Integrate with LlamaIndex in 2 minutes

What this tutorial will cover:

• How can you use LlamaIndex’s connectors to get data.
• How you can then store/retrieve data in Twilix to then generate answers

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Installation

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Before You Start

All API requests requires an API key. To get your API key, sign up for free at https://app.twilix.io.

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Installing via Python

You can install Twilix's BlitzChain package in Python running the following pip command:

pip install -U blitzchain

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Using LlamaIndex’s Loaders

Before starting, make sure you install Llama Index using the pip install below.

pip install -q llama-index

You can then get any loader from LlamaIndex. Here is an example:

from llama_index import download_loader

YoutubeTranscriptReader = download_loader("YoutubeTranscriptReader")

loader = YoutubeTranscriptReader()
documents = loader.load_data(ytlinks=['https://www.youtube.com/watch?v=-hxeDjAxvJ8'])

Once you have retrieved the documents, you will want to insert them into Twilix. To help with this, we built out our Python package which we called BlitzChain.

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Instantiating Twilix

All API requests requires an API key. To get your API key, sign up for free at https://app.twilix.io.

from blitzchain import Client
TWILIX_API_KEY = "..."
client = Client(TWILIX_API_KEY)
collection = client.Collection("marcAndreessenInterviewDemo")

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Pre-processing

We convert everything to a string to avoid any potential inconsistent data structure issues.

docs = [d.dict() for d in documents]
# Convert everything to a string
for d in docs:
  for k, v in d.items():
    d[k] = str(v)

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Inserting into Twilix

result = collection.insert_objects(
  objects=docs,
  # Here you can specify you only want to split on the text field
  split_on_field='text',
  # Here, you can specify that you ONLY want to vectorize the text field
  fields_for_vectorization=['text'],
)
print(result)

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Check using count

You can then run a count of the documents to check they have been inserted properly. Below, we can see what happened.

# You can check the count of the documents here
collection.count()

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Run question-answering

Running question-answering now is as simple as 1 line of code:

collection.generative_qa(
    # Ask it any query
    user_input="Summarize this please",
    # Use your answer field
    prompt_fields=['text'],
)

If you are interested in further tweaking this, we recommend checking out

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Create a copilot

You can build a copilot to do more complex things based on your data such as integrating with your product to do advanced reasoning tasks or analysis.

collection.copilot(
    # Ask it any query
    user_input="Summarize this please",
    # Use your answer field
    prompt_fields=['text'],
)

If you are interested in further tweaking this, we recommend checking out