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Personal Knowledge Assistant: Extract & Retrieve Summaries via Telegram & Google Sheets
https://n8nworkflows.xyz/workflows/personal-knowledge-assistant--extract---retrieve-summaries-via-telegram---google-sheets-7215
# Personal Knowledge Assistant: Extract & Retrieve Summaries via Telegram & Google Sheets
### 1. Workflow Overview
This workflow, titled **Personal Knowledge Assistant: Extract & Retrieve Summaries via Telegram & Google Sheets**, automates the extraction, summarization, storage, and retrieval of knowledge from YouTube videos and article links. It is designed for users who want to build a personal knowledge base by:
- Automatically extracting transcripts or article content from URLs added to a Google Sheet,
- Using Google Gemini AI models to summarize the content,
- Storing summarized data in a structured Google Sheet,
- Enabling querying of the stored summaries via Telegram chat.
The workflow is logically divided into three main functional blocks:
- **1.1 Input Reception and Query Handling:** Accepts user requests through Telegram and queries stored summaries in Google Sheets.
- **1.2 Data Ingestion and Processing:** Detects new URLs added to a Google Sheet, fetches content (YouTube transcripts or article data), and processes it.
- **1.3 Summarization and Storage:** Uses Google Gemini AI models to generate refined summaries and appends them back to Google Sheets for future querying.
---
### 2. Block-by-Block Analysis
#### 1.1 Input Reception and Query Handling
**Overview:**
This block manages incoming user queries via Telegram, retrieves relevant data from Google Sheets, and responds with the best-matched summary or an apology if no data is found.
**Nodes Involved:**
- Telegram Trigger
- AI Agent
- Google Gemini Chat Model
- Get row(s) in sheet in Google Sheets
- Send a text message
**Node Details:**
- **Telegram Trigger**
- *Type:* Trigger node for Telegram messages
- *Role:* Listens for incoming Telegram messages (updates of type "message").
- *Configuration:* Uses Telegram API credentials connected to a Telegram bot.
- *Inputs:* Incoming Telegram messages
- *Outputs:* Passes message JSON to AI Agent
- *Edge cases:* Telegram API connectivity issues, user sending unsupported message types.
- **AI Agent**
- *Type:* LangChain Agent for AI-driven conversation
- *Role:* Processes user message text to query stored summaries.
- *Configuration:* Prompts the AI to answer using Google Sheets data or apologize politely if no data found.
- *Key Expression:* `User Request: {{ $json.message.text }}`
- *Inputs:* Telegram message JSON
- *Outputs:* AI-generated response text
- *Dependencies:* Uses "Google Gemini Chat Model" as language model and "Get row(s) in sheet in Google Sheets" as tool to search data.
- *Edge cases:* AI model response errors, empty or ambiguous user queries, token limits.
- **Google Gemini Chat Model**
- *Type:* Google PaLM AI chat model
- *Role:* Provides AI language comprehension and generation for the AI Agent.
- *Configuration:* Uses Google Gemini API credentials.
- *Inputs:* Text prompts from AI Agent
- *Outputs:* AI-generated text responses
- *Edge cases:* API quota exceeded, authentication errors.
- **Get row(s) in sheet in Google Sheets**
- *Type:* Google Sheets node (read operation)
- *Role:* Searches for relevant rows matching user queries in the Google Sheet "YouTube Video and Article Data."
- *Configuration:* Reads from Sheet1 (gid=0) within the specified spreadsheet.
- *Inputs:* Query parameters from AI Agent
- *Outputs:* Rows matching query for AI Agent to process
- *Edge cases:* API authentication errors, empty sheet, query mismatch.
- **Send a text message**
- *Type:* Telegram node for sending messages
- *Role:* Sends AI Agent's response back to the Telegram chat.
- *Configuration:* Uses Telegram bot credentials; sends message text from AI Agent output; targets chat ID from Telegram Trigger.
- *Inputs:* AI Agent response text
- *Outputs:* Message sent confirmation
- *Edge cases:* Telegram API errors, rate limits.
---
#### 1.2 Data Ingestion and Processing
**Overview:**
This block detects when a new URL is added to a designated Google Sheet, filters for new entries, and determines if the URL is a YouTube video or an article link to fetch the appropriate content.
**Nodes Involved:**
- Google Sheets Trigger
- Filter
- If
- HTTP Request (YouTube transcript fetch)
- HTTP Request1 (Article fetch)
**Node Details:**
- **Google Sheets Trigger**
- *Type:* Trigger node for Google Sheets row additions
- *Role:* Monitors a specific sheet (Sheet2, gid=800289465) for new rows added containing URLs.
- *Configuration:* Polls every minute, uses Google Sheets OAuth credentials.
- *Inputs:* New rows added to Sheet2
- *Outputs:* New row JSON data forwarded to Filter.
- *Edge cases:* Polling delays, API rate limiting.
- **Filter**
- *Type:* Filter node
- *Role:* Ensures the incoming row has a URL and has not been marked as "Stored" (i.e., new, unprocessed).
- *Configuration:*
- Checks that "URL " field exists and is non-empty.
- Checks that "Stored" field is empty.
- *Inputs:* New row data from Google Sheets Trigger
- *Outputs:* Passes only new, unprocessed URLs to "If" node.
- *Edge cases:* Malformed or missing fields, empty rows.
- **If**
- *Type:* Conditional node
- *Role:* Determines if the URL corresponds to a YouTube video link or an article.
- *Configuration:* Checks if the URL contains "youtu.be" or "youtube.com".
- *Inputs:* Filtered row JSON with URL
- *Outputs:*
- True branch to YouTube transcript HTTP Request
- False branch to article HTTP Request
- *Edge cases:* URLs not matching either criteria, malformed URLs.
- **HTTP Request (YouTube transcript fetch)**
- *Type:* HTTP POST request node
- *Role:* Calls Apify Actor "YouTube Transcript Ninja" endpoint to retrieve transcript JSON for the YouTube URL.
- *Configuration:*
- POST with JSON body including language (English), no timestamps, and startUrls with the URL parameter.
- URL is parameterized with the new row's "URL ".
- *Inputs:* YouTube URL from If node
- *Outputs:* Transcript JSON to Information Extractor1.
- *Edge cases:* Apify API key missing or invalid, transcript not available, HTTP errors.
- **HTTP Request1 (Article fetch)**
- *Type:* HTTP GET request node
- *Role:* Fetches raw article HTML/data from the provided URL.
- *Configuration:* Direct GET request to the URL from the Google Sheet.
- *Inputs:* Article URL from If node
- *Outputs:* HTML content to Markdown node.
- *Edge cases:* HTTP errors, invalid URLs, timeouts, non-HTML responses.
---
#### 1.3 Summarization and Storage
**Overview:**
This block converts raw content (YouTube transcripts or article HTML) into summaries using Google Gemini AI models, then stores the summaries and metadata back into Google Sheets to build a structured knowledge base.
**Nodes Involved:**
- Information Extractor1 (YouTube transcript)
- Google Gemini Chat Model2 (YouTube)
- Append row in sheet (YouTube)
- Append or update row in sheet (YouTube)
- Markdown (Article HTML to text)
- Information Extractor (Article)
- Google Gemini Chat Model1 (Article)
- Append row in sheet1 (Article)
- Append or update row in sheet2 (Article)
**Node Details:**
- **Markdown**
- *Type:* Markdown node
- *Role:* Converts raw HTML article content into markdown/plain text for summarization.
- *Configuration:* Uses the field `data` from HTTP Request1 node as HTML input.
- *Inputs:* HTML content from HTTP Request1
- *Outputs:* Markdown/plain text to Information Extractor.
- *Edge cases:* Malformed HTML, empty content.
- **Information Extractor1 (YouTube transcript)**
- *Type:* LangChain Information Extractor
- *Role:* Extracts structured information (Title and refined summary) from YouTube transcript text.
- *Configuration:*
- Text template: `"YouTube Video Transcript: {{ $json.transcript }}"`
- Extracts two required attributes:
- Title (a title for the video)
- Article Refined Data (detailed summary, with instruction not to start with "this video")
- *Inputs:* Transcript JSON from HTTP Request
- *Outputs:* Structured extracted data to Append row in sheet.
- *AI Model:* Uses Google Gemini Chat Model2 as AI language model.
- *Edge cases:* Extraction failures, incomplete transcripts.
- **Google Gemini Chat Model2**
- *Type:* Google PaLM AI chat model
- *Role:* Provides AI summarization for YouTube transcripts.
- *Configuration:* Connected to Information Extractor1.
- *Edge cases:* API errors, quota limits.
- **Append row in sheet (YouTube)**
- *Type:* Google Sheets append operation
- *Role:* Appends extracted video title and summary to Sheet1 (main data sheet).
- *Configuration:* Columns: Title and Data (summary).
- *Inputs:* Output from Information Extractor1
- *Outputs:* Confirmation to Append or update row in sheet.
- *Edge cases:* API errors, permission issues.
- **Append or update row in sheet (YouTube)**
- *Type:* Google Sheets append or update operation
- *Role:* Updates Sheet2 to mark the URL as "Stored" ✅ indicating processing complete.
- *Configuration:* Matches rows by "URL " column; sets "Stored" to "✅".
- *Inputs:* URL from Google Sheets Trigger node JSON.
- *Outputs:* Final confirmation.
- *Edge cases:* Concurrency issues, partial updates.
- **Information Extractor (Article)**
- *Type:* LangChain Information Extractor
- *Role:* Extracts structured information (Title and refined summary) from article markdown text.
- *Configuration:*
- Text template: `"Article: {{ $json.data }}"`
- Extracts two attributes similar to YouTube extractor: Title and Article Refined Data.
- *Inputs:* Markdown text from Markdown node
- *Outputs:* Structured extracted data to Append row in sheet1.
- *AI Model:* Uses Google Gemini Chat Model1 as AI language model.
- *Edge cases:* Extraction quality affected by markdown conversion.
- **Google Gemini Chat Model1**
- *Type:* Google PaLM AI chat model
- *Role:* Summarizes article content for Information Extractor.
- *Configuration:* Connected to Information Extractor.
- *Edge cases:* API limits.
- **Append row in sheet1 (Article)**
- *Type:* Google Sheets append operation
- *Role:* Appends article title and summary to Sheet1.
- *Inputs:* Extractor output
- *Outputs:* Confirmation to Append or update row in sheet2.
- *Edge cases:* Same as Append row in sheet (YouTube).
- **Append or update row in sheet2 (Article)**
- *Type:* Google Sheets append or update operation
- *Role:* Marks the article URL as "Stored" ✅ in Sheet2 to avoid reprocessing.
- *Inputs:* URL from Google Sheets Trigger JSON
- *Outputs:* Final confirmation.
- *Edge cases:* Same as YouTube append/update node.
---
### 3. Summary Table
| Node Name | Node Type | Functional Role | Input Node(s) | Output Node(s) | Sticky Note |
|---------------------------|----------------------------------------|----------------------------------------------|-----------------------------|-------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Telegram Trigger | telegramTrigger | Listen to Telegram messages | — | AI Agent | |
| AI Agent | langchain.agent | Process user query, search Google Sheets | Telegram Trigger, Google Gemini Chat Model, Get row(s) in sheet in Google Sheets | Send a text message | |
| Google Gemini Chat Model | langchain.lmChatGoogleGemini | AI language model for AI Agent | AI Agent | AI Agent | |
| Get row(s) in sheet in Google Sheets | googleSheetsTool | Search stored summaries in Google Sheets | AI Agent | AI Agent | |
| Send a text message | telegram | Reply to Telegram user | AI Agent | — | |
| Google Sheets Trigger | googleSheetsTrigger | Trigger on new URLs added to Sheet2 | — | Filter | |
| Filter | filter | Filter new URLs not yet processed | Google Sheets Trigger | If | |
| If | if | Detect if URL is YouTube or Article | Filter | HTTP Request (YouTube), HTTP Request1 (Article) | |
| HTTP Request | httpRequest | Fetch YouTube transcript via Apify API | If (YouTube branch) | Information Extractor1 | |
| Information Extractor1 | langchain.informationExtractor | Extract title and summary from transcript | HTTP Request | Append row in sheet | |
| Google Gemini Chat Model2 | langchain.lmChatGoogleGemini | Summarize YouTube transcript | Information Extractor1 | Information Extractor1 | |
| Append row in sheet | googleSheets | Append YouTube summary to Sheet1 | Information Extractor1 | Append or update row in sheet | |
| Append or update row in sheet | googleSheets | Mark YouTube URL as stored in Sheet2 | Append row in sheet | — | |
| HTTP Request1 | httpRequest | Fetch article HTML content | If (Article branch) | Markdown | |
| Markdown | markdown | Convert HTML article to markdown | HTTP Request1 | Information Extractor | |
| Information Extractor | langchain.informationExtractor | Extract title and summary from article | Markdown | Append row in sheet1 | |
| Google Gemini Chat Model1 | langchain.lmChatGoogleGemini | Summarize article content | Information Extractor | Information Extractor | |
| Append row in sheet1 | googleSheets | Append article summary to Sheet1 | Information Extractor | Append or update row in sheet2 | |
| Append or update row in sheet2 | googleSheets | Mark article URL as stored in Sheet2 | Append row in sheet1 | — | |
| Sticky Note | stickyNote | Note: Requesting Data | — | — | # Requesting Data |
| Sticky Note1 | stickyNote | Note: Storing Data through YouTube/Article URLs | — | — | # Storing Data through YouTube Video URL / Article Link |
| Sticky Note2 | stickyNote | Setup guide | — | — | # 🛠 Setup Guide ...\n\nAuthor: [Rakin Jakaria](https://www.youtube.com/@rakinjakaria)\n\nLinks to Telegram Bot, Apify, Gemini, Google Sheets provided |
| Sticky Note3 | stickyNote | Purpose of Agent | — | — | # 1️⃣ Purpose of This Agent\n\nExtract summaries, store, and query via Telegram. |
| Sticky Note4 | stickyNote | How to Use | — | — | # 2️⃣ How to Use\n\nAdd URLs to Sheet2, auto-process, query via Telegram. |
---
### 4. Reproducing the Workflow from Scratch
1. **Create Telegram Trigger node**
- Type: Telegram Trigger
- Configure with your Telegram bot credentials.
- Set to listen for "message" updates.
2. **Create Google Gemini Chat Model node (for AI Agent)**
- Type: LangChain Google Gemini Chat Model
- Configure with your Google PaLM API credentials.
3. **Create Get row(s) in sheet in Google Sheets node**
- Type: Google Sheets Tool (read)
- Connect to your Google Sheets account with OAuth2.
- Configure to read from your knowledge base sheet (Sheet1, gid=0) in your specific spreadsheet ID.
4. **Create AI Agent node**
- Type: LangChain Agent
- Set input text to `User Request: {{ $json.message.text }}`
- System message: "when user asks something give the answer from the given google sheet and after searching the google sheet if you don't found then politely apologies to the user."
- Add the Google Gemini Chat Model as AI language model.
- Add the Google Sheets read node as a tool.
- Connect Telegram Trigger to AI Agent.
5. **Create Send a text message node**
- Type: Telegram
- Configure credentials same as Telegram Trigger.
- Set chatId to `{{ $('Telegram Trigger').item.json.message.chat.id }}`
- Set text to `{{ $json.output }}` from AI Agent.
- Connect AI Agent to this node.
6. **Create Google Sheets Trigger node**
- Type: Google Sheets Trigger
- Configure OAuth2 for Google Sheets.
- Set to watch for row additions on Sheet2 (gid=800289465) in your spreadsheet.
7. **Create Filter node**
- Type: Filter
- Conditions:
- "URL " field exists and is not empty
- "Stored" field is empty
- Connect Google Sheets Trigger to Filter.
8. **Create If node**
- Type: If
- Condition: Check if "URL " contains "youtu.be" OR "youtube.com"
- Connect Filter to If.
9. **Create HTTP Request node (YouTube transcript)**
- Type: HTTP Request (POST)
- URL: Apify Actor YouTube Transcript Ninja endpoint (replace with your Apify API URL)
- Body (JSON):
```json
{
"includeTimestamps": "No",
"language": "English",
"startUrls": ["{{ $json['URL '] }}"]
}
```
- Connect If true branch to this node.
10. **Create HTTP Request1 node (Article fetch)**
- Type: HTTP Request (GET)
- URL: `{{ $json['URL '] }}`
- Connect If false branch to this node.
11. **Create Markdown node**
- Type: Markdown
- Set HTML input to `{{ $json.data }}` from HTTP Request1 node.
- Connect HTTP Request1 to Markdown.
12. **Create Google Gemini Chat Model1 node (for Article summarization)**
- Configure with Google PaLM credentials.
- Connect as AI language model to Information Extractor.
13. **Create Information Extractor node (Article)**
- Type: LangChain Information Extractor
- Text: `Article: {{ $json.data }}`
- Attributes:
- Title (required, description: "a title for this article")
- Article Refined Data (required, description: "a detailed summary from the article to add that in my Supabase. Don't start with this article or this thing.")
- Connect Markdown node output to this node.
- Link Google Gemini Chat Model1 as its AI language model.
14. **Create Append row in sheet1 node**
- Type: Google Sheets Append
- Document ID and Sheet: Sheet1 (gid=0) of your spreadsheet
- Columns: Title and Data mapped from