PaperQA2

PaperQA2 is a package for doing high-accuracy retrieval augmented generation (RAG) on PDFs or text 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.

• Quickstart
  • Example Output
• What is PaperQA2
  • PaperQA2 vs PaperQA
  • What's New in Version 5 (aka PaperQA2)?
  • 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

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.

bash 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

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

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 models.

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
7. pybtex

PaperQA2 vs PaperQA

We've been working on hard on fundamental upgrades for a while and mostly followed SemVer. 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.

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

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.

PaperQA2 Algorithm

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

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

bash 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 (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

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

bash 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/.

bash 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:

bash 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

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

and then you can use it with

bash 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.

bash 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:

bash pqa -i nanomaterials index

Now I can search for papers about thermoelectrics:

bash pqa -i nanomaterials search thermoelectrics

or I can use the normal ask

bash 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:

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

Bundled Settings

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

| Setting Name | Description | | ------------ | ---------------------------------------------------------------------------------------------------------------------------- | | highquality | Highly performant, relatively expensive (due to having `evidencek= 15) query using aToolSelectoragent. | | 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 usetier<1-5>_limits` to specify the tier. |

Rate Limits

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.

bash 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:

bash 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:

```python from paperqa import Settings, ask

answerresponse = ask( "What is PaperQA2?", settings=Settings( llmconfig={"ratelimit": {"gpt-4o-2024-11-20": "30000 per 1 minute"}}, summaryllmconfig={"ratelimit": {"gpt-4o-2024-11-20": "30000 per 1 minute"}}, ), ) ```

Library Usage

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

```python from paperqa import Settings, ask

answerresponse = ask( "What is PaperQA2?", settings=Settings(temperature=0.5, paperdirectory="my_papers"), ) ```

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

Agentic Adding/Querying Documents

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:

```python from paperqa import Settings, ask

answerresponse = ask( "What is PaperQA2?", settings=Settings(temperature=0.5, paperdirectory="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:

```python from paperqa import Settings, agent_query

answerresponse = await agentquery( query="What is PaperQA2?", settings=Settings(temperature=0.5, paperdirectory="mypapers"), ) ```

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

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.

```python from paperqa import Docs, Settings

valid extensions include .pdf, .txt, .md, and .html

doc_paths = ("myfile.pdf", "myotherfile.pdf")

Prepare the Docs object by adding a bunch of documents

docs = Docs() for docpath in docpaths: 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.answermaxsources = 3

Query the Docs object to get an answer

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

Async

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:

```python import asyncio from paperqa import Docs

async def main() -> None: docs = Docs() # valid extensions include .pdf, .txt, .md, and .html 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

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:

```python from paperqa import Settings, ask

answerresponse = ask( "What is PaperQA2?", settings=Settings( llm="gpt-4o-mini", summaryllm="gpt-4o-mini", paperdirectory="mypapers" ), ) ```

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.

```python from paperqa import Settings, ask from paperqa.settings import AgentSettings

answerresponse = ask( "What is PaperQA2?", settings=Settings( llm="claude-3-5-sonnet-20240620", summaryllm="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

```python from paperqa import Settings, ask from paperqa.settings import AgentSettings

answerresponse = ask( "What is PaperQA2?", settings=Settings( llm="gemini/gemini-2.0-flash", summaryllm="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.

```python from paperqa import Settings, ask

localllmconfig = dict( modellist=[ dict( modelname="myllmmodel", litellmparams=dict( model="my-llm-model", apibase="http://localhost:8080/v1", apikey="sk-no-key-required", temperature=0.1, frequencypenalty=1.5, max_tokens=512, ), ) ] )

answerresponse = ask( "What is PaperQA2?", settings=Settings( llm="my-llm-model", llmconfig=localllmconfig, summaryllm="my-llm-model", summaryllmconfig=localllm_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.

```python from paperqa import Settings, ask

localllmconfig = { "modellist": [ { "modelname": "ollama/llama3.2", "litellmparams": { "model": "ollama/llama3.2", "apibase": "http://localhost:11434", }, } ] }

answerresponse = ask( "What is PaperQA2?", settings=Settings( llm="ollama/llama3.2", llmconfig=localllmconfig, summaryllm="ollama/llama3.2", summaryllmconfig=localllm_config, embedding="ollama/mxbai-embed-large", ), ) ```

Embedding Model

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:

```python 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:

```python 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:

```python 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.

sh pip install paper-qa[local]

and then prefix embedding model names with st-:

```python 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

```python 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

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.

```python from paperqa import Settings

settings = Settings() settings.answer.answermaxsources = 3 settings.answer.evidence_k = 5

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

Using Code or HTML

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.

```python 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

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.

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.

Using External DB/Vector DB and Caching

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:

```python from paperqa import Docs, Doc, Text

docs = Docs()

for ... in mydocs: doc = Doc(docname=..., citation=..., dockey=..., citation=...) texts = [Text(text=..., name=..., doc=doc) for ... in mytexts] docs.add_texts(texts, doc) ```

Creating Index

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

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

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

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 Settings 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

```python import os

from paperqa import Settings from paperqa.agents.main import agentquery from paperqa.agents.search import getdirectory_index

async def amain(folderofpapers: str | os.PathLike) -> None: settings = Settings(paperdirectory=folderof_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

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:

```python from paperqa.clients import DocMetadataClient, ALL_CLIENTS

client = DocMetadataClient(metadataclients=ALLCLIENTS) 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:

python 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

| Setting | Default | Description | | -------------------------------------------- | -------------------------------------- | ------------------------------------------------------------------------------------------------------- | | llm | "gpt-4o-2024-11-20" | Default LLM for most things, including answers. Should be 'best' LLM. | | llm_config | None | Optional configuration for llm. | | summary_llm | "gpt-4o-2024-11-20" | Default LLM for summaries and parsing citations. | | summary_llm_config | None | Optional configuration for summary_llm. | | embedding | "text-embedding-3-small" | Default embedding model for texts. | | 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_detailed_citations | True | Include detailed citations in summaries. | | 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.chunk_size | 5000 | Characters per chunk (0 for no chunking). | | parsing.page_size_limit | 1,280,000 | Character limit per page. | | parsing.pdfs_use_block_parsing | False | Opt-in flag for block-based PDF parsing over text-based PDF parsing. | | parsing.use_doc_details | True | Whether to get metadata details for docs. | | parsing.overlap | 250 | Characters to overlap chunks. | | parsing.multimodal | True | Flag to parse both text and images from applicable documents. | | 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.chunking_algorithm | ChunkingOptions.SIMPLE_OVERLAP | Algorithm for chunking. | | parsing.doc_filters | None | Optional filters for allowed documents. | | parsing.use_human_readable_clinical_trials | False | Parse clinical trial JSONs into readable text. | | prompt.summary | summary_prompt | Template for summarizing text, must contain variables matching summary_prompt. | | prompt.qa | qa_prompt | Template for QA, must contain variables matching qa_prompt. | | prompt.select | select_paper_prompt | Template for selecting papers, must contain variables matching select_paper_prompt. | | prompt.pre | None | Optional pre-prompt templated with just the original question to append information before a qa prompt. | | prompt.post | None | Optional post-processing prompt that can access PQASession fields. | | prompt.system | default_system_prompt | System prompt for the model. | | prompt.use_json | True | Whether to use JSON formatting. | | prompt.summary_json | summary_json_prompt | JSON-specific summary prompt. | | prompt.summary_json_system | summary_json_system_prompt | System prompt for JSON summaries. | | prompt.context_outer | CONTEXT_OUTER_PROMPT | Prompt for how to format all contexts in generate answer. | | prompt.context_inner | CONTEXT_INNER_PROMPT | Prompt for how to format a single context in generate answer. Must contain 'name' and 'text' variables. | | agent.agent_llm | "gpt-4o-2024-11-20" | Model to use for 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.should_pre_search | False | Whether to run search tool before invoking agent. | | agent.tool_names | None | Optional override on tools to provide the agent. | | agent.max_timesteps | None | Optional upper limit on environment steps. | | 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.files_filter | lambda f: f.suffix in {...} | Filter function to mark files in the paper directory to index. |

Where do I get papers?

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

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:

```python 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

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

You can customize any of the prompts using settings.

```python from paperqa import Docs, Settings

myqaprompt = ( "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 = myqaprompt await docs.aquery("What is PaperQA2?", settings=settings) ```

Pre and Post Prompts

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

How come I get different results than your papers?

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?

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 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?

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.

```python 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

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

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

bibtex @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}, }

bibtex @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} }

bibtex @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} }