Metadata-Version: 2.1
Name: paper-qa
Version: 4.6.1
Summary: LLM Chain for answering questions from docs
Author-email: Andrew White <white.d.andrew@gmail.com>
Maintainer-email: James Braza <jamesbraza@gmail.com>, Andrew White <white.d.andrew@gmail.com>
License: Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright [yyyy] [name of copyright owner]
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
        
Project-URL: repository, https://github.com/whitead/paper-qa
Keywords: question answering
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: PyCryptodome
Requires-Dist: html2text
Requires-Dist: numpy
Requires-Dist: openai >=1
Requires-Dist: pydantic >=2
Requires-Dist: pypdf
Requires-Dist: tiktoken >=0.4.0
Provides-Extra: dev
Requires-Dist: anthropic ; extra == 'dev'
Requires-Dist: faiss-cpu ; extra == 'dev'
Requires-Dist: langchain-community ; extra == 'dev'
Requires-Dist: langchain-openai ; extra == 'dev'
Requires-Dist: pymupdf ; extra == 'dev'
Requires-Dist: python-dotenv ; extra == 'dev'
Requires-Dist: pyzotero ; extra == 'dev'
Requires-Dist: requests ; extra == 'dev'
Requires-Dist: sentence-transformers ; extra == 'dev'
Requires-Dist: voyageai ; extra == 'dev'
Requires-Dist: build ; extra == 'dev'
Requires-Dist: pre-commit ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: types-requests ; extra == 'dev'
Requires-Dist: types-setuptools ; extra == 'dev'
Requires-Dist: pytest-asyncio ; extra == 'dev'
Requires-Dist: pytest-sugar ; extra == 'dev'
Requires-Dist: pytest-timer ; extra == 'dev'

# PaperQA

[![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/whitead/paper-qa)
[![tests](https://github.com/whitead/paper-qa/actions/workflows/tests.yml/badge.svg)](https://github.com/whitead/paper-qa)
[![PyPI version](https://badge.fury.io/py/paper-qa.svg)](https://badge.fury.io/py/paper-qa)

This is a minimal package for doing question and answering from
PDFs or text files (which can be raw HTML). It strives to give very good answers, with no hallucinations, by grounding responses with in-text citations.

By default, it uses [OpenAI Embeddings](https://platform.openai.com/docs/guides/embeddings) with a simple numpy vector DB to embed and search documents. However, via [langchain](https://github.com/hwchase17/langchain) you can use open-source models or embeddings (see details below).

paper-qa uses the process shown below:

1. embed docs into vectors
2. embed query into vector
3. search for top k passages in docs
4. create summary of each passage relevant to query
5. score and select only relevant summaries
6. put summaries into prompt
7. generate answer with prompt

See our paper for more details:

```bibtex
@article{lala2023paperqa,
  title={PaperQA: Retrieval-Augmented Generative Agent for Scientific Research},
  author={L{\'a}la, Jakub and O'Donoghue, Odhran and Shtedritski, Aleksandar and Cox, Sam and Rodriques, Samuel G and White, Andrew D},
  journal={arXiv preprint arXiv:2312.07559},
  year={2023}
}
```

## Output Example

Question: How can carbon nanotubes be manufactured at a large scale?

Carbon nanotubes can be manufactured at a large scale using the electric-arc technique (Journet6644). This technique involves creating an arc between two electrodes in a reactor under a helium atmosphere and using a mixture of a metallic catalyst and graphite powder in the anode. Yields of 80% of entangled carbon filaments can be achieved, which consist of smaller aligned SWNTs self-organized into bundle-like crystallites (Journet6644). Additionally, carbon nanotubes can be synthesized and self-assembled using various methods such as DNA-mediated self-assembly, nanoparticle-assisted alignment, chemical self-assembly, and electro-addressed functionalization (Tulevski2007). These methods have been used to fabricate large-area nanostructured arrays, high-density integration, and freestanding networks (Tulevski2007). 98% semiconducting CNT network solution can also be used and is separated from metallic nanotubes using a density gradient ultracentrifugation approach (Chen2014). The substrate is incubated in the solution and then rinsed with deionized water and dried with N2 air gun, leaving a uniform carbon network (Chen2014).

### References

Journet6644: Journet, Catherine, et al. "Large-scale production of single-walled carbon nanotubes by the electric-arc technique." nature 388.6644 (1997): 756-758.

Tulevski2007: Tulevski, George S., et al. "Chemically assisted directed assembly of carbon nanotubes for the fabrication of large-scale device arrays." Journal of the American Chemical Society 129.39 (2007): 11964-11968.

Chen2014: Chen, Haitian, et al. "Large-scale complementary macroelectronics using hybrid integration of carbon nanotubes and IGZO thin-film transistors." Nature communications 5.1 (2014): 4097.

## What's New?

Version 4 removed langchain from the package because it no longer supports pickling. This also simplifies the package a bit - especially prompts. Langchain can still be used, but it's not required. You can use any LLMs from langchain, but you will need to use the `LangchainLLMModel` class to wrap the model.

## Install

Install with pip:

```bash
pip install paper-qa
```

You need to have an LLM to use paper-qa. You can use OpenAI, llama.cpp (via Server), or any LLMs from langchain. OpenAI just works, as long as you have set your OpenAI API key (`export OPENAI_API_KEY=sk-...`). See instructions below for other LLMs.

## Usage

To use paper-qa, you need to have a list of paths/files/urls (valid extensions include: .pdf, .txt). You can then use the `Docs` class to add the documents and then query them. `Docs` will try to guess citation formats from the content of the files, but you can also provide them yourself.

```python

from paperqa import Docs

my_docs = ...# get a list of paths

docs = Docs()
for d in my_docs:
    docs.add(d)

answer = docs.query("What manufacturing challenges are unique to bispecific antibodies?")
print(answer.formatted_answer)
```

The answer object has the following attributes: `formatted_answer`, `answer` (answer alone), `question` , and `context` (the summaries of passages found for answer).

### Async

paper-qa 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.add_url`       |
| `Docs.get_evidence` | `Docs.aget_evidence` |
| `Docs.query`        | `Docs.aquery`        |

The synchronous version just call 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:

```py
from paperqa import Docs

my_docs = ...# get a list of paths

docs = Docs()
for d in my_docs:
    await docs.aadd(d)

answer = await docs.aquery("What manufacturing challenges are unique to bispecific antibodies?")
```

### Adding Documents

`add` will add from paths. You can also use `add_file` (expects a file object) or `add_url` to work with other sources.

### Choosing Model

By default, it uses a hybrid of `gpt-3.5-turbo` and `gpt-4-turbo`. You can adjust this:

```py
docs = Docs(llm='gpt-3.5-turbo')
```

or you can use any other model available in [langchain](https://github.com/hwchase17/langchain):

```py
from paperqa import Docs
from langchain_community.chat_models import ChatAnthropic
docs = Docs(llm="langchain",
            client=ChatAnthropic())
```

Note we split the model into the wrapper and `client`, which is `ChatAnthropic` here. This is because `client` stores the non-pickleable part and langchain LLMs are only sometimes serializable/pickleable. The paper-qa `Docs` must always serializable. Thus, we split the model into two parts.

```py
import pickle
docs = Docs(llm="langchain",
            client=ChatAnthropic())
model_str = pickle.dumps(docs)
docs = pickle.loads(model_str)
# but you have to set the client after loading
docs.set_client(ChatAnthropic())
```

#### Locally Hosted

You can use llama.cpp to be the LLM. Note that you should be using relatively large models, because paper-qa 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](https://github.com/Mozilla-Ocho/llamafile) and execute it with `-cb -np 4 -a my-llm-model --embedding` which will enable continuous batching and embeddings.

```py
from paperqa import Docs, LlamaEmbeddingModel
from openai import AsyncOpenAI

# start llamap.cpp client with

local_client = AsyncOpenAI(
    base_url="http://localhost:8080/v1",
    api_key = "sk-no-key-required"
)

docs = Docs(client=local_client,
            embedding_model=LlamaEmbeddingModel(),
            llm_model=OpenAILLMModel(config=dict(model="my-llm-model", temperature=0.1, frequency_penalty=1.5, max_tokens=512)))
```

### Changing Embedding Model

You can use langchain embedding models, or the [SentenceTransformer](https://www.sbert.net/) models. For example

```py
from paperqa import Docs, SentenceTransformerEmbeddingModel
from openai import AsyncOpenAI

# start llamap.cpp client with

local_client = AsyncOpenAI(
    base_url="http://localhost:8080/v1",
    api_key = "sk-no-key-required"
)

docs = Docs(client=local_client,
            embedding_model=SentenceTransformerEmbeddingModel(),
            llm_model=OpenAILLMModel(config=dict(model="my-llm-model", temperature=0.1, frequency_penalty=1.5, max_tokens=512)))
```

Just like in the above examples, we have to split the Langchain model into a client and model to keep `Docs` serializable.

```py

from paperqa import Docs, LangchainEmbeddingModel

docs = Docs(embedding_model=LangchainEmbeddingModel(), embedding_client=OpenAIEmbeddings())
```

### 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.

```py
docs.query("What manufacturing challenges are unique to bispecific antibodies?", k = 5, max_sources = 2)
```

### 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

source_files = glob.glob('**/*.js')

docs = Docs()
for f in source_files:
    # this assumes the file names are unique in code
    docs.add(f, citation='File ' + os.path.name(f), docname=os.path.name(f))
answer = docs.query("Where is the search bar in the header defined?")
print(answer)
```

### 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:

```py

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)
```

If you want to use an external vector store, you can also do that directly via langchain. For example, to use the [FAISS](https://ai.meta.com/tools/faiss/) vector store from langchain:

```py
from paperqa import LangchainVectorStore, Docs
from langchain_community.vector_store import FAISS
from langchain_openai import OpenAIEmbeddings

my_index = LangchainVectorStore(cls=FAISS, embedding_model=OpenAIEmbeddings())
docs = Docs(texts_index=my_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.

### Zotero

If you use [Zotero](https://www.zotero.org/) to organize your personal bibliography,
you can use the `paperqa.contrib.ZoteroDB` to query papers from your library,
which relies on [pyzotero](https://github.com/urschrei/pyzotero).

Install `pyzotero` to use this feature:

```bash
pip install pyzotero
```

First, note that `paperqa` 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](https://www.zotero.org/settings/keys) 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](https://www.zotero.org/settings/keys/new) 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 `paperqa`:

```py
from paperqa.contrib import ZoteroDB

docs = paperqa.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
    docs.add(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:

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

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

### Paper Scraper

If you want to search for papers outside of your own collection, I've found an unrelated project called [paper-scraper](https://github.com/blackadad/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.

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

## PDF Reading Options

By default [PyPDF](https://pypi.org/project/pypdf/) is used since it's pure python and easy to install. For faster PDF reading, paper-qa will detect and use [PymuPDF (fitz)](https://pymupdf.readthedocs.io/en/latest/):

```sh
pip install pymupdf
```

## Callbacks Factory

To execute a function on each chunk of LLM completions, you need to provide a function that when called with the name of the step produces a list of functions to execute on each chunk. For example, to get a typewriter view of the completions, you can do:

```python
def make_typewriter(step_name):
    def typewriter(chunk):
        print(chunk, end="")
    return [typewriter] # <- note that this is a list of functions
...
docs.query("What manufacturing challenges are unique to bispecific antibodies?", get_callbacks=make_typewriter)
```

### Caching Embeddings

In general, embeddings are cached when you pickle a `Docs` regardless of what vector store you use. See above for details on more explicit management of them.

## Customizing Prompts

You can customize any of the prompts, using the `PromptCollection` class. For example, if you want to change the prompt for the question, you can do:

```python
from paperqa import Docs, Answer, PromptCollection

my_qaprompt = "Answer the question '{question}' "
    "Use the context below if helpful. "
    "You can cite the context using the key "
    "like (Example2012). "
    "If there is insufficient context, write a poem "
    "about how you cannot answer.\n\n"
    "Context: {context}\n\n"
prompts=PromptCollection(qa=my_qaprompt)
docs = Docs(prompts=prompts)
```

### 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 is this different from LlamaIndex?

It's not that different! This is similar to the tree response method in LlamaIndex. I just have included some prompts I find useful, readers that give page numbers/line numbers, and am focused on one task - answering technical questions with cited sources.

### How is this different from LangChain?

There has been some great work on retrievers in langchain and you could say this is an example of a retriever.

### 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)

docs.set_client() #defaults to OpenAI
```
