From c64f98e2bb8f2b4519108f55a6505ffab227b81b Mon Sep 17 00:00:00 2001 From: Harrison Chase Date: Fri, 10 Feb 2023 10:07:26 -0800 Subject: [PATCH] Harrison/format agent instructions (#973) Co-authored-by: Andrew White Co-authored-by: Harrison Chase Co-authored-by: Peng Qu <82029664+pengqu123@users.noreply.github.com> --- .../document_loaders/examples/pdf.ipynb | 129 +++++- .../document_loaders/how_to_guides.rst | 2 + langchain/agents/conversational/base.py | 5 +- langchain/agents/mrkl/base.py | 10 +- langchain/document_loaders/__init__.py | 2 + langchain/document_loaders/paged_pdf.py | 36 ++ poetry.lock | 420 ++++++++++-------- pyproject.toml | 5 +- tests/integration_tests/examples/hello.pdf | Bin 0 -> 3991 bytes .../test_pdf_pagesplitter.py | 19 + 10 files changed, 441 insertions(+), 187 deletions(-) create mode 100644 langchain/document_loaders/paged_pdf.py create mode 100644 tests/integration_tests/examples/hello.pdf create mode 100644 tests/integration_tests/test_pdf_pagesplitter.py diff --git a/docs/modules/document_loaders/examples/pdf.ipynb b/docs/modules/document_loaders/examples/pdf.ipynb index b7e589e8..f4659df0 100644 --- a/docs/modules/document_loaders/examples/pdf.ipynb +++ b/docs/modules/document_loaders/examples/pdf.ipynb @@ -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, diff --git a/docs/modules/document_loaders/how_to_guides.rst b/docs/modules/document_loaders/how_to_guides.rst index e1fda659..ae4ae3dc 100644 --- a/docs/modules/document_loaders/how_to_guides.rst +++ b/docs/modules/document_loaders/how_to_guides.rst @@ -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. diff --git a/langchain/agents/conversational/base.py b/langchain/agents/conversational/base.py index d97fc417..3371d34f 100644 --- a/langchain/agents/conversational/base.py +++ b/langchain/agents/conversational/base.py @@ -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( diff --git a/langchain/agents/mrkl/base.py b/langchain/agents/mrkl/base.py index 4477cb6d..8c09e23f 100644 --- a/langchain/agents/mrkl/base.py +++ b/langchain/agents/mrkl/base.py @@ -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, diff --git a/langchain/document_loaders/__init__.py b/langchain/document_loaders/__init__.py index 7956cea0..df7cf4ea 100644 --- a/langchain/document_loaders/__init__.py +++ b/langchain/document_loaders/__init__.py @@ -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", ] diff --git a/langchain/document_loaders/paged_pdf.py b/langchain/document_loaders/paged_pdf.py new file mode 100644 index 00000000..b7bb6aa3 --- /dev/null +++ b/langchain/document_loaders/paged_pdf.py @@ -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 diff --git a/poetry.lock b/poetry.lock index d61dc42f..bf4292d3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -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 = 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- {file = "types-toml-0.10.8.1.tar.gz", hash = "sha256:171bdb3163d79a520560f24ba916a9fc9bff81659c5448a9fea89240923722be"}, - {file = "types_toml-0.10.8.1-py3-none-any.whl", hash = "sha256:b7b5c4977f96ab7b5ac06d8a6590d17c0bf252a96efc03b109c2711fb3e0eafd"}, + {file = "types-toml-0.10.8.3.tar.gz", hash = "sha256:f37244eff4cd7eace9cb70d0bac54d3eba77973aa4ef26c271ac3d1c6503a48e"}, + {file = "types_toml-0.10.8.3-py3-none-any.whl", hash = "sha256:a2286a053aea6ab6ff814659272b1d4a05d86a1dd52b807a87b23511993b46c5"}, ] [[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 = [ - {file = "types-urllib3-1.26.25.4.tar.gz", hash = "sha256:eec5556428eec862b1ac578fb69aab3877995a99ffec9e5a12cf7fbd0cc9daee"}, - {file = "types_urllib3-1.26.25.4-py3-none-any.whl", hash = "sha256:ed6b9e8a8be488796f72306889a06a3fc3cb1aa99af02ab8afb50144d7317e49"}, + {file = "types-urllib3-1.26.25.5.tar.gz", hash = "sha256:5630e578246d170d91ebe3901788cd28d53c4e044dc2e2488e3b0d55fb6895d8"}, + {file = "types_urllib3-1.26.25.5-py3-none-any.whl", hash = "sha256:e8f25c8bb85cde658c72ee931e56e7abd28803c26032441eea9ff4a4df2b0c31"}, ] [[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 = [ - {file = "websocket-client-1.5.0.tar.gz", hash = "sha256:561ca949e5bbb5d33409a37235db55c279235c78ee407802f1d2314fff8a8536"}, - {file = "websocket_client-1.5.0-py3-none-any.whl", hash = "sha256:fb5d81b95d350f3a54838ebcb4c68a5353bbd1412ae8f068b1e5280faeb13074"}, + {file = "websocket-client-1.5.1.tar.gz", hash = "sha256:3f09e6d8230892547132177f575a4e3e73cfdf06526e20cc02aa1c3b47184d40"}, + {file = "websocket_client-1.5.1-py3-none-any.whl", hash = "sha256:cdf5877568b7e83aa7cf2244ab56a3213de587bbe0ce9d8b9600fc77b455d89e"}, ] [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 = [ - {file = "zipp-3.12.0-py3-none-any.whl", hash = "sha256:9eb0a4c5feab9b08871db0d672745b53450d7f26992fd1e4653aa43345e97b86"}, - {file = "zipp-3.12.0.tar.gz", hash = "sha256:73efd63936398aac78fd92b6f4865190119d6c91b531532e798977ea8dd402eb"}, + {file = "zipp-3.13.0-py3-none-any.whl", hash = "sha256:e8b2a36ea17df80ffe9e2c4fda3f693c3dad6df1697d3cd3af232db680950b0b"}, + {file = "zipp-3.13.0.tar.gz", hash = "sha256:23f70e964bc11a34cef175bc90ba2914e1e4545ea1e3e2f67c079671883f9cb6"}, ] [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" diff --git a/pyproject.toml b/pyproject.toml index 2e2078dd..a42b37d3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -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" diff --git a/tests/integration_tests/examples/hello.pdf b/tests/integration_tests/examples/hello.pdf new file mode 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zok*8^3ln(DznZ@azM(EtvM}*FOm7pvQq*dlI;4nxa1dw3Z25_jmhv-=X1B<}$h3g8 zr|}@3DR8s|HLoyBaIc32Gm#-#YaO(OMI!Fq#?kSPk#$f`@|dc0nB`C@CTz3KJ1r@8 zZbQSRXHsU_71PRvqi$OSWAyroRfJiBALx@7p)u9otak=9+x_NUu2uILuiOARbj zZ?}@{#5wlOWFdHQ?d5D6Ols6(HFZ!gowd1s=qakf^s~JJ|Hf!vN;>@d=({Hwow1FK z{e=nYa<5DUob-3Z7UV5H?NZurxNk@LE3rd?7tW0ypHG1_SD26&?u%^n-4 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