Welcome to docGPT, an easily implemented document intelligence program written as an abstraction over langchain. Simply put all the unstructured data (eg PDF files) in the data folder and start asking questions about them!
Useful for Retrieval and Summarization tasks.
Place your files in the data folder. Install dependencies and run
from docGPT_core import *
from RVS import *
set_tokens(OPENAI_TOKEN='OPENAI TOKEN HERE',HF_TOKEN="HF TOKEN HERE")
llm = Llm(model_type='gpt-3.5-turbo')
openai_embeddings = Embedding(model_type='openai')
persona = Persona(personality_type='explainer')
vs = VectorStore(embedding_model=openai_embeddings)
chain = Chain(retriever=vs, llm=llm, persona=persona)
You can start chatting with your data now.
chain.qa(inputs={"query": "Your question"})
Clone this repo. Install dependencies langchain, gradio, numpy and sklearn using pip install -r requirements.txt
Import docGPT_core and RVS modules.
from docGPT_core import *
from RVS import *
You have to define the OpenAI token, the LLM to be used and the embedding model to be used. The default LLM is OpenAI gpt-3.5-turbo and default embedding model is text-embedding-ada-002. If you are planning on using Huggingface models, HugingFace token also needs to be set.
#setting tokens
set_tokens(OPENAI_TOKEN='OPENAI TOKEN HERE',HF_TOKEN="HF TOKEN HERE")
#setting models
llm = Llm(model_type='gpt-3.5-turbo')
openai_embeddings = Embedding(model_type='openai')
#setting the personality of the model
persona = Persona(personality_type='explainer')
Place all the files you want to create the vectorstore for, in the data folder and run call the VectorStore function. You can pass the chunk size and the overlaps if necessary.
vs = VectorStore(embedding_model=openai_embeddings)
This might take some time depending on the size of the documents. If the data in the data folder need OCR, additional packages including Tesseract OCR needs to be installed.
You can save the created vectorstore in disk to avoid re-creating it with vs.save('my_vectorstore.pkl') and reload it later with load_vectorstore('my_vectorstore.pkl')
The QA chain can be initiated with one line of code
chain = Chain(retriever=vs, llm=llm, persona=persona),
The retriever used in the chain object creation in above example is the default retriever attached to the vectorstore. If you want to retrieve n-number of documents, not the default 3 documents, use Retriever class.
Eg: chain = Chain(retriever=Retriever(vs, k=4), llm=llm, persona=persona)
and can query the vectorstore with
chain.qa(inputs={"query": "Your question"})
RVS module provides functions for summarization.
summarize() function returns the summary of the vectorstore as a passage or as key points.
eg: summarize(vectorstore=vs, llm=llm, max_tokens=2500, summary=False, keypoints=True)
Toggle summary and keypoints booleans to return either kind of summary. The method uses Representative Vector Summarization method explained in https://arxiv.org/abs/2308.00479v1.
Change the max_tokens parameter, depending on the number of chunks to be included and the size of each chunk. Suggestion is to start with a value like 2000, and depending on the answer, fine tune.
Mentioned above is the highest level API for creating the QA engine. Please refer this document for finer control.