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Merge branch 'dev' into feat/aws

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Yuki Sekiya 2023-10-18 22:43:44 +09:00 • committed by GitHub
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92
src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py Normal file
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@ -0,0 +1,92 @@
from typing import Optional
from langflow import CustomComponent
from langchain.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
from langchain.llms.base import BaseLLM
class QianfanChatEndpointComponent(CustomComponent):
display_name: str = "QianfanChatEndpoint"
description: str = (
"Baidu Qianfan chat models. Get more detail from "
"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint."
)
def build_config(self):
return {
"model": {
"display_name": "Model Name",
"options": [
"ERNIE-Bot",
"ERNIE-Bot-turbo",
"BLOOMZ-7B",
"Llama-2-7b-chat",
"Llama-2-13b-chat",
"Llama-2-70b-chat",
"Qianfan-BLOOMZ-7B-compressed",
"Qianfan-Chinese-Llama-2-7B",
"ChatGLM2-6B-32K",
"AquilaChat-7B",
],
"info": "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint",
"required": True,
},
"qianfan_ak": {
"display_name": "Qianfan Ak",
"required": True,
"password": True,
"info": "which you could get from https://cloud.baidu.com/product/wenxinworkshop",
},
"qianfan_sk": {
"display_name": "Qianfan Sk",
"required": True,
"password": True,
"info": "which you could get from https://cloud.baidu.com/product/wenxinworkshop",
},
"top_p": {
"display_name": "Top p",
"field_type": "float",
"info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
"value": 0.8,
},
"temperature": {
"display_name": "Temperature",
"field_type": "float",
"info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
"value": 0.95,
},
"penalty_score": {
"display_name": "Penalty Score",
"field_type": "float",
"info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
"value": 1.0,
},
"endpoint": {
"display_name": "Endpoint",
"info": "Endpoint of the Qianfan LLM, required if custom model used.",
},
"code": {"show": False},
}
def build(
self,
model: str = "ERNIE-Bot-turbo",
qianfan_ak: Optional[str] = None,
qianfan_sk: Optional[str] = None,
top_p: Optional[float] = None,
temperature: Optional[float] = None,
penalty_score: Optional[float] = None,
endpoint: Optional[str] = None,
) -> BaseLLM:
try:
output = QianfanChatEndpoint( # type: ignore
model=model,
qianfan_ak=qianfan_ak,
qianfan_sk=qianfan_sk,
top_p=top_p,
temperature=temperature,
penalty_score=penalty_score,
endpoint=endpoint,
)
except Exception as e:
raise ValueError("Could not connect to Baidu Qianfan API.") from e
return output # type: ignore

92
src/backend/langflow/components/llms/BaiduQianfanLLMEndpoints.py Normal file
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@ -0,0 +1,92 @@
from typing import Optional
from langflow import CustomComponent
from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint
from langchain.llms.base import BaseLLM
class QianfanLLMEndpointComponent(CustomComponent):
display_name: str = "QianfanLLMEndpoint"
description: str = (
"Baidu Qianfan hosted open source or customized models. "
"Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint"
)
def build_config(self):
return {
"model": {
"display_name": "Model Name",
"options": [
"ERNIE-Bot",
"ERNIE-Bot-turbo",
"BLOOMZ-7B",
"Llama-2-7b-chat",
"Llama-2-13b-chat",
"Llama-2-70b-chat",
"Qianfan-BLOOMZ-7B-compressed",
"Qianfan-Chinese-Llama-2-7B",
"ChatGLM2-6B-32K",
"AquilaChat-7B",
],
"info": "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint",
"required": True,
},
"qianfan_ak": {
"display_name": "Qianfan Ak",
"required": True,
"password": True,
"info": "which you could get from https://cloud.baidu.com/product/wenxinworkshop",
},
"qianfan_sk": {
"display_name": "Qianfan Sk",
"required": True,
"password": True,
"info": "which you could get from https://cloud.baidu.com/product/wenxinworkshop",
},
"top_p": {
"display_name": "Top p",
"field_type": "float",
"info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
"value": 0.8,
},
"temperature": {
"display_name": "Temperature",
"field_type": "float",
"info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
"value": 0.95,
},
"penalty_score": {
"display_name": "Penalty Score",
"field_type": "float",
"info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
"value": 1.0,
},
"endpoint": {
"display_name": "Endpoint",
"info": "Endpoint of the Qianfan LLM, required if custom model used.",
},
"code": {"show": False},
}
def build(
self,
model: str = "ERNIE-Bot-turbo",
qianfan_ak: Optional[str] = None,
qianfan_sk: Optional[str] = None,
top_p: Optional[float] = None,
temperature: Optional[float] = None,
penalty_score: Optional[float] = None,
endpoint: Optional[str] = None,
) -> BaseLLM:
try:
output = QianfanLLMEndpoint( # type: ignore
model=model,
qianfan_ak=qianfan_ak,
qianfan_sk=qianfan_sk,
top_p=top_p,
temperature=temperature,
penalty_score=penalty_score,
endpoint=endpoint,
)
except Exception as e:
raise ValueError("Could not connect to Baidu Qianfan API.") from e
return output # type: ignore

8
src/backend/langflow/components/vectorstores/Vectara.py
View file

@ -5,7 +5,6 @@ from langchain.vectorstores import Vectara
from langchain.schema import Document
from langchain.vectorstores.base import VectorStore
from langchain.schema import BaseRetriever
from langchain.embeddings.base import Embeddings
class VectaraComponent(CustomComponent):
@ -22,7 +21,6 @@ class VectaraComponent(CustomComponent):
"vectara_api_key": {"display_name": "Vectara API Key", "password": True},
"code": {"show": False},
"documents": {"display_name": "Documents"},
"embedding": {"display_name": "Embedding"},
}
def build(
@ -30,21 +28,21 @@ class VectaraComponent(CustomComponent):
vectara_customer_id: str,
vectara_corpus_id: str,
vectara_api_key: str,
embedding: Optional[Embeddings] = None,
documents: Optional[Document] = None,
) -> Union[VectorStore, BaseRetriever]:
# If documents, then we need to create a Vectara instance using .from_documents
if documents is not None and embedding is not None:
if documents is not None:
return Vectara.from_documents(
documents=documents, # type: ignore
vectara_customer_id=vectara_customer_id,
vectara_corpus_id=vectara_corpus_id,
vectara_api_key=vectara_api_key,
embedding=embedding,
source="langflow",
)
return Vectara(
vectara_customer_id=vectara_customer_id,
vectara_corpus_id=vectara_corpus_id,
vectara_api_key=vectara_api_key,
source="langflow",
)

2
src/backend/langflow/template/frontend_node/base.py
View file

@ -44,7 +44,7 @@ class FieldFormatters(BaseModel):
class FrontendNode(BaseModel):
template: Template
description: str
description: Optional[str] = None
base_classes: List[str]
name: str = ""
display_name: str = ""

5
src/frontend/src/contexts/tabsContext.tsx
View file

@ -260,9 +260,6 @@ export function TabsProvider({ children }: { children: ReactNode }) {
// simulate a click on the link element to trigger the download
link.click();
setNoticeData({
title: "Warning: Critical data, JSON file may include API keys.",
});
}
function downloadFlows() {
@ -300,7 +297,7 @@ export function TabsProvider({ children }: { children: ReactNode }) {
let fileData = JSON.parse(text);
if (fileData.flows) {
fileData.flows.forEach((flow: FlowType) => {
id = addFlow(flow, newProject);
id = addFlow(newProject, flow);
});
}
// parse the text into a JSON object

17
src/frontend/src/modals/exportModal/index.tsx
View file

@ -4,6 +4,7 @@ import IconComponent from "../../components/genericIconComponent";
import { Button } from "../../components/ui/button";
import { Checkbox } from "../../components/ui/checkbox";
import { EXPORT_DIALOG_SUBTITLE } from "../../constants/constants";
import { alertContext } from "../../contexts/alertContext";
import { TabsContext } from "../../contexts/tabsContext";
import { removeApiKeys } from "../../utils/reactflowUtils";
import BaseModal from "../baseModal";
@ -11,7 +12,8 @@ import BaseModal from "../baseModal";
const ExportModal = forwardRef(
(props: { children: ReactNode }, ref): JSX.Element => {
const { flows, tabId, downloadFlow } = useContext(TabsContext);
const [checked, setChecked] = useState(false);
const { setNoticeData } = useContext(alertContext);
const [checked, setChecked] = useState(true);
const flow = flows.find((f) => f.id === tabId);
useEffect(() => {
setName(flow!.name);
@ -44,6 +46,7 @@ const ExportModal = forwardRef(
<div className="mt-3 flex items-center space-x-2">
<Checkbox
id="terms"
checked={checked}
onCheckedChange={(event: boolean) => {
setChecked(event);
}}
@ -52,18 +55,26 @@ const ExportModal = forwardRef(
Save with my API keys
</label>
</div>
<span className="text-xs text-destructive">
Caution: Uncheck this box only removes API keys from fields
specifically designated for API keys.
</span>
</BaseModal.Content>
<BaseModal.Footer>
<Button
onClick={() => {
if (checked)
if (checked) {
downloadFlow(
flows.find((flow) => flow.id === tabId)!,
name!,
description
);
else
setNoticeData({
title:
"Warning: Critical data, JSON file may include API keys.",
});
} else
downloadFlow(
removeApiKeys(flows.find((flow) => flow.id === tabId)!),
name!,

2684
src/frontend/tests/onlyFront/assets/collection.json Normal file
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96
src/frontend/tests/onlyFront/assets/flow.json Normal file
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@ -0,0 +1,96 @@
{
"description": "Engineered for Excellence, Built for Business.",
"name": "Fluffy Sinoussi",
"data": {
"nodes": [
{
"id": "AgentInitializer-Zza0A",
"type": "genericNode",
"position": { "x": 595, "y": 224.25 },
"data": {
"type": "AgentInitializer",
"node": {
"template": {
"llm": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "llm",
"display_name": "LLM",
"advanced": false,
"dynamic": false,
"info": "",
"type": "BaseLanguageModel",
"list": false
},
"memory": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "memory",
"advanced": false,
"dynamic": false,
"info": "",
"type": "BaseChatMemory",
"list": false
},
"tools": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "tools",
"advanced": false,
"dynamic": false,
"info": "",
"type": "Tool",
"list": true
},
"agent": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"value": "zero-shot-react-description",
"password": false,
"options": [
"zero-shot-react-description",
"react-docstore",
"self-ask-with-search",
"conversational-react-description",
"openai-functions",
"openai-multi-functions"
],
"name": "agent",
"advanced": false,
"dynamic": false,
"info": "",
"type": "str",
"list": true
},
"_type": "initialize_agent"
},
"description": "Construct a zero shot agent from an LLM and tools.",
"base_classes": ["AgentExecutor", "function"],
"display_name": "AgentInitializer",
"custom_fields": {},
"output_types": [],
"documentation": "https://python.langchain.com/docs/modules/agents/agent_types/",
"beta": false,
"error": null
},
"id": "AgentInitializer-Zza0A"
},
"positionAbsolute": { "x": 595, "y": 224.25 }
}
],
"edges": [],
"viewport": { "x": 0, "y": 0, "zoom": 1 }
},
"id": "84c4b46f-063b-4d48-bf7f-6c668013064f"
}

87
src/frontend/tests/onlyFront/dragAndDrop.spec.ts Normal file
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@ -0,0 +1,87 @@
import { expect, test } from "@playwright/test";
import { readFileSync } from "fs";
test.describe("drag and drop test", () => {
/// <reference lib="dom"/>
test("drop collection", async ({ page }) => {
await page.routeFromHAR("harFiles/langflow.har", {
url: "**/api/v1/**",
update: false,
});
await page.route("**/api/v1/flows/", async (route) => {
const json = {
id: "e9ac1bdc-429b-475d-ac03-d26f9a2a3210",
};
await route.fulfill({ json, status: 201 });
});
await page.goto("http:localhost:3000/");
await page.locator("span").filter({ hasText: "My Collection" }).isVisible();
// Read your file into a buffer.
const jsonContent = readFileSync(
"tests/onlyFront/assets/collection.json",
"utf-8"
);
// Create the DataTransfer and File
const dataTransfer = await page.evaluateHandle((data) => {
const dt = new DataTransfer();
// Convert the buffer to a hex array
const file = new File([data], "collection.json", {
type: "application/json",
});
dt.items.add(file);
return dt;
}, jsonContent);
// Now dispatch
await page.dispatchEvent('//*[@id="root"]/div/div[2]/div[2]', "drop", {
dataTransfer,
});
expect(
await page
.locator(".main-page-flows-display")
.evaluate((el) => el.children)
).toBeTruthy();
});
test("drop flow", async ({ page }) => {
await page.routeFromHAR("harFiles/langflow.har", {
url: "**/api/v1/**",
update: false,
});
await page.route("**/api/v1/flows/", async (route) => {
const json = {
id: "e9ac1bdc-429b-475d-ac03-d26f9a2a3210",
};
await route.fulfill({ json, status: 201 });
});
await page.goto("http:localhost:3000/");
await page.locator("span").filter({ hasText: "My Collection" }).isVisible();
// Read your file into a buffer.
const jsonContent = readFileSync(
"tests/onlyFront/assets/flow.json",
"utf-8"
);
// Create the DataTransfer and File
const dataTransfer = await page.evaluateHandle((data) => {
const dt = new DataTransfer();
// Convert the buffer to a hex array
const file = new File([data], "flow.json", {
type: "application/json",
});
dt.items.add(file);
return dt;
}, jsonContent);
// Now dispatch
await page.dispatchEvent('//*[@id="root"]/div/div[2]/div[2]', "drop", {
dataTransfer,
});
expect(
await page
.locator(".main-page-flows-display")
.evaluate((el) => el.children)
).toBeTruthy();
});
});