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Code documentation Q&A bot example with LangChain
This Q&A bot will allow you to query your own documentation easily using questions. We'll also demonstrate the use of LangChain and LanceDB using the OpenAI API.
In this example we'll Numpy 1.26 documentation, but, this could be replaced for your own docs as well
Installing dependencies
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First, let's get some setup out of the way. As we're using the OpenAI API, ensure that you've set your key (and organization if needed):
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We're going to use the power of LangChain to help us create our Q&A bot. It comes with several APIs that can make our development much easier as well as a LanceDB integration for vectorstore.
Importing all libraries
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To make this easier, we've downloaded Pandas documentation and stored the raw HTML files for you to download. We'll download them and then use LangChain's HTML document readers to parse them and store them in LanceDB as a vector store, along with relevant metadata. By default we'll use numpy docs as it is much smaller than pandas docs, but you can replace this with your own docs as well.
Get the data
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We'll create a simple helper function that can help to extract metadata, so we can use this downstream when we're wanting to query with filters. In this case, we want to keep the lineage of the uri or path for each document that we process:
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Pre-processing and loading the documentation
Next, let's pre-process and load the documentation. To make sure we don't need to do this repeatedly if we were updating code, we're caching it using pickle so we can retrieve it again (this could take a few minutes to run the first time you do it). We'll also add some more metadata to the docs here such as the title and version of the code:
If there is some issue with nltk package, kindly try using
import nltk
nltk.download('punkt')
or try to manually install the nltk_data package and unzip the punkt tokenizer zip and the averaged_perceptron_tagger zip file in the packages folder.
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[nltk_data] Downloading package punkt to /root/nltk_data... [nltk_data] Unzipping tokenizers/punkt.zip.
True
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Generating emebeddings from our docs
Now that we have our raw documents loaded, we need to pre-process them to generate embeddings:
[16]
Storing
Let's connect to LanceDB so we can store our documents, It requires 0 setup !
For LanceDB cloud use the below cell :
[ ]
For LanceDB local use the below cell :
[18]
Now let's create our RetrievalQA chain using the LanceDB vector store:
[19]
And thats it! We're all setup. The next step is to run some queries, let's try a few:
Querying
[20]
{'query': 'tell me about the numpy library?',
, 'result': ' The NumPy library is a Python library that provides multidimensional array and matrix data structures, as well as various routines for efficient operations on these structures. It is commonly used for scientific computing and has a vast library of mathematical functions. It also has a fixed array size and is used extensively in other popular data science packages such as Pandas and scikit-learn.'} [21]
{'query': "What's the current version of numpy?",
, 'result': ' The current version of NumPy is 1.26.'} [22]
{'query': 'What kind of linear algebra related operations can be done in numpy?',
, 'result': "\n\nSome possible operations that can be performed using numpy's linear algebra functions include matrix multiplication, finding eigenvalues and eigenvectors, calculating determinants and inverses, solving systems of linear equations, and performing decompositions such as LU, QR, and SVD."} Thanks !