from langchain_community.chat_models.azure_openai import AzureChatOpenAI
|
||||
from langchain.chat_models.azure_openai import AzureChatOpenAI
|
||||
|
||||
|
||||
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",
|
||||
|
|
@ -19,21 +18,11 @@ 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",
|
||||
"2023-09-01-preview",
|
||||
"2023-12-01-preview",
|
||||
]
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"model": {
|
||||
"display_name": "Model Name",
|
||||
"value": self.AZURE_OPENAI_MODELS[0],
|
||||
"value": "gpt-35-turbo",
|
||||
"options": self.AZURE_OPENAI_MODELS,
|
||||
"required": True,
|
||||
},
|
||||
|
|
@ -48,8 +37,7 @@ class AzureChatOpenAIComponent(CustomComponent):
|
|||
},
|
||||
"api_version": {
|
||||
"display_name": "API Version",
|
||||
"options": self.AZURE_OPENAI_API_VERSIONS,
|
||||
"value": self.AZURE_OPENAI_API_VERSIONS[-1],
|
||||
"value": "2023-05-15",
|
||||
"required": True,
|
||||
"advanced": True,
|
||||
},
|
||||
|
|
@ -66,7 +54,6 @@ class AzureChatOpenAIComponent(CustomComponent):
|
|||
"required": False,
|
||||
"field_type": "int",
|
||||
"advanced": True,
|
||||
"info": "Maximum number of tokens to generate.",
|
||||
},
|
||||
"code": {"show": False},
|
||||
}
|
||||
|
|
@ -77,20 +64,16 @@ class AzureChatOpenAIComponent(CustomComponent):
|
|||
azure_endpoint: str,
|
||||
azure_deployment: str,
|
||||
api_key: str,
|
||||
api_version: str,
|
||||
api_version: str = "2023-05-15",
|
||||
temperature: float = 0.7,
|
||||
max_tokens: Optional[int] = 1000,
|
||||
) -> BaseLanguageModel:
|
||||
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
|
||||
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,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
|
||||
from langchain.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
|
||||
from langchain.llms.base import BaseLLM
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
|
|
|
|||
|
|
@ -1,33 +0,0 @@
|
|||
from typing import Dict, Optional
|
||||
|
||||
from langchain_community.llms.ctransformers import CTransformers
|
||||
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
class CTransformersComponent(CustomComponent):
|
||||
display_name = "CTransformers"
|
||||
description = "C Transformers LLM models"
|
||||
documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"model": {"display_name": "Model", "required": True},
|
||||
"model_file": {
|
||||
"display_name": "Model File",
|
||||
"required": False,
|
||||
"field_type": "file",
|
||||
"file_types": [".bin"],
|
||||
},
|
||||
"model_type": {"display_name": "Model Type", "required": True},
|
||||
"config": {
|
||||
"display_name": "Config",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
"field_type": "dict",
|
||||
"value": '{"top_k":40,"top_p":0.95,"temperature":0.8,"repetition_penalty":1.1,"last_n_tokens":64,"seed":-1,"max_new_tokens":256,"stop":"","stream":"False","reset":"True","batch_size":8,"threads":-1,"context_length":-1,"gpu_layers":0}',
|
||||
},
|
||||
}
|
||||
|
||||
def build(self, model: str, model_file: str, model_type: str, config: Optional[Dict] = None) -> CTransformers:
|
||||
return CTransformers(model=model, model_file=model_file, model_type=model_type, config=config) # type: ignore
|
||||
|
|
@ -1,47 +0,0 @@
|
|||
from pydantic import SecretStr
|
||||
from langflow import CustomComponent
|
||||
from typing import Optional, Union, Callable
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
from langchain_community.chat_models.anthropic import ChatAnthropic
|
||||
|
||||
|
||||
class ChatAnthropicComponent(CustomComponent):
|
||||
display_name = "ChatAnthropic"
|
||||
description = "`Anthropic` chat large language models."
|
||||
documentation = "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"anthropic_api_key": {
|
||||
"display_name": "Anthropic API Key",
|
||||
"field_type": "str",
|
||||
"password": True,
|
||||
},
|
||||
"anthropic_api_url": {
|
||||
"display_name": "Anthropic API URL",
|
||||
"field_type": "str",
|
||||
},
|
||||
"model_kwargs": {
|
||||
"display_name": "Model Kwargs",
|
||||
"field_type": "dict",
|
||||
"advanced": True,
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
anthropic_api_key: str,
|
||||
anthropic_api_url: Optional[str] = None,
|
||||
model_kwargs: dict = {},
|
||||
temperature: Optional[float] = None,
|
||||
) -> Union[BaseLanguageModel, Callable]:
|
||||
return ChatAnthropic(
|
||||
anthropic_api_key=SecretStr(anthropic_api_key),
|
||||
anthropic_api_url=anthropic_api_url,
|
||||
model_kwargs=model_kwargs,
|
||||
temperature=temperature,
|
||||
)
|
||||
|
|
@ -1,8 +1,8 @@
|
|||
from typing import Any, Dict, List, Optional
|
||||
|
||||
# from langchain_community.chat_models import ChatOllama
|
||||
from langchain_community.chat_models import ChatOllama
|
||||
from langchain_core.language_models.chat_models import BaseChatModel
|
||||
from langchain.chat_models import ChatOllama
|
||||
from langchain.chat_models.base import BaseChatModel
|
||||
|
||||
# from langchain.chat_models import ChatOllama
|
||||
from langflow import CustomComponent
|
||||
|
|
|
|||
|
|
@ -1,86 +0,0 @@
|
|||
from typing import Optional, Union
|
||||
|
||||
from langchain.llms import BaseLLM
|
||||
from langchain_community.chat_models.openai import ChatOpenAI
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel, NestedDict
|
||||
|
||||
|
||||
class ChatOpenAIComponent(CustomComponent):
|
||||
display_name = "ChatOpenAI"
|
||||
description = "`OpenAI` Chat large language models API."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"field_type": "int",
|
||||
"advanced": False,
|
||||
"required": False,
|
||||
},
|
||||
"model_kwargs": {
|
||||
"display_name": "Model Kwargs",
|
||||
"field_type": "NestedDict",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
},
|
||||
"model_name": {
|
||||
"display_name": "Model Name",
|
||||
"field_type": "str",
|
||||
"advanced": False,
|
||||
"required": False,
|
||||
"options": [
|
||||
"gpt-4-turbo-preview",
|
||||
"gpt-4-0125-preview",
|
||||
"gpt-4-1106-preview",
|
||||
"gpt-4-vision-preview",
|
||||
"gpt-3.5-turbo-0125",
|
||||
"gpt-3.5-turbo-1106",
|
||||
],
|
||||
},
|
||||
"openai_api_base": {
|
||||
"display_name": "OpenAI API Base",
|
||||
"field_type": "str",
|
||||
"advanced": False,
|
||||
"required": False,
|
||||
"info": (
|
||||
"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\n"
|
||||
"You can change this to use other APIs like JinaChat, LocalAI and Prem."
|
||||
),
|
||||
},
|
||||
"openai_api_key": {
|
||||
"display_name": "OpenAI API Key",
|
||||
"field_type": "str",
|
||||
"advanced": False,
|
||||
"required": False,
|
||||
"password": True,
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
"advanced": False,
|
||||
"required": False,
|
||||
"value": 0.7,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
max_tokens: Optional[int] = 256,
|
||||
model_kwargs: NestedDict = {},
|
||||
model_name: str = "gpt-4-1106-preview",
|
||||
openai_api_base: Optional[str] = None,
|
||||
openai_api_key: Optional[str] = None,
|
||||
temperature: float = 0.7,
|
||||
) -> Union[BaseLanguageModel, BaseLLM]:
|
||||
if not openai_api_base:
|
||||
openai_api_base = "https://api.openai.com/v1"
|
||||
return ChatOpenAI(
|
||||
max_tokens=max_tokens,
|
||||
model_kwargs=model_kwargs,
|
||||
model=model_name,
|
||||
base_url=openai_api_base,
|
||||
api_key=openai_api_key,
|
||||
temperature=temperature,
|
||||
)
|
||||
|
|
@ -1,87 +0,0 @@
|
|||
from typing import List, Optional, Union
|
||||
|
||||
from langchain.llms import BaseLLM
|
||||
from langchain_community.chat_models.vertexai import ChatVertexAI
|
||||
from langchain_core.messages.base import BaseMessage
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
|
||||
|
||||
class ChatVertexAIComponent(CustomComponent):
|
||||
display_name = "ChatVertexAI"
|
||||
description = "`Vertex AI` Chat large language models API."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"credentials": {
|
||||
"display_name": "Credentials",
|
||||
"field_type": "file",
|
||||
"file_types": [".json"],
|
||||
"file_path": None,
|
||||
},
|
||||
"examples": {
|
||||
"display_name": "Examples",
|
||||
"multiline": True,
|
||||
},
|
||||
"location": {
|
||||
"display_name": "Location",
|
||||
"value": "us-central1",
|
||||
},
|
||||
"max_output_tokens": {
|
||||
"display_name": "Max Output Tokens",
|
||||
"value": 128,
|
||||
"advanced": True,
|
||||
},
|
||||
"model_name": {
|
||||
"display_name": "Model Name",
|
||||
"value": "chat-bison",
|
||||
},
|
||||
"project": {
|
||||
"display_name": "Project",
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"value": 0.0,
|
||||
},
|
||||
"top_k": {
|
||||
"display_name": "Top K",
|
||||
"value": 40,
|
||||
"advanced": True,
|
||||
},
|
||||
"top_p": {
|
||||
"display_name": "Top P",
|
||||
"value": 0.95,
|
||||
"advanced": True,
|
||||
},
|
||||
"verbose": {
|
||||
"display_name": "Verbose",
|
||||
"value": False,
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
credentials: Optional[str],
|
||||
project: str,
|
||||
examples: Optional[List[BaseMessage]] = [],
|
||||
location: str = "us-central1",
|
||||
max_output_tokens: int = 128,
|
||||
model_name: str = "chat-bison",
|
||||
temperature: float = 0.0,
|
||||
top_k: int = 40,
|
||||
top_p: float = 0.95,
|
||||
verbose: bool = False,
|
||||
) -> Union[BaseLanguageModel, BaseLLM]:
|
||||
return ChatVertexAI(
|
||||
credentials=credentials,
|
||||
examples=examples,
|
||||
location=location,
|
||||
max_output_tokens=max_output_tokens,
|
||||
model_name=model_name,
|
||||
project=project,
|
||||
temperature=temperature,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
verbose=verbose,
|
||||
)
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
from langchain_community.llms.cohere import Cohere
|
||||
from langchain_core.language_models.base import BaseLanguageModel
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
class CohereComponent(CustomComponent):
|
||||
display_name = "Cohere"
|
||||
description = "Cohere large language models."
|
||||
documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"cohere_api_key": {"display_name": "Cohere API Key", "type": "password", "password": True},
|
||||
"max_tokens": {"display_name": "Max Tokens", "default": 256, "type": "int", "show": True},
|
||||
"temperature": {"display_name": "Temperature", "default": 0.75, "type": "float", "show": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
cohere_api_key: str,
|
||||
max_tokens: int = 256,
|
||||
temperature: float = 0.75,
|
||||
) -> BaseLanguageModel:
|
||||
return Cohere(cohere_api_key=cohere_api_key, max_tokens=max_tokens, temperature=temperature) # type: ignore
|
||||
|
|
@ -1,129 +0,0 @@
|
|||
from typing import Optional, List, Dict, Any
|
||||
from langflow import CustomComponent
|
||||
from langchain_community.llms.llamacpp import LlamaCpp
|
||||
|
||||
|
||||
class LlamaCppComponent(CustomComponent):
|
||||
display_name = "LlamaCpp"
|
||||
description = "llama.cpp model."
|
||||
documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"grammar": {"display_name": "Grammar", "advanced": True},
|
||||
"cache": {"display_name": "Cache", "advanced": True},
|
||||
"client": {"display_name": "Client", "advanced": True},
|
||||
"echo": {"display_name": "Echo", "advanced": True},
|
||||
"f16_kv": {"display_name": "F16 KV", "advanced": True},
|
||||
"grammar_path": {"display_name": "Grammar Path", "advanced": True},
|
||||
"last_n_tokens_size": {"display_name": "Last N Tokens Size", "advanced": True},
|
||||
"logits_all": {"display_name": "Logits All", "advanced": True},
|
||||
"logprobs": {"display_name": "Logprobs", "advanced": True},
|
||||
"lora_base": {"display_name": "Lora Base", "advanced": True},
|
||||
"lora_path": {"display_name": "Lora Path", "advanced": True},
|
||||
"max_tokens": {"display_name": "Max Tokens", "advanced": True},
|
||||
"metadata": {"display_name": "Metadata", "advanced": True},
|
||||
"model_kwargs": {"display_name": "Model Kwargs", "advanced": True},
|
||||
"model_path": {
|
||||
"display_name": "Model Path",
|
||||
"field_type": "file",
|
||||
"file_types": [".bin"],
|
||||
"required": True,
|
||||
},
|
||||
"n_batch": {"display_name": "N Batch", "advanced": True},
|
||||
"n_ctx": {"display_name": "N Ctx", "advanced": True},
|
||||
"n_gpu_layers": {"display_name": "N GPU Layers", "advanced": True},
|
||||
"n_parts": {"display_name": "N Parts", "advanced": True},
|
||||
"n_threads": {"display_name": "N Threads", "advanced": True},
|
||||
"repeat_penalty": {"display_name": "Repeat Penalty", "advanced": True},
|
||||
"rope_freq_base": {"display_name": "Rope Freq Base", "advanced": True},
|
||||
"rope_freq_scale": {"display_name": "Rope Freq Scale", "advanced": True},
|
||||
"seed": {"display_name": "Seed", "advanced": True},
|
||||
"stop": {"display_name": "Stop", "advanced": True},
|
||||
"streaming": {"display_name": "Streaming", "advanced": True},
|
||||
"suffix": {"display_name": "Suffix", "advanced": True},
|
||||
"tags": {"display_name": "Tags", "advanced": True},
|
||||
"temperature": {"display_name": "Temperature"},
|
||||
"top_k": {"display_name": "Top K", "advanced": True},
|
||||
"top_p": {"display_name": "Top P", "advanced": True},
|
||||
"use_mlock": {"display_name": "Use Mlock", "advanced": True},
|
||||
"use_mmap": {"display_name": "Use Mmap", "advanced": True},
|
||||
"verbose": {"display_name": "Verbose", "advanced": True},
|
||||
"vocab_only": {"display_name": "Vocab Only", "advanced": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
model_path: str,
|
||||
grammar: Optional[str] = None,
|
||||
cache: Optional[bool] = None,
|
||||
client: Optional[Any] = None,
|
||||
echo: Optional[bool] = False,
|
||||
f16_kv: bool = True,
|
||||
grammar_path: Optional[str] = None,
|
||||
last_n_tokens_size: Optional[int] = 64,
|
||||
logits_all: bool = False,
|
||||
logprobs: Optional[int] = None,
|
||||
lora_base: Optional[str] = None,
|
||||
lora_path: Optional[str] = None,
|
||||
max_tokens: Optional[int] = 256,
|
||||
metadata: Optional[Dict] = None,
|
||||
model_kwargs: Dict = {},
|
||||
n_batch: Optional[int] = 8,
|
||||
n_ctx: int = 512,
|
||||
n_gpu_layers: Optional[int] = 1,
|
||||
n_parts: int = -1,
|
||||
n_threads: Optional[int] = 1,
|
||||
repeat_penalty: Optional[float] = 1.1,
|
||||
rope_freq_base: float = 10000.0,
|
||||
rope_freq_scale: float = 1.0,
|
||||
seed: int = -1,
|
||||
stop: Optional[List[str]] = [],
|
||||
streaming: bool = True,
|
||||
suffix: Optional[str] = "",
|
||||
tags: Optional[List[str]] = [],
|
||||
temperature: Optional[float] = 0.8,
|
||||
top_k: Optional[int] = 40,
|
||||
top_p: Optional[float] = 0.95,
|
||||
use_mlock: bool = False,
|
||||
use_mmap: Optional[bool] = True,
|
||||
verbose: bool = True,
|
||||
vocab_only: bool = False,
|
||||
) -> LlamaCpp:
|
||||
return LlamaCpp(
|
||||
model_path=model_path,
|
||||
grammar=grammar,
|
||||
cache=cache,
|
||||
client=client,
|
||||
echo=echo,
|
||||
f16_kv=f16_kv,
|
||||
grammar_path=grammar_path,
|
||||
last_n_tokens_size=last_n_tokens_size,
|
||||
logits_all=logits_all,
|
||||
logprobs=logprobs,
|
||||
lora_base=lora_base,
|
||||
lora_path=lora_path,
|
||||
max_tokens=max_tokens,
|
||||
metadata=metadata,
|
||||
model_kwargs=model_kwargs,
|
||||
n_batch=n_batch,
|
||||
n_ctx=n_ctx,
|
||||
n_gpu_layers=n_gpu_layers,
|
||||
n_parts=n_parts,
|
||||
n_threads=n_threads,
|
||||
repeat_penalty=repeat_penalty,
|
||||
rope_freq_base=rope_freq_base,
|
||||
rope_freq_scale=rope_freq_scale,
|
||||
seed=seed,
|
||||
stop=stop,
|
||||
streaming=streaming,
|
||||
suffix=suffix,
|
||||
tags=tags,
|
||||
temperature=temperature,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
use_mlock=use_mlock,
|
||||
use_mmap=use_mmap,
|
||||
verbose=verbose,
|
||||
vocab_only=vocab_only,
|
||||
)
|
||||
|
|
@ -1,7 +1,7 @@
|
|||
from typing import List, Optional
|
||||
from typing import Optional, List
|
||||
|
||||
from langchain.llms import Ollama
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langchain_community.llms.ollama import Ollama
|
||||
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
|
@ -133,25 +133,30 @@ class OllamaLLM(CustomComponent):
|
|||
mirostat_eta = None
|
||||
mirostat_tau = None
|
||||
|
||||
try:
|
||||
llm = Ollama(
|
||||
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,
|
||||
)
|
||||
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
|
||||
|
||||
|
|
|
|||
|
|
@ -1,147 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from langchain.llms import BaseLLM
|
||||
from typing import Optional, Union, Callable, Dict
|
||||
from langchain_community.llms.vertexai import VertexAI
|
||||
|
||||
|
||||
class VertexAIComponent(CustomComponent):
|
||||
display_name = "VertexAI"
|
||||
description = "Google Vertex AI large language models"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"credentials": {
|
||||
"display_name": "Credentials",
|
||||
"field_type": "file",
|
||||
"file_types": [".json"],
|
||||
"required": False,
|
||||
"value": None,
|
||||
},
|
||||
"location": {
|
||||
"display_name": "Location",
|
||||
"type": "str",
|
||||
"advanced": True,
|
||||
"value": "us-central1",
|
||||
"required": False,
|
||||
},
|
||||
"max_output_tokens": {
|
||||
"display_name": "Max Output Tokens",
|
||||
"field_type": "int",
|
||||
"value": 128,
|
||||
"required": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"max_retries": {
|
||||
"display_name": "Max Retries",
|
||||
"type": "int",
|
||||
"value": 6,
|
||||
"required": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"metadata": {
|
||||
"display_name": "Metadata",
|
||||
"field_type": "dict",
|
||||
"required": False,
|
||||
"default": {},
|
||||
},
|
||||
"model_name": {
|
||||
"display_name": "Model Name",
|
||||
"type": "str",
|
||||
"value": "text-bison",
|
||||
"required": False,
|
||||
},
|
||||
"n": {
|
||||
"advanced": True,
|
||||
"display_name": "N",
|
||||
"field_type": "int",
|
||||
"value": 1,
|
||||
"required": False,
|
||||
},
|
||||
"project": {
|
||||
"display_name": "Project",
|
||||
"type": "str",
|
||||
"required": False,
|
||||
"default": None,
|
||||
},
|
||||
"request_parallelism": {
|
||||
"display_name": "Request Parallelism",
|
||||
"field_type": "int",
|
||||
"value": 5,
|
||||
"required": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"streaming": {
|
||||
"display_name": "Streaming",
|
||||
"field_type": "bool",
|
||||
"value": False,
|
||||
"required": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
"value": 0.0,
|
||||
"required": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"top_k": {"display_name": "Top K", "type": "int", "default": 40, "required": False, "advanced": True},
|
||||
"top_p": {
|
||||
"display_name": "Top P",
|
||||
"field_type": "float",
|
||||
"value": 0.95,
|
||||
"required": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"tuned_model_name": {
|
||||
"display_name": "Tuned Model Name",
|
||||
"type": "str",
|
||||
"required": False,
|
||||
"value": None,
|
||||
"advanced": True,
|
||||
},
|
||||
"verbose": {
|
||||
"display_name": "Verbose",
|
||||
"field_type": "bool",
|
||||
"value": False,
|
||||
"required": False,
|
||||
},
|
||||
"name": {"display_name": "Name", "field_type": "str"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
credentials: Optional[str] = None,
|
||||
location: str = "us-central1",
|
||||
max_output_tokens: int = 128,
|
||||
max_retries: int = 6,
|
||||
metadata: Dict = {},
|
||||
model_name: str = "text-bison",
|
||||
n: int = 1,
|
||||
name: Optional[str] = None,
|
||||
project: Optional[str] = None,
|
||||
request_parallelism: int = 5,
|
||||
streaming: bool = False,
|
||||
temperature: float = 0.0,
|
||||
top_k: int = 40,
|
||||
top_p: float = 0.95,
|
||||
tuned_model_name: Optional[str] = None,
|
||||
verbose: bool = False,
|
||||
) -> Union[BaseLLM, Callable]:
|
||||
return VertexAI(
|
||||
credentials=credentials,
|
||||
location=location,
|
||||
max_output_tokens=max_output_tokens,
|
||||
max_retries=max_retries,
|
||||
metadata=metadata,
|
||||
model_name=model_name,
|
||||
n=n,
|
||||
name=name,
|
||||
project=project,
|
||||
request_parallelism=request_parallelism,
|
||||
streaming=streaming,
|
||||
temperature=temperature,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
tuned_model_name=tuned_model_name,
|
||||
verbose=verbose,
|
||||
)
|
||||
|
|
@ -1,49 +0,0 @@
|
|||
from typing import Callable, Optional, Union
|
||||
|
||||
from langchain.retrievers import MultiQueryRetriever
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import BaseLLM, BaseRetriever, PromptTemplate
|
||||
|
||||
|
||||
class MultiQueryRetrieverComponent(CustomComponent):
|
||||
display_name = "MultiQueryRetriever"
|
||||
description = "Initialize from llm using default template."
|
||||
documentation = "https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"llm": {"display_name": "LLM"},
|
||||
"prompt": {
|
||||
"display_name": "Prompt",
|
||||
"default": {
|
||||
"input_variables": ["question"],
|
||||
"input_types": {},
|
||||
"output_parser": None,
|
||||
"partial_variables": {},
|
||||
"template": "You are an AI language model assistant. Your task is \n"
|
||||
"to generate 3 different versions of the given user \n"
|
||||
"question to retrieve relevant documents from a vector database. \n"
|
||||
"By generating multiple perspectives on the user question, \n"
|
||||
"your goal is to help the user overcome some of the limitations \n"
|
||||
"of distance-based similarity search. Provide these alternative \n"
|
||||
"questions separated by newlines. Original question: {question}",
|
||||
"template_format": "f-string",
|
||||
"validate_template": False,
|
||||
"_type": "prompt",
|
||||
},
|
||||
},
|
||||
"retriever": {"display_name": "Retriever"},
|
||||
"parser_key": {"display_name": "Parser Key", "default": "lines"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
llm: BaseLLM,
|
||||
retriever: BaseRetriever,
|
||||
prompt: Optional[PromptTemplate] = None,
|
||||
parser_key: str = "lines",
|
||||
) -> Union[Callable, MultiQueryRetriever]:
|
||||
if not prompt:
|
||||
return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, parser_key=parser_key)
|
||||
else:
|
||||
return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, prompt=prompt, parser_key=parser_key)
|
||||
|
|
@ -15,21 +15,29 @@ class VectaraSelfQueryRetriverComponent(CustomComponent):
|
|||
|
||||
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"
|
||||
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",
|
||||
"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)',
|
||||
},
|
||||
"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(
|
||||
|
|
@ -39,19 +47,24 @@ class VectaraSelfQueryRetriverComponent(CustomComponent):
|
|||
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.")
|
||||
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"],
|
||||
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
|
||||
)
|
||||
llm,
|
||||
vectorstore,
|
||||
document_content_description,
|
||||
metadata_field_obj,
|
||||
verbose=True
|
||||
)
|
||||
|
|
@ -1,30 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from langchain.text_splitter import CharacterTextSplitter
|
||||
from langchain_core.documents.base import Document
|
||||
from typing import List
|
||||
|
||||
|
||||
class CharacterTextSplitterComponent(CustomComponent):
|
||||
display_name = "CharacterTextSplitter"
|
||||
description = "Splitting text that looks at characters."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"documents": {"display_name": "Documents"},
|
||||
"chunk_overlap": {"display_name": "Chunk Overlap", "default": 200},
|
||||
"chunk_size": {"display_name": "Chunk Size", "default": 1000},
|
||||
"separator": {"display_name": "Separator", "default": "\n"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
documents: List[Document],
|
||||
chunk_overlap: int = 200,
|
||||
chunk_size: int = 1000,
|
||||
separator: str = "\n",
|
||||
) -> List[Document]:
|
||||
return CharacterTextSplitter(
|
||||
chunk_overlap=chunk_overlap,
|
||||
chunk_size=chunk_size,
|
||||
separator=separator,
|
||||
).split_documents(documents)
|
||||
|
|
@ -1,9 +1,7 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain.text_splitter import Language
|
||||
from langchain_core.documents import Document
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langchain.text_splitter import Language
|
||||
from langchain.schema import Document
|
||||
|
||||
|
||||
class LanguageRecursiveTextSplitterComponent(CustomComponent):
|
||||
|
|
@ -50,7 +48,7 @@ class LanguageRecursiveTextSplitterComponent(CustomComponent):
|
|||
documents: list[Document],
|
||||
chunk_size: Optional[int] = 1000,
|
||||
chunk_overlap: Optional[int] = 200,
|
||||
separator_type: str = "Python",
|
||||
separator_type: Optional[str] = "Python",
|
||||
) -> list[Document]:
|
||||
"""
|
||||
Split text into chunks of a specified length.
|
||||
|
|
|
|||
|
|
@ -1,10 +1,7 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_core.documents import Document
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langchain.schema import Document
|
||||
from langflow.utils.util import build_loader_repr_from_documents
|
||||
from langchain.text_splitter import RecursiveCharacterTextSplitter
|
||||
|
||||
|
||||
class RecursiveCharacterTextSplitterComponent(CustomComponent):
|
||||
|
|
@ -57,6 +54,7 @@ class RecursiveCharacterTextSplitterComponent(CustomComponent):
|
|||
Returns:
|
||||
list[str]: The chunks of text.
|
||||
"""
|
||||
from langchain.text_splitter import RecursiveCharacterTextSplitter
|
||||
|
||||
if separators == "":
|
||||
separators = None
|
||||
|
|
|
|||
|
|
@ -1,16 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from langchain_community.tools.json.tool import JsonSpec
|
||||
from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
|
||||
|
||||
|
||||
class JsonToolkitComponent(CustomComponent):
|
||||
display_name = "JsonToolkit"
|
||||
description = "Toolkit for interacting with a JSON spec."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"spec": {"display_name": "Spec", "type": JsonSpec},
|
||||
}
|
||||
|
||||
def build(self, spec: JsonSpec) -> JsonToolkit:
|
||||
return JsonToolkit(spec=spec)
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from langflow.field_typing import AgentExecutor
|
||||
from typing import Callable
|
||||
from langchain_community.utilities.requests import TextRequestsWrapper
|
||||
from langchain_community.agent_toolkits.openapi.toolkit import OpenAPIToolkit
|
||||
|
||||
|
||||
class OpenAPIToolkitComponent(CustomComponent):
|
||||
display_name = "OpenAPIToolkit"
|
||||
description = "Toolkit for interacting with an OpenAPI API."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"json_agent": {"display_name": "JSON Agent"},
|
||||
"requests_wrapper": {"display_name": "Text Requests Wrapper"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
json_agent: AgentExecutor,
|
||||
requests_wrapper: TextRequestsWrapper,
|
||||
) -> Callable:
|
||||
return OpenAPIToolkit(json_agent=json_agent, requests_wrapper=requests_wrapper)
|
||||
|
|
@ -1,26 +0,0 @@
|
|||
from typing import Callable, Union
|
||||
|
||||
from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo
|
||||
from langchain_community.vectorstores import VectorStore
|
||||
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
class VectorStoreInfoComponent(CustomComponent):
|
||||
display_name = "VectorStoreInfo"
|
||||
description = "Information about a VectorStore"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"vectorstore": {"display_name": "VectorStore"},
|
||||
"description": {"display_name": "Description", "multiline": True},
|
||||
"name": {"display_name": "Name"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
vectorstore: VectorStore,
|
||||
description: str,
|
||||
name: str,
|
||||
) -> Union[VectorStoreInfo, Callable]:
|
||||
return VectorStoreInfo(vectorstore=vectorstore, description=description, name=name)
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from typing import List, Union
|
||||
from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit
|
||||
from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo
|
||||
from langflow.field_typing import BaseLanguageModel, Tool
|
||||
|
||||
|
||||
class VectorStoreRouterToolkitComponent(CustomComponent):
|
||||
display_name = "VectorStoreRouterToolkit"
|
||||
description = "Toolkit for routing between Vector Stores."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"vectorstores": {"display_name": "Vector Stores"},
|
||||
"llm": {"display_name": "LLM"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self, vectorstores: List[VectorStoreInfo], llm: BaseLanguageModel
|
||||
) -> Union[Tool, VectorStoreRouterToolkit]:
|
||||
print("vectorstores", vectorstores)
|
||||
print("llm", llm)
|
||||
return VectorStoreRouterToolkit(vectorstores=vectorstores, llm=llm)
|
||||
|
|
@ -1,28 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit
|
||||
from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo
|
||||
from langflow.field_typing import (
|
||||
BaseLanguageModel,
|
||||
)
|
||||
from langflow.field_typing import (
|
||||
Tool,
|
||||
)
|
||||
from typing import Union
|
||||
|
||||
|
||||
class VectorStoreToolkitComponent(CustomComponent):
|
||||
display_name = "VectorStoreToolkit"
|
||||
description = "Toolkit for interacting with a Vector Store."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"vectorstore_info": {"display_name": "Vector Store Info"},
|
||||
"llm": {"display_name": "LLM"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
vectorstore_info: VectorStoreInfo,
|
||||
llm: BaseLanguageModel,
|
||||
) -> Union[Tool, VectorStoreToolkit]:
|
||||
return VectorStoreToolkit(vectorstore_info=vectorstore_info, llm=llm)
|
||||
|
|
@ -1,31 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
|
||||
# Assuming `BingSearchAPIWrapper` is a class that exists in the context
|
||||
# and has the appropriate methods and attributes.
|
||||
# We need to make sure this class is importable from the context where this code will be running.
|
||||
from langchain_community.utilities.bing_search import BingSearchAPIWrapper
|
||||
|
||||
|
||||
class BingSearchAPIWrapperComponent(CustomComponent):
|
||||
display_name = "BingSearchAPIWrapper"
|
||||
description = "Wrapper for Bing Search API."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"bing_search_url": {"display_name": "Bing Search URL"},
|
||||
"bing_subscription_key": {
|
||||
"display_name": "Bing Subscription Key",
|
||||
"password": True,
|
||||
},
|
||||
"k": {"display_name": "Number of results", "advanced": True},
|
||||
# 'k' is not included as it is not shown (show=False)
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
bing_search_url: str,
|
||||
bing_subscription_key: str,
|
||||
k: int = 10,
|
||||
) -> BingSearchAPIWrapper:
|
||||
# 'k' has a default value and is not shown (show=False), so it is hardcoded here
|
||||
return BingSearchAPIWrapper(bing_search_url=bing_search_url, bing_subscription_key=bing_subscription_key, k=k)
|
||||
|
|
@ -1,9 +1,8 @@
|
|||
from typing import Optional
|
||||
|
||||
import requests
|
||||
from langchain_core.documents import Document
|
||||
from langflow import CustomComponent
|
||||
from langchain.schema import Document
|
||||
from langflow.services.database.models.base import orjson_dumps
|
||||
import requests
|
||||
from typing import Optional
|
||||
|
||||
|
||||
class GetRequest(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -1,21 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from typing import Union, Callable
|
||||
from langchain_community.utilities.google_search import GoogleSearchAPIWrapper
|
||||
|
||||
|
||||
class GoogleSearchAPIWrapperComponent(CustomComponent):
|
||||
display_name = "GoogleSearchAPIWrapper"
|
||||
description = "Wrapper for Google Search API."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"google_api_key": {"display_name": "Google API Key", "password": True},
|
||||
"google_cse_id": {"display_name": "Google CSE ID", "password": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
google_api_key: str,
|
||||
google_cse_id: str,
|
||||
) -> Union[GoogleSearchAPIWrapper, Callable]:
|
||||
return GoogleSearchAPIWrapper(google_api_key=google_api_key, google_cse_id=google_cse_id)
|
||||
|
|
@ -1,47 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from typing import Dict, Optional
|
||||
|
||||
# Assuming the existence of GoogleSerperAPIWrapper class in the serper module
|
||||
# If this class does not exist, you would need to create it or import the appropriate class from another module
|
||||
from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
|
||||
|
||||
|
||||
class GoogleSerperAPIWrapperComponent(CustomComponent):
|
||||
display_name = "GoogleSerperAPIWrapper"
|
||||
description = "Wrapper around the Serper.dev Google Search API."
|
||||
|
||||
def build_config(self) -> Dict[str, Dict]:
|
||||
return {
|
||||
"result_key_for_type": {
|
||||
"display_name": "Result Key for Type",
|
||||
"show": True,
|
||||
"multiline": False,
|
||||
"password": False,
|
||||
"name": "result_key_for_type",
|
||||
"advanced": False,
|
||||
"dynamic": False,
|
||||
"info": "",
|
||||
"field_type": "dict",
|
||||
"list": False,
|
||||
"value": {"news": "news", "places": "places", "images": "images", "search": "organic"},
|
||||
},
|
||||
"serper_api_key": {
|
||||
"display_name": "Serper API Key",
|
||||
"show": True,
|
||||
"multiline": False,
|
||||
"password": True,
|
||||
"name": "serper_api_key",
|
||||
"advanced": False,
|
||||
"dynamic": False,
|
||||
"info": "",
|
||||
"type": "str",
|
||||
"list": False,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
serper_api_key: str,
|
||||
result_key_for_type: Optional[Dict[str, str]] = None,
|
||||
) -> GoogleSerperAPIWrapper:
|
||||
return GoogleSerperAPIWrapper(result_key_for_type=result_key_for_type, serper_api_key=serper_api_key)
|
||||
|
|
@ -11,7 +11,7 @@
|
|||
|
||||
# - **Document:** The Document containing the JSON object.
|
||||
|
||||
from langchain_core.documents import Document
|
||||
from langchain.schema import Document
|
||||
from langflow import CustomComponent
|
||||
from langflow.services.database.models.base import orjson_dumps
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +1,8 @@
|
|||
from typing import Optional
|
||||
|
||||
import requests
|
||||
from langchain_core.documents import Document
|
||||
from langflow import CustomComponent
|
||||
from langchain.schema import Document
|
||||
from langflow.services.database.models.base import orjson_dumps
|
||||
import requests
|
||||
from typing import Optional
|
||||
|
||||
|
||||
class PostRequest(CustomComponent):
|
||||
|
|
|
|||
|
|
@ -1,33 +0,0 @@
|
|||
from langflow import CustomComponent
|
||||
from typing import Optional, Dict
|
||||
from langchain_community.utilities.searx_search import SearxSearchWrapper
|
||||
|
||||
|
||||
class SearxSearchWrapperComponent(CustomComponent):
|
||||
display_name = "SearxSearchWrapper"
|
||||
description = "Wrapper for Searx API."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"headers": {
|
||||
"field_type": "dict",
|
||||
"display_name": "Headers",
|
||||
"multiline": True,
|
||||
"value": '{"Authorization": "Bearer <token>"}',
|
||||
},
|
||||
"k": {"display_name": "k", "advanced": True, "field_type": "int", "value": 10},
|
||||
"searx_host": {
|
||||
"display_name": "Searx Host",
|
||||
"field_type": "str",
|
||||
"value": "https://searx.example.com",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
k: int = 10,
|
||||
headers: Optional[Dict[str, str]] = None,
|
||||
searx_host: str = "https://searx.example.com",
|
||||
) -> SearxSearchWrapper:
|
||||
return SearxSearchWrapper(headers=headers, k=k, searx_host=searx_host)
|
||||
|
|
@ -1,31 +0,0 @@
|
|||
from typing import Callable, Union
|
||||
|
||||
from langchain_community.utilities.serpapi import SerpAPIWrapper
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
class SerpAPIWrapperComponent(CustomComponent):
|
||||
display_name = "SerpAPIWrapper"
|
||||
description = "Wrapper around SerpAPI"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"serpapi_api_key": {"display_name": "SerpAPI API Key", "type": "str", "password": True},
|
||||
"params": {
|
||||
"display_name": "Parameters",
|
||||
"type": "dict",
|
||||
"advanced": True,
|
||||
"multiline": True,
|
||||
"value": '{"engine": "google","google_domain": "google.com","gl": "us","hl": "en"}',
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
serpapi_api_key: str,
|
||||
params: dict,
|
||||
) -> Union[SerpAPIWrapper, Callable]: # Removed quotes around SerpAPIWrapper
|
||||
return SerpAPIWrapper( # type: ignore
|
||||
serpapi_api_key=serpapi_api_key,
|
||||
params=params,
|
||||
)
|
||||
|
|
@ -1,8 +1,7 @@
|
|||
from typing import List, Optional
|
||||
|
||||
import requests
|
||||
from langchain_core.documents import Document
|
||||
from langflow import CustomComponent
|
||||
from langchain.schema import Document
|
||||
from langflow.services.database.models.base import orjson_dumps
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,30 +0,0 @@
|
|||
from typing import Callable, Union
|
||||
|
||||
from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
|
||||
from langflow import CustomComponent
|
||||
|
||||
# Assuming WikipediaAPIWrapper is a class that needs to be imported.
|
||||
# The import statement is not included as it is not provided in the JSON
|
||||
# and the actual implementation details are unknown.
|
||||
|
||||
|
||||
class WikipediaAPIWrapperComponent(CustomComponent):
|
||||
display_name = "WikipediaAPIWrapper"
|
||||
description = "Wrapper around WikipediaAPI."
|
||||
|
||||
def build_config(self):
|
||||
return {}
|
||||
|
||||
def build(
|
||||
self,
|
||||
top_k_results: int = 3,
|
||||
lang: str = "en",
|
||||
load_all_available_meta: bool = False,
|
||||
doc_content_chars_max: int = 4000,
|
||||
) -> Union[WikipediaAPIWrapper, Callable]:
|
||||
return WikipediaAPIWrapper( # type: ignore
|
||||
top_k_results=top_k_results,
|
||||
lang=lang,
|
||||
load_all_available_meta=load_all_available_meta,
|
||||
doc_content_chars_max=doc_content_chars_max,
|
||||
)
|
||||
|
|
@ -1,18 +0,0 @@
|
|||
from typing import Callable, Union
|
||||
|
||||
from langchain_community.utilities.wolfram_alpha import WolframAlphaAPIWrapper
|
||||
from langflow import CustomComponent
|
||||
|
||||
# Since all the fields in the JSON have show=False, we will only create a basic component
|
||||
# without any configurable fields.
|
||||
|
||||
|
||||
class WolframAlphaAPIWrapperComponent(CustomComponent):
|
||||
display_name = "WolframAlphaAPIWrapper"
|
||||
description = "Wrapper for Wolfram Alpha."
|
||||
|
||||
def build_config(self):
|
||||
return {"appid": {"display_name": "App ID", "type": "str", "password": True}}
|
||||
|
||||
def build(self, appid: str) -> Union[Callable, WolframAlphaAPIWrapper]:
|
||||
return WolframAlphaAPIWrapper(wolfram_alpha_appid=appid) # type: ignore
|
||||
|
|
@ -3,8 +3,9 @@ from typing import List, Optional, Union
|
|||
import chromadb # type: ignore
|
||||
from langchain.embeddings.base import Embeddings
|
||||
from langchain.schema import BaseRetriever, Document
|
||||
from langchain_community.vectorstores import VectorStore
|
||||
from langchain_community.vectorstores.chroma import Chroma
|
||||
from langchain.vectorstores import Chroma
|
||||
from langchain.vectorstores.base import VectorStore
|
||||
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,26 +0,0 @@
|
|||
from typing import List, Union
|
||||
|
||||
from langchain.schema import BaseRetriever
|
||||
from langchain_community.vectorstores import VectorStore
|
||||
from langchain_community.vectorstores.faiss import FAISS
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import Document, Embeddings
|
||||
|
||||
|
||||
class FAISSComponent(CustomComponent):
|
||||
display_name = "FAISS"
|
||||
description = "Construct FAISS wrapper from raw documents."
|
||||
documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"documents": {"display_name": "Documents"},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
embedding: Embeddings,
|
||||
documents: List[Document],
|
||||
) -> Union[VectorStore, FAISS, BaseRetriever]:
|
||||
return FAISS.from_documents(documents=documents, embedding=embedding)
|
||||
|
|
@ -1,47 +0,0 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import (
|
||||
Document,
|
||||
Embeddings,
|
||||
NestedDict,
|
||||
)
|
||||
|
||||
|
||||
class MongoDBAtlasComponent(CustomComponent):
|
||||
display_name = "MongoDB Atlas"
|
||||
description = "Construct a `MongoDB Atlas Vector Search` vector store from raw documents."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"documents": {"display_name": "Documents"},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
"collection_name": {"display_name": "Collection Name"},
|
||||
"db_name": {"display_name": "Database Name"},
|
||||
"index_name": {"display_name": "Index Name"},
|
||||
"mongodb_atlas_cluster_uri": {"display_name": "MongoDB Atlas Cluster URI"},
|
||||
"search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
documents: List[Document],
|
||||
embedding: Embeddings,
|
||||
collection_name: str = "",
|
||||
db_name: str = "",
|
||||
index_name: str = "",
|
||||
mongodb_atlas_cluster_uri: str = "",
|
||||
search_kwargs: Optional[NestedDict] = None,
|
||||
) -> MongoDBAtlasVectorSearch:
|
||||
search_kwargs = search_kwargs or {}
|
||||
return MongoDBAtlasVectorSearch(
|
||||
documents=documents,
|
||||
embedding=embedding,
|
||||
collection_name=collection_name,
|
||||
db_name=db_name,
|
||||
index_name=index_name,
|
||||
mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,
|
||||
search_kwargs=search_kwargs,
|
||||
)
|
||||
|
|
@ -1,62 +0,0 @@
|
|||
import os
|
||||
from typing import List, Optional, Union
|
||||
|
||||
import pinecone # type: ignore
|
||||
from langchain.schema import BaseRetriever
|
||||
from langchain_community.vectorstores import VectorStore
|
||||
from langchain_community.vectorstores.pinecone import Pinecone
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import Document, Embeddings
|
||||
|
||||
|
||||
class PineconeComponent(CustomComponent):
|
||||
display_name = "Pinecone"
|
||||
description = "Construct Pinecone wrapper from raw documents."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"documents": {"display_name": "Documents"},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
"index_name": {"display_name": "Index Name"},
|
||||
"namespace": {"display_name": "Namespace"},
|
||||
"pinecone_api_key": {"display_name": "Pinecone API Key", "default": "", "password": True, "required": True},
|
||||
"pinecone_env": {"display_name": "Pinecone Environment", "default": "", "required": True},
|
||||
"search_kwargs": {"display_name": "Search Kwargs", "default": "{}"},
|
||||
"pool_threads": {"display_name": "Pool Threads", "default": 1, "advanced": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
embedding: Embeddings,
|
||||
pinecone_env: str,
|
||||
documents: List[Document],
|
||||
index_name: Optional[str] = None,
|
||||
pinecone_api_key: Optional[str] = None,
|
||||
text_key: Optional[str] = "text",
|
||||
namespace: Optional[str] = "default",
|
||||
pool_threads: Optional[int] = None,
|
||||
) -> Union[VectorStore, Pinecone, BaseRetriever]:
|
||||
if pinecone_api_key is None or pinecone_env is None:
|
||||
raise ValueError("Pinecone API Key and Environment are required.")
|
||||
if os.getenv("PINECONE_API_KEY") is None and pinecone_api_key is None:
|
||||
raise ValueError("Pinecone API Key is required.")
|
||||
|
||||
pinecone.init(api_key=pinecone_api_key, environment=pinecone_env) # type: ignore
|
||||
if documents:
|
||||
return Pinecone.from_documents(
|
||||
documents=documents,
|
||||
embedding=embedding,
|
||||
index_name=index_name,
|
||||
pool_threads=pool_threads,
|
||||
namespace=namespace,
|
||||
text_key=text_key,
|
||||
)
|
||||
|
||||
return Pinecone.from_existing_index(
|
||||
index_name=index_name,
|
||||
embedding=embedding,
|
||||
text_key=text_key,
|
||||
namespace=namespace,
|
||||
pool_threads=pool_threads,
|
||||
)
|
||||
|
|
@ -1,76 +0,0 @@
|
|||
from typing import List, Optional, Union
|
||||
|
||||
from langchain.schema import BaseRetriever
|
||||
from langchain_community.vectorstores import VectorStore
|
||||
from langchain_community.vectorstores.qdrant import Qdrant
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import Document, Embeddings, NestedDict
|
||||
|
||||
|
||||
class QdrantComponent(CustomComponent):
|
||||
display_name = "Qdrant"
|
||||
description = "Construct Qdrant wrapper from a list of texts."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"documents": {"display_name": "Documents"},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
"api_key": {"display_name": "API Key", "password": True},
|
||||
"collection_name": {"display_name": "Collection Name"},
|
||||
"content_payload_key": {"display_name": "Content Payload Key", "advanced": True},
|
||||
"distance_func": {"display_name": "Distance Function", "advanced": True},
|
||||
"grpc_port": {"display_name": "gRPC Port", "advanced": True},
|
||||
"host": {"display_name": "Host", "advanced": True},
|
||||
"https": {"display_name": "HTTPS", "advanced": True},
|
||||
"location": {"display_name": "Location", "advanced": True},
|
||||
"metadata_payload_key": {"display_name": "Metadata Payload Key", "advanced": True},
|
||||
"path": {"display_name": "Path", "advanced": True},
|
||||
"port": {"display_name": "Port", "advanced": True},
|
||||
"prefer_grpc": {"display_name": "Prefer gRPC", "advanced": True},
|
||||
"prefix": {"display_name": "Prefix", "advanced": True},
|
||||
"search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
|
||||
"timeout": {"display_name": "Timeout", "advanced": True},
|
||||
"url": {"display_name": "URL", "advanced": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
embedding: Embeddings,
|
||||
documents: List[Document],
|
||||
api_key: Optional[str] = None,
|
||||
collection_name: Optional[str] = None,
|
||||
content_payload_key: str = "page_content",
|
||||
distance_func: str = "Cosine",
|
||||
grpc_port: Optional[int] = 6334,
|
||||
host: Optional[str] = None,
|
||||
https: bool = False,
|
||||
location: str = ":memory:",
|
||||
metadata_payload_key: str = "metadata",
|
||||
path: Optional[str] = None,
|
||||
port: Optional[int] = 6333,
|
||||
prefer_grpc: bool = False,
|
||||
prefix: Optional[str] = None,
|
||||
search_kwargs: Optional[NestedDict] = None,
|
||||
timeout: Optional[float] = None,
|
||||
url: Optional[str] = None,
|
||||
) -> Union[VectorStore, Qdrant, BaseRetriever]:
|
||||
return Qdrant.from_documents(
|
||||
documents=documents,
|
||||
embedding=embedding,
|
||||
api_key=api_key,
|
||||
collection_name=collection_name,
|
||||
content_payload_key=content_payload_key,
|
||||
distance_func=distance_func,
|
||||
grpc_port=grpc_port,
|
||||
host=host,
|
||||
https=https,
|
||||
location=location,
|
||||
metadata_payload_key=metadata_payload_key,
|
||||
path=path,
|
||||
port=port,
|
||||
prefer_grpc=prefer_grpc,
|
||||
prefix=prefix,
|
||||
search_kwargs=search_kwargs,
|
||||
timeout=timeout,
|
||||
url=url,
|
||||
)
|
||||
|
|
@ -1,13 +1,11 @@
|
|||
from typing import Optional, Union
|
||||
|
||||
from langchain.embeddings.base import Embeddings
|
||||
from langchain_community.vectorstores import VectorStore
|
||||
from langchain_community.vectorstores.redis import Redis
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.retrievers import BaseRetriever
|
||||
|
||||
from typing import Optional
|
||||
from langflow import CustomComponent
|
||||
|
||||
from langchain.vectorstores.redis import Redis
|
||||
from langchain.schema import Document
|
||||
from langchain.vectorstores.base import VectorStore
|
||||
from langchain.embeddings.base import Embeddings
|
||||
|
||||
|
||||
class RedisComponent(CustomComponent):
|
||||
"""
|
||||
|
|
@ -44,7 +42,7 @@ class RedisComponent(CustomComponent):
|
|||
redis_server_url: str,
|
||||
redis_index_name: str,
|
||||
documents: Optional[Document] = None,
|
||||
) -> Union[VectorStore, BaseRetriever]:
|
||||
) -> VectorStore:
|
||||
"""
|
||||
Builds the Vector Store or BaseRetriever object.
|
||||
|
||||
|
|
@ -57,19 +55,10 @@ class RedisComponent(CustomComponent):
|
|||
Returns:
|
||||
- VectorStore: The Vector Store object.
|
||||
"""
|
||||
if documents is None:
|
||||
redis_vs = Redis.from_existing_index(
|
||||
embedding=embedding,
|
||||
index_name=redis_index_name,
|
||||
schema=None,
|
||||
key_prefix=None,
|
||||
redis_url=redis_server_url,
|
||||
)
|
||||
else:
|
||||
redis_vs = Redis.from_documents(
|
||||
documents=documents, # type: ignore
|
||||
embedding=embedding,
|
||||
redis_url=redis_server_url,
|
||||
index_name=redis_index_name,
|
||||
)
|
||||
return redis_vs
|
||||
|
||||
return Redis.from_documents(
|
||||
documents=documents, # type: ignore
|
||||
embedding=embedding,
|
||||
redis_url=redis_server_url,
|
||||
index_name=redis_index_name,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,44 +0,0 @@
|
|||
from typing import List, Union
|
||||
|
||||
from langchain.schema import BaseRetriever
|
||||
from langchain_community.vectorstores import VectorStore
|
||||
from langchain_community.vectorstores.supabase import SupabaseVectorStore
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import Document, Embeddings, NestedDict
|
||||
from supabase.client import Client, create_client
|
||||
|
||||
|
||||
class SupabaseComponent(CustomComponent):
|
||||
display_name = "Supabase"
|
||||
description = "Return VectorStore initialized from texts and embeddings."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"documents": {"display_name": "Documents"},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
"query_name": {"display_name": "Query Name"},
|
||||
"search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
|
||||
"supabase_service_key": {"display_name": "Supabase Service Key"},
|
||||
"supabase_url": {"display_name": "Supabase URL"},
|
||||
"table_name": {"display_name": "Table Name", "advanced": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
embedding: Embeddings,
|
||||
documents: List[Document],
|
||||
query_name: str = "",
|
||||
search_kwargs: NestedDict = {},
|
||||
supabase_service_key: str = "",
|
||||
supabase_url: str = "",
|
||||
table_name: str = "",
|
||||
) -> Union[VectorStore, SupabaseVectorStore, BaseRetriever]:
|
||||
supabase: Client = create_client(supabase_url, supabase_key=supabase_service_key)
|
||||
return SupabaseVectorStore.from_documents(
|
||||
documents=documents,
|
||||
embedding=embedding,
|
||||
query_name=query_name,
|
||||
search_kwargs=search_kwargs,
|
||||
client=supabase,
|
||||
table_name=table_name,
|
||||
)
|
||||
|
|
@ -1,14 +1,14 @@
|
|||
import tempfile
|
||||
import urllib
|
||||
import urllib.request
|
||||
from typing import List, Optional, Union
|
||||
|
||||
from langchain_community.embeddings import FakeEmbeddings
|
||||
from langchain_community.vectorstores.vectara import Vectara
|
||||
from langchain_core.vectorstores import VectorStore
|
||||
|
||||
from typing import Optional, Union, List
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import BaseRetriever, Document
|
||||
import tempfile
|
||||
import urllib.request
|
||||
import urllib
|
||||
|
||||
from langchain.vectorstores import Vectara
|
||||
from langchain.schema import Document
|
||||
from langchain.vectorstores.base import VectorStore
|
||||
from langchain.schema import BaseRetriever
|
||||
from langchain.embeddings import FakeEmbeddings
|
||||
|
||||
|
||||
class VectaraComponent(CustomComponent):
|
||||
|
|
@ -19,18 +19,25 @@ class VectaraComponent(CustomComponent):
|
|||
field_config = {
|
||||
"vectara_customer_id": {
|
||||
"display_name": "Vectara Customer ID",
|
||||
"required": True,
|
||||
},
|
||||
"vectara_corpus_id": {
|
||||
"display_name": "Vectara Corpus ID",
|
||||
"required": True,
|
||||
},
|
||||
"vectara_api_key": {
|
||||
"display_name": "Vectara API Key",
|
||||
"password": True,
|
||||
"required": True,
|
||||
},
|
||||
"code": {"show": False},
|
||||
"documents": {
|
||||
"display_name": "Documents",
|
||||
"info": "Pass in either for Self Query Retriever or for making a Vectara Object",
|
||||
},
|
||||
"documents": {"display_name": "Documents", "info": "If provided, will be upserted to corpus (optional)"},
|
||||
"files_url": {
|
||||
"display_name": "Files Url",
|
||||
"info": "Make vectara object using url of files (optional)",
|
||||
"info": "Make vectara object using url of files(documents not needed)",
|
||||
},
|
||||
}
|
||||
|
||||
|
|
@ -42,8 +49,6 @@ class VectaraComponent(CustomComponent):
|
|||
files_url: Optional[List[str]] = None,
|
||||
documents: Optional[Document] = None,
|
||||
) -> Union[VectorStore, BaseRetriever]:
|
||||
source = "Langflow"
|
||||
|
||||
if documents is not None:
|
||||
return Vectara.from_documents(
|
||||
documents=documents,
|
||||
|
|
@ -51,7 +56,6 @@ class VectaraComponent(CustomComponent):
|
|||
vectara_customer_id=vectara_customer_id,
|
||||
vectara_corpus_id=vectara_corpus_id,
|
||||
vectara_api_key=vectara_api_key,
|
||||
source=source,
|
||||
)
|
||||
|
||||
if files_url is not None:
|
||||
|
|
@ -67,12 +71,10 @@ class VectaraComponent(CustomComponent):
|
|||
vectara_customer_id=vectara_customer_id,
|
||||
vectara_corpus_id=vectara_corpus_id,
|
||||
vectara_api_key=vectara_api_key,
|
||||
source=source,
|
||||
)
|
||||
|
||||
return Vectara(
|
||||
vectara_customer_id=vectara_customer_id,
|
||||
vectara_corpus_id=vectara_corpus_id,
|
||||
vectara_api_key=vectara_api_key,
|
||||
source=source,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,12 +1,13 @@
|
|||
from typing import Optional, Union
|
||||
|
||||
import weaviate # type: ignore
|
||||
from langchain.embeddings.base import Embeddings
|
||||
from langchain.schema import BaseRetriever, Document
|
||||
from langchain_community.vectorstores import VectorStore, Weaviate
|
||||
|
||||
from typing import Optional, Union
|
||||
from langflow import CustomComponent
|
||||
|
||||
from langchain.vectorstores import Weaviate
|
||||
from langchain.schema import Document
|
||||
from langchain.vectorstores.base import VectorStore
|
||||
from langchain.schema import BaseRetriever
|
||||
from langchain.embeddings.base import Embeddings
|
||||
|
||||
|
||||
class WeaviateVectorStore(CustomComponent):
|
||||
display_name: str = "Weaviate"
|
||||
|
|
@ -44,7 +45,7 @@ class WeaviateVectorStore(CustomComponent):
|
|||
search_by_text: bool = False,
|
||||
api_key: Optional[str] = None,
|
||||
index_name: Optional[str] = None,
|
||||
text_key: str = "text",
|
||||
text_key: Optional[str] = "text",
|
||||
embedding: Optional[Embeddings] = None,
|
||||
documents: Optional[Document] = None,
|
||||
attributes: Optional[list] = None,
|
||||
|
|
|
|||
|
|
@ -1,15 +1,13 @@
|
|||
from typing import Optional, Union
|
||||
|
||||
from langchain.embeddings.base import Embeddings
|
||||
from langchain_community.vectorstores import VectorStore
|
||||
from langchain_community.vectorstores.pgvector import PGVector
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.retrievers import BaseRetriever
|
||||
|
||||
from typing import Optional, List
|
||||
from langflow import CustomComponent
|
||||
|
||||
from langchain.vectorstores.pgvector import PGVector
|
||||
from langchain.schema import Document
|
||||
from langchain.vectorstores.base import VectorStore
|
||||
from langchain.embeddings.base import Embeddings
|
||||
|
||||
class PGVectorComponent(CustomComponent):
|
||||
|
||||
class PostgresqlVectorComponent(CustomComponent):
|
||||
"""
|
||||
A custom component for implementing a Vector Store using PostgreSQL.
|
||||
"""
|
||||
|
|
@ -17,6 +15,7 @@ class PGVectorComponent(CustomComponent):
|
|||
display_name: str = "PGVector"
|
||||
description: str = "Implementation of Vector Store using PostgreSQL"
|
||||
documentation = "https://python.langchain.com/docs/integrations/vectorstores/pgvector"
|
||||
beta = True
|
||||
|
||||
def build_config(self):
|
||||
"""
|
||||
|
|
@ -26,7 +25,8 @@ class PGVectorComponent(CustomComponent):
|
|||
- dict: A dictionary containing the configuration options for the component.
|
||||
"""
|
||||
return {
|
||||
"code": {"show": False},
|
||||
"index_name": {"display_name": "Index Name", "value": "your_index"},
|
||||
"code": {"show": True, "display_name": "Code"},
|
||||
"documents": {"display_name": "Documents", "is_list": True},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
"pg_server_url": {
|
||||
|
|
@ -41,8 +41,8 @@ class PGVectorComponent(CustomComponent):
|
|||
embedding: Embeddings,
|
||||
pg_server_url: str,
|
||||
collection_name: str,
|
||||
documents: Optional[Document] = None,
|
||||
) -> Union[VectorStore, BaseRetriever]:
|
||||
documents: Optional[List[Document]] = None,
|
||||
) -> VectorStore:
|
||||
"""
|
||||
Builds the Vector Store or BaseRetriever object.
|
||||
|
||||
|
|
@ -58,13 +58,13 @@ class PGVectorComponent(CustomComponent):
|
|||
|
||||
try:
|
||||
if documents is None:
|
||||
vector_store = PGVector.from_existing_index(
|
||||
return PGVector.from_existing_index(
|
||||
embedding=embedding,
|
||||
collection_name=collection_name,
|
||||
connection_string=pg_server_url,
|
||||
)
|
||||
|
||||
vector_store = PGVector.from_documents(
|
||||
return PGVector.from_documents(
|
||||
embedding=embedding,
|
||||
documents=documents,
|
||||
collection_name=collection_name,
|
||||
|
|
@ -72,4 +72,3 @@ class PGVectorComponent(CustomComponent):
|
|||
)
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to build PGVector: {e}")
|
||||
return vector_store
|
||||
|
|
|
|||
|
|
@ -106,8 +106,6 @@ 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:
|
||||
|
|
@ -174,8 +172,6 @@ prompts:
|
|||
textsplitters:
|
||||
CharacterTextSplitter:
|
||||
documentation: "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter"
|
||||
RecursiveCharacterTextSplitter:
|
||||
documentation: "https://python.langchain.com/docs/modules/data_connection/document_transformers/recursive_text_splitter"
|
||||
toolkits:
|
||||
OpenAPIToolkit:
|
||||
documentation: ""
|
||||
|
|
@ -278,8 +274,6 @@ 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:
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ from langchain.schema.language_model import BaseLanguageModel
|
|||
from langchain.schema.memory import BaseMemory
|
||||
from langchain.text_splitter import TextSplitter
|
||||
from langchain.tools import Tool
|
||||
from langchain_community.vectorstores import VectorStore
|
||||
from langchain.vectorstores.base import VectorStore
|
||||
|
||||
# Type alias for more complex dicts
|
||||
NestedDict = Dict[str, Union[str, Dict]]
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ from langflow.interface.retrievers.base import retriever_creator
|
|||
from langflow.interface.text_splitters.base import textsplitter_creator
|
||||
from langflow.interface.toolkits.base import toolkits_creator
|
||||
from langflow.interface.tools.base import tool_creator
|
||||
from langflow.interface.vector_store.base import vectorstore_creator
|
||||
from langflow.interface.wrappers.base import wrapper_creator
|
||||
from langflow.utils.lazy_load import LazyLoadDictBase
|
||||
|
||||
|
|
@ -45,7 +46,7 @@ class VertexTypesDict(LazyLoadDictBase):
|
|||
**{t: types.LLMVertex for t in llm_creator.to_list()},
|
||||
**{t: types.MemoryVertex for t in memory_creator.to_list()},
|
||||
**{t: types.EmbeddingVertex for t in embedding_creator.to_list()},
|
||||
# **{t: types.VectorStoreVertex for t in vectorstore_creator.to_list()},
|
||||
**{t: types.VectorStoreVertex for t in vectorstore_creator.to_list()},
|
||||
**{t: types.DocumentLoaderVertex for t in documentloader_creator.to_list()},
|
||||
**{t: types.TextSplitterVertex for t in textsplitter_creator.to_list()},
|
||||
**{t: types.OutputParserVertex for t in output_parser_creator.to_list()},
|
||||
|
|
|
|||
|
|
@ -2,10 +2,14 @@ from typing import Any, Optional
|
|||
|
||||
from langchain.agents import AgentExecutor, ZeroShotAgent
|
||||
from langchain.agents.agent_toolkits import (
|
||||
SQLDatabaseToolkit,
|
||||
VectorStoreInfo,
|
||||
VectorStoreRouterToolkit,
|
||||
VectorStoreToolkit,
|
||||
)
|
||||
from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
|
||||
from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
|
||||
from langchain.agents.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX
|
||||
from langchain.agents.agent_toolkits.vectorstore.prompt import PREFIX as VECTORSTORE_PREFIX
|
||||
from langchain.agents.agent_toolkits.vectorstore.prompt import ROUTER_PREFIX as VECTORSTORE_ROUTER_PREFIX
|
||||
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
|
||||
|
|
@ -13,14 +17,9 @@ from langchain.base_language import BaseLanguageModel
|
|||
from langchain.chains.llm import LLMChain
|
||||
from langchain.sql_database import SQLDatabase
|
||||
from langchain.tools.sql_database.prompt import QUERY_CHECKER
|
||||
from langchain_community.agent_toolkits import SQLDatabaseToolkit
|
||||
from langchain_community.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
|
||||
from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
|
||||
from langchain_community.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX
|
||||
from langchain_experimental.agents.agent_toolkits.pandas.prompt import PREFIX as PANDAS_PREFIX
|
||||
from langchain_experimental.agents.agent_toolkits.pandas.prompt import SUFFIX_WITH_DF as PANDAS_SUFFIX
|
||||
from langchain_experimental.tools.python.tool import PythonAstREPLTool
|
||||
|
||||
from langflow.interface.base import CustomAgentExecutor
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -67,9 +67,7 @@ Human: {input}
|
|||
class MidJourneyPromptChain(BaseCustomConversationChain):
|
||||
"""MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts."""
|
||||
|
||||
template: Optional[
|
||||
str
|
||||
] = """I want you to act as a prompt generator for Midjourney's artificial intelligence program.
|
||||
template: Optional[str] = """I want you to act as a prompt generator for Midjourney's artificial intelligence program.
|
||||
Your job is to provide detailed and creative descriptions that will inspire unique and interesting images from the AI.
|
||||
Keep in mind that the AI is capable of understanding a wide range of language and can interpret abstract concepts, so feel free to be as imaginative and descriptive as possible.
|
||||
For example, you could describe a scene from a futuristic city, or a surreal landscape filled with strange creatures.
|
||||
|
|
@ -83,9 +81,7 @@ class MidJourneyPromptChain(BaseCustomConversationChain):
|
|||
|
||||
|
||||
class TimeTravelGuideChain(BaseCustomConversationChain):
|
||||
template: Optional[
|
||||
str
|
||||
] = """I want you to act as my time travel guide. You are helpful and creative. I will provide you with the historical period or future time I want to visit and you will suggest the best events, sights, or people to experience. Provide the suggestions and any necessary information.
|
||||
template: Optional[str] = """I want you to act as my time travel guide. You are helpful and creative. I will provide you with the historical period or future time I want to visit and you will suggest the best events, sights, or people to experience. Provide the suggestions and any necessary information.
|
||||
Current conversation:
|
||||
{history}
|
||||
Human: {input}
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@ import ast
|
|||
import os
|
||||
import zlib
|
||||
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from loguru import logger
|
||||
|
||||
|
||||
|
|
@ -67,18 +66,18 @@ class DirectoryReader:
|
|||
def filter_loaded_components(self, data: dict, with_errors: bool) -> dict:
|
||||
from langflow.interface.custom.utils import build_component
|
||||
|
||||
items = []
|
||||
for menu in data["menu"]:
|
||||
components = []
|
||||
for component in menu["components"]:
|
||||
try:
|
||||
if component["error"] if with_errors else not component["error"]:
|
||||
component_tuple = (*build_component(component), component)
|
||||
components.append(component_tuple)
|
||||
except Exception as e:
|
||||
logger.error(f"Error while loading component: {e}")
|
||||
continue
|
||||
items.append({"name": menu["name"], "path": menu["path"], "components": components})
|
||||
items = [
|
||||
{
|
||||
"name": menu["name"],
|
||||
"path": menu["path"],
|
||||
"components": [
|
||||
(*build_component(component), component)
|
||||
for component in menu["components"]
|
||||
if (component["error"] if with_errors else not component["error"])
|
||||
],
|
||||
}
|
||||
for menu in data["menu"]
|
||||
]
|
||||
filtered = [menu for menu in items if menu["components"]]
|
||||
logger.debug(f'Filtered components {"with errors" if with_errors else ""}: {len(filtered)}')
|
||||
return {"menu": filtered}
|
||||
|
|
@ -246,18 +245,9 @@ class DirectoryReader:
|
|||
else:
|
||||
component_name_camelcase = component_name
|
||||
|
||||
if validation_result:
|
||||
try:
|
||||
output_types = self.get_output_types_from_code(result_content)
|
||||
except Exception as exc:
|
||||
logger.exception(f"Error while getting output types from code: {str(exc)}")
|
||||
output_types = [component_name_camelcase]
|
||||
else:
|
||||
output_types = [component_name_camelcase]
|
||||
|
||||
component_info = {
|
||||
"name": "CustomComponent",
|
||||
"output_types": output_types,
|
||||
"output_types": [component_name_camelcase],
|
||||
"file": filename,
|
||||
"code": result_content if validation_result else "",
|
||||
"error": "" if validation_result else result_content,
|
||||
|
|
@ -269,13 +259,3 @@ class DirectoryReader:
|
|||
response["menu"].append(menu_result)
|
||||
logger.debug("-------------------- Component menu list built --------------------")
|
||||
return response
|
||||
|
||||
@staticmethod
|
||||
def get_output_types_from_code(code: str) -> list:
|
||||
"""
|
||||
Get the output types from the code.
|
||||
"""
|
||||
custom_component = CustomComponent(code=code)
|
||||
types_list = custom_component.get_function_entrypoint_return_type
|
||||
# Get the name of types classes
|
||||
return [type_.__name__ for type_ in types_list if hasattr(type_, "__name__")]
|
||||
|
|
|
|||
|
|
@ -7,6 +7,8 @@ from typing import Any, Dict, List, Optional, Union
|
|||
from uuid import UUID
|
||||
|
||||
from fastapi import HTTPException
|
||||
from loguru import logger
|
||||
|
||||
from langflow.field_typing.range_spec import RangeSpec
|
||||
from langflow.interface.custom.code_parser.utils import extract_inner_type
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
|
@ -19,7 +21,6 @@ from langflow.interface.importing.utils import eval_custom_component_code
|
|||
from langflow.template.field.base import TemplateField
|
||||
from langflow.template.frontend_node.custom_components import CustomComponentFrontendNode
|
||||
from langflow.utils.util import get_base_classes
|
||||
from loguru import logger
|
||||
|
||||
|
||||
def add_output_types(frontend_node: CustomComponentFrontendNode, return_types: List[str]):
|
||||
|
|
|
|||
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