Tutorials/Querying With Clinical Trials Copy Section

PaperQA2 for Clinical Trials

PaperQA2 now natively supports querying clinical trials in addition to any documents supplied by the user. It
uses a new tool, the aptly named clinical_trials_search tool. Users don't have to provide any clinical
trials to the tool itself, it uses the clinicaltrials.gov API to retrieve them on the fly. As of
January 2025, the tool is not enabled by default, but it's easy to configure. Here's an example
where we query only clinical trials, without using any documents:

from paperqa import Settings, agent_query
answer_response = await agent_query(
    query="What drugs have been found to effectively treat Ulcerative Colitis?",
    settings=Settings.from_name("search_only_clinical_trials"),
)
print(answer_response.session.answer)

Output Copy Section

Several drugs have been found to effectively treat Ulcerative Colitis (UC),
targeting different mechanisms of the disease.

Golimumab, a tumor necrosis factor (TNF) inhibitor marketed as Simponi®, has demonstrated efficacy
in treating moderate-to-severe UC. Administered subcutaneously, it was shown to maintain clinical
response through Week 54 in patients, as assessed by the Partial Mayo Score (NCT02092285).

Mesalazine, an anti-inflammatory drug, is commonly used for UC treatment. In a study comparing
mesalazine enemas to faecal microbiota transplantation (FMT) for left-sided UC,
mesalazine enemas (4g daily) were effective in inducing clinical remission (Mayo score ≤ 2) (NCT03104036).

Antibiotics have also shown potential in UC management. A combination of doxycycline,
amoxicillin, and metronidazole induced remission in 60-70% of patients with moderate-to-severe
UC in prior studies. These antibiotics are thought to alter gut microbiota, reducing pathobionts
and promoting beneficial bacteria (NCT02217722, NCT03986996).

Roflumilast, a phosphodiesterase-4 (PDE4) inhibitor, is being investigated for mild-to-moderate UC.
Preliminary findings suggest it may improve disease severity and biochemical markers when
added to conventional treatments (NCT05684484).

These treatments highlight diverse therapeutic approaches, including immunosuppression,
microbiota modulation, and anti-inflammatory mechanisms.

You can see the in-line citations for each clinical trial used as a response for each query. If you'd like
to see more data on the specific contexts that were used to answer the query:

print(answer_response.session.contexts)

[Context(context='The excerpt mentions that a search on ClinicalTrials.gov for clinical trials related to drugs
treating Ulcerative Colitis yielded 689 trials. However, it does not provide specific information about which
drugs have been found effective for treating Ulcerative Colitis.', text=Text(text='', name=...

Using Settings.from_name('search_only_clinical_trials') is a shortcut, but note that you can easily
add clinical_trial_search into any custom Settings by just explicitly naming it as a tool:

from pathlib import Path
from paperqa import Settings, agent_query, AgentSetting
from paperqa.agents.tools import DEFAULT_TOOL_NAMES
you can start with the default list of PaperQA tools

print(DEFAULT_TOOL_NAMES)
>>> ['paper_search', 'gather_evidence', 'gen_answer', 'reset', 'complete'],
we can start with a directory with a potentially useful paper in it

print(list(Path("my_papers").iterdir()))
now let's query using standard tools + clinical_trials

answer_response = await agent_query(
    query="What drugs have been found to effectively treat Ulcerative Colitis?",
    settings=Settings(
        paper_directory="my_papers",
        agent={"tool_names": DEFAULT_TOOL_NAMES + ["clinical_trials_search"]},
    ),
)
let's check out the formatted answer (with references included)

print(answer_response.session.formatted_answer)

Question: What drugs have been found to effectively treat Ulcerative Colitis?

Several drugs have been found effective in treating Ulcerative Colitis (UC), with treatment
strategies varying based on disease severity and extent. For mild-to-moderate UC, 5-aminosalicylic
acid (5-ASA) is the first-line therapy. Topical 5-ASA, such as mesalazine suppositories (1 g/day),
is effective for proctitis or distal colitis, inducing remission in 31-80% of patients. Oral mesalazine
at higher doses (e.g., 4.8 g/day) can accelerate clinical improvement in more extensive disease
(meier2011currenttreatmentof pages 1-2; meier2011currenttreatmentof pages 3-4).

For moderate-to-severe cases, corticosteroids are commonly used. Oral steroids like prednisolone
(40-60 mg/day) or intravenous steroids such as methylprednisolone (60 mg/day) and hydrocortisone
(400 mg/day) are standard for inducing remission (meier2011currenttreatmentof pages 3-4). Tumor
necrosis factor (TNF)-α blockers, such as infliximab, are effective for steroid-refractory cases
(meier2011currenttreatmentof pages 2-3; meier2011currenttreatmentof pages 3-4).

Immunosuppressive agents, including azathioprine and 6-mercaptopurine, are used for maintenance
therapy in steroid-dependent or refractory cases (meier2011currenttreatmentof pages 2-3;
meier2011currenttreatmentof pages 3-4). Antibiotics, such as combinations of penicillin,
tetracycline, and metronidazole, have shown promise in altering the microbiota and inducing
remission in some patients, though their efficacy varies (NCT02217722).

References

1. (meier2011currenttreatmentof pages 2-3): Johannes Meier and Andreas Sturm. Current treatment
of ulcerative colitis. World journal of gastroenterology, 17 27:3204-12, 2011.
URL: https://doi.org/10.3748/wjg.v17.i27.3204, doi:10.3748/wjg.v17.i27.3204.

2. (meier2011currenttreatmentof pages 3-4): Johannes Meier and Andreas Sturm. Current treatment
of ulcerative colitis. World journal of gastroenterology, 17 27:3204-12, 2011. URL:
https://doi.org/10.3748/wjg.v17.i27.3204, doi:10.3748/wjg.v17.i27.3204.

3. (NCT02217722): Prof. Arie Levine. Use of the Ulcerative Colitis Diet for Induction of
Remission. Prof. Arie Levine. 2014. ClinicalTrials.gov Identifier: NCT02217722

4. (meier2011currenttreatmentof pages 1-2): Johannes Meier and Andreas Sturm. Current
treatment of ulcerative colitis. World journal of gastroenterology, 17 27:3204-12, 2011.
URL: https://doi.org/10.3748/wjg.v17.i27.3204, doi:10.3748/wjg.v17.i27.3204.

We now see both papers and clinical trials cited in our response. For convenience, we have a
Settings.from_name that works as well:

from paperqa import Settings, agent_query
answer_response = await agent_query(
    query="What drugs have been found to effectively treat Ulcerative Colitis?",
    settings=Settings.from_name("clinical_trials"),
)

And, this works with the pqa cli as well:

>>> pqa --settings 'search_only_clinical_trials' ask 'what is Ibuprofen effective at treating?'

...
[13:29:50] Completing 'what is Ibuprofen effective at treating?' as 'certain'.
Answer: Ibuprofen is a non-steroidal anti-inflammatory drug (NSAID) effective
in treating various conditions, including pain, inflammation, and fever.
It is widely used for tension-type
headaches, with studies showing that ibuprofen sodium provides significant
pain relief and reduces pain intensity compared to standard ibuprofen and placebo
over a 3-hour period (NCT01362491).
Intravenous ibuprofen is effective in managing postoperative pain, particularly
in orthopedic surgeries, and helps control the inflammatory process. When combined
with opioids, it reduces opioid
consumption and associated side effects, making it a key component of
multimodal analgesia (NCT05401916, NCT01773005).

Ibuprofen is also effective in pediatric populations as a first-line
anti-inflammatory and antipyretic agent due to its relatively
low adverse effects compared to other NSAIDs (NCT01478022).
Additionally, it has been studied for its potential use in managing
chronic periodontitis through subgingival irrigation with a 2% ibuprofen
mouthwash, which reduces periodontal pocket depth and
bleeding on probing, improving periodontal health (NCT02538237).

These findings highlight ibuprofen's versatility in treating pain, inflammation,
fever, and specific conditions like tension headaches, postoperative pain, and periodontal diseases.

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Tutorials/Running On Lfrqa Copy Section

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Measuring PaperQA2 with LFRQA

This tutorial is available as a Jupyter notebook here

Overview Copy Chapter

The LFRQA dataset was introduced in the paper _RAG-QA Arena: Evaluating Domain Robustness for Long-Form Retrieval-Augmented Question Answering_. It features 1,404 science questions (along with other categories) that have been human-annotated with answers. This tutorial walks through the process of setting up the dataset for use and benchmarking.

Download the Annotations Copy Chapter

First, we need to obtain the annotated dataset from the official repository:

Create a new directory for the dataset

!mkdir -p data/rag-qa-benchmarking
Get the annotated questions

!curl https://raw.githubusercontent.com/awslabs/rag-qa-arena/refs/heads/main/data/\
annotations_science_with_citation.jsonl \
-o data/rag-qa-benchmarking/annotations_science_with_citation.jsonl

Download the Robust-QA Documents Copy Chapter

LFRQA is built upon Robust-QA, so we must download the relevant documents:

Download the Lotte dataset, which includes the required documents

!curl https://downloads.cs.stanford.edu/nlp/data/colbert/colbertv2/lotte.tar.gz --output lotte.tar.gz
Extract the dataset

!tar -xvzf lotte.tar.gz
Move the science test collection to our dataset folder

!cp lotte/science/test/collection.tsv ./data/rag-qa-benchmarking/science_test_collection.tsv
Clean up unnecessary files

!rm lotte.tar.gz
!rm -rf lotte

For more details, refer to the original paper: _RAG-QA Arena: Evaluating Domain Robustness for Long-Form Retrieval-Augmented Question Answering_.

Load the Data Copy Chapter

We now load the documents into a pandas dataframe:

import os
import pandas as pd
Load questions and answers dataset

rag_qa_benchmarking_dir = os.path.join("data", "rag-qa-benchmarking")
Load documents dataset

lfrqa_docs_df = pd.read_csv(
    os.path.join(rag_qa_benchmarking_dir, "science_test_collection.tsv"),
    sep="\t",
    names=["doc_id", "doc_text"],
)

Select the Documents to Use Copy Chapter

To run a test, we can use 1% of the dataset. This will be accomplished by selecting the first 1% available documents and the questions referent to these documents.

proportion_to_use = 1 / 100
amount_of_docs_to_use = int(len(lfrqa_docs_df) * proportion_to_use)
print(f"Using {amount_of_docs_to_use} out of {len(lfrqa_docs_df)} documents")

Prepare the Document Files Copy Chapter

partial_docs = lfrqa_docs_df.head(amount_of_docs_to_use)
lfrqa_directory = os.path.join(rag_qa_benchmarking_dir, "lfrqa")
os.makedirs(
    os.path.join(lfrqa_directory, "science_docs_for_paperqa", "files"), exist_ok=True
)
for i, row in partial_docs.iterrows():
    doc_id = row["doc_id"]
    doc_text = row["doc_text"]
    with open(
        os.path.join(
            lfrqa_directory, "science_docs_for_paperqa", "files", f"{doc_id}.txt"
        ),
        "w",
        encoding="utf-8",
    ) as f:
        f.write(doc_text)
    if i % int(len(partial_docs) * 0.05) == 0:
        progress = (i + 1) / len(partial_docs)
        print(f"Progress: {progress:.2%}")

Create the Manifest File Copy Chapter

manifest = partial_docs.copy()
manifest["file_location"] = manifest["doc_id"].apply(lambda x: f"files/{x}.txt")
manifest["doi"] = ""
manifest["title"] = manifest["doc_id"]
manifest["key"] = manifest["doc_id"]
manifest["docname"] = manifest["doc_id"]
manifest["citation"] = "_"
manifest = manifest.drop(columns=["doc_id", "doc_text"])
manifest.to_csv(
    os.path.join(lfrqa_directory, "science_docs_for_paperqa", "manifest.csv"),
    index=False,
)

Filter and Save Questions Copy Chapter

questions_df = pd.read_json(
    os.path.join(rag_qa_benchmarking_dir, "annotations_science_with_citation.jsonl"),
    lines=True,
)
partial_questions = questions_df[
    questions_df.gold_doc_ids.apply(
        lambda ids: all(_id < amount_of_docs_to_use for _id in ids)
    )
]
partial_questions.to_csv(
    os.path.join(lfrqa_directory, "questions.csv"),
    index=False,
)
print("Using", len(partial_questions), "questions")

Install paperqa Copy Chapter

!pip install paper-qa

Index the Documents Copy Chapter

Now we will build an index for the LFRQA documents. The index is a Tantivy index, which is a fast, full-text search engine library written in Rust. Tantivy is designed to handle large datasets efficiently, making it ideal for searching through a vast collection of papers or documents.

Feel free to adjust the concurrency settings as you like. Because we defined a manifest, we don’t need any API keys for building this index because we don't discern any citation metadata, but you do need LLM API keys to answer questions.

Remember that this process is quick for small portions of the dataset, but can take around 3 hours for the whole dataset.

import nest_asyncio
nest_asyncio.apply()

We add the line above to handle async code within a notebook.

However, to improve compatibility and speed up the indexing process, we strongly recommend running the following code in a separate .py file

import os
from paperqa import Settings
from paperqa.agents import build_index
from paperqa.settings import AgentSettings, IndexSettings, ParsingSettings
settings = Settings(
    agent=AgentSettings(
        index=IndexSettings(
            name="lfrqa_science_index",
            paper_directory=os.path.join(
                "data", "rag-qa-benchmarking", "lfrqa", "science_docs_for_paperqa"
            ),
            index_directory=os.path.join(
                "data", "rag-qa-benchmarking", "lfrqa", "science_docs_for_paperqa_index"
            ),
            manifest_file="manifest.csv",
            concurrency=10_000,
            batch_size=10_000,
        )
    ),
    parsing=ParsingSettings(
        use_doc_details=False,
        defer_embedding=True,
    ),
)
build_index(settings=settings)

After this runs, you will have an index ready to use!

Benchmark! Copy Chapter

To run this, you will need to have the ldp and [fhaviary[lfrqa]](https://github.com/Future-House/aviary/blob/main/packages/lfrqa/README.md#installation) packages installed.

!pip install ldp "fhaviary[lfrqa]"

/ Detailed source-code truncated for AI context efficiency. /

After running this, you can find the results in the data/rag-qa-benchmarking/results folder. Here is an example of how to read them:

import glob
json_files = glob.glob(os.path.join(rag_qa_benchmarking_dir, "results", "*.json"))
data = []
for file in json_files:
    with open(file) as f:
        json_data = json.load(f)
        json_data["qid"] = file.split("/")[-1].replace(".json", "")
        data.append(json_data)
results_df = pd.DataFrame(data).set_index("qid")
results_df["winner"].value_counts(normalize=True)

---

Tutorials/Settings Tutorial Copy Section

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Setup

This tutorial is available as a Jupyter notebook here.

This tutorial aims to show how to use the Settings class to configure PaperQA.
Firstly, we will be using OpenAI and Anthropic models, so we need to set the OPENAI_API_KEY and ANTHROPIC_API_KEY environment variables.
We will use both models to make it clear when paperqa agent is using either one or the other.
We use python-dotenv to load the environment variables from a .env file.
Hence, our first step is to create a .env file and install the required packages.

fmt: off

Create .env file with OpenAI API and Anthropic API keys

Replace <your-openai-api-key> and <your-anthropic-api-key> with your actual API keys

!echo "OPENAI_API_KEY=<your-openai-api-key>" > .env # fmt: skip
!echo "ANTHROPIC_API_KEY=<your-anthropic-api-key>" >> .env # fmt: skip
!uv pip install -q nest-asyncio python-dotenv aiohttp fhlmi "paper-qa[local]"
fmt: on

import os
import aiohttp
import nest_asyncio
from dotenv import load_dotenv
nest_asyncio.apply()
load_dotenv(".env")

print("You have set the following environment variables:")
print(
    f"OPENAI_API_KEY:    {'is set' if os.environ['OPENAI_API_KEY'] else 'is not set'}"
)
print(
    f"ANTHROPIC_API_KEY: {'is set' if os.environ['ANTHROPIC_API_KEY'] else 'is not set'}"
)

We will use the lmi package to get the model names and the .papers directory to save documents we will use.

from lmi import CommonLLMNames
llm_openai = CommonLLMNames.OPENAI_TEST.value
llm_anthropic = CommonLLMNames.ANTHROPIC_TEST.value
Create the papers directory if it doesn't exist

os.makedirs("papers", exist_ok=True)
Download the paper from arXiv and save it to the papers directory

url = "https://arxiv.org/pdf/2407.01603"
async with aiohttp.ClientSession() as session, session.get(url, timeout=60) as response:
    content = await response.read()
    with open("papers/2407.01603.pdf", "wb") as f:
        f.write(content)

The Settings class is used to configure the PaperQA settings.
Official documentation can be found here and the open source code can be found here.

Here is a basic example of how to use the Settings class. We will be unnecessarily verbose for the sake of clarity. Please notice that most of the settings are optional and the defaults are good for most cases. Refer to the descriptions of each setting for more information.

Within this Settings object, I'd like to discuss specifically how the llms are configured and how paperqa looks for papers.

A common source of confusion is that multiple llms are used in paperqa. We have llm, summary_llm, agent_llm, and embedding. Hence, if llm is set to an Anthropic model, summary_llm and agent_llm will still require a OPENAI_API_KEY, since OpenAI models are the default.

Among the objects that use llms in paperqa, we have llm, summary_llm, agent_llm, and embedding:

- llm: Main LLM used by the agent to reason about the question, extract metadata from documents, etc.
- summary_llm: LLM used to summarize the papers.
- agent_llm: LLM used to answer questions and select tools.
- embedding: Embedding model used to embed the papers.

Let's see some examples around this concept. First, we define the settings with llm set to an OpenAI model. Please notice this is not an complete list of settings. But take your time to read through this Settings class and all customization that can be done.

/ Detailed source-code truncated for AI context efficiency. /

As it is evident, Paperqa is absolutely customizable. And here we reinterate that despite this possible fine customization, the defaults are good for most cases. Although, the user is welcome to explore the settings and customize the paperqa to their needs.

We also set settings.verbosity to 1, which will print the agent configuration. Feel free to set it to 0 to silence the logging after your first run.

from paperqa import ask
response = ask(
    "What are the most relevant language models used for chemistry?", settings=settings
)

Which probably worked fine. Let's now try to remove OPENAI_API_KEY and run again the same question with the same settings.

os.environ["OPENAI_API_KEY"] = ""
print("You have set the following environment variables:")
print(
    f"OPENAI_API_KEY:    {'is set' if os.environ['OPENAI_API_KEY'] else 'is not set'}"
)
print(
    f"ANTHROPIC_API_KEY: {'is set' if os.environ['ANTHROPIC_API_KEY'] else 'is not set'}"
)

response = ask(
    "What are the most relevant language models used for chemistry?", settings=settings
)

It would obviously fail. We don't have a valid OPENAI_API_KEY, so the agent will not be able to use OpenAI models. Let's change it to an Anthropic model and see if it works.

settings.llm = llm_anthropic
settings.llm_config = {
    "model_list": [
        {
            "model_name": llm_anthropic,
            "litellm_params": {
                "model": llm_anthropic,
                "temperature": 0.1,
                "max_tokens": 512,
            },
        }
    ],
    "rate_limit": {
        llm_anthropic: "30000 per 1 minute",
    },
}
settings.summary_llm = llm_anthropic
settings.summary_llm_config = {
    "rate_limit": {
        llm_anthropic: "30000 per 1 minute",
    },
}
settings.agent = AgentSettings(
    agent_llm=llm_anthropic,
    agent_llm_config={
        "rate_limit": {
            llm_anthropic: "30000 per 1 minute",
        },
    },
    index=IndexSettings(
        paper_directory=pathlib.Path.cwd().joinpath("papers"),
        manifest_file=None,
        index_directory=pathlib.Path.cwd().joinpath("papers/index"),
    ),
)
settings.embedding = "st-multi-qa-MiniLM-L6-cos-v1"
response = ask(
    "What are the most relevant language models used for chemistry?", settings=settings
)

Now the agent is able to use Anthropic models only and although we don't have a valid OPENAI_API_KEY,
the question is answered because the agent will not use OpenAI models.
See that we also changed the embedding because it was using text-embedding-3-small by default,
which is a OpenAI model. Paperqa implements a few embedding models.
Please refer to the documentation for more information.

In addition, notice that this is a very verbose example for the sake of clarity. We could have just set only the llms names and used default settings for the rest:

llm_anthropic_config = {
    "model_list": [{
            "model_name": llm_anthropic,
    }]
}
settings.llm = llm_anthropic
settings.llm_config = llm_anthropic_config
settings.summary_llm = llm_anthropic
settings.summary_llm_config = llm_anthropic_config
settings.agent = AgentSettings(
    agent_llm=llm_anthropic,
    agent_llm_config=llm_anthropic_config,
    index=IndexSettings(
        paper_directory=pathlib.Path.cwd().joinpath("papers"),
        manifest_file=None,
        index_directory=pathlib.Path.cwd().joinpath("papers/index"),
    ),
)
settings.embedding = "st-multi-qa-MiniLM-L6-cos-v1"

The output

Paperqa returns a PQASession object, which contains not only the answer but also all the information gatheres to answer the questions. We recommend printing the PQASession object (print(response.session)) to understand the information it contains. Let's check the PQASession object:

print(response.session)

print("Let's examine the PQASession object returned by paperqa:\n")
print(f"Status: {response.status.value}")
print("1. Question asked:")
print(f"{response.session.question}\n")
print("2. Answer provided:")
print(f"{response.session.answer}\n")

In addition to the answer, the PQASession object contains all the references and contexts used to generate the answer.

Because paperqa splits the documents into chunks, each chunk is a valid reference. You can see that it also references the page where the context was found.

print("3. References cited:")
print(f"{response.session.references}\n")

Lastly, PQASession.session.contexts contains the contexts used to generate the answer. Each context has a score, which is the similarity between the question and the context.
Paperqa uses this score to choose what contexts is more relevant to answer the question.

print("4. Contexts used to generate the answer:")
print(
    "These are the relevant text passages that were retrieved and used to formulate the answer:"
)
for i, ctx in enumerate(response.session.contexts, 1):
    print(f"\nContext {i}:")
    print(f"Source: {ctx.text.name}")
    print(f"Content: {ctx.context}")
    print(f"Score: {ctx.score}")

---

Tutorials/Where Do I Get Papers Copy Section

Where to get papers

OpenReview Copy Chapter

You can use papers from https://openreview.net/ as your database!
Here's a helper that fetches a list of all papers from a selected conference (like ICLR, ICML, NeurIPS), queries this list to find relevant papers using LLM, and downloads those relevant papers to a local directory which can be used with paper-qa on the next step. Install openreview-py with

pip install paper-qa[openreview]

and get your username and password from the website. You can put them into .env file under OPENREVIEW_USERNAME and OPENREVIEW_PASSWORD variables, or pass them in the code directly.

from paperqa import Settings
from paperqa.contrib.openreview_paper_helper import OpenReviewPaperHelper
these settings require gemini api key you can get from https://aistudio.google.com/

import os; os.environ["GEMINI_API_KEY"] = os.getenv("GEMINI_API_KEY")

1Mil context window helps to suggest papers. These settings are not required, but useful for an initial setup.

settings = Settings.from_name("openreview")
helper = OpenReviewPaperHelper(settings, venue_id="ICLR.cc/2025/Conference")
if you don't know venue_id you can find it via

helper.get_venues()
Now we can query LLM to select relevant papers and download PDFs

question = "What is the progress on brain activity research?"
submissions = helper.fetch_relevant_papers(question)
There's also a function that saves tokens by using openreview metadata for citations

docs = await helper.aadd_docs(submissions)
Now you can continue asking like in the main tutorial

session = await docs.aquery(question, settings=settings)
print(session.answer)

Zotero Copy Chapter

_It's been a while since we've tested this - so let us know if it runs into issues!_

If you use Zotero to organize your personal bibliography,
you can use the paperqa.contrib.ZoteroDB to query papers from your library,
which relies on pyzotero.

Install pyzotero via the zotero extra for this feature:

pip install paper-qa[zotero]

First, note that PaperQA2 parses the PDFs of papers to store in the database,
so all relevant papers should have PDFs stored inside your database.
You can get Zotero to automatically do this by highlighting the references
you wish to retrieve, right clicking, and selecting _"Find Available PDFs"_.
You can also manually drag-and-drop PDFs onto each reference.

To download papers, you need to get an API key for your account.

1. Get your library ID, and set it as the environment variable ZOTERO_USER_ID.
- For personal libraries, this ID is given here at the part "_Your userID for use in API calls is XXXXXX_".
- For group libraries, go to your group page https://www.zotero.org/groups/groupname, and hover over the settings link. The ID is the integer after /groups/. (_h/t pyzotero!_)
2. Create a new API key here and set it as the environment variable ZOTERO_API_KEY.
- The key will need read access to the library.

With this, we can download papers from our library and add them to PaperQA2:

from paperqa import Docs
from paperqa.contrib import ZoteroDB
docs = Docs()
zotero = ZoteroDB(library_type="user")  # "group" if group library
for item in zotero.iterate(limit=20):
    if item.num_pages > 30:
        continue  # skip long papers
    await docs.aadd(item.pdf, docname=item.key)

which will download the first 20 papers in your Zotero database and add
them to the Docs object.

We can also do specific queries of our Zotero library and iterate over the results:

for item in zotero.iterate(
    q="large language models",
    qmode="everything",
    sort="date",
    direction="desc",
    limit=100,
):
    print("Adding", item.title)
    await docs.aadd(item.pdf, docname=item.key)

You can read more about the search syntax by typing zotero.iterate? in IPython.

Paper Scraper Copy Chapter

If you want to search for papers outside of your own collection, 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.

from paperqa import Docs
keyword_search = "bispecific antibody manufacture"
papers = paperscraper.search_papers(keyword_search)
docs = Docs()
for path, data in papers.items():
    try:
        await docs.aadd(path)
    except ValueError as e:
        # sometimes this happens if PDFs aren't downloaded or readable
        print("Could not read", path, e)
session = await docs.aquery(
    "What manufacturing challenges are unique to bispecific antibodies?"
)
print(session)

---

CONTRIBUTING Copy Section

Contributing to PaperQA

Thank you for your interest in contributing to PaperQA!
Here are some guidelines to help you get started.

Setting up the development environment Copy Chapter

We use uv for our local development.

1. Install uv by following the instructions on the uv website.
2. Run the following command to install all dependencies and set up the development environment:

uv sync

Installing the package for development Copy Chapter

If you prefer to use pip for installing the package in development mode, you can do so by running:

pip install -e ".[dev]"

Where the dev extra includes development dependencies such as pytest.

Running tests and other tooling Copy Chapter

Use the following commands:

- Run tests (requires an OpenAI key in your environment)

pytest
  # or for multiprocessing based parallelism
  pytest -n auto

- Run pre-commit for formatting and type checking

pre-commit run --all-files

- Run mypy, refurb, or pylint directly:

mypy paperqa
  # or
  refurb paperqa
  # or
  pylint paperqa

See our GitHub Actions tests.yml for further reference.

Using pytest-recording and VCR cassettes pytest-recording and VCR cassettes')" title="Copy chapter prompt for LLMs"> Copy Chapter

We use the pytest-recording plugin
to create VCR cassettes to cache HTTP requests,
making our unit tests more deterministic.

To record a new VCR cassette:

uv run pytest --record-mode=once tests/desired_test_module.py

And the new cassette(s) should appear in tests/cassettes.

Our configuration for pytest-recording can be found in tests/conftest.py.
This includes header removals (e.g. OpenAI authorization key)
from responses to ensure sensitive information is excluded from the cassettes.

Please ensure cassettes are less than 1 MB
to keep tests loading quickly.

Happy coding!

---

README Copy Section

PaperQA2

[](https://github.com/Future-House/paper-qa)
[](https://badge.fury.io/py/paper-qa)
[](https://github.com/Future-House/paper-qa)

PaperQA2 is a package for doing high-accuracy retrieval augmented generation (RAG) on PDFs, text files, Microsoft Office documents, and source code files,
with a focus on the scientific literature.
See our recent 2024 paper
to see examples of PaperQA2's superhuman performance in scientific tasks like
question answering, summarization, and contradiction detection.

---

Table of Contents

- Quickstart
- Example Output
- What is PaperQA2
- PaperQA2 vs PaperQA
- PaperQA2 Goes CalVer in December 2025
- What's New in Version 5 (aka PaperQA2)?
- What's New in December 2025?
- PaperQA2 Algorithm
- Installation
- CLI Usage
- Bundled Settings
- Rate Limits
- Library Usage
- Agentic Adding/Querying Documents
- Manual (No Agent) Adding/Querying Documents
- Async
- Choosing Model
- Locally Hosted
- Embedding Model
- Specifying the Embedding Model
- Local Embedding Models (Sentence Transformers)
- Adjusting number of sources
- Using Code or HTML
- Multimodal Support
- Using External DB/Vector DB and Caching
- Creating Index
- Manifest Files
- Reusing Index
- Using Clients Directly
- Settings Cheatsheet
- Where do I get papers?
- Callbacks
- Caching Embeddings
- Customizing Prompts
- Pre and Post Prompts
- FAQ
- How come I get different results than your papers?
- How is this different from LlamaIndex or LangChain?
- Can I save or load?
- Reproduction
- Citation

---

Quickstart Copy Chapter

In this example we take a folder of research paper PDFs,
magically get their metadata - including citation counts with a retraction check,
then parse and cache PDFs into a full-text search index,
and finally answer the user question with an LLM agent.

pip install paper-qa
mkdir my_papers
curl -o my_papers/PaperQA2.pdf https://arxiv.org/pdf/2409.13740
cd my_papers
pqa ask 'What is PaperQA2?'

Example Output Copy Section

Question: Has anyone designed neural networks that compute with proteins or DNA?

The claim that neural networks have been designed to compute with DNA is supported by multiple sources.

The work by Qian, Winfree, and Bruck demonstrates the use of DNA strand displacement cascades

to construct neural network components, such as artificial neurons and associative memories,

using a DNA-based system (Qian2011Neural pages 1-2, Qian2011Neural pages 15-16, Qian2011Neural pages 54-56).

This research includes the implementation of a 3-bit XOR gate and a four-neuron Hopfield associative memory,

showcasing the potential of DNA for neural network computation.

Additionally, the application of deep learning techniques to genomics,

which involves computing with DNA sequences, is well-documented.

Studies have applied convolutional neural networks (CNNs) to predict genomic features such as

transcription factor binding and DNA accessibility (Eraslan2019Deep pages 4-5, Eraslan2019Deep pages 5-6).

These models leverage DNA sequences as input data,

effectively using neural networks to compute with DNA.

While the provided excerpts do not explicitly mention protein-based neural network computation,

they do highlight the use of neural networks in tasks related to protein sequences,

such as predicting DNA-protein binding (Zeng2016Convolutional pages 1-2).

However, the primary focus remains on DNA-based computation.

What is PaperQA2 Copy Chapter

PaperQA2 is engineered to be the best agentic RAG model for working with scientific papers.
Here are some features:

- A simple interface to get good answers with grounded responses containing in-text citations.
- State-of-the-art implementation including document metadata-awareness
in embeddings and LLM-based re-ranking and contextual summarization (RCS).
- Support for agentic RAG, where a language agent can iteratively refine queries and answers.
- Automatic redundant fetching of paper metadata,
including citation and journal quality data from multiple providers.
- A usable full-text search engine for a local repository of PDF/text files.
- A robust interface for customization, with default support for all [LiteLLM][LiteLLM providers] models.

[LiteLLM providers]: https://docs.litellm.ai/docs/providers
[LiteLLM general docs]: https://docs.litellm.ai/docs/

By default, it uses OpenAI embeddings
and models with a Numpy vector DB to embed and search documents.
However, you can easily use other closed-source, open-source models or embeddings (see details below).

PaperQA2 depends on some awesome libraries/APIs that make our repo possible.
Here are some in no particular order:

1. Semantic Scholar
2. Crossref
3. Unpaywall
4. Pydantic
5. tantivy
6. [LiteLLM][LiteLLM general docs]
7. pybtex

PaperQA2 vs PaperQA Copy Section

We've been working hard on fundamental upgrades for a while
and mostly followed SemVer, until December 2025.
Meaning we've incremented the major version number on each breaking change.
This brings us to the current major version number v5.
So why call is the repo now called PaperQA2?
We wanted to remark on the fact though that we've
exceeded human performance on many important metrics.
So we arbitrarily call version 5 and onward PaperQA2,
and versions before it as PaperQA1 to denote the significant change in performance.
We recognize that we are challenged at naming and counting at FutureHouse,
so we reserve the right at any time to arbitrarily change the name to PaperCrow.

PaperQA2 Goes CalVer in December 2025 Copy Section

Prior to December 2025 we used semantic versioning.
This eventually led to confusion in two ways:

1. Developers: should we major version bump based on
settings or fundamental system capabilities?
What if a bug fix requires breaking changes to the agent's behaviors?
2. Speaking: should one use terminology from our publications
(e.g. PaperQA1,
PaperQA2)
or the Git tags (e.g. v5) from this repo/package?
When someone says "PaperQA" -- what version do they mean?

To resolve these confusions, in December 2025,
we moved to calendar versioning.
The developer burden is diminished because
we're basically removing guarantees of backwards compatibility across releases
(as CalVer is ZeroVer bound to dates).
It solves the "speaking" issue because Git tags are now
quite different from publication terminology (e.g. PaperQA2 vs v2025.12.17).
When someone says "PaperQA" it will just refer to the system,
not a particular snapshot of agentic behaviors.
When someone says "PaperQA2" it will refer to paper-qa>=5,
which applies to both SemVer tags v5.0.0 and the new CalVer tags v2025.12.17.

This switch is backwards compatible for version 5's SemVer,
as the year 2025 is strictly greater than major version 5.

What's New in Version 5 (aka PaperQA2)? Copy Section

Version 5 added:

- A CLI pqa
- Agentic workflows invoking tools for
paper search, gathering evidence, and generating an answer
- Removed much of the statefulness from the Docs object
- A migration to LiteLLM for compatibility with many LLM providers
as well as centralized rate limits and cost tracking
- A bundled set of configurations (read this section here))
containing known-good hyperparameters

Note that Docs objects pickled from prior versions of PaperQA are incompatible with version 5,
and will need to be rebuilt.
Also, our minimum Python version was increased to Python 3.11.

What's New in December 2025? Copy Section

The last four months since version 5.29.1 have seen many changes:

- New modalities: tables, figures, non-English languages, math equations
- More and better readers
- Two new _model-based_ PDF readers: Docling
and Nvidia nemotron-parse
- All PDF readers now can parse images and tables, report page numbers,
support DPI
- A reader for Microsoft Office data types
- Multimodal contextual summarization
- Media objects are also passed to the summary_llm during creation
- Media objects' embedding space is enhanced using an enrichment_llm prompt
- Simpler and performant HTTP stack
- Consolidation from aiohttp and httpx to just httpx
- Integration with httpx-aiohttp for performance
- Context relevance is simplified and some assumptions were removed
- Many minor features such as
retrying Context creation upon invalid JSON,
compatibility with fall 2025's frontier LLMs,
and improved prompt templates
- Multiple fixes in metadata processing via Semantic Scholar and OpenAlex,
and metadata processing
(e.g. incorrectly inferring identical document IDs for main text and SI)
- Completed the deprecations accrued over the past year

PaperQA2 Algorithm Copy Section

To understand PaperQA2, let's start with the pieces of the underlying algorithm.
The default workflow of PaperQA2 is as follows:

| Phase | PaperQA2 Actions |
| ---------------------- | ------------------------------------------------------------------------- |
| 1. Paper Search | - Get candidate papers from LLM-generated keyword query |
| | - Chunk, embed, and add candidate papers to state |
| 2. Gather Evidence | - Embed query into vector |
| | - Rank top _k_ document chunks in current state |
| | - Create scored summary of each chunk in the context of the current query |
| | - Use LLM to re-score and select most relevant summaries |
| 3. Generate Answer | - Put best summaries into prompt with context |
| | - Generate answer with prompt |

The tools can be invoked in any order by a language agent.
For example, an LLM agent might do a narrow and broad search,
or using different phrasing for the gather evidence step from the generate answer step.

Installation Copy Chapter

For a non-development setup,
install PaperQA2 (aka version 5) from PyPI.
Note version 5 requires Python 3.11+.

pip install paper-qa>=5

For development setup,
please refer to the CONTRIBUTING.md file.

PaperQA2 uses an LLM to operate,
so you'll need to either set an appropriate [API key environment variable][LiteLLM providers]
(i.e. export OPENAI_API_KEY=sk-...)
or set up an open source LLM server (i.e. using llamafile.
Any LiteLLM compatible model can be configured to use with PaperQA2.

If you need to index a large set of papers (100+),
you will likely want an API key for both
Crossref
and Semantic Scholar,
which will allow you to avoid hitting public rate limits using these metadata services.
Those can be exported as CROSSREF_API_KEY and SEMANTIC_SCHOLAR_API_KEY variables.

CLI Usage Copy Chapter

The fastest way to test PaperQA2 is via the CLI. First navigate to a directory with some papers and use the pqa cli:

pqa ask 'What is PaperQA2?'

You will see PaperQA2 index your local PDF files,
gathering the necessary metadata for each of them
(using Crossref and Semantic Scholar),
search over that index, then break the files into chunked evidence contexts,
rank them, and ultimately generate an answer.
The next time this directory is queried,
your index will already be built (save for any differences detected, like new added papers),
so it will skip the indexing and chunking steps.

All prior answers will be indexed and stored,
you can view them by querying via the search subcommand,
or access them yourself in your PQA_HOME directory,
which defaults to ~/.pqa/.

pqa -i 'answers' search 'ranking and contextual summarization'

PaperQA2 is highly configurable, when running from the command line,
pqa --help shows all options and short descriptions.
For example to run with a higher temperature:

pqa --temperature 0.5 ask 'What is PaperQA2?'

You can view all settings with pqa view.
Another useful thing is to change to other templated settings - for example
fast is a setting that answers more quickly
and you can see it with pqa -s fast view

Maybe you have some new settings you want to save? You can do that with

pqa -s my_new_settings --temperature 0.5 --llm foo-bar-5 save

and then you can use it with

pqa -s my_new_settings ask 'What is PaperQA2?'

If you run pqa with a command which requires a new indexing,
say if you change the default chunk_size,
a new index will automatically be created for you.

pqa --parsing.chunk_size 5000 ask 'What is PaperQA2?'

You can also use pqa to do full-text search with use of LLMs view the search command.
For example, let's save the index from a directory and give it a name:

pqa -i nanomaterials index

Now I can search for papers about thermoelectrics:

pqa -i nanomaterials search thermoelectrics

or I can use the normal ask

pqa -i nanomaterials ask 'Are there nm scale features in thermoelectric materials?'

Both the CLI and module have pre-configured settings based on prior performance and our publications,
they can be invoked as follows:

pqa --settings <setting name> \
    ask 'Are there nm scale features in thermoelectric materials?'

Bundled Settings Copy Section

Inside src/paperqa/configs we bundle known useful settings:

| Setting Name | Description |
| ------------ | ---------------------------------------------------------------------------------------------------------------------------- |
| high_quality | Highly performant, relatively expensive (due to having evidence_k = 15) query using a ToolSelector agent. |
| fast | Setting to get answers cheaply and quickly. |
| wikicrow | Setting to emulate the Wikipedia article writing used in our WikiCrow publication. |
| contracrow | Setting to find contradictions in papers, your query should be a claim that needs to be flagged as a contradiction (or not). |
| debug | Setting useful solely for debugging, but not in any actual application beyond debugging. |
| tier1_limits | Settings that match OpenAI rate limits for each tier, you can use tier<1-5>_limits to specify the tier. |

Rate Limits Copy Section

If you are hitting rate limits, say with the OpenAI Tier 1 plan, you can add them into PaperQA2.
For each OpenAI tier, a pre-built setting exists to limit usage.

pqa --settings 'tier1_limits' ask 'What is PaperQA2?'

This will limit your system to use the tier1_limits,
and slow down your queries to accommodate.

You can also specify them manually with any rate limit string that matches the specification in
the limits module:

pqa --summary_llm_config '{"rate_limit": {"gpt-4o-2024-11-20": "30000 per 1 minute"}}' \
    ask 'What is PaperQA2?'

Or by adding into a Settings object, if calling imperatively:

from paperqa import Settings, ask
answer_response = ask(
    "What is PaperQA2?",
    settings=Settings(
        llm_config={"rate_limit": {"gpt-4o-2024-11-20": "30000 per 1 minute"}},
        summary_llm_config={"rate_limit": {"gpt-4o-2024-11-20": "30000 per 1 minute"}},
    ),
)

Library Usage Copy Chapter

PaperQA2's full workflow can be accessed via Python directly:

from paperqa import Settings, ask
answer_response = ask(
    "What is PaperQA2?",
    settings=Settings(temperature=0.5, paper_directory="my_papers"),
)

Please see our installation docs for how to install the package from PyPI.

Agentic Adding/Querying Documents Copy Section

The answer object has the following attributes:
formatted_answer, answer (answer alone), question , and context (the summaries of passages found for answer).
ask will use the SearchPapers tool, which will query a local index of files,
you can specify this location via the Settings object:

from paperqa import Settings, ask
answer_response = ask(
    "What is PaperQA2?",
    settings=Settings(
        temperature=0.5, agent={"index": {"paper_directory": "my_papers"}}
    ),
)

ask is just a convenience wrapper around the real entrypoint,
which can be accessed if you'd like to run concurrent asynchronous workloads:

from paperqa import Settings, agent_query
answer_response = await agent_query(
    query="What is PaperQA2?",
    settings=Settings(
        temperature=0.5, agent={"index": {"paper_directory": "my_papers"}}
    ),
)

The default agent will use an LLM based agent,
but you can also specify a "fake" agent to use a hard coded call path of
search -> gather evidence -> answer to reduce token usage.

Manual (No Agent) Adding/Querying Documents Copy Section

Normally via agent execution, the agent invokes the search tool,
which adds documents to the Docs object for you behind the scenes.
However, if you prefer fine-grained control,
you can directly interact with the Docs object.

Note that manually adding and querying Docs does not impact performance.
It just removes the automation associated with an agent picking the documents to add.

from paperqa import Docs, Settings
valid extensions include .pdf, .txt, .md, .html, .docx, .xlsx, .pptx, and code files (e.g., .py, .ts, .yaml)

doc_paths = ("myfile.pdf", "myotherfile.pdf")
Prepare the Docs object by adding a bunch of documents

docs = Docs()
for doc_path in doc_paths:
    await docs.aadd(doc_path)
Set up how we want to query the Docs object

settings = Settings()
settings.llm = "claude-3-5-sonnet-20240620"
settings.answer.answer_max_sources = 3
Query the Docs object to get an answer

session = await docs.aquery("What is PaperQA2?", settings=settings)
print(session)

Async Copy Section

PaperQA2 is written to be used asynchronously.
The synchronous API is just a wrapper around the async.
Here are the methods and their async equivalents:

| Sync | Async |
| ------------------- | -------------------- |
| Docs.add | Docs.aadd |
| Docs.add_file | Docs.aadd_file |
| Docs.add_url | Docs.aadd_url |
| Docs.get_evidence | Docs.aget_evidence |
| Docs.query | Docs.aquery |

The synchronous version just calls the async version in a loop.
Most modern python environments support async natively (including Jupyter notebooks!).
So you can do this in a Jupyter Notebook:

import asyncio
from paperqa import Docs

async def main() -> None:
    docs = Docs()
    # valid extensions include .pdf, .txt, .md, .html, .docx, .xlsx, .pptx, and code files (e.g., .py, .ts, .yaml)
    for doc in ("myfile.pdf", "myotherfile.pdf"):
        await docs.aadd(doc)
    session = await docs.aquery("What is PaperQA2?")
    print(session)

asyncio.run(main())

Choosing Model Copy Section

By default, PaperQA2 uses OpenAI's gpt-4o-2024-11-20 model for the
summary_llm, llm, and agent_llm.
Please see the Settings Cheatsheet
for more information on these settings.
PaperQA2 also defaults to using OpenAI's text-embedding-3-small model for the embedding setting.
If you don't have an OpenAI API key, you can use a different embedding model.
More information about embedding models can be found in the "Embedding Model" section.

We use the lmi package for our LLM interface,
which in turn uses litellm to support many LLM providers.
You can adjust this easily to use any model supported by litellm:

from paperqa import Settings, ask
answer_response = ask(
    "What is PaperQA2?",
    settings=Settings(
        llm="gpt-4o-mini", summary_llm="gpt-4o-mini", agent={"index": {"paper_directory": "my_papers"}}
    ),
)

To use Claude, make sure you set the ANTHROPIC_API_KEY environment variable.
In this example, we also use a different embedding model.
Please make sure to pip install paper-qa[local] to use a local embedding model.

from paperqa import Settings, ask
from paperqa.settings import AgentSettings
answer_response = ask(
    "What is PaperQA2?",
    settings=Settings(
        llm="claude-3-5-sonnet-20240620",
        summary_llm="claude-3-5-sonnet-20240620",
        agent=AgentSettings(agent_llm="claude-3-5-sonnet-20240620"),
        # SEE: https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1
        embedding="st-multi-qa-MiniLM-L6-cos-v1",
    ),
)

Or Gemini, by setting the GEMINI_API_KEY from Google AI Studio

from paperqa import Settings, ask
from paperqa.settings import AgentSettings
answer_response = ask(
    "What is PaperQA2?",
    settings=Settings(
        llm="gemini/gemini-2.0-flash",
        summary_llm="gemini/gemini-2.0-flash",
        agent=AgentSettings(agent_llm="gemini/gemini-2.0-flash"),
        embedding="gemini/text-embedding-004",
    ),
)

#### Locally Hosted

You can use llama.cpp to be the LLM.
Note that you should be using relatively large models,
because PaperQA2 requires following a lot of instructions.
You won't get good performance with 7B models.

The easiest way to get set-up is to download a llama file
and execute it with -cb -np 4 -a my-llm-model --embedding
which will enable continuous batching and embeddings.

from paperqa import Settings, ask
local_llm_config = dict(
    model_list=[
        dict(
            model_name="my_llm_model",
            litellm_params=dict(
                model="my-llm-model",
                api_base="http://localhost:8080/v1",
                api_key="sk-no-key-required",
                temperature=0.1,
                frequency_penalty=1.5,
                max_tokens=512,
            ),
        )
    ]
)
answer_response = ask(
    "What is PaperQA2?",
    settings=Settings(
        llm="my-llm-model",
        llm_config=local_llm_config,
        summary_llm="my-llm-model",
        summary_llm_config=local_llm_config,
    ),
)

Models hosted with ollama are also supported.
To run the example below make sure you have downloaded llama3.2 and mxbai-embed-large via ollama.

from paperqa import Settings, ask
local_llm_config = {
    "model_list": [
        {
            "model_name": "ollama/llama3.2",
            "litellm_params": {
                "model": "ollama/llama3.2",
                "api_base": "http://localhost:11434",
            },
        }
    ]
}
answer_response = ask(
    "What is PaperQA2?",
    settings=Settings(
        llm="ollama/llama3.2",
        llm_config=local_llm_config,
        summary_llm="ollama/llama3.2",
        summary_llm_config=local_llm_config,
        embedding="ollama/mxbai-embed-large",
    ),
)

Embedding Model Copy Section

Embeddings are used to retrieve k texts (where k is specified via Settings.answer.evidence_k)
for re-ranking and contextual summarization.
If you don't want to use embeddings, but instead just fetch all chunks,
disable "evidence retrieval" via the Settings.answer.evidence_retrieval setting.

PaperQA2 defaults to using OpenAI (text-embedding-3-small) embeddings,
but has flexible options for both vector stores and embedding choices.

#### Specifying the Embedding Model

The simplest way to specify the embedding model is via Settings.embedding:

from paperqa import Settings, ask
answer_response = ask(
    "What is PaperQA2?",
    settings=Settings(embedding="text-embedding-3-large"),
)

embedding accepts any embedding model name supported by litellm.
PaperQA2 also supports an embedding input of "hybrid-<model_name>"
i.e. "hybrid-text-embedding-3-small" to use a hybrid sparse keyword (based on a token modulo embedding)
and dense vector embedding, where any litellm model can be used in the dense model name.
"sparse" can be used to use a sparse keyword embedding only.

Embedding models are used to create PaperQA2's index of the full-text embedding vectors (texts_index argument).
The embedding model can be specified as a setting when you are adding new papers to the Docs object:

from paperqa import Docs, Settings
docs = Docs()
for doc in ("myfile.pdf", "myotherfile.pdf"):
    await docs.aadd(doc, settings=Settings(embedding="text-embedding-large-3"))

Note that PaperQA2 uses Numpy as a dense vector store.
Its design of using a keyword search initially reduces the number of chunks
needed for each answer to a relatively small number < 1k.
Therefore, NumpyVectorStore is a good place to start, it's a simple in-memory store, without an index.
However, if a larger-than-memory vector store is needed,
you can an external vector database like Qdrant via the QdrantVectorStore class.

The hybrid embeddings can be customized:

from paperqa import (
    Docs,
    HybridEmbeddingModel,
    SparseEmbeddingModel,
    LiteLLMEmbeddingModel,
)

model = HybridEmbeddingModel(
    models=[LiteLLMEmbeddingModel(), SparseEmbeddingModel(ndim=1024)]
)
docs = Docs()
for doc in ("myfile.pdf", "myotherfile.pdf"):
    await docs.aadd(doc, embedding_model=model)

The sparse embedding (keyword) models default to having 256 dimensions,
but this can be specified via the ndim argument.

#### Local Embedding Models (Sentence Transformers)

You can use a SentenceTransformerEmbeddingModel model if you install sentence-transformers,
which is a local embedding library with support for HuggingFace models and more.
You can install it by adding the local extras.

pip install paper-qa[local]

and then prefix embedding model names with st-:

from paperqa import Settings, ask
answer_response = ask(
    "What is PaperQA2?",
    settings=Settings(embedding="st-multi-qa-MiniLM-L6-cos-v1"),
)

or with a hybrid model

from paperqa import Settings, ask
answer_response = ask(
    "What is PaperQA2?",
    settings=Settings(embedding="hybrid-st-multi-qa-MiniLM-L6-cos-v1"),
)

Adjusting number of sources Copy Section

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.

from paperqa import Settings
settings = Settings()
settings.answer.answer_max_sources = 3
settings.answer.evidence_k = 5
await docs.aquery(
    "What is PaperQA2?",
    settings=settings,
)

Using Code or HTML Copy Section

You do not need to use papers -- you can use code or raw HTML.
Note that this tool is focused on answering questions,
so it won't do well at writing code.
One note is that the tool cannot infer citations from code,
so you will need to provide them yourself.

import glob
import os
from paperqa import Docs
source_files = glob.glob("/*.js")
docs = Docs()
for f in source_files:
    # this assumes the file names are unique in code
    await docs.aadd(
        f, citation="File " + os.path.basename(f), docname=os.path.basename(f)
    )
session = await docs.aquery("Where is the search bar in the header defined?")
print(session)

Multimodal Support Copy Section

Multimodal support centers on:

- Standalone images
- Images or tables in PDFs

The Docs object stores media via a ParsedMedia object.
When chunking a document, media are not split at chunk boundaries,
so it's possible 2+ chunks can correspond with the same media.
This means within PaperQA each chunk
has a one-to-many relationship between ParsedMedia and chunks.

Depending on the source document, the same image can appear multiple times
(e.g. each page of a PDF has a logo in the margins).
Thus, clients should consider media databases
to have a many-to-many relationship with chunks.

Since PaperQA's evidence gathering process centers on text-based retrieval,
it's possible relevant image(s) or table(s) aren't retrieved
because their associated text content is irrelevant.
For a concrete example, imagine the figure in a paper has a terse caption
and is placed one page after relevant main-text discussion.
To solve this problem, PaperQA supports media enrichment at document read-time.
Basically after reading in the PDF,
the parsing.enrichment_llm is given the parsing.enrichment_prompt
and co-located text to generate a synthetic caption for every image/table.
The synthetic captions are used to shift the embeddings of each text chunk,
but are kept separate from the actual source text.
This way evidence gathering can fetch relevant images/tables
without risk of polluting contextual summaries with LLM-generated captions.

If you want multimodal PDF reading, but do not want enrichment
(since adds one LLM prompt/media at read-time),
enrichment can be disabled by setting parsing.multimodal to ON_WITHOUT_ENRICHMENT.

When creating contextual summaries on a given chunk (a Text),
the summary LLM is passed both the chunk's text and the chunk's associated media,
but the output contextual summary itself remains text-only.

If you would like,
specifying the prompt paperqa.prompts.summary_json_multimodal_system_prompt
to the setting prompt.summary_json_system
will include a used_images flag attributing
usage of images in any contextual summarizations.

Using External DB/Vector DB and Caching Copy Section

You may want to cache parsed texts and embeddings in an external database or file.
You can then build a Docs object from those directly:

from paperqa import Docs, Doc, Text
docs = Docs()
for ... in my_docs:
    doc = Doc(docname=..., citation=..., dockey=..., citation=...)
    texts = [Text(text=..., name=..., doc=doc) for ... in my_texts]
    docs.add_texts(texts, doc)

Creating Index Copy Section

Indexes will be placed in the [home directory][home dir] by default.
This can be controlled via the PQA_HOME environment variable.

Indexes are made by reading files in the IndexSettings.paper_directory.
By default, we recursively read from subdirectories of the paper directory,
unless disabled using IndexSettings.recurse_subdirectories.
The paper directory is not modified in any way, it's just read from.

[home dir]: https://docs.python.org/3/library/pathlib.html#pathlib.Path.home

#### Manifest Files

The indexing process attempts to infer paper metadata like title and DOI
using LLM-powered text processing.
You can avoid this point of uncertainty using a "manifest" file,
which is a CSV containing DocDetails fields (order doesn't matter).
For example:

- file_location: relative path to the paper's PDF within the index directory
- doi: DOI of the paper
- title: title of the paper

By providing this information,
we ensure queries to metadata providers like Crossref are accurate.

To ease creating a manifest, there is a helper class method Doc.to_csv,
which also works when called on DocDetails.

Reusing Index Copy Section

The local search indexes are built based on a hash of the current Settings object.
So make sure you properly specify the paper_directory to your IndexSettings object.
In general, it's advisable to:

1. Pre-build an index given a folder of papers (can take several minutes)
2. Reuse the index to perform many queries

import os
from paperqa import Settings
from paperqa.agents.main import agent_query
from paperqa.agents.search import get_directory_index

async def amain(folder_of_papers: str | os.PathLike) -> None:
    settings = Settings(agent={"index": {"paper_directory": folder_of_papers}})
    # 1. Build the index. Note an index name is autogenerated when unspecified
    built_index = await get_directory_index(settings=settings)
    print(settings.get_index_name())  # Display the autogenerated index name
    print(await built_index.index_files)  # Display the index contents
    # 2. Use the settings as many times as you want with ask
    answer_response_1 = await agent_query(
        query="What is a cool retrieval augmented generation technique?",
        settings=settings,
    )
    answer_response_2 = await agent_query(
        query="What is PaperQA2?",
        settings=settings,
    )

Using Clients Directly Copy Section

One of the most powerful features of PaperQA2 is its ability to combine data from multiple metadata sources.
For example, Unpaywall can provide open access status/direct links to PDFs,
Crossref can provide bibtex,
and Semantic Scholar can provide citation licenses.
Here's a short demo of how to do this:

from paperqa.clients import DocMetadataClient, ALL_CLIENTS
client = DocMetadataClient(metadata_clients=ALL_CLIENTS)
details = await client.query(title="Augmenting language models with chemistry tools")
print(details.formatted_citation)
Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari,

Andrew D. White, and Philippe Schwaller.

 Augmenting large language models with chemistry tools. Nature Machine Intelligence,

6:525-535, May 2024. URL: https://doi.org/10.1038/s42256-024-00832-8,

doi:10.1038/s42256-024-00832-8.

This article has 243 citations and is from a domain leading peer-reviewed journal.
print(details.citation_count)
243
print(details.license)
cc-by
print(details.pdf_url)
https://www.nature.com/articles/s42256-024-00832-8.pdf

the client.query is meant to check for exact matches of title.
It's a bit robust (like to casing, missing a word).
There are duplicates for titles though - so you can also add authors to disambiguate.
Or you can provide a doi directly client.query(doi="10.1038/s42256-024-00832-8").

If you're doing this at a large scale,
you may not want to use ALL_CLIENTS (just omit the argument)
and you can specify which specific fields you want to speed up queries.
For example:

details = await client.query(
    title="Augmenting large language models with chemistry tools",
    authors=["Andres M. Bran", "Sam Cox"],
    fields=["title", "doi"],
)

will return much faster than the first query and we'll be certain the authors match.

Settings Cheatsheet Copy Chapter

| Setting | Default | Description |
| -------------------------------------------- | -------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| llm | "gpt-4o-2024-11-20" | LLM for general use including metadata inference (see Docs.aadd) and answer generation (see Docs.aquery and gen_answer tool). |
| llm_config | None | Optional configuration for llm. |
| summary_llm | "gpt-4o-2024-11-20" | LLM for creating contextual summaries (see Docs.aget_evidence and gather_evidence tool). |
| summary_llm_config | None | Optional configuration for summary_llm. |
| embedding | "text-embedding-3-small" | Embedding model for embedding text chunks when adding papers. |
| embedding_config | None | Optional configuration for embedding. |
| temperature | 0.0 | Temperature for LLMs. |
| batch_size | 1 | Batch size for calling LLMs. |
| texts_index_mmr_lambda | 1.0 | Lambda for MMR in text index. |
| verbosity | 0 | Integer verbosity level for logging (0-3). 3 = all LLM/Embeddings calls logged. |
| custom_context_serializer | None | Custom async function (see typing for signature) to override the default answer context serialization. |
| answer.evidence_k | 10 | Number of evidence pieces to retrieve. |
| answer.evidence_retrieval | True | Use retrieval vs processing all docs. |
| answer.evidence_summary_length | "about 100 words" | Length of evidence summary. |
| answer.evidence_skip_summary | False | Whether to skip summarization. |
| answer.evidence_text_only_fallback | False | Whether to allow context creation to retry without media present. |
| answer.answer_max_sources | 5 | Max number of sources for an answer. |
| answer.max_answer_attempts | None | Max attempts to generate an answer. |
| answer.answer_length | "about 200 words, but can be longer" | Length of final answer. |
| answer.max_concurrent_requests | 4 | Max concurrent requests to LLMs. |
| answer.answer_filter_extra_background | False | Whether to cite background info from model. |
| answer.get_evidence_if_no_contexts | True | Allow lazy evidence gathering. |
| answer.group_contexts_by_question | False | Groups the final contexts by the underlying gather_evidence question in the final context prompt. |
| answer.evidence_relevance_score_cutoff | 1 | Cutoff evidence relevance score to include in the answer context (inclusive) |
| answer.skip_evidence_citation_strip | False | Skip removal of citations from the gather_evidence contexts |
| parsing.page_size_limit | 1,280,000 | Character limit per page. |
| parsing.use_doc_details | True | Whether to get metadata details for docs. |
| parsing.reader_config | dict | Optional keyword arguments for the document reader. |
| parsing.multimodal | True | Control to parse both text and media from applicable documents, as well as potentially enriching them with text descriptions. |
| parsing.defer_embedding | False | Whether to defer embedding until summarization. |
| parsing.parse_pdf | paperqa_pypdf.parse_pdf_to_pages | Function to parse PDF files. |
| parsing.configure_pdf_parser | No-op | Callable to configure the PDF parser within parse_pdf, useful for behaviors such as enabling logging. |
| parsing.doc_filters | None | Optional filters for allowed documents. |
| parsing.use_human_readable_clinical_trials | False | Parse clinical trial JSONs into readable text. |
| parsing.enrichment_llm | "gpt-4o-2024-11-20" | LLM for media enrichment. |
| parsing.enrichment_llm_config | None | Optional configuration for enrichment_llm. |
| parsing.enrichment_page_radius | 1 | Page radius for context text in enrichment. |
| parsing.enrichment_prompt | image_enrichment_prompt_template | Prompt template for enriching media. |
| parsing.citation_prompt | citation_prompt | Prompt to create citation from peeking one chunk. |
| parsing.structured_citation_prompt | structured_citation_prompt | Prompt to create a citation (in JSON) from peeking one chunk. |
| parsing.disable_doc_valid_check | False | Flag to disable checking if a document looks like text (was parsed correctly). |
| prompts.summary | summary_prompt | Template for summarizing text, must contain variables matching summary_prompt. |
| prompts.qa | qa_prompt | Template for QA, must contain variables matching qa_prompt. |
| prompts.select | select_paper_prompt | Template for selecting papers, must contain variables matching select_paper_prompt. |
| prompts.pre | None | Optional pre-prompt templated with just the original question to append information before a qa prompt. |
| prompts.post | None | Optional post-processing prompt that can access PQASession fields. |
| prompts.system | default_system_prompt | System prompt for the model. |
| prompts.use_json | True | Whether to use JSON formatting. |
| prompts.summary_json | summary_json_prompt | JSON-specific summary prompt. |
| prompts.summary_json_system | summary_json_system_prompt | System prompt for JSON summaries. |
| prompts.context_outer | CONTEXT_OUTER_PROMPT | Prompt for how to format all contexts in generate answer. |
| prompts.context_inner | CONTEXT_INNER_PROMPT | Prompt for how to format a single context in generate answer. Must contain 'name' and 'text' variables. |
| prompts.answer_iteration_prompt | answer_iteration_prompt_template | Prompt to inject existing prior answers to allow iteration. Default injects no prior answers. |
| agent.agent_llm | "gpt-4o-2024-11-20" | LLM inside the agent making tool selections. |
| agent.agent_llm_config | None | Optional configuration for agent_llm. |
| agent.agent_type | "ToolSelector" | Type of agent to use. |
| agent.agent_config | None | Optional kwarg for AGENT constructor. |
| agent.agent_system_prompt | env_system_prompt | Optional system prompt message. |
| agent.agent_prompt | env_reset_prompt | Agent prompt. |
| agent.return_paper_metadata | False | Whether to include paper title/year in search tool results. |
| agent.search_count | 8 | Search count. |
| agent.timeout | 500.0 | Timeout on agent execution (seconds). |
| agent.tool_names | None | Optional override on tools to provide the agent. |
| agent.max_timesteps | None | Optional upper limit on environment steps. |
| agent.agent_evidence_n | 1 | Top n ranked evidences shown to the agent after gathering evidence. |
| agent.rebuild_index | True | Flag to rebuild the index at the start of agent runners. |
| agent.callbacks | {} | Named lists of callables to be invoked with environment state. |
| agent.index.name | None | Optional name of the index. |
| agent.index.paper_directory | Current working directory | Directory containing papers to be indexed. |
| agent.index.manifest_file | None | Path to manifest CSV with document attributes. |
| agent.index.index_directory | pqa_directory("indexes") | Directory to store PQA indexes. |
| agent.index.use_absolute_paper_directory | False | Whether to use absolute paper directory path. |
| agent.index.recurse_subdirectories | True | Whether to recurse into subdirectories when indexing. |
| agent.index.concurrency | 5 | Number of concurrent filesystem reads. |
| agent.index.sync_with_paper_directory | True | Whether to sync index with paper directory on load. |
| agent.index.batch_size | 1 | Number of files to process before committing to the index. |
| agent.index.files_filter | lambda f: f.suffix in {...} | Filter function to mark files in the paper directory to index. |

Where do I get papers? Copy Chapter

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.

See detailed docs about zotero, openreview and parsing

Callbacks Copy Chapter

To execute a function on each chunk of LLM completions,
you need to provide a function that can be executed on each chunk.
For example, to get a typewriter view of the completions, you can do:

from paperqa import Docs

def typewriter(chunk: str) -> None:
    print(chunk, end="")

docs = Docs()
add some docs...
await docs.aquery("What is PaperQA2?", callbacks=[typewriter])

Caching Embeddings Copy Section

In general, embeddings are cached when you pickle a Docs regardless of what vector store you use.
So as long as you save your underlying Docs object,
you should be able to avoid re-embedding your documents.

Customizing Prompts Copy Chapter

You can customize any of the prompts using settings.

from paperqa import Docs, Settings
my_qa_prompt = (
    "Answer the question '{question}'\n"
    "Use the context below if helpful. "
    "You can cite the context using the key like (pqac-abcd1234). "
    "If there is insufficient context, write a poem "
    "about how you cannot answer.\n\n"
    "Context: {context}"
)
docs = Docs()
settings = Settings()
settings.prompts.qa = my_qa_prompt
await docs.aquery("What is PaperQA2?", settings=settings)

Pre and Post Prompts Copy Section

Following the syntax above, you can also include prompts that
are executed after the query and before the query.
For example, you can use this to critique the answer.

FAQ Copy Chapter

How come I get different results than your papers? Copy Section

Internally at FutureHouse, we have a slightly different set of tools.
We're trying to get some of them, like citation traversal, into this repo.
However, we have APIs and licenses to access research papers that we cannot share openly.
Similarly, in our research papers' results we do not start with the known relevant PDFs.
Our agent has to identify them using keyword search over all papers, rather than just a subset.
We're gradually aligning these two versions of PaperQA,
but until there is an open-source way to freely access papers (even just open source papers)
you will need to provide PDFs yourself.

How is this different from LlamaIndex or LangChain? Copy Section

LangChain
and LlamaIndex
are both frameworks for working with LLM applications,
with abstractions made for agentic workflows and retrieval augmented generation.

Over time, the PaperQA team over time chose to become framework-agnostic,
instead outsourcing LLM drivers to [LiteLLM][LiteLLM general docs]
and no framework besides Pydantic for its tools.
PaperQA focuses on scientific papers and their metadata.

PaperQA can be reimplemented using either LlamaIndex or LangChain.
For example, our GatherEvidence tool can be reimplemented
as a retriever with an LLM-based re-ranking and contextual summary.
There is similar work with the tree response method in LlamaIndex.

Can I save or load? Copy Section

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.

import pickle
save

with open("my_docs.pkl", "wb") as f:
    pickle.dump(docs, f)
load

with open("my_docs.pkl", "rb") as f:
    docs = pickle.load(f)

Reproduction Copy Chapter

Contained in docs/2024-10-16_litqa2-splits.json5
are the question IDs used in train, evaluation, and test splits,
as well as paper DOIs used to build the splits' indexes.

- Train and eval splits: question IDs come from
LAB-Bench's LitQA2 question IDs.
- Test split: questions IDs come from
aviary-paper-data's LitQA2 question IDs.

There are multiple papers slowly building PaperQA, shown below in Citation.
To reproduce:

- skarlinski2024language: train and eval splits are applicable.
The test split remains held out.
- narayanan2024aviarytraininglanguageagents: train, eval, and test splits are applicable.

Example on how to use LitQA for evaluation can be found in
aviary.litqa.

Citation Copy Chapter

Please read and cite the following papers if you use this software:

@article{narayanan2024aviarytraininglanguageagents,
      title = {Aviary: training language agents on challenging scientific tasks},
      author = {
      Siddharth Narayanan and
 James D. Braza and
 Ryan-Rhys Griffiths and
 Manu Ponnapati and
 Albert Bou and
 Jon Laurent and
 Ori Kabeli and
 Geemi Wellawatte and
 Sam Cox and
 Samuel G. Rodriques and
 Andrew D. White},
      journal = {arXiv preprent arXiv:2412.21154},
      year = {2024},
      url = {https://doi.org/10.48550/arXiv.2412.21154},
}

@article{skarlinski2024language,
    title = {Language agents achieve superhuman synthesis of scientific knowledge},
    author = {
    Michael D. Skarlinski and
 Sam Cox and
 Jon M. Laurent and
 James D. Braza and
 Michaela Hinks and
 Michael J. Hammerling and
 Manvitha Ponnapati and
 Samuel G. Rodriques and
 Andrew D. White},
    journal = {arXiv preprent arXiv:2409.13740},
    year = {2024},
    url = {https://doi.org/10.48550/arXiv.2409.13740}
}

@article{lala2023paperqa,
    title = {PaperQA: Retrieval-Augmented Generative Agent for Scientific Research},
    author = {
    Jakub Lála and
 Odhran O'Donoghue and
 Aleksandar Shtedritski and
 Sam Cox and
 Samuel G. Rodriques and
 Andrew D. White},
    journal = {arXiv preprint arXiv:2312.07559},
    year = {2023},
    url = {https://doi.org/10.48550/arXiv.2312.07559}
}

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