This is a minimal package for doing question and answering from PDFs or text files (which can be raw HTML). It strives to give very good answers, with no hallucinations, by grounding responses with in-text citations. It uses OpenAI Embeddings with a vector DB called FAISS to embed and search documents. langchain helps generate answers.
It uses this process
embed docs into vectors -> embed query into vector -> search for top k passages in docs
create summary of each passage relevant to query -> put summaries into prompt -> generate answer
- Made it possible to switch models besides OpenAI.
- Can access the raw passages and references from the answer object.
Question: How can carbon nanotubes be manufactured at a large scale?
Carbon nanotubes can be manufactured at a large scale using the electric-arc technique (Journet6644). This technique involves creating an arc between two electrodes in a reactor under a helium atmosphere and using a mixture of a metallic catalyst and graphite powder in the anode. Yields of 80% of entangled carbon filaments can be achieved, which consist of smaller aligned SWNTs self-organized into bundle-like crystallites (Journet6644). Additionally, carbon nanotubes can be synthesized and self-assembled using various methods such as DNA-mediated self-assembly, nanoparticle-assisted alignment, chemical self-assembly, and electro-addressed functionalization (Tulevski2007). These methods have been used to fabricate large-area nanostructured arrays, high-density integration, and freestanding networks (Tulevski2007). 98% semiconducting CNT network solution can also be used and is separated from metallic nanotubes using a density gradient ultracentrifugation approach (Chen2014). The substrate is incubated in the solution and then rinsed with deionized water and dried with N2 air gun, leaving a uniform carbon network (Chen2014).
Journet6644: Journet, Catherine, et al. "Large-scale production of single-walled carbon nanotubes by the electric-arc technique." nature 388.6644 (1997): 756-758.
Tulevski2007: Tulevski, George S., et al. "Chemically assisted directed assembly of carbon nanotubes for the fabrication of large-scale device arrays." Journal of the American Chemical Society 129.39 (2007): 11964-11968.
Chen2014: Chen, Haitian, et al. "Large-scale complementary macroelectronics using hybrid integration of carbon nanotubes and IGZO thin-film transistors." Nature communications 5.1 (2014): 4097.
Install with pip:
pip install paper-qaMake sure you have set your OPENAI_API_KEY environment variable to your openai api key
To use paper-qa, you need to have a list of paths (valid extensions include: .pdf, .txt) and a list of citations (strings) that correspond to the paths. You can then use the Docs class to add the documents and then query them.
This uses a lot of tokens!! About 5-10k tokens per answer + embedding cost (negligible unless many documents used). That is up to $0.20 per answer with current GPT-3 pricing. Use wisely.
from paperqa import Docs
# get a list of paths, citations
docs = Docs()
for d, c in zip(my_docs, my_citations):
docs.add(d, c)
# takes ~ 1 min and costs $0.10-$0.20 to execute this line
answer = docs.query("What manufacturing challenges are unique to bispecific antibodies?")
print(answer.formatted_answer)The answer object has the following attributes: formatted_answer, answer (answer alone), question, context (the summaries of passages found for answer), references (the docs from which the passages came), and passages which contain the raw text of the passages as a dictionary.
You can adjust the numbers of sources (passages of text) to reduce token usage or add more context. k refers to the top k most relevant and diverse (may from different sources) passages. Each passage is sent to the LLM to summarize, or determine if it is irrelevant. After this step, a limit of max_sources is applied so that the final answer can fit into the LLM context window. Thus, k > max_sources and max_sources is the number of sources used in the final answer.
docs.query("What manufacturing challenges are unique to bispecific antibodies?", k = 5, max_sources = 2)Well that's a really good question! It's probably best to just download PDFs of papers you think will help answer your question and start from there.
If you want to do it automatically, I've found an unrelated project called paper-scraper that looks like it might help. But beware, this project looks like it uses some scraping tools that may violate publisher's rights or be in a gray area of legality.
keyword_search = 'bispecific antibody manufacture'
papers = paperscraper.search_papers(keyword_search)
docs = paperqa.Docs()
for path,data in papers.items():
try:
docs.add(path, data['citation'], data['key'])
except ValueError as e:
# sometimes this happens if PDFs aren't downloaded or readable
print('Could not read', path, e)
# takes ~ 1 min and costs $0.50 to execute this line
answer = docs.query("What manufacturing challenges are unique to bispecific antibodies?")
print(answer.formatted_answer)gpt-index does generate answers, but in a somewhat opinionated way. It doesn't have a great way to track where text comes from and it's not easy to force it to pull from multiple documents. I don't know which way is better, but for writing scholarly text I found it to work better to pull from multiple relevant documents and then generate an answer. I would like to PR to do this to gpt-index but it looks pretty involved right now.
I use some of my own code to pull papers from Google Scholar. This code is not included because it may enable people to violate Google's terms of service and publisher's terms of service.
The Docs class can be pickled and unpickled. This is useful if you want to save the embeddings of the documents and then load them later. The database is stored in $HOME/.paperqa/{name} where name is default, or you can pass a name when you instantiate the paperqa doc object.
import pickle
with open("my_docs.pkl", "wb") as f:
pickle.dump(docs, f)
with open("my_docs.pkl", "rb") as f:
docs = pickle.load(f)