___
## Use media files in a flow
diff --git a/docs/3. Product Features/02. Speed Sends.md b/docs/3. Product Features/02. Speed Sends.md
index 122acd9db..4de807235 100644
--- a/docs/3. Product Features/02. Speed Sends.md
+++ b/docs/3. Product Features/02. Speed Sends.md
@@ -56,7 +56,7 @@ Speed Sends is a functionality in Glific to create a message or save the message
## Save Speed Sends
1. You can save any existing messages from chat from the little dropdown arrow shown next to the message. It will be saved in the Speed Sends list.
-
+
diff --git a/docs/3. Product Features/03. Flows/2. Flow Actions/12. Link Google Sheets.md b/docs/3. Product Features/03. Flows/2. Flow Actions/12. Link Google Sheets.md
index cfe7caa34..ee9938918 100644
--- a/docs/3. Product Features/03. Flows/2. Flow Actions/12. Link Google Sheets.md
+++ b/docs/3. Product Features/03. Flows/2. Flow Actions/12. Link Google Sheets.md
@@ -74,6 +74,32 @@ Click on `OK` and use the column name like below and access the value of cell va
___
+## Send template messages using Google Sheets
+
+Template messages could be sent using Google Sheets using `Read Google Sheets`. This is generally useful when different template messages need to be scheduled & sent regularly to the beneficiaries.
+Let us dive in a bit deeper by looking at the steps involved :
+
+1. Create the template messages that you'd like to send via Google Sheets using the normal method. Refer [Creating Template Messages](https://glific.github.io/docs/docs/Product%20Features/Templates/#hsm-template-creation--approval-submission)
+
+2. Once the template is created, make note of the UUID of the template messages you need to share using Google Sheets. Refer the screenshot below :
+ 
+
+3. Create a Google Sheet with two headers, `Key` and `template_uuid` as shown below. Usually the `key` column will be having dates on which the individual templates need to be sent.
+ 
+4. Now let us setup the flow. The flow starts with a `Link Google Sheets` node which has the Google Sheets we created linked. This is how the node looks like :
+
+
+
+As you can see, the row input field is given an expression `@calendar.current_date` which tries to match the current date value with a `Key` value. If a match is found, then that row is chosen.
+
+5. In the next step, `Send contact a message` is used and an expression is given in place of the template selection drop-down. The expression reads `<%= Glific.Clients.ColoredCow.send_template("@results.sheet.template_uuid", ["@contact.name"]) %>`
+
+
+
+In this expression, the `template_uuid` matching the row in the given date gets send to the user. The expression also passes the value of the contact's name. You can pass multiple variables in this expression, if the template message demands it, using comma separation.
+
+In this way, you can use Google Sheets for sending template messages. With the use of [Triggers](https://glific.github.io/docs/docs/Product%20Features/Triggers/) you'll be able to schedule different template messages for specific dates using Google Sheets, enabling you to do it in a single flow, instead of creating separate flows for separate templates. This is particularly useful, when you plan to share a set of instructions/ advisory/ content in a scheduled manner in specific dates & let's you use dynamic content for the same.
+
## Add Writable Google Sheet
### Enable Google Sheet APIs and Add Credentials
@@ -184,4 +210,4 @@ To handle blank cells in between the Google Sheet use [Split by Expression](http
___
## Advanced Feature Webinar
-
\ No newline at end of file
+
diff --git a/docs/3. Product Features/03. Flows/2. Flow Actions/Split By/01. Custom Expression.md b/docs/3. Product Features/03. Flows/2. Flow Actions/Split By/01. Custom Expression.md
index e803065ae..9bc1cb74d 100644
--- a/docs/3. Product Features/03. Flows/2. Flow Actions/Split By/01. Custom Expression.md
+++ b/docs/3. Product Features/03. Flows/2. Flow Actions/Split By/01. Custom Expression.md
@@ -10,7 +10,7 @@ You can use the `Split by a custom expression` option to make that happen.
`Split by a custom expression` uses a coding syntax to match the response received or profile attribute.
-The syntax will be like `<%= if (Variable name = 'String' / number ), do: 1, else: 0 %>`
+The syntax will be like `<%= if (Variable name == 'String' / number ), do: 1, else: 0 %>`
___
## Custom Expressions cheatsheet
diff --git a/docs/3. Product Features/03. Flows/4. Others/01. Flows not working - Troubleshoot checklist.md b/docs/3. Product Features/03. Flows/4. Others/01. Flows not working - Troubleshoot checklist.md
index 498b9c000..a53870988 100644
--- a/docs/3. Product Features/03. Flows/4. Others/01. Flows not working - Troubleshoot checklist.md
+++ b/docs/3. Product Features/03. Flows/4. Others/01. Flows not working - Troubleshoot checklist.md
@@ -46,7 +46,7 @@ ___
1. `Could not find Sub Flow: Help Workflow` - The subflow was created and exists no more.
-2. `The next message after a long wait for time should be an HSM template` - After `Wait for time`, it should be an HSM message
+2. `The next message after a long wait for time should be an HSM template` - After `Wait for time`, it should be an HSM message. The same error will be shown when you tick `Continue when there is no response` checkbox and the delay set is 1 day or more.
3. `Could not find Contact: SaaS Admin`- When staff member added to Send Broadcast node is deleted but in the node it is not updated
diff --git a/docs/3. Product Features/09. Reporting & Dashboard/02. DataStudio Integration for Visualizations & Reports.md b/docs/3. Product Features/09. Reporting & Dashboard/02. DataStudio Integration for Visualizations & Reports.md
index c10842a01..d76427e29 100644
--- a/docs/3. Product Features/09. Reporting & Dashboard/02. DataStudio Integration for Visualizations & Reports.md
+++ b/docs/3. Product Features/09. Reporting & Dashboard/02. DataStudio Integration for Visualizations & Reports.md
@@ -2,17 +2,15 @@
## Steps to integrate DataStudio for newly onboarded organization
-1 . Copy the Glific Report template.
+1 . Copy the Glific Report template.
-2 . Add data sources.
+2 . Add data sources.
-3 . Connect all tables one by one to get the data from BigQuery tables.
-
-4 . Get data in Flat Fields (Refer : [Populate data in Field Views (User info) report.](https://glific.github.io/docs/docs/Product%20Features/Reporting%20&%20Dashboard/Populate%20Data%20in%20Field%20Views-%20User%20Info%20Report/)
-
-5 . Your reports will be ready.
+3 . Connect all tables one by one to get the data from BigQuery tables.
+4 . Get data in Flat Fields (Refer : [Populate data in Field Views (User info) report.](https://glific.github.io/docs/docs/Product%20Features/Reporting%20&%20Dashboard/Populate%20Data%20in%20Field%20Views-%20User%20Info%20Report/)
+5 . Your reports will be ready.
**Below are the detailed steps with screenshots to integrate Data studio.**
@@ -20,43 +18,30 @@
1 . Click on the link of the reports template (shared publicly with view access). Use the latest **version 4.0** for your reports.
-
-
-- Template v2.3: [https://datastudio.google.com/s/hUkQeA0pjH4](https://datastudio.google.com/s/hUkQeA0pjH4)
+- Template v2.3: [https://datastudio.google.com/s/hUkQeA0pjH4](https://datastudio.google.com/s/hUkQeA0pjH4)
- Template v3.0: [https://datastudio.google.com/reporting/676f97b8-9da0-41a6-a50a-6c05190eac2c](https://datastudio.google.com/reporting/676f97b8-9da0-41a6-a50a-6c05190eac2c)
- Template v4.0: [https://datastudio.google.com/u/0/reporting/fdc97942-72e5-4d7d-92c4-4a4c7da9be86/page/p_t8287rtztc](https://datastudio.google.com/u/0/reporting/fdc97942-72e5-4d7d-92c4-4a4c7da9be86/page/p_t8287rtztc)
-1.
+
+1.
2 . Click on the `Make a copy` icon at the top right corner, from the 3 dots dropdown next to the Share button.
-
-
3 . Click on `Copy Report`.
**_Note_**_: Use the original data source, for now, don’t change it for any data source, it will break a few charts which have calculated or renamed fields._
-
-
-
-
-
-
4. In the new tab of the created copy of the report, select `Manage added data sources`from the Resources Menu
-
-
5. Click on `edit` one by one for each tables. Start with **contact** and **messages** tables
-
-
6. Select BigQuery data source
a. Select `MY PROJECTS`
@@ -69,60 +54,42 @@ d. Select the `Table`. Click on the `Reconnect` button.
-
-
f . You might get a warning "`Configuration has changed. Do you want to apply the changes?`”. That’s ok, click on `APPLY`
-
-
g . Click on `DONE` in fields listing page
-
-
**_Repeat steps 5 and 6 for the following tables. Contacts, Messages_**
-
-
To get the project ID click on the phone number on BigQuery.
-
-
-6 . For Flat fields data source update the custom query with **project ID** (from
+6 . For Flat fields data source update the custom query with **project ID** (from
the Glific settings interface for big query) and phone number
-
-
and click on `Reconnect`to update.
-
-
-7. For Flow context data source update the custom query with **project ID** (from
+7. For Flow context data source update the custom query with **project ID** (from
the Glific settings interface for big query) and phone number. There will be four place where you need to update the **project ID**
-
-
-8. Follow the same steps as above for `Monthly active users` the data source. Replace the **project ID** and **phone number** as per your setup.
+8. Follow the same steps as above for `Monthly active users` the data source. Replace the **project ID** and **phone number** as per your setup.
9 . For Flow results. Go to your big Query account. Open a new Tab and run below query. Replace `tides-saas-309509.917302307943` with your **Project ID**
-
-
```
CREATE OR REPLACE FUNCTION `tides-saas-309509.917302307943.jsonObjectKeys`(input STRING) RETURNS ARRAY
-___
+---
+
**_Note:_** _If you have never used Data Studio before then you will be prompted to provide some basic information and agree to their terms and conditins. Do so and then repeat step 2._
-___
+
+---
3 . Click on `Copy Report`. Refer to the image below:
-___
+---
+
**_Note:_** _Do not click on any of the drop downs for **New Data Source**. Directly click on_ 'Copy Report'
-___
+---
## Connect data sources to your BigQuery Account
-___
-4 . A new tab will open with the copy of the report and some visuals not functioning, select `Manage added data sources`from the Resources Menu. Refer to the image below:
+---
+
+4 . A new tab will open with the copy of the report and some visuals not functioning, select `Manage added data sources`from the Resources Menu. Refer to the image below:
-___
+---
### Connect `contacts`, `messages`, and `message conversations` through 'My Projects'
-___
+---
5 . Click on `edit` one by one for each data source. Start with **contacts**
@@ -241,42 +203,43 @@ d. Select the `Table` with the matching name. Click on the blue `Reconnect` butt
-e . You might get a warning "`Configuration has changed. Do you want to apply the changes?`”. That’s okay, click on `APPLY`
+e . You might get a warning "`Configuration has changed. Do you want to apply the changes?`”. That’s okay, click on `APPLY`
f . The fields listing page will open up. Verify that the fields are set to the correct type. Most will be correct by default.
-**What to check for:**
+**What to check for:**
+
1. All fields with 'id' in their name should be of the type 'text'
2. 'phone' should be of the type 'text'
3. 'language' should be of the type 'text'
-3. All fields with 'at'/'time' in their name should be of the type 'Date and Time'
-4. Latitude/Longitude should be of the type latitude, longitude.
+4. All fields with 'at'/'time' in their name should be of the type 'Date and Time'
+5. Latitude/Longitude should be of the type latitude, longitude.
g. Click on `DONE` in fields listing page
-
7 . **_Repeat steps 5 and 6 for the following data sources; Messages, Message Conversations_**
-___
+---
**_Note: To get the Dataset ID click on the phone number on BigQuery._**
-___
+---
### Connect `flat fields`, `monthly active users`, `Contact collections`, `success messages` and `flow context` by replacing your Dataset ID into the preset 'Custom Query'.
-___
-8 . For the Flat Fields data source, select Edit and then update the custom query with **Dataset ID** (from the Glific settings interface for big query as shown in the note above). Replace `tides-saas-309509.917302307943` with your **Dataset ID** (Refer to the image below)
+---
+8 . For the Flat Fields data source, select Edit and then update the custom query with **Dataset ID** (from the Glific settings interface for big query as shown in the note above). Replace `tides-saas-309509.917302307943` with your **Dataset ID** (Refer to the image below)
**Note:** Ensure that you only replace the **Dataset ID**, that is, ".contacts" should not be deleted. This applies for all steps where you replace the **Dataset ID**, ensure you do not select and paste over the remaining text.
-___
+
+---
@@ -286,22 +249,22 @@ ___
10 . Verify the field types. (Refer Step 6.f.) And then click `Done`
-11 . Follow steps 8 to 10 as above for; `Monthly active users`, `Contact Collections` and `Success Messages`, Replace the **Dataset ID** as per your setup.
+11 . Follow steps 8 to 10 as above for; `Monthly active users`, `Contact Collections` and `Success Messages`, Replace the **Dataset ID** as per your setup.
12 . For Flow context data source update the custom query with **Dataset ID** (from the Glific settings interface for big query). There will be four places where you need to update the **Dataset ID**
-___
+---
+
### Connect `flow Results` and `Contact Collection` by running a query in BigQuery before connecting the data sources.
-___
+
+---
13 . For Flow results. First go to your BigQuery account. Open a new tab and run below query. Replace `tides-saas-309509.917302307943` with your **Dataset ID**
-
-
```
CREATE OR REPLACE FUNCTION `tides-saas-309509.917302307943.jsonObjectKeys`(input STRING) RETURNS ARRAY
-
18 . Go back to DataStudio and follow the same steps as we did for `contacts` (6 and 7) above to connect the `Contact Collection` data source.
-
-19 . Click `CLOSE` on the top right, and your reports will be ready
+19 . Click `CLOSE` on the top right, and your reports will be ready
+20 . Your reports are ready for use.
-20 . Your reports are ready for use.
+---
-___
**_Note: To create your own custom reports follow the steps in the below document ._**
[https://support.google.com/datastudio/answer/6292570?hl=en](https://support.google.com/datastudio/answer/6292570?hl=en)
-___
+---
diff --git a/docs/3. Product Features/09. Reporting & Dashboard/06. Understanding V5 Data Studio Reports.md b/docs/3. Product Features/09. Reporting & Dashboard/06. Understanding V5 Data Studio Reports.md
index 806474a15..5db472191 100644
--- a/docs/3. Product Features/09. Reporting & Dashboard/06. Understanding V5 Data Studio Reports.md
+++ b/docs/3. Product Features/09. Reporting & Dashboard/06. Understanding V5 Data Studio Reports.md
@@ -5,7 +5,7 @@ ___
- [GCS Setup](https://glific.github.io/docs/docs/Onboarding/GCS%20Setup/Google%20Cloud%20Storage%20Setup)
- [BigQuery Setup](https://glific.github.io/docs/docs/Product%20Features/Reporting%20&%20Dashboard/BigQuery%20Setup%20and%20link%20with%20Glific)
-- [Sample Dashboard](https://lookerstudio.google.com/u/0/reporting/e790512e-01d4-4f55-a514-dc5dbf4b6c51/page/p_eq3uecja2c)
+- [Sample Dashboard](https://lookerstudio.google.com/u/0/reporting/6f6d2bd7-7bd0-45d0-afad-1e9620f266d8/page/CoQkB)
___
## Video Tutorial
diff --git a/docs/3. Product Features/10. Others/Tags.md b/docs/3. Product Features/10. Others/Tags.md
new file mode 100644
index 000000000..c9bc3c0c4
--- /dev/null
+++ b/docs/3. Product Features/10. Others/Tags.md
@@ -0,0 +1,47 @@
+> ### **2 minutes read `Beginner`**
+
+**Glific currently supports the use of `Tags`, which is used for categorizing flows, HSM Templates & Interactive Messages. Tags help you manage your flows/ templates/ interactive messages faster and enables you to filter out and view items belonging to each Tag separately, making the search process easier.**
+
+
++ +## Tags in Flows + +
+
+In the Flow listing page, you will find the option for selecting a Tag from a dropdown list. Tags could be helpful for bundling all flows which are similar to each other.
+
+Tags could be created during the creation of a flow. Please refer the screenshot below :
+
+
+
+As you can see, while creating a flow, you can either select a Tag from the dropdown or create a new Tag which indicates the type of Flow. Here since it is a user registration flow, we’re tagging it as ‘Onboarding’. Similarly, other Tags could be used like ‘Support’, ‘Menu’ for bundling all flows which handle support or which contain menu respectively.
++## Tags in HSM Templates +
+
+Tags could also be useful in HSM Templates, for bundling all similar template messages.
+As you can see in the screenshot above, the Tag option could be found just above the `Submit for Approval` option.
+
+Similar to what we saw in `Tags in Flows`, during Template message creation, Tags could either be created or selected from the available set of options. It can also be ignored if not required.
+
+
+
+As you can see, on choosing the `Weather` tag, we are able to filter out all HSM templates which are tagged as ‘Weather’. Since only one template is tagged as ‘Weather’, just the template we just created is being shown. In this way, you will be able to categorize & at a later stage, search & find templates easily by making use of `Tags`.
++## Tags in Interactive Messages + +
+
+Tags can also be used in Interactive Messages. It will particularly be useful for categorizing interactive messages which has some common attributes. This makes finding them at a later stage useful.
+
+Tags could either be selected from a dropdown or a new Tag could be created altogether while creating an interactive message.
+
+
+
+In the screenshot above, we can see that the interactive message we’re creating is tagged with a new tag called ‘Registration’. So when you’re searching for the Tag at any later point, you’ll be able to find it out easily without scrolling over the whole Interactive Message listing page. Tags make sure that you end up categorizing similar interactive messages to the same bucket, bringing a structure in the flow listing page, improving the searchability & helps managing them easier.
+
+
+
+## Note
+It is important to note that selecting or creating tags are not mandatory during creation of a Flow, Template or Interactive Message. It is an additional feature in case you’ve large number of flows/ templates/ interactive messages & managing them becomes cumbersome.
diff --git a/docs/4. Integrations/Bhashini ASR b/docs/4. Integrations/Bhashini ASR
new file mode 100644
index 000000000..d855f9333
--- /dev/null
+++ b/docs/4. Integrations/Bhashini ASR
@@ -0,0 +1,78 @@
+> ### **4 minute read `Advanced`**
+
+**“Bhashini” is a project or initiative aimed at providing easy access to the Internet and digital services for all Indians in their native languages. The primary goal of the project is to increase the amount of content available in Indian languages, thereby promoting digital inclusivity and accessibility for a broader population**
+
+---
+
+## Bhashini in Glific
+- Bhashini APIs are used in Glific for speech recognition
+- Whatsapp user can share an audio file, which could be converted to text (in the language chosen by the user)
+- The converted text could be stored in Google Sheets, used in flows/ used as per the use case
+
+---
+
+## Sample Chat
+
+
+---
+
+## Steps to integrate Bhashini ASR in Glific flows
+1. Create an account in [Bhashini website](https://bhashini.gov.in/ulca/user/register#) using name & email ID. Please verify the account using the email received.
+
+
+2. Go to ‘My Profile’ on the top-right corner.
+
+
+3. Generate ‘ULCA API Key’ (top-right corner). Also make note of the User ID on top of the button. This will be used inside the webhook call in Glific flow.
+
+
+4. Make note of the API Key generated. This is required inside the webhook call used in the Glific flow for calling the Bhashini API. (Starting with 01af.. here)
+
+
+5. Now login to your Glific instance and create a flow. In this example we’ll use a flow called Maven ASR Bhashini, used for recording incoming audio queries in a Google Sheet. The flow starts with asking the user to share the query as a voice note and it is saved as ‘speech’.
+
+The download link for a few sample flows are given below to test it for yourself.
+
+
+
+6. After the audio is captured, the Bhashini ASR API is called using a Webhook function called ‘speech_to_text_with_bhasini’ and the response is stored as ‘speech2text’.
+
+- The function name is pre-defined, you should always use the name ‘speech_to_text_with_bhasini’ to call the Bhashini API
+- The response stored (speech2text here) could be given any name of your liking, just like any other flow variable
+
+
+
+7. The webhook body is shown below. Please update the parameters as shown :
+
+- speech : It should be updated with the result name given for the audio file captured. Here, it is saved as ‘speech’ (Step 5), hence the value is @results.speech.input (If the audio note captured was saved as ‘query’, then the value will be @results.query.input)
+- userID : The userID is updated with the userID captured from Bhashini website. (Refer Step 3)
+- ulcaApiKey : The API Key needs to be updated with the ULCA API key captured from Bhashini website. (Refer Step 4)
+- pipelineId : Keep the value as given in the screenshot below - “64392f96daac500b55c543cd”
+- base_url : Keep the value as given in the screenshot below - “https://meity-auth.ulcacontrib.org/ulca/apis/v0/model/”
+- contact : Keep the value as given in the screenshot below - “@contact”
+You can read more about the variables used inside the webhook body/ Bhashini APIs [here](https://bhashini.gitbook.io/bhashini-apis/)
+
+
+
+8. Once the webhook is updated, you could always refer to the translated text as ‘@results.speech2text.asr_response_text’ to use it inside the flow.
+
+The output of the text response from the Bhashini depends on the language preference of the user. For instance if a user has selected Hindi language, the response from Glific will be in Hindi script.
+
+9. You could additionally link the webhook to a ‘Link Google Sheet’ node to record the translated text into a spreadsheet as shown in the flow below.
+
+
+
+10. The Google Sheet node is configured as shown below. The fourth variable being used is ‘@results.speech2text.asr_response_text’ which will capture the translated text in the fourth column.
+
+
+11. The incoming audio notes in this flow will be captured in Google Sheets as shown below
+
+
+## Sample Flow Links
+*Test some sample flows for yourself by importing these flows to your Glific instance.*
+
+- [Maven ASR Bhashini](https://drive.google.com/file/d/1qXxDIaP4MMDl18NsgVuhF1H8cIetJNiM/view?usp=sharing)
+- [Bhasini API](https://drive.google.com/file/d/1K-KcJ0NFDIcVN2w8E1sTnev36AEGdv5I/view?usp=sharing)
+
+## Blogs
+- [Getting started with Bhashini ASR](https://glific.org/getting-started-using-asr-with-bhashini-api/)
diff --git a/docs/4. Integrations/Bhashini ASR.md b/docs/4. Integrations/Bhashini ASR.md
new file mode 100644
index 000000000..c1e60aa0c
--- /dev/null
+++ b/docs/4. Integrations/Bhashini ASR.md
@@ -0,0 +1,78 @@
+> ### **4 minute read `Advanced`**
+
+**`Bhashini` is a project or initiative aimed at providing easy access to the Internet and digital services for all Indians in their native languages. The primary goal of the project is to increase the amount of content available in Indian languages, thereby promoting digital inclusivity and accessibility for a broader population**
+
+---
+
+## Bhashini in Glific
+- Bhashini APIs are used in Glific for speech recognition
+- Whatsapp user can share an audio file, which could be converted to text (in the language chosen by the user)
+- The converted text could be stored in Google Sheets, used in flows/ used as per the use case
+
+---
+
+## Sample Chat
+
+
+---
+
+## Steps to integrate Bhashini ASR in Glific flows
+1. Create an account in [Bhashini website](https://bhashini.gov.in/ulca/user/register#) using name & email ID. Please verify the account using the email received.
+
+
+2. Go to ‘My Profile’ on the top-right corner.
+
+
+3. Generate ‘ULCA API Key’ (top-right corner). Also make note of the User ID on top of the button. This will be used inside the webhook call in Glific flow.
+
+
+4. Make note of the API Key generated. This is required inside the webhook call used in the Glific flow for calling the Bhashini API. (Starting with 01af.. here)
+
+
+5. Now login to your Glific instance and create a flow. In this example we’ll use a flow called Maven ASR Bhashini, used for recording incoming audio queries in a Google Sheet. The flow starts with asking the user to share the query as a voice note and it is saved as ‘speech’.
+
+The download link for a few sample flows are given below to test it for yourself.
+
+
+
+6. After the audio is captured, the Bhashini ASR API is called using a Webhook function called ‘speech_to_text_with_bhasini’ and the response is stored as ‘speech2text’.
+
+- The function name is pre-defined, you should always use the name ‘speech_to_text_with_bhasini’ to call the Bhashini API
+- The response stored (speech2text here) could be given any name of your liking, just like any other flow variable
+
+
+
+7. The webhook body is shown below. Please update the parameters as shown :
+
+- speech : It should be updated with the result name given for the audio file captured. Here, it is saved as ‘speech’ (Step 5), hence the value is @results.speech.input (If the audio note captured was saved as ‘query’, then the value will be @results.query.input)
+- userID : The userID is updated with the userID captured from Bhashini website. (Refer Step 3)
+- ulcaApiKey : The API Key needs to be updated with the ULCA API key captured from Bhashini website. (Refer Step 4)
+- pipelineId : Keep the value as given in the screenshot below - “64392f96daac500b55c543cd”
+- base_url : Keep the value as given in the screenshot below - “https://meity-auth.ulcacontrib.org/ulca/apis/v0/model/”
+- contact : Keep the value as given in the screenshot below - “@contact”
+You can read more about the variables used inside the webhook body/ Bhashini APIs [here](https://bhashini.gitbook.io/bhashini-apis/)
+
+
+
+8. Once the webhook is updated, you could always refer to the translated text as ‘@results.speech2text.asr_response_text’ to use it inside the flow.
+
+The output of the text response from the Bhashini depends on the language preference of the user. For instance if a user has selected Hindi language, the response from Glific will be in Hindi script.
+
+9. You could additionally link the webhook to a ‘Link Google Sheet’ node to record the translated text into a spreadsheet as shown in the flow below.
+
+
+
+10. The Google Sheet node is configured as shown below. The fourth variable being used is ‘@results.speech2text.asr_response_text’ which will capture the translated text in the fourth column.
+
+
+11. The incoming audio notes in this flow will be captured in Google Sheets as shown below
+
+
+## Sample Flow Links
+*Test some sample flows for yourself by importing these flows to your Glific instance.*
+
+- [Maven ASR Bhashini](https://drive.google.com/file/d/1qXxDIaP4MMDl18NsgVuhF1H8cIetJNiM/view?usp=sharing)
+- [Bhasini API](https://drive.google.com/file/d/1K-KcJ0NFDIcVN2w8E1sTnev36AEGdv5I/view?usp=sharing)
+
+## Blogs
+- [Getting started with Bhashini ASR](https://glific.org/getting-started-using-asr-with-bhashini-api/)
diff --git a/docs/4. Integrations/Conversing in voice notes over custom knowledge base.md b/docs/4. Integrations/Conversing in voice notes over custom knowledge base.md
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@@ -0,0 +1,47 @@
+
+
+1. In the flow- by calling the “jugalbandi-voice” function and the above inputs in the webhook from the glific interface we can get a text response in desired language as well as a voice note. Send voice notes and get voice notes in return using the Jugalbandi API.
+
+- Prerequisites: knowledge base will have to uploaded in jugalbandi platform. [Read More](https://glific.github.io/docs/docs/Integrations/ChatGPT%20Integration/)
+- [Example flow](https://drive.google.com/file/d/1xLnLGWg_95Zhi5v0QIQpaRz_cLA9d0Er/view) (json format) which you can upload and re-use as a starting point
+
+
+Elements involved
+2. [Jugalbandi Platform](https://api.jugalbandi.ai/docs#/)
+Using Jugalbandi platform for generating voice response.
+
+
+
+
+3. Call the Glific webhook
+
+Function to use - “jugalbandi-voice”
+
+
+
+## Function Body
+
+
+
+
+“Input_language” : specify as per the jugalbandi platform or you can use the @contact.language
+“output_format” : text or voice
+“query_text” : if the input is in text format
+“audio_url” : send the flow variable of the voice note received
+“prompt” : any contextual or system prompt
+“uuid_number” : custom knowledge base which has been uploaded in the jugalbandi platform
+
+## How to get the response back and get the voice note
+- The first node in the “success” pathway is pulling the responses
+
+
+
+
+
+- The second node (screenshot below) is sending the computer generated voice note of the response.
+
+
+
+
+
+
diff --git a/docs/6. FAQ/20. Configure read receipts in Gupshup account..md b/docs/6. FAQ/20. Configure read receipts in Gupshup account..md
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index e568bafb6..000000000
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@@ -1,17 +0,0 @@
-Steps to configure read receipts in the Gupshup account.
-
-1 . Log in to your Gupshp account.
-
-
-
-
-
-2 . Click on the settings icon on your app.
-
-
-
-
-
-3. Scroll down to the `Message Events` section and active all the checkboxes.
-
-
diff --git a/docs/6. FAQ/20. Read Receipts.md b/docs/6. FAQ/20. Read Receipts.md
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+The read receipts data is important for tracking user engagement. It lets you categorize the status of different messages you've sent into `Sent`, `Delivered`, `Read (blue tick)`etc
+
+## Steps to configure read receipts in the Gupshup account.
+
+
+
+1 . Log in to your Gupshp account.
+
+
+
+
+2 . Click on the settings icon on your app.
+
+
+
+3. Scroll down to the `Message Events` section and active all the checkboxes.
+
+
+
+- Once you configure Gupshup, you will be able to see the status (delivered, read, sent etc) in the column name `bsp_status` of `Messages table` in BigQuery.
+
+
+
+
+- You can add this table in your dashboards to create a Funnel Chart which will help you with more insights on the different drop-off areas/ leakages in your flow. This helps you understand your flow using data & helps you tweak it to enhance engagement.