[Artificial Intelligence] Building ChatBot for your PDF files with LangChain

By See Hiong

May 7, 2023 - 2 minutes read - 345 words

Extending the use case on the previous post, I will demostrate how you could ingest your own PDF file to your own LLaMa model in local machine.

Preparation

(2 mins)

Let’s start off by installing Chroma, the open-source embedding database:

pip install chromadb pypdf

langchain-chromadb-install

Ingesting your PDF

(5 mins)

For a start, I will ingest this Java-Design-Patterns file:

python3

from langchain.document_loaders import PyPDFLoader 

loader = PyPDFLoader("/home/pi/java-design-patterns-101.pdf")
document = loader.load()

langchain-ingest-pdf

Next, I will split the document into chunks. Please change the chunk_size attributes based on your source contents and requirements.

from langchain.text_splitter import CharacterTextSplitter

text_splitter = CharacterTextSplitter(chunk_size=1500, separator="\n")
texts = text_splitter.split_documents(document)

We will be using the default Chroma’s embeddings. By default, Chroma uses all-MiniLM-L6-v2.

from langchain.vectorstores import Chroma

vectordb = Chroma.from_documents(texts, persist_directory=".", metadatas=[{"source": f"{i}-jdp"} for i in range(len(texts))])
vectordb.persist()

The chroma DB will be stored in the current directory, stored as chroma-collections.parquet, chroma-embeddings.parquest and index folder langchain-chroma-embeddings

NOTE: It seems like the current codebase does not use default embedding function anymore. langchain-chroma-embeddings-failed

If you face the above issue, you may provide the embeddings as such:

from langchain.embeddings import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vectordb = Chroma.from_documents(texts, embeddings, persist_directory=".", metadatas=[{"source": f"{i}-jdp"} for i in range(len(texts))])
vectordb.persist()

To load from the persisted DB, you can use this instead:

from langchain.vectorstores import Chroma
vectordb = Chroma(persist_directory="/home/pi")

To load our local llama model on the local machine:

from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.llms import LlamaCpp

callback = [StreamingStdOutCallbackHandler()]
llm = LlamaCpp(
    model_path="/home/pi/llama.cpp/models/7B/ggml-model-q4_0.bin", callbacks=callback, verbose=True, n_ctx=4096
)

langchain-llm-local-machine-4096

Question and Answering

(10 mins)

Let’s do a question-answering with the PDF source over the vector database, with RetrievalQAWithSourcesChain

retriever = vectordb.as_retriever()

query="What is a Singleton pattern as described by David Gallardo? Summarize in less than 100 words for a beginner"
docs=retriever.get_relevant_documents(query)

from langchain.chains.question_answering import load_qa_chain
chain = load_qa_chain(llm, chain_type="stuff")
chain.run(input_documents=docs, question=query)

langchain-qa-chain

You may learn about Index-related chains for combing your own data and start exploring question ansewering over your own documents! Happy chaining!

Running with OpenBLAS (optional)

(2 mins)

Referencing to issues#32, I manage to get it run with BLAS by re-installating llama-cpp-python:

LLAMA_OPENBLAS=on pip install --force-reinstall --ignore-installed --no-cache-dir llama-cpp-python

This is the run with BLAS=1: langchain-llm-blas-on