Sunucu yanıtını kontrol edin
Sunucu yanıtı
NS kayıtları
Whois alanı
301 Moved Permanently - vrdmn.com
HTTP Status: 301
User-Agent: Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; [email protected])
Location: https://www.vrdmn.com/
Content-Type: text/html; charset=UTF-8
Date: Sun, 25 May 2025 10:30:42 GMT
Expires: Sun, 25 May 2025 10:30:42 GMT
Cache-Control: private, max-age=0
X-Content-Type-Options: nosniff
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Content-Security-Policy: frame-ancestors 'self'
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Server: GSE
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Vary: Accept-Encoding
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content-type: text/html; charset=UTF-8
expires: Sun, 25 May 2025 10:30:42 GMT
date: Sun, 25 May 2025 10:30:42 GMT
cache-control: private, max-age=0
last-modified: Fri, 23 May 2025 01:04:00 GMT
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HTTP Kodu 301 Kalıcı Olarak Taşındı
301 http kodu, istenen kaynağın kalıcı olarak yeni bir URL'ye taşındığı anlamına gelir. Gelecekteki tüm istekler yeni adresi kullanmalıdır.
Kod 301 ne zaman kullanılır?
- Bir web sitesi alan adını değiştirirken
- URL yapılarını değiştirirken
- SEO için yönlendirmeleri ayarlarken
Kod 301 kullanıcı için ne anlama geliyor?
Tarayıcı kullanıcıyı otomatik olarak yeni adrese yönlendirecek ve arama motorları dizinlerini güncelleyecektir.
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Vardhaman Deshpande
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<h2 class='date-header'><span>Sunday, 2 February 2025</span></h2>
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<h3 class='post-title entry-title' itemprop='name'>
<a href='https://www.vrdmn.com/2025/02/building-agent-for-microsoft-365.html'>Building an Agent for Microsoft 365 Copilot: Adding an API plugin</a>
</h3>
<div class='post-header'>
<div class='post-header-line-1'></div>
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<div class='post-body entry-content' id='post-body-285749958596253646' itemprop='description articleBody'>
<p>
In the previous post, <a href="https://www.vrdmn.com/2025/01/building-agent-for-microsoft-365-copilot.html" target="_blank"><span style="color: #2b00fe;">we saw how to get started building agents for Microsoft 365 Copilot</span></a>. We saw the different agent manifest files and how to configure
them. There are some great out of the box capabilities available for agents
like using Web search, Code Interpreter and Creating Images.
</p>
<p>
However, a very common scenario is to bring in data from external systems into
Copilot and pass it to the LLM. So in this post, we will have a look at how to
connect Copilot with external systems using API Plugins.
</p>
<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgBFLrZX-JhJR6oiMI-uHqSBuNwj0D_2huk_pXu4AJFc-KEgcBwDpfioY_58RiyWJmcHWnvPDtdA6gtddn7nR0rvhKyuRwS8hDrpjJhkEIEDdWb1MFyDRQk3JP86fjkhHxEva_IAZihbcIfSyD3Q-S4bxLMooSdEcyxvTYqvx29uBB5dNKkirgkR5lNI7E/s1024/A%20minimalistic%20and%20abstract%20illustration%20inspired%20by%20the%20concept%20of%20an%20AI%20AGENT%20working%20with%20information.%20AGENT%20is%20using%20tools%20at%20hand%20to%20perform%20tasks.%20Subtle%20geometric%20shapes%20and%20lines%20gently%20flow%20across%20the%20canv.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1024" data-original-width="1024" height="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgBFLrZX-JhJR6oiMI-uHqSBuNwj0D_2huk_pXu4AJFc-KEgcBwDpfioY_58RiyWJmcHWnvPDtdA6gtddn7nR0rvhKyuRwS8hDrpjJhkEIEDdWb1MFyDRQk3JP86fjkhHxEva_IAZihbcIfSyD3Q-S4bxLMooSdEcyxvTYqvx29uBB5dNKkirgkR5lNI7E/w400-h400/A%20minimalistic%20and%20abstract%20illustration%20inspired%20by%20the%20concept%20of%20an%20AI%20AGENT%20working%20with%20information.%20AGENT%20is%20using%20tools%20at%20hand%20to%20perform%20tasks.%20Subtle%20geometric%20shapes%20and%20lines%20gently%20flow%20across%20the%20canv.png" width="400" /></a></div>
<p><b><span style="color: red;">In simple terms, API Plugins are AI "wrappers" on existing APIs.</span></b> We inform the LLM
about an API and give it instructions on when to call these APIs and which
parameters to pass to them.
</p>
<p>
To connect your API to the Copilot agent, we have to create an "Action" and
include it in the <b><span style="color: #ffa400;">declarativeAgent.json</span></b> file we saw in the previous post:
</p>
<script src="https://gist.github.com/vman/f29b1663279ac47b4e4b096355809884.js"></script>
<p>
The ai-plugin.json file contains information on how the LLM will understand
our API. We will come to this file later.
</p>
<p>
Before that, let's understand how our API looks. We have a simple API that can
retrieve some sample data about repairs:
</p>
<script src="https://gist.github.com/vman/a3e2483697360b1247dab9ce33e43e98.js"></script>
<p>Next, we will need the OpenAI specification for this API</p>
<script src="https://gist.github.com/vman/866f672c2204cfe6c2c73b9a4355713b.js"></script>
<p>The OpenAPI specification is important as it describes in detail the exact signatures of our APIs to the LLM.</p>
<p>
Finally, we will come to the most important file which is the <b><span style="color: #ffa400;">ai-plugin.json</span></b>
file. It does several things. It informs Copilot which API methods are available, which parameters they
expect and when to call them. This is done in simple human readable language so that the LLM can best understand it.</p>
<p>
Additionally, it also handles formatting of the data before it's shown to the
user in Copilot. Whether we want to use Adaptive Cards to show the data or if
we want to run any other processing like removing direct links from the
responses.
</p><p><b><span style="color: #ffa400;">If you are doing plugin development, chances are that you will be spending most of your time on this file.</span></b></p>
<script src="https://gist.github.com/vman/49a2a4308d2c10b0b7e488b90ea4c938.js"></script>
<p>Once everything is in place, we will run our plugin with simple instructions and this is how it will fetch the data from external database </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEitjyrAzkOQ44hvo-E09cip07joDBBTy8zqNro9wYwb5jg2T5brMVu8_8XC5b_goAmp0dbUh53PDQ6WQxCusJmLakOwMNA-uhXNIV0w1ldqiWFhT5mq-A3MWQvZaNViCAzxXVzRrPsROT3GmkED2yWU0aXJnZyWn4rYVrBiqUallPDKoemmir7T_cv_NtE/s1269/367156485-9939972e-0449-410c-b237-d9d748cd6628.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="761" data-original-width="1269" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEitjyrAzkOQ44hvo-E09cip07joDBBTy8zqNro9wYwb5jg2T5brMVu8_8XC5b_goAmp0dbUh53PDQ6WQxCusJmLakOwMNA-uhXNIV0w1ldqiWFhT5mq-A3MWQvZaNViCAzxXVzRrPsROT3GmkED2yWU0aXJnZyWn4rYVrBiqUallPDKoemmir7T_cv_NtE/w400-h240/367156485-9939972e-0449-410c-b237-d9d748cd6628.png" width="400" /></a></div><br /><p>Hope this helps!</p>
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<h2 class='date-header'><span>Monday, 6 January 2025</span></h2>
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<h3 class='post-title entry-title' itemprop='name'>
<a href='https://www.vrdmn.com/2025/01/building-agent-for-microsoft-365-copilot.html'>Building an Agent for Microsoft 365 Copilot Chat</a>
</h3>
<div class='post-header'>
<div class='post-header-line-1'></div>
</div>
<div class='post-body entry-content' id='post-body-1045027176581956469' itemprop='description articleBody'>
<p>
Microsoft 365 Copilot Chat is a powerful, general-purpose assistant that greatly
improves personal productivity, helping users manage tasks like emails,
calendars, document searches, and more.
</p>
<p>
<span><b><span style="color: red;">However, the true potential of M365 Copilot lies in its
extensibility.</span></b>
By building specialized "vertical" agents on top of M365 Copilot, you can
unlock team productivity as well as automate business processes. These
custom agents not only help in individual productivity, but also help in
building workflows across groups of people.</span>
</p>
<p>
Agents for Microsoft 365 Copilot leverage the same robust foundation—its
orchestrator, foundation models, and trusted AI services—that powers M365
Copilot itself. This ensures consistency, reliability, and security at
scale.
</p>
<p>
So in this post, let's take a look at how to build Agents on top of
Microsoft 365 Copilot.
</p>
<p>
We will be building a Declarative Agent with the help of the Teams toolkit.
Before we start, we need the following prerequisites:
</p>
<p>
<span style="color: #2b00fe;"><span><a href="https://www.microsoft.com/en-us/microsoft-365/copilot/enterprise" target="_blank">A Microsoft 365 Copilot license</a></span>
</span>
</p>
<p>
<span style="color: #2b00fe;"><span><a href="https://marketplace.visualstudio.com/items?itemName=TeamsDevApp.ms-teams-vscode-extension" target="_blank">Teams Toolkit Visual Studio Code extension</a></span>
</span>
</p>
<p>
<span style="color: #2b00fe;"><a href="https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/prerequisites#building-with-teams-toolkit" target="_blank">Enable side loading of Teams apps</a></span>
</p>
<p>
Once everything is in place, we will go to Visual Studio Code
</p>
<p>
In the Teams Toolkit extension, To create an agent, we will click on
"Create new app"
</p>
<p>Then, click on "Agent"</p>
<p></p>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/a/AVvXsEjetmOlipuqbaTFum57vxPnGwdVH-QA69NJW5ABWv-UuWx7iAAtnxRsaNEdn6QDsuEg2tg5Qj-28nRkGSSngl2LPuDPZN3tQ1ISNK6a3Uo56FPWotFyKCj0ABcRwdFLUSaQwBXJfvf6G2NFeRGTzc8X-sQ5HrLFPh8R07sAg_01srESq2ewN6suOOswbDY" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="416" data-original-width="1153" height="230" src="https://blogger.googleusercontent.com/img/a/AVvXsEjetmOlipuqbaTFum57vxPnGwdVH-QA69NJW5ABWv-UuWx7iAAtnxRsaNEdn6QDsuEg2tg5Qj-28nRkGSSngl2LPuDPZN3tQ1ISNK6a3Uo56FPWotFyKCj0ABcRwdFLUSaQwBXJfvf6G2NFeRGTzc8X-sQ5HrLFPh8R07sAg_01srESq2ewN6suOOswbDY=w640-h230" width="640" /></a>
</div>
<br />
<p></p>
<p>
When the agent is created, we see a bunch of files getting created as part
of the scaffolding. So let's take a look the difference moving pieces
of the agent:
</p>
<p>
<span style="background-color: white; color: #161616; font-size: 16px;"></span>
</p>
<div class="separator" style="clear: both; text-align: center;">
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</div>
<h3 style="text-align: left;">
<span style="color: #38761d;">manifest.json</span>
</h3>
<p>
If you have been doing M365 Apps (and Teams apps) for a while, you are
familiar with this file. This is the file which represents our Agent in the
M365 App catalog. It contains the various details like name, description and
capabilities of the app.
</p>
<script src="https://gist.github.com/vman/7b486b72d610f536a499d3cd8f5f994b.js"></script>
<p>
However, you will notice the new property in this file which is
<b><span style="color: red;">"copilotAgents"</span></b>
This property will be pointing to a file containing the description of our new
declarative agent. So let's look at how that file looks next:
</p>
<h3 style="text-align: left;">
<span style="color: #38761d;">declarativeAgent.json</span>
</h3>
<script src="https://gist.github.com/vman/35bace9d20fa8a90b06693b83fce23c9.js"></script>
Lots of interesting things are happening here. <div><br />
<div>First the <b><span style="color: red;">"instructions"</span></b> property is
pointing to a file which will contain the "System prompt" of our agent. We
will have a look at this file later.
</div>
<div>
<div><br /></div><div>
Next, the
<b><span style="color: red;">"conversation_starters"</span></b> property
contains ready to go prompts which the user can ask the agent. Our agent
will be trained to respond to these prompts. This is so that the user is
properly onboarded when they land on our agent.
</div>
<div><br /></div>
<div>
Finally there will be the
<b><span style="color: red;">actions</span></b> and
<b><span style="color: red;">capabilities</span></b> properties:
</div>
<div><br /></div>
<div>
Actions property contains connections to external APIs which the agent can
invoke.
</div>
<div><br /></div>
<div>
Capabilities property contains the different out of the box "Tools" which we
want to allow in our agent. E.g. SharePoint and OneDrive search, image
creation, code interpreter to generate charts etc
</div>
<div><br /></div>
<div>
We will talk about both these properties in more details in subsequent blog
posts.<p>
</p>
<h3 style="text-align: left;">
<span style="color: #38761d;">instruction.txt</span>
</h3>
<p>
And finally we have the instructions file where we can specify the system
prompt for the agent. Here, we can guide the agent and assign it
personality. We can make it aware of the tools and capabilities it has
available and when the use them. We can provide one shot or few shot
examples to "train" the agent on responding to users.
</p>
<script src="https://gist.github.com/vman/4308147103c55d8e4640b56c3519dc3d.js"></script>
Once all files are in place, you can click "Provision" from the Teams
toolkit extension:
</div>
<div>
<div class="separator" style="clear: both; text-align: center;"><br /></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh4lWw7HuGmhdP1Y_0lv80c2veoN4lmcd-eIrpu31zgVG428WgHDafDDvU6IohxqbP99gFL20cweeWXGW_h72zs-3oEPbgHdo7jAGxYJk__-qDvd308qHSs_SdKFilZH6fVewL3SOVj08z0AtAgR1DCiTeryZe4AIOdWUog4Rh7VPmUQp0UhMk9SuG6gXQ/s977/Screenshot%202025-01-06%20102414.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="673" data-original-width="977" height="440" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh4lWw7HuGmhdP1Y_0lv80c2veoN4lmcd-eIrpu31zgVG428WgHDafDDvU6IohxqbP99gFL20cweeWXGW_h72zs-3oEPbgHdo7jAGxYJk__-qDvd308qHSs_SdKFilZH6fVewL3SOVj08z0AtAgR1DCiTeryZe4AIOdWUog4Rh7VPmUQp0UhMk9SuG6gXQ/w640-h440/Screenshot%202025-01-06%20102414.png" width="640" /></a>
</div>
<br />Then navigate to <a href="https://www.microsoft365.com/chat" target="_blank"><b>https://www.microsoft365.com/chat</b></a>
</div>
<div><br /></div>
<div>And our new agent will be ready!</div>
<div><br /></div>
<div>
Here is how the agent will provide the conversation starters when we first
lang on it:
</div>
<div><br /></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgFlL4Xj2AOw6yiaFi_Zx2a1xR4Z_F6j0hx3Ve758fl1FePeJ7lO68_n9myaIv4lkkYGtoKrkRcWuO1n2_ixntGcy9gJgJ1enKvZ9aWPWXUmJo_vvPcDHzJWZgPdwUROjX8Jie9dUP4GsJznY1Tny3TIyNUduyM7gVBTRFkk7VUkidogZHe6HvQc5YcCfU/s1051/Screenshot%202025-01-06%20102816.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="597" data-original-width="1051" height="365" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgFlL4Xj2AOw6yiaFi_Zx2a1xR4Z_F6j0hx3Ve758fl1FePeJ7lO68_n9myaIv4lkkYGtoKrkRcWuO1n2_ixntGcy9gJgJ1enKvZ9aWPWXUmJo_vvPcDHzJWZgPdwUROjX8Jie9dUP4GsJznY1Tny3TIyNUduyM7gVBTRFkk7VUkidogZHe6HvQc5YcCfU/w640-h365/Screenshot%202025-01-06%20102816.png" width="640" /></a>
</div>
<div><br /></div>
<div>Simple conversation using Web search capability:</div>
<br />
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<div>Conversation using Web search and Code Interpreter capabilities:</div>
<br />
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<br />
<div><br /></div>
<div>
Hope this was helpful! We will explore M365 Copilot Agents more in
subsequent blog posts.<br /><br />
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<h2 class='date-header'><span>Monday, 9 December 2024</span></h2>
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<a href='https://www.vrdmn.com/2024/12/search-sharepoint-and-onedrive-files-in.html'>Search SharePoint and OneDrive files in natural language with OpenAI function calling and Microsoft Graph Search API</a>
</h3>
<div class='post-header'>
<div class='post-header-line-1'></div>
</div>
<div class='post-body entry-content' id='post-body-6555527554161231977' itemprop='description articleBody'>
<p>
By now, we have seen "Chat with your documents" functionality being introduced
in many Microsoft 365 applications. It is typically built by combining Large
Language Models (LLMs) and vector databases.
</p>
<p>
To make the documents "chat ready", they have to be converted to embeddings
and stored in vector databases like Azure AI Search. However, indexing the
documents and keeping the index in sync are not trivial tasks. There are many
moving pieces involved. Also, many times there is no need for "similarity
search" or "vector search" where the search is made based on meaning of the
query.
</p>
<p>
In such cases, a simple "keyword" search can do the trick. The advantage of
using keyword search in Microsoft 365 applications is that the Microsoft
Search indexes are already available as part of the service. APIs like the
Microsoft Graph Search API and the SharePoint Search REST API give us "ready
to consume" endpoints which can be used to query documents across SharePoint
and OneDrive. Keeping these search indexes in sync with the changes in the
documents is also handled by the Microsoft 365 service itself.
</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhdVoUqs_dG8ci7O4HWehyphenhyphenyk_95hnr013beyBWi2G6BDsqya8jKV7zGu0Z1gyTcXYSybbVVDyAjIvftsfLOJV_Tzcgr_GpNFhcQvEmQ4i_w669pGt7f4VPgR829Bz6k4fpvfEawBRQAh3y6n9kWBbfr7nxMoGTTVBXu-lmwAjYsJF4GrR9GMAc6Wp2cZmQ/s1024/generated_00%20(2).png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1024" data-original-width="1024" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhdVoUqs_dG8ci7O4HWehyphenhyphenyk_95hnr013beyBWi2G6BDsqya8jKV7zGu0Z1gyTcXYSybbVVDyAjIvftsfLOJV_Tzcgr_GpNFhcQvEmQ4i_w669pGt7f4VPgR829Bz6k4fpvfEawBRQAh3y6n9kWBbfr7nxMoGTTVBXu-lmwAjYsJF4GrR9GMAc6Wp2cZmQ/w640-h640/generated_00%20(2).png" width="640" /></a></div>
<p>
So in this post, let's have a look at how we can combine OpenAI's <b><span style="color: red;">gpt-4o</span></b> Large Language
Model with <b><span style="color: red;">Microsoft Graph Search API</span></b> to query SharePoint and OneDrive
documents in natural language.
</p>
<p>
On a high level we will be using OpenAI function calling to achieve this. Our
steps are going to be:
</p>
<h4 style="background-color: white; color: #666666; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; font-size: 13.2px; margin: 0px; position: relative;">
<span style="color: red;">1. Define an OpenAI function and make it available to the
LLM. </span>
</h4>
<h4 style="background-color: white; color: #666666; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; font-size: 13.2px; margin: 0px; position: relative;">
<span style="color: red;"><br />2. During the course of the chat, if the LLM thinks that to respond
to the user, it needs to call our function, it will respond with the
function name along with the parameters.<br /><br /></span>
</h4>
<h4 style="background-color: white; color: #666666; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; font-size: 13.2px; margin: 0px; position: relative;">
<span style="color: red;">3. Call the Microsoft Graph Search API based on the parameters provided by
the LLM.<br /><br /></span>
</h4>
<h4 style="background-color: white; color: #666666; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; font-size: 13.2px; margin: 0px; position: relative;">
<span style="color: red;">4. Send the results returned from the Microsoft Graph back to the LLM to
generate a response in natural language.</span>
</h4>
<p>
So let's see how to achieve this. In this code I have used the following nuget
packages:
</p>
<p>
<span style="color: #2b00fe;"><a href="https://www.nuget.org/packages/Azure.AI.OpenAI/2.1.0" target="_blank">https://www.nuget.org/packages/Azure.AI.OpenAI/2.1.0</a></span>
</p>
<p>
<a href="https://www.nuget.org/packages/Microsoft.Graph/5.64.0/" target="_blank">https://www.nuget.org/packages/Microsoft.Graph/5.64.0</a>
</p>
<p>The first thing we will look at is our OpenAI function definition:</p>
<script src="https://gist.github.com/vman/6166539fd7b02fd3561762674d773017.js"></script>
In this function we are informing the LLM that if needs to search any files as
part of providing the responses, it can call this function. The function name
will be returned in the response and the relevant parameter will be provided as
well. Now let's see how our orchestrator function looks:
<br />
<br />
<script src="https://gist.github.com/vman/d93b9944af5a9f951b5875ca29ab2ed9.js"></script>
<p>
There is a lot to unpack here as this function is the one which does the heavy
lifting. This code is responsible for handling the chat with OpenAI, calling
the MS Graph and also responding back to the user based on the response from
the Graph.
</p>
<p>
Next, let's have a look at the code which calls the Microsoft Graph based on
the parameters provided by the LLM.
</p>
<p>
Before executing this code, you will need to have created an App registration.
Here is how to do that:
<a href="https://learn.microsoft.com/en-us/azure/active-directory/develop/quickstart-register-app">https://learn.microsoft.com/en-us/azure/active-directory/develop/quickstart-register-app</a>
</p>
<p>
Since we are calling the Microsoft Graph
<b><span style="color: red;">/search</span></b> endpoint with
<span style="color: red;"><b>delegated permissions</b></span>, the app registration will need a minimum of the
<b><span style="color: red;">User.Read</span></b> and
<span style="color: red;"><b>Files.Read.All</b></span> permissions granted.
<a href="https://learn.microsoft.com/en-us/graph/api/user-list?view=graph-rest-1.0&tabs=http">https://learn.microsoft.com/en-us/graph/api/search-query?view=graph-rest-1.0&tabs=http
</a>
</p>
<script src="https://gist.github.com/vman/39c7deb8d81b01c88b8dae5d47161327.js"></script>
This code get the parameters sent from the LLM and uses the Microsoft Graph .NET
SDK to call the <b><span style="color: red;">/search</span></b> endpoint and fetch the files based on the <b><span style="color: red;">searchQuery</span></b>
properties. Once the files are returned, their summary value is concatenated
into a string and returned to the orchestrator function so that it can be sent
again to the LLM.
<div><br /></div>
<div>
Finally, lets have a look at our CallOpenAI function which is responsible for
talking to the Open AI chat api.</div><div>
</div>
<script src="https://gist.github.com/vman/910d3cfafb8b4224e86f43e0ec62da82.js"></script>
<div>
This code defines the Open AI function which will be included in our Chat API
calls. Also, the user's search query is sent to the API to determine if the
function needs to be called. This function is also called again after the
response from the Microsoft Graph is fetched. At that time, this function
contains the details fetched from the Graph to generate an output in natural
language. This way, we can use Open AI function calling together with
Microsoft Graph API to search files in SharePoint and OneDrive.
</div><div><br /></div><div>Hope this helps!</div>
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<span itemprop='name'>Vardhaman Deshpande</span>
</a>
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</span>
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at
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<h2 class='date-header'><span>Tuesday, 5 November 2024</span></h2>
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<a href='https://www.vrdmn.com/2024/11/working-with-openai-assistants-using.html'>Working with OpenAI Assistants: Using code interpreter to generate charts</a>
</h3>
<div class='post-header'>
<div class='post-header-line-1'></div>
</div>
<div class='post-body entry-content' id='post-body-2249403873734555514' itemprop='description articleBody'>
<p>
This is the fourth post in the series where we explore the OpenAI Assistants
API. In this post, we will be looking at the code interpreter tool which
allows us to generate charts based on some data. This is very powerful for
scenarios where you have to do data analysis on JSON, csv or Microsoft Excel
files and generate charts and reports based on them.
</p>
<p>See the following posts for the entire series:</p>
<p>
<span style="color: #2b00fe;"
><b
><a
href="https://www.vrdmn.com/2024/10/working-with-openai-assistants-api.html"
target="_blank"
>Working with the OpenAI Assistants API: Create a simple assistant</a
></b
></span
>
</p>
<p>
<span style="color: #2b00fe;"
><b
><a
href="https://www.vrdmn.com/2024/10/working-with-openai-assistants-api_14.html"
target="_blank"
>Working with the OpenAI Assistants API: Using file search</a
></b
></span
>
</p>
<p>
<span style="color: #2b00fe;"
><b
><a
href="https://www.vrdmn.com/2024/11/working-with-openai-assistants-chat.html"
target="_blank"
>Working with the OpenAI Assistants API: Chat with Excel files using
Code interpreter</a
></b
></span
>
</p>
<p>
<span style="color: #2b00fe;"
><b>Working with the OpenAI Assistants API: </b></span
><span style="color: #2b00fe;"
><b>Using code interpreter to generate charts</b></span
> (this post)
</p>
<p>
The Code Interpreter tool has access to a sandboxed python code execution
environment within the Assistants API. This can provide very useful as the
Assistants API can iteratively run code against the files provided to it and
generate charts!
</p>
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<a
href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhQm-AUBiUHlcQGiZSI8Sn8RujIHIuv0tRey7XH2nZd8ZlvOHV-qiDZHdTw3LoDorfXmMdaLoKnAIlLF9Iddow6TlOYGQBn825iSisRHM22PcGgh-f2juHtmNtumNVANb86FKrr8IYuv20Z9XoCn0c9TMf-utUgN5EvjtBTSZLVHcB0E89XlGE6xgrxumk/s1024/a%20robot%20drawing%20a%20bar%20chart,%20casual%20pencil%20drawing%20style.jpg"
style="margin-left: 1em; margin-right: 1em;"
><img border="0" data-original-height="1024" data-original-width="1024" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhQm-AUBiUHlcQGiZSI8Sn8RujIHIuv0tRey7XH2nZd8ZlvOHV-qiDZHdTw3LoDorfXmMdaLoKnAIlLF9Iddow6TlOYGQBn825iSisRHM22PcGgh-f2juHtmNtumNVANb86FKrr8IYuv20Z9XoCn0c9TMf-utUgN5EvjtBTSZLVHcB0E89XlGE6xgrxumk/w640-h640/a%20robot%20drawing%20a%20bar%20chart,%20casual%20pencil%20drawing%20style.jpg" width="640"
/></a>
</div>
<p>
So in this post, let's see how we can generate charts based on an excel file
with the code interpreter tool. The excel file we will be querying will be the
same one we used in the last post. It contains details of customers like their
name and the licenses purchased of a fictional product by them:
</p>
<p></p>
<div class="separator" style="clear: both; text-align: center;">
<a
href="https://blogger.googleusercontent.com/img/a/AVvXsEhwffTKWJVfqHz1W6FmJl7hHls6lM1wA3Z5zeBuvY0iH9IlgRUKRfT7QGvFBDTrygiFoYr2gLBlXCfR4zI-8HaKLXzqPRe6wsKAV4-BJIIX1JncBhgIZxuM-uaJ0gCIcbONhW7OaLiq1P_wTHxzlyn1skRGt5dGW5J_r2TO12Q3OOApbXGEEAT57vJTWbs"
style="margin-left: 1em; margin-right: 1em;"
><img alt="" data-original-height="156" data-original-width="672" src="https://blogger.googleusercontent.com/img/a/AVvXsEhwffTKWJVfqHz1W6FmJl7hHls6lM1wA3Z5zeBuvY0iH9IlgRUKRfT7QGvFBDTrygiFoYr2gLBlXCfR4zI-8HaKLXzqPRe6wsKAV4-BJIIX1JncBhgIZxuM-uaJ0gCIcbONhW7OaLiq1P_wTHxzlyn1skRGt5dGW5J_r2TO12Q3OOApbXGEEAT57vJTWbs=s16000"
/></a>
</div>
<p></p>
<p>
To generate charts using the Code interpreter, we have to use the following
moving pieces:
</p>
<div>
<ul>
<li>
First, we need to upload the excel file using the <b
><span style="color: #ffa400;">Open AI File client</span></b
>
</li>
<li>
Then, we need to <b
><span style="color: #ffa400;"
>connect the uploaded file to the Code Interpreter tool in either an
assistant or a thread</span
></b
> which would enable the assistant to generate a chart on the
document.
</li>
</ul>
<div>
For the demo code, we will be using the <a
href="https://azure.microsoft.com/en-in/products/ai-services/openai-service"
target="_blank"
><span style="color: #2b00fe;">Azure OpenAI service</span></a
> for working with the OpenAI gpt-4o model and since we will be using
.NET code, we will need the <a
href="https://www.nuget.org/packages/Azure.AI.OpenAI/2.1.0-beta.1"
target="_blank"
><span style="color: #2b00fe;">Azure OpenAI .NET SDK</span></a
> as well as <a
href="https://www.nuget.org/packages/Azure.AI.OpenAI.Assistants/1.0.0-beta.4/"
><span style="color: #2b00fe;">Azure.AI.OpenAI.Assistants</span></a
> nuget packages.
</div>
<div><br /></div>
</div>
<script src="https://gist.github.com/vman/e1402f25d02e891741af84bd93d877c5.js"></script>
<p>And this is the file generated by the code interpreter tool:</p>
<div class="separator" style="clear: both; text-align: center;">
<a
href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjjroUOVLc1m4vWRqITadtW0PCW-6kmQQieW2pxm044Mt7hk2GLUXm-INWjbmL8xUZXsNhT4VT8AAcLowhA2zpb4bL1gD8D5HnHai09Sk-z6B3_Q1Oe269BLu6xF00QyPWdFfhBbD8qTc9wrPf-rXV7vgecXS-OwaNrOW39hy3ekh4g6rhNX2bKiFbJBX0/s1979/557d1041-62b8-4667-8425-96178b6fcd4c.png"
style="margin-left: 1em; margin-right: 1em;"
><img border="0" data-original-height="1180" data-original-width="1979" height="382" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjjroUOVLc1m4vWRqITadtW0PCW-6kmQQieW2pxm044Mt7hk2GLUXm-INWjbmL8xUZXsNhT4VT8AAcLowhA2zpb4bL1gD8D5HnHai09Sk-z6B3_Q1Oe269BLu6xF00QyPWdFfhBbD8qTc9wrPf-rXV7vgecXS-OwaNrOW39hy3ekh4g6rhNX2bKiFbJBX0/w640-h382/557d1041-62b8-4667-8425-96178b6fcd4c.png" width="640"
/></a>
</div>
<p>
As you can see the code interpreter tool takes a few passes at the data. It
tries to understand the document before generating the chart. This is a really
powerful feature and the possibilities are endless!
</p>
<p>Hope this helps.</p>
<div style='clear: both;'></div>
</div>
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<div class="date-outer">
<h2 class='date-header'><span>Monday, 4 November 2024</span></h2>
<div class="date-posts">
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<meta content='https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhoBMmG7ABt-QRwsmjLyFuU46fxernAlFYkaQXxfxypKunXDVUftzU_FT5WLQMxVlq0FXMDwPbuhfdII05VYz-M7jnAMS0zH6y8Xy4NIcCAoWuKe70Gg-wmwvBeTeYlKOkVKfPQpWw8I1Af8mHE6q2ZVRfL0_4odpvaCxoem0ZcqG-2YjYw5gZoGZ8G7Iw/w400-h400/a%20robot%20running%20code%20on%20an%20excel%20file,%20casual%20pencil%20drawing%20style.jpg' itemprop='image_url'/>
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<h3 class='post-title entry-title' itemprop='name'>
<a href='https://www.vrdmn.com/2024/11/working-with-openai-assistants-chat.html'>Working with OpenAI Assistants: Chat with Excel files using Code interpreter</a>
</h3>
<div class='post-header'>
<div class='post-header-line-1'></div>
</div>
<div class='post-body entry-content' id='post-body-8216900863811474093' itemprop='description articleBody'>
<p>
This is the third post in the series where we explore the OpenAI Assistants
API. In this post, we will be looking at the code interpreter tool which
allows us to upload files to the Assistants API and write python code against
them. This is very powerful for scenarios where you have to do data analysis
on csv or Microsoft Excel files and generate charts and reports on them.
</p>
<p>See the following posts for the entire series:</p>
<p>
<span style="color: #2b00fe;"><b><a href="https://www.vrdmn.com/2024/10/working-with-openai-assistants-api.html" target="_blank">Working with the OpenAI Assistants: Create a simple assistant</a></b></span>
</p>
<p>
<span style="color: #2b00fe;"><b><a href="https://www.vrdmn.com/2024/10/working-with-openai-assistants-api_14.html" target="_blank">Working with the OpenAI Assistants: Using file search</a></b></span>
</p>
<p>
<span style="color: #2b00fe;"><b>Working with the OpenAI Assistants: Chat with Excel files using code interpreter</b></span> (this post)
</p><p><b style="color: #2b00fe;"><a href="https://www.vrdmn.com/2024/11/working-with-openai-assistants-using.html" target="_blank">Working with OpenAI Assistants: Using code interpreter to generate charts</a></b></p>
<p>
The Retrieval Augmented Generation (RAG) pattern, which was discussed in
previous posts, works great for text based files like Microsoft Word and PDF
documents. However, when it comes to structured data files like csv or excel,
it comes out short. An this where the Code Interpreter tool can come in very handy. It
can repetitively run python code on documents until it is confident that the
user's question has been answered.
</p>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhoBMmG7ABt-QRwsmjLyFuU46fxernAlFYkaQXxfxypKunXDVUftzU_FT5WLQMxVlq0FXMDwPbuhfdII05VYz-M7jnAMS0zH6y8Xy4NIcCAoWuKe70Gg-wmwvBeTeYlKOkVKfPQpWw8I1Af8mHE6q2ZVRfL0_4odpvaCxoem0ZcqG-2YjYw5gZoGZ8G7Iw/s1024/a%20robot%20running%20code%20on%20an%20excel%20file,%20casual%20pencil%20drawing%20style.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1024" data-original-width="1024" height="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhoBMmG7ABt-QRwsmjLyFuU46fxernAlFYkaQXxfxypKunXDVUftzU_FT5WLQMxVlq0FXMDwPbuhfdII05VYz-M7jnAMS0zH6y8Xy4NIcCAoWuKe70Gg-wmwvBeTeYlKOkVKfPQpWw8I1Af8mHE6q2ZVRfL0_4odpvaCxoem0ZcqG-2YjYw5gZoGZ8G7Iw/w400-h400/a%20robot%20running%20code%20on%20an%20excel%20file,%20casual%20pencil%20drawing%20style.jpg" width="400" /></a>
</div>
<p>
So in this post, let's see how we can query an excel file with the code
interpreter tool. The excel file we will be querying will contain details of customers like their name and the licenses purchased of a fictional product by them:
</p>
<p></p>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/a/AVvXsEhwffTKWJVfqHz1W6FmJl7hHls6lM1wA3Z5zeBuvY0iH9IlgRUKRfT7QGvFBDTrygiFoYr2gLBlXCfR4zI-8HaKLXzqPRe6wsKAV4-BJIIX1JncBhgIZxuM-uaJ0gCIcbONhW7OaLiq1P_wTHxzlyn1skRGt5dGW5J_r2TO12Q3OOApbXGEEAT57vJTWbs" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="156" data-original-width="672" src="https://blogger.googleusercontent.com/img/a/AVvXsEhwffTKWJVfqHz1W6FmJl7hHls6lM1wA3Z5zeBuvY0iH9IlgRUKRfT7QGvFBDTrygiFoYr2gLBlXCfR4zI-8HaKLXzqPRe6wsKAV4-BJIIX1JncBhgIZxuM-uaJ0gCIcbONhW7OaLiq1P_wTHxzlyn1skRGt5dGW5J_r2TO12Q3OOApbXGEEAT57vJTWbs=s16000" /></a>
</div>
<p></p>
<p>
To upload and analyse documents using the Code interpreter, we have to use the
following moving pieces:
</p>
<div>
<ul>
<li>
First, we need to upload files using the <b><span style="color: #ffa400;">Open AI File client</span></b>
</li>
<li>
Then, we need to <b><span style="color: #ffa400;">connect the uploaded file to the Code Interpreter tool in either an
assistant or a thread</span></b> which would enable the assistant to answer questions based on the
document.
</li>
</ul>
<div>
For the demo code, we will be using the <a href="https://azure.microsoft.com/en-in/products/ai-services/openai-service" target="_blank"><span style="color: #2b00fe;">Azure OpenAI service</span></a> for working with the OpenAI gpt-4o model and since we will be using
.NET code, we will need the <a href="https://www.nuget.org/packages/Azure.AI.OpenAI/2.1.0-beta.1" target="_blank"><span style="color: #2b00fe;">Azure OpenAI .NET SDK</span></a> as well as <a href="https://www.nuget.org/packages/Azure.AI.OpenAI.Assistants/1.0.0-beta.4/"><span style="color: #2b00fe;">Azure.AI.OpenAI.Assistants</span></a> nuget packages.
</div>
<div><br /></div>
</div>
<script src="https://gist.github.com/vman/20e6f2711219a3d3478a0bf710fcb736.js"></script>
<p>
As you can see the code interpreter tool takes a few passes at the data. It
tries to understand the document before answering the question. This is a
really powerful feature and the possibilities are endless!
</p>
<p>Hope this helps.</p>
<div style='clear: both;'></div>
</div>
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<h2 class='date-header'><span>Monday, 14 October 2024</span></h2>
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<a href='https://www.vrdmn.com/2024/10/working-with-openai-assistants-api_14.html'>Working with OpenAI Assistants: Using file search</a>
</h3>
<div class='post-header'>
<div class='post-header-line-1'></div>
</div>
<div class='post-body entry-content' id='post-body-7504094186511594233' itemprop='description articleBody'>
<p>
This is the second post in the series where we explore the OpenAI Assistants
API. In this post, we will be looking at the file search capabilities which
allows us to upload files to the Assistants API and chat with them. See the
following posts for the entire series:
</p>
<p>
<span style="color: #2b00fe;"><b><a href="https://www.vrdmn.com/2024/10/working-with-openai-assistants-api.html" target="_blank">Working with OpenAI Assistants: Create a simple assistant</a></b></span>
</p>
<p>
<span style="color: #2b00fe;"><b>Working with OpenAI Assistants: Using file search</b></span> (this post)
</p>
<p>
<span style="color: #2b00fe;"><b><a href="https://www.vrdmn.com/2024/11/working-with-openai-assistants-chat.html" target="_blank">Working with OpenAI Assistants: Chat with Excel files using code interpreter</a></b></span></p><p><span style="color: #2b00fe;"><b><a href="https://www.vrdmn.com/2024/11/working-with-openai-assistants-using.html" target="_blank">Working with OpenAI Assistants: Using code interpreter to generate charts</a></b></span></p>
<p>
The file search API uses the Retrieval Augmented Generation (RAG) pattern
which has been made popular recently. The added advantage of using the
Assistants API for this is that the API manages document chunking, vectorizing
and indexing for us. Whereas without the Assistants API we would have to use a
separate service like Azure AI Search and manage the document indexing
ourselves. </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhcCrWAXRMCBSXX69sHgJnyO6b2sVQfKfhhkgtj7lRHrVAjwaJPdsmSzKvMWHnDth5P6181oreXjRy8d2gstDAjNZQgKhcOeoYwQ0Tum8utTvwlpnvnkcPRljVzoZHShHiz1WMFHyeloO7icdrZ7DsCUZ98dj5G8xCC751EcIHvlaNe_TiuD8rkdf-9ZVI/s1024/A%20minimalistic%20and%20abstract%20illustration%20inspired%20by%20the%20concept%20of%20AI-powered%20file%20search.%20Subtle%20geometric%20shapes%20and%20lines%20gently%20flow%20across%20the%20canvas,%20representing%20the%20idea%20of%20interconnected%20data.%20Abstrac%20(1).jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1024" data-original-width="1024" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhcCrWAXRMCBSXX69sHgJnyO6b2sVQfKfhhkgtj7lRHrVAjwaJPdsmSzKvMWHnDth5P6181oreXjRy8d2gstDAjNZQgKhcOeoYwQ0Tum8utTvwlpnvnkcPRljVzoZHShHiz1WMFHyeloO7icdrZ7DsCUZ98dj5G8xCC751EcIHvlaNe_TiuD8rkdf-9ZVI/w640-h640/A%20minimalistic%20and%20abstract%20illustration%20inspired%20by%20the%20concept%20of%20AI-powered%20file%20search.%20Subtle%20geometric%20shapes%20and%20lines%20gently%20flow%20across%20the%20canvas,%20representing%20the%20idea%20of%20interconnected%20data.%20Abstrac%20(1).jpg" width="640" /></a></div><p>To upload and chat with documents using the Assistants API, we have to use the
following moving pieces: </p>
<div>
<ul style="text-align: left;">
<li>
First, we need to create a
<b><span style="color: #ffa400;">Vector Store</span></b> in the Assistants
API.
</li>
<li>
Then, we need to upload files using the
<b><span style="color: #ffa400;">Open AI File client</span></b> and add
them to the vector store.
</li>
<li>
Finally, we need to
<b><span style="color: #ffa400;">connect the vector store to either an assistant or a thread</span></b> which would enable to assistant to answer questions based on the
document.</li>
</ul><div><br /></div>
<div>
For the demo code, we will be using the <a href="https://azure.microsoft.com/en-in/products/ai-services/openai-service" target="_blank"><span style="color: #2b00fe;">Azure OpenAI service</span></a> for working with the OpenAI gpt-4o model and since we will be using
.NET code, we will need the <a href="https://www.nuget.org/packages/Azure.AI.OpenAI/2.1.0-beta.1" target="_blank"><span style="color: #2b00fe;">Azure OpenAI .NET SDK</span></a> as well as <a href="https://www.nuget.org/packages/Azure.AI.OpenAI.Assistants/1.0.0-beta.4/"><span style="color: #2b00fe;">Azure.AI.OpenAI.Assistants</span></a> nuget packages.</div><div><br /></div>
</div>
<div>
<script src="https://gist.github.com/vman/3dd75f596d7443cdeb8a2dc3f9372d5b.js"></script>
<h3 style="text-align: left;">
<span style="background-color: white; color: #38761d;">Limitations</span>
</h3>
<div style="text-align: left;"><br /></div><div style="text-align: left;">
As per
<a href="https://platform.openai.com/docs/assistants/tools/file-search" target="_blank"><span style="color: #2b00fe;">OpenAI docs</span></a>, there are some limitations for the file search tool:
</div>
<div style="text-align: left;"><br /></div>
<div style="text-align: left;">
<ul style="text-align: left;">
<li>Each vector store can hold up to 10,000 files.</li>
<li>
The maximum file size of a file which can be uploaded is 512 MB. Each
file should contain no more than 5,000,000 tokens per file (computed
automatically when you attach a file).
</li>
</ul>
</div>
<div style="text-align: left;"><br /></div>
<div style="text-align: left;">
When querying for the documents in the vector store, we have to be aware of
the following things which are not possible right now. However, the OpenAI
team are working on this and some of these features will be available soon:
</div>
<div style="text-align: left;">
<span style="font-weight: normal;"><br /></span>
</div>
<div style="text-align: left;">
<ul style="text-align: left;">
<li>
<span style="font-weight: normal;">Support for deterministic pre-search filtering using custom
metadata.</span>
</li>
<li>
<span style="font-weight: normal;">Support for parsing images within documents (including images of
charts, graphs, tables etc.)</span>
</li>
<li>
<span style="font-weight: normal;">Support for retrievals over structured file formats (like csv or
jsonl).</span>
</li>
<li>
<span style="font-weight: normal;">Better support for summarization — the tool today is optimized for
search queries.</span>
</li>
</ul>
</div>
<div style="text-align: left;">
<span style="font-weight: normal;"><br /></span>
</div>
<div style="text-align: left;">
<span style="font-weight: normal;">Current supported files types can be found in the
<a href="https://platform.openai.com/docs/assistants/tools/file-search/supported-files" target="_blank"><span style="color: #2b00fe;">OpenAI docs</span></a></span>
</div><div style="text-align: left;"><br /></div><div style="text-align: left;">Hope this helps!</div>
</div>
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<h2 class='date-header'><span>Monday, 7 October 2024</span></h2>
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<h3 class='post-title entry-title' itemprop='name'>
<a href='https://www.vrdmn.com/2024/10/working-with-openai-assistants-api.html'>Working with OpenAI Assistants: Create a simple assistant</a>
</h3>
<div class='post-header'>
<div class='post-header-line-1'></div>
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<p>
With
<a href="https://platform.openai.com/docs/assistants/overview" target="_blank"><span style="color: #2b00fe;">OpenAI's recently released Assistants API</span></a>, building AI bots becomes a lot easier. Using the API, an assistant can leverage
custom instructions, files and tools (previously called functions) and answer
user questions based on them.
</p>
<p>
Before the Assistants API, building such assistants was possible but for a lot
of things, we had to use our own services e.g. vector storage for file search,
database for maintaining chat history etc.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg55YifOTO0wmPllJ5W79jDgkfTK5bgW1t0mb0zzGVEPLNW20W5aXIs7LAZq1ddmmIBvcECDSF0T6-5ubFTf24v2OelfyeE70-C1l-KebsTYThqQlHBF7lNN56j-ASQPPICNZLPECc_S0YVATo-mE_wSYdp2JbaBYF6bQ-g6Z4GVMPoKvkoqbSeVMYobTA/s1024/ai%20assistant,%20casual%20drawing,%20no%20robts.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1024" data-original-width="1024" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg55YifOTO0wmPllJ5W79jDgkfTK5bgW1t0mb0zzGVEPLNW20W5aXIs7LAZq1ddmmIBvcECDSF0T6-5ubFTf24v2OelfyeE70-C1l-KebsTYThqQlHBF7lNN56j-ASQPPICNZLPECc_S0YVATo-mE_wSYdp2JbaBYF6bQ-g6Z4GVMPoKvkoqbSeVMYobTA/w640-h640/ai%20assistant,%20casual%20drawing,%20no%20robts.jpg" width="640" /></a></div><p>The Assistants API gives us a handy wrapper on top of all these disparate
services and a single endpoint to work with. So in this series of posts, let's
have a look at what the Assistants API can do.</p><p></p>
<p style="text-align: left;">
<span style="color: #2b00fe;"><b>
Working with OpenAI Assistants: Create a simple assistant</b></span>
<span style="font-weight: normal;">(this post) </span>
</p>
<p>
<span style="color: #2b00fe;"><b><a href="https://www.vrdmn.com/2024/10/working-with-openai-assistants-api_14.html" target="_blank">Working with OpenAI Assistants: Using file search</a></b></span></p>
<p>
<span style="color: #2b00fe;"><b><a href="https://www.vrdmn.com/2024/11/working-with-openai-assistants-chat.html" target="_blank">Working with OpenAI Assistants: Chat with Excel files using code interpreter</a></b></span></p><p><span style="color: #2b00fe;"><b><a href="https://www.vrdmn.com/2024/11/working-with-openai-assistants-using.html" target="_blank">Working with OpenAI Assistants: Using code interpreter to generate charts</a></b></span></p>
<p>
The first thing we are going to do is build a simple assistant which has a
"SharePoint Tutor" personality. It will be used to answer questions for users
who are learning to use SharePoint. Before deep diving into the code, lets
understand the different moving pieces of the Assistants API:
</p>
<p>
An <span style="color: #ffa400;"><b>assistant</b></span> is a container in
which all operations between the AI and the user are managed.
</p>
<p>
A <b><span style="color: #ffa400;">thread</span></b> is a list of messages
which were exchanged between the user and AI. The thread is also responsible for maintaining the conversation history.
</p>
<p>
A <b><span style="color: #ffa400;">run</span></b> is a single invocation of an
assistant based on the history in the thread as well as the tools available to
the assistant. After a run is executed, new messages are generated and added
to the thread.
</p><p>For the demo code, we will be using the <a href="https://azure.microsoft.com/en-in/products/ai-services/openai-service" target="_blank"><span style="color: #2b00fe;">Azure OpenAI service</span></a> for working with the OpenAI gpt-4o model and since we will be using .NET code, we will need the <a href="https://www.nuget.org/packages/Azure.AI.OpenAI/2.1.0-beta.1" target="_blank"><span style="color: #2b00fe;">Azure OpenAI .NET SDK</span></a> as well as <a href="https://www.nuget.org/packages/Azure.AI.OpenAI.Assistants/1.0.0-beta.4/"><span style="color: #2b00fe;">Azure.AI.OpenAI.Assistants</span></a> nuget packages.</p>
<script src="https://gist.github.com/vman/a6c059d64219fb6292a6ad5156b0f530.js"></script>
This was a simple assistant creation just to get us familiar with the Assitants
API. In the next posts, we will dive deeper into the API and explore the more
advanced concepts. Stay tuned!
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<span style="font-family: verdana; font-size: 8pt;color:#BB3333;"><b>v uh r dha m aa n</b></span><span style="font-family: Arial; font-size: 12px;"> </span><br style="font-family: Arial; font-size: 12px;" /><br style="font-family: Arial; font-size: 12px;" /><table border="0" cellpadding="0" cellspacing="0" style="font-family: Arial;"><tbody><tr><td align="center" class="td_bck_keys1" nowrap="" style="background-color: #eeeeee; border-color: rgb(255, 255, 255) rgb(153, 153, 153) rgb(153, 153, 153) rgb(255, 255, 255); border-style: solid; border-width: 1px; color: #bb3333; font-family: verdana; font-size: 8pt; letter-spacing: 0.1em; padding: 5px;" width="20"><b>v</b></td><td align="center" class="td_bck_keys1" nowrap="" style="background-color: #eeeeee; border-color: rgb(255, 255, 255) rgb(153, 153, 153) rgb(153, 153, 153) rgb(255, 255, 255); border-style: solid; border-width: 1px; color: #bb3333; font-family: verdana; font-size: 8pt; letter-spacing: 0.1em; padding: 5px;" width="20"><b>uh</b></td><td align="center" class="td_bck_keys1" nowrap="" style="background-color: #eeeeee; border-color: rgb(255, 255, 255) rgb(153, 153, 153) rgb(153, 153, 153) rgb(255, 255, 255); border-style: solid; border-width: 1px; color: #bb3333; font-family: verdana; font-size: 8pt; letter-spacing: 0.1em; padding: 5px;" width="20"><b>r</b></td><td align="center" class="td_bck_keys1" nowrap="" style="background-color: #eeeeee; border-color: rgb(255, 255, 255) rgb(153, 153, 153) rgb(153, 153, 153) rgb(255, 255, 255); border-style: solid; border-width: 1px; color: #bb3333; font-family: verdana; font-size: 8pt; letter-spacing: 0.1em; padding: 5px;" width="20"><b>dha</b></td><td align="center" class="td_bck_keys1" nowrap="" style="background-color: #eeeeee; border-color: rgb(255, 255, 255) rgb(153, 153, 153) rgb(153, 153, 153) rgb(255, 255, 255); border-style: solid; border-width: 1px; color: #bb3333; font-family: verdana; font-size: 8pt; letter-spacing: 0.1em; padding: 5px;" width="20"><b>m</b></td><td align="center" class="td_bck_keys1" nowrap="" style="background-color: #eeeeee; border-color: rgb(255, 255, 255) rgb(153, 153, 153) rgb(153, 153, 153) rgb(255, 255, 255); border-style: solid; border-width: 1px; color: #bb3333; font-family: verdana; font-size: 8pt; letter-spacing: 0.1em; padding: 5px;" width="20"><b>aa</b></td><td align="center" class="td_bck_keys1" nowrap="" style="background-color: #eeeeee; border-color: rgb(255, 255, 255) rgb(153, 153, 153) rgb(153, 153, 153) rgb(255, 255, 255); border-style: solid; border-width: 1px; color: #bb3333; font-family: verdana; font-size: 8pt; letter-spacing: 0.1em; padding: 5px;" width="20"><b>n</b></td></tr><tr><td align="center" class="td_bck_keys1" nowrap="" style="background-color: #eeeeee; border-color: rgb(255, 255, 255) rgb(153, 153, 153) rgb(153, 153, 153) rgb(255, 255, 255); border-style: solid; border-width: 1px; color: #bb3333; font-family: verdana; font-size: 8pt; letter-spacing: 0.1em; padding: 5px;"><span style="color:#000000;"><span class="data_key" style="color: #bb3333; font-size: 8pt; font-weight: bold;">v</span>ery</span></td><td align="center" class="td_bck_keys1" nowrap="" style="background-color: #eeeeee; border-color: rgb(255, 255, 255) rgb(153, 153, 153) rgb(153, 153, 153) rgb(255, 255, 255); border-style: solid; border-width: 1px; color: #bb3333; font-family: verdana; font-size: 8pt; letter-spacing: 0.1em; padding: 5px;"><span style="color:#000000;"><span class="data_key" style="color: #bb3333; font-size: 8pt; font-weight: bold;">u</span>p</span></td><td align="center" class="td_bck_keys1" nowrap="" style="background-color: #eeeeee; border-color: rgb(255, 255, 255) rgb(153, 153, 153) rgb(153, 153, 153) rgb(255, 255, 255); border-style: solid; border-width: 1px; color: #bb3333; font-family: verdana; font-size: 8pt; letter-spacing: 0.1em; padding: 5px;"><span style="color:#000000;"><span class="data_key" style="color: #bb3333; font-size: 8pt; font-weight: bold;">r</span>at</span></td><td align="center" class="td_bck_keys1" nowrap="" style="background-color: #eeeeee; border-color: rgb(255, 255, 255) rgb(153, 153, 153) rgb(153, 153, 153) rgb(255, 255, 255); border-style: solid; border-width: 1px; color: #bb3333; font-family: verdana; font-size: 8pt; letter-spacing: 0.1em; padding: 5px;"><span style="color:#000000;"><span class="data_key" style="color: #bb3333; font-size: 8pt; font-weight: bold;">the</span></span></td><td align="center" class="td_bck_keys1" nowrap="" style="background-color: #eeeeee; border-color: rgb(255, 255, 255) rgb(153, 153, 153) rgb(153, 153, 153) rgb(255, 255, 255); border-style: solid; border-width: 1px; color: #bb3333; font-family: verdana; font-size: 8pt; letter-spacing: 0.1em; padding: 5px;"><span style="color:#000000;"><span class="data_key" style="color: #bb3333; font-size: 8pt; font-weight: bold;">m</span>e</span></td><td align="center" class="td_bck_keys1" nowrap="" style="background-color: #eeeeee; border-color: rgb(255, 255, 255) rgb(153, 153, 153) rgb(153, 153, 153) rgb(255, 255, 255); border-style: solid; border-width: 1px; color: #bb3333; font-family: verdana; font-size: 8pt; letter-spacing: 0.1em; padding: 5px;"><span style="color:#000000;">c<span class="data_key" style="color: #bb3333; font-size: 8pt; font-weight: bold;">a</span>r</span></td><td align="center" class="td_bck_keys1" nowrap="" style="background-color: #eeeeee; border-color: rgb(255, 255, 255) rgb(153, 153, 153) rgb(153, 153, 153) rgb(255, 255, 255); border-style: solid; border-width: 1px; color: #bb3333; font-family: verdana; font-size: 8pt; letter-spacing: 0.1em; padding: 5px;"><span style="color:#000000;"><span class="data_key" style="color: #bb3333; font-size: 8pt; font-weight: bold;">n</span>o</span></td></tr></tbody></table>
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<a class='post-count-link' href='https://www.vrdmn.com/2020/04/'>
April
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2020/03/'>
March
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2020/02/'>
February
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2020/01/'>
January
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2019/'>
2019
</a>
<span class='post-count' dir='ltr'>(12)</span>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
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► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2019/12/'>
December
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2019/11/'>
November
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2019/10/'>
October
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2019/09/'>
September
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2019/07/'>
July
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2019/04/'>
April
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2019/03/'>
March
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2019/02/'>
February
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2019/01/'>
January
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2018/'>
2018
</a>
<span class='post-count' dir='ltr'>(13)</span>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2018/12/'>
December
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2018/10/'>
October
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2018/08/'>
August
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2018/07/'>
July
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2018/06/'>
June
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2018/05/'>
May
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2018/04/'>
April
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2018/03/'>
March
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2018/02/'>
February
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2018/01/'>
January
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2017/'>
2017
</a>
<span class='post-count' dir='ltr'>(15)</span>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
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► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2017/08/'>
August
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2017/07/'>
July
</a>
<span class='post-count' dir='ltr'>(5)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2017/06/'>
June
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2017/05/'>
May
</a>
<span class='post-count' dir='ltr'>(3)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2017/04/'>
April
</a>
<span class='post-count' dir='ltr'>(4)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2017/01/'>
January
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2016/'>
2016
</a>
<span class='post-count' dir='ltr'>(17)</span>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
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► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2016/11/'>
November
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2016/10/'>
October
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2016/09/'>
September
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2016/08/'>
August
</a>
<span class='post-count' dir='ltr'>(5)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2016/07/'>
July
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2016/06/'>
June
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2016/05/'>
May
</a>
<span class='post-count' dir='ltr'>(4)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2016/03/'>
March
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2015/'>
2015
</a>
<span class='post-count' dir='ltr'>(20)</span>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2015/11/'>
November
</a>
<span class='post-count' dir='ltr'>(4)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2015/08/'>
August
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2015/07/'>
July
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2015/05/'>
May
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2015/04/'>
April
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2015/03/'>
March
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2015/01/'>
January
</a>
<span class='post-count' dir='ltr'>(8)</span>
</li>
</ul>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2014/'>
2014
</a>
<span class='post-count' dir='ltr'>(11)</span>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2014/11/'>
November
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2014/09/'>
September
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2014/08/'>
August
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2014/06/'>
June
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2014/05/'>
May
</a>
<span class='post-count' dir='ltr'>(3)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2014/03/'>
March
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2013/'>
2013
</a>
<span class='post-count' dir='ltr'>(13)</span>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2013/11/'>
November
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2013/10/'>
October
</a>
<span class='post-count' dir='ltr'>(1)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2013/07/'>
July
</a>
<span class='post-count' dir='ltr'>(4)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2013/03/'>
March
</a>
<span class='post-count' dir='ltr'>(3)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2013/02/'>
February
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2013/01/'>
January
</a>
<span class='post-count' dir='ltr'>(2)</span>
</li>
</ul>
</li>
</ul>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
<a class='post-count-link' href='https://www.vrdmn.com/2012/'>
2012
</a>
<span class='post-count' dir='ltr'>(18)</span>
<ul class='hierarchy'>
<li class='archivedate collapsed'>
<a class='toggle' href='javascript:void(0)'>
<span class='zippy'>
► 
</span>
</a>
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Vardhaman Deshpande
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