diff --git a/docs/docs/integrations/vectorstores/pathway.ipynb b/docs/docs/integrations/vectorstores/pathway.ipynb new file mode 100644 index 0000000000000..256e22336c504 --- /dev/null +++ b/docs/docs/integrations/vectorstores/pathway.ipynb @@ -0,0 +1,297 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " # Pathway\n", + " >[Pathway](https://pathway.com/) is an open data processing framework. It allows you to easily develop data transformation pipelines and Machine Learning applications that work with live data sources and changing data.\n", + "\n", + " This notebook shows how to use the `Pathway` to deploy a live data indexing pipeline which can be queried from your chains just like a regular vector store. However, under the hood, Pathway updates the index on each data change giving you always up-to-date answers.\n", + "\n", + " In this notebook, we will use a simple document processing pipeline that:\n", + " 1. Monitors several data sources (files, S3 folders, cloud storages) for data changes.\n", + " 2. Parses, splits and embeds the documents using `LangChain` methods.\n", + " 3. Builds a vector index for the data.\n", + "\n", + " We will connect to the index using a `VectorStore` client, which implements the `similarity_search` function to retrieve matching documents.\n", + "\n", + " The basic pipeline described in this document allows to effortlessly build a simple vector index of files stored in a cloud location. However, Pathway provides everything needed to build realtime data pipelines and apps, including SQL-like able operations such as groupby-reductions and joins between disparate data sources, time-based grouping and windowing of data, and a wide array of connectors.\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " # Prerequisites\n", + " Install the `pathway` package. To use the most powerful, `unstructured.io` based parser also install unstructured package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # !pip install pathway\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " # Building the data pipeline\n", + "\n", + " First, make suer you have an API key ith an LLM provider such as OpenAI." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "if \"OPENAI_API_KEY\" not in os.environ:\n", + " os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " We will now assemble the data vectorization pipeline, using a simple `UTF8` file parser, `CharacterTextSplitter`, and `OpenAIEmbeddings`.\n", + "\n", + " First, we define the data sources. For simplicity we use the files-based one, but any supported `pathway` [connector](https://pathway.com/developers/api-docs/pathway-io/), such as [s3](https://pathway.com/developers/api-docs/pathway-io-s3/) or [google drive](https://pathway.com/developers/api-docs/pathway-io-gdrive/#pathway.io.gdrive.read) will work too.\n", + "\n", + " Then, we now define the `LangChain` components that define how the data will be processed.\n", + "\n", + " Last, we assemble the data pipeline. We will start it running in a background thread, to be able to query it immediately from the demonstration. Please note that in a production deployment, the server will be running in another process, possibly on another machine. For the quick-start, we keep the server and client as different threads of the same python process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pathway as pw\n", + "from langchain.embeddings.openai import OpenAIEmbeddings\n", + "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain.vectorstores import PathwayVectorServer\n", + "\n", + "# This creates a `pathway` connector that reads a single file.\n", + "data_sources = []\n", + "data_sources.append(\n", + " pw.io.fs.read(\n", + " \"../../templates/rag-pathway/sample_documents\",\n", + " format=\"binary\",\n", + " mode=\"streaming\",\n", + " with_metadata=True,\n", + " )\n", + ")\n", + "\n", + "# This creates a connector that tracks files in Google drive.\n", + "# please follow the instructions at https://pathway.com/developers/tutorials/connectors/gdrive-connector/ to get credentials\n", + "# data_sources.append(\n", + "# pw.io.gdrive.read(object_id=\"17H4YpBOAKQzEJ93xmC2z170l0bP2npMy\", service_user_credentials_file=\"credentials.json\", with_metadata=True))\n", + "\n", + "# Choose proper LangChain document transformers\n", + "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", + "embeddings_model = OpenAIEmbeddings(openai_api_key=os.environ[\"OPENAI_API_KEY\"])\n", + "\n", + "# The PathwayVectorServer is a wrapper over pathway.xpacks.llm.vector_store to accept LangChain transforments\n", + "# Fell free to fork it to develop bespoke document processing pipelines.\n", + "vector_server = PathwayVectorServer(\n", + " *data_sources,\n", + " embedder=embeddings_model,\n", + " splitter=text_splitter,\n", + ")\n", + "vector_server.run_server(host=\"127.0.0.1\", port=\"8765\", threaded=True, with_cache=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " We now instantiate and configure the client" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.vectorstores import PathwayVectorClient\n", + "\n", + "client = PathwayVectorClient(\n", + " host=\"127.0.0.1\",\n", + " port=\"8765\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " And we can start asking queries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "query = \"What is Pathway?\"\n", + "docs = client.similarity_search(query)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(docs[0].page_content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " **Your turn!** Now make a change to the source documents or make a fresh one and retry the query!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ## Using `unstructured.io` document parser\n", + "\n", + " The vectorization pipeline supports pluggable parsers. The default parser simply decodes files as UTF8. However, we also provide an https://unstructured.io/ parser that can extract text out of many kinds of documents:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # !pip install unstructured[all-docs] # if you will need to parse complex documents\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathway.xpacks.llm import parsers\n", + "\n", + "vector_server = PathwayVectorServer(\n", + " *data_sources,\n", + " parser=parsers.ParseUnstructured(),\n", + " embedder=embeddings_model,\n", + " splitter=text_splitter,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ## Getting information on indexed files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " `PathwayVectorClient.get_vectorstore_statistics()` gives essential statistics on the state of the vector store, like the number of indexed files and the timestamp of last updated one. You can use it in your chains to tell the user how fresh is your knowledge base." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "client.get_vectorstore_statistics()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ## Filtering based on file metadata\n", + "\n", + " We support document filtering using [jmespath](https://jmespath.org/) expressions, for instance:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# take into account only sources modified later than unix timestamp\n", + "docs = client.similarity_search(query, metadata_filter=\"modified_at >= `1702672093`\")\n", + "\n", + "# take into account only sources modified later than unix timestamp\n", + "docs = client.similarity_search(query, metadata_filter=\"owner == `james`\")\n", + "\n", + "# take into account only sources with path containing 'repo_readme'\n", + "docs = client.similarity_search(query, metadata_filter=\"contains(path, 'repo_readme')\")\n", + "\n", + "# and of two conditions\n", + "docs = client.similarity_search(\n", + " query, metadata_filter=\"owner == `james` && modified_at >= `1702672093`\"\n", + ")\n", + "\n", + "# or of two conditions\n", + "docs = client.similarity_search(\n", + " query, metadata_filter=\"owner == `james` || modified_at >= `1702672093`\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " # Running in production\n", + "\n", + " ## Running in a separate process\n", + " A production deployment will typically run the server in a separate process. We provide a template application under [`templates`](https://github.com/langchain-ai/langchain/tree/master/templates/rag-pathway). We recommend to run the Pathway data indexing pipeline in a container-based deployment environment, such as Docker or Kubernetes. For more info see [Pathway's deployment guide](https://pathway.com/developers/user-guide/deployment/).\n", + "\n", + " ## Configuring the cache\n", + " The Pathway vectorizing pipeline comes with an embeddings cache:\n", + " ```python\n", + " vector_server.run_server(..., with_cache=True)\n", + " ```\n", + "\n", + " The default cache configuration is the locally hosted disk cache, stored in the `./Cache` directory. However, it is possible to customize it by explicitly specifying the caching backend that can be chosen among several persistent backends [options](https://pathway.com/developers/api-docs/persistence-api/#pathway.persistence.Backend)." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": 3 + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/libs/community/langchain_community/vectorstores/__init__.py b/libs/community/langchain_community/vectorstores/__init__.py index df64f984cc18f..93fb786b6c019 100644 --- a/libs/community/langchain_community/vectorstores/__init__.py +++ b/libs/community/langchain_community/vectorstores/__init__.py @@ -276,6 +276,18 @@ def _import_opensearch_vector_search() -> Any: return OpenSearchVectorSearch +def _import_pathway_vector_client() -> Any: + from langchain_community.vectorstores.pathway import PathwayVectorClient + + return PathwayVectorClient + + +def _import_pathway_vector_server() -> Any: + from langchain_community.vectorstores.pathway import PathwayVectorServer + + return PathwayVectorServer + + def _import_pgembedding() -> Any: from langchain_community.vectorstores.pgembedding import PGEmbedding @@ -525,6 +537,10 @@ def __getattr__(name: str) -> Any: return _import_neo4j_vector() elif name == "OpenSearchVectorSearch": return _import_opensearch_vector_search() + elif name == "PathwayVectorClient": + return _import_pathway_vector_client() + elif name == "PathwayVectorServer": + return _import_pathway_vector_server() elif name == "PGEmbedding": return _import_pgembedding() elif name == "PGVector": @@ -624,6 +640,8 @@ def __getattr__(name: str) -> Any: "MyScaleSettings", "Neo4jVector", "OpenSearchVectorSearch", + "PathwayVectorClient", + "PathwayVectorServer", "PGEmbedding", "PGVector", "Pinecone", diff --git a/libs/community/langchain_community/vectorstores/pathway.py b/libs/community/langchain_community/vectorstores/pathway.py new file mode 100644 index 0000000000000..838743e0e8e35 --- /dev/null +++ b/libs/community/langchain_community/vectorstores/pathway.py @@ -0,0 +1,141 @@ +""" +Pathway vector store server and client. + + +The PathwayVectorServer builds a pipeline which indexes all files in a given folder, +embeds them and builds a vector index. The pipeline reacts to changes in source files, +automatically updating appropriate index entries. + +The PathwayVectorClient implements the LangChain VectorStore interface and queries the +PathwayVectorServer to retrieve up-to-date documents. + +""" + +from typing import Callable, List, Optional + +import pathway as pw +from langchain_core.documents import BaseDocumentTransformer, Document +from langchain_core.embeddings import Embeddings +from langchain_core.vectorstores import VectorStore +from pathway.xpacks.llm import vector_store + + +class PathwayVectorServer: + """ + Build an autoupdating document indexing pipeline + for approximate nearest neighbor search. + + Args: + embedder - embedding model e.g. OpenAIEmbeddings + parser - callable that parses file contents into a list of documents + splitter - docuent splitter, e.g. CharacterTextSplitter + """ + + def __init__( + self, + *docs: pw.Table, + embedder: Embeddings, + parser: Optional[Callable[[bytes], List[Document]]] = None, + splitter: Optional[BaseDocumentTransformer] = None, + **kwargs, + ) -> None: + generic_parser = None + if parser: + generic_parser = lambda x: [ # noqa + (doc.page_content, doc.metadata) for doc in parser(x) + ] + + generic_splitter = None + if splitter: + generic_splitter = lambda x: [ # noqa + (doc.page_content, doc.metadata) + for doc in splitter.transform_documents([Document(page_content=x)]) + ] + generic_embedded = lambda x: embedder.embed_documents([x])[0] # noqa + + self.vector_store_server = vector_store.VectorStoreServer( + *docs, + embedder=generic_embedded, + parser=generic_parser, + splitter=generic_splitter, + **kwargs, + ) + + def run_server( + self, + host, + port, + threaded=False, + with_cache=True, + cache_backend: pw.persistence.Backend + | None = pw.persistence.Backend.filesystem("./Cache"), + ): + """ + Run the server and start answering queries. + + Args: + - host: host to bind the HTTP listener + - port: port to bind the HTTP listener + - docs: pathway tables typically coming out of connectors which contain + source documents. + - threaded: if True, run in a thread. Else block computation + - with_cache: if True, embedding requests for the same contents are cached + - cache_backend: the backend to use for caching if it is enabled. The + default is the disk cache, hosted locally in the folder ``./Cache``. You + can use ``Backend`` class of the [`persistence API`] + (/developers/api-docs/persistence-api/#pathway.persistence.Backend) + to override it. + + Returns: + If threaded, return the Thread object. Else, does not return. + """ + return self.vector_store_server.run_server( + host, + port, + threaded=threaded, + with_cache=with_cache, + cache_backend=cache_backend, + ) + + +class PathwayVectorClient(VectorStore): + """ + VectorStore connecting to PathwayVectorServer realtime data pipeline. + """ + + def __init__( + self, + host, + port, + ) -> None: + self.client = vector_store.VectorStoreClient(host, port) + + def add_texts( + self, + *args, + **kwargs, + ) -> List[str]: + """Pathway is not suitable for this method.""" + raise NotImplementedError( + "Pathway vector store does not support adding or removing texts" + " from client." + ) + + @classmethod + def from_texts(cls, *args, **kwargs): + raise NotImplementedError( + "Pathway vector store does not support initializing from_texts." + ) + + def similarity_search( + self, query: str, k: int = 4, metadata_filter: str | None = None + ) -> List[Document]: + rets = self.client(query=query, k=k, metadata_filter=metadata_filter) + + return [ + Document(page_content=ret["text"], metadata=ret["metadata"]) for ret in rets + ] + + def get_vectorstore_statistics(self): + """Fetch basic statistics about the Vector Store.""" + return self.client.get_vectorstore_statistics() diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock index 160e328cffa44..5d731e646d4cd 100644 --- a/libs/community/poetry.lock +++ b/libs/community/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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"flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"] +[[package]] +name = "uritemplate" +version = "4.1.1" +description = "Implementation of RFC 6570 URI Templates" +optional = true +python-versions = ">=3.6" +files = [ + {file = "uritemplate-4.1.1-py2.py3-none-any.whl", hash = "sha256:830c08b8d99bdd312ea4ead05994a38e8936266f84b9a7878232db50b044e02e"}, + {file = "uritemplate-4.1.1.tar.gz", hash = "sha256:4346edfc5c3b79f694bccd6d6099a322bbeb628dbf2cd86eea55a456ce5124f0"}, +] + [[package]] name = "urllib3" version = "1.26.18" @@ -8360,6 +9005,17 @@ files = [ {file = "xxhash-3.4.1.tar.gz", hash = "sha256:0379d6cf1ff987cd421609a264ce025e74f346e3e145dd106c0cc2e3ec3f99a9"}, ] +[[package]] +name = "xyzservices" +version = "2023.10.1" +description = "Source of XYZ tiles providers" +optional = true +python-versions = ">=3.8" +files = [ + {file = "xyzservices-2023.10.1-py3-none-any.whl", hash = "sha256:6a4c38d3a9f89d3e77153eff9414b36a8ee0850c9e8b85796fd1b2a85b8dfd68"}, + {file = "xyzservices-2023.10.1.tar.gz", hash = "sha256:091229269043bc8258042edbedad4fcb44684b0473ede027b5672ad40dc9fa02"}, +] + [[package]] name = "yarl" version = "1.9.3" @@ -8485,4 +9141,4 @@ extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "as [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "e3bacf389a13d283c4dd29e3a673e1863826b4e98785c666fefc10cf714c2f6f" +content-hash = "efbec6fb2c618a5350d9e5da339504c1c20871935e73de3bcf1fbe98895c0ff2" diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml index 2b2868005fdf1..67a826da9862d 100644 --- a/libs/community/pyproject.toml +++ b/libs/community/pyproject.toml @@ -83,6 +83,7 @@ msal = {version = "^1.25.0", optional = true} databricks-vectorsearch = {version = "^0.21", optional = true} dgml-utils = {version = "^0.3.0", optional = true} datasets = {version = "^2.15.0", optional = true} +pathway = {version = "^0.7.5", optional = true, python = ">=3.10"} [tool.poetry.group.test] optional = true diff --git a/libs/community/tests/integration_tests/vectorstores/test_pathway.py b/libs/community/tests/integration_tests/vectorstores/test_pathway.py new file mode 100644 index 0000000000000..dc977dba9b8bd --- /dev/null +++ b/libs/community/tests/integration_tests/vectorstores/test_pathway.py @@ -0,0 +1,72 @@ +"""Test Pathway vector store functionality.""" +import os +import shutil +import sys +import time +from multiprocessing import Process + +import pytest + +from langchain_community.vectorstores.pathway import ( + PathwayVectorClient, + PathwayVectorServer, +) +from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings + +PATHWAY_HOST = "127.0.0.1" +PATHWAY_PORT = 8754 +PATHWAY_DIR = "/tmp/pathway-samples/" + + +def pathway_server(): + import pathway as pw + + data_sources = [] + data_sources.append( + pw.io.fs.read( + PATHWAY_DIR, + format="binary", + mode="streaming", + with_metadata=True, + ) + ) + + embeddings_model = FakeEmbeddings() + + vector_server = PathwayVectorServer(*data_sources, embedder=embeddings_model) + thread = vector_server.run_server( + host=PATHWAY_HOST, + port=PATHWAY_PORT, + threaded=True, + with_cache=False, + ) + thread.join() + + +@pytest.mark.skipif( + sys.version_info < (3, 10), + reason="Pathway requires python 3.10 or higher", +) +@pytest.mark.requires("pathway") +class TestPathway: + @classmethod + def setup_class(cls) -> None: + os.mkdir(PATHWAY_DIR) + with open(f"{PATHWAY_DIR}file_one.txt", "w+") as f: + f.write("foo") + + @classmethod + def teardown_class(cls): + shutil.rmtree(PATHWAY_DIR) + pass + + def test_similarity_search_without_metadata(self) -> None: + p = Process(target=pathway_server) + p.start() + time.sleep(6) + client = PathwayVectorClient(host=PATHWAY_HOST, port=PATHWAY_PORT) + output = client.similarity_search("foo") + p.terminate() + time.sleep(2) + assert len(output) == 1 + assert output[0].page_content == "foo" diff --git a/libs/langchain/langchain/vectorstores/__init__.py b/libs/langchain/langchain/vectorstores/__init__.py index 5c9b08b5d75c2..d051171ea8693 100644 --- a/libs/langchain/langchain/vectorstores/__init__.py +++ b/libs/langchain/langchain/vectorstores/__init__.py @@ -266,6 +266,18 @@ def _import_opensearch_vector_search() -> Any: return OpenSearchVectorSearch +def _import_pathway_vector_client() -> Any: + from langchain.vectorstores.pathway import PathwayVectorClient + + return PathwayVectorClient + + +def _import_pathway_vector_server() -> Any: + from langchain.vectorstores.pathway import PathwayVectorServer + + return PathwayVectorServer + + def _import_pgembedding() -> Any: from langchain.vectorstores.pgembedding import PGEmbedding @@ -515,6 +527,10 @@ def __getattr__(name: str) -> Any: return _import_neo4j_vector() elif name == "OpenSearchVectorSearch": return _import_opensearch_vector_search() + elif name == "PathwayVectorClient": + return _import_pathway_vector_client() + elif name == "PathwayVectorServer": + return _import_pathway_vector_server() elif name == "PGEmbedding": return _import_pgembedding() elif name == "PGVector": @@ -614,6 +630,8 @@ def __getattr__(name: str) -> Any: "MyScaleSettings", "Neo4jVector", "OpenSearchVectorSearch", + "PathwayVectorClient", + "PathwayVectorServer", "PGEmbedding", "PGVector", "Pinecone", diff --git a/libs/langchain/langchain/vectorstores/pathway.py b/libs/langchain/langchain/vectorstores/pathway.py new file mode 100644 index 0000000000000..41dbd54298933 --- /dev/null +++ b/libs/langchain/langchain/vectorstores/pathway.py @@ -0,0 +1,5 @@ +from langchain_community.vectorstores.pathway import ( + PathwayVectorServer, PathwayVectorClient +) + +__all__ = ["PathwayVectorServer", "PathwayVectorClient"] diff --git a/templates/rag-pathway/LICENSE b/templates/rag-pathway/LICENSE new file mode 100644 index 0000000000000..426b65090341f --- /dev/null +++ b/templates/rag-pathway/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/rag-pathway/README.md b/templates/rag-pathway/README.md new file mode 100644 index 0000000000000..2e80db591abff --- /dev/null +++ b/templates/rag-pathway/README.md @@ -0,0 +1,72 @@ +# rag-pathway + +Pathway is a data streaming tool that can be utilized as vector store that is always up to date. +Once the engine is listening to your local files, Drive, Dropbox, s3 or any other connector, +You don't have to run any ETL jobs, embedding services or LLM Api. + +Your answers to questions will be always up to date with latest changes. +You can listen to many sources simultaneously. + + +## Environment Setup + +OPENAI_API_KEY + +## Usage + +To use this package, you should first have the LangChain CLI installed: + +```shell +pip install -U langchain-cli +``` + +To create a new LangChain project and install this as the only package, you can do: + +```shell +langchain app new my-app --package rag-pathway +``` + +If you want to add this to an existing project, you can just run: + +```shell +langchain app add rag-pathway +``` + +And add the following code to your `server.py` file: +```python +from rag_pathway import chain as rag_pathway_chain + +add_routes(app, rag_pathway_chain, path="/rag-pathway") +``` + +(Optional) Let's now configure LangSmith. +LangSmith will help us trace, monitor and debug LangChain applications. +LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). +If you don't have access, you can skip this section + + +```shell +export LANGCHAIN_TRACING_V2=true +export LANGCHAIN_API_KEY= +export LANGCHAIN_PROJECT= # if not specified, defaults to "default" +``` + +If you are inside this directory, then you can spin up a LangServe instance directly by: + +```shell +langchain serve +``` + +This will start the FastAPI app with a server is running locally at +[http://localhost:8000](http://localhost:8000) + +We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) +We can access the playground at [http://127.0.0.1:8000/rag-pathway/playground](http://127.0.0.1:8000/rag-pathway/playground) + +We can access the template from code with: + +```python +from langserve.client import RemoteRunnable + +runnable = RemoteRunnable("http://localhost:8000/rag-pathway") +``` diff --git a/templates/rag-pathway/poetry.lock b/templates/rag-pathway/poetry.lock new file mode 100644 index 0000000000000..80be828af368c --- /dev/null +++ b/templates/rag-pathway/poetry.lock @@ -0,0 +1,3422 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.9.1" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = 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= "poetry.core.masonry.api" diff --git a/templates/rag-pathway/rag_pathway/__init__.py b/templates/rag-pathway/rag_pathway/__init__.py new file mode 100644 index 0000000000000..614c54fa33a4a --- /dev/null +++ b/templates/rag-pathway/rag_pathway/__init__.py @@ -0,0 +1,3 @@ +from rag_pathway.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-pathway/rag_pathway/chain.py b/templates/rag-pathway/rag_pathway/chain.py new file mode 100644 index 0000000000000..d1f4d2bbab5cd --- /dev/null +++ b/templates/rag-pathway/rag_pathway/chain.py @@ -0,0 +1,90 @@ +import pathway as pw +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.prompts import ChatPromptTemplate +from langchain.pydantic_v1 import BaseModel +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableParallel, RunnablePassthrough +from langchain.text_splitter import CharacterTextSplitter +from langchain.vectorstores import PathwayVectorClient, PathwayVectorServer + +HOST = "127.0.0.1" +PORT = 8780 + +# If you have a running Pathway Vectorstore instance you can connect to it via client. +# If not, you can run Vectorstore as follows: +create_vectorstore = True +if create_vectorstore: + # Example for document loading (from local folders), splitting, + # and creating vectorstore with Pathway + + data_sources = [] + data_sources.append( + pw.io.fs.read( + "./sample_documents", format="binary", mode="streaming", with_metadata=True + ) # This creates a `pathway` connector that tracks + # all the files in the sample_documents directory + ) + + # This creates a connector that tracks files in Google drive. + # please follow the instructions at: + # https://pathway.com/developers/tutorials/connectors/gdrive-connector/ + # to get credentials, see pathway documentation for more options including: + # s3, Dropbox, etc. + data_sources.append( + pw.io.gdrive.read( + object_id="17H4YpBOAKQzEJ93xmC2z170l0bP2npMy", + service_user_credentials_file="credentials.json", + with_metadata=True, + ) + ) + + text_splitter = CharacterTextSplitter( + separator="\n\n", + chunk_size=10, + chunk_overlap=5, + length_function=len, + is_separator_regex=False, + ) + + # Embed + embeddings_model = OpenAIEmbeddings() + + # Launch VectorDB + vector_server = PathwayVectorServer( + *data_sources, embedder=embeddings_model, splitter=text_splitter + ) + vector_server.run_server(host=HOST, port=PORT, threaded=True) + +# Initalize client +client = PathwayVectorClient( + host=HOST, + port=PORT, +) + +retriever = client.as_retriever() + + +# RAG prompt +template = """Answer the question based only on the following context: +{context} +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +# RAG +model = ChatOpenAI() +chain = ( + RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) + | prompt + | model + | StrOutputParser() +) + + +# Add typing for input +class Question(BaseModel): + __root__: str + + +chain = chain.with_types(input_type=Question) diff --git a/templates/rag-pathway/sample_documents/repo_readme.md b/templates/rag-pathway/sample_documents/repo_readme.md new file mode 100644 index 0000000000000..49caef1aed6fa --- /dev/null +++ b/templates/rag-pathway/sample_documents/repo_readme.md @@ -0,0 +1,159 @@ +Pathway is an open framework for high-throughput and low-latency real-time data processing. It is used to create Python code which seamlessly combines batch processing, streaming, and real-time API's for LLM apps. Pathway's distributed runtime (🦀-🐍) provides fresh results of your data pipelines whenever new inputs and requests are received. + +In the first place, Pathway was designed to be a life-saver (or at least a time-saver) for Python developers and ML/AI engineers faced with live data sources, where you need to react quickly to fresh data. Still, Pathway is a powerful tool that can be used for a lot of things. If you want to do streaming in Python, build an AI data pipeline, or if you are looking for your next Python data processing framework, keep reading. + +Pathway provides a high-level programming interface in Python for defining data transformations, aggregations, and other operations on data streams. +With Pathway, you can effortlessly design and deploy sophisticated data workflows that efficiently handle high volumes of data in real time. + +Pathway is interoperable with various data sources and sinks such as Kafka, CSV files, SQL/noSQL databases, and REST API's, allowing you to connect and process data from different storage systems. + +Typical use-cases of Pathway include realtime data processing, ETL (Extract, Transform, Load) pipelines, data analytics, monitoring, anomaly detection, and recommendation. Pathway can also independently provide the backbone of a light LLMops stack for real-time LLM applications. + +In Pathway, data is represented in the form of Tables. Live data streams are also treated as Tables. The library provides a rich set of operations like filtering, joining, grouping, and windowing. + +For any questions, you will find the community and team behind the project on Discord. + +Screencast animation of converting batch code to streaming by changing one keyword argument in the script. + +## Getting started + + +### Installation + +Pathway requires Python 3.10 or above. + +You can install the current release of Pathway using `pip`: + +``` +$ pip install -U pathway +``` + +⚠️ Pathway is available on MacOS and Linux. Users of other systems should run Pathway on a Virtual Machine. + +### Running Pathway locally + +To use Pathway, you only need to import it: + +```python +import pathway as pw +``` + +Now, you can easily create your processing pipeline, and let Pathway handle the updates. Once your pipeline is created, you can launch the computation on streaming data with a one-line command: + +```python +pw.run() +``` + +You can then run your Pathway project (say, `main.py`) just like a normal Python script: `$ python main.py`. Alternatively, use the pathway'ish version: + +``` +$ pathway spawn python main.py +``` + +Pathway natively supports multithreading. +To launch your application with 3 threads, you can do as follows: +``` +$ pathway spawn --threads 3 python main.py +``` + +To jumpstart a Pathway project, you can use our cookiecutter template + + +### Example + +```python +import pathway as pw + +# Using the `demo` module to create a data stream +table = pw.demo.range_stream(nb_rows=50) +# Storing the stream into a CSV file +pw.io.csv.write(table, "output_table.csv") + +# Summing all the values in a new table +sum_table = table.reduce(sum=pw.reducers.sum(pw.this.value)) +# Storing the sum (which is a stream) in another CSV file +pw.io.csv.write(sum_table, "sum_table.csv") + +# Now that the pipeline is built, the computation is started +pw.run() +``` + +Run this example in Google Colab + +## Deployment + +Do you feel limited by a local run? +If you want to scale your Pathway application, you may be interested in our Pathway for Enterprise. +Pathway for Enterprise is specially tailored towards end-to-end data processing and real time intelligent analytics. +It scales using distributed computing on the cloud and supports Kubernetes deployment. + +You can learn more about the features of Pathway for Enterprise on our website. + +If you are interested, don't hesitate to contact us to learn more. + +## Monitoring Pathway + +Pathway comes with a monitoring dashboard that allows you to keep track of the number of messages sent by each connector and the latency of the system. The dashboard also includes log messages. + +This dashboard is enabled by default; you can disable it by passing `monitoring_level = pathway.MonitoringLevel.NONE` to `pathway.run()`. + +In addition to Pathway's built-in dashboard, you can use Prometheus to monitor your Pathway application. + +## Resources + +See also: Pathway Documentation (https://pathway.com/developers/) webpage (including API Docs). + +### Videos about Pathway +[▶️ Building an LLM Application without a vector database](https://www.youtube.com/watch?v=kcrJSk00duw) - by [Jan Chorowski](https://scholar.google.com/citations?user=Yc94070AAAAJ) (7min 56s) + +[▶️ Linear regression on a Kafka Stream](https://vimeo.com/805069039) - by [Richard Pelgrim](https://twitter.com/richardpelgrim) (7min 53s) + +[▶️ Introduction to reactive data processing](https://pathway.com/developers/user-guide/concepts/welcome) - by [Adrian Kosowski](https://scholar.google.com/citations?user=om8De_0AAAAJ) (27min 54s) + +If you would like to share with us some Pathway-related content, please give an admin a shout on Discord https://discord.gg/pathway. + +### Manul conventions + +Manuls (aka Pallas's Cats) [are creatures with fascinating habits](https://www.youtube.com/watch?v=rlSTBvViflc). As a tribute to them, we usually read `pw`, one of the most frequent tokens in Pathway code, as: `"paw"`. + +## Performance + +Pathway is made to outperform state-of-the-art technologies designed for streaming and batch data processing tasks, including: Flink, Spark, and Kafka Streaming. It also makes it possible to implement a lot of algorithms/UDF's in streaming mode which are not readily supported by other streaming frameworks (especially: temporal joins, iterative graph algorithms, machine learning routines). + +If you are curious, here are some benchmarks to play with https://github.com/pathwaycom/pathway-benchmarks. + +If you try your own benchmarks, please don't hesitate to let us know. We investigate situations in which Pathway is underperforming on par with bugs (i.e., to our knowledge, they shouldn't happen...). + +## Coming soon + +Here are some features we plan to incorporate in the near future: + +- Enhanced monitoring, observability, and data drift detection (integrates with Grafana visualization and other dashboarding tools). +- New connectors: interoperability with Delta Lake and Snowflake data sources. +- Easier connection setup for MongoDB. +- More performant garbage collection. + + +## Dependencies + +Pathway is made to run in a "clean" Linux/MacOS + Python environment. When installing the pathway package with `pip` (from a wheel), you are likely to encounter a small number of Python package dependencies, such as sqlglot (used in the SQL API) and python-sat (useful for resolving dependencies during compilation). All necessary Rust crates are pre-built; the Rust compiler is not required to install Pathway, unless building from sources. A modified version of Timely/Differential Dataflow (which provides a dataflow assembly layer) is part of this repo. + +## License + +Pathway is distributed on a BSL 1.1 License https://github.com/pathwaycom/pathway/blob/main/LICENSE.txt which allows for unlimited non-commercial use, as well as use of the Pathway package for most commercial purposes https://pathway.com/license/, free of charge. Code in this repository automatically converts to Open Source (Apache 2.0 License) after 4 years. Some public repos https://github.com/pathwaycom which are complementary to this one (examples, libraries, connectors, etc.) are licensed as Open Source, under the MIT license. + + +## Contribution guidelines + +If you develop a library or connector which you would like to integrate with this repo, we suggest releasing it first as a separate repo on a MIT/Apache 2.0 license. + +For all concerns regarding core Pathway functionalities, Issues are encouraged. For further information, don't hesitate to engage with Pathway's Discord https://discord.gg/pathway. + +## Get Help + +If you have any questions, issues, or just want to chat about Pathway, we're here to help! Feel free to: +- Check out the documentation in https://pathway.com/developers/ for detailed information. +- Reach out to us via email at contact@pathway.com. + +Our team is always happy to help you and ensure that you get the most out of Pathway. +If you would like to better understand how best to use Pathway in your project, please don't hesitate to reach out to us. diff --git a/templates/rag-pathway/sample_documents/what-you-get.md b/templates/rag-pathway/sample_documents/what-you-get.md new file mode 100644 index 0000000000000..2be67ff9718e5 --- /dev/null +++ b/templates/rag-pathway/sample_documents/what-you-get.md @@ -0,0 +1,92 @@ +--- +title: What you get +description: 'What to expect when using Pathway: experience and performance' +--- + +# What you get with Pathway + +## 1. Python + Rust: the best of both worlds + +Pathway provides a Python interface and experience created with data developers in mind. You can easily build pipelines by manipulating Pathway tables and rely on the vast resources and libraries of the Python ecosystem. Also, Pathway can seamlessly be integrated into your CI/CD chain as it is inherently compatible with popular tools such as mypy or pytest. +Your Pathway pipelines can be automatically tested, built, and deployed, like any other Python workflow. + +Pathway also relies on a powerful Rust engine to ensure high performance for your pipelines, no matter if you are dealing with batch or streaming data. +Pathway engine makes the utmost of Rust speed and memory safety to provide efficient parallel and distributed processing without being limited by Python's [GIL](https://en.wikipedia.org/w/index.php?title=Global_interpreter_lock&oldid=1144836295). + +Pathway takes the best of both worlds and efficiently associates the convenience of Python with the power of Rust. + + +## 2. Incremental computation + +Pathway's engine incrementally processes data updates. This means that the minimum work needed by any algorithm or transformation is performed to refresh its results when fresh data arrives. + + + +## 3. An ML-friendly code life cycle + +### Writing your code + +As a Pathway user, you write code in Python, with Pathway imported as a Python module. +Pathway provides a cookiecutter template in https://github.com/pathwaycom/cookiecutter-pathway to help start Pathway projects. + +Data manipulation syntax is built around a Table API, which closely resembles the DataFrame API of PySpark which in turn resembles DataFrames in pandas. + +The same code developed with the Pathway module can be used for batch and streaming use cases, depending only on data connector settings and method of launching. +For many operations, Pathway returns exactly the same results when run in batch and streaming modes. +Exceptions are out-of-order data for which streaming mode may optionally ignore and functions that explicitly depend on processing time. + +### Running code and prototyping + +During rapid prototyping, Pathway code can be written and tested interactively, without waiting for compilation or deployment, with the computation graph being built in the background. +This is helpful for instance during data science work in Jupyter notebooks. + +The developed code prototype can then run on streaming data sources. +The computation graph is handed down to the runtime engine when executing the line `pathway.run()`. + +In terms of the interplay between interactive usability and launching compute graphs, Pathway takes direct inspiration from TensorFlow and PyTorch: just like TensorFlow, we explicitly represent computations as graphs that are executed with a `run()` command, however, similarly to PyTorch, we aim to offer the best in class interactive work environment for hands-on, data-driven algorithm design.\ +\ +(Footnote: Our advisor and Business Angel, Lukasz Kaiser, was a co-author of TensorFlow, and our CTO, Jan Chorowski, was a contributor to Theano). + +Pathway calls into the same Rust runtime in any deployment - on either streaming data sources or in interactive mode. + +## 4. Consistency of results + +Pathway computes consistent results: each produced output is computed based on data contained in prefixes of the input data streams. +For most of the operations, exactly the same outputs are produced: + +1. If the input data was sent to the engine all at once in batch mode. +2. If the inputs were sent in several smaller batches, or in streaming mode. + +This equivalence of stream and batch processing facilitates easy development of data processing logic: one can reason in terms of steady-state and consistent results and not worry about all intermediate and wrong states through which an eventually consistent system may pass. + +Consider a Kafka topic containing messages, each holding a list (pack) of several events that co-occurred together in a banking system. +Events indicate that an account was debited or credited money. +Clearly, a transaction touches two accounts and consists of two events that must be processed atomically (either both or none at all), or some outputs, e.g., account balances, will be inconsistent. +Kafka is atomic with respect to individual messages. Thus, this event-pack design ensures atomic delivery of all events that form a transaction. +However, processing event-packs is complex, and logic is much easier if the event-pack stream is unpacked into a single message stream (e.g., we can then group events by account and compute the balances). + +Since Kafka only guarantees the atomic processing of single messages, consistency is lost once the event-packs are flattened into a single-even stream. +On the other hand, Pathway guarantees consistency. +If a Pathway computation unpacks the event-pack into individual events, all messages that form a transaction will be consistently processed, ensuring that every produced output depends on either all or no events that were grouped together into a single event-pack. + +## 5. Containerized deployments + +Pathway is meant to be deployed in a containerized manner. + +Single-machine deployments can easily be achieved using Docker. +The deployment can run concurrently on multiple cores using multiple processes or threads. + +We provide a pathway spawn command to aid in launching multi-process and multi-threaded jobs. + +The choice between threads and multiple processes depends on the nature of the computation. +While communication between threads is faster, Python-heavy workloads may require multiprocess parallelism to bypass the GIL. +We are eagerly awaiting the GIL-less Python era! + +For using Pathway on large workloads beyond a single machine, see Distributed deployment. + +## 6. Easy testing and CI/CD + +Pathway tests on offline data snippets can be run locally in any CI/CD pipeline with Python. +Tests can cover the handling of temporal (late, out of order) aspects of data by comparing results on multiple revisions. +Pathway supports several session-replay mechanisms, such as the demo API. +These allow recreating streaming scenarios predictably within standard CI/CD pipelines (Jenkins, GitHub Actions, etc.) diff --git a/templates/rag-pathway/tests/__init__.py b/templates/rag-pathway/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d