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Harrison/format agent instructions (#973)

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Co-authored-by: Andrew White <white.d.andrew@gmail.com>
Co-authored-by: Harrison Chase <harrisonchase@Harrisons-MBP.attlocal.net>
Co-authored-by: Peng Qu <82029664+pengqu123@users.noreply.github.com>
makefile-update-1
Harrison Chase 1 year ago committed by GitHub
parent 5469d898a9
commit c64f98e2bb
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10 changed files with 441 additions and 187 deletions
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129
docs/modules/document_loaders/examples/pdf.ipynb
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@ -10,6 +10,133 @@
"This covers how to load pdfs into a document format that we can use downstream."
]
},
{
"cell_type": "markdown",
"id": "743f9413",
"metadata": {},
"source": [
"## Using PyPDF\n",
"\n",
"Allows for tracking of page numbers as well."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c428b0c5",
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import PagedPDFSplitter\n",
"\n",
"loader = PagedPDFSplitter(\"example_data/layout-parser-paper.pdf\")\n",
"pages = loader.load_and_split()"
]
},
{
"cell_type": "markdown",
"id": "ebd895e4",
"metadata": {},
"source": [
"An advantage of this approach is that documents can be retrieved with page numbers."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "87fa7b3a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9: 10 Z. Shen et al.\n",
"Fig. 4: Illustration of (a) the original historical Japanese document with layout\n",
"detection results and (b) a recreated version of the document image that achieves\n",
"much better character recognition recall. The reorganization algorithm rearranges\n",
"the tokens based on the their detected bounding boxes given a maximum allowed\n",
"height.\n",
"4LayoutParser Community Platform\n",
"Another focus of LayoutParser is promoting the reusability of layout detection\n",
"models and full digitization pipelines. Similar to many existing deep learning\n",
"libraries, LayoutParser comes with a community model hub for distributing\n",
"layout models. End-users can upload their self-trained models to the model hub,\n",
"and these models can be loaded into a similar interface as the currently available\n",
"LayoutParser pre-trained models. For example, the model trained on the News\n",
"Navigator dataset [17] has been incorporated in the model hub.\n",
"Beyond DL models, LayoutParser also promotes the sharing of entire doc-\n",
"ument digitization pipelines. For example, sometimes the pipeline requires the\n",
"combination of multiple DL models to achieve better accuracy. Currently, pipelines\n",
"are mainly described in academic papers and implementations are often not pub-\n",
"licly available. To this end, the LayoutParser community platform also enables\n",
"the sharing of layout pipelines to promote the discussion and reuse of techniques.\n",
"For each shared pipeline, it has a dedicated project page, with links to the source\n",
"code, documentation, and an outline of the approaches. A discussion panel is\n",
"provided for exchanging ideas. Combined with the core LayoutParser library,\n",
"users can easily build reusable components based on the shared pipelines and\n",
"apply them to solve their unique problems.\n",
"5 Use Cases\n",
"The core objective of LayoutParser is to make it easier to create both large-scale\n",
"and light-weight document digitization pipelines. Large-scale document processing\n",
"3: 4 Z. Shen et al.\n",
"Efficient Data AnnotationC u s t o m i z e d M o d e l T r a i n i n gModel Cust omizationDI A Model HubDI A Pipeline SharingCommunity PlatformLa y out Detection ModelsDocument Images \n",
"T h e C o r e L a y o u t P a r s e r L i b r a r yOCR ModuleSt or age & VisualizationLa y out Data Structur e\n",
"Fig. 1: The overall architecture of LayoutParser . For an input document image,\n",
"the core LayoutParser library provides a set of o\u000b",
"-the-shelf tools for layout\n",
"detection, OCR, visualization, and storage, backed by a carefully designed layout\n",
"data structure. LayoutParser also supports high level customization via e\u000ecient\n",
"layout annotation and model training functions. These improve model accuracy\n",
"on the target samples. The community platform enables the easy sharing of DIA\n",
"models and whole digitization pipelines to promote reusability and reproducibility.\n",
"A collection of detailed documentation, tutorials and exemplar projects make\n",
"LayoutParser easy to learn and use.\n",
"AllenNLP [ 8] and transformers [ 34] have provided the community with complete\n",
"DL-based support for developing and deploying models for general computer\n",
"vision and natural language processing problems. LayoutParser , on the other\n",
"hand, specializes speci\f",
"cally in DIA tasks. LayoutParser is also equipped with a\n",
"community platform inspired by established model hubs such as Torch Hub [23]\n",
"andTensorFlow Hub [1]. It enables the sharing of pretrained models as well as\n",
"full document processing pipelines that are unique to DIA tasks.\n",
"There have been a variety of document data collections to facilitate the\n",
"development of DL models. Some examples include PRImA [ 3](magazine layouts),\n",
"PubLayNet [ 38](academic paper layouts), Table Bank [ 18](tables in academic\n",
"papers), Newspaper Navigator Dataset [ 16,17](newspaper \f",
"gure layouts) and\n",
"HJDataset [31](historical Japanese document layouts). A spectrum of models\n",
"trained on these datasets are currently available in the LayoutParser model zoo\n",
"to support di\u000b",
"erent use cases.\n",
"3 The Core LayoutParser Library\n",
"At the core of LayoutParser is an o\u000b",
"-the-shelf toolkit that streamlines DL-\n",
"based document image analysis. Five components support a simple interface\n",
"with comprehensive functionalities: 1) The layout detection models enable using\n",
"pre-trained or self-trained DL models for layout detection with just four lines\n",
"of code. 2) The detected layout information is stored in carefully engineered\n"
]
}
],
"source": [
"from langchain.vectorstores import FAISS\n",
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"\n",
"faiss_index = FAISS.from_documents(pages, OpenAIEmbeddings())\n",
"docs = faiss_index.similarity_search(\"How will the community be engaged?\", k=2)\n",
"for doc in docs:\n",
" print(str(doc.metadata[\"page\"]) + \":\", doc.page_content)"
]
},
{
"cell_type": "markdown",
"id": "09d64998",
"metadata": {},
"source": [
"## Using Unstructured"
]
},
{
"cell_type": "code",
"execution_count": 1,
@ -65,7 +192,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.9.1"
}
},
"nbformat": 4,

2
docs/modules/document_loaders/how_to_guides.rst
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@ -27,6 +27,8 @@ There are a lot of different document loaders that LangChain supports. Below are
`Roam <./examples/roam.html>`_: A walkthrough of how to load data from a Roam file export.
`EveryNote <./examples/everynote.html>`_: A walkthrough of how to load data from a EveryNote (`.enex`) file.
`YouTube <./examples/youtube.html>`_: A walkthrough of how to load the transcript from a YouTube video.
`s3 File <./examples/s3_file.html>`_: A walkthrough of how to load a file from s3.

5
langchain/agents/conversational/base.py
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@ -39,6 +39,7 @@ class ConversationalAgent(Agent):
tools: List[Tool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
ai_prefix: str = "AI",
human_prefix: str = "Human",
input_variables: Optional[List[str]] = None,
@ -61,7 +62,7 @@ class ConversationalAgent(Agent):
[f"> {tool.name}: {tool.description}" for tool in tools]
)
tool_names = ", ".join([tool.name for tool in tools])
format_instructions = FORMAT_INSTRUCTIONS.format(
format_instructions = format_instructions.format(
tool_names=tool_names, ai_prefix=ai_prefix, human_prefix=human_prefix
)
template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
@ -93,6 +94,7 @@ class ConversationalAgent(Agent):
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
ai_prefix: str = "AI",
human_prefix: str = "Human",
input_variables: Optional[List[str]] = None,
@ -106,6 +108,7 @@ class ConversationalAgent(Agent):
human_prefix=human_prefix,
prefix=prefix,
suffix=suffix,
format_instructions=format_instructions,
input_variables=input_variables,
)
llm_chain = LLMChain(

10
langchain/agents/mrkl/base.py
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@ -72,6 +72,7 @@ class ZeroShotAgent(Agent):
tools: List[Tool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
) -> PromptTemplate:
"""Create prompt in the style of the zero shot agent.
@ -88,7 +89,7 @@ class ZeroShotAgent(Agent):
"""
tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
tool_names = ", ".join([tool.name for tool in tools])
format_instructions = FORMAT_INSTRUCTIONS.format(tool_names=tool_names)
format_instructions = format_instructions.format(tool_names=tool_names)
template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
@ -102,13 +103,18 @@ class ZeroShotAgent(Agent):
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
**kwargs: Any,
) -> Agent:
"""Construct an agent from an LLM and tools."""
cls._validate_tools(tools)
prompt = cls.create_prompt(
tools, prefix=prefix, suffix=suffix, input_variables=input_variables
tools,
prefix=prefix,
suffix=suffix,
format_instructions=format_instructions,
input_variables=input_variables,
)
llm_chain = LLMChain(
llm=llm,

2
langchain/document_loaders/__init__.py
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@ -14,6 +14,7 @@ from langchain.document_loaders.html import UnstructuredHTMLLoader
from langchain.document_loaders.imsdb import IMSDbLoader
from langchain.document_loaders.notion import NotionDirectoryLoader
from langchain.document_loaders.obsidian import ObsidianLoader
from langchain.document_loaders.paged_pdf import PagedPDFSplitter
from langchain.document_loaders.pdf import UnstructuredPDFLoader
from langchain.document_loaders.powerpoint import UnstructuredPowerPointLoader
from langchain.document_loaders.readthedocs import ReadTheDocsLoader
@ -47,5 +48,6 @@ __all__ = [
"AZLyricsLoader",
"CollegeConfidentialLoader",
"GutenbergLoader",
"PagedPDFSplitter",
"EveryNoteLoader",
]

36
langchain/document_loaders/paged_pdf.py
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@ -0,0 +1,36 @@
"""Loads a PDF with pypdf and chunks at character level."""
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
class PagedPDFSplitter(BaseLoader):
"""Loads a PDF with pypdf and chunks at character level.
Loader also stores page numbers in metadatas.
"""
def __init__(self, file_path: str):
"""Initialize with file path."""
try:
import pypdf # noqa:F401
except ImportError:
raise ValueError(
"pypdf package not found, please install it with " "`pip install pypdf`"
)
self._file_path = file_path
def load(self) -> List[Document]:
"""Load given path as pages."""
import pypdf
pdf_file_obj = open(self._file_path, "rb")
pdf_reader = pypdf.PdfReader(pdf_file_obj)
docs = []
for i, page in enumerate(pdf_reader.pages):
text = page.extract_text()
metadata = {"source": self._file_path, "page": i}
docs.append(Document(page_content=text, metadata=metadata))
pdf_file_obj.close()
return docs

420
poetry.lock generated
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@ -384,14 +384,14 @@ files = [
[[package]]
name = "beautifulsoup4"
version = "4.11.1"
version = "4.11.2"
description = "Screen-scraping library"
category = "main"
optional = false
python-versions = ">=3.6.0"
files = [
{file = "beautifulsoup4-4.11.1-py3-none-any.whl", hash = "sha256:58d5c3d29f5a36ffeb94f02f0d786cd53014cf9b3b3951d42e0080d8a9498d30"},
{file = "beautifulsoup4-4.11.1.tar.gz", hash = "sha256:ad9aa55b65ef2808eb405f46cf74df7fcb7044d5cbc26487f96eb2ef2e436693"},
{file = "beautifulsoup4-4.11.2-py3-none-any.whl", hash = "sha256:0e79446b10b3ecb499c1556f7e228a53e64a2bfcebd455f370d8927cb5b59e39"},
{file = "beautifulsoup4-4.11.2.tar.gz", hash = "sha256:bc4bdda6717de5a2987436fb8d72f45dc90dd856bdfd512a1314ce90349a0106"},
]
[package.dependencies]
@ -403,32 +403,46 @@ lxml = ["lxml"]
[[package]]
name = "black"
version = "22.12.0"
version = "23.1.0"
description = "The uncompromising code formatter."
category = "dev"
category = "main"
optional = false
python-versions = ">=3.7"
files = [
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[package.dependencies]
click = ">=8.0.0"
mypy-extensions = ">=0.4.3"
packaging = ">=22.0"
pathspec = ">=0.9.0"
platformdirs = ">=2"
tomli = {version = ">=1.1.0", markers = "python_full_version < \"3.11.0a7\""}
tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
[package.extras]
@ -787,47 +801,49 @@ toml = ["tomli"]
[[package]]
name = "cryptography"
version = "39.0.0"
version = "39.0.1"
description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
category = "main"
optional = false
python-versions = ">=3.6"
files = [
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[package.dependencies]
@ -6125,7 +6183,7 @@ files = [
name = "tomli"
version = "2.0.1"
description = "A lil' TOML parser"
category = "dev"
category = "main"
optional = false
python-versions = ">=3.7"
files = [
@ -6257,14 +6315,14 @@ telegram = ["requests"]
[[package]]
name = "traitlets"
version = "5.8.1"
version = "5.9.0"
description = "Traitlets Python configuration system"
category = "dev"
optional = false
python-versions = ">=3.7"
files = [
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[package.extras]
@ -6273,14 +6331,14 @@ test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"]
[[package]]
name = "transformers"
version = "4.26.0"
version = "4.26.1"
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
category = "main"
optional = true
python-versions = ">=3.7.0"
files = [
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[package.dependencies]
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[[package]]
name = "types-pyyaml"
version = "6.0.12.3"
version = "6.0.12.5"
description = "Typing stubs for PyYAML"
category = "dev"
optional = false
python-versions = "*"
files = [
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[[package]]
name = "types-redis"
version = "4.4.0.4"
version = "4.4.0.6"
description = "Typing stubs for redis"
category = "dev"
optional = false
python-versions = "*"
files = [
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[package.dependencies]
@ -6404,14 +6462,14 @@ types-pyOpenSSL = "*"
[[package]]
name = "types-requests"
version = "2.28.11.8"
version = "2.28.11.12"
description = "Typing stubs for requests"
category = "dev"
optional = false
python-versions = "*"
files = [
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[package.dependencies]
@ -6419,26 +6477,26 @@ types-urllib3 = "<1.27"
[[package]]
name = "types-toml"
version = "0.10.8.1"
version = "0.10.8.3"
description = "Typing stubs for toml"
category = "dev"
optional = false
python-versions = "*"
files = [
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]
[[package]]
name = "types-urllib3"
version = "1.26.25.4"
version = "1.26.25.5"
description = "Typing stubs for urllib3"
category = "dev"
optional = false
python-versions = "*"
files = [
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[[package]]
@ -6642,14 +6700,14 @@ files = [
[[package]]
name = "websocket-client"
version = "1.5.0"
version = "1.5.1"
description = "WebSocket client for Python with low level API options"
category = "dev"
optional = false
python-versions = ">=3.7"
files = [
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[package.extras]
@ -6929,14 +6987,14 @@ multidict = ">=4.0"
[[package]]
name = "zipp"
version = "3.12.0"
version = "3.13.0"
description = "Backport of pathlib-compatible object wrapper for zip files"
category = "dev"
optional = false
python-versions = ">=3.7"
files = [
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[package.extras]
@ -6944,10 +7002,10 @@ docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker
testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
[extras]
all = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "elasticsearch", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text"]
all = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "elasticsearch", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf"]
llms = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "b190d518d7a99484ccb0aaf0ed43dfaf9c8cc74481b00da9d4fadd9d02c0dda2"
content-hash = "55ff8e2f70840a299ca72a27468cf18ec732514bdc2aa2ed9e8faf9bc5caa71f"

5
pyproject.toml
Unescape Escape View File

@ -44,6 +44,8 @@ huggingface_hub = {version = "^0", optional = true}
google-search-results = {version = "^2", optional = true}
sentence-transformers = {version = "^2", optional = true}
aiohttp = "^3.8.3"
pypdf = {version = "^3.4.0", optional = true}
black = "^23.1.0"
[tool.poetry.group.docs.dependencies]
@ -72,7 +74,6 @@ pytest-asyncio = "^0.20.3"
[tool.poetry.group.lint.dependencies]
flake8-docstrings = "^1.6.0"
black = "^22.10.0"
isort = "^5.10.1"
flake8 = "^6.0.0"
types-toml = "^0.10.8.1"
@ -92,7 +93,7 @@ playwright = "^1.28.0"
[tool.poetry.extras]
llms = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
all = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "elasticsearch", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text"]
all = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "elasticsearch", "google-search-results", "faiss-cpu", "sentence_transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf"]
[tool.isort]
profile = "black"

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tests/integration_tests/examples/hello.pdf
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tests/integration_tests/test_pdf_pagesplitter.py
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"""Test splitting with page numbers included."""
import os
from langchain.document_loaders import PagedPDFSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
def test_pdf_pagesplitter() -> None:
"""Test splitting with page numbers included."""
script_dir = os.path.dirname(__file__)
loader = PagedPDFSplitter(os.path.join(script_dir, "examples/hello.pdf"))
docs = loader.load()
assert "page" in docs[0].metadata
assert "source" in docs[0].metadata
faiss_index = FAISS.from_documents(docs, OpenAIEmbeddings())
docs = faiss_index.similarity_search("Complete this sentence: Hello", k=1)
assert "Hello world" in docs[0].page_content
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