Flowise

https://docs.flowiseai.com/
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This project provides in-depth guides on core Flowise functionalities.
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### Querying Documentation via GET Request

Source: https://docs.flowiseai.com/integrations/3rd-party-platform-integration/streamlit

This example shows how to query the documentation dynamically. Send a GET request to the page URL with an 'ask' query parameter containing your question in natural language to get a direct answer and relevant excerpts.

```bash
GET https://docs.flowiseai.com/integrations/3rd-party-platform-integration/streamlit.md?ask=<question>
```

--------------------------------

### Dynamic Documentation Query Example

Source: https://docs.flowiseai.com/integrations/llamaindex/response-synthesizer/tree-summarize

Example of how to perform an HTTP GET request to query the documentation dynamically. This is useful for retrieving specific information not explicitly present on the current page.

```http
GET https://docs.flowiseai.com/integrations/llamaindex/response-synthesizer/tree-summarize.md?ask=<question>
```

--------------------------------

### Agent Instructions: Querying Documentation

Source: https://docs.flowiseai.com/integrations/langchain/document-loaders/serpapi-for-web-search

Example of how to query the documentation dynamically using an HTTP GET request with an 'ask' query parameter.

```http
GET https://docs.flowiseai.com/integrations/langchain/document-loaders/serpapi-for-web-search.md?ask=<question>
```

--------------------------------

### Start Flowise Application

Source: https://docs.flowiseai.com/configuration/deployment/replit

Start the Flowise application after global installation using the npx command.

```bash
npx flowise start
```

--------------------------------

### Dynamic Documentation Query Example

Source: https://docs.flowiseai.com/configuration/deployment/hugging-face

Demonstrates how to perform an HTTP GET request to query the documentation dynamically by appending an 'ask' query parameter with a natural language question.

```http
GET https://docs.flowiseai.com/configuration/deployment/hugging-face.md?ask=<question>
```

--------------------------------

### Install Certbot and Dependencies

Source: https://docs.flowiseai.com/configuration/deployment/digital-ocean

Set up a virtual environment and install Certbot with the Nginx plugin to enable HTTPS. This requires Python 3.10+ virtual environment setup.

```bash
apt install python3.10-venv
sudo python3 -m venv /opt/certbot/
sudo /opt/certbot/bin/pip install --upgrade pip
```

```bash
sudo /opt/certbot/bin/pip install certbot certbot-nginx
```

--------------------------------

### Enable and Start Docker Service

Source: https://docs.flowiseai.com/configuration/deployment/aws

Enables the Docker service to start on boot and then starts the service.

```bash
sudo systemctl enable docker.service
```

```bash
sudo systemctl start docker.service
```

--------------------------------

### Dynamic Documentation Query Example

Source: https://docs.flowiseai.com/integrations/langchain/retrievers/multi-query-retriever

Perform an HTTP GET request to the current page URL with the 'ask' query parameter to dynamically query documentation. The question should be specific and in natural language.

```bash
GET https://docs.flowiseai.com/integrations/langchain/retrievers/multi-query-retriever.md?ask=<question>
```

--------------------------------

### Start Nginx Service

Source: https://docs.flowiseai.com/configuration/deployment/aws

Starts the Nginx service.

```bash
sudo systemctl start nginx
```

--------------------------------

### Start Node Configuration

Source: https://docs.flowiseai.com/tutorials/agentic-rag

Configure the Start node to accept chat input and initialize a 'query' state variable.

```json
{
  "name": "Start",
  "type": "start",
  "template": {
    "inputtype": "chat input",
    "flowstate": [
      {
        "key": "query",
        "value": ""
      }
    ]
  }
}
```

--------------------------------

### Install Nginx

Source: https://docs.flowiseai.com/configuration/deployment/aws

Installs the Nginx web server using yum.

```bash
sudo yum install nginx
```

--------------------------------

### Install Docker

Source: https://docs.flowiseai.com/configuration/deployment/digital-ocean

Installs Docker and Docker Compose on your Droplet. Ensure you have root privileges for these commands.

```bash
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
```

```bash
sudo curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
```

--------------------------------

### Install Git

Source: https://docs.flowiseai.com/configuration/deployment/aws

Installs Git on the system using yum with the -y flag to automatically confirm installation.

```bash
sudo yum install git -y
```

--------------------------------

### Install Dependencies and Build Flowise

Source: https://docs.flowiseai.com/configuration/deployment/gcp

Installs project dependencies using pnpm and builds the Flowise application.

```bash
cd Flowise
pnpm install
pnpm build
```

--------------------------------

### Install Docker Compose Plugin

Source: https://docs.flowiseai.com/configuration/deployment/aws

Installs the Docker Compose plugin using yum.

```bash
sudo yum install docker-compose-plugin
```

--------------------------------

### Install Nginx

Source: https://docs.flowiseai.com/configuration/deployment/digital-ocean

Installs Nginx on your Droplet and allows HTTP traffic through the firewall. Verify Nginx status afterwards.

```bash
sudo apt update
sudo apt install nginx
```

```bash
sudo ufw allow 'Nginx HTTP'
```

```bash
systemctl status nginx
```

--------------------------------

### Embed Configuration Example

Source: https://docs.flowiseai.com/using-flowise/embed

Example of how to configure the theme, date/time toggles, and footer for the Flowise embeddable chat widget.

```javascript
new FlowiseChatEmbed({
    theme: {
      chatWindowHeader: {
        title: 'Flowise AI',
        color: '#303235',
      },
      dateTimeToggle: {
        date: true,
        time: true,
      },
      footer: {
        textColor: '#303235',
        text: 'Powered by',
        company: 'Flowise',
        companyLink: 'https://flowiseai.com',
      },
    },
  });
```

--------------------------------

### Start Main Server

Source: https://docs.flowiseai.com/configuration/running-flowise-using-queue

Run the main Flowise server. Ensure environment variables are configured.

```bash
pnpm start
```

--------------------------------

### Install Docker on AWS EC2

Source: https://docs.flowiseai.com/configuration/deployment/aws

Commands to update the system, search for Docker, get version info, and install Docker on an AWS EC2 instance using yum.

```bash
sudo yum update
```

```bash
sudo yum search docker
```

```bash
sudo yum info docker
```

```bash
sudo yum install docker
```

--------------------------------

### Start Flowise with Username and Password

Source: https://docs.flowiseai.com/configuration/authorization/app-level

Starts the Flowise application with specified username and password credentials for authentication. This method is deprecated but useful for initial setup or specific environments.

```bash
npx flowise start --FLOWISE_USERNAME=user --FLOWISE_PASSWORD=1234
```

--------------------------------

### Agent Tool Usage Example

Source: https://docs.flowiseai.com/integrations/langchain/tools/google-drive

Demonstrates how to use the Google Drive tool with an Agent, allowing for intelligent selection of actions and optional parameter overrides.

```text
GET https://docs.flowiseai.com/integrations/langchain/tools/google-drive.md?ask=<question>
```

--------------------------------

### Environment Variables for Self-Hosted Enterprise Admin Setup

Source: https://docs.flowiseai.com/using-flowise/workspaces

These environment variables are required for setting up an admin account in a self-hosted enterprise Flowise installation. Ensure these are configured before proceeding with the admin setup.

```environment
JWT_AUTH_TOKEN_SECRET
JWT_REFRESH_TOKEN_SECRET
JWT_ISSUER
JWT_AUDIENCE
JWT_TOKEN_EXPIRY_IN_MINUTES
JWT_REFRESH_TOKEN_EXPIRY_IN_MINUTES
PASSWORD_RESET_TOKEN_EXPIRY_IN_MINS
PASSWORD_SALT_HASH_ROUNDS
TOKEN_HASH_SECRET
```

--------------------------------

### Start OpenTelemetry Collector with Docker Compose

Source: https://docs.flowiseai.com/using-flowise/monitoring

Use this command to start the OpenTelemetry Collector container. Navigate to the 'Flowise/metrics/otel' directory before running the command. This setup uses the provided 'otel.config.yml' for collector configuration.

```bash
cd Flowise
cd metrics && cd otel
docker compose up -d
```

--------------------------------

### Query Documentation with Momento Cache

Source: https://docs.flowiseai.com/integrations/langchain/cache/momento-cache

Demonstrates how to perform an HTTP GET request to query the documentation dynamically. This is useful for retrieving specific information not explicitly present on the page.

```bash
GET https://docs.flowiseai.com/integrations/langchain/cache/momento-cache.md?ask=<question>
```

--------------------------------

### Dockerfile for Hugging Face Space

Source: https://docs.flowiseai.com/configuration/deployment/hugging-face

This Dockerfile installs Node.js, Flowise, and necessary dependencies, configuring directories and setting up the command to start Flowise.

```dockerfile
FROM node:20-alpine
USER root

# Arguments that can be passed at build time
ARG FLOWISE_PATH=/usr/local/lib/node_modules/flowise
ARG BASE_PATH=/root/.flowise
ARG DATABASE_PATH=$BASE_PATH
ARG SECRETKEY_PATH=$BASE_PATH
ARG LOG_PATH=$BASE_PATH/logs
ARG BLOB_STORAGE_PATH=$BASE_PATH/storage

# Install dependencies
RUN apk add --no-cache git python3 py3-pip make g++ build-base cairo-dev pango-dev chromium

ENV PUPPETEER_SKIP_DOWNLOAD=true
ENV PUPPETEER_EXECUTABLE_PATH=/usr/bin/chromium-browser

# Install Flowise globally
RUN npm install -g flowise

# Configure Flowise directories using the ARG
RUN mkdir -p $LOG_PATH $FLOWISE_PATH/uploads && chmod -R 777 $LOG_PATH $FLOWISE_PATH

WORKDIR /data

CMD ["npx", "flowise", "start"]
```

--------------------------------

### Query Documentation with ChatGroq

Source: https://docs.flowiseai.com/integrations/llamaindex/chat-models/chatgroq

Demonstrates how to perform an HTTP GET request to query the documentation dynamically. Use this when the answer is not explicitly present on the page or for clarification.

```bash
GET https://docs.flowiseai.com/integrations/llamaindex/chat-models/chatgroq.md?ask=<question>
```

--------------------------------

### Customer Support Interaction Examples

Source: https://docs.flowiseai.com/tutorials/customer-support

Provide examples of how the agent should respond to various customer queries. This helps the agent understand the desired tone, information to provide, and how to handle different scenarios.

```text
Here are a few examples of how you can interact with customers:

&#x3C;example 1>
H: Hi, do you organize birthday parties for kids?

A: Hi there! I'm John from Acme Events—thanks for reaching out. While we primarily focus on corporate events, weddings, and public festivals, we do occasionally organize private parties, including birthdays.

Could you share more details about the party you're planning (e.g., number of guests, location, and date)? I'd be happy to see how we can help!
&#x3C;/example 1>

&#x3C;example 2>
H: What are your business hours?

A: Our business hours are Monday to Friday, 9 AM to 5 PM EST.

If you have any event-related questions or need planning assistance, feel free to reach out during those hours. How can I assist you today?
&#x3C;/example 2>

&#x3C;example 3>
H: Do you handle hybrid conferences?

A: Absolutely! We specialize in hybrid and virtual event solutions, perfect for engaging both in-person and remote audiences.

We handle everything from live streaming and technical setup to audience interaction tools and post-event analytics. Are you planning a hybrid event soon? I’d love to hear more about your requirements.
&#x3C;/example 3>

&#x3C;example 4>
H: I’m looking to plan a wedding. Can you help with that?

A: Congratulations! Yes, we’d love to help you plan your big day. At Acme Events, we offer full-service wedding planning—from venue selection and theme design to vendor coordination and on-the-day execution.

Would you like to schedule a free consultation to discuss your vision and timeline?
&#x3C;/example 4>

&#x3C;example 5>
H: Can I get a quote for a corporate event?

A: Of course! To prepare an accurate quote, I’ll need a few details:

1. What type of corporate event are you planning (e.g., seminar, gala, retreat)?

<strong>2. Estimated number of attendees?
</strong>
3. Preferred date and location?

4. Any specific services you need (e.g., catering, AV setup, branding)?

Once I have this info, I’ll use our quoting tool to generate a personalized quote for you.
&#x3C;/example 5>
```

--------------------------------

### Get All Upsert History Records

Source: https://docs.flowiseai.com/api-reference/upsert-history

Retrieve all upsert history records. Supports filtering by chatflow ID, sort order, start date, and end date.

```json
{
  "tags": [
    {
      "name": "upsert-history"
    }
  ],
  "security": [
    {
      "bearerAuth": []
    }
  ],
  "components": {
    "securitySchemes": {
      "bearerAuth": {
        "type": "http",
        "scheme": "bearer",
        "bearerFormat": "JWT"
      }
    },
    "schemas": {
      "UpsertHistoryResponse": {
        "type": "object",
        "properties": {
          "id": {
            "type": "string",
            "format": "uuid",
            "description": "Unique identifier for the upsert history record"
          },
          "chatflowid": {
            "type": "string",
            "description": "ID of the chatflow associated with the upsert history"
          },
          "result": {
            "type": "string",
            "description": "Result of the upsert operation, stored as a JSON string"
          },
          "flowData": {
            "type": "string",
            "description": "Flow data associated with the upsert operation, stored as a JSON string"
          },
          "date": {
            "type": "string",
            "format": "date-time",
            "description": "Date and time when the upsert operation was performed"
          }
        }
      }
    }
  },
  "paths": {
    "/upsert-history/{id}": {
      "get": {
        "tags": [
          "upsert-history"
        ],
        "summary": "Get all upsert history records",
        "description": "Retrieve all upsert history records with optional filters",
        "operationId": "getAllUpsertHistory",
        "parameters": [
          {
            "in": "path",
            "name": "id",
            "required": true,
            "schema": {
              "type": "string"
            },
            "description": "Chatflow ID to filter records by"
          },
          {
            "in": "query",
            "name": "order",
            "required": false,
            "schema": {
              "type": "string",
              "enum": [
                "ASC",
                "DESC"
              ],
              "default": "ASC"
            },
            "description": "Sort order of the results (ascending or descending)"
          },
          {
            "in": "query",
            "name": "startDate",
            "required": false,
            "schema": {
              "type": "string",
              "format": "date-time"
            },
            "description": "Filter records from this start date (inclusive)"
          },
          {
            "in": "query",
            "name": "endDate",
            "required": false,
            "schema": {
              "type": "string",
              "format": "date-time"
            },
            "description": "Filter records until this end date (inclusive)"
          }
        ],
        "responses": {
          "200": {
            "description": "Successful operation",
            "content": {
              "application/json": {
                "schema": {
                  "type": "array",
                  "items": {
                    "$ref": "#/components/schemas/UpsertHistoryResponse"
                  }
                }
              }
            }
          },
          "500": {
            "description": "Internal server error"
          }
        }
      }
    }
  }
}
```

--------------------------------

### Multiple Files Processing Example

Source: https://docs.flowiseai.com/integrations/langchain/document-loaders/text-file

Shows how to configure the Text File loader for processing multiple files, including specifying metadata and omitting certain metadata keys.

```json
{
  "txtFile": ["doc1.txt", "doc2.md", "code.py"],
  "metadata": {
    "batch": "docs-2024",
    "processor": "text-loader"
  },
  "omitMetadataKeys": "source, timestamp"
}
```

--------------------------------

### Routing Condition Setup

Source: https://docs.flowiseai.com/tutorials/supervisor-and-workers

Configure condition nodes to route the workflow based on the supervisor's decision stored in the flow state. This example sets up conditions for 'SOFTWARE' and 'REVIEWER' workers, with an 'Else' branch for 'FINISH'.

```json
{
  "condition": "{{ $flow.state.next }}",
  "value": "SOFTWARE"
}
```

```json
{
  "condition": "{{ $flow.state.next }}",
  "value": "REVIEWER"
}
```

--------------------------------

### Upload Image by URL Example

Source: https://docs.flowiseai.com/using-flowise/prediction

This Python code snippet demonstrates how to call the `upload_image_url` function to upload an image from a public URL to Flowise for analysis. Ensure you have the Flowise client library installed and replace 'your-flow-id' with your actual Flowise flow ID.

```python
result = upload_image_url(
    flow_id="your-flow-id",
    question="What's in this image? Analyze it in detail.",
    image_url="https://example.com/path/to/image.jpg",
    image_name="example_image.jpg"
)

print(result)
```

--------------------------------

### Query Documentation with AutoGPT

Source: https://docs.flowiseai.com/integrations/langchain/agents/autogpt

Use this GET request to ask questions about the documentation dynamically. Replace <question> with your specific query.

```bash
GET https://docs.flowiseai.com/integrations/langchain/agents/autogpt.md?ask=<question>
```

--------------------------------

### Query Documentation via HTTP GET

Source: https://docs.flowiseai.com/integrations/langchain/llms/ollama

Use this method to ask questions about the documentation dynamically. The question should be specific and in natural language. The response includes the answer and relevant excerpts.

```bash
GET https://docs.flowiseai.com/integrations/langchain/llms/ollama.md?ask=<question>
```

--------------------------------

### Query Documentation with GET Request

Source: https://docs.flowiseai.com/integrations/langchain/embeddings/openai-embeddings

To get additional information not directly available on the page, perform an HTTP GET request to the current page URL with the 'ask' query parameter. The question should be specific and in natural language. Use this to get direct answers, clarifications, or related documentation.

```bash
GET https://docs.flowiseai.com/integrations/langchain/embeddings/openai-embeddings.md?ask=<question>
```

--------------------------------

### Setup Certbot Virtual Environment

Source: https://docs.flowiseai.com/configuration/deployment/aws

Creates a virtual environment for Certbot using Python 3 and upgrades pip within that environment.

```bash
sudo python3 -m venv /opt/certbot/
sudo /opt/certbot/bin/pip install --upgrade pip
```

--------------------------------

### Querying Documentation via HTTP GET

Source: https://docs.flowiseai.com/integrations/llamaindex/response-synthesizer/refine

To get additional information not directly present on the page, perform an HTTP GET request with an 'ask' query parameter. The question should be specific and self-contained.

```http
GET https://docs.flowiseai.com/integrations/llamaindex/response-synthesizer/refine.md?ask=<question>
```

--------------------------------

### Starting Flowise with NPM and Custom Port/Debug

Source: https://docs.flowiseai.com/configuration/environment-variables

Use the 'npx flowise start' command to run Flowise and set custom environment variables like PORT and DEBUG.

```bash
npx flowise start --PORT=3000 --DEBUG=true
```

--------------------------------

### Querying Documentation via HTTP GET

Source: https://docs.flowiseai.com/integrations/langchain/chat-models/chatcometapi

To get information not directly on the page, perform an HTTP GET request to the current page URL with an 'ask' query parameter containing your question.

```bash
GET https://docs.flowiseai.com/integrations/langchain/chat-models/chatcometapi.md?ask=<question>
```

--------------------------------

### Start LiteLLM Proxy with Docker

Source: https://docs.flowiseai.com/integrations/litellm

Starts the LiteLLM proxy service using Docker, mounting a local configuration file and exposing the proxy on port 4000. The --detailed_debug flag enables verbose logging.

```shell
docker run \
    -v $(pwd)/litellm_config.yaml:/app/config.yaml \
    -p 4000:4000 \
    ghcr.io/berriai/litellm:main-latest \
    --config /app/config.yaml --detailed_debug
```

--------------------------------

### Query Documentation via GET Request

Source: https://docs.flowiseai.com/integrations/langchain/embeddings/localai-embeddings

To get additional information not present on the page, make an HTTP GET request to the page URL with an 'ask' query parameter. The question should be specific and self-contained.

```http
GET https://docs.flowiseai.com/integrations/langchain/embeddings/localai-embeddings.md?ask=<question>
```

--------------------------------

### Querying Documentation via HTTP GET

Source: https://docs.flowiseai.com/integrations/langchain/document-loaders/custom-document-loader

To get additional information not directly on the page, perform an HTTP GET request to the current page URL with the 'ask' query parameter followed by your question.

```http
GET https://docs.flowiseai.com/integrations/langchain/document-loaders/custom-document-loader.md?ask=<question>
```

--------------------------------

### Query Documentation via HTTP GET

Source: https://docs.flowiseai.com/integrations/langchain/cache/upstash-redis-cache

Demonstrates how to perform an HTTP GET request to query the documentation dynamically. This is useful for retrieving specific information not immediately apparent on the page.

```bash
GET https://docs.flowiseai.com/integrations/langchain/cache/upstash-redis-cache.md?ask=<question>
```

--------------------------------

### Configure Start Node for Email Inquiry

Source: https://docs.flowiseai.com/tutorials/human-in-the-loop

Set up the Start node with 'Form Input' to capture structured email data including subject, body, and sender.

```json
{
  "inputType": "Form Input",
  "formTitle": "Email Inquiry",
  "formDescription": "Incoming email inquiry",
  "formInputTypes": [
    {
      "name": "Subject",
      "type": "String"
    },
    {
      "name": "Body",
      "type": "String"
    },
    {
      "name": "From",
      "type": "String"
    }
  ]
}
```

--------------------------------

### SerpApi Additional Metadata Example

Source: https://docs.flowiseai.com/integrations/langchain/document-loaders/serpapi-for-web-search

Example of additional metadata to be included with search results.

```typescript
metadata: {
  "source": "serpapi",
  "category": "tech",
  "timestamp": "2024-03-21"
}
```

--------------------------------

### SerpApi Search Query Example

Source: https://docs.flowiseai.com/integrations/langchain/document-loaders/serpapi-for-web-search

Example of a search query to be used with the SerpApi loader.

```typescript
query: "artificial intelligence latest developments"
```

--------------------------------

### Querying Documentation with AWS ChatBedrock

Source: https://docs.flowiseai.com/integrations/langchain/chat-models/aws-chatbedrock

Demonstrates how to perform an HTTP GET request to query documentation dynamically using the 'ask' query parameter. This is useful for retrieving specific information or clarification not directly present on the page.

```http
GET https://docs.flowiseai.com/integrations/langchain/chat-models/aws-chatbedrock.md?ask=<question>
```

--------------------------------

### Query Documentation with GET Request

Source: https://docs.flowiseai.com/integrations/llamaindex/embeddings/openai-embedding

To get additional information not present on the page, perform an HTTP GET request to the current page URL with the 'ask' query parameter. The question should be specific and in natural language.

```http
GET https://docs.flowiseai.com/integrations/llamaindex/embeddings/openai-embedding.md?ask=<question>
```

--------------------------------

### Querying Documentation via GET Request

Source: https://docs.flowiseai.com/integrations/langchain/tools/google-sheets

To get additional information not directly on the page, make an HTTP GET request to the current page URL with the 'ask' query parameter. The question should be specific and self-contained.

```http
GET https://docs.flowiseai.com/integrations/langchain/tools/google-sheets.md?ask=<question>
```

--------------------------------

### Terraform Initialization Command

Source: https://docs.flowiseai.com/configuration/deployment/azure

Initializes the Terraform project, downloading necessary plugins and binary files.

```bash
terraform init
```

--------------------------------

### Query Documentation Dynamically

Source: https://docs.flowiseai.com/integrations/3rd-party-platform-integration

Use this method to ask questions about the documentation. Perform an HTTP GET request on the current page URL with the 'ask' query parameter. The question should be specific and in natural language.

```bash
GET https://docs.flowiseai.com/integrations/3rd-party-platform-integration.md?ask=<question>
```

--------------------------------

### Query Documentation via HTTP GET

Source: https://docs.flowiseai.com/integrations/langchain/embeddings/aws-bedrock-embeddings

To get information not explicitly present on the page, perform an HTTP GET request to the current page URL with the 'ask' query parameter. The question should be specific and self-contained.

```bash
GET https://docs.flowiseai.com/integrations/langchain/embeddings/aws-bedrock-embeddings.md?ask=<question>
```

--------------------------------

### Querying Documentation Dynamically

Source: https://docs.flowiseai.com/integrations/langchain/tools/retriever-tool

Use this GET request to ask specific questions about the documentation. The response includes direct answers and relevant excerpts.

```http
GET https://docs.flowiseai.com/integrations/langchain/tools/retriever-tool.md?ask=<question>
```

--------------------------------

### Query Documentation with GET Request

Source: https://docs.flowiseai.com/integrations/langchain/document-loaders/cheerio-web-scraper

To get additional information not directly present on the page, perform an HTTP GET request to the current page URL with the 'ask' query parameter. The question should be specific and self-contained.

```bash
GET https://docs.flowiseai.com/integrations/langchain/document-loaders/cheerio-web-scraper.md?ask=<question>
```
Tokens:
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