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Harrison Chase committed 2023-04-15 21:06:39 -07:00
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docs/_static/css/custom.css
Vendored
+4
View File
@@ -11,3 +11,7 @@ pre {
max-width: 2560px !important;
}
}
#my-component-root *, #headlessui-portal-root * {
z-index: 1000000000000;
}
docs/_static/js/mendablesearch.js
Vendored
+58
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@@ -0,0 +1,58 @@
document.addEventListener('DOMContentLoaded', () => {
// Load the external dependencies
function loadScript(src, onLoadCallback) {
const script = document.createElement('script');
script.src = src;
script.onload = onLoadCallback;
document.head.appendChild(script);
}
function createRootElement() {
const rootElement = document.createElement('div');
rootElement.id = 'my-component-root';
document.body.appendChild(rootElement);
return rootElement;
}
function initializeMendable() {
const rootElement = createRootElement();
const { MendableFloatingButton } = Mendable;
const iconSpan1 = React.createElement('span', {
}, '🦜');
const iconSpan2 = React.createElement('span', {
}, '🔗');
const icon = React.createElement('p', {
style: { color: '#ffffff', fontSize: '22px',width: '48px', height: '48px', margin: '0px', padding: '0px', display: 'flex', alignItems: 'center', justifyContent: 'center', textAlign: 'center' },
}, [iconSpan1, iconSpan2]);
const mendableFloatingButton = React.createElement(
MendableFloatingButton,
{
style: { darkMode: false, accentColor: '#010810' },
floatingButtonStyle: { color: '#ffffff', backgroundColor: '#010810' },
anon_key: '82842b36-3ea6-49b2-9fb8-52cfc4bde6bf', // Mendable Search Public ANON key, ok to be public
messageSettings: {
openSourcesInNewTab: false,
},
icon: icon,
}
);
ReactDOM.render(mendableFloatingButton, rootElement);
}
loadScript('https://unpkg.com/react@17/umd/react.production.min.js', () => {
loadScript('https://unpkg.com/react-dom@17/umd/react-dom.production.min.js', () => {
loadScript('https://unpkg.com/@mendable/search@0.0.83/dist/umd/mendable.min.js', initializeMendable);
});
});
});
docs/conf.py
+5
View File
@@ -103,5 +103,10 @@ html_static_path = ["_static"]
html_css_files = [
"css/custom.css",
]
html_js_files = [
"js/mendablesearch.js",
]
nb_execution_mode = "off"
myst_enable_extensions = ["colon_fence"]
docs/deployments.md
+5
View File
@@ -33,12 +33,17 @@ It implements a Question Answering app and contains instructions for deploying t
A minimal example on how to run LangChain on Vercel using Flask.
## [Digitalocean App Platform](https://github.com/homanp/digitalocean-langchain)
A minimal example on how to deploy LangChain to DigitalOcean App Platform.
## [SteamShip](https://github.com/steamship-core/steamship-langchain/)
This repository contains LangChain adapters for Steamship, enabling LangChain developers to rapidly deploy their apps on Steamship.
This includes: production ready endpoints, horizontal scaling across dependencies, persistant storage of app state, multi-tenancy support, etc.
## [Langchain-serve](https://github.com/jina-ai/langchain-serve)
This repository allows users to serve local chains and agents as RESTful, gRPC, or Websocket APIs thanks to [Jina](https://docs.jina.ai/). Deploy your chains & agents with ease and enjoy independent scaling, serverless and autoscaling APIs, as well as a Streamlit playground on Jina AI Cloud.
## [BentoML](https://github.com/ssheng/BentoChain)
docs/ecosystem/comet_tracking.ipynb
+26 -26
View File
@@ -1,7 +1,6 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -9,7 +8,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -17,7 +15,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -31,7 +28,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -39,7 +35,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -52,14 +47,13 @@
"metadata": {},
"outputs": [],
"source": [
"!pip install comet_ml\n",
"!pip install langchain\n",
"!pip install openai\n",
"!pip install google-search-results"
"%pip install comet_ml langchain openai google-search-results spacy textstat pandas\n",
"\n",
"import sys\n",
"!{sys.executable} -m spacy download en_core_web_sm"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -67,7 +61,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -86,7 +79,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -94,7 +86,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -109,12 +100,12 @@
"source": [
"import os\n",
"\n",
"%env OPENAI_API_KEY=\"...\"\n",
"%env SERPAPI_API_KEY=\"...\""
"os.environ[\"OPENAI_API_KEY\"] = \"...\"\n",
"#os.environ[\"OPENAI_ORGANIZATION\"] = \"...\"\n",
"os.environ[\"SERPAPI_API_KEY\"] = \"...\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -149,7 +140,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -185,12 +175,11 @@
"synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
"\n",
"test_prompts = [{\"title\": \"Documentary about Bigfoot in Paris\"}]\n",
"synopsis_chain.apply(test_prompts)\n",
"print(synopsis_chain.apply(test_prompts))\n",
"comet_callback.flush_tracker(synopsis_chain, finish=True)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -232,7 +221,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -240,7 +228,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -256,7 +243,7 @@
"metadata": {},
"outputs": [],
"source": [
"!pip install rouge-score"
"%pip install rouge-score"
]
},
{
@@ -336,16 +323,29 @@
" \"\"\"\n",
" }\n",
"]\n",
"synopsis_chain.apply(test_prompts)\n",
"print(synopsis_chain.apply(test_prompts))\n",
"comet_callback.flush_tracker(synopsis_chain, finish=True)"
]
}
],
"metadata": {
"language_info": {
"name": "python"
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"orig_nbformat": 4
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
docs/ecosystem/gpt4all.md
+2 -2
View File
@@ -36,7 +36,7 @@ from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8, callback_handler=callback_handler, verbose=True)
# Generate text. Tokens are streamed throught the callback manager.
# Generate text. Tokens are streamed through the callback manager.
model("Once upon a time, ")
```
@@ -44,4 +44,4 @@ model("Once upon a time, ")
You can find links to model file downloads in the [pyllamacpp](https://github.com/nomic-ai/pyllamacpp) repository.
For a more detailed walkthrough of this, see [this notebook](../modules/models/llms/integrations/gpt4all.ipynb)
For a more detailed walkthrough of this, see [this notebook](../modules/models/llms/integrations/gpt4all.ipynb)
docs/gallery.rst
+2 -2
View File
@@ -1,5 +1,5 @@
LangChain Gallery
=============
=================
Lots of people have built some pretty awesome stuff with LangChain.
This is a collection of our favorites.
@@ -223,7 +223,7 @@ Open Source
Answer questions about the documentation of any project
Misc. Colab Notebooks
~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~
.. panels::
:body: text-center
docs/modules/chains/examples/sqlite.ipynb
+7 -5
View File
@@ -9,9 +9,9 @@
}
},
"source": [
"# SQLite example\n",
"# SQL Chain example\n",
"\n",
"This example showcases hooking up an LLM to answer questions over a database."
"This example demonstrates the use of the `SQLDatabaseChain` for answering questions over a database."
]
},
{
@@ -23,8 +23,10 @@
}
},
"source": [
"This uses the example Chinook database.\n",
"To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository."
"Under the hood, LangChain uses SQLAlchemy to connect to SQL databases. The `SQLDatabaseChain` can therefore be used with any SQL dialect supported by SQLAlchemy, such as MS SQL, MySQL, MariaDB, PostgreSQL, Oracle SQL, and SQLite. Please refer to the SQLAlchemy documentation for more information about requirements for connecting to your database. For example, a connection to MySQL requires an appropriate connector such as PyMySQL. A URI for a MySQL connection might look like: `mysql+pymysql://user:pass@some_mysql_db_address/db_name`\n",
"\n",
"This demonstration uses SQLite and the example Chinook database.\n",
"To set it up, follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository."
]
},
{
@@ -679,7 +681,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.10.10"
}
},
"nbformat": 4,
docs/modules/indexes/document_loaders/examples/csv.ipynb
+1 -1
View File
@@ -106,7 +106,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Specify a column to be used identify the document source\n",
"## Specify a column to be used identify the document source\n",
"\n",
"Use the `source_column` argument to specify a column to be set as the source for the document created from each row. Otherwise `file_path` will be used as the source for all documents created from the csv file.\n",
"\n",
docs/modules/indexes/document_loaders/examples/example_data/test_repo1
Submodule
+1
Submodule docs/modules/indexes/document_loaders/examples/example_data/test_repo1 added at 7e525a3b91.
docs/modules/indexes/document_loaders/examples/git.ipynb
+192
View File
@@ -0,0 +1,192 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Git\n",
"\n",
"This notebook shows how to load text files from Git repository."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load existing repository from disk"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from git import Repo\n",
"\n",
"repo = Repo.clone_from(\n",
" \"https://github.com/hwchase17/langchain\", to_path=\"./example_data/test_repo1\"\n",
")\n",
"branch = repo.head.reference"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import GitLoader"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"loader = GitLoader(repo_path=\"./example_data/test_repo1/\", branch=branch)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"data = loader.load()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(data)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"page_content='.venv\\n.github\\n.git\\n.mypy_cache\\n.pytest_cache\\nDockerfile' metadata={'file_path': '.dockerignore', 'file_name': '.dockerignore', 'file_type': ''}\n"
]
}
],
"source": [
"print(data[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clone repository from url"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import GitLoader"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"loader = GitLoader(\n",
" clone_url=\"https://github.com/hwchase17/langchain\",\n",
" repo_path=\"./example_data/test_repo2/\",\n",
" branch=\"master\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"data = loader.load()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1074"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Filtering files to load"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import GitLoader\n",
"\n",
"# eg. loading only python files\n",
"loader = GitLoader(repo_path=\"./example_data/test_repo1/\", file_filter=lambda file_path: file_path.endswith(\".py\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
docs/modules/indexes/document_loaders/examples/slack_directory.ipynb
+81
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@@ -0,0 +1,81 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "1dc7df1d",
"metadata": {},
"source": [
"# Slack (Local Exported Zipfile)\n",
"\n",
"This notebook covers how to load documents from a Zipfile generated from a Slack export.\n",
"\n",
"In order to get this Slack export, follow these instructions:\n",
"\n",
"## 🧑 Instructions for ingesting your own dataset\n",
"\n",
"Export your Slack data. You can do this by going to your Workspace Management page and clicking the Import/Export option ({your_slack_domain}.slack.com/services/export). Then, choose the right date range and click `Start export`. Slack will send you an email and a DM when the export is ready.\n",
"\n",
"The download will produce a `.zip` file in your Downloads folder (or wherever your downloads can be found, depending on your OS configuration).\n",
"\n",
"Copy the path to the `.zip` file, and assign it as `LOCAL_ZIPFILE` below."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "007c5cbf",
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import SlackDirectoryLoader "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a1caec59",
"metadata": {},
"outputs": [],
"source": [
"# Optionally set your Slack URL. This will give you proper URLs in the docs sources.\n",
"SLACK_WORKSPACE_URL = \"https://xxx.slack.com\"\n",
"LOCAL_ZIPFILE = \"\" # Paste the local paty to your Slack zip file here.\n",
"\n",
"loader = SlackDirectoryLoader(LOCAL_ZIPFILE, SLACK_WORKSPACE_URL)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b1c30ff7",
"metadata": {},
"outputs": [],
"source": [
"docs = loader.load()\n",
"docs"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
docs/modules/indexes/document_loaders/examples/url.ipynb
+74 -1
View File
@@ -112,6 +112,79 @@
"source": [
"data = loader.load()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "a2c1c79f",
"metadata": {},
"source": [
"# Playwright URL Loader\n",
"\n",
"This covers how to load HTML documents from a list of URLs using the `PlaywrightURLLoader`.\n",
"\n",
"As in the Selenium case, Playwright allows us to load pages that need JavaScript to render.\n",
"\n",
"## Setup\n",
"\n",
"To use the `PlaywrightURLLoader`, you will need to install `playwright` and `unstructured`. Additionally, you will need to install the Playwright Chromium browser:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "53158417",
"metadata": {},
"outputs": [],
"source": [
"# Install playwright\n",
"!pip install \"playwright\"\n",
"!pip install \"unstructured\"\n",
"!playwright install"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ab4e115",
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import PlaywrightURLLoader"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ce5a9a0a",
"metadata": {},
"outputs": [],
"source": [
"urls = [\n",
" \"https://www.youtube.com/watch?v=dQw4w9WgXcQ\",\n",
" \"https://goo.gl/maps/NDSHwePEyaHMFGwh8\"\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2dc3e0bc",
"metadata": {},
"outputs": [],
"source": [
"loader = PlaywrightURLLoader(urls=urls, remove_selectors=[\"header\", \"footer\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "10b79f80",
"metadata": {},
"outputs": [],
"source": [
"data = loader.load()"
]
}
],
"metadata": {
@@ -130,7 +203,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
"version": "3.10.6"
}
},
"nbformat": 4,
docs/modules/indexes/document_loaders/examples/web_base.ipynb
+2 -2
View File
@@ -89,7 +89,7 @@
"id": "150988e6",
"metadata": {},
"source": [
"# Loading multiple webpages\n",
"## Loading multiple webpages\n",
"\n",
"You can also load multiple webpages at once by passing in a list of urls to the loader. This will return a list of documents in the same order as the urls passed in."
]
@@ -123,7 +123,7 @@
"id": "641be294",
"metadata": {},
"source": [
"## Load multiple urls concurrently\n",
"### Load multiple urls concurrently\n",
"\n",
"You can speed up the scraping process by scraping and parsing multiple urls concurrently.\n",
"\n",
docs/modules/indexes/getting_started.ipynb
+1 -1
View File
@@ -99,7 +99,7 @@
"outputs": [],
"source": [
"from langchain.document_loaders import TextLoader\n",
"loader = TextLoader('../state_of_the_union.txt')"
"loader = TextLoader('../state_of_the_union.txt', encoding='utf8')"
]
},
{
docs/modules/indexes/retrievers/examples/svm_retriever.ipynb
+128
View File
@@ -0,0 +1,128 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "ab66dd43",
"metadata": {},
"source": [
"# SVM Retriever\n",
"\n",
"This notebook goes over how to use a retriever that under the hood uses an SVM using scikit-learn.\n",
"\n",
"Largely based on https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "393ac030",
"metadata": {},
"outputs": [],
"source": [
"from langchain.retrievers import SVMRetriever\n",
"from langchain.embeddings import OpenAIEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a801b57c",
"metadata": {},
"outputs": [],
"source": [
"# !pip install scikit-learn"
]
},
{
"cell_type": "markdown",
"id": "aaf80e7f",
"metadata": {},
"source": [
"## Create New Retriever with Texts"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "98b1c017",
"metadata": {},
"outputs": [],
"source": [
"retriever = SVMRetriever.from_texts([\"foo\", \"bar\", \"world\", \"hello\", \"foo bar\"], OpenAIEmbeddings())"
]
},
{
"cell_type": "markdown",
"id": "08437fa2",
"metadata": {},
"source": [
"## Use Retriever\n",
"\n",
"We can now use the retriever!"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c0455218",
"metadata": {},
"outputs": [],
"source": [
"result = retriever.get_relevant_documents(\"foo\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7dfa5c29",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='foo', metadata={}),\n",
" Document(page_content='foo bar', metadata={}),\n",
" Document(page_content='hello', metadata={}),\n",
" Document(page_content='world', metadata={})]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "74bd9256",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
docs/modules/indexes/retrievers/examples/time_weighted_vectorstore.ipynb
+32 -27
View File
@@ -69,7 +69,7 @@
{
"data": {
"text/plain": [
"['73f8f585-9536-4240-aef7-cea205b336bd']"
"['f6303531-d3a5-44af-b7c8-e3cf76916ce5']"
]
},
"execution_count": 3,
@@ -79,7 +79,7 @@
],
"source": [
"retriever.add_documents([Document(page_content=\"hello world\")])\n",
"time.sleep(1)\n",
"time.sleep(20)\n",
"retriever.add_documents([Document(page_content=\"hello foo\")])"
]
},
@@ -90,19 +90,24 @@
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "normalize_score_fn must be provided to FAISS constructor to normalize scores",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mretriever\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_relevant_documents\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhello world\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/workplace/langchain/langchain/retrievers/time_weighted_retriever.py:94\u001b[0m, in \u001b[0;36mTimeWeightedVectorStoreRetriever.get_relevant_documents\u001b[0;34m(self, query)\u001b[0m\n\u001b[1;32m 89\u001b[0m docs_and_scores \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 90\u001b[0m doc\u001b[38;5;241m.\u001b[39mmetadata[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbuffer_idx\u001b[39m\u001b[38;5;124m\"\u001b[39m]: (doc, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_salience)\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m doc \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmemory_stream[\u001b[38;5;241m-\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mk :]\n\u001b[1;32m 92\u001b[0m }\n\u001b[1;32m 93\u001b[0m \u001b[38;5;66;03m# If a doc is considered salient, update the salience score\u001b[39;00m\n\u001b[0;32m---> 94\u001b[0m docs_and_scores\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_salient_docs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 95\u001b[0m rescored_docs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 96\u001b[0m (doc, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_combined_score(doc, salience, current_time))\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m doc, salience \u001b[38;5;129;01min\u001b[39;00m docs_and_scores\u001b[38;5;241m.\u001b[39mvalues()\n\u001b[1;32m 98\u001b[0m ]\n\u001b[1;32m 99\u001b[0m rescored_docs\u001b[38;5;241m.\u001b[39msort(key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[38;5;241m1\u001b[39m], reverse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
"File \u001b[0;32m~/workplace/langchain/langchain/retrievers/time_weighted_retriever.py:75\u001b[0m, in \u001b[0;36mTimeWeightedVectorStoreRetriever.get_salient_docs\u001b[0;34m(self, query)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return documents that are salient to the query.\"\"\"\u001b[39;00m\n\u001b[1;32m 73\u001b[0m docs_and_scores: List[Tuple[Document, \u001b[38;5;28mfloat\u001b[39m]]\n\u001b[1;32m 74\u001b[0m docs_and_scores \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m---> 75\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvectorstore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msimilarity_search_with_normalized_similarities\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 76\u001b[0m \u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearch_kwargs\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 78\u001b[0m )\n\u001b[1;32m 79\u001b[0m results \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m fetched_doc, cosine_distance \u001b[38;5;129;01min\u001b[39;00m docs_and_scores:\n",
"File \u001b[0;32m~/workplace/langchain/langchain/vectorstores/base.py:94\u001b[0m, in \u001b[0;36mVectorStore.similarity_search_with_normalized_similarities\u001b[0;34m(self, query, k, **kwargs)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msimilarity_search_with_normalized_similarities\u001b[39m(\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 86\u001b[0m query: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m 87\u001b[0m k: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m,\n\u001b[1;32m 88\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 89\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[Tuple[Document, \u001b[38;5;28mfloat\u001b[39m]]:\n\u001b[1;32m 90\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return docs and similarity scores, normalized on a scale from 0 to 1.\u001b[39;00m\n\u001b[1;32m 91\u001b[0m \n\u001b[1;32m 92\u001b[0m \u001b[38;5;124;03m 0 is dissimilar, 1 is most similar.\u001b[39;00m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 94\u001b[0m docs_and_similarities \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_similarity_search_with_normalized_similarities\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 95\u001b[0m \u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(\n\u001b[1;32m 98\u001b[0m similarity \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0.0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m similarity \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _, similarity \u001b[38;5;129;01min\u001b[39;00m docs_and_similarities\n\u001b[1;32m 100\u001b[0m ):\n\u001b[1;32m 101\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 102\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNormalized similarity scores must be between\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m 0 and 1, got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdocs_and_similarities\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 104\u001b[0m )\n",
"File \u001b[0;32m~/workplace/langchain/langchain/vectorstores/faiss.py:435\u001b[0m, in \u001b[0;36mFAISS._similarity_search_with_normalized_similarities\u001b[0;34m(self, query, k, **kwargs)\u001b[0m\n\u001b[1;32m 433\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return docs and their similarity scores on a scale from 0 to 1.\"\"\"\u001b[39;00m\n\u001b[1;32m 434\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnormalize_score_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 435\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 436\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnormalize_score_fn must be provided to\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 437\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m FAISS constructor to normalize scores\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 438\u001b[0m )\n\u001b[1;32m 439\u001b[0m docs_and_scores \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimilarity_search_with_score(query, k\u001b[38;5;241m=\u001b[39mk)\n\u001b[1;32m 440\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [(doc, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnormalize_score_fn(score)) \u001b[38;5;28;01mfor\u001b[39;00m doc, score \u001b[38;5;129;01min\u001b[39;00m docs_and_scores]\n",
"\u001b[0;31mValueError\u001b[0m: normalize_score_fn must be provided to FAISS constructor to normalize scores"
"name": "stdout",
"output_type": "stream",
"text": [
"0.9999994359334345\n",
"1.8408203353689756\n",
"0.9999428041917008\n",
"1.9999408025741263\n"
]
},
{
"data": {
"text/plain": [
"[Document(page_content='hello world', metadata={'last_accessed_at': datetime.datetime(2023, 4, 15, 21, 4, 41, 457055), 'created_at': datetime.datetime(2023, 4, 15, 21, 4, 20, 437090), 'buffer_idx': 0})]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
@@ -120,7 +125,7 @@
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 5,
"id": "dc37669b",
"metadata": {},
"outputs": [],
@@ -131,35 +136,35 @@
"embedding_size = 1536\n",
"index = faiss.IndexFlatL2(embedding_size)\n",
"vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})\n",
"retriever = TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, decay_factor=.00000001, k=1) "
"retriever = TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, decay_factor=.0000000000000000000000001, k=1) "
]
},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": 6,
"id": "fa284384",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['3c156c9a-ad6b-47dc-befc-87437a603973']"
"['f063e5b2-c2eb-42fc-8894-79c7a7a9038e']"
]
},
"execution_count": 31,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retriever.add_documents([Document(page_content=\"hello world\")])\n",
"time.sleep(1)\n",
"time.sleep(20)\n",
"retriever.add_documents([Document(page_content=\"hello foo\")])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 7,
"id": "7558f94d",
"metadata": {},
"outputs": [
@@ -167,19 +172,19 @@
"name": "stdout",
"output_type": "stream",
"text": [
"0.8416762384156714\n",
"1.6166791948060522\n",
"0.5781175231321124\n",
"1.577325318264064\n"
"0.9978079943966991\n",
"1.8412067636745604\n",
"0.7235696005754303\n",
"1.7230094271411442\n"
]
},
{
"data": {
"text/plain": [
"[Document(page_content='hello foo', metadata={'last_accessed_at': datetime.datetime(2023, 4, 13, 23, 35, 54, 905605), 'created_at': datetime.datetime(2023, 4, 13, 23, 35, 54, 242988), 'buffer_idx': 1})]"
"[Document(page_content='hello foo', metadata={'last_accessed_at': datetime.datetime(2023, 4, 15, 21, 5, 2, 243331), 'created_at': datetime.datetime(2023, 4, 15, 21, 5, 1, 579028), 'buffer_idx': 1})]"
]
},
"execution_count": 32,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
docs/modules/indexes/vectorstores/examples/deeplake.ipynb
+285 -35
View File
@@ -17,34 +17,36 @@
"metadata": {},
"outputs": [],
"source": [
"!python3 -m pip install openai deeplake"
"!python3 -m pip install openai deeplake tiktoken"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.vectorstores import DeepLake\n",
"from langchain.document_loaders import TextLoader"
"from langchain.vectorstores import DeepLake"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ['OPENAI_API_KEY'] = 'sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'"
"import getpass\n",
"\n",
"os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')\n",
"embeddings = OpenAIEmbeddings()"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -60,9 +62,38 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mem://langchain loaded successfully.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Evaluating ingest: 100%|██████████| 1/1 [00:04<00:00\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset(path='mem://langchain', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
"\n",
" tensor htype shape dtype compression\n",
" ------- ------- ------- ------- ------- \n",
" embedding generic (4, 1536) float32 None \n",
" ids text (4, 1) str None \n",
" metadata json (4, 1) str None \n",
" text text (4, 1) str None \n"
]
}
],
"source": [
"db = DeepLake.from_documents(docs, embeddings)\n",
"\n",
@@ -72,9 +103,23 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
"\n",
"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
"\n",
"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
"\n",
"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
]
}
],
"source": [
"print(docs[0].page_content)"
]
@@ -89,9 +134,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/media/sdb/davit/.local/lib/python3.10/site-packages/langchain/llms/openai.py:624: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
" warnings.warn(\n"
]
}
],
"source": [
"from langchain.chains import RetrievalQA\n",
"from langchain.llms import OpenAIChat\n",
@@ -101,9 +155,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"'The president nominated Circuit Court of Appeals Judge Ketanji Brown Jackson for the United States Supreme Court and praised her qualifications and broad support from both Democrats and Republicans.'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = 'What did the president say about Ketanji Brown Jackson'\n",
"qa.run(query)"
@@ -119,9 +184,43 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 11,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mem://langchain loaded successfully.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Evaluating ingest: 100%|██████████| 1/1 [00:04<00:00\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset(path='mem://langchain', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
"\n",
" tensor htype shape dtype compression\n",
" ------- ------- ------- ------- ------- \n",
" embedding generic (42, 1536) float32 None \n",
" ids text (42, 1) str None \n",
" metadata json (42, 1) str None \n",
" text text (42, 1) str None \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": []
}
],
"source": [
"import random\n",
"\n",
@@ -133,9 +232,30 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 42/42 [00:00<00:00, 3456.17it/s]\n"
]
},
{
"data": {
"text/plain": [
"[Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013}),\n",
" Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \\n\\nFirst, beat the opioid epidemic.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013}),\n",
" Document(page_content='Vice President Harris and I ran for office with a new economic vision for America. \\n\\nInvest in America. Educate Americans. Grow the workforce. Build the economy from the bottom up \\nand the middle out, not from the top down. \\n\\nBecause we know that when the middle class grows, the poor have a ladder up and the wealthy do very well. \\n\\nAmerica used to have the best roads, bridges, and airports on Earth. \\n\\nNow our infrastructure is ranked 13th in the world. \\n\\nWe won’t be able to compete for the jobs of the 21st Century if we don’t fix that. \\n\\nThat’s why it was so important to pass the Bipartisan Infrastructure Law—the most sweeping investment to rebuild America in history. \\n\\nThis was a bipartisan effort, and I want to thank the members of both parties who worked to make it happen. \\n\\nWe’re done talking about infrastructure weeks. \\n\\nWe’re going to have an infrastructure decade.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013}),\n",
" Document(page_content='It is going to transform America and put us on a path to win the economic competition of the 21st Century that we face with the rest of the world—particularly with China. \\n\\nAs I’ve told Xi Jinping, it is never a good bet to bet against the American people. \\n\\nWe’ll create good jobs for millions of Americans, modernizing roads, airports, ports, and waterways all across America. \\n\\nAnd we’ll do it all to withstand the devastating effects of the climate crisis and promote environmental justice. \\n\\nWe’ll build a national network of 500,000 electric vehicle charging stations, begin to replace poisonous lead pipes—so every child—and every American—has clean water to drink at home and at school, provide affordable high-speed internet for every American—urban, suburban, rural, and tribal communities. \\n\\n4,000 projects have already been announced. \\n\\nAnd tonight, I’m announcing that this year we will start fixing over 65,000 miles of highway and 1,500 bridges in disrepair.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013})]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db.similarity_search('What did the president say about Ketanji Brown Jackson', filter={'year': 2013})"
]
@@ -151,9 +271,23 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 13,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012}),\n",
" Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013}),\n",
" Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \\n\\nFirst, beat the opioid epidemic.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013}),\n",
" Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave. \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2014})]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db.similarity_search('What did the president say about Ketanji Brown Jackson?', distance_metric='cos')"
]
@@ -169,9 +303,23 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012}),\n",
" Document(page_content='One was stationed at bases and breathing in toxic smoke from “burn pits” that incinerated wastes of war—medical and hazard material, jet fuel, and more. \\n\\nWhen they came home, many of the world’s fittest and best trained warriors were never the same. \\n\\nHeadaches. Numbness. Dizziness. \\n\\nA cancer that would put them in a flag-draped coffin. \\n\\nI know. \\n\\nOne of those soldiers was my son Major Beau Biden. \\n\\nWe don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. \\n\\nBut I’m committed to finding out everything we can. \\n\\nCommitted to military families like Danielle Robinson from Ohio. \\n\\nThe widow of Sergeant First Class Heath Robinson. \\n\\nHe was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq. \\n\\nStationed near Baghdad, just yards from burn pits the size of football fields. \\n\\nHeath’s widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2014}),\n",
" Document(page_content='As Ohio Senator Sherrod Brown says, “It’s time to bury the label “Rust Belt.” \\n\\nIt’s time. \\n\\nBut with all the bright spots in our economy, record job growth and higher wages, too many families are struggling to keep up with the bills. \\n\\nInflation is robbing them of the gains they might otherwise feel. \\n\\nI get it. That’s why my top priority is getting prices under control. \\n\\nLook, our economy roared back faster than most predicted, but the pandemic meant that businesses had a hard time hiring enough workers to keep up production in their factories. \\n\\nThe pandemic also disrupted global supply chains. \\n\\nWhen factories close, it takes longer to make goods and get them from the warehouse to the store, and prices go up. \\n\\nLook at cars. \\n\\nLast year, there weren’t enough semiconductors to make all the cars that people wanted to buy. \\n\\nAnd guess what, prices of automobiles went up. \\n\\nSo—we have a choice. \\n\\nOne way to fight inflation is to drive down wages and make Americans poorer.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012}),\n",
" Document(page_content='We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \\n\\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \\n\\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \\n\\nOfficer Mora was 27 years old. \\n\\nOfficer Rivera was 22. \\n\\nBoth Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \\n\\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \\n\\nI’ve worked on these issues a long time. \\n\\nI know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012})]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db.max_marginal_relevance_search('What did the president say about Ketanji Brown Jackson?')"
]
@@ -187,21 +335,87 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"!activeloop login -t <token>"
"os.environ['ACTIVELOOP_TOKEN'] = getpass.getpass('Activeloop Token:')"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 17,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your Deep Lake dataset has been successfully created!\n",
"The dataset is private so make sure you are logged in!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\\"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/linkedin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"hub://davitbun/linkedin loaded successfully.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Evaluating ingest: 100%|██████████| 1/1 [00:23<00:00\n",
"/"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset(path='hub://davitbun/linkedin', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
"\n",
" tensor htype shape dtype compression\n",
" ------- ------- ------- ------- ------- \n",
" embedding generic (42, 1536) float32 None \n",
" ids text (42, 1) str None \n",
" metadata json (42, 1) str None \n",
" text text (42, 1) str None \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
}
],
"source": [
"# Embed and store the texts\n",
"dataset_path = \"hub://{username}/{dataset_name}\" # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n",
"dataset_path = f\"hub://{USERNAME}/{DATASET_NAME}\" # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n",
"\n",
"embedding = OpenAIEmbeddings()\n",
"vectordb = DeepLake.from_documents(documents=docs, embedding=embedding, dataset_path=dataset_path)"
@@ -209,9 +423,23 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 18,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
"\n",
"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
"\n",
"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
"\n",
"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
]
}
],
"source": [
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
"docs = db.similarity_search(query)\n",
@@ -220,11 +448,35 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset(path='hub://davitbun/linkedin', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
"\n",
" tensor htype shape dtype compression\n",
" ------- ------- ------- ------- ------- \n",
" embedding generic (42, 1536) float32 None \n",
" ids text (42, 1) str None \n",
" metadata json (42, 1) str None \n",
" text text (42, 1) str None \n"
]
}
],
"source": [
"vectordb.ds.summary()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"vectordb.ds.summary()"
"embeddings = vectordb.ds.embedding.numpy()"
]
},
{
@@ -232,9 +484,7 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"embeddings = vectordb.ds.embedding.numpy()"
]
"source": []
}
],
"metadata": {
docs/modules/memory/examples/agent_with_memory_in_db.ipynb
+1 -1
View File
@@ -16,7 +16,7 @@
"In order to add a memory with an external message store to an agent we are going to do the following steps:\n",
"\n",
"1. We are going to create a `RedisChatMessageHistory` to connect to an external database to store the messages in.\n",
"2. We are going to create an `LLMChain` useing that chat history as memory.\n",
"2. We are going to create an `LLMChain` using that chat history as memory.\n",
"3. We are going to use that `LLMChain` to create a custom Agent.\n",
"\n",
"For the purposes of this exercise, we are going to create a simple custom Agent that has access to a search tool and utilizes the `ConversationBufferMemory` class."
docs/modules/memory/types/vectorstore_retriever_memory.ipynb
+80 -39
View File
@@ -7,9 +7,11 @@
"source": [
"# VectorStore-Backed Memory\n",
"\n",
"`VectorStoreRetrieverMemory` stores interactions in a VectorDB and queries the top-K most \"salient\" interactions every type it is called.\n",
"`VectorStoreRetrieverMemory` stores memories in a VectorDB and queries the top-K most \"salient\" docs every time it is called.\n",
"\n",
"This differs from most of the other Memory classes in that "
"This differs from most of the other Memory classes in that it doesn't explicitly track the order of interactions.\n",
"\n",
"In this case, the \"docs\" are previous conversation snippets. This can be useful to refer to relevant pieces of information that the AI was told earlier in the conversation."
]
},
{
@@ -25,7 +27,8 @@
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.llms import OpenAI\n",
"from langchain.memory import VectorStoreRetrieverMemory\n",
"from langchain.chains import ConversationChain"
"from langchain.chains import ConversationChain\n",
"from langchain.prompts import PromptTemplate"
]
},
{
@@ -40,7 +43,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 29,
"id": "eef56f65",
"metadata": {
"tags": []
@@ -66,12 +69,12 @@
"source": [
"### Create your the VectorStoreRetrieverMemory\n",
"\n",
"The memory object is instantiated from "
"The memory object is instantiated from any VectorStoreRetriever."
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 30,
"id": "e00d4938",
"metadata": {
"tags": []
@@ -84,14 +87,14 @@
"memory = VectorStoreRetrieverMemory(retriever=retriever)\n",
"\n",
"# When added to an agent, the memory object can save pertinent information from conversations or used tools\n",
"memory.save_context({\"input\": \"check the latest scores of the Warriors game\"}, {\"output\": \"the Warriors are up against the Astros 88 to 84\"})\n",
"memory.save_context({\"input\": \"I need help doing my taxes - what's the standard deduction this year?\"}, {\"output\": \"...\"})\n",
"memory.save_context({\"input\": \"What's the the time?\"}, {\"output\": f\"It's {datetime.now()}\"}) # "
"memory.save_context({\"input\": \"My favorite food is pizza\"}, {\"output\": \"thats good to know\"})\n",
"memory.save_context({\"input\": \"My favorite sport is soccer\"}, {\"output\": \"...\"})\n",
"memory.save_context({\"input\": \"I don't the Celtics\"}, {\"output\": \"ok\"}) # "
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 31,
"id": "2fe28a28",
"metadata": {
"tags": []
@@ -101,7 +104,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"input: I need help doing my taxes - what's the standard deduction this year?\n",
"input: My favorite sport is soccer\n",
"output: ...\n"
]
}
@@ -109,7 +112,7 @@
"source": [
"# Notice the first result returned is the memory pertaining to tax help, which the language model deems more semantically relevant\n",
"# to a 1099 than the other documents, despite them both containing numbers.\n",
"print(memory.load_memory_variables({\"prompt\": \"What's a 1099?\"})[\"history\"])"
"print(memory.load_memory_variables({\"prompt\": \"what sport should i watch?\"})[\"history\"])"
]
},
{
@@ -123,7 +126,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 32,
"id": "ebd68c10",
"metadata": {
"tags": []
@@ -139,9 +142,13 @@
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
"\n",
"Relevant pieces of previous conversation:\n",
"input: My favorite food is pizza\n",
"output: thats good to know\n",
"\n",
"(You do not need to use these pieces of information if not relevant)\n",
"\n",
"Current conversation:\n",
"input: I need help doing my taxes - what's the standard deduction this year?\n",
"output: ...\n",
"Human: Hi, my name is Perry, what's up?\n",
"AI:\u001b[0m\n",
"\n",
@@ -151,18 +158,32 @@
{
"data": {
"text/plain": [
"\" Hi Perry, my name is AI. I'm doing great, how about you? I understand you need help with your taxes. What specifically do you need help with?\""
"\" Hi Perry, I'm doing well. How about you?\""
]
},
"execution_count": 5,
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"llm = OpenAI(temperature=0) # Can be any valid LLM\n",
"_DEFAULT_TEMPLATE = \"\"\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
"\n",
"Relevant pieces of previous conversation:\n",
"{history}\n",
"\n",
"(You do not need to use these pieces of information if not relevant)\n",
"\n",
"Current conversation:\n",
"Human: {input}\n",
"AI:\"\"\"\n",
"PROMPT = PromptTemplate(\n",
" input_variables=[\"history\", \"input\"], template=_DEFAULT_TEMPLATE\n",
")\n",
"conversation_with_summary = ConversationChain(\n",
" llm=llm, \n",
" prompt=PROMPT,\n",
" # We set a very low max_token_limit for the purposes of testing.\n",
" memory=memory,\n",
" verbose=True\n",
@@ -172,7 +193,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 33,
"id": "86207a61",
"metadata": {
"tags": []
@@ -188,10 +209,14 @@
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
"\n",
"Relevant pieces of previous conversation:\n",
"input: My favorite sport is soccer\n",
"output: ...\n",
"\n",
"(You do not need to use these pieces of information if not relevant)\n",
"\n",
"Current conversation:\n",
"input: check the latest scores of the Warriors game\n",
"output: the Warriors are up against the Astros 88 to 84\n",
"Human: If the Cavaliers were to face off against the Warriers or the Astros, who would they most stand a chance to beat?\n",
"Human: what's my favorite sport?\n",
"AI:\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
@@ -200,22 +225,22 @@
{
"data": {
"text/plain": [
"\" It's hard to say without knowing the current form of the teams. However, based on the current scores, it looks like the Cavaliers would have a better chance of beating the Astros than the Warriors.\""
"' You told me earlier that your favorite sport is soccer.'"
]
},
"execution_count": 6,
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Here, the basketball related content is surfaced\n",
"conversation_with_summary.predict(input=\"If the Cavaliers were to face off against the Warriers or the Astros, who would they most stand a chance to beat?\")"
"conversation_with_summary.predict(input=\"what's my favorite sport?\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 34,
"id": "8c669db1",
"metadata": {
"tags": []
@@ -231,10 +256,14 @@
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
"\n",
"Relevant pieces of previous conversation:\n",
"input: My favorite food is pizza\n",
"output: thats good to know\n",
"\n",
"(You do not need to use these pieces of information if not relevant)\n",
"\n",
"Current conversation:\n",
"input: What's the the time?\n",
"output: It's 2023-04-13 09:18:55.623736\n",
"Human: What day is it tomorrow?\n",
"Human: Whats my favorite food\n",
"AI:\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
@@ -243,10 +272,10 @@
{
"data": {
"text/plain": [
"' Tomorrow is 2023-04-14.'"
"' You said your favorite food is pizza.'"
]
},
"execution_count": 7,
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
@@ -254,12 +283,12 @@
"source": [
"# Even though the language model is stateless, since relavent memory is fetched, it can \"reason\" about the time.\n",
"# Timestamping memories and data is useful in general to let the agent determine temporal relevance\n",
"conversation_with_summary.predict(input=\"What day is it tomorrow?\")"
"conversation_with_summary.predict(input=\"Whats my favorite food\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 35,
"id": "8c09a239",
"metadata": {
"tags": []
@@ -275,10 +304,14 @@
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
"\n",
"Current conversation:\n",
"Relevant pieces of previous conversation:\n",
"input: Hi, my name is Perry, what's up?\n",
"response: Hi Perry, my name is AI. I'm doing great, how about you? I understand you need help with your taxes. What specifically do you need help with?\n",
"Human: What's your name?\n",
"response: Hi Perry, I'm doing well. How about you?\n",
"\n",
"(You do not need to use these pieces of information if not relevant)\n",
"\n",
"Current conversation:\n",
"Human: What's my name?\n",
"AI:\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
@@ -287,10 +320,10 @@
{
"data": {
"text/plain": [
"\" My name is AI. It's nice to meet you, Perry.\""
"' Your name is Perry.'"
]
},
"execution_count": 8,
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
@@ -299,8 +332,16 @@
"# The memories from the conversation are automatically stored,\n",
"# since this query best matches the introduction chat above,\n",
"# the agent is able to 'remember' the user's name.\n",
"conversation_with_summary.predict(input=\"What's your name?\")"
"conversation_with_summary.predict(input=\"What's my name?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "df27c7dc",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
@@ -319,7 +360,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
"version": "3.9.1"
}
},
"nbformat": 4,
docs/modules/models/chat/integrations/promptlayer_chatopenai.ipynb
+1 -1
View File
@@ -115,7 +115,7 @@
"id": "a2d76826",
"metadata": {},
"source": [
"**The above request should now appear on your [PromptLayer dashboard](https://ww.promptlayer.com).**"
"**The above request should now appear on your [PromptLayer dashboard](https://www.promptlayer.com).**"
]
},
{
docs/modules/models/llms/examples/token_usage_tracking.ipynb
+5 -9
View File
@@ -43,22 +43,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Total Tokens: 39\n",
"Prompt Tokens: 4\n",
"Completion Tokens: 35\n",
"Tokens Used: 42\n",
"\tPrompt Tokens: 4\n",
"\tCompletion Tokens: 38\n",
"Successful Requests: 1\n",
"Total Cost (USD): $0.0007800000000000001\n"
"Total Cost (USD): $0.00084\n"
]
}
],
"source": [
"with get_openai_callback() as cb:\n",
" result = llm(\"Tell me a joke\")\n",
" print(f\"Total Tokens: {cb.total_tokens}\")\n",
" print(f\"Prompt Tokens: {cb.prompt_tokens}\")\n",
" print(f\"Completion Tokens: {cb.completion_tokens}\")\n",
" print(f\"Successful Requests: {cb.successful_requests}\")\n",
" print(f\"Total Cost (USD): ${cb.total_cost}\")"
" print(cb)"
]
},
{
docs/modules/models/llms/getting_started.ipynb
+1 -1
View File
@@ -186,7 +186,7 @@
"source": [
"**Number of Tokens:** You can also estimate how many tokens a piece of text will be in that model. This is useful because models have a context length (and cost more for more tokens), which means you need to be aware of how long the text you are passing in is.\n",
"\n",
"Notice that by default the tokens are estimated using [tiktoken](https://github.com/openai/tiktoken) (except for legacy version <3.8, where a HuggingFace tokenizer is used)"
"Notice that by default the tokens are estimated using [tiktoken](https://github.com/openai/tiktoken) (except for legacy version <3.8, where a Hugging Face tokenizer is used)"
]
},
{
docs/use_cases/agents/baby_agi.ipynb
+61 -34
View File
@@ -38,7 +38,7 @@
"from langchain.llms import BaseLLM\n",
"from langchain.vectorstores.base import VectorStore\n",
"from pydantic import BaseModel, Field\n",
"from langchain.chains.base import Chain\n"
"from langchain.chains.base import Chain"
]
},
{
@@ -73,6 +73,7 @@
"embeddings_model = OpenAIEmbeddings()\n",
"# Initialize the vectorstore as empty\n",
"import faiss\n",
"\n",
"embedding_size = 1536\n",
"index = faiss.IndexFlatL2(embedding_size)\n",
"vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})"
@@ -116,7 +117,12 @@
" )\n",
" prompt = PromptTemplate(\n",
" template=task_creation_template,\n",
" input_variables=[\"result\", \"task_description\", \"incomplete_tasks\", \"objective\"],\n",
" input_variables=[\n",
" \"result\",\n",
" \"task_description\",\n",
" \"incomplete_tasks\",\n",
" \"objective\",\n",
" ],\n",
" )\n",
" return cls(prompt=prompt, llm=llm, verbose=verbose)"
]
@@ -147,7 +153,7 @@
" template=task_prioritization_template,\n",
" input_variables=[\"task_names\", \"next_task_id\", \"objective\"],\n",
" )\n",
" return cls(prompt=prompt, llm=llm, verbose=verbose)\n"
" return cls(prompt=prompt, llm=llm, verbose=verbose)"
]
},
{
@@ -173,7 +179,7 @@
" template=execution_template,\n",
" input_variables=[\"objective\", \"context\", \"task\"],\n",
" )\n",
" return cls(prompt=prompt, llm=llm, verbose=verbose)\n"
" return cls(prompt=prompt, llm=llm, verbose=verbose)"
]
},
{
@@ -193,11 +199,22 @@
"metadata": {},
"outputs": [],
"source": [
"def get_next_task(task_creation_chain: LLMChain, result: Dict, task_description: str, task_list: List[str], objective: str) -> List[Dict]:\n",
"def get_next_task(\n",
" task_creation_chain: LLMChain,\n",
" result: Dict,\n",
" task_description: str,\n",
" task_list: List[str],\n",
" objective: str,\n",
") -> List[Dict]:\n",
" \"\"\"Get the next task.\"\"\"\n",
" incomplete_tasks = \", \".join(task_list)\n",
" response = task_creation_chain.run(result=result, task_description=task_description, incomplete_tasks=incomplete_tasks, objective=objective)\n",
" new_tasks = response.split('\\n')\n",
" response = task_creation_chain.run(\n",
" result=result,\n",
" task_description=task_description,\n",
" incomplete_tasks=incomplete_tasks,\n",
" objective=objective,\n",
" )\n",
" new_tasks = response.split(\"\\n\")\n",
" return [{\"task_name\": task_name} for task_name in new_tasks if task_name.strip()]"
]
},
@@ -208,12 +225,19 @@
"metadata": {},
"outputs": [],
"source": [
"def prioritize_tasks(task_prioritization_chain: LLMChain, this_task_id: int, task_list: List[Dict], objective: str) -> List[Dict]:\n",
"def prioritize_tasks(\n",
" task_prioritization_chain: LLMChain,\n",
" this_task_id: int,\n",
" task_list: List[Dict],\n",
" objective: str,\n",
") -> List[Dict]:\n",
" \"\"\"Prioritize tasks.\"\"\"\n",
" task_names = [t[\"task_name\"] for t in task_list]\n",
" next_task_id = int(this_task_id) + 1\n",
" response = task_prioritization_chain.run(task_names=task_names, next_task_id=next_task_id, objective=objective)\n",
" new_tasks = response.split('\\n')\n",
" response = task_prioritization_chain.run(\n",
" task_names=task_names, next_task_id=next_task_id, objective=objective\n",
" )\n",
" new_tasks = response.split(\"\\n\")\n",
" prioritized_task_list = []\n",
" for task_string in new_tasks:\n",
" if not task_string.strip():\n",
@@ -239,9 +263,12 @@
" if not results:\n",
" return []\n",
" sorted_results, _ = zip(*sorted(results, key=lambda x: x[1], reverse=True))\n",
" return [str(item.metadata['task']) for item in sorted_results]\n",
" return [str(item.metadata[\"task\"]) for item in sorted_results]\n",
"\n",
"def execute_task(vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5) -> str:\n",
"\n",
"def execute_task(\n",
" vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5\n",
") -> str:\n",
" \"\"\"Execute a task.\"\"\"\n",
" context = _get_top_tasks(vectorstore, query=objective, k=k)\n",
" return execution_chain.run(objective=objective, context=context, task=task)"
@@ -254,7 +281,6 @@
"metadata": {},
"outputs": [],
"source": [
"\n",
"class BabyAGI(Chain, BaseModel):\n",
" \"\"\"Controller model for the BabyAGI agent.\"\"\"\n",
"\n",
@@ -265,9 +291,10 @@
" task_id_counter: int = Field(1)\n",
" vectorstore: VectorStore = Field(init=False)\n",
" max_iterations: Optional[int] = None\n",
" \n",
"\n",
" class Config:\n",
" \"\"\"Configuration for this pydantic object.\"\"\"\n",
"\n",
" arbitrary_types_allowed = True\n",
"\n",
" def add_task(self, task: Dict):\n",
@@ -285,18 +312,18 @@
" def print_task_result(self, result: str):\n",
" print(\"\\033[93m\\033[1m\" + \"\\n*****TASK RESULT*****\\n\" + \"\\033[0m\\033[0m\")\n",
" print(result)\n",
" \n",
"\n",
" @property\n",
" def input_keys(self) -> List[str]:\n",
" return [\"objective\"]\n",
" \n",
"\n",
" @property\n",
" def output_keys(self) -> List[str]:\n",
" return []\n",
"\n",
" def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n",
" \"\"\"Run the agent.\"\"\"\n",
" objective = inputs['objective']\n",
" objective = inputs[\"objective\"]\n",
" first_task = inputs.get(\"first_task\", \"Make a todo list\")\n",
" self.add_task({\"task_id\": 1, \"task_name\": first_task})\n",
" num_iters = 0\n",
@@ -325,7 +352,11 @@
"\n",
" # Step 4: Create new tasks and reprioritize task list\n",
" new_tasks = get_next_task(\n",
" self.task_creation_chain, result, task[\"task_name\"], [t[\"task_name\"] for t in self.task_list], objective\n",
" self.task_creation_chain,\n",
" result,\n",
" task[\"task_name\"],\n",
" [t[\"task_name\"] for t in self.task_list],\n",
" objective,\n",
" )\n",
" for new_task in new_tasks:\n",
" self.task_id_counter += 1\n",
@@ -333,27 +364,26 @@
" self.add_task(new_task)\n",
" self.task_list = deque(\n",
" prioritize_tasks(\n",
" self.task_prioritization_chain, this_task_id, list(self.task_list), objective\n",
" self.task_prioritization_chain,\n",
" this_task_id,\n",
" list(self.task_list),\n",
" objective,\n",
" )\n",
" )\n",
" num_iters += 1\n",
" if self.max_iterations is not None and num_iters == self.max_iterations:\n",
" print(\"\\033[91m\\033[1m\" + \"\\n*****TASK ENDING*****\\n\" + \"\\033[0m\\033[0m\")\n",
" print(\n",
" \"\\033[91m\\033[1m\" + \"\\n*****TASK ENDING*****\\n\" + \"\\033[0m\\033[0m\"\n",
" )\n",
" break\n",
" return {}\n",
"\n",
" @classmethod\n",
" def from_llm(\n",
" cls,\n",
" llm: BaseLLM,\n",
" vectorstore: VectorStore,\n",
" verbose: bool = False,\n",
" **kwargs\n",
" cls, llm: BaseLLM, vectorstore: VectorStore, verbose: bool = False, **kwargs\n",
" ) -> \"BabyAGI\":\n",
" \"\"\"Initialize the BabyAGI Controller.\"\"\"\n",
" task_creation_chain = TaskCreationChain.from_llm(\n",
" llm, verbose=verbose\n",
" )\n",
" task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose)\n",
" task_prioritization_chain = TaskPrioritizationChain.from_llm(\n",
" llm, verbose=verbose\n",
" )\n",
@@ -363,7 +393,7 @@
" task_prioritization_chain=task_prioritization_chain,\n",
" execution_chain=execution_chain,\n",
" vectorstore=vectorstore,\n",
" **kwargs\n",
" **kwargs,\n",
" )"
]
},
@@ -405,14 +435,11 @@
"outputs": [],
"source": [
"# Logging of LLMChains\n",
"verbose=False\n",
"verbose = False\n",
"# If None, will keep on going forever\n",
"max_iterations: Optional[int] = 3\n",
"baby_agi = BabyAGI.from_llm(\n",
" llm=llm,\n",
" vectorstore=vectorstore,\n",
" verbose=verbose,\n",
" max_iterations=max_iterations\n",
" llm=llm, vectorstore=vectorstore, verbose=verbose, max_iterations=max_iterations\n",
")"
]
},
docs/use_cases/agents/baby_agi_with_agent.ipynb
+79 -45
View File
@@ -34,7 +34,7 @@
"from langchain.llms import BaseLLM\n",
"from langchain.vectorstores.base import VectorStore\n",
"from pydantic import BaseModel, Field\n",
"from langchain.chains.base import Chain\n"
"from langchain.chains.base import Chain"
]
},
{
@@ -54,7 +54,9 @@
"metadata": {},
"outputs": [],
"source": [
"%pip install faiss-cpu > /dev/null%pip install google-search-results > /dev/nullfrom langchain.vectorstores import FAISS\n",
"%pip install faiss-cpu > /dev/null\n",
"%pip install google-search-results > /dev/null\n",
"from langchain.vectorstores import FAISS\n",
"from langchain.docstore import InMemoryDocstore"
]
},
@@ -69,6 +71,7 @@
"embeddings_model = OpenAIEmbeddings()\n",
"# Initialize the vectorstore as empty\n",
"import faiss\n",
"\n",
"embedding_size = 1536\n",
"index = faiss.IndexFlatL2(embedding_size)\n",
"vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})"
@@ -115,7 +118,12 @@
" )\n",
" prompt = PromptTemplate(\n",
" template=task_creation_template,\n",
" input_variables=[\"result\", \"task_description\", \"incomplete_tasks\", \"objective\"],\n",
" input_variables=[\n",
" \"result\",\n",
" \"task_description\",\n",
" \"incomplete_tasks\",\n",
" \"objective\",\n",
" ],\n",
" )\n",
" return cls(prompt=prompt, llm=llm, verbose=verbose)"
]
@@ -146,7 +154,7 @@
" template=task_prioritization_template,\n",
" input_variables=[\"task_names\", \"next_task_id\", \"objective\"],\n",
" )\n",
" return cls(prompt=prompt, llm=llm, verbose=verbose)\n"
" return cls(prompt=prompt, llm=llm, verbose=verbose)"
]
},
{
@@ -158,20 +166,23 @@
"source": [
"from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n",
"from langchain import OpenAI, SerpAPIWrapper, LLMChain\n",
"todo_prompt = PromptTemplate.from_template(\"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}\")\n",
"\n",
"todo_prompt = PromptTemplate.from_template(\n",
" \"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}\"\n",
")\n",
"todo_chain = LLMChain(llm=OpenAI(temperature=0), prompt=todo_prompt)\n",
"search = SerpAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name = \"Search\",\n",
" name=\"Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events\"\n",
" description=\"useful for when you need to answer questions about current events\",\n",
" ),\n",
" Tool(\n",
" name = \"TODO\",\n",
" name=\"TODO\",\n",
" func=todo_chain.run,\n",
" description=\"useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is!\"\n",
" )\n",
" description=\"useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is!\",\n",
" ),\n",
"]\n",
"\n",
"\n",
@@ -179,10 +190,10 @@
"suffix = \"\"\"Question: {task}\n",
"{agent_scratchpad}\"\"\"\n",
"prompt = ZeroShotAgent.create_prompt(\n",
" tools, \n",
" prefix=prefix, \n",
" suffix=suffix, \n",
" input_variables=[\"objective\", \"task\", \"context\",\"agent_scratchpad\"]\n",
" tools,\n",
" prefix=prefix,\n",
" suffix=suffix,\n",
" input_variables=[\"objective\", \"task\", \"context\", \"agent_scratchpad\"],\n",
")"
]
},
@@ -203,11 +214,22 @@
"metadata": {},
"outputs": [],
"source": [
"def get_next_task(task_creation_chain: LLMChain, result: Dict, task_description: str, task_list: List[str], objective: str) -> List[Dict]:\n",
"def get_next_task(\n",
" task_creation_chain: LLMChain,\n",
" result: Dict,\n",
" task_description: str,\n",
" task_list: List[str],\n",
" objective: str,\n",
") -> List[Dict]:\n",
" \"\"\"Get the next task.\"\"\"\n",
" incomplete_tasks = \", \".join(task_list)\n",
" response = task_creation_chain.run(result=result, task_description=task_description, incomplete_tasks=incomplete_tasks, objective=objective)\n",
" new_tasks = response.split('\\n')\n",
" response = task_creation_chain.run(\n",
" result=result,\n",
" task_description=task_description,\n",
" incomplete_tasks=incomplete_tasks,\n",
" objective=objective,\n",
" )\n",
" new_tasks = response.split(\"\\n\")\n",
" return [{\"task_name\": task_name} for task_name in new_tasks if task_name.strip()]"
]
},
@@ -218,12 +240,19 @@
"metadata": {},
"outputs": [],
"source": [
"def prioritize_tasks(task_prioritization_chain: LLMChain, this_task_id: int, task_list: List[Dict], objective: str) -> List[Dict]:\n",
"def prioritize_tasks(\n",
" task_prioritization_chain: LLMChain,\n",
" this_task_id: int,\n",
" task_list: List[Dict],\n",
" objective: str,\n",
") -> List[Dict]:\n",
" \"\"\"Prioritize tasks.\"\"\"\n",
" task_names = [t[\"task_name\"] for t in task_list]\n",
" next_task_id = int(this_task_id) + 1\n",
" response = task_prioritization_chain.run(task_names=task_names, next_task_id=next_task_id, objective=objective)\n",
" new_tasks = response.split('\\n')\n",
" response = task_prioritization_chain.run(\n",
" task_names=task_names, next_task_id=next_task_id, objective=objective\n",
" )\n",
" new_tasks = response.split(\"\\n\")\n",
" prioritized_task_list = []\n",
" for task_string in new_tasks:\n",
" if not task_string.strip():\n",
@@ -249,9 +278,12 @@
" if not results:\n",
" return []\n",
" sorted_results, _ = zip(*sorted(results, key=lambda x: x[1], reverse=True))\n",
" return [str(item.metadata['task']) for item in sorted_results]\n",
" return [str(item.metadata[\"task\"]) for item in sorted_results]\n",
"\n",
"def execute_task(vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5) -> str:\n",
"\n",
"def execute_task(\n",
" vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5\n",
") -> str:\n",
" \"\"\"Execute a task.\"\"\"\n",
" context = _get_top_tasks(vectorstore, query=objective, k=k)\n",
" return execution_chain.run(objective=objective, context=context, task=task)"
@@ -264,7 +296,6 @@
"metadata": {},
"outputs": [],
"source": [
"\n",
"class BabyAGI(Chain, BaseModel):\n",
" \"\"\"Controller model for the BabyAGI agent.\"\"\"\n",
"\n",
@@ -275,9 +306,10 @@
" task_id_counter: int = Field(1)\n",
" vectorstore: VectorStore = Field(init=False)\n",
" max_iterations: Optional[int] = None\n",
" \n",
"\n",
" class Config:\n",
" \"\"\"Configuration for this pydantic object.\"\"\"\n",
"\n",
" arbitrary_types_allowed = True\n",
"\n",
" def add_task(self, task: Dict):\n",
@@ -295,18 +327,18 @@
" def print_task_result(self, result: str):\n",
" print(\"\\033[93m\\033[1m\" + \"\\n*****TASK RESULT*****\\n\" + \"\\033[0m\\033[0m\")\n",
" print(result)\n",
" \n",
"\n",
" @property\n",
" def input_keys(self) -> List[str]:\n",
" return [\"objective\"]\n",
" \n",
"\n",
" @property\n",
" def output_keys(self) -> List[str]:\n",
" return []\n",
"\n",
" def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n",
" \"\"\"Run the agent.\"\"\"\n",
" objective = inputs['objective']\n",
" objective = inputs[\"objective\"]\n",
" first_task = inputs.get(\"first_task\", \"Make a todo list\")\n",
" self.add_task({\"task_id\": 1, \"task_name\": first_task})\n",
" num_iters = 0\n",
@@ -335,7 +367,11 @@
"\n",
" # Step 4: Create new tasks and reprioritize task list\n",
" new_tasks = get_next_task(\n",
" self.task_creation_chain, result, task[\"task_name\"], [t[\"task_name\"] for t in self.task_list], objective\n",
" self.task_creation_chain,\n",
" result,\n",
" task[\"task_name\"],\n",
" [t[\"task_name\"] for t in self.task_list],\n",
" objective,\n",
" )\n",
" for new_task in new_tasks:\n",
" self.task_id_counter += 1\n",
@@ -343,40 +379,41 @@
" self.add_task(new_task)\n",
" self.task_list = deque(\n",
" prioritize_tasks(\n",
" self.task_prioritization_chain, this_task_id, list(self.task_list), objective\n",
" self.task_prioritization_chain,\n",
" this_task_id,\n",
" list(self.task_list),\n",
" objective,\n",
" )\n",
" )\n",
" num_iters += 1\n",
" if self.max_iterations is not None and num_iters == self.max_iterations:\n",
" print(\"\\033[91m\\033[1m\" + \"\\n*****TASK ENDING*****\\n\" + \"\\033[0m\\033[0m\")\n",
" print(\n",
" \"\\033[91m\\033[1m\" + \"\\n*****TASK ENDING*****\\n\" + \"\\033[0m\\033[0m\"\n",
" )\n",
" break\n",
" return {}\n",
"\n",
" @classmethod\n",
" def from_llm(\n",
" cls,\n",
" llm: BaseLLM,\n",
" vectorstore: VectorStore,\n",
" verbose: bool = False,\n",
" **kwargs\n",
" cls, llm: BaseLLM, vectorstore: VectorStore, verbose: bool = False, **kwargs\n",
" ) -> \"BabyAGI\":\n",
" \"\"\"Initialize the BabyAGI Controller.\"\"\"\n",
" task_creation_chain = TaskCreationChain.from_llm(\n",
" llm, verbose=verbose\n",
" )\n",
" task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose)\n",
" task_prioritization_chain = TaskPrioritizationChain.from_llm(\n",
" llm, verbose=verbose\n",
" )\n",
" llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
" tool_names = [tool.name for tool in tools]\n",
" agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)\n",
" agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)\n",
" agent_executor = AgentExecutor.from_agent_and_tools(\n",
" agent=agent, tools=tools, verbose=True\n",
" )\n",
" return cls(\n",
" task_creation_chain=task_creation_chain,\n",
" task_prioritization_chain=task_prioritization_chain,\n",
" execution_chain=agent_executor,\n",
" vectorstore=vectorstore,\n",
" **kwargs\n",
" **kwargs,\n",
" )"
]
},
@@ -418,14 +455,11 @@
"outputs": [],
"source": [
"# Logging of LLMChains\n",
"verbose=False\n",
"verbose = False\n",
"# If None, will keep on going forever\n",
"max_iterations: Optional[int] = 3\n",
"baby_agi = BabyAGI.from_llm(\n",
" llm=llm,\n",
" vectorstore=vectorstore,\n",
" verbose=verbose,\n",
" max_iterations=max_iterations\n",
" llm=llm, vectorstore=vectorstore, verbose=verbose, max_iterations=max_iterations\n",
")"
]
},
docs/use_cases/code.md
+1
View File
@@ -23,3 +23,4 @@ Query Understanding: GPT-4 processes user queries, grasping the context and extr
The full tutorial is available below.
- [Twitter the-algorithm codebase analysis with Deep Lake](code/twitter-the-algorithm-analysis-deeplake.ipynb): A notebook walking through how to parse github source code and run queries conversation.
- [LangChain codebase analysis with Deep Lake](code/code-analysis-deeplake.ipynb): A notebook walking through how to analyze and do question answering over THIS code base.
docs/use_cases/code/code-analysis-deeplake.ipynb
+644
View File
@@ -0,0 +1,644 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use LangChain, GPT and Deep Lake to work with code base\n",
"In this tutorial, we are going to use Langchain + Deep Lake with GPT to analyze the code base of the LangChain itself. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Design"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Prepare data:\n",
" 1. Upload all python project files using the `langchain.document_loaders.TextLoader`. We will call these files the **documents**.\n",
" 2. Split all documents to chunks using the `langchain.text_splitter.CharacterTextSplitter`.\n",
" 3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain.vectorstores.DeepLake`\n",
"2. Question-Answering:\n",
" 1. Build a chain from `langchain.chat_models.ChatOpenAI` and `langchain.chains.ConversationalRetrievalChain`\n",
" 2. Prepare questions.\n",
" 3. Get answers running the chain.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementation"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"### Integration preparations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need to set up keys for external services and install necessary python libraries."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#!python3 -m pip install --upgrade langchain deeplake openai"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set up OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate. \n",
"\n",
"For full documentation of Deep Lake please follow https://docs.activeloop.ai/ and API reference https://docs.deeplake.ai/en/latest/"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ········\n"
]
}
],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
"os.environ['OPENAI_API_KEY'] = getpass()\n",
"# Please manually enter OpenAI Key"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Authenticate into Deep Lake if you want to create your own dataset and publish it. You can get an API key from the platform at [app.activeloop.ai](https://app.activeloop.ai)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ········\n"
]
}
],
"source": [
"os.environ['ACTIVELOOP_TOKEN'] = getpass.getpass('Activeloop Token:')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare data "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load all repository files. Here we assume this notebook is downloaded as the part of the langchain fork and we work with the python files of the `langchain` repo.\n",
"\n",
"If you want to use files from different repo, change `root_dir` to the root dir of your repo."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1147\n"
]
}
],
"source": [
"from langchain.document_loaders import TextLoader\n",
"\n",
"root_dir = '../../../..'\n",
"\n",
"docs = []\n",
"for dirpath, dirnames, filenames in os.walk(root_dir):\n",
" for file in filenames:\n",
" if file.endswith('.py') and '/.venv/' not in dirpath:\n",
" try: \n",
" loader = TextLoader(os.path.join(dirpath, file), encoding='utf-8')\n",
" docs.extend(loader.load_and_split())\n",
" except Exception as e: \n",
" pass\n",
"print(f'{len(docs)}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, chunk the files"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Created a chunk of size 1620, which is longer than the specified 1000\n",
"Created a chunk of size 1213, which is longer than the specified 1000\n",
"Created a chunk of size 1263, which is longer than the specified 1000\n",
"Created a chunk of size 1448, which is longer than the specified 1000\n",
"Created a chunk of size 1120, which is longer than the specified 1000\n",
"Created a chunk of size 1148, which is longer than the specified 1000\n",
"Created a chunk of size 1826, which is longer than the specified 1000\n",
"Created a chunk of size 1260, which is longer than the specified 1000\n",
"Created a chunk of size 1195, which is longer than the specified 1000\n",
"Created a chunk of size 2147, which is longer than the specified 1000\n",
"Created a chunk of size 1410, which is longer than the specified 1000\n",
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"Created a chunk of size 1030, which is longer than the specified 1000\n",
"Created a chunk of size 1046, which is longer than the specified 1000\n",
"Created a chunk of size 1024, which is longer than the specified 1000\n",
"Created a chunk of size 1026, which is longer than the specified 1000\n",
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"Created a chunk of size 1999, which is longer than the specified 1000\n",
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"Created a chunk of size 1403, which is longer than the specified 1000\n",
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"Created a chunk of size 1825, which is longer than the specified 1000\n",
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"Created a chunk of size 1206, which is longer than the specified 1000\n",
"Created a chunk of size 1102, which is longer than the specified 1000\n",
"Created a chunk of size 1184, which is longer than the specified 1000\n",
"Created a chunk of size 1002, which is longer than the specified 1000\n",
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"Created a chunk of size 1754, which is longer than the specified 1000\n",
"Created a chunk of size 2413, which is longer than the specified 1000\n",
"Created a chunk of size 1771, which is longer than the specified 1000\n",
"Created a chunk of size 2054, which is longer than the specified 1000\n",
"Created a chunk of size 2000, which is longer than the specified 1000\n",
"Created a chunk of size 2061, which is longer than the specified 1000\n",
"Created a chunk of size 1066, which is longer than the specified 1000\n",
"Created a chunk of size 1419, which is longer than the specified 1000\n",
"Created a chunk of size 1368, which is longer than the specified 1000\n",
"Created a chunk of size 1008, which is longer than the specified 1000\n",
"Created a chunk of size 1227, which is longer than the specified 1000\n",
"Created a chunk of size 1745, which is longer than the specified 1000\n",
"Created a chunk of size 2296, which is longer than the specified 1000\n",
"Created a chunk of size 1083, which is longer than the specified 1000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"3477\n"
]
}
],
"source": [
"from langchain.text_splitter import CharacterTextSplitter\n",
"\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_documents(docs)\n",
"print(f\"{len(texts)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then embed chunks and upload them to the DeepLake.\n",
"\n",
"This can take several minutes. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', document_model_name='text-embedding-ada-002', query_model_name='text-embedding-ada-002', embedding_ctx_length=8191, openai_api_key=None, openai_organization=None, allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"\n",
"embeddings = OpenAIEmbeddings()\n",
"embeddings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.vectorstores import DeepLake\n",
"\n",
"db = DeepLake.from_documents(texts, embeddings, dataset_path=f\"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code\")\n",
"db"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question Answering\n",
"First load the dataset, construct the retriever, then construct the Conversational Chain"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"-"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/user_name/langchain-code\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"hub://user_name/langchain-code loaded successfully.\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Deep Lake Dataset in hub://user_name/langchain-code already exists, loading from the storage\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset(path='hub://user_name/langchain-code', read_only=True, tensors=['embedding', 'ids', 'metadata', 'text'])\n",
"\n",
" tensor htype shape dtype compression\n",
" ------- ------- ------- ------- ------- \n",
" embedding generic (3477, 1536) float32 None \n",
" ids text (3477, 1) str None \n",
" metadata json (3477, 1) str None \n",
" text text (3477, 1) str None \n"
]
}
],
"source": [
"db = DeepLake(dataset_path=f\"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code\", read_only=True, embedding_function=embeddings)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"retriever = db.as_retriever()\n",
"retriever.search_kwargs['distance_metric'] = 'cos'\n",
"retriever.search_kwargs['fetch_k'] = 20\n",
"retriever.search_kwargs['maximal_marginal_relevance'] = True\n",
"retriever.search_kwargs['k'] = 20"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also specify user defined functions using [Deep Lake filters](https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def filter(x):\n",
" # filter based on source code\n",
" if 'something' in x['text'].data()['value']:\n",
" return False\n",
" \n",
" # filter based on path e.g. extension\n",
" metadata = x['metadata'].data()['value']\n",
" return 'only_this' in metadata['source'] or 'also_that' in metadata['source']\n",
"\n",
"### turn on below for custom filtering\n",
"# retriever.search_kwargs['filter'] = filter"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.chains import ConversationalRetrievalChain\n",
"\n",
"model = ChatOpenAI(model='gpt-3.5-turbo') # 'ada' 'gpt-3.5-turbo' 'gpt-4',\n",
"qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"questions = [\n",
" \"What is the class hierarchy?\",\n",
" # \"What classes are derived from the Chain class?\",\n",
" # \"What classes and functions in the ./langchain/utilities/ forlder are not covered by unit tests?\",\n",
" # \"What one improvement do you propose in code in relation to the class herarchy for the Chain class?\",\n",
"] \n",
"chat_history = []\n",
"\n",
"for question in questions: \n",
" result = qa({\"question\": question, \"chat_history\": chat_history})\n",
" chat_history.append((question, result['answer']))\n",
" print(f\"-> **Question**: {question} \\n\")\n",
" print(f\"**Answer**: {result['answer']} \\n\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"-> **Question**: What is the class hierarchy? \n",
"\n",
"**Answer**: There are several class hierarchies in the provided code, so I'll list a few:\n",
"\n",
"1. `BaseModel` -> `ConstitutionalPrinciple`: `ConstitutionalPrinciple` is a subclass of `BaseModel`.\n",
"2. `BasePromptTemplate` -> `StringPromptTemplate`, `AIMessagePromptTemplate`, `BaseChatPromptTemplate`, `ChatMessagePromptTemplate`, `ChatPromptTemplate`, `HumanMessagePromptTemplate`, `MessagesPlaceholder`, `SystemMessagePromptTemplate`, `FewShotPromptTemplate`, `FewShotPromptWithTemplates`, `Prompt`, `PromptTemplate`: All of these classes are subclasses of `BasePromptTemplate`.\n",
"3. `APIChain`, `Chain`, `MapReduceDocumentsChain`, `MapRerankDocumentsChain`, `RefineDocumentsChain`, `StuffDocumentsChain`, `HypotheticalDocumentEmbedder`, `LLMChain`, `LLMBashChain`, `LLMCheckerChain`, `LLMMathChain`, `LLMRequestsChain`, `PALChain`, `QAWithSourcesChain`, `VectorDBQAWithSourcesChain`, `VectorDBQA`, `SQLDatabaseChain`: All of these classes are subclasses of `Chain`.\n",
"4. `BaseLoader`: `BaseLoader` is a subclass of `ABC`.\n",
"5. `BaseTracer` -> `ChainRun`, `LLMRun`, `SharedTracer`, `ToolRun`, `Tracer`, `TracerException`, `TracerSession`: All of these classes are subclasses of `BaseTracer`.\n",
"6. `OpenAIEmbeddings`, `HuggingFaceEmbeddings`, `CohereEmbeddings`, `JinaEmbeddings`, `LlamaCppEmbeddings`, `HuggingFaceHubEmbeddings`, `TensorflowHubEmbeddings`, `SagemakerEndpointEmbeddings`, `HuggingFaceInstructEmbeddings`, `SelfHostedEmbeddings`, `SelfHostedHuggingFaceEmbeddings`, `SelfHostedHuggingFaceInstructEmbeddings`, `FakeEmbeddings`, `AlephAlphaAsymmetricSemanticEmbedding`, `AlephAlphaSymmetricSemanticEmbedding`: All of these classes are subclasses of `BaseLLM`. \n",
"\n",
"\n",
"-> **Question**: What classes are derived from the Chain class? \n",
"\n",
"**Answer**: There are multiple classes that are derived from the Chain class. Some of them are:\n",
"- APIChain\n",
"- AnalyzeDocumentChain\n",
"- ChatVectorDBChain\n",
"- CombineDocumentsChain\n",
"- ConstitutionalChain\n",
"- ConversationChain\n",
"- GraphQAChain\n",
"- HypotheticalDocumentEmbedder\n",
"- LLMChain\n",
"- LLMCheckerChain\n",
"- LLMRequestsChain\n",
"- LLMSummarizationCheckerChain\n",
"- MapReduceChain\n",
"- OpenAPIEndpointChain\n",
"- PALChain\n",
"- QAWithSourcesChain\n",
"- RetrievalQA\n",
"- RetrievalQAWithSourcesChain\n",
"- SequentialChain\n",
"- SQLDatabaseChain\n",
"- TransformChain\n",
"- VectorDBQA\n",
"- VectorDBQAWithSourcesChain\n",
"\n",
"There might be more classes that are derived from the Chain class as it is possible to create custom classes that extend the Chain class.\n",
"\n",
"\n",
"-> **Question**: What classes and functions in the ./langchain/utilities/ forlder are not covered by unit tests? \n",
"\n",
"**Answer**: All classes and functions in the `./langchain/utilities/` folder seem to have unit tests written for them. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
docs/use_cases/code/twitter-the-algorithm-analysis-deeplake.ipynb
+43 -28
View File
@@ -18,31 +18,13 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Define OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate. For full documentation of Deep Lake please follow https://docs.activeloop.ai/ and API reference https://docs.deeplake.ai/en/latest/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.vectorstores import DeepLake\n",
"Define OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate. For full documentation of Deep Lake please follow [docs](https://docs.activeloop.ai/) and [API reference](https://docs.deeplake.ai/en/latest/).\n",
"\n",
"os.environ['OPENAI_API_KEY']='sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'\n",
"embeddings = OpenAIEmbeddings()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Authenticate into Deep Lake if you want to create your own dataset and publish it. You can get an API key from the platform at https://app.activeloop.ai"
"Authenticate into Deep Lake if you want to create your own dataset and publish it. You can get an API key from the [platform](https://app.activeloop.ai)"
]
},
{
@@ -51,7 +33,15 @@
"metadata": {},
"outputs": [],
"source": [
"!activeloop login -t <TOKEN>"
"import os\n",
"import getpass\n",
"\n",
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.vectorstores import DeepLake\n",
"\n",
"os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')\n",
"os.environ['ACTIVELOOP_TOKEN'] = getpass.getpass('Activeloop Token:')\n",
"embeddings = OpenAIEmbeddings()"
]
},
{
@@ -143,15 +133,35 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"-"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/twitter-algorithm\n",
"\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"-"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"hub://davitbun/twitter-algorithm loaded successfully.\n",
"\n"
]
@@ -184,7 +194,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -205,7 +215,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -224,7 +234,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -267,9 +277,14 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"-> **Question**: What does favCountParams do? \n",
"\n",
"**Answer**: `favCountParams` is an optional ThriftLinearFeatureRankingParams instance that represents the parameters related to the \"favorite count\" feature in the ranking process. It is used to control the weight of the favorite count feature while ranking tweets. The favorite count is the number of times a tweet has been marked as a favorite by users, and it is considered an important signal in the ranking of tweets. By using `favCountParams`, the system can adjust the importance of the favorite count while calculating the final ranking score of a tweet. \n",
"\n",
"-> **Question**: is it Likes + Bookmarks, or not clear from the code?\n",
"\n",
"**Answer**: From the provided code, it is not clear if the favorite count metric is determined by the sum of likes and bookmarks. The favorite count is mentioned in the code, but there is no explicit reference to how it is calculated in terms of likes and bookmarks. \n",
@@ -423,7 +438,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.10.0"
}
},
"nbformat": 4,
docs/use_cases/evaluation.rst
+2 -2
View File
@@ -1,5 +1,5 @@
Evaluation
==============
==========
.. note::
`Conceptual Guide <https://docs.langchain.com/docs/use-cases/evaluation>`_
@@ -83,7 +83,7 @@ The existing examples we have are:
Other Examples
------------
--------------
In addition, we also have some more generic resources for evaluation.
langchain/agents/agent_toolkits/openapi/spec.py
+2 -2
View File
@@ -68,12 +68,12 @@ def reduce_openapi_spec(spec: dict, dereference: bool = True) -> ReducedOpenAPIS
I was hoping https://openapi.tools/ would have some useful bits
to this end, but doesn't seem so.
"""
# 1. Consider only get, post endpoints.
# 1. Consider only get, post, patch, delete endpoints.
endpoints = [
(f"{operation_name.upper()} {route}", docs.get("description"), docs)
for route, operation in spec["paths"].items()
for operation_name, docs in operation.items()
if operation_name in ["get", "post"]
if operation_name in ["get", "post", "patch", "delete"]
]
# 2. Replace any refs so that complete docs are retrieved.
langchain/callbacks/comet_ml_callback.py
+5 -7
View File
@@ -34,12 +34,10 @@ def _get_experiment(
) -> Any:
comet_ml = import_comet_ml()
experiment = comet_ml.config.get_global_experiment()
if experiment is None:
experiment = comet_ml.Experiment( # type: ignore
workspace=workspace,
project_name=project_name,
)
experiment = comet_ml.Experiment( # type: ignore
workspace=workspace,
project_name=project_name,
)
return experiment
@@ -132,7 +130,7 @@ class CometCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
warning = (
"The comet_ml callback is currently in beta and is subject to change "
"based on updates to `langchain`. Please report any issues to "
"https://github.com/comet_ml/issue_tracking/issues with the tag "
"https://github.com/comet-ml/issue_tracking/issues with the tag "
"`langchain`."
)
comet_ml.LOGGER.warning(warning)
langchain/callbacks/openai_info.py
+9
View File
@@ -53,6 +53,15 @@ class OpenAICallbackHandler(BaseCallbackHandler):
successful_requests: int = 0
total_cost: float = 0.0
def __repr__(self) -> str:
return (
f"Tokens Used: {self.total_tokens}\n"
f"\tPrompt Tokens: {self.prompt_tokens}\n"
f"\tCompletion Tokens: {self.completion_tokens}\n"
f"Successful Requests: {self.successful_requests}\n"
f"Total Cost (USD): ${self.total_cost}"
)
@property
def always_verbose(self) -> bool:
"""Whether to call verbose callbacks even if verbose is False."""
langchain/chains/llm_math/base.py
+1 -1
View File
@@ -7,8 +7,8 @@ from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_math.prompt import PROMPT
from langchain.prompts.base import BasePromptTemplate
from langchain.python import PythonREPL
from langchain.schema import BaseLanguageModel
from langchain.utilities import PythonREPL
class LLMMathChain(Chain):
langchain/chains/pal/base.py
+1 -1
View File
@@ -13,8 +13,8 @@ from langchain.chains.llm import LLMChain
from langchain.chains.pal.colored_object_prompt import COLORED_OBJECT_PROMPT
from langchain.chains.pal.math_prompt import MATH_PROMPT
from langchain.prompts.base import BasePromptTemplate
from langchain.python import PythonREPL
from langchain.schema import BaseLanguageModel
from langchain.utilities import PythonREPL
class PALChain(Chain):
langchain/chains/question_answering/__init__.py
+1 -1
View File
@@ -196,7 +196,7 @@ def load_qa_chain(
Args:
llm: Language Model to use in the chain.
chain_type: Type of document combining chain to use. Should be one of "stuff",
"map_reduce", and "refine".
"map_reduce", "map_rerank", and "refine".
verbose: Whether chains should be run in verbose mode or not. Note that this
applies to all chains that make up the final chain.
callback_manager: Callback manager to use for the chain.
langchain/chains/sql_database/base.py
+6 -5
View File
@@ -1,13 +1,13 @@
"""Chain for interacting with SQL Database."""
from __future__ import annotations
from typing import Any, Dict, List
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.sql_database.prompt import DECIDER_PROMPT, PROMPT
from langchain.chains.sql_database.prompt import DECIDER_PROMPT, PROMPT, SQL_PROMPTS
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import BaseLanguageModel
from langchain.sql_database import SQLDatabase
@@ -28,7 +28,7 @@ class SQLDatabaseChain(Chain):
"""LLM wrapper to use."""
database: SQLDatabase = Field(exclude=True)
"""SQL Database to connect to."""
prompt: BasePromptTemplate = PROMPT
prompt: Optional[BasePromptTemplate] = None
"""Prompt to use to translate natural language to SQL."""
top_k: int = 5
"""Number of results to return from the query"""
@@ -65,8 +65,9 @@ class SQLDatabaseChain(Chain):
return [self.output_key, "intermediate_steps"]
def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)
input_text = f"{inputs[self.input_key]} \nSQLQuery:"
prompt = self.prompt or SQL_PROMPTS.get(self.database.dialect, PROMPT)
llm_chain = LLMChain(llm=self.llm, prompt=prompt)
input_text = f"{inputs[self.input_key]}\nSQLQuery:"
self.callback_manager.on_text(input_text, verbose=self.verbose)
# If not present, then defaults to None which is all tables.
table_names_to_use = inputs.get("table_names_to_use")
langchain/chains/sql_database/prompt.py
+147
View File
@@ -2,6 +2,7 @@
from langchain.output_parsers.list import CommaSeparatedListOutputParser
from langchain.prompts.prompt import PromptTemplate
_DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.
Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.
@@ -38,3 +39,149 @@ DECIDER_PROMPT = PromptTemplate(
template=_DECIDER_TEMPLATE,
output_parser=CommaSeparatedListOutputParser(),
)
_mssql_prompt = """You are an MS SQL expert. Given an input question, first create a syntactically correct MS SQL query to run, then look at the results of the query and return the answer to the input question.
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the TOP clause as per MS SQL. You can order the results to return the most informative data in the database.
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in square brackets ([]) to denote them as delimited identifiers.
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
Use the following format:
Question: "Question here"
SQLQuery: "SQL Query to run"
SQLResult: "Result of the SQLQuery"
Answer: "Final answer here"
Only use the following tables:
{table_info}
Question: {input}"""
MSSQL_PROMPT = PromptTemplate(
input_variables=["input", "table_info", "top_k"], template=_mssql_prompt
)
_mysql_prompt = """You are a MySQL expert. Given an input question, first create a syntactically correct MySQL query to run, then look at the results of the query and return the answer to the input question.
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per MySQL. You can order the results to return the most informative data in the database.
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in backticks (`) to denote them as delimited identifiers.
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
Use the following format:
Question: "Question here"
SQLQuery: "SQL Query to run"
SQLResult: "Result of the SQLQuery"
Answer: "Final answer here"
Only use the following tables:
{table_info}
Question: {input}"""
MYSQL_PROMPT = PromptTemplate(
input_variables=["input", "table_info", "top_k"],
template=_mysql_prompt,
)
_mariadb_prompt = """You are a MariaDB expert. Given an input question, first create a syntactically correct MariaDB query to run, then look at the results of the query and return the answer to the input question.
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per MariaDB. You can order the results to return the most informative data in the database.
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in backticks (`) to denote them as delimited identifiers.
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
Use the following format:
Question: "Question here"
SQLQuery: "SQL Query to run"
SQLResult: "Result of the SQLQuery"
Answer: "Final answer here"
Only use the following tables:
{table_info}
Question: {input}"""
MARIADB_PROMPT = PromptTemplate(
input_variables=["input", "table_info", "top_k"],
template=_mariadb_prompt,
)
_oracle_prompt = """You are an Oracle SQL expert. Given an input question, first create a syntactically correct Oracle SQL query to run, then look at the results of the query and return the answer to the input question.
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the FETCH FIRST n ROWS ONLY clause as per Oracle SQL. You can order the results to return the most informative data in the database.
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
Use the following format:
Question: "Question here"
SQLQuery: "SQL Query to run"
SQLResult: "Result of the SQLQuery"
Answer: "Final answer here"
Only use the following tables:
{table_info}
Question: {input}"""
ORACLE_PROMPT = PromptTemplate(
input_variables=["input", "table_info", "top_k"],
template=_oracle_prompt,
)
_postgres_prompt = """You are a PostgreSQL expert. Given an input question, first create a syntactically correct PostgreSQL query to run, then look at the results of the query and return the answer to the input question.
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per PostgreSQL. You can order the results to return the most informative data in the database.
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
Use the following format:
Question: "Question here"
SQLQuery: "SQL Query to run"
SQLResult: "Result of the SQLQuery"
Answer: "Final answer here"
Only use the following tables:
{table_info}
Question: {input}"""
POSTGRES_PROMPT = PromptTemplate(
input_variables=["input", "table_info", "top_k"], template=_postgres_prompt
)
_sqlite_prompt = """You are a SQLite expert. Given an input question, first create a syntactically correct SQLite query to run, then look at the results of the query and return the answer to the input question.
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per SQLite. You can order the results to return the most informative data in the database.
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
Use the following format:
Question: "Question here"
SQLQuery: "SQL Query to run"
SQLResult: "Result of the SQLQuery"
Answer: "Final answer here"
Only use the following tables:
{table_info}
Question: {input}"""
SQLITE_PROMPT = PromptTemplate(
input_variables=["input", "table_info", "top_k"],
template=_sqlite_prompt,
)
SQL_PROMPTS = {
"mssql": MSSQL_PROMPT,
"mysql": MYSQL_PROMPT,
"mariadb": MARIADB_PROMPT,
"oracle": ORACLE_PROMPT,
"postgresql": POSTGRES_PROMPT,
"sqlite": SQLITE_PROMPT,
}
langchain/chat_models/__init__.py
+2 -1
View File
@@ -1,5 +1,6 @@
from langchain.chat_models.anthropic import ChatAnthropic
from langchain.chat_models.azure_openai import AzureChatOpenAI
from langchain.chat_models.openai import ChatOpenAI
from langchain.chat_models.promptlayer_openai import PromptLayerChatOpenAI
__all__ = ["ChatOpenAI", "AzureChatOpenAI", "PromptLayerChatOpenAI"]
__all__ = ["ChatOpenAI", "AzureChatOpenAI", "PromptLayerChatOpenAI", "ChatAnthropic"]
langchain/chat_models/anthropic.py
+139
View File
@@ -0,0 +1,139 @@
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.chat_models.base import BaseChatModel
from langchain.llms.anthropic import _AnthropicCommon
from langchain.schema import (
AIMessage,
BaseMessage,
ChatGeneration,
ChatMessage,
ChatResult,
HumanMessage,
SystemMessage,
)
class ChatAnthropic(BaseChatModel, _AnthropicCommon):
r"""Wrapper around Anthropic's large language model.
To use, you should have the ``anthropic`` python package installed, and the
environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
import anthropic
from langchain.llms import Anthropic
model = ChatAnthropic(model="<model_name>", anthropic_api_key="my-api-key")
"""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "anthropic-chat"
def _convert_one_message_to_text(self, message: BaseMessage) -> str:
if isinstance(message, ChatMessage):
message_text = f"\n\n{message.role.capitalize()}: {message.content}"
elif isinstance(message, HumanMessage):
message_text = f"{self.HUMAN_PROMPT} {message.content}"
elif isinstance(message, AIMessage):
message_text = f"{self.AI_PROMPT} {message.content}"
elif isinstance(message, SystemMessage):
message_text = f"{self.HUMAN_PROMPT} <admin>{message.content}</admin>"
else:
raise ValueError(f"Got unknown type {message}")
return message_text
def _convert_messages_to_text(self, messages: List[BaseMessage]) -> str:
"""Format a list of strings into a single string with necessary newlines.
Args:
messages (List[BaseMessage]): List of BaseMessage to combine.
Returns:
str: Combined string with necessary newlines.
"""
return "".join(
self._convert_one_message_to_text(message) for message in messages
)
def _convert_messages_to_prompt(self, messages: List[BaseMessage]) -> str:
"""Format a list of messages into a full prompt for the Anthropic model
Args:
messages (List[BaseMessage]): List of BaseMessage to combine.
Returns:
str: Combined string with necessary HUMAN_PROMPT and AI_PROMPT tags.
"""
if not self.AI_PROMPT:
raise NameError("Please ensure the anthropic package is loaded")
if not isinstance(messages[-1], AIMessage):
messages.append(AIMessage(content=""))
text = self._convert_messages_to_text(messages)
return (
text.rstrip()
) # trim off the trailing ' ' that might come from the "Assistant: "
def _generate(
self, messages: List[BaseMessage], stop: Optional[List[str]] = None
) -> ChatResult:
prompt = self._convert_messages_to_prompt(messages)
params: Dict[str, Any] = {"prompt": prompt, **self._default_params}
if stop:
params["stop_sequences"] = stop
if self.streaming:
completion = ""
stream_resp = self.client.completion_stream(**params)
for data in stream_resp:
delta = data["completion"][len(completion) :]
completion = data["completion"]
self.callback_manager.on_llm_new_token(
delta,
verbose=self.verbose,
)
else:
response = self.client.completion(**params)
completion = response["completion"]
message = AIMessage(content=completion)
return ChatResult(generations=[ChatGeneration(message=message)])
async def _agenerate(
self, messages: List[BaseMessage], stop: Optional[List[str]] = None
) -> ChatResult:
prompt = self._convert_messages_to_prompt(messages)
params: Dict[str, Any] = {"prompt": prompt, **self._default_params}
if stop:
params["stop_sequences"] = stop
if self.streaming:
completion = ""
stream_resp = await self.client.acompletion_stream(**params)
async for data in stream_resp:
delta = data["completion"][len(completion) :]
completion = data["completion"]
if self.callback_manager.is_async:
await self.callback_manager.on_llm_new_token(
delta,
verbose=self.verbose,
)
else:
self.callback_manager.on_llm_new_token(
delta,
verbose=self.verbose,
)
else:
response = await self.client.acompletion(**params)
completion = response["completion"]
message = AIMessage(content=completion)
return ChatResult(generations=[ChatGeneration(message=message)])
langchain/chat_models/openai.py
+4 -4
View File
@@ -32,8 +32,8 @@ logger = logging.getLogger(__file__)
def _create_retry_decorator(llm: ChatOpenAI) -> Callable[[Any], Any]:
import openai
min_seconds = 4
max_seconds = 10
min_seconds = 1
max_seconds = 60
# Wait 2^x * 1 second between each retry starting with
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
return retry(
@@ -199,8 +199,8 @@ class ChatOpenAI(BaseChatModel):
def _create_retry_decorator(self) -> Callable[[Any], Any]:
import openai
min_seconds = 4
max_seconds = 10
min_seconds = 1
max_seconds = 60
# Wait 2^x * 1 second between each retry starting with
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
return retry(
langchain/document_loaders/__init__.py
+6
View File
@@ -27,6 +27,7 @@ from langchain.document_loaders.evernote import EverNoteLoader
from langchain.document_loaders.facebook_chat import FacebookChatLoader
from langchain.document_loaders.gcs_directory import GCSDirectoryLoader
from langchain.document_loaders.gcs_file import GCSFileLoader
from langchain.document_loaders.git import GitLoader
from langchain.document_loaders.gitbook import GitbookLoader
from langchain.document_loaders.googledrive import GoogleDriveLoader
from langchain.document_loaders.gutenberg import GutenbergLoader
@@ -55,6 +56,7 @@ from langchain.document_loaders.roam import RoamLoader
from langchain.document_loaders.s3_directory import S3DirectoryLoader
from langchain.document_loaders.s3_file import S3FileLoader
from langchain.document_loaders.sitemap import SitemapLoader
from langchain.document_loaders.slack_directory import SlackDirectoryLoader
from langchain.document_loaders.srt import SRTLoader
from langchain.document_loaders.telegram import TelegramChatLoader
from langchain.document_loaders.text import TextLoader
@@ -63,6 +65,7 @@ from langchain.document_loaders.unstructured import (
UnstructuredFileLoader,
)
from langchain.document_loaders.url import UnstructuredURLLoader
from langchain.document_loaders.url_playwright import PlaywrightURLLoader
from langchain.document_loaders.url_selenium import SeleniumURLLoader
from langchain.document_loaders.web_base import WebBaseLoader
from langchain.document_loaders.whatsapp_chat import WhatsAppChatLoader
@@ -81,6 +84,7 @@ __all__ = [
"UnstructuredFileIOLoader",
"UnstructuredURLLoader",
"SeleniumURLLoader",
"PlaywrightURLLoader",
"DirectoryLoader",
"NotionDirectoryLoader",
"NotionDBLoader",
@@ -138,4 +142,6 @@ __all__ = [
"DuckDBLoader",
"BigQueryLoader",
"BiliBiliLoader",
"SlackDirectoryLoader",
"GitLoader",
]
langchain/document_loaders/git.py
+87
View File
@@ -0,0 +1,87 @@
import os
from typing import Callable, List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
class GitLoader(BaseLoader):
"""Loads files from a Git repository into a list of documents.
Repository can be local on disk available at `repo_path`,
or remote at `clone_url` that will be cloned to `repo_path`.
Currently supports only text files.
Each document represents one file in the repository. The `path` points to
the local Git repository, and the `branch` specifies the branch to load
files from. By default, it loads from the `main` branch.
"""
def __init__(
self,
repo_path: str,
clone_url: Optional[str] = None,
branch: Optional[str] = "main",
file_filter: Optional[Callable[[str], bool]] = None,
):
self.repo_path = repo_path
self.clone_url = clone_url
self.branch = branch
self.file_filter = file_filter
def load(self) -> List[Document]:
try:
from git import Blob, Repo # type: ignore
except ImportError as ex:
raise ImportError(
"Could not import git python package. "
"Please install it with `pip install GitPython`."
) from ex
if not os.path.exists(self.repo_path) and self.clone_url is None:
raise ValueError(f"Path {self.repo_path} does not exist")
elif self.clone_url:
repo = Repo.clone_from(self.clone_url, self.repo_path)
repo.git.checkout(self.branch)
else:
repo = Repo(self.repo_path)
repo.git.checkout(self.branch)
docs: List[Document] = []
for item in repo.tree().traverse():
if not isinstance(item, Blob):
continue
file_path = os.path.join(self.repo_path, item.path)
ignored_files = repo.ignored([file_path])
if len(ignored_files):
continue
# uses filter to skip files
if self.file_filter and not self.file_filter(file_path):
continue
rel_file_path = os.path.relpath(file_path, self.repo_path)
try:
with open(file_path, "rb") as f:
content = f.read()
file_type = os.path.splitext(item.name)[1]
# loads only text files
try:
text_content = content.decode("utf-8")
except UnicodeDecodeError:
continue
metadata = {
"file_path": rel_file_path,
"file_name": item.name,
"file_type": file_type,
}
doc = Document(page_content=text_content, metadata=metadata)
docs.append(doc)
except Exception as e:
print(f"Error reading file {file_path}: {e}")
return docs
langchain/document_loaders/slack_directory.py
+112
View File
@@ -0,0 +1,112 @@
"""Loader for documents from a Slack export."""
import json
import zipfile
from pathlib import Path
from typing import Dict, List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
class SlackDirectoryLoader(BaseLoader):
"""Loader for loading documents from a Slack directory dump."""
def __init__(self, zip_path: str, workspace_url: Optional[str] = None):
"""Initialize the SlackDirectoryLoader.
Args:
zip_path (str): The path to the Slack directory dump zip file.
workspace_url (Optional[str]): The Slack workspace URL.
Including the URL will turn
sources into links. Defaults to None.
"""
self.zip_path = Path(zip_path)
self.workspace_url = workspace_url
self.channel_id_map = self._get_channel_id_map(self.zip_path)
@staticmethod
def _get_channel_id_map(zip_path: Path) -> Dict[str, str]:
"""Get a dictionary mapping channel names to their respective IDs."""
with zipfile.ZipFile(zip_path, "r") as zip_file:
try:
with zip_file.open("channels.json", "r") as f:
channels = json.load(f)
return {channel["name"]: channel["id"] for channel in channels}
except KeyError:
return {}
def load(self) -> List[Document]:
"""Load and return documents from the Slack directory dump."""
docs = []
with zipfile.ZipFile(self.zip_path, "r") as zip_file:
for channel_path in zip_file.namelist():
channel_name = Path(channel_path).parent.name
if not channel_name:
continue
if channel_path.endswith(".json"):
messages = self._read_json(zip_file, channel_path)
for message in messages:
document = self._convert_message_to_document(
message, channel_name
)
docs.append(document)
return docs
def _read_json(self, zip_file: zipfile.ZipFile, file_path: str) -> List[dict]:
"""Read JSON data from a zip subfile."""
with zip_file.open(file_path, "r") as f:
data = json.load(f)
return data
def _convert_message_to_document(
self, message: dict, channel_name: str
) -> Document:
"""
Convert a message to a Document object.
Args:
message (dict): A message in the form of a dictionary.
channel_name (str): The name of the channel the message belongs to.
Returns:
Document: A Document object representing the message.
"""
text = message.get("text", "")
metadata = self._get_message_metadata(message, channel_name)
return Document(
page_content=text,
metadata=metadata,
)
def _get_message_metadata(self, message: dict, channel_name: str) -> dict:
"""Create and return metadata for a given message and channel."""
timestamp = message.get("ts", "")
user = message.get("user", "")
source = self._get_message_source(channel_name, user, timestamp)
return {
"source": source,
"channel": channel_name,
"timestamp": timestamp,
"user": user,
}
def _get_message_source(self, channel_name: str, user: str, timestamp: str) -> str:
"""
Get the message source as a string.
Args:
channel_name (str): The name of the channel the message belongs to.
user (str): The user ID who sent the message.
timestamp (str): The timestamp of the message.
Returns:
str: The message source.
"""
if self.workspace_url:
channel_id = self.channel_id_map.get(channel_name, "")
return (
f"{self.workspace_url}/archives/{channel_id}"
+ f"/p{timestamp.replace('.', '')}"
)
else:
return f"{channel_name} - {user} - {timestamp}"
langchain/document_loaders/url_playwright.py
+87
View File
@@ -0,0 +1,87 @@
"""Loader that uses Playwright to load a page, then uses unstructured to load the html.
"""
import logging
from typing import List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__file__)
class PlaywrightURLLoader(BaseLoader):
"""Loader that uses Playwright and to load a page and unstructured to load the html.
This is useful for loading pages that require javascript to render.
Attributes:
urls (List[str]): List of URLs to load.
continue_on_failure (bool): If True, continue loading other URLs on failure.
headless (bool): If True, the browser will run in headless mode.
"""
def __init__(
self,
urls: List[str],
continue_on_failure: bool = True,
headless: bool = True,
remove_selectors: Optional[List[str]] = None,
):
"""Load a list of URLs using Playwright and unstructured."""
try:
import playwright # noqa:F401
except ImportError:
raise ValueError(
"playwright package not found, please install it with "
"`pip install playwright`"
)
try:
import unstructured # noqa:F401
except ImportError:
raise ValueError(
"unstructured package not found, please install it with "
"`pip install unstructured`"
)
self.urls = urls
self.continue_on_failure = continue_on_failure
self.headless = headless
self.remove_selectors = remove_selectors
def load(self) -> List[Document]:
"""Load the specified URLs using Playwright and create Document instances.
Returns:
List[Document]: A list of Document instances with loaded content.
"""
from playwright.sync_api import sync_playwright
from unstructured.partition.html import partition_html
docs: List[Document] = list()
with sync_playwright() as p:
browser = p.chromium.launch(headless=self.headless)
for url in self.urls:
try:
page = browser.new_page()
page.goto(url)
for selector in self.remove_selectors or []:
element = page.locator(selector)
if element.is_visible():
element.evaluate("element => element.remove()")
page_source = page.content()
elements = partition_html(text=page_source)
text = "\n\n".join([str(el) for el in elements])
metadata = {"source": url}
docs.append(Document(page_content=text, metadata=metadata))
except Exception as e:
if self.continue_on_failure:
logger.error(
f"Error fetching or processing {url}, exception: {e}"
)
else:
raise e
browser.close()
return docs
langchain/llms/anthropic.py
+90 -82
View File
@@ -1,15 +1,100 @@
"""Wrapper around Anthropic APIs."""
import re
from typing import Any, Dict, Generator, List, Mapping, Optional
from typing import Any, Callable, Dict, Generator, List, Mapping, Optional
from pydantic import Extra, root_validator
from pydantic import BaseModel, Extra, root_validator
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
class Anthropic(LLM):
r"""Wrapper around Anthropic large language models.
class _AnthropicCommon(BaseModel):
client: Any = None #: :meta private:
model: str = "claude-v1"
"""Model name to use."""
max_tokens_to_sample: int = 256
"""Denotes the number of tokens to predict per generation."""
temperature: Optional[float] = None
"""A non-negative float that tunes the degree of randomness in generation."""
top_k: Optional[int] = None
"""Number of most likely tokens to consider at each step."""
top_p: Optional[float] = None
"""Total probability mass of tokens to consider at each step."""
streaming: bool = False
"""Whether to stream the results."""
anthropic_api_key: Optional[str] = None
HUMAN_PROMPT: Optional[str] = None
AI_PROMPT: Optional[str] = None
count_tokens: Optional[Callable[[str], int]] = None
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
anthropic_api_key = get_from_dict_or_env(
values, "anthropic_api_key", "ANTHROPIC_API_KEY"
)
try:
import anthropic
values["client"] = anthropic.Client(anthropic_api_key)
values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT
values["AI_PROMPT"] = anthropic.AI_PROMPT
values["count_tokens"] = anthropic.count_tokens
except ImportError:
raise ValueError(
"Could not import anthropic python package. "
"Please it install it with `pip install anthropic`."
)
return values
@property
def _default_params(self) -> Mapping[str, Any]:
"""Get the default parameters for calling Anthropic API."""
d = {
"max_tokens_to_sample": self.max_tokens_to_sample,
"model": self.model,
}
if self.temperature is not None:
d["temperature"] = self.temperature
if self.top_k is not None:
d["top_k"] = self.top_k
if self.top_p is not None:
d["top_p"] = self.top_p
return d
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{}, **self._default_params}
def _get_anthropic_stop(self, stop: Optional[List[str]] = None) -> List[str]:
if not self.HUMAN_PROMPT or not self.AI_PROMPT:
raise NameError("Please ensure the anthropic package is loaded")
if stop is None:
stop = []
# Never want model to invent new turns of Human / Assistant dialog.
stop.extend([self.HUMAN_PROMPT])
return stop
def get_num_tokens(self, text: str) -> int:
"""Calculate number of tokens."""
if not self.count_tokens:
raise NameError("Please ensure the anthropic package is loaded")
return self.count_tokens(text)
class Anthropic(LLM, _AnthropicCommon):
r"""Wrapper around Anthropic's large language models.
To use, you should have the ``anthropic`` python package installed, and the
environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass
@@ -32,73 +117,15 @@ class Anthropic(LLM):
response = model(prompt)
"""
client: Any #: :meta private:
model: str = "claude-v1"
"""Model name to use."""
max_tokens_to_sample: int = 256
"""Denotes the number of tokens to predict per generation."""
temperature: float = 1.0
"""A non-negative float that tunes the degree of randomness in generation."""
top_k: int = 0
"""Number of most likely tokens to consider at each step."""
top_p: float = 1
"""Total probability mass of tokens to consider at each step."""
streaming: bool = False
"""Whether to stream the results."""
anthropic_api_key: Optional[str] = None
HUMAN_PROMPT: Optional[str] = None
AI_PROMPT: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
anthropic_api_key = get_from_dict_or_env(
values, "anthropic_api_key", "ANTHROPIC_API_KEY"
)
try:
import anthropic
values["client"] = anthropic.Client(anthropic_api_key)
values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT
values["AI_PROMPT"] = anthropic.AI_PROMPT
except ImportError:
raise ValueError(
"Could not import anthropic python package. "
"Please install it with `pip install anthropic`."
)
return values
@property
def _default_params(self) -> Mapping[str, Any]:
"""Get the default parameters for calling Anthropic API."""
return {
"max_tokens_to_sample": self.max_tokens_to_sample,
"temperature": self.temperature,
"top_k": self.top_k,
"top_p": self.top_p,
}
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{"model": self.model}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "anthropic"
return "anthropic-llm"
def _wrap_prompt(self, prompt: str) -> str:
if not self.HUMAN_PROMPT or not self.AI_PROMPT:
@@ -115,18 +142,6 @@ class Anthropic(LLM):
# As a last resort, wrap the prompt ourselves to emulate instruct-style.
return f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT} Sure, here you go:\n"
def _get_anthropic_stop(self, stop: Optional[List[str]] = None) -> List[str]:
if not self.HUMAN_PROMPT or not self.AI_PROMPT:
raise NameError("Please ensure the anthropic package is loaded")
if stop is None:
stop = []
# Never want model to invent new turns of Human / Assistant dialog.
stop.extend([self.HUMAN_PROMPT, self.AI_PROMPT])
return stop
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
r"""Call out to Anthropic's completion endpoint.
@@ -148,10 +163,8 @@ class Anthropic(LLM):
stop = self._get_anthropic_stop(stop)
if self.streaming:
stream_resp = self.client.completion_stream(
model=self.model,
prompt=self._wrap_prompt(prompt),
stop_sequences=stop,
stream=True,
**self._default_params,
)
current_completion = ""
@@ -163,7 +176,6 @@ class Anthropic(LLM):
)
return current_completion
response = self.client.completion(
model=self.model,
prompt=self._wrap_prompt(prompt),
stop_sequences=stop,
**self._default_params,
@@ -175,10 +187,8 @@ class Anthropic(LLM):
stop = self._get_anthropic_stop(stop)
if self.streaming:
stream_resp = await self.client.acompletion_stream(
model=self.model,
prompt=self._wrap_prompt(prompt),
stop_sequences=stop,
stream=True,
**self._default_params,
)
current_completion = ""
@@ -195,7 +205,6 @@ class Anthropic(LLM):
)
return current_completion
response = await self.client.acompletion(
model=self.model,
prompt=self._wrap_prompt(prompt),
stop_sequences=stop,
**self._default_params,
@@ -227,7 +236,6 @@ class Anthropic(LLM):
"""
stop = self._get_anthropic_stop(stop)
return self.client.completion_stream(
model=self.model,
prompt=self._wrap_prompt(prompt),
stop_sequences=stop,
**self._default_params,
langchain/llms/openai.py
+60 -37
View File
@@ -114,20 +114,7 @@ async def acompletion_with_retry(
class BaseOpenAI(BaseLLM):
"""Wrapper around OpenAI large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.llms import OpenAI
openai = OpenAI(model_name="text-davinci-003")
"""
"""Wrapper around OpenAI large language models."""
client: Any #: :meta private:
model_name: str = "text-davinci-003"
@@ -476,13 +463,6 @@ class BaseOpenAI(BaseLLM):
def modelname_to_contextsize(self, modelname: str) -> int:
"""Calculate the maximum number of tokens possible to generate for a model.
text-davinci-003: 4,097 tokens
text-curie-001: 2,048 tokens
text-babbage-001: 2,048 tokens
text-ada-001: 2,048 tokens
code-davinci-002: 8,000 tokens
code-cushman-001: 2,048 tokens
Args:
modelname: The modelname we want to know the context size for.
@@ -494,20 +474,37 @@ class BaseOpenAI(BaseLLM):
max_tokens = openai.modelname_to_contextsize("text-davinci-003")
"""
if modelname == "text-davinci-003":
return 4097
elif modelname == "text-curie-001":
return 2048
elif modelname == "text-babbage-001":
return 2048
elif modelname == "text-ada-001":
return 2048
elif modelname == "code-davinci-002":
return 8000
elif modelname == "code-cushman-001":
return 2048
else:
return 4097
model_token_mapping = {
"gpt-4": 8192,
"gpt-4-0314": 8192,
"gpt-4-32k": 32768,
"gpt-4-32k-0314": 32768,
"gpt-3.5-turbo": 4096,
"gpt-3.5-turbo-0301": 4096,
"text-ada-001": 2049,
"ada": 2049,
"text-babbage-001": 2040,
"babbage": 2049,
"text-curie-001": 2049,
"curie": 2049,
"davinci": 2049,
"text-davinci-003": 4097,
"text-davinci-002": 4097,
"code-davinci-002": 8001,
"code-davinci-001": 8001,
"code-cushman-002": 2048,
"code-cushman-001": 2048,
}
context_size = model_token_mapping.get(modelname, None)
if context_size is None:
raise ValueError(
f"Unknown model: {modelname}. Please provide a valid OpenAI model name."
"Known models are: " + ", ".join(model_token_mapping.keys())
)
return context_size
def max_tokens_for_prompt(self, prompt: str) -> int:
"""Calculate the maximum number of tokens possible to generate for a prompt.
@@ -531,7 +528,20 @@ class BaseOpenAI(BaseLLM):
class OpenAI(BaseOpenAI):
"""Generic OpenAI class that uses model name."""
"""Wrapper around OpenAI large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.llms import OpenAI
openai = OpenAI(model_name="text-davinci-003")
"""
@property
def _invocation_params(self) -> Dict[str, Any]:
@@ -539,7 +549,20 @@ class OpenAI(BaseOpenAI):
class AzureOpenAI(BaseOpenAI):
"""Azure specific OpenAI class that uses deployment name."""
"""Wrapper around Azure-specific OpenAI large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.llms import AzureOpenAI
openai = AzureOpenAI(model_name="text-davinci-003")
"""
deployment_name: str = ""
"""Deployment name to use."""
langchain/llms/rwkv.py
+51 -25
View File
@@ -3,7 +3,7 @@
Based on https://github.com/saharNooby/rwkv.cpp/blob/master/rwkv/chat_with_bot.py
https://github.com/BlinkDL/ChatRWKV/blob/main/v2/chat.py
"""
from typing import Any, Dict, List, Mapping, Optional, Set, SupportsIndex
from typing import Any, Dict, List, Mapping, Optional, Set
from pydantic import BaseModel, Extra, root_validator
@@ -58,7 +58,7 @@ class RWKV(LLM, BaseModel):
CHUNK_LEN: int = 256
"""Batch size for prompt processing."""
max_tokens_per_generation: SupportsIndex = 256
max_tokens_per_generation: int = 256
"""Maximum number of tokens to generate."""
client: Any = None #: :meta private:
@@ -69,6 +69,8 @@ class RWKV(LLM, BaseModel):
model_tokens: Any = None #: :meta private:
model_state: Any = None #: :meta private:
class Config:
"""Configuration for this pydantic object."""
@@ -139,42 +141,66 @@ class RWKV(LLM, BaseModel):
"""Return the type of llm."""
return "rwkv-4"
def rwkv_generate(self, prompt: str) -> str:
tokens = self.tokenizer.encode(prompt).ids
def run_rnn(self, _tokens: List[str], newline_adj: int = 0) -> Any:
AVOID_REPEAT_TOKENS = []
AVOID_REPEAT = "，：？！"
for i in AVOID_REPEAT:
dd = self.pipeline.encode(i)
assert len(dd) == 1
AVOID_REPEAT_TOKENS += dd
logits = None
state = None
tokens = [int(x) for x in _tokens]
self.model_tokens += tokens
occurrence = {}
out: Any = None
# Feed in the input string
while len(tokens) > 0:
logits, state = self.client.forward(tokens[: self.CHUNK_LEN], state)
out, self.model_state = self.client.forward(
tokens[: self.CHUNK_LEN], self.model_state
)
tokens = tokens[self.CHUNK_LEN :]
END_OF_LINE = 187
out[END_OF_LINE] += newline_adj # adjust \n probability
if self.model_tokens[-1] in AVOID_REPEAT_TOKENS:
out[self.model_tokens[-1]] = -999999999
return out
def rwkv_generate(self, prompt: str) -> str:
self.model_state = None
self.model_tokens = []
logits = self.run_rnn(self.tokenizer.encode(prompt).ids)
begin = len(self.model_tokens)
out_last = begin
occurrence: Dict = {}
decoded = ""
for i in range(self.max_tokens_per_generation):
token = self.pipeline.sample_logits(
logits, temperature=self.temperature, top_p=self.top_p
)
if token not in occurrence:
occurrence[token] = 1
else:
occurrence[token] += 1
decoded += self.tokenizer.decode([token])
if "\n" in decoded:
break
# feed back in
logits, state = self.client.forward([token], state)
for n in occurrence:
logits[n] -= (
self.penalty_alpha_presence
+ occurrence[n] * self.penalty_alpha_frequency
)
token = self.pipeline.sample_logits(
logits, temperature=self.temperature, top_p=self.top_p
)
END_OF_TEXT = 0
if token == END_OF_TEXT:
break
if token not in occurrence:
occurrence[token] = 1
else:
occurrence[token] += 1
logits = self.run_rnn([token])
xxx = self.tokenizer.decode(self.model_tokens[out_last:])
if "\ufffd" not in xxx: # avoid utf-8 display issues
decoded += xxx
out_last = begin + i + 1
if i >= self.max_tokens_per_generation - 100:
break
return decoded
langchain/python.py
+3 -25
View File
@@ -1,26 +1,4 @@
"""Mock Python REPL."""
import sys
from io import StringIO
from typing import Dict, Optional
"""For backwards compatibility."""
from langchain.utilities.python import PythonREPL
from pydantic import BaseModel, Field
class PythonREPL(BaseModel):
"""Simulates a standalone Python REPL."""
globals: Optional[Dict] = Field(default_factory=dict, alias="_globals")
locals: Optional[Dict] = Field(default_factory=dict, alias="_locals")
def run(self, command: str) -> str:
"""Run command with own globals/locals and returns anything printed."""
old_stdout = sys.stdout
sys.stdout = mystdout = StringIO()
try:
exec(command, self.globals, self.locals)
sys.stdout = old_stdout
output = mystdout.getvalue()
except Exception as e:
sys.stdout = old_stdout
output = str(e)
return output
__all__ = ["PythonREPL"]
langchain/retrievers/__init__.py
+2
View File
@@ -4,6 +4,7 @@ from langchain.retrievers.elastic_search_bm25 import ElasticSearchBM25Retriever
from langchain.retrievers.metal import MetalRetriever
from langchain.retrievers.pinecone_hybrid_search import PineconeHybridSearchRetriever
from langchain.retrievers.remote_retriever import RemoteLangChainRetriever
from langchain.retrievers.svm import SVMRetriever
from langchain.retrievers.tfidf import TFIDFRetriever
from langchain.retrievers.time_weighted_retriever import (
TimeWeightedVectorStoreRetriever,
@@ -20,4 +21,5 @@ __all__ = [
"WeaviateHybridSearchRetriever",
"DataberryRetriever",
"TimeWeightedVectorStoreRetriever",
"SVMRetriever",
]
langchain/retrievers/svm.py
+61
View File
@@ -0,0 +1,61 @@
"""SMV Retriever.
Largely based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""
from __future__ import annotations
from typing import Any, List
import numpy as np
from pydantic import BaseModel
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever, Document
def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
return np.array([embeddings.embed_query(split) for split in contexts])
class SVMRetriever(BaseRetriever, BaseModel):
embeddings: Embeddings
index: Any
texts: List[str]
k: int = 4
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@classmethod
def from_texts(
cls, texts: List[str], embeddings: Embeddings, **kwargs: Any
) -> SVMRetriever:
index = create_index(texts, embeddings)
return cls(embeddings=embeddings, index=index, texts=texts, **kwargs)
def get_relevant_documents(self, query: str) -> List[Document]:
from sklearn import svm
query_embeds = np.array(self.embeddings.embed_query(query))
x = np.concatenate([query_embeds[None, ...], self.index])
y = np.zeros(x.shape[0])
y[0] = 1
clf = svm.LinearSVC(
class_weight="balanced", verbose=False, max_iter=10000, tol=1e-6, C=0.1
)
clf.fit(x, y)
similarities = clf.decision_function(x)
sorted_ix = np.argsort(-similarities)
top_k_results = []
for row in sorted_ix[1 : self.k + 1]:
top_k_results.append(Document(page_content=self.texts[row - 1]))
return top_k_results
async def aget_relevant_documents(self, query: str) -> List[Document]:
raise NotImplementedError
langchain/retrievers/time_weighted_retriever.py
+7 -7
View File
@@ -50,7 +50,7 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
def _get_combined_score(
self,
document: Document,
vector_salience: Optional[float],
vector_relevance: Optional[float],
current_time: datetime,
) -> float:
"""Return the combined score for a document."""
@@ -63,8 +63,8 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
for key in self.other_score_keys:
if key in document.metadata:
score += document.metadata[key]
if vector_salience is not None:
score += vector_salience
if vector_relevance is not None:
score += vector_relevance
print(score)
return score
@@ -75,10 +75,10 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
query, **self.search_kwargs
)
results = {}
for fetched_doc, cosine_distance in docs_and_scores:
for fetched_doc, relevance in docs_and_scores:
buffer_idx = fetched_doc.metadata["buffer_idx"]
doc = self.memory_stream[buffer_idx]
results[buffer_idx] = (doc, (1 - cosine_distance))
results[buffer_idx] = (doc, relevance)
return results
def get_relevant_documents(self, query: str) -> List[Document]:
@@ -91,8 +91,8 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
# If a doc is considered salient, update the salience score
docs_and_scores.update(self.get_salient_docs(query))
rescored_docs = [
(doc, self._get_combined_score(doc, salience, current_time))
for doc, salience in docs_and_scores.values()
(doc, self._get_combined_score(doc, relevance, current_time))
for doc, relevance in docs_and_scores.values()
]
rescored_docs.sort(key=lambda x: x[1], reverse=True)
result = []
langchain/tools/python/tool.py
+1 -1
View File
@@ -7,8 +7,8 @@ from typing import Dict, Optional
from pydantic import Field, root_validator
from langchain.python import PythonREPL
from langchain.tools.base import BaseTool
from langchain.utilities import PythonREPL
def _get_default_python_repl() -> PythonREPL:
langchain/tools/sql_database/tool.py
+16 -14
View File
@@ -1,6 +1,7 @@
# flake8: noqa
"""Tools for interacting with a SQL database."""
from pydantic import BaseModel, Extra, Field, validator
from pydantic import BaseModel, Extra, Field, validator, root_validator
from typing import Any, Dict
from langchain.chains.llm import LLMChain
from langchain.prompts import PromptTemplate
@@ -81,28 +82,29 @@ class QueryCheckerTool(BaseSQLDatabaseTool, BaseTool):
template: str = QUERY_CHECKER
llm: BaseLLM
llm_chain: LLMChain = Field(
default_factory=lambda: LLMChain(
llm=QueryCheckerTool.llm,
prompt=PromptTemplate(
template=QueryCheckerTool.template, input_variables=["query", "dialect"]
),
)
)
llm_chain: LLMChain = Field(init=False)
name = "query_checker_sql_db"
description = """
Use this tool to double check if your query is correct before executing it.
Always use this tool before executing a query with query_sql_db!
"""
@validator("llm_chain")
def validate_llm_chain_input_variables(cls, llm_chain: LLMChain) -> LLMChain:
"""Make sure the LLM chain has the correct input variables."""
if llm_chain.prompt.input_variables != ["query", "dialect"]:
@root_validator(pre=True)
def initialize_llm_chain(cls, values: Dict[str, Any]) -> Dict[str, Any]:
if "llm_chain" not in values:
values["llm_chain"] = LLMChain(
llm=values.get("llm"),
prompt=PromptTemplate(
template=QUERY_CHECKER, input_variables=["query", "dialect"]
),
)
if values["llm_chain"].prompt.input_variables != ["query", "dialect"]:
raise ValueError(
"LLM chain for QueryCheckerTool must have input variables ['query', 'dialect']"
)
return llm_chain
return values
def _run(self, query: str) -> str:
"""Use the LLM to check the query."""
langchain/utilities/__init__.py
+2 -2
View File
@@ -1,5 +1,4 @@
"""General utilities."""
from langchain.python import PythonREPL
from langchain.requests import TextRequestsWrapper
from langchain.utilities.apify import ApifyWrapper
from langchain.utilities.bash import BashProcess
@@ -7,6 +6,7 @@ from langchain.utilities.bing_search import BingSearchAPIWrapper
from langchain.utilities.google_search import GoogleSearchAPIWrapper
from langchain.utilities.google_serper import GoogleSerperAPIWrapper
from langchain.utilities.openweathermap import OpenWeatherMapAPIWrapper
from langchain.utilities.python import PythonREPL
from langchain.utilities.searx_search import SearxSearchWrapper
from langchain.utilities.serpapi import SerpAPIWrapper
from langchain.utilities.wikipedia import WikipediaAPIWrapper
@@ -16,7 +16,6 @@ __all__ = [
"ApifyWrapper",
"BashProcess",
"TextRequestsWrapper",
"PythonREPL",
"GoogleSearchAPIWrapper",
"GoogleSerperAPIWrapper",
"WolframAlphaAPIWrapper",
@@ -25,4 +24,5 @@ __all__ = [
"BingSearchAPIWrapper",
"WikipediaAPIWrapper",
"OpenWeatherMapAPIWrapper",
"PythonREPL",
]
langchain/utilities/openweathermap.py
+2 -2
View File
@@ -13,8 +13,8 @@ class OpenWeatherMapAPIWrapper(BaseModel):
Docs for using:
1. Go to OpenWeatherMap and sign up for an API key
3. Save your API KEY into OPENWEATHERMAP_API_KEY env variable
4. pip install wolframalpha
2. Save your API KEY into OPENWEATHERMAP_API_KEY env variable
3. pip install pyowm
"""
owm: Any
langchain/utilities/python.py
+25
View File
@@ -0,0 +1,25 @@
import sys
from io import StringIO
from typing import Dict, Optional
from pydantic import BaseModel, Field
class PythonREPL(BaseModel):
"""Simulates a standalone Python REPL."""
globals: Optional[Dict] = Field(default_factory=dict, alias="_globals")
locals: Optional[Dict] = Field(default_factory=dict, alias="_locals")
def run(self, command: str) -> str:
"""Run command with own globals/locals and returns anything printed."""
old_stdout = sys.stdout
sys.stdout = mystdout = StringIO()
try:
exec(command, self.globals, self.locals)
sys.stdout = old_stdout
output = mystdout.getvalue()
except Exception as e:
sys.stdout = old_stdout
output = str(e)
return output
langchain/vectorstores/base.py
+5 -5
View File
@@ -153,7 +153,7 @@ class VectorStore(ABC):
return await asyncio.get_event_loop().run_in_executor(None, func)
def max_marginal_relevance_search(
self, query: str, k: int = 4, fetch_k: int = 20
self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
@@ -171,18 +171,18 @@ class VectorStore(ABC):
raise NotImplementedError
async def amax_marginal_relevance_search(
self, query: str, k: int = 4, fetch_k: int = 20
self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance."""
# This is a temporary workaround to make the similarity search
# asynchronous. The proper solution is to make the similarity search
# asynchronous in the vector store implementations.
func = partial(self.max_marginal_relevance_search, query, k, fetch_k)
func = partial(self.max_marginal_relevance_search, query, k, fetch_k, **kwargs)
return await asyncio.get_event_loop().run_in_executor(None, func)
def max_marginal_relevance_search_by_vector(
self, embedding: List[float], k: int = 4, fetch_k: int = 20
self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
@@ -200,7 +200,7 @@ class VectorStore(ABC):
raise NotImplementedError
async def amax_marginal_relevance_search_by_vector(
self, embedding: List[float], k: int = 4, fetch_k: int = 20
self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance."""
raise NotImplementedError
langchain/vectorstores/chroma.py
+2
View File
@@ -193,6 +193,7 @@ class Chroma(VectorStore):
k: int = 4,
fetch_k: int = 20,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
@@ -227,6 +228,7 @@ class Chroma(VectorStore):
k: int = 4,
fetch_k: int = 20,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
langchain/vectorstores/deeplake.py
+10 -6
View File
@@ -97,6 +97,7 @@ class DeepLake(VectorStore):
read_only: Optional[bool] = False,
ingestion_batch_size: int = 1024,
num_workers: int = 4,
**kwargs: Any,
) -> None:
"""Initialize with Deep Lake client."""
self.ingestion_batch_size = ingestion_batch_size
@@ -113,14 +114,18 @@ class DeepLake(VectorStore):
self._deeplake = deeplake
if deeplake.exists(dataset_path, token=token):
self.ds = deeplake.load(dataset_path, token=token, read_only=read_only)
self.ds = deeplake.load(
dataset_path, token=token, read_only=read_only, **kwargs
)
logger.warning(
f"Deep Lake Dataset in {dataset_path} already exists, "
f"loading from the storage"
)
self.ds.summary()
else:
self.ds = deeplake.empty(dataset_path, token=token, overwrite=True)
self.ds = deeplake.empty(
dataset_path, token=token, overwrite=True, **kwargs
)
with self.ds:
self.ds.create_tensor(
@@ -386,7 +391,7 @@ class DeepLake(VectorStore):
)
def max_marginal_relevance_search_by_vector(
self, embedding: List[float], k: int = 4, fetch_k: int = 20
self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
@@ -406,7 +411,7 @@ class DeepLake(VectorStore):
)
def max_marginal_relevance_search(
self, query: str, k: int = 4, fetch_k: int = 20
self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
@@ -466,8 +471,7 @@ class DeepLake(VectorStore):
DeepLake: Deep Lake dataset.
"""
deeplake_dataset = cls(
dataset_path=dataset_path,
embedding_function=embedding,
dataset_path=dataset_path, embedding_function=embedding, **kwargs
)
deeplake_dataset.add_texts(texts=texts, metadatas=metadatas, ids=ids)
return deeplake_dataset
langchain/vectorstores/elastic_vector_search.py
+2 -2
View File
@@ -200,8 +200,8 @@ class ElasticVectorSearch(VectorStore, ABC):
"""
embedding = self.embedding.embed_query(query)
script_query = _default_script_query(embedding)
response = self.client.search(index=self.index_name, query=script_query)
hits = [hit["_source"] for hit in response["hits"]["hits"][:k]]
response = self.client.search(index=self.index_name, query=script_query, size=k)
hits = [hit["_source"] for hit in response["hits"]["hits"]]
documents = [
Document(page_content=hit["text"], metadata=hit["metadata"]) for hit in hits
]
langchain/vectorstores/faiss.py
+20 -8
View File
@@ -227,7 +227,7 @@ class FAISS(VectorStore):
return [doc for doc, _ in docs_and_scores]
def max_marginal_relevance_search_by_vector(
self, embedding: List[float], k: int = 4, fetch_k: int = 20
self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
@@ -262,7 +262,11 @@ class FAISS(VectorStore):
return docs
def max_marginal_relevance_search(
self, query: str, k: int = 4, fetch_k: int = 20
self,
query: str,
k: int = 4,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
@@ -404,40 +408,48 @@ class FAISS(VectorStore):
**kwargs,
)
def save_local(self, folder_path: str) -> None:
def save_local(self, folder_path: str, index_name: str = "index") -> None:
"""Save FAISS index, docstore, and index_to_docstore_id to disk.
Args:
folder_path: folder path to save index, docstore,
and index_to_docstore_id to.
index_name: for saving with a specific index file name
"""
path = Path(folder_path)
path.mkdir(exist_ok=True, parents=True)
# save index separately since it is not picklable
faiss = dependable_faiss_import()
faiss.write_index(self.index, str(path / "index.faiss"))
faiss.write_index(
self.index, str(path / "{index_name}.faiss".format(index_name=index_name))
)
# save docstore and index_to_docstore_id
with open(path / "index.pkl", "wb") as f:
with open(path / "{index_name}.pkl".format(index_name=index_name), "wb") as f:
pickle.dump((self.docstore, self.index_to_docstore_id), f)
@classmethod
def load_local(cls, folder_path: str, embeddings: Embeddings) -> FAISS:
def load_local(
cls, folder_path: str, embeddings: Embeddings, index_name: str = "index"
) -> FAISS:
"""Load FAISS index, docstore, and index_to_docstore_id to disk.
Args:
folder_path: folder path to load index, docstore,
and index_to_docstore_id from.
embeddings: Embeddings to use when generating queries
index_name: for saving with a specific index file name
"""
path = Path(folder_path)
# load index separately since it is not picklable
faiss = dependable_faiss_import()
index = faiss.read_index(str(path / "index.faiss"))
index = faiss.read_index(
str(path / "{index_name}.faiss".format(index_name=index_name))
)
# load docstore and index_to_docstore_id
with open(path / "index.pkl", "rb") as f:
with open(path / "{index_name}.pkl".format(index_name=index_name), "rb") as f:
docstore, index_to_docstore_id = pickle.load(f)
return cls(embeddings.embed_query, index, docstore, index_to_docstore_id)
langchain/vectorstores/pinecone.py
+1 -1
View File
@@ -218,7 +218,7 @@ class Pinecone(VectorStore):
else:
raise ValueError(
f"Index '{index_name}' not found in your Pinecone project. "
"Did you mean one of the following indexes: {', '.join(indexes)}"
f"Did you mean one of the following indexes: {', '.join(indexes)}"
)
for i in range(0, len(texts), batch_size):
langchain/vectorstores/qdrant.py
+7 -3
View File
@@ -81,7 +81,7 @@ class Qdrant(VectorStore):
ids = [uuid.uuid4().hex for _ in texts]
self.client.upsert(
collection_name=self.collection_name,
points=rest.Batch(
points=rest.Batch.construct(
ids=ids,
vectors=[self.embedding_function(text) for text in texts],
payloads=self._build_payloads(
@@ -147,7 +147,11 @@ class Qdrant(VectorStore):
]
def max_marginal_relevance_search(
self, query: str, k: int = 4, fetch_k: int = 20
self,
query: str,
k: int = 4,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
@@ -314,7 +318,7 @@ class Qdrant(VectorStore):
client.upsert(
collection_name=collection_name,
points=rest.Batch(
points=rest.Batch.construct(
ids=[uuid.uuid4().hex for _ in texts],
vectors=embeddings,
payloads=cls._build_payloads(
langchain/vectorstores/redis.py
+20 -13
View File
@@ -70,6 +70,9 @@ class Redis(VectorStore):
redis_url: str,
index_name: str,
embedding_function: Callable,
content_key: str = "content",
metadata_key: str = "metadata",
vector_key: str = "content_vector",
**kwargs: Any,
):
"""Initialize with necessary components."""
@@ -92,6 +95,9 @@ class Redis(VectorStore):
raise ValueError(f"Redis failed to connect: {e}")
self.client = redis_client
self.content_key = content_key
self.metadata_key = metadata_key
self.vector_key = vector_key
def add_texts(
self,
@@ -112,11 +118,11 @@ class Redis(VectorStore):
pipeline.hset(
key,
mapping={
"content": text,
"content_vector": np.array(
self.content_key: text,
self.vector_key: np.array(
self.embedding_function(text), dtype=np.float32
).tobytes(),
"metadata": json.dumps(metadata),
self.metadata_key: json.dumps(metadata),
},
)
ids.append(key)
@@ -191,8 +197,8 @@ class Redis(VectorStore):
embedding = self.embedding_function(query)
# Prepare the Query
return_fields = ["metadata", "content", "vector_score"]
vector_field = "content_vector"
return_fields = [self.metadata_key, self.content_key, "vector_score"]
vector_field = self.vector_key
hybrid_fields = "*"
base_query = (
f"{hybrid_fields}=>[KNN {k} @{vector_field} $vector AS vector_score]"
@@ -232,6 +238,9 @@ class Redis(VectorStore):
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
index_name: Optional[str] = None,
content_key: str = "content",
metadata_key: str = "metadata",
vector_key: str = "content_vector",
**kwargs: Any,
) -> Redis:
"""Construct RediSearch wrapper from raw documents.
@@ -287,10 +296,10 @@ class Redis(VectorStore):
"COSINE" # distance metric for the vectors (ex. COSINE, IP, L2)
)
schema = (
TextField(name="content"),
TextField(name="metadata"),
TextField(name=content_key),
TextField(name=metadata_key),
VectorField(
"content_vector",
vector_key,
"FLAT",
{
"TYPE": "FLOAT32",
@@ -313,11 +322,9 @@ class Redis(VectorStore):
pipeline.hset(
key,
mapping={
"content": text,
"content_vector": np.array(
embeddings[i], dtype=np.float32
).tobytes(),
"metadata": json.dumps(metadata),
content_key: text,
vector_key: np.array(embeddings[i], dtype=np.float32).tobytes(),
metadata_key: json.dumps(metadata),
},
)
pipeline.execute()
langchain/vectorstores/weaviate.py
+96 -4
View File
@@ -6,9 +6,22 @@ from uuid import uuid4
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore
def _default_schema(index_name: str) -> Dict:
return {
"class": index_name,
"properties": [
{
"name": "text",
"dataType": ["text"],
}
],
}
class Weaviate(VectorStore):
"""Wrapper around Weaviate vector database.
@@ -70,14 +83,24 @@ class Weaviate(VectorStore):
data_properties[key] = metadatas[i][key]
_id = get_valid_uuid(uuid4())
batch.add_data_object(data_properties, self._index_name, _id)
batch.add_data_object(
data_object=data_properties, class_name=self._index_name, uuid=_id
)
ids.append(_id)
return ids
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Look up similar documents in weaviate."""
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
content: Dict[str, Any] = {"concepts": [query]}
if kwargs.get("search_distance"):
content["certainty"] = kwargs.get("search_distance")
@@ -114,5 +137,74 @@ class Weaviate(VectorStore):
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> Weaviate:
"""Not implemented for Weaviate yet."""
raise NotImplementedError("weaviate does not currently support `from_texts`.")
"""Construct Weaviate wrapper from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new index for the embeddings in the Weaviate instance.
3. Adds the documents to the newly created Weaviate index.
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores.weaviate import Weaviate
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
weaviate = Weaviate.from_texts(
texts,
embeddings,
weaviate_url="http://localhost:8080"
)
"""
weaviate_url = get_from_dict_or_env(kwargs, "weaviate_url", "WEAVIATE_URL")
try:
from weaviate import Client
from weaviate.util import get_valid_uuid
except ImportError:
raise ValueError(
"Could not import weaviate python package. "
"Please install it with `pip instal weaviate-client`"
)
client = Client(weaviate_url)
index_name = kwargs.get("index_name", f"LangChain_{uuid4().hex}")
embeddings = embedding.embed_documents(texts) if embedding else None
text_key = "text"
schema = _default_schema(index_name)
attributes = list(metadatas[0].keys()) if metadatas else None
# check whether the index already exists
if not client.schema.contains(schema):
client.schema.create_class(schema)
with client.batch as batch:
for i, text in enumerate(texts):
data_properties = {
text_key: text,
}
if metadatas is not None:
for key in metadatas[i].keys():
data_properties[key] = metadatas[i][key]
_id = get_valid_uuid(uuid4())
# if an embedding strategy is not provided, we let
# weaviate create the embedding. Note that this will only
# work if weaviate has been installed with a vectorizer module
# like text2vec-contextionary for example
params = {
"uuid": _id,
"data_object": data_properties,
"class_name": index_name,
}
if embeddings is not None:
params["vector"] = (embeddings[i],)
batch.add_data_object(**params)
batch.flush()
return cls(client, index_name, text_key, attributes)
poetry.lock
Generated
+143 -119
View File
@@ -499,7 +499,7 @@ name = "authlib"
version = "1.2.0"
description = "The ultimate Python library in building OAuth and OpenID Connect servers and clients."
category = "main"
optional = true
optional = false
python-versions = "*"
files = [
{file = "Authlib-1.2.0-py2.py3-none-any.whl", hash = "sha256:4ddf4fd6cfa75c9a460b361d4bd9dac71ffda0be879dbe4292a02e92349ad55a"},
@@ -743,7 +743,7 @@ name = "cachetools"
version = "5.3.0"
description = "Extensible memoizing collections and decorators"
category = "main"
optional = true
optional = false
python-versions = "~=3.7"
files = [
{file = "cachetools-5.3.0-py3-none-any.whl", hash = "sha256:429e1a1e845c008ea6c85aa35d4b98b65d6a9763eeef3e37e92728a12d1de9d4"},
@@ -1338,20 +1338,20 @@ files = [
[[package]]
name = "deeplake"
version = "3.2.21"
version = "3.2.22"
description = "Activeloop Deep Lake"
category = "main"
optional = false
python-versions = "*"
files = [
{file = "deeplake-3.2.21.tar.gz", hash = "sha256:3ad6f81f666ba9e2951aff697babb845249b54dfdd72f22e3c7ef5838ae2e9d7"},
{file = "deeplake-3.2.22.tar.gz", hash = "sha256:068280561366dd1bd891d3ffda8638ec59860a23b9426815a484b0591ab467a6"},
]
[package.dependencies]
aioboto3 = {version = "10.4.0", markers = "python_version >= \"3.7\" and sys_platform != \"win32\""}
boto3 = "*"
click = "*"
humbug = ">=0.2.6"
humbug = ">=0.3.1"
nest_asyncio = {version = "*", markers = "python_version >= \"3.7\" and sys_platform != \"win32\""}
numcodecs = "*"
numpy = "*"
@@ -1955,14 +1955,14 @@ uritemplate = ">=3.0.1,<5"
[[package]]
name = "google-auth"
version = "2.17.2"
version = "2.17.3"
description = "Google Authentication Library"
category = "main"
optional = true
python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*"
files = [
{file = "google-auth-2.17.2.tar.gz", hash = "sha256:295c80ebb95eac74003c07a696cf3ef6b414e9230ae8894f3843f8215fd2aa56"},
{file = "google_auth-2.17.2-py2.py3-none-any.whl", hash = "sha256:544536a43d44dff0f64222e4d027d124989fcb9c10979687e589e1694fba9c94"},
{file = "google-auth-2.17.3.tar.gz", hash = "sha256:ce311e2bc58b130fddf316df57c9b3943c2a7b4f6ec31de9663a9333e4064efc"},
{file = "google_auth-2.17.3-py2.py3-none-any.whl", hash = "sha256:f586b274d3eb7bd932ea424b1c702a30e0393a2e2bc4ca3eae8263ffd8be229f"},
]
[package.dependencies]
@@ -2064,14 +2064,14 @@ grpc = ["grpcio (>=1.44.0,<2.0.0dev)"]
[[package]]
name = "gptcache"
version = "0.1.8"
description = "GPT Cache, a powerful caching library that can be used to speed up and lower the cost of chat applications that rely on the LLM service. GPT Cache works as a memcache for AIGC applications, similar to how Redis works for traditional applications."
version = "0.1.9"
description = "GPTCache, a powerful caching library that can be used to speed up and lower the cost of chat applications that rely on the LLM service. GPTCache works as a memcache for AIGC applications, similar to how Redis works for traditional applications."
category = "main"
optional = true
optional = false
python-versions = ">=3.8.1"
files = [
{file = "gptcache-0.1.8-py3-none-any.whl", hash = "sha256:953662291819471e5461920c89367084f905237a8506f1a1605729f3e633f147"},
{file = "gptcache-0.1.8.tar.gz", hash = "sha256:23200cc0783776210cce85a588ae68222d522ce9456f74b7836945ebe8b15820"},
{file = "gptcache-0.1.9-py3-none-any.whl", hash = "sha256:c603dea4a6e835032875ccc21a9205bb4d8ef0f2696a8df3ac13ac3a060c4cf6"},
{file = "gptcache-0.1.9.tar.gz", hash = "sha256:b92f30e4d8bc22a017a71fb4801f469739d4c83734a874fea51ca6998ef833d4"},
]
[package.dependencies]
@@ -2954,14 +2954,14 @@ websockets = ["websockets"]
[[package]]
name = "jina-hubble-sdk"
version = "0.35.0"
version = "0.36.0"
description = "SDK for Hubble API at Jina AI."
category = "main"
optional = true
python-versions = ">=3.7.0"
files = [
{file = "jina-hubble-sdk-0.35.0.tar.gz", hash = "sha256:f0826dc6d22aa8da983e4f67af8fa5b78dd852274da71adea7a38d9620438b37"},
{file = "jina_hubble_sdk-0.35.0-py3-none-any.whl", hash = "sha256:957e4ce5e9400ee27aad7a6e728172cdcffb8bb6667e430fa3c97f000650beb3"},
{file = "jina-hubble-sdk-0.36.0.tar.gz", hash = "sha256:ba1a72c7a5c14963fdad9af1ff4c3bba26a03ddcced08111bd11a95b153249ec"},
{file = "jina_hubble_sdk-0.36.0-py3-none-any.whl", hash = "sha256:56db142147d7c72142ed1b6505020c3b4d8070b1603d07ff796e56d491dde294"},
]
[package.dependencies]
@@ -4794,14 +4794,14 @@ tests = ["pytest", "pytest-cov", "pytest-pep8"]
[[package]]
name = "packaging"
version = "23.0"
version = "23.1"
description = "Core utilities for Python packages"
category = "main"
optional = false
python-versions = ">=3.7"
files = [
{file = "packaging-23.0-py3-none-any.whl", hash = "sha256:714ac14496c3e68c99c29b00845f7a2b85f3bb6f1078fd9f72fd20f0570002b2"},
{file = "packaging-23.0.tar.gz", hash = "sha256:b6ad297f8907de0fa2fe1ccbd26fdaf387f5f47c7275fedf8cce89f99446cf97"},
{file = "packaging-23.1-py3-none-any.whl", hash = "sha256:994793af429502c4ea2ebf6bf664629d07c1a9fe974af92966e4b8d2df7edc61"},
{file = "packaging-23.1.tar.gz", hash = "sha256:a392980d2b6cffa644431898be54b0045151319d1e7ec34f0cfed48767dd334f"},
]
[[package]]
@@ -5188,14 +5188,14 @@ testing = ["pytest", "pytest-benchmark"]
[[package]]
name = "posthog"
version = "2.4.2"
version = "2.5.0"
description = "Integrate PostHog into any python application."
category = "dev"
optional = false
python-versions = "*"
files = [
{file = "posthog-2.4.2-py2.py3-none-any.whl", hash = "sha256:8c7c37de997d955aea61bf0aa0069970e71f0d9d79c9e6b3a134e6593d5aa3d6"},
{file = "posthog-2.4.2.tar.gz", hash = "sha256:652a628623aab26597e8421a7ddf9caaf19dd93cc1288901a6b23db9693d34e5"},
{file = "posthog-2.5.0-py2.py3-none-any.whl", hash = "sha256:3d3ece06c3fe4135497c239f211c82344962b93e4fd4ba5afd20bd4ad12d77be"},
{file = "posthog-2.5.0.tar.gz", hash = "sha256:7601ef75b483eb67a6229cafec02da9624f6d46df61066771ca9e3f986284fc3"},
]
[package.dependencies]
@@ -5309,6 +5309,22 @@ files = [
[package.dependencies]
wcwidth = "*"
[[package]]
name = "promptlayer"
version = "0.1.80"
description = "PromptLayer is a package to keep track of your GPT models training"
category = "dev"
optional = false
python-versions = "*"
files = [
{file = "promptlayer-0.1.80.tar.gz", hash = "sha256:1012018ed3e4bca4f0d9c9164cb00b7ca0936cba6a40d4de53e87ef08fdff62f"},
]
[package.dependencies]
langchain = "*"
openai = "*"
requests = "*"
[[package]]
name = "protobuf"
version = "3.19.6"
@@ -6228,14 +6244,14 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
[[package]]
name = "qdrant-client"
version = "1.1.3"
version = "1.1.4"
description = "Client library for the Qdrant vector search engine"
category = "main"
optional = true
python-versions = ">=3.7,<3.12"
files = [
{file = "qdrant_client-1.1.3-py3-none-any.whl", hash = "sha256:c95f59fb9e3e89d163517f8992ee4557eccb45c252147e11e45c608ef1c7dd29"},
{file = "qdrant_client-1.1.3.tar.gz", hash = "sha256:2b7de2b987fc456c643a06878a4150947c3d3d6c6515f6c29f6e707788daa6e7"},
{file = "qdrant_client-1.1.4-py3-none-any.whl", hash = "sha256:12ad9dba63228cc5493e137bf35c59af56d84ca3a2b088c4298825d4893c7100"},
{file = "qdrant_client-1.1.4.tar.gz", hash = "sha256:92ad225bd770fb6a7ac10f75e38f53ffebe63c7f239b02fc7d2bc993246eb74c"},
]
[package.dependencies]
@@ -6473,14 +6489,14 @@ files = [
[[package]]
name = "rich"
version = "13.3.3"
version = "13.3.4"
description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
category = "main"
optional = true
python-versions = ">=3.7.0"
files = [
{file = "rich-13.3.3-py3-none-any.whl", hash = "sha256:540c7d6d26a1178e8e8b37e9ba44573a3cd1464ff6348b99ee7061b95d1c6333"},
{file = "rich-13.3.3.tar.gz", hash = "sha256:dc84400a9d842b3a9c5ff74addd8eb798d155f36c1c91303888e0a66850d2a15"},
{file = "rich-13.3.4-py3-none-any.whl", hash = "sha256:22b74cae0278fd5086ff44144d3813be1cedc9115bdfabbfefd86400cb88b20a"},
{file = "rich-13.3.4.tar.gz", hash = "sha256:b5d573e13605423ec80bdd0cd5f8541f7844a0e71a13f74cf454ccb2f490708b"},
]
[package.dependencies]
@@ -6675,49 +6691,57 @@ transformers = ">=4.6.0,<5.0.0"
[[package]]
name = "sentencepiece"
version = "0.1.97"
version = "0.1.98"
description = "SentencePiece python wrapper"
category = "main"
optional = false
python-versions = "*"
files = [
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[package.extras]
apple = ["thinc-apple-ops (>=0.1.0.dev0,<1.0.0)"]
cuda = ["cupy (>=5.0.0b4,<12.0.0)"]
cuda-autodetect = ["cupy-wheel (>=11.0.0,<12.0.0)"]
cuda100 = ["cupy-cuda100 (>=5.0.0b4,<12.0.0)"]
cuda101 = ["cupy-cuda101 (>=5.0.0b4,<12.0.0)"]
cuda102 = ["cupy-cuda102 (>=5.0.0b4,<12.0.0)"]
cuda110 = ["cupy-cuda110 (>=5.0.0b4,<12.0.0)"]
cuda111 = ["cupy-cuda111 (>=5.0.0b4,<12.0.0)"]
cuda112 = ["cupy-cuda112 (>=5.0.0b4,<12.0.0)"]
cuda113 = ["cupy-cuda113 (>=5.0.0b4,<12.0.0)"]
cuda114 = ["cupy-cuda114 (>=5.0.0b4,<12.0.0)"]
cuda115 = ["cupy-cuda115 (>=5.0.0b4,<12.0.0)"]
cuda116 = ["cupy-cuda116 (>=5.0.0b4,<12.0.0)"]
cuda117 = ["cupy-cuda117 (>=5.0.0b4,<12.0.0)"]
cuda11x = ["cupy-cuda11x (>=11.0.0,<12.0.0)"]
cuda80 = ["cupy-cuda80 (>=5.0.0b4,<12.0.0)"]
cuda90 = ["cupy-cuda90 (>=5.0.0b4,<12.0.0)"]
cuda91 = ["cupy-cuda91 (>=5.0.0b4,<12.0.0)"]
cuda92 = ["cupy-cuda92 (>=5.0.0b4,<12.0.0)"]
cuda = ["cupy (>=5.0.0b4,<13.0.0)"]
cuda-autodetect = ["cupy-wheel (>=11.0.0,<13.0.0)"]
cuda100 = ["cupy-cuda100 (>=5.0.0b4,<13.0.0)"]
cuda101 = ["cupy-cuda101 (>=5.0.0b4,<13.0.0)"]
cuda102 = ["cupy-cuda102 (>=5.0.0b4,<13.0.0)"]
cuda110 = ["cupy-cuda110 (>=5.0.0b4,<13.0.0)"]
cuda111 = ["cupy-cuda111 (>=5.0.0b4,<13.0.0)"]
cuda112 = ["cupy-cuda112 (>=5.0.0b4,<13.0.0)"]
cuda113 = ["cupy-cuda113 (>=5.0.0b4,<13.0.0)"]
cuda114 = ["cupy-cuda114 (>=5.0.0b4,<13.0.0)"]
cuda115 = ["cupy-cuda115 (>=5.0.0b4,<13.0.0)"]
cuda116 = ["cupy-cuda116 (>=5.0.0b4,<13.0.0)"]
cuda117 = ["cupy-cuda117 (>=5.0.0b4,<13.0.0)"]
cuda11x = ["cupy-cuda11x (>=11.0.0,<13.0.0)"]
cuda80 = ["cupy-cuda80 (>=5.0.0b4,<13.0.0)"]
cuda90 = ["cupy-cuda90 (>=5.0.0b4,<13.0.0)"]
cuda91 = ["cupy-cuda91 (>=5.0.0b4,<13.0.0)"]
cuda92 = ["cupy-cuda92 (>=5.0.0b4,<13.0.0)"]
ja = ["sudachidict-core (>=20211220)", "sudachipy (>=0.5.2,!=0.6.1)"]
ko = ["natto-py (>=0.9.0)"]
lookups = ["spacy-lookups-data (>=1.0.3,<1.1.0)"]
@@ -8096,14 +8120,14 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.
[[package]]
name = "types-pyopenssl"
version = "23.1.0.1"
version = "23.1.0.2"
description = "Typing stubs for pyOpenSSL"
category = "dev"
optional = false
python-versions = "*"
files = [
{file = "types-pyOpenSSL-23.1.0.1.tar.gz", hash = "sha256:59044283c475eaa5a29b36a903c123d52bdf4a7e012f0a1ca0e41115b99216da"},
{file = "types_pyOpenSSL-23.1.0.1-py3-none-any.whl", hash = "sha256:ac7fbc240930c2f9a1cbd2d04f9cb14ad0f15b0ad8d6528732a83747b1b2086e"},
{file = "types-pyOpenSSL-23.1.0.2.tar.gz", hash = "sha256:20b80971b86240e8432a1832bd8124cea49c3088c7bfc77dfd23be27ffe4a517"},
{file = "types_pyOpenSSL-23.1.0.2-py3-none-any.whl", hash = "sha256:b050641aeff6dfebf231ad719bdac12d53b8ee818d4afb67b886333484629957"},
]
[package.dependencies]
@@ -8336,7 +8360,7 @@ name = "validators"
version = "0.20.0"
description = "Python Data Validation for Humans™."
category = "main"
optional = true
optional = false
python-versions = ">=3.4"
files = [
{file = "validators-0.20.0.tar.gz", hash = "sha256:24148ce4e64100a2d5e267233e23e7afeb55316b47d30faae7eb6e7292bc226a"},
@@ -8473,7 +8497,7 @@ name = "weaviate-client"
version = "3.15.5"
description = "A python native weaviate client"
category = "main"
optional = true
optional = false
python-versions = ">=3.7"
files = [
{file = "weaviate-client-3.15.5.tar.gz", hash = "sha256:6da7e5d08dc9bb8b7879661d1a457c50af7d73e621a5305efe131160e83da69e"},
@@ -9011,4 +9035,4 @@ qdrant = ["qdrant-client"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "26b1bbfbc3a228b892b2466af3561b799238a6d379853d325dc3c798776df0d8"
content-hash = "7e343fa8e31d8fcf1023cbda592f64c05e80015c4e0e23c1d387d2e9671ce995"
pyproject.toml
+7 -3
View File
@@ -1,6 +1,6 @@
[tool.poetry]
name = "langchain"
version = "0.0.139"
version = "0.0.141"
description = "Building applications with LLMs through composability"
authors = []
license = "MIT"
@@ -28,7 +28,7 @@ spacy = {version = "^3", optional = true}
nltk = {version = "^3", optional = true}
transformers = {version = "^4", optional = true}
beautifulsoup4 = {version = "^4", optional = true}
torch = {version = "^1", optional = true}
torch = {version = ">=1,<3", optional = true}
jinja2 = {version = "^3", optional = true}
tiktoken = {version = "^0.3.2", optional = true, python="^3.9"}
pinecone-client = {version = "^2", optional = true}
@@ -36,7 +36,7 @@ pinecone-text = {version = "^0.4.2", optional = true}
weaviate-client = {version = "^3", optional = true}
google-api-python-client = {version = "2.70.0", optional = true}
wolframalpha = {version = "5.0.0", optional = true}
anthropic = {version = "^0.2.4", optional = true}
anthropic = {version = "^0.2.6", optional = true}
qdrant-client = {version = "^1.1.2", optional = true, python = ">=3.8.1,<3.12"}
dataclasses-json = "^0.5.7"
tensorflow-text = {version = "^2.11.0", optional = true, python = "^3.10, <3.12"}
@@ -101,9 +101,13 @@ pgvector = "^0.1.6"
transformers = "^4.27.4"
pandas = "^2.0.0"
deeplake = "^3.2.21"
weaviate-client = "^3.15.5"
torch = "^1.0.0"
chromadb = "^0.3.21"
tiktoken = "^0.3.3"
python-dotenv = "^1.0.0"
gptcache = "^0.1.9"
promptlayer = "^0.1.80"
[tool.poetry.group.lint.dependencies]
ruff = "^0.0.249"
tests/README.md
+7
View File
@@ -39,6 +39,11 @@ cd tests/integration_tests/vectorstores/docker-compose
docker-compose -f elasticsearch.yml up
```
### Prepare environment variables for local testing:
- copy `tests/.env.example` to `tests/.env`
- set variables in `tests/.env` file, e.g `OPENAI_API_KEY`
Additionally, it's important to note that some integration tests may require certain
environment variables to be set, such as `OPENAI_API_KEY`. Be sure to set any required
environment variables before running the tests to ensure they run correctly.
@@ -54,7 +59,9 @@ cassettes. You can use the --vcr-record=none command-line option to disable reco
new cassettes. Here's an example:
```bash
pytest --log-cli-level=10 tests/integration_tests/vectorstores/test_pinecone.py --vcr-record=none
pytest tests/integration_tests/vectorstores/test_elasticsearch.py --vcr-record=none
```
### Run some tests with coverage:
tests/integration_tests/.env.example
+9
View File
@@ -0,0 +1,9 @@
# openai
# your api key from https://platform.openai.com/account/api-keys
OPENAI_API_KEY=
# pinecone
# your api key from left menu "API Keys" in https://app.pinecone.io
PINECONE_API_KEY=your_pinecone_api_key_here
# your pinecone environment from left menu "API Keys" in https://app.pinecone.io
PINECONE_ENVIRONMENT=us-west4-gcp
tests/integration_tests/chat_models/test_anthropic.py
+83
View File
@@ -0,0 +1,83 @@
"""Test Anthropic API wrapper."""
from typing import List
import pytest
from langchain.callbacks.base import CallbackManager
from langchain.chat_models.anthropic import ChatAnthropic
from langchain.schema import (
AIMessage,
BaseMessage,
ChatGeneration,
HumanMessage,
LLMResult,
)
from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
def test_anthropic_call() -> None:
"""Test valid call to anthropic."""
chat = ChatAnthropic(model="test")
message = HumanMessage(content="Hello")
response = chat([message])
assert isinstance(response, AIMessage)
assert isinstance(response.content, str)
def test_anthropic_streaming() -> None:
"""Test streaming tokens from anthropic."""
chat = ChatAnthropic(model="test", streaming=True)
message = HumanMessage(content="Hello")
response = chat([message])
assert isinstance(response, AIMessage)
assert isinstance(response.content, str)
def test_anthropic_streaming_callback() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
chat = ChatAnthropic(
model="test",
streaming=True,
callback_manager=callback_manager,
verbose=True,
)
message = HumanMessage(content="Write me a sentence with 10 words.")
chat([message])
assert callback_handler.llm_streams > 1
@pytest.mark.asyncio
async def test_anthropic_async_streaming_callback() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
chat = ChatAnthropic(
model="test",
streaming=True,
callback_manager=callback_manager,
verbose=True,
)
chat_messages: List[BaseMessage] = [
HumanMessage(content="How many toes do dogs have?")
]
result: LLMResult = await chat.agenerate([chat_messages])
assert callback_handler.llm_streams > 1
assert isinstance(result, LLMResult)
for response in result.generations[0]:
assert isinstance(response, ChatGeneration)
assert isinstance(response.text, str)
assert response.text == response.message.content
def test_formatting() -> None:
chat = ChatAnthropic()
chat_messages: List[BaseMessage] = [HumanMessage(content="Hello")]
result = chat._convert_messages_to_prompt(chat_messages)
assert result == "\n\nHuman: Hello\n\nAssistant:"
chat_messages = [HumanMessage(content="Hello"), AIMessage(content="Answer:")]
result = chat._convert_messages_to_prompt(chat_messages)
assert result == "\n\nHuman: Hello\n\nAssistant: Answer:"
tests/integration_tests/conftest.py
+21 -15
View File
@@ -1,10 +1,31 @@
import os
from pathlib import Path
import pytest
# Getting the absolute path of the current file's directory
ABS_PATH = os.path.dirname(os.path.abspath(__file__))
# Getting the absolute path of the project's root directory
PROJECT_DIR = os.path.abspath(os.path.join(ABS_PATH, os.pardir, os.pardir))
# Loading the .env file if it exists
def _load_env() -> None:
dotenv_path = os.path.join(PROJECT_DIR, "tests", "integration_tests", ".env")
if os.path.exists(dotenv_path):
from dotenv import load_dotenv
load_dotenv(dotenv_path)
_load_env()
@pytest.fixture(scope="module")
def test_dir() -> Path:
return Path(os.path.join(PROJECT_DIR, "tests", "integration_tests"))
# This fixture returns a string containing the path to the cassette directory for the
# current module
@@ -15,18 +36,3 @@ def vcr_cassette_dir(request: pytest.FixtureRequest) -> str:
"cassettes",
os.path.basename(request.module.__file__).replace(".py", ""),
)
# This fixture returns a dictionary containing filter_headers options
# for replacing certain headers with dummy values during cassette playback
# Specifically, it replaces the authorization header with a dummy value to
# prevent sensitive data from being recorded in the cassette.
@pytest.fixture(scope="module")
def vcr_config() -> dict:
return {
"filter_headers": [
("authorization", "authorization-DUMMY"),
("X-OpenAI-Client-User-Agent", "X-OpenAI-Client-User-Agent-DUMMY"),
("User-Agent", "User-Agent-DUMMY"),
],
}
tests/integration_tests/document_loaders/test_slack.py
+23
View File
@@ -0,0 +1,23 @@
"""Tests for the Slack directory loader"""
from pathlib import Path
from langchain.document_loaders import SlackDirectoryLoader
def test_slack_directory_loader() -> None:
"""Test Slack directory loader."""
file_path = Path(__file__).parent.parent / "examples/slack_export.zip"
loader = SlackDirectoryLoader(str(file_path))
docs = loader.load()
assert len(docs) == 5
def test_slack_directory_loader_urls() -> None:
"""Test workspace URLS are passed through in the SlackDirectoryloader."""
file_path = Path(__file__).parent.parent / "examples/slack_export.zip"
workspace_url = "example_workspace.com"
loader = SlackDirectoryLoader(str(file_path), workspace_url)
docs = loader.load()
for doc in docs:
assert doc.metadata["source"].startswith(workspace_url)
tests/integration_tests/examples/slack_export.zip
BIN
View File
Binary file not shown.
tests/integration_tests/llms/test_anthropic.py
-2
View File
@@ -32,7 +32,6 @@ def test_anthropic_streaming_callback() -> None:
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
llm = Anthropic(
model="claude-v1",
streaming=True,
callback_manager=callback_manager,
verbose=True,
@@ -55,7 +54,6 @@ async def test_anthropic_async_streaming_callback() -> None:
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
llm = Anthropic(
model="claude-v1",
streaming=True,
callback_manager=callback_manager,
verbose=True,
tests/integration_tests/llms/test_openai.py
+11
View File
@@ -211,3 +211,14 @@ async def test_openai_chat_async_streaming_callback() -> None:
result = await llm.agenerate(["Write me a sentence with 100 words."])
assert callback_handler.llm_streams != 0
assert isinstance(result, LLMResult)
def test_openai_modelname_to_contextsize_valid() -> None:
"""Test model name to context size on a valid model."""
assert OpenAI().modelname_to_contextsize("davinci") == 2049
def test_openai_modelname_to_contextsize_invalid() -> None:
"""Test model name to context size on an invalid model."""
with pytest.raises(ValueError):
OpenAI().modelname_to_contextsize("foobar")
tests/integration_tests/vectorstores/cassettes/test_pinecone/TestPinecone.test_from_texts.yaml
+489
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tests/integration_tests/vectorstores/conftest.py
+58 -2
View File
@@ -1,15 +1,56 @@
import os
from typing import Generator, List
from typing import Generator, List, Union
import pytest
from vcr.request import Request
from langchain.document_loaders import TextLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema import Document
from langchain.text_splitter import CharacterTextSplitter
# Those environment variables turn on Deep Lake pytest mode.
# It significantly makes tests run much faster.
# Need to run before `import deeplake`
os.environ["BUGGER_OFF"] = "true"
os.environ["DEEPLAKE_DOWNLOAD_PATH"] = "./testing/local_storage"
os.environ["DEEPLAKE_PYTEST_ENABLED"] = "true"
# This fixture returns a dictionary containing filter_headers options
# for replacing certain headers with dummy values during cassette playback
# Specifically, it replaces the authorization header with a dummy value to
# prevent sensitive data from being recorded in the cassette.
# It also filters request to certain hosts (specified in the `ignored_hosts` list)
# to prevent data from being recorded in the cassette.
@pytest.fixture(scope="module")
def vcr_config() -> dict:
skipped_host = ["pinecone.io"]
def before_record_response(response: dict) -> Union[dict, None]:
return response
def before_record_request(request: Request) -> Union[Request, None]:
for host in skipped_host:
if request.host.startswith(host) or request.host.endswith(host):
return None
return request
return {
"before_record_request": before_record_request,
"before_record_response": before_record_response,
"filter_headers": [
("authorization", "authorization-DUMMY"),
("X-OpenAI-Client-User-Agent", "X-OpenAI-Client-User-Agent-DUMMY"),
("Api-Key", "Api-Key-DUMMY"),
("User-Agent", "User-Agent-DUMMY"),
],
"ignore_localhost": True,
}
# Define a fixture that yields a generator object returning a list of documents
@pytest.fixture(scope="module")
@pytest.fixture(scope="function")
def documents() -> Generator[List[Document], None, None]:
"""Return a generator that yields a list of documents."""
@@ -23,3 +64,18 @@ def documents() -> Generator[List[Document], None, None]:
# Yield the documents split into chunks
yield text_splitter.split_documents(documents)
@pytest.fixture(scope="function")
def texts() -> Generator[List[str], None, None]:
# Load the documents from a file located in the fixtures directory
documents = TextLoader(
os.path.join(os.path.dirname(__file__), "fixtures", "sharks.txt")
).load()
yield [doc.page_content for doc in documents]
@pytest.fixture(scope="module")
def embedding_openai() -> OpenAIEmbeddings:
return OpenAIEmbeddings()
tests/integration_tests/vectorstores/docker-compose/weaviate.yml
+22
View File
@@ -0,0 +1,22 @@
version: '3.4'
services:
weaviate:
command:
- --host
- 0.0.0.0
- --port
- '8080'
- --scheme
- http
image: semitechnologies/weaviate:1.18.2
ports:
- 8080:8080
restart: on-failure:0
environment:
QUERY_DEFAULTS_LIMIT: 25
AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
DEFAULT_VECTORIZER_MODULE: 'none'
ENABLE_MODULES: ''
CLUSTER_HOSTNAME: 'node1'
tests/integration_tests/vectorstores/test_elasticsearch.py
+20 -18
View File
@@ -21,6 +21,11 @@ docker-compose -f elasticsearch.yml up
class TestElasticsearch:
@classmethod
def setup_class(cls) -> None:
if not os.getenv("OPENAI_API_KEY"):
raise ValueError("OPENAI_API_KEY environment variable is not set")
@pytest.fixture(scope="class", autouse=True)
def elasticsearch_url(self) -> Union[str, Generator[str, None, None]]:
"""Return the elasticsearch url."""
@@ -34,15 +39,6 @@ class TestElasticsearch:
# print(index_name)
es.indices.delete(index=index_name)
@pytest.fixture(scope="class", autouse=True)
def openai_api_key(self) -> Union[str, Generator[str, None, None]]:
"""Return the OpenAI API key."""
openai_api_key = os.getenv("OPENAI_API_KEY")
if not openai_api_key:
raise ValueError("OPENAI_API_KEY environment variable is not set")
yield openai_api_key
def test_similarity_search_without_metadata(self, elasticsearch_url: str) -> None:
"""Test end to end construction and search without metadata."""
texts = ["foo", "bar", "baz"]
@@ -67,15 +63,17 @@ class TestElasticsearch:
@pytest.mark.vcr(ignore_localhost=True)
def test_default_index_from_documents(
self, documents: List[Document], openai_api_key: str, elasticsearch_url: str
self,
documents: List[Document],
embedding_openai: OpenAIEmbeddings,
elasticsearch_url: str,
) -> None:
"""This test checks the construction of a default
ElasticSearch index using the 'from_documents'."""
embedding = OpenAIEmbeddings(openai_api_key=openai_api_key)
elastic_vector_search = ElasticVectorSearch.from_documents(
documents=documents,
embedding=embedding,
embedding=embedding_openai,
elasticsearch_url=elasticsearch_url,
)
@@ -86,16 +84,18 @@ class TestElasticsearch:
@pytest.mark.vcr(ignore_localhost=True)
def test_custom_index_from_documents(
self, documents: List[Document], openai_api_key: str, elasticsearch_url: str
self,
documents: List[Document],
embedding_openai: OpenAIEmbeddings,
elasticsearch_url: str,
) -> None:
"""This test checks the construction of a custom
ElasticSearch index using the 'from_documents'."""
index_name = f"custom_index_{uuid.uuid4().hex}"
embedding = OpenAIEmbeddings(openai_api_key=openai_api_key)
elastic_vector_search = ElasticVectorSearch.from_documents(
documents=documents,
embedding=embedding,
embedding=embedding_openai,
elasticsearch_url=elasticsearch_url,
index_name=index_name,
)
@@ -110,15 +110,17 @@ class TestElasticsearch:
@pytest.mark.vcr(ignore_localhost=True)
def test_custom_index_add_documents(
self, documents: List[Document], openai_api_key: str, elasticsearch_url: str
self,
documents: List[Document],
embedding_openai: OpenAIEmbeddings,
elasticsearch_url: str,
) -> None:
"""This test checks the construction of a custom
ElasticSearch index using the 'add_documents'."""
index_name = f"custom_index_{uuid.uuid4().hex}"
embedding = OpenAIEmbeddings(openai_api_key=openai_api_key)
elastic_vector_search = ElasticVectorSearch(
embedding=embedding,
embedding=embedding_openai,
elasticsearch_url=elasticsearch_url,
index_name=index_name,
)
tests/integration_tests/vectorstores/test_pinecone.py
+195 -84
View File
@@ -1,97 +1,208 @@
"""Test Pinecone functionality."""
import importlib
import os
import uuid
from typing import List
import pinecone
import pytest
from langchain.docstore.document import Document
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores.pinecone import Pinecone
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
pinecone.init(api_key="YOUR_API_KEY", environment="YOUR_ENV")
# if the index already exists, delete it
try:
pinecone.delete_index("langchain-demo")
except Exception:
pass
index = pinecone.Index("langchain-demo")
index_name = "langchain-test-index" # name of the index
namespace_name = "langchain-test-namespace" # name of the namespace
dimension = 1536 # dimension of the embeddings
def test_pinecone() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Pinecone.from_texts(
texts, FakeEmbeddings(), index_name="langchain-demo", namespace="test"
)
output = docsearch.similarity_search("foo", k=1, namespace="test")
assert output == [Document(page_content="foo")]
def reset_pinecone() -> None:
assert os.environ.get("PINECONE_API_KEY") is not None
assert os.environ.get("PINECONE_ENVIRONMENT") is not None
import pinecone
def test_pinecone_with_metadatas() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Pinecone.from_texts(
texts,
FakeEmbeddings(),
index_name="langchain-demo",
metadatas=metadatas,
namespace="test-metadata",
)
output = docsearch.similarity_search("foo", k=1, namespace="test-metadata")
assert output == [Document(page_content="foo", metadata={"page": 0})]
importlib.reload(pinecone)
def test_pinecone_with_scores() -> None:
"""Test end to end construction and search with scores and IDs."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Pinecone.from_texts(
texts,
FakeEmbeddings(),
index_name="langchain-demo",
metadatas=metadatas,
namespace="test-metadata-score",
)
output = docsearch.similarity_search_with_score(
"foo", k=3, namespace="test-metadata-score"
)
docs = [o[0] for o in output]
scores = [o[1] for o in output]
assert docs == [
Document(page_content="foo", metadata={"page": 0}),
Document(page_content="bar", metadata={"page": 1}),
Document(page_content="baz", metadata={"page": 2}),
]
assert scores[0] > scores[1] > scores[2]
def test_pinecone_with_namespaces() -> None:
"Test that namespaces are properly handled." ""
# Create two indexes with the same name but different namespaces
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
Pinecone.from_texts(
texts,
FakeEmbeddings(),
index_name="langchain-demo",
metadatas=metadatas,
namespace="test-namespace",
pinecone.init(
api_key=os.environ.get("PINECONE_API_KEY"),
environment=os.environ.get("PINECONE_ENVIRONMENT"),
)
texts = ["foo2", "bar2", "baz2"]
metadatas = [{"page": i} for i in range(len(texts))]
Pinecone.from_texts(
texts,
FakeEmbeddings(),
index_name="langchain-demo",
metadatas=metadatas,
namespace="test-namespace2",
)
# Search with namespace
docsearch = Pinecone.from_existing_index(
"langchain-demo", embedding=FakeEmbeddings(), namespace="test-namespace"
)
output = docsearch.similarity_search("foo", k=6)
# check that we don't get results from the other namespace
page_contents = [o.page_content for o in output]
assert set(page_contents) == set(["foo", "bar", "baz"])
class TestPinecone:
index: pinecone.Index
@classmethod
def setup_class(cls) -> None:
reset_pinecone()
cls.index = pinecone.Index(index_name)
if index_name in pinecone.list_indexes():
index_stats = cls.index.describe_index_stats()
if index_stats["dimension"] == dimension:
# delete all the vectors in the index if the dimension is the same
# from all namespaces
index_stats = cls.index.describe_index_stats()
for _namespace_name in index_stats["namespaces"].keys():
cls.index.delete(delete_all=True, namespace=_namespace_name)
else:
pinecone.delete_index(index_name)
pinecone.create_index(name=index_name, dimension=dimension)
else:
pinecone.create_index(name=index_name, dimension=dimension)
# insure the index is empty
index_stats = cls.index.describe_index_stats()
assert index_stats["dimension"] == dimension
if index_stats["namespaces"].get(namespace_name) is not None:
assert index_stats["namespaces"][namespace_name]["vector_count"] == 0
@classmethod
def teardown_class(cls) -> None:
index_stats = cls.index.describe_index_stats()
for _namespace_name in index_stats["namespaces"].keys():
cls.index.delete(delete_all=True, namespace=_namespace_name)
reset_pinecone()
@pytest.fixture(autouse=True)
def setup(self) -> None:
# delete all the vectors in the index
index_stats = self.index.describe_index_stats()
for _namespace_name in index_stats["namespaces"].keys():
self.index.delete(delete_all=True, namespace=_namespace_name)
reset_pinecone()
@pytest.mark.vcr()
def test_from_texts(
self, texts: List[str], embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and search."""
unique_id = uuid.uuid4().hex
needs = f"foobuu {unique_id} booo"
texts.insert(0, needs)
docsearch = Pinecone.from_texts(
texts=texts,
embedding=embedding_openai,
index_name=index_name,
namespace=namespace_name,
)
output = docsearch.similarity_search(unique_id, k=1, namespace=namespace_name)
assert output == [Document(page_content=needs)]
@pytest.mark.vcr()
def test_from_texts_with_metadatas(
self, texts: List[str], embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and search."""
unique_id = uuid.uuid4().hex
needs = f"foobuu {unique_id} booo"
texts.insert(0, needs)
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Pinecone.from_texts(
texts,
embedding_openai,
index_name=index_name,
metadatas=metadatas,
namespace=namespace_name,
)
output = docsearch.similarity_search(needs, k=1, namespace=namespace_name)
# TODO: why metadata={"page": 0.0}) instead of {"page": 0}?
assert output == [Document(page_content=needs, metadata={"page": 0.0})]
@pytest.mark.vcr()
def test_from_texts_with_scores(self, embedding_openai: OpenAIEmbeddings) -> None:
"""Test end to end construction and search with scores and IDs."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Pinecone.from_texts(
texts,
embedding_openai,
index_name=index_name,
metadatas=metadatas,
namespace=namespace_name,
)
output = docsearch.similarity_search_with_score(
"foo", k=3, namespace=namespace_name
)
docs = [o[0] for o in output]
scores = [o[1] for o in output]
sorted_documents = sorted(docs, key=lambda x: x.metadata["page"])
# TODO: why metadata={"page": 0.0}) instead of {"page": 0}, etc???
assert sorted_documents == [
Document(page_content="foo", metadata={"page": 0.0}),
Document(page_content="bar", metadata={"page": 1.0}),
Document(page_content="baz", metadata={"page": 2.0}),
]
assert scores[0] > scores[1] > scores[2]
def test_from_existing_index_with_namespaces(
self, embedding_openai: OpenAIEmbeddings
) -> None:
"""Test that namespaces are properly handled."""
# Create two indexes with the same name but different namespaces
texts_1 = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts_1))]
Pinecone.from_texts(
texts_1,
embedding_openai,
index_name=index_name,
metadatas=metadatas,
namespace=f"{index_name}-1",
)
texts_2 = ["foo2", "bar2", "baz2"]
metadatas = [{"page": i} for i in range(len(texts_2))]
Pinecone.from_texts(
texts_2,
embedding_openai,
index_name=index_name,
metadatas=metadatas,
namespace=f"{index_name}-2",
)
# Search with namespace
docsearch = Pinecone.from_existing_index(
index_name=index_name,
embedding=embedding_openai,
namespace=f"{index_name}-1",
)
output = docsearch.similarity_search("foo", k=20, namespace=f"{index_name}-1")
# check that we don't get results from the other namespace
page_contents = sorted(set([o.page_content for o in output]))
assert all(content in ["foo", "bar", "baz"] for content in page_contents)
assert all(content not in ["foo2", "bar2", "baz2"] for content in page_contents)
def test_add_documents_with_ids(
self, texts: List[str], embedding_openai: OpenAIEmbeddings
) -> None:
ids = [uuid.uuid4().hex for _ in range(len(texts))]
Pinecone.from_texts(
texts=texts,
ids=ids,
embedding=embedding_openai,
index_name=index_name,
namespace=index_name,
)
index_stats = self.index.describe_index_stats()
assert index_stats["namespaces"][index_name]["vector_count"] == len(texts)
ids_1 = [uuid.uuid4().hex for _ in range(len(texts))]
Pinecone.from_texts(
texts=texts,
ids=ids_1,
embedding=embedding_openai,
index_name=index_name,
namespace=index_name,
)
index_stats = self.index.describe_index_stats()
assert index_stats["namespaces"][index_name]["vector_count"] == len(texts) * 2
tests/integration_tests/vectorstores/test_weaviate.py
+51
View File
@@ -0,0 +1,51 @@
"""Test Weaviate functionality."""
import logging
from typing import Generator, Union
import pytest
from weaviate import Client
from langchain.docstore.document import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.weaviate import Weaviate
logging.basicConfig(level=logging.DEBUG)
"""
cd tests/integration_tests/vectorstores/docker-compose
docker compose -f weaviate.yml up
"""
class TestWeaviate:
@pytest.fixture(scope="class", autouse=True)
def weaviate_url(self) -> Union[str, Generator[str, None, None]]:
"""Return the weaviate url."""
url = "http://localhost:8080"
yield url
# Clear the test index
client = Client(url)
client.schema.delete_all()
def test_similarity_search_without_metadata(self, weaviate_url: str) -> None:
"""Test end to end construction and search without metadata."""
texts = ["foo", "bar", "baz"]
docsearch = Weaviate.from_texts(
texts,
OpenAIEmbeddings(),
weaviate_url=weaviate_url,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
def test_similarity_search_with_metadata(self, weaviate_url: str) -> None:
"""Test end to end construction and search with metadata."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Weaviate.from_texts(
texts, OpenAIEmbeddings(), metadatas=metadatas, weaviate_url=weaviate_url
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"page": 0})]
tests/unit_tests/agents/test_sql.py
+18
View File
@@ -0,0 +1,18 @@
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from tests.unit_tests.llms.fake_llm import FakeLLM
def test_create_sql_agent() -> None:
db = SQLDatabase.from_uri("sqlite:///:memory:")
queries = {"foo": "Final Answer: baz"}
llm = FakeLLM(queries=queries, sequential_responses=True)
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
agent_executor = create_sql_agent(
llm=llm,
toolkit=toolkit,
)
assert agent_executor.run("hello") == "baz"
tests/unit_tests/llms/fake_llm.py
+25 -2
View File
@@ -1,5 +1,7 @@
"""Fake LLM wrapper for testing purposes."""
from typing import Any, List, Mapping, Optional
from typing import Any, List, Mapping, Optional, cast
from pydantic import validator
from langchain.llms.base import LLM
@@ -8,6 +10,18 @@ class FakeLLM(LLM):
"""Fake LLM wrapper for testing purposes."""
queries: Optional[Mapping] = None
sequential_responses: Optional[bool] = False
response_index: int = 0
@validator("queries", always=True)
def check_queries_required(
cls, queries: Optional[Mapping], values: Mapping[str, Any]
) -> Optional[Mapping]:
if values.get("sequential_response") and not queries:
raise ValueError(
"queries is required when sequential_response is set to True"
)
return queries
@property
def _llm_type(self) -> str:
@@ -15,7 +29,9 @@ class FakeLLM(LLM):
return "fake"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""First try to lookup in queries, else return 'foo' or 'bar'."""
if self.sequential_responses:
return self._get_next_response_in_sequence
if self.queries is not None:
return self.queries[prompt]
if stop is None:
@@ -26,3 +42,10 @@ class FakeLLM(LLM):
@property
def _identifying_params(self) -> Mapping[str, Any]:
return {}
@property
def _get_next_response_in_sequence(self) -> str:
queries = cast(Mapping, self.queries)
response = queries[list(queries.keys())[self.response_index]]
self.response_index = self.response_index + 1
return response
tests/unit_tests/test_python.py
+1 -1
View File
@@ -3,8 +3,8 @@ import sys
import pytest
from langchain.python import PythonREPL
from langchain.tools.python.tool import PythonAstREPLTool, PythonREPLTool
from langchain.utilities import PythonREPL
_SAMPLE_CODE = """
```