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Chatbot For Your Google Paperwork Utilizing Langchain And OpenAI


On this article, we’ll create a Chatbot to your Google Paperwork with OpenAI and Langchain. Now why do we now have to do that within the first place? It could get tedious to repeat and paste your Google Docs contents to OpenAI. OpenAI has a personality token restrict the place you’ll be able to solely add particular quantities of data. So if you wish to do that at scale otherwise you need to do it programmatically, you’re going to wish a library that will help you out; with that, Langchain comes into the image. You may create a enterprise affect by connecting Langchain with Google Drive and open AI in an effort to summarize your paperwork and ask associated questions. These paperwork might be your product paperwork, your analysis paperwork, or your inside data base that your organization is utilizing.

Chatbot For Your Google Documents | Langchain | Openai

Studying Targets

  • You may learn to fetch your Google paperwork content material utilizing Langchain.
  • Learn to combine your Google docs content material with OpenAI LLM.
  • You may be taught to summarize and ask questions on your doc’s content material.
  • You may learn to create a Chatbot that solutions questions based mostly in your paperwork.

This text was printed as part of the Information Science Blogathon.

Load Your Paperwork

Earlier than we get began, we have to arrange our paperwork in google drive.  The important half here’s a doc loader that langchain gives known as GoogleDriveLoader. Utilizing this, you’ll be able to initialize this class after which cross it a listing of doc IDs.

from langchain.document_loaders import GoogleDriveLoader
import os
loader = GoogleDriveLoader(document_ids=["YOUR DOCUMENT ID's'"],
                          credentials_path="PATH TO credentials.json FILE")
docs = loader.load()

Yow will discover your doc id out of your doc hyperlink. Yow will discover the id between the ahead slashes after /d/ within the hyperlink.

For instance, in case your doc hyperlink is then your doc id is “1zqC3_bYM8Jw4NgF”.

You may cross the listing of those doc IDs to document_ids parameter, and the cool half about that is you can even cross a Google Drive folder ID that incorporates your paperwork. In case your folder hyperlink is then the folder ID is “OuKkeghlPiGgWZdM1TzuzM”.

Authorize Google Drive Credentials

Step 1:

Allow the GoogleDrive API by utilizing this hyperlink Please guarantee you’re logged into the identical Gmail account the place your paperwork are saved within the drive.

Chatbot For Your Google Documents | Langchain | Openai

Step 2: Go to the Google Cloud console by clicking this hyperlink . Choose “OAuth consumer ID”. Give software kind as Desktop app.

Chatbot For Your Google Documents | Langchain | Openai
Chatbot For Your Google Documents | Langchain | Openai

Step 3: After creating the OAuth consumer, obtain the secrets and techniques file by clicking “DOWNLOAD JSON”. You may comply with Google’s steps in case you have any doubts whereas making a credentials file.

Chatbot For Your Google Documents | Langchain | Openai

Step 4: Improve your Google API Python consumer by working beneath pip command

pip set up --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib

Then we have to cross our json file path into GoogleDriveLoader.

Summarizing Your Paperwork

Ensure you have your OpenAI API Keys accessible with you. If not, comply with the beneath steps:

1. Go to ‘ and create your account.

2. Login into your account and choose ‘API’ in your dashboard.

3. Now click on in your profile icon, then choose ‘View API Keys’.

4. Choose ‘Create new secret key’, copy it, and put it aside.

Subsequent, we have to load our OpenAI LLM. Let’s summarize the loaded docs utilizing OpenAI. Within the beneath code, we used a summarization algorithm known as summarize_chain supplied by langchain to create a summarization course of which we saved in a variable named chain that takes enter paperwork and produces concise summaries utilizing the map_reduce method. Exchange your API key within the beneath code.

from langchain.llms import OpenAI
from langchain.chains.summarize import load_summarize_chain
llm = OpenAI(temperature=0, openai_api_key=os.environ['OPENAI_API_KEY'])
chain = load_summarize_chain(llm, chain_type="map_reduce", verbose= False)

You’re going to get a abstract of your paperwork when you run this code. If you wish to see what LangChain was doing beneath the covers, change verbose to True, after which you’ll be able to see the logic that Langchain is utilizing and the way it’s considering. You may observe that LangChain will robotically insert the question to summarize your doc, and the whole textual content(question+ doc content material) might be handed to OpenAI. Now OpenAI will generate the abstract.

Beneath is a use case the place I despatched a doc in Google Drive associated to a product SecondaryEquityHub and summarized the doc utilizing the map_reduce chain kind and load_summarize_chain() operate. I’ve set verbose=True to see how Langchain is working internally.

from langchain.document_loaders import GoogleDriveLoader
import os
loader = GoogleDriveLoader(document_ids=["ceHbuZXVTJKe1BT5apJMTUvG9_59-yyknQsz9ZNIEwQ8"],
docs = loader.load()
from langchain.llms import OpenAI
from langchain.chains.summarize import load_summarize_chain
llm = OpenAI(temperature=0, openai_api_key=os.environ['OPENAI_API_KEY'])
chain = load_summarize_chain(llm, chain_type="map_reduce", verbose=True)


 Source: Author

We are able to observe that Langchain inserted the immediate to generate a abstract for a given doc.

 Source: Author

We are able to see the concise abstract and the product options current within the doc generated by Langchain utilizing OpenAI LLM.

Extra Use Circumstances

1. Analysis: We are able to use this performance whereas doing analysis, As a substitute of intensively studying the whole analysis paper phrase by phrase, we will use the summarizing performance to get a look on the paper rapidly.

2. Training: Instructional establishments can get curated textbook content material summaries from in depth information, educational books, and papers.

3. Enterprise Intelligence: Information analysts should undergo a big set of paperwork to extract insights from paperwork. Utilizing this performance, they will cut back the large quantity of effort.

4. Authorized Case Evaluation: Legislation practising professionals can use this performance to rapidly get important arguments extra effectively from their huge quantity of earlier comparable case paperwork.

Let’s say we needed to ask questions on content material in a given doc, we have to load in a special chain named load_qa_chain . Subsequent, we initialise this chain with a chain_type parameter. In our case, we used chain_type as “stuff” It is a simple chain kind; it takes all of the content material, concatenates, and passes to LLM.

Different chain_types:

  • map_reduce: In the beginning, the mannequin will individually appears into every doc and shops its insights, and on the finish, it combines all these insights and once more appears into these mixed insights to get the ultimate response.
  • refine: It iteratively appears into every doc given within the document_id listing, then it refines the solutions with the current info it discovered within the doc because it goes.
  • Map re-rank: The mannequin will individually look into every doc and assigns a rating to the insights. Lastly, it is going to return the one with the very best rating.

Subsequent, we run our chain by passing the enter paperwork and question.

from langchain.chains.question_answering import load_qa_chain
question = "Who's founding father of analytics vidhya?"
chain = load_qa_chain(llm, chain_type="stuff"), query=question)

Once you run this code, langchain robotically inserts the immediate together with your doc content material earlier than sending this to OpenAI LLM. Below the hood, langchain helps us with immediate engineering by offering optimized prompts to extract the required content material from paperwork. If you wish to see what prompts they’re utilizing internally, simply set verbose=True, then you’ll be able to see the immediate within the output.

from langchain.chains.question_answering import load_qa_chain
question = "Who's founding father of analytics vidhya?"
chain = load_qa_chain(llm, chain_type="stuff", verbose=True), query=question)

Construct Your Chatbot

Now we have to discover a technique to make this mannequin a question-answering Chatbot. Primarily we have to comply with beneath three issues to create a Chatbot.

1. Chatbot ought to keep in mind the chat historical past to know the context relating to the continuing dialog.

2. Chat historical past needs to be up to date after every immediate the consumer asks to bot.

2. Chatbot ought to work till the consumer desires to exit the dialog.

from langchain.chains.question_answering import load_qa_chain

# Perform to load the Langchain question-answering chain
def load_langchain_qa():
    llm = OpenAI(temperature=0, openai_api_key=os.environ['OPENAI_API_KEY'])  
    chain = load_qa_chain(llm, chain_type="stuff", verbose=True)
    return chain

# Perform to deal with consumer enter and generate responses
def chatbot():
    print("Chatbot: Hello! I am your pleasant chatbot. Ask me something or kind 'exit' to finish the dialog.")
    from langchain.document_loaders import GoogleDriveLoader
    loader = GoogleDriveLoader(document_ids=["YOUR DOCUMENT ID's'"],
                          credentials_path="PATH TO credentials.json FILE")
    docs = loader
    # Initialize the Langchain question-answering chain
    chain = load_langchain_qa()
    # Record to retailer chat historical past
    chat_history = []
    whereas True:
        user_input = enter("You: ")
        if user_input.decrease() == "exit":
            print("Chatbot: Goodbye! Have a fantastic day.")

        # Append the consumer's query to speak historical past

        # Course of the consumer's query utilizing the question-answering chain
        response =, query=user_input)
        # Extract the reply from the response
        reply = response['answers'][0]['answer'] if response['answers'] else "I could not discover a solution to your query."

        # Append the chatbot's response to speak historical past
        chat_history.append("Chatbot: " + reply)

        # Print the chatbot's response
        print("Chatbot:", reply)

if __name__ == "__main__":

We initialized our google drive paperwork and OpenAI LLM. Subsequent, we created a listing to retailer the chat historical past, and we up to date the listing after each immediate. Then we created an infinite whereas loop that stops when the consumer offers “exit” as a immediate.


On this article, we now have seen the way to create a Chatbot to present insights about your Google paperwork contents. Integrating Langchain, OpenAI, and Google Drive is without doubt one of the most useful use circumstances in any discipline, whether or not medical, analysis, industrial, or engineering. As a substitute of studying complete information and analyzing the info to get insights which prices a variety of human time and effort. We are able to implement this know-how to automate describing, summarizing, analyzing, and extracting insights from our information recordsdata.

Key Takeaways

  • Google paperwork might be fetched into Python utilizing Python’s GoogleDriveLoader class and Google Drive API credentials.
  • By integrating OpenAI LLM with Langchain, we will summarize our paperwork and ask questions associated to the paperwork.
  • We are able to get insights from a number of paperwork by selecting applicable chain sorts like map_reduce, stuff, refine, and map rerank.

Often Requested Questions

Q1. The way to construct a sensible chatbot with Langchain and ChatGPT?

A. To construct an clever chatbot, it’s good to have applicable information, then it’s good to give entry to ChatGPT for this information. Lastly, it’s good to present dialog reminiscence to the bot to retailer the chat historical past to know the context.

Q2. How do I share a Google Doc with OpenAI’s ChatGPT?

A. One of many options is you should use Langchain’s GoogleDriveLoader to fetch a Google Doc then, you’ll be able to initialize the OpenAI LLM utilizing your API keys, then you’ll be able to share the file to this LLM.

Q3. How do I hyperlink ChatGPT on to a Google Drive file?

A. First, it’s good to allow Google Drive API, then get your credentials for Google Drive API, then you’ll be able to cross the doc id of your file to the OpenAI ChatGPT mannequin utilizing Langchain GoogleDriveLoader.

This autumn. Can ChatGPT entry drive paperwork?

A. ChatGPT can’t entry our paperwork straight. Nevertheless, we will both copy and paste the content material into ChatGPT or straight fetch the contents of paperwork utilizing Langchain then, we will cross the contents to ChatGPT by initializing it utilizing secret keys.

The media proven on this article just isn’t owned by Analytics Vidhya and is used on the Creator’s discretion.



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