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Erick Friis 2024-10-15 11:23:33 -04:00
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2
docs/docs/concepts.mdx
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@ -713,7 +713,7 @@ Callback handlers can either be `sync` or `async`:
* Sync callback handlers implement the [BaseCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html) interface. * Sync callback handlers implement the [BaseCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html) interface.
* Async callback handlers implement the [AsyncCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) interface. * Async callback handlers implement the [AsyncCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) interface.
During run-time LangChain configures an appropriate callback manager (e.g., [CallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.CallbackManager.html) or [AsyncCallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.AsyncCallbackManager.html) which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered. During run-time LangChain configures an appropriate callback manager (e.g., [CallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.CallbackManager.html) or [AsyncCallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.AsyncCallbackManager.html)) which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered.
#### Passing callbacks #### Passing callbacks

2
docs/docs/how_to/custom_tools.ipynb
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@ -22,7 +22,7 @@
"2. LangChain [Runnables](/docs/concepts#runnable-interface);\n", "2. LangChain [Runnables](/docs/concepts#runnable-interface);\n",
"3. By sub-classing from [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.BaseTool.html) -- This is the most flexible method, it provides the largest degree of control, at the expense of more effort and code.\n", "3. By sub-classing from [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.BaseTool.html) -- This is the most flexible method, it provides the largest degree of control, at the expense of more effort and code.\n",
"\n", "\n",
"Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.tool.html#langchain_core.tools.tool). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.StructuredTool.html#langchain_core.tools.StructuredTool.from_function) class method.\n", "Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.tool.html#langchain_core.tools.tool). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.structured.StructuredTool.html#langchain_core.tools.structured.StructuredTool.from_function) class method.\n",
"\n", "\n",
"In this guide we provide an overview of these methods.\n", "In this guide we provide an overview of these methods.\n",
"\n", "\n",

2
docs/docs/integrations/chat/sambastudio.ipynb
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@ -19,7 +19,7 @@
"source": [ "source": [
"# ChatSambaStudio\n", "# ChatSambaStudio\n",
"\n", "\n",
"This will help you getting started with SambaNovaCloud [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html).\n", "This will help you getting started with SambaStudio [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html).\n",
"\n", "\n",
"**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://docs.sambanova.ai/sambastudio/latest/sambastudio-intro.html) SambaStudio is a rich, GUI-based platform that provides the functionality to train, deploy, and manage models in SambaNova [DataScale](https://sambanova.ai/products/datascale) systems.\n", "**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://docs.sambanova.ai/sambastudio/latest/sambastudio-intro.html) SambaStudio is a rich, GUI-based platform that provides the functionality to train, deploy, and manage models in SambaNova [DataScale](https://sambanova.ai/products/datascale) systems.\n",
"\n", "\n",

223
docs/docs/integrations/document_loaders/firecrawl.ipynb
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@ -26,33 +26,32 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Setup\n", "## Setup"
"\n",
"### Credentials \n",
"\n",
"You will need to get your own API key. Go to [this page](https://firecrawl.dev) to learn more."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": null,
"metadata": {}, "metadata": {
"scrolled": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"import getpass\n", "pip install firecrawl-py"
"import os\n",
"\n",
"if \"FIRECRAWL_API_KEY\" not in os.environ:\n",
" os.environ[\"FIRECRAWL_API_KEY\"] = getpass.getpass(\"Enter your Firecrawl API key: \")"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### Installation\n", "## Usage"
"\n", ]
"You will need to install both the `langchain_community` and `firecrawl-py` pacakges:" },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You will need to get your own API key. See https://firecrawl.dev"
] ]
}, },
{ {
@ -61,42 +60,12 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"%pip install -qU firecrawl-py==0.0.20 langchain_community" "from langchain_community.document_loaders.firecrawl import FireCrawlLoader"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialization\n",
"\n",
"### Modes\n",
"\n",
"- `scrape`: Scrape single url and return the markdown.\n",
"- `crawl`: Crawl the url and all accessible sub pages and return the markdown for each one."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.document_loaders import FireCrawlLoader\n",
"\n",
"loader = FireCrawlLoader(url=\"https://firecrawl.dev\", mode=\"crawl\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -111,40 +80,14 @@
} }
], ],
"source": [ "source": [
"docs = loader.load()\n", "loader = FireCrawlLoader(\n",
"\n", " api_key=\"YOUR_API_KEY\", url=\"https://firecrawl.dev\", mode=\"scrape\"\n",
"docs[0]" ")"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'ogUrl': 'https://www.firecrawl.dev/', 'title': 'Home - Firecrawl', 'robots': 'follow, index', 'ogImage': 'https://www.firecrawl.dev/og.png?123', 'ogTitle': 'Firecrawl', 'sitemap': {'lastmod': '2024-08-12T00:28:16.681Z', 'changefreq': 'weekly'}, 'keywords': 'Firecrawl,Markdown,Data,Mendable,Langchain', 'sourceURL': 'https://www.firecrawl.dev/', 'ogSiteName': 'Firecrawl', 'description': 'Firecrawl crawls and converts any website into clean markdown.', 'ogDescription': 'Turn any website into LLM-ready data.', 'pageStatusCode': 200, 'ogLocaleAlternate': []}\n"
]
}
],
"source": [
"print(docs[0].metadata)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lazy Load\n",
"\n",
"You can use lazy loading to minimize memory requirements."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -160,39 +103,61 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"len(pages)" "pages"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modes\n",
"\n",
"- `scrape`: Scrape single url and return the markdown.\n",
"- `crawl`: Crawl the url and all accessible sub pages and return the markdown for each one.\n",
"- `map`: Maps the URL and returns a list of semantically related pages."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Crawl\n",
"\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{ "source": [
"name": "stdout", "loader = FireCrawlLoader(\n",
"output_type": "stream", " api_key=\"YOUR_API_KEY\",\n",
"text": [ " url=\"https://firecrawl.dev\",\n",
"Introducing [Smart Crawl!](https://www.firecrawl.dev/smart-crawl)\n", " mode=\"crawl\",\n",
" Join the waitlist to turn any web\n", ")"
"{'ogUrl': 'https://www.firecrawl.dev/blog/introducing-fire-engine-for-firecrawl', 'title': 'Introducing Fire Engine for Firecrawl', 'robots': 'follow, index', 'ogImage': 'https://www.firecrawl.dev/images/blog/fire-engine-launch.png', 'ogTitle': 'Introducing Fire Engine for Firecrawl', 'sitemap': {'lastmod': '2024-08-06T00:00:00.000Z', 'changefreq': 'weekly'}, 'keywords': 'firecrawl,fireengine,web crawling,dashboard,web scraping,LLM,data extraction', 'sourceURL': 'https://www.firecrawl.dev/blog/introducing-fire-engine-for-firecrawl', 'ogSiteName': 'Firecrawl', 'description': 'The most scalable, reliable, and fast way to get web data for Firecrawl.', 'ogDescription': 'The most scalable, reliable, and fast way to get web data for Firecrawl.', 'pageStatusCode': 200, 'ogLocaleAlternate': []}\n" ]
] },
} {
], "cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"data = loader.load()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [ "source": [
"print(pages[0].page_content[:100])\n", "print(pages[0].page_content[:100])\n",
"print(pages[0].metadata)" "print(pages[0].metadata)"
@ -202,10 +167,54 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Crawler Options\n", "#### Crawl Options\n",
"\n", "\n",
"You can also pass `params` to the loader. This is a dictionary of options to pass to the crawler. See the [FireCrawl API documentation](https://github.com/mendableai/firecrawl-py) for more information.\n", "You can also pass `params` to the loader. This is a dictionary of options to pass to the crawler. See the [FireCrawl API documentation](https://github.com/mendableai/firecrawl-py) for more information."
"\n" ]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"### Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"loader = FireCrawlLoader(api_key=\"YOUR_API_KEY\", url=\"firecrawl.dev\", mode=\"map\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"docs = loader.load()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"docs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Map Options\n",
"\n",
"You can also pass `params` to the loader. This is a dictionary of options to pass to the loader. See the [FireCrawl API documentation](https://github.com/mendableai/firecrawl-py) for more information."
] ]
}, },
{ {
@ -220,7 +229,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "langchain", "display_name": "Python 3 (ipykernel)",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },

18
docs/docs/integrations/tools/databricks.ipynb
View File

@ -74,6 +74,24 @@
")" ")"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(Optional) To increase the retry time for getting a function execution response, set environment variable UC_TOOL_CLIENT_EXECUTION_TIMEOUT. Default retry time value is 120s."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"UC_TOOL_CLIENT_EXECUTION_TIMEOUT\"] = \"200\""
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 4,

2
docs/docs/tutorials/chatbot.ipynb
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@ -438,7 +438,7 @@
"app = workflow.compile(checkpointer=MemorySaver())\n", "app = workflow.compile(checkpointer=MemorySaver())\n",
"\n", "\n",
"# Async invocation:\n", "# Async invocation:\n",
"output = await app.ainvoke({\"messages\": input_messages}, config):\n", "output = await app.ainvoke({\"messages\": input_messages}, config)\n",
"output[\"messages\"][-1].pretty_print()\n", "output[\"messages\"][-1].pretty_print()\n",
"```\n", "```\n",
"\n", "\n",

3
docs/package.json
View File

@ -26,13 +26,14 @@
"@docusaurus/preset-classic": "3.5.2", "@docusaurus/preset-classic": "3.5.2",
"@docusaurus/remark-plugin-npm2yarn": "^3.5.2", "@docusaurus/remark-plugin-npm2yarn": "^3.5.2",
"@docusaurus/theme-mermaid": "3.5.2", "@docusaurus/theme-mermaid": "3.5.2",
"prism-react-renderer": "^2.1.0", "@giscus/react": "^3.0.0",
"@mdx-js/react": "^3", "@mdx-js/react": "^3",
"@supabase/supabase-js": "^2.39.7", "@supabase/supabase-js": "^2.39.7",
"clsx": "^1.2.1", "clsx": "^1.2.1",
"cookie": "^0.6.0", "cookie": "^0.6.0",
"isomorphic-dompurify": "^2.7.0", "isomorphic-dompurify": "^2.7.0",
"json-loader": "^0.5.7", "json-loader": "^0.5.7",
"prism-react-renderer": "^2.1.0",
"process": "^0.11.10", "process": "^0.11.10",
"react": "^18", "react": "^18",
"react-dom": "^18", "react-dom": "^18",

14
docs/src/theme/DocItem/Paginator/index.js
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@ -1,11 +1,25 @@
import React from 'react'; import React from 'react';
import Paginator from '@theme-original/DocItem/Paginator'; import Paginator from '@theme-original/DocItem/Paginator';
import Feedback from "@theme/Feedback"; import Feedback from "@theme/Feedback";
import Giscus from "@giscus/react";
export default function PaginatorWrapper(props) { export default function PaginatorWrapper(props) {
return ( return (
<> <>
<Feedback /> <Feedback />
<Giscus
repo="langchain-ai/langchain"
repoId="R_kgDOIPDwlg"
category="Docs Discussions"
categoryId="DIC_kwDOIPDwls4CjJYb"
mapping="pathname"
strict="0"
reactionsEnabled="0"
emitMetadata="0"
inputPosition="bottom"
theme="preferred_color_scheme"
lang="en"
loading="lazy" />
<Paginator {...props} /> <Paginator {...props} />
</> </>
); );

12
docs/src/theme/Feedback.js
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@ -220,10 +220,6 @@ export default function Feedback() {
onMouseUp: (e) => (e.currentTarget.style.backgroundColor = "#f0f0f0"), onMouseUp: (e) => (e.currentTarget.style.backgroundColor = "#f0f0f0"),
}; };
const newGithubIssueURL = pathname
? `https://github.com/langchain-ai/langchain/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CIssue+related+to+${pathname}%3E&url=https://python.langchain.com${pathname}`
: "https://github.com/langchain-ai/langchain/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E";
return ( return (
<div style={{ display: "flex", flexDirection: "column" }}> <div style={{ display: "flex", flexDirection: "column" }}>
<hr /> <hr />
@ -296,14 +292,6 @@ export default function Feedback() {
</div> </div>
</> </>
)} )}
<br />
<h4>
You can also leave detailed feedback{" "}
<a target="_blank" href={newGithubIssueURL}>
on GitHub
</a>
.
</h4>
</div> </div>
); );
} }

53
docs/yarn.lock
View File

@ -1709,6 +1709,13 @@
resolved "https://registry.yarnpkg.com/@eslint/js/-/js-8.57.1.tgz#de633db3ec2ef6a3c89e2f19038063e8a122e2c2" resolved "https://registry.yarnpkg.com/@eslint/js/-/js-8.57.1.tgz#de633db3ec2ef6a3c89e2f19038063e8a122e2c2"
integrity sha512-d9zaMRSTIKDLhctzH12MtXvJKSSUhaHcjV+2Z+GK+EEY7XKpP5yR4x+N3TAcHTcu963nIr+TMcCb4DBCYX1z6Q== integrity sha512-d9zaMRSTIKDLhctzH12MtXvJKSSUhaHcjV+2Z+GK+EEY7XKpP5yR4x+N3TAcHTcu963nIr+TMcCb4DBCYX1z6Q==
"@giscus/react@^3.0.0":
version "3.0.0"
resolved "https://registry.yarnpkg.com/@giscus/react/-/react-3.0.0.tgz#fdadce2c7e4023eb4fdbcc219cdd97f6d7aa17f0"
integrity sha512-hgCjLpg3Wgh8VbTF5p8ZLcIHI74wvDk1VIFv12+eKhenNVUDjgwNg2B1aq/3puyHOad47u/ZSyqiMtohjy/OOA==
dependencies:
giscus "^1.5.0"
"@hapi/hoek@^9.0.0", "@hapi/hoek@^9.3.0": "@hapi/hoek@^9.0.0", "@hapi/hoek@^9.3.0":
version "9.3.0" version "9.3.0"
resolved "https://registry.yarnpkg.com/@hapi/hoek/-/hoek-9.3.0.tgz#8368869dcb735be2e7f5cb7647de78e167a251fb" resolved "https://registry.yarnpkg.com/@hapi/hoek/-/hoek-9.3.0.tgz#8368869dcb735be2e7f5cb7647de78e167a251fb"
@ -1835,6 +1842,18 @@
resolved "https://registry.yarnpkg.com/@leichtgewicht/ip-codec/-/ip-codec-2.0.5.tgz#4fc56c15c580b9adb7dc3c333a134e540b44bfb1" resolved "https://registry.yarnpkg.com/@leichtgewicht/ip-codec/-/ip-codec-2.0.5.tgz#4fc56c15c580b9adb7dc3c333a134e540b44bfb1"
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"@lit-labs/ssr-dom-shim@^1.2.0":
version "1.2.1"
resolved "https://registry.yarnpkg.com/@lit-labs/ssr-dom-shim/-/ssr-dom-shim-1.2.1.tgz#2f3a8f1d688935c704dbc89132394a41029acbb8"
integrity sha512-wx4aBmgeGvFmOKucFKY+8VFJSYZxs9poN3SDNQFF6lT6NrQUnHiPB2PWz2sc4ieEcAaYYzN+1uWahEeTq2aRIQ==
"@lit/reactive-element@^2.0.4":
version "2.0.4"
resolved "https://registry.yarnpkg.com/@lit/reactive-element/-/reactive-element-2.0.4.tgz#8f2ed950a848016383894a26180ff06c56ae001b"
integrity sha512-GFn91inaUa2oHLak8awSIigYz0cU0Payr1rcFsrkf5OJ5eSPxElyZfKh0f2p9FsTiZWXQdWGJeXZICEfXXYSXQ==
dependencies:
"@lit-labs/ssr-dom-shim" "^1.2.0"
"@mdx-js/mdx@^3.0.0": "@mdx-js/mdx@^3.0.0":
version "3.0.1" version "3.0.1"
resolved "https://registry.yarnpkg.com/@mdx-js/mdx/-/mdx-3.0.1.tgz#617bd2629ae561fdca1bb88e3badd947f5a82191" resolved "https://registry.yarnpkg.com/@mdx-js/mdx/-/mdx-3.0.1.tgz#617bd2629ae561fdca1bb88e3badd947f5a82191"
@ -2569,7 +2588,7 @@
dependencies: dependencies:
"@types/node" "*" "@types/node" "*"
"@types/trusted-types@*": "@types/trusted-types@*", "@types/trusted-types@^2.0.2":
version "2.0.7" version "2.0.7"
resolved "https://registry.yarnpkg.com/@types/trusted-types/-/trusted-types-2.0.7.tgz#baccb07a970b91707df3a3e8ba6896c57ead2d11" resolved "https://registry.yarnpkg.com/@types/trusted-types/-/trusted-types-2.0.7.tgz#baccb07a970b91707df3a3e8ba6896c57ead2d11"
integrity sha512-ScaPdn1dQczgbl0QFTeTOmVHFULt394XJgOQNoyVhZ6r2vLnMLJfBPd53SB52T/3G36VI1/g2MZaX0cwDuXsfw== integrity sha512-ScaPdn1dQczgbl0QFTeTOmVHFULt394XJgOQNoyVhZ6r2vLnMLJfBPd53SB52T/3G36VI1/g2MZaX0cwDuXsfw==
@ -5549,6 +5568,13 @@ get-symbol-description@^1.0.2:
es-errors "^1.3.0" es-errors "^1.3.0"
get-intrinsic "^1.2.4" get-intrinsic "^1.2.4"
giscus@^1.5.0:
version "1.5.0"
resolved "https://registry.yarnpkg.com/giscus/-/giscus-1.5.0.tgz#8299fa056b2ed31ec8b05d4645871e016982b4b2"
integrity sha512-t3LL0qbSO3JXq3uyQeKpF5CegstGfKX/0gI6eDe1cmnI7D56R7j52yLdzw4pdKrg3VnufwCgCM3FDz7G1Qr6lg==
dependencies:
lit "^3.1.2"
github-slugger@^1.5.0: github-slugger@^1.5.0:
version "1.5.0" version "1.5.0"
resolved "https://registry.yarnpkg.com/github-slugger/-/github-slugger-1.5.0.tgz#17891bbc73232051474d68bd867a34625c955f7d" resolved "https://registry.yarnpkg.com/github-slugger/-/github-slugger-1.5.0.tgz#17891bbc73232051474d68bd867a34625c955f7d"
@ -6882,6 +6908,31 @@ lines-and-columns@^1.1.6:
resolved "https://registry.yarnpkg.com/lines-and-columns/-/lines-and-columns-1.2.4.tgz#eca284f75d2965079309dc0ad9255abb2ebc1632" resolved "https://registry.yarnpkg.com/lines-and-columns/-/lines-and-columns-1.2.4.tgz#eca284f75d2965079309dc0ad9255abb2ebc1632"
integrity sha512-7ylylesZQ/PV29jhEDl3Ufjo6ZX7gCqJr5F7PKrqc93v7fzSymt1BpwEU8nAUXs8qzzvqhbjhK5QZg6Mt/HkBg== integrity sha512-7ylylesZQ/PV29jhEDl3Ufjo6ZX7gCqJr5F7PKrqc93v7fzSymt1BpwEU8nAUXs8qzzvqhbjhK5QZg6Mt/HkBg==
lit-element@^4.1.0:
version "4.1.1"
resolved "https://registry.yarnpkg.com/lit-element/-/lit-element-4.1.1.tgz#07905992815076e388cf6f1faffc7d6866c82007"
integrity sha512-HO9Tkkh34QkTeUmEdNYhMT8hzLid7YlMlATSi1q4q17HE5d9mrrEHJ/o8O2D0cMi182zK1F3v7x0PWFjrhXFew==
dependencies:
"@lit-labs/ssr-dom-shim" "^1.2.0"
"@lit/reactive-element" "^2.0.4"
lit-html "^3.2.0"
lit-html@^3.2.0:
version "3.2.1"
resolved "https://registry.yarnpkg.com/lit-html/-/lit-html-3.2.1.tgz#8fc49e3531ee5947e4d93e8a5aa642ab1649833b"
integrity sha512-qI/3lziaPMSKsrwlxH/xMgikhQ0EGOX2ICU73Bi/YHFvz2j/yMCIrw4+puF2IpQ4+upd3EWbvnHM9+PnJn48YA==
dependencies:
"@types/trusted-types" "^2.0.2"
lit@^3.1.2:
version "3.2.1"
resolved "https://registry.yarnpkg.com/lit/-/lit-3.2.1.tgz#d6dd15eac20db3a098e81e2c85f70a751ff55592"
integrity sha512-1BBa1E/z0O9ye5fZprPtdqnc0BFzxIxTTOO/tQFmyC/hj1O3jL4TfmLBw0WEwjAokdLwpclkvGgDJwTIh0/22w==
dependencies:
"@lit/reactive-element" "^2.0.4"
lit-element "^4.1.0"
lit-html "^3.2.0"
loader-runner@^4.2.0: loader-runner@^4.2.0:
version "4.3.0" version "4.3.0"
resolved "https://registry.yarnpkg.com/loader-runner/-/loader-runner-4.3.0.tgz#c1b4a163b99f614830353b16755e7149ac2314e1" resolved "https://registry.yarnpkg.com/loader-runner/-/loader-runner-4.3.0.tgz#c1b4a163b99f614830353b16755e7149ac2314e1"

4
libs/community/langchain_community/document_loaders/base_o365.py
View File

@ -118,8 +118,8 @@ class O365BaseLoader(BaseLoader, BaseModel):
metadata_dict[file.name] = { metadata_dict[file.name] = {
"source": file.web_url, "source": file.web_url,
"mime_type": file.mime_type, "mime_type": file.mime_type,
"created": file.created, "created": str(file.created),
"modified": file.modified, "modified": str(file.modified),
"created_by": str(file.created_by), "created_by": str(file.created_by),
"modified_by": str(file.modified_by), "modified_by": str(file.modified_by),
"description": file.description, "description": file.description,

219
libs/community/langchain_community/document_loaders/firecrawl.py
View File

@ -1,3 +1,4 @@
import warnings
from typing import Iterator, Literal, Optional from typing import Iterator, Literal, Optional
from langchain_core.document_loaders import BaseLoader from langchain_core.document_loaders import BaseLoader
@ -48,7 +49,6 @@ class FireCrawlLoader(BaseLoader):
Join the waitlist to turn any web Join the waitlist to turn any web
{'ogUrl': 'https://www.firecrawl.dev/', 'title': 'Home - Firecrawl', 'robots': 'follow, index', 'ogImage': 'https://www.firecrawl.dev/og.png?123', 'ogTitle': 'Firecrawl', 'sitemap': {'lastmod': '2024-08-12T00:28:16.681Z', 'changefreq': 'weekly'}, 'keywords': 'Firecrawl,Markdown,Data,Mendable,Langchain', 'sourceURL': 'https://www.firecrawl.dev/', 'ogSiteName': 'Firecrawl', 'description': 'Firecrawl crawls and converts any website into clean markdown.', 'ogDescription': 'Turn any website into LLM-ready data.', 'pageStatusCode': 200, 'ogLocaleAlternate': []} {'ogUrl': 'https://www.firecrawl.dev/', 'title': 'Home - Firecrawl', 'robots': 'follow, index', 'ogImage': 'https://www.firecrawl.dev/og.png?123', 'ogTitle': 'Firecrawl', 'sitemap': {'lastmod': '2024-08-12T00:28:16.681Z', 'changefreq': 'weekly'}, 'keywords': 'Firecrawl,Markdown,Data,Mendable,Langchain', 'sourceURL': 'https://www.firecrawl.dev/', 'ogSiteName': 'Firecrawl', 'description': 'Firecrawl crawls and converts any website into clean markdown.', 'ogDescription': 'Turn any website into LLM-ready data.', 'pageStatusCode': 200, 'ogLocaleAlternate': []}
Async load: Async load:
.. code-block:: python .. code-block:: python
@ -64,13 +64,169 @@ class FireCrawlLoader(BaseLoader):
""" # noqa: E501 """ # noqa: E501
def legacy_crawler_options_adapter(self, params: dict) -> dict:
use_legacy_options = False
legacy_keys = [
"includes",
"excludes",
"allowBackwardCrawling",
"allowExternalContentLinks",
"pageOptions",
]
for key in legacy_keys:
if params.get(key):
use_legacy_options = True
break
if use_legacy_options:
warnings.warn(
"Deprecated parameters detected. See Firecrawl v1 docs for updates.",
DeprecationWarning,
)
if "includes" in params:
if params["includes"] is True:
params["includePaths"] = params["includes"]
del params["includes"]
if "excludes" in params:
if params["excludes"] is True:
params["excludePaths"] = params["excludes"]
del params["excludes"]
if "allowBackwardCrawling" in params:
if params["allowBackwardCrawling"] is True:
params["allowBackwardLinks"] = params["allowBackwardCrawling"]
del params["allowBackwardCrawling"]
if "allowExternalContentLinks" in params:
if params["allowExternalContentLinks"] is True:
params["allowExternalLinks"] = params["allowExternalContentLinks"]
del params["allowExternalContentLinks"]
if "pageOptions" in params:
if isinstance(params["pageOptions"], dict):
params["scrapeOptions"] = self.legacy_scrape_options_adapter(
params["pageOptions"]
)
del params["pageOptions"]
return params
def legacy_scrape_options_adapter(self, params: dict) -> dict:
use_legacy_options = False
formats = ["markdown"]
if "extractorOptions" in params:
if "mode" in params["extractorOptions"]:
if (
params["extractorOptions"]["mode"] == "llm-extraction"
or params["extractorOptions"]["mode"]
== "llm-extraction-from-raw-html"
or params["extractorOptions"]["mode"]
== "llm-extraction-from-markdown"
):
use_legacy_options = True
if "extractionPrompt" in params["extractorOptions"]:
if params["extractorOptions"]["extractionPrompt"]:
params["prompt"] = params["extractorOptions"][
"extractionPrompt"
]
else:
params["prompt"] = params["extractorOptions"].get(
"extractionPrompt",
"Extract page information based on the schema.",
)
if "extractionSchema" in params["extractorOptions"]:
if params["extractorOptions"]["extractionSchema"]:
params["schema"] = params["extractorOptions"][
"extractionSchema"
]
if "userPrompt" in params["extractorOptions"]:
if params["extractorOptions"]["userPrompt"]:
params["prompt"] = params["extractorOptions"]["userPrompt"]
del params["extractorOptions"]
scrape_keys = [
"includeMarkdown",
"includeHtml",
"includeRawHtml",
"includeExtract",
"includeLinks",
"screenshot",
"fullPageScreenshot",
"onlyIncludeTags",
"removeTags",
]
for key in scrape_keys:
if params.get(key):
use_legacy_options = True
break
if use_legacy_options:
warnings.warn(
"Deprecated parameters detected. See Firecrawl v1 docs for updates.",
DeprecationWarning,
)
if "includeMarkdown" in params:
if params["includeMarkdown"] is False:
formats.remove("markdown")
del params["includeMarkdown"]
if "includeHtml" in params:
if params["includeHtml"] is True:
formats.append("html")
del params["includeHtml"]
if "includeRawHtml" in params:
if params["includeRawHtml"] is True:
formats.append("rawHtml")
del params["includeRawHtml"]
if "includeExtract" in params:
if params["includeExtract"] is True:
formats.append("extract")
del params["includeExtract"]
if "includeLinks" in params:
if params["includeLinks"] is True:
formats.append("links")
del params["includeLinks"]
if "screenshot" in params:
if params["screenshot"] is True:
formats.append("screenshot")
del params["screenshot"]
if "fullPageScreenshot" in params:
if params["fullPageScreenshot"] is True:
formats.append("screenshot@fullPage")
del params["fullPageScreenshot"]
if "onlyIncludeTags" in params:
if params["onlyIncludeTags"] is True:
params["includeTags"] = params["onlyIncludeTags"]
del params["onlyIncludeTags"]
if "removeTags" in params:
if params["removeTags"] is True:
params["excludeTags"] = params["removeTags"]
del params["removeTags"]
if "formats" not in params:
params["formats"] = formats
return params
def __init__( def __init__(
self, self,
url: str, url: str,
*, *,
api_key: Optional[str] = None, api_key: Optional[str] = None,
api_url: Optional[str] = None, api_url: Optional[str] = None,
mode: Literal["crawl", "scrape"] = "crawl", mode: Literal["crawl", "scrape", "map"] = "crawl",
params: Optional[dict] = None, params: Optional[dict] = None,
): ):
"""Initialize with API key and url. """Initialize with API key and url.
@ -82,8 +238,9 @@ class FireCrawlLoader(BaseLoader):
api_url: The Firecrawl API URL. If not specified will be read from env var api_url: The Firecrawl API URL. If not specified will be read from env var
FIRECRAWL_API_URL or defaults to https://api.firecrawl.dev. FIRECRAWL_API_URL or defaults to https://api.firecrawl.dev.
mode: The mode to run the loader in. Default is "crawl". mode: The mode to run the loader in. Default is "crawl".
Options include "scrape" (single url) and Options include "scrape" (single url),
"crawl" (all accessible sub pages). "crawl" (all accessible sub pages),
"map" (returns list of links that are semantically related).
params: The parameters to pass to the Firecrawl API. params: The parameters to pass to the Firecrawl API.
Examples include crawlerOptions. Examples include crawlerOptions.
For more details, visit: https://github.com/mendableai/firecrawl-py For more details, visit: https://github.com/mendableai/firecrawl-py
@ -95,30 +252,58 @@ class FireCrawlLoader(BaseLoader):
raise ImportError( raise ImportError(
"`firecrawl` package not found, please run `pip install firecrawl-py`" "`firecrawl` package not found, please run `pip install firecrawl-py`"
) )
if mode not in ("crawl", "scrape"): if mode not in ("crawl", "scrape", "search", "map"):
raise ValueError( raise ValueError(
f"Unrecognized mode '{mode}'. Expected one of 'crawl', 'scrape'." f"Invalid mode '{mode}'. Allowed: 'crawl', 'scrape', 'search', 'map'."
) )
if not url:
raise ValueError("Url must be provided")
api_key = api_key or get_from_env("api_key", "FIRECRAWL_API_KEY") api_key = api_key or get_from_env("api_key", "FIRECRAWL_API_KEY")
self.firecrawl = FirecrawlApp(api_key=api_key, api_url=api_url) self.firecrawl = FirecrawlApp(api_key=api_key, api_url=api_url)
self.url = url self.url = url
self.mode = mode self.mode = mode
self.params = params self.params = params or {}
def lazy_load(self) -> Iterator[Document]: def lazy_load(self) -> Iterator[Document]:
if self.mode == "scrape": if self.mode == "scrape":
firecrawl_docs = [self.firecrawl.scrape_url(self.url, params=self.params)] firecrawl_docs = [
self.firecrawl.scrape_url(
self.url, params=self.legacy_scrape_options_adapter(self.params)
)
]
elif self.mode == "crawl": elif self.mode == "crawl":
firecrawl_docs = self.firecrawl.crawl_url(self.url, params=self.params) if not self.url:
raise ValueError("URL is required for crawl mode")
crawl_response = self.firecrawl.crawl_url(
self.url, params=self.legacy_crawler_options_adapter(self.params)
)
firecrawl_docs = crawl_response.get("data", [])
elif self.mode == "map":
if not self.url:
raise ValueError("URL is required for map mode")
firecrawl_docs = self.firecrawl.map_url(self.url, params=self.params)
elif self.mode == "search":
raise ValueError(
"Search mode is not supported in this version, please downgrade."
)
else: else:
raise ValueError( raise ValueError(
f"Unrecognized mode '{self.mode}'. Expected one of 'crawl', 'scrape'." f"Invalid mode '{self.mode}'. Allowed: 'crawl', 'scrape', 'map'."
) )
for doc in firecrawl_docs: for doc in firecrawl_docs:
metadata = doc.get("metadata", {}) if self.mode == "map":
if (self.params is not None) and self.params.get( page_content = doc
"extractorOptions", {} metadata = {}
).get("mode") == "llm-extraction": else:
metadata["llm_extraction"] = doc.get("llm_extraction") page_content = (
doc.get("markdown") or doc.get("html") or doc.get("rawHtml", "")
yield Document(page_content=doc.get("markdown", ""), metadata=metadata) )
metadata = doc.get("metadata", {})
if not page_content:
continue
yield Document(
page_content=page_content,
metadata=metadata,
)

4
libs/community/langchain_community/graphs/neo4j_graph.py
View File

@ -430,7 +430,7 @@ class Neo4jGraph(GraphStore):
try: try:
data, _, _ = self._driver.execute_query( data, _, _ = self._driver.execute_query(
Query(text=query, timeout=self.timeout), Query(text=query, timeout=self.timeout),
database=self._database, database_=self._database,
parameters_=params, parameters_=params,
) )
json_data = [r.data() for r in data] json_data = [r.data() for r in data]
@ -457,7 +457,7 @@ class Neo4jGraph(GraphStore):
): ):
raise raise
# fallback to allow implicit transactions # fallback to allow implicit transactions
with self._driver.session() as session: with self._driver.session(database=self._database) as session:
data = session.run(Query(text=query, timeout=self.timeout), params) data = session.run(Query(text=query, timeout=self.timeout), params)
json_data = [r.data() for r in data] json_data = [r.data() for r in data]
if self.sanitize: if self.sanitize:

2
libs/community/langchain_community/llms/bedrock.py
View File

@ -396,7 +396,7 @@ class BedrockBase(BaseModel, ABC):
"""Validate that AWS credentials to and python package exists in environment.""" """Validate that AWS credentials to and python package exists in environment."""
# Skip creating new client if passed in constructor # Skip creating new client if passed in constructor
if values["client"] is not None: if values.get("client") is not None:
return values return values
try: try:

53
libs/community/langchain_community/tools/databricks/_execution.py
View File

@ -1,5 +1,8 @@
import inspect import inspect
import json import json
import logging
import os
import time
from dataclasses import dataclass from dataclasses import dataclass
from io import StringIO from io import StringIO
from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional
@ -7,7 +10,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional
if TYPE_CHECKING: if TYPE_CHECKING:
from databricks.sdk import WorkspaceClient from databricks.sdk import WorkspaceClient
from databricks.sdk.service.catalog import FunctionInfo from databricks.sdk.service.catalog import FunctionInfo
from databricks.sdk.service.sql import StatementParameterListItem from databricks.sdk.service.sql import StatementParameterListItem, StatementState
EXECUTE_FUNCTION_ARG_NAME = "__execution_args__" EXECUTE_FUNCTION_ARG_NAME = "__execution_args__"
DEFAULT_EXECUTE_FUNCTION_ARGS = { DEFAULT_EXECUTE_FUNCTION_ARGS = {
@ -15,6 +18,9 @@ DEFAULT_EXECUTE_FUNCTION_ARGS = {
"row_limit": 100, "row_limit": 100,
"byte_limit": 4096, "byte_limit": 4096,
} }
UC_TOOL_CLIENT_EXECUTION_TIMEOUT = "UC_TOOL_CLIENT_EXECUTION_TIMEOUT"
DEFAULT_UC_TOOL_CLIENT_EXECUTION_TIMEOUT = "120"
_logger = logging.getLogger(__name__)
def is_scalar(function: "FunctionInfo") -> bool: def is_scalar(function: "FunctionInfo") -> bool:
@ -174,13 +180,42 @@ def execute_function(
parameters=parametrized_statement.parameters, parameters=parametrized_statement.parameters,
**execute_statement_args, # type: ignore **execute_statement_args, # type: ignore
) )
status = response.status if response.status and job_pending(response.status.state) and response.statement_id:
assert status is not None, f"Statement execution failed: {response}" statement_id = response.statement_id
if status.state != StatementState.SUCCEEDED: wait_time = 0
error = status.error retry_cnt = 0
client_execution_timeout = int(
os.environ.get(
UC_TOOL_CLIENT_EXECUTION_TIMEOUT,
DEFAULT_UC_TOOL_CLIENT_EXECUTION_TIMEOUT,
)
)
while wait_time < client_execution_timeout:
wait = min(2**retry_cnt, client_execution_timeout - wait_time)
_logger.debug(
f"Retrying {retry_cnt} time to get statement execution "
f"status after {wait} seconds."
)
time.sleep(wait)
response = ws.statement_execution.get_statement(statement_id) # type: ignore
if response.status is None or not job_pending(response.status.state):
break
wait_time += wait
retry_cnt += 1
if response.status and job_pending(response.status.state):
return FunctionExecutionResult(
error=f"Statement execution is still pending after {wait_time} "
"seconds. Please increase the wait_timeout argument for executing "
f"the function or increase {UC_TOOL_CLIENT_EXECUTION_TIMEOUT} "
"environment variable for increasing retrying time, default is "
f"{DEFAULT_UC_TOOL_CLIENT_EXECUTION_TIMEOUT} seconds."
)
assert response.status is not None, f"Statement execution failed: {response}"
if response.status.state != StatementState.SUCCEEDED:
error = response.status.error
assert ( assert (
error is not None error is not None
), "Statement execution failed but no error message was provided." ), f"Statement execution failed but no error message was provided: {response}"
return FunctionExecutionResult(error=f"{error.error_code}: {error.message}") return FunctionExecutionResult(error=f"{error.error_code}: {error.message}")
manifest = response.manifest manifest = response.manifest
assert manifest is not None assert manifest is not None
@ -211,3 +246,9 @@ def execute_function(
return FunctionExecutionResult( return FunctionExecutionResult(
format="CSV", value=csv_buffer.getvalue(), truncated=truncated format="CSV", value=csv_buffer.getvalue(), truncated=truncated
) )
def job_pending(state: Optional["StatementState"]) -> bool:
from databricks.sdk.service.sql import StatementState
return state in (StatementState.PENDING, StatementState.RUNNING)

267
libs/community/langchain_community/vectorstores/cassandra.py
View File

@ -1,6 +1,7 @@
from __future__ import annotations from __future__ import annotations
import asyncio import asyncio
import importlib.metadata
import typing import typing
import uuid import uuid
from typing import ( from typing import (
@ -18,6 +19,7 @@ from typing import (
) )
import numpy as np import numpy as np
from packaging.version import Version # this is a lancghain-core dependency
if typing.TYPE_CHECKING: if typing.TYPE_CHECKING:
from cassandra.cluster import Session from cassandra.cluster import Session
@ -30,6 +32,7 @@ from langchain_community.utilities.cassandra import SetupMode
from langchain_community.vectorstores.utils import maximal_marginal_relevance from langchain_community.vectorstores.utils import maximal_marginal_relevance
CVST = TypeVar("CVST", bound="Cassandra") CVST = TypeVar("CVST", bound="Cassandra")
MIN_CASSIO_VERSION = Version("0.1.10")
class Cassandra(VectorStore): class Cassandra(VectorStore):
@ -110,6 +113,15 @@ class Cassandra(VectorStore):
"Could not import cassio python package. " "Could not import cassio python package. "
"Please install it with `pip install cassio`." "Please install it with `pip install cassio`."
) )
cassio_version = Version(importlib.metadata.version("cassio"))
if cassio_version is not None and cassio_version < MIN_CASSIO_VERSION:
msg = (
"Cassio version not supported. Please upgrade cassio "
f"to version {MIN_CASSIO_VERSION} or higher."
)
raise ImportError(msg)
if not table_name: if not table_name:
raise ValueError("Missing required parameter 'table_name'.") raise ValueError("Missing required parameter 'table_name'.")
self.embedding = embedding self.embedding = embedding
@ -143,6 +155,9 @@ class Cassandra(VectorStore):
**kwargs, **kwargs,
) )
if self.session is None:
self.session = self.table.session
@property @property
def embeddings(self) -> Embeddings: def embeddings(self) -> Embeddings:
return self.embedding return self.embedding
@ -231,6 +246,70 @@ class Cassandra(VectorStore):
await self.adelete_by_document_id(document_id) await self.adelete_by_document_id(document_id)
return True return True
def delete_by_metadata_filter(
self,
filter: dict[str, Any],
*,
batch_size: int = 50,
) -> int:
"""Delete all documents matching a certain metadata filtering condition.
This operation does not use the vector embeddings in any way, it simply
removes all documents whose metadata match the provided condition.
Args:
filter: Filter on the metadata to apply. The filter cannot be empty.
batch_size: amount of deletions per each batch (until exhaustion of
the matching documents).
Returns:
A number expressing the amount of deleted documents.
"""
if not filter:
msg = (
"Method `delete_by_metadata_filter` does not accept an empty "
"filter. Use the `clear()` method if you really want to empty "
"the vector store."
)
raise ValueError(msg)
return self.table.find_and_delete_entries(
metadata=filter,
batch_size=batch_size,
)
async def adelete_by_metadata_filter(
self,
filter: dict[str, Any],
*,
batch_size: int = 50,
) -> int:
"""Delete all documents matching a certain metadata filtering condition.
This operation does not use the vector embeddings in any way, it simply
removes all documents whose metadata match the provided condition.
Args:
filter: Filter on the metadata to apply. The filter cannot be empty.
batch_size: amount of deletions per each batch (until exhaustion of
the matching documents).
Returns:
A number expressing the amount of deleted documents.
"""
if not filter:
msg = (
"Method `delete_by_metadata_filter` does not accept an empty "
"filter. Use the `clear()` method if you really want to empty "
"the vector store."
)
raise ValueError(msg)
return await self.table.afind_and_delete_entries(
metadata=filter,
batch_size=batch_size,
)
def add_texts( def add_texts(
self, self,
texts: Iterable[str], texts: Iterable[str],
@ -333,6 +412,180 @@ class Cassandra(VectorStore):
await asyncio.gather(*tasks) await asyncio.gather(*tasks)
return ids return ids
def replace_metadata(
self,
id_to_metadata: dict[str, dict],
*,
batch_size: int = 50,
) -> None:
"""Replace the metadata of documents.
For each document to update, identified by its ID, the new metadata
dictionary completely replaces what is on the store. This includes
passing empty metadata `{}` to erase the currently-stored information.
Args:
id_to_metadata: map from the Document IDs to modify to the
new metadata for updating.
Keys in this dictionary that do not correspond to an existing
document will not cause an error, rather will result in new
rows being written into the Cassandra table but without an
associated vector: hence unreachable through vector search.
batch_size: Number of concurrent requests to send to the server.
Returns:
None if the writes succeed (otherwise an error is raised).
"""
ids_and_metadatas = list(id_to_metadata.items())
for i in range(0, len(ids_and_metadatas), batch_size):
batch_i_m = ids_and_metadatas[i : i + batch_size]
futures = [
self.table.put_async(
row_id=doc_id,
metadata=doc_md,
)
for doc_id, doc_md in batch_i_m
]
for future in futures:
future.result()
return
async def areplace_metadata(
self,
id_to_metadata: dict[str, dict],
*,
concurrency: int = 50,
) -> None:
"""Replace the metadata of documents.
For each document to update, identified by its ID, the new metadata
dictionary completely replaces what is on the store. This includes
passing empty metadata `{}` to erase the currently-stored information.
Args:
id_to_metadata: map from the Document IDs to modify to the
new metadata for updating.
Keys in this dictionary that do not correspond to an existing
document will not cause an error, rather will result in new
rows being written into the Cassandra table but without an
associated vector: hence unreachable through vector search.
concurrency: Number of concurrent queries to the database.
Defaults to 50.
Returns:
None if the writes succeed (otherwise an error is raised).
"""
ids_and_metadatas = list(id_to_metadata.items())
sem = asyncio.Semaphore(concurrency)
async def send_concurrently(doc_id: str, doc_md: dict) -> None:
async with sem:
await self.table.aput(
row_id=doc_id,
metadata=doc_md,
)
for doc_id, doc_md in ids_and_metadatas:
tasks = [asyncio.create_task(send_concurrently(doc_id, doc_md))]
await asyncio.gather(*tasks)
return
@staticmethod
def _row_to_document(row: Dict[str, Any]) -> Document:
return Document(
id=row["row_id"],
page_content=row["body_blob"],
metadata=row["metadata"],
)
def get_by_document_id(self, document_id: str) -> Document | None:
"""Get by document ID.
Args:
document_id: the document ID to get.
"""
row = self.table.get(row_id=document_id)
if row is None:
return None
return self._row_to_document(row=row)
async def aget_by_document_id(self, document_id: str) -> Document | None:
"""Get by document ID.
Args:
document_id: the document ID to get.
"""
row = await self.table.aget(row_id=document_id)
if row is None:
return None
return self._row_to_document(row=row)
def metadata_search(
self,
metadata: dict[str, Any] = {}, # noqa: B006
n: int = 5,
) -> Iterable[Document]:
"""Get documents via a metadata search.
Args:
metadata: the metadata to query for.
"""
rows = self.table.find_entries(metadata=metadata, n=n)
return [self._row_to_document(row=row) for row in rows if row]
async def ametadata_search(
self,
metadata: dict[str, Any] = {}, # noqa: B006
n: int = 5,
) -> Iterable[Document]:
"""Get documents via a metadata search.
Args:
metadata: the metadata to query for.
"""
rows = await self.table.afind_entries(metadata=metadata, n=n)
return [self._row_to_document(row=row) for row in rows]
async def asimilarity_search_with_embedding_id_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[Dict[str, str]] = None,
body_search: Optional[Union[str, List[str]]] = None,
) -> List[Tuple[Document, List[float], str]]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter on the metadata to apply.
body_search: Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns:
List of (Document, embedding, id), the most similar to the query vector.
"""
kwargs: Dict[str, Any] = {}
if filter is not None:
kwargs["metadata"] = filter
if body_search is not None:
kwargs["body_search"] = body_search
hits = await self.table.aann_search(
vector=embedding,
n=k,
**kwargs,
)
return [
(
self._row_to_document(row=hit),
hit["vector"],
hit["row_id"],
)
for hit in hits
]
@staticmethod @staticmethod
def _search_to_documents( def _search_to_documents(
hits: Iterable[Dict[str, Any]], hits: Iterable[Dict[str, Any]],
@ -341,10 +594,7 @@ class Cassandra(VectorStore):
# (1=most relevant), as required by this class' contract. # (1=most relevant), as required by this class' contract.
return [ return [
( (
Document( Cassandra._row_to_document(row=hit),
page_content=hit["body_blob"],
metadata=hit["metadata"],
),
0.5 + 0.5 * hit["distance"], 0.5 + 0.5 * hit["distance"],
hit["row_id"], hit["row_id"],
) )
@ -375,7 +625,6 @@ class Cassandra(VectorStore):
kwargs["metadata"] = filter kwargs["metadata"] = filter
if body_search is not None: if body_search is not None:
kwargs["body_search"] = body_search kwargs["body_search"] = body_search
hits = self.table.metric_ann_search( hits = self.table.metric_ann_search(
vector=embedding, vector=embedding,
n=k, n=k,
@ -712,13 +961,7 @@ class Cassandra(VectorStore):
for pf_index, pf_hit in enumerate(prefetch_hits) for pf_index, pf_hit in enumerate(prefetch_hits)
if pf_index in mmr_chosen_indices if pf_index in mmr_chosen_indices
] ]
return [ return [Cassandra._row_to_document(row=hit) for hit in mmr_hits]
Document(
page_content=hit["body_blob"],
metadata=hit["metadata"],
)
for hit in mmr_hits
]
def max_marginal_relevance_search_by_vector( def max_marginal_relevance_search_by_vector(
self, self,

8
libs/community/langchain_community/vectorstores/clickhouse.py
View File

@ -309,6 +309,14 @@ class Clickhouse(VectorStore):
**kwargs, **kwargs,
) )
# Enable JSON type # Enable JSON type
try:
self.client.command("SET allow_experimental_json_type=1")
except Exception as _:
logger.debug(
f"Clickhouse version={self.client.server_version} - "
"There is no allow_experimental_json_type parameter."
)
self.client.command("SET allow_experimental_object_type=1") self.client.command("SET allow_experimental_object_type=1")
if self.config.index_type: if self.config.index_type:
# Enable index # Enable index

239
libs/community/langchain_community/vectorstores/infinispanvs.py
View File

@ -5,9 +5,10 @@ from __future__ import annotations
import json import json
import logging import logging
import uuid import uuid
from typing import Any, Iterable, List, Optional, Tuple, Type, cast import warnings
from typing import Any, Iterable, List, Optional, Tuple, Type, Union, cast
import requests from httpx import Response
from langchain_core.documents import Document from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore from langchain_core.vectorstores import VectorStore
@ -49,7 +50,7 @@ class InfinispanVS(VectorStore):
embedding=RGBEmbeddings(), embedding=RGBEmbeddings(),
output_fields: ["texture", "color"], output_fields: ["texture", "color"],
lambda_key: lambda text,meta: str(meta["_key"]), lambda_key: lambda text,meta: str(meta["_key"]),
lambda_content: lambda item: item["color"]}) lambda_content: lambda item: item["color"])
""" """
def __init__( def __init__(
@ -58,13 +59,48 @@ class InfinispanVS(VectorStore):
ids: Optional[List[str]] = None, ids: Optional[List[str]] = None,
**kwargs: Any, **kwargs: Any,
): ):
"""
Parameters
----------
cache_name: str
Embeddings cache name. Default "vector"
entity_name: str
Protobuf entity name for the embeddings. Default "vector"
text_field: str
Protobuf field name for text. Default "text"
vector_field: str
Protobuf field name for vector. Default "vector"
lambda_content: lambda
Lambda returning the content part of an item. Default returns text_field
lambda_metadata: lambda
Lambda returning the metadata part of an item. Default returns items
fields excepts text_field, vector_field, _type
output_fields: List[str]
List of fields to be returned from item, if None return all fields.
Default None
kwargs: Any
Rest of arguments passed to Infinispan. See docs"""
self.ispn = Infinispan(**kwargs) self.ispn = Infinispan(**kwargs)
self._configuration = kwargs self._configuration = kwargs
self._cache_name = str(self._configuration.get("cache_name", "vector")) self._cache_name = str(self._configuration.get("cache_name", "vector"))
self._entity_name = str(self._configuration.get("entity_name", "vector")) self._entity_name = str(self._configuration.get("entity_name", "vector"))
self._embedding = embedding self._embedding = embedding
self._textfield = self._configuration.get("textfield", "text") self._textfield = self._configuration.get("textfield", "")
self._vectorfield = self._configuration.get("vectorfield", "vector") if self._textfield == "":
self._textfield = self._configuration.get("text_field", "text")
else:
warnings.warn(
"`textfield` is deprecated. Please use `text_field` " "param.",
DeprecationWarning,
)
self._vectorfield = self._configuration.get("vectorfield", "")
if self._vectorfield == "":
self._vectorfield = self._configuration.get("vector_field", "vector")
else:
warnings.warn(
"`vectorfield` is deprecated. Please use `vector_field` " "param.",
DeprecationWarning,
)
self._to_content = self._configuration.get( self._to_content = self._configuration.get(
"lambda_content", lambda item: self._default_content(item) "lambda_content", lambda item: self._default_content(item)
) )
@ -121,7 +157,7 @@ repeated float %s = 1;
metadata_proto += "}\n" metadata_proto += "}\n"
return metadata_proto return metadata_proto
def schema_create(self, proto: str) -> requests.Response: def schema_create(self, proto: str) -> Response:
"""Deploy the schema for the vector db """Deploy the schema for the vector db
Args: Args:
proto(str): protobuf schema proto(str): protobuf schema
@ -130,14 +166,14 @@ repeated float %s = 1;
""" """
return self.ispn.schema_post(self._entity_name + ".proto", proto) return self.ispn.schema_post(self._entity_name + ".proto", proto)
def schema_delete(self) -> requests.Response: def schema_delete(self) -> Response:
"""Delete the schema for the vector db """Delete the schema for the vector db
Returns: Returns:
An http Response containing the result of the operation An http Response containing the result of the operation
""" """
return self.ispn.schema_delete(self._entity_name + ".proto") return self.ispn.schema_delete(self._entity_name + ".proto")
def cache_create(self, config: str = "") -> requests.Response: def cache_create(self, config: str = "") -> Response:
"""Create the cache for the vector db """Create the cache for the vector db
Args: Args:
config(str): configuration of the cache. config(str): configuration of the cache.
@ -172,14 +208,14 @@ repeated float %s = 1;
) )
return self.ispn.cache_post(self._cache_name, config) return self.ispn.cache_post(self._cache_name, config)
def cache_delete(self) -> requests.Response: def cache_delete(self) -> Response:
"""Delete the cache for the vector db """Delete the cache for the vector db
Returns: Returns:
An http Response containing the result of the operation An http Response containing the result of the operation
""" """
return self.ispn.cache_delete(self._cache_name) return self.ispn.cache_delete(self._cache_name)
def cache_clear(self) -> requests.Response: def cache_clear(self) -> Response:
"""Clear the cache for the vector db """Clear the cache for the vector db
Returns: Returns:
An http Response containing the result of the operation An http Response containing the result of the operation
@ -193,14 +229,14 @@ repeated float %s = 1;
""" """
return self.ispn.cache_exists(self._cache_name) return self.ispn.cache_exists(self._cache_name)
def cache_index_clear(self) -> requests.Response: def cache_index_clear(self) -> Response:
"""Clear the index for the vector db """Clear the index for the vector db
Returns: Returns:
An http Response containing the result of the operation An http Response containing the result of the operation
""" """
return self.ispn.index_clear(self._cache_name) return self.ispn.index_clear(self._cache_name)
def cache_index_reindex(self) -> requests.Response: def cache_index_reindex(self) -> Response:
"""Rebuild the for the vector db """Rebuild the for the vector db
Returns: Returns:
An http Response containing the result of the operation An http Response containing the result of the operation
@ -325,12 +361,16 @@ repeated float %s = 1;
def configure(self, metadata: dict, dimension: int) -> None: def configure(self, metadata: dict, dimension: int) -> None:
schema = self.schema_builder(metadata, dimension) schema = self.schema_builder(metadata, dimension)
output = self.schema_create(schema) output = self.schema_create(schema)
assert output.ok, "Unable to create schema. Already exists? " assert (
output.status_code == self.ispn.Codes.OK
), "Unable to create schema. Already exists? "
"Consider using clear_old=True" "Consider using clear_old=True"
assert json.loads(output.text)["error"] is None assert json.loads(output.text)["error"] is None
if not self.cache_exists(): if not self.cache_exists():
output = self.cache_create() output = self.cache_create()
assert output.ok, "Unable to create cache. Already exists? " assert (
output.status_code == self.ispn.Codes.OK
), "Unable to create cache. Already exists? "
"Consider using clear_old=True" "Consider using clear_old=True"
# Ensure index is clean # Ensure index is clean
self.cache_index_clear() self.cache_index_clear()
@ -350,7 +390,24 @@ repeated float %s = 1;
auto_config: Optional[bool] = True, auto_config: Optional[bool] = True,
**kwargs: Any, **kwargs: Any,
) -> InfinispanVS: ) -> InfinispanVS:
"""Return VectorStore initialized from texts and embeddings.""" """Return VectorStore initialized from texts and embeddings.
In addition to parameters described by the super method, this
implementation provides other configuration params if different
configuration from default is needed.
Parameters
----------
ids : List[str]
Additional list of keys associated to the embedding. If not
provided UUIDs will be generated
clear_old : bool
Whether old data must be deleted. Default True
auto_config: bool
Whether to do a complete server setup (caches,
protobuf definition...). Default True
kwargs: Any
Rest of arguments passed to InfinispanVS. See docs"""
infinispanvs = cls(embedding=embedding, ids=ids, **kwargs) infinispanvs = cls(embedding=embedding, ids=ids, **kwargs)
if auto_config and len(metadatas or []) > 0: if auto_config and len(metadatas or []) > 0:
if clear_old: if clear_old:
@ -381,20 +438,83 @@ class Infinispan:
https://github.com/rigazilla/infinispan-vector#run-infinispan https://github.com/rigazilla/infinispan-vector#run-infinispan
""" """
def __init__(self, **kwargs: Any): def __init__(
self._configuration = kwargs self,
self._schema = str(self._configuration.get("schema", "http")) schema: str = "http",
self._host = str(self._configuration.get("hosts", ["127.0.0.1:11222"])[0]) user: str = "",
self._default_node = self._schema + "://" + self._host password: str = "",
self._cache_url = str(self._configuration.get("cache_url", "/rest/v2/caches")) hosts: List[str] = ["127.0.0.1:11222"],
self._schema_url = str(self._configuration.get("cache_url", "/rest/v2/schemas")) cache_url: str = "/rest/v2/caches",
self._use_post_for_query = str( schema_url: str = "/rest/v2/schemas",
self._configuration.get("use_post_for_query", True) use_post_for_query: bool = True,
) http2: bool = True,
verify: bool = True,
**kwargs: Any,
):
"""
Parameters
----------
schema: str
Schema for HTTP request: "http" or "https". Default "http"
user, password: str
User and password if auth is required. Default None
hosts: List[str]
List of server addresses. Default ["127.0.0.1:11222"]
cache_url: str
URL endpoint for cache API. Default "/rest/v2/caches"
schema_url: str
URL endpoint for schema API. Default "/rest/v2/schemas"
use_post_for_query: bool
Whether POST method should be used for query. Default True
http2: bool
Whether HTTP/2 protocol should be used. `pip install "httpx[http2]"` is
needed for HTTP/2. Default True
verify: bool
Whether TLS certificate must be verified. Default True
"""
def req_query( try:
self, query: str, cache_name: str, local: bool = False import httpx
) -> requests.Response: except ImportError:
raise ImportError(
"Could not import httpx python package. "
"Please install it with `pip install httpx`"
'or `pip install "httpx[http2]"` if you need HTTP/2.'
)
self.Codes = httpx.codes
self._configuration = kwargs
self._schema = schema
self._user = user
self._password = password
self._host = hosts[0]
self._default_node = self._schema + "://" + self._host
self._cache_url = cache_url
self._schema_url = schema_url
self._use_post_for_query = use_post_for_query
self._http2 = http2
if self._user and self._password:
if self._schema == "http":
auth: Union[Tuple[str, str], httpx.DigestAuth] = httpx.DigestAuth(
username=self._user, password=self._password
)
else:
auth = (self._user, self._password)
self._h2c = httpx.Client(
http2=self._http2,
http1=not self._http2,
auth=auth,
verify=verify,
)
else:
self._h2c = httpx.Client(
http2=self._http2,
http1=not self._http2,
verify=verify,
)
def req_query(self, query: str, cache_name: str, local: bool = False) -> Response:
"""Request a query """Request a query
Args: Args:
query(str): query requested query(str): query requested
@ -409,7 +529,7 @@ class Infinispan:
def _query_post( def _query_post(
self, query_str: str, cache_name: str, local: bool = False self, query_str: str, cache_name: str, local: bool = False
) -> requests.Response: ) -> Response:
api_url = ( api_url = (
self._default_node self._default_node
+ self._cache_url + self._cache_url
@ -420,9 +540,9 @@ class Infinispan:
) )
data = {"query": query_str} data = {"query": query_str}
data_json = json.dumps(data) data_json = json.dumps(data)
response = requests.post( response = self._h2c.post(
api_url, api_url,
data_json, content=data_json,
headers={"Content-Type": "application/json"}, headers={"Content-Type": "application/json"},
timeout=REST_TIMEOUT, timeout=REST_TIMEOUT,
) )
@ -430,7 +550,7 @@ class Infinispan:
def _query_get( def _query_get(
self, query_str: str, cache_name: str, local: bool = False self, query_str: str, cache_name: str, local: bool = False
) -> requests.Response: ) -> Response:
api_url = ( api_url = (
self._default_node self._default_node
+ self._cache_url + self._cache_url
@ -441,10 +561,10 @@ class Infinispan:
+ "&local=" + "&local="
+ str(local) + str(local)
) )
response = requests.get(api_url, timeout=REST_TIMEOUT) response = self._h2c.get(api_url, timeout=REST_TIMEOUT)
return response return response
def post(self, key: str, data: str, cache_name: str) -> requests.Response: def post(self, key: str, data: str, cache_name: str) -> Response:
"""Post an entry """Post an entry
Args: Args:
key(str): key of the entry key(str): key of the entry
@ -454,15 +574,15 @@ class Infinispan:
An http Response containing the result of the operation An http Response containing the result of the operation
""" """
api_url = self._default_node + self._cache_url + "/" + cache_name + "/" + key api_url = self._default_node + self._cache_url + "/" + cache_name + "/" + key
response = requests.post( response = self._h2c.post(
api_url, api_url,
data, content=data,
headers={"Content-Type": "application/json"}, headers={"Content-Type": "application/json"},
timeout=REST_TIMEOUT, timeout=REST_TIMEOUT,
) )
return response return response
def put(self, key: str, data: str, cache_name: str) -> requests.Response: def put(self, key: str, data: str, cache_name: str) -> Response:
"""Put an entry """Put an entry
Args: Args:
key(str): key of the entry key(str): key of the entry
@ -472,15 +592,15 @@ class Infinispan:
An http Response containing the result of the operation An http Response containing the result of the operation
""" """
api_url = self._default_node + self._cache_url + "/" + cache_name + "/" + key api_url = self._default_node + self._cache_url + "/" + cache_name + "/" + key
response = requests.put( response = self._h2c.put(
api_url, api_url,
data, content=data,
headers={"Content-Type": "application/json"}, headers={"Content-Type": "application/json"},
timeout=REST_TIMEOUT, timeout=REST_TIMEOUT,
) )
return response return response
def get(self, key: str, cache_name: str) -> requests.Response: def get(self, key: str, cache_name: str) -> Response:
"""Get an entry """Get an entry
Args: Args:
key(str): key of the entry key(str): key of the entry
@ -489,12 +609,12 @@ class Infinispan:
An http Response containing the entry or errors An http Response containing the entry or errors
""" """
api_url = self._default_node + self._cache_url + "/" + cache_name + "/" + key api_url = self._default_node + self._cache_url + "/" + cache_name + "/" + key
response = requests.get( response = self._h2c.get(
api_url, headers={"Content-Type": "application/json"}, timeout=REST_TIMEOUT api_url, headers={"Content-Type": "application/json"}, timeout=REST_TIMEOUT
) )
return response return response
def schema_post(self, name: str, proto: str) -> requests.Response: def schema_post(self, name: str, proto: str) -> Response:
"""Deploy a schema """Deploy a schema
Args: Args:
name(str): name of the schema. Will be used as a key name(str): name of the schema. Will be used as a key
@ -503,10 +623,10 @@ class Infinispan:
An http Response containing the result of the operation An http Response containing the result of the operation
""" """
api_url = self._default_node + self._schema_url + "/" + name api_url = self._default_node + self._schema_url + "/" + name
response = requests.post(api_url, proto, timeout=REST_TIMEOUT) response = self._h2c.post(api_url, content=proto, timeout=REST_TIMEOUT)
return response return response
def cache_post(self, name: str, config: str) -> requests.Response: def cache_post(self, name: str, config: str) -> Response:
"""Create a cache """Create a cache
Args: Args:
name(str): name of the cache. name(str): name of the cache.
@ -515,15 +635,15 @@ class Infinispan:
An http Response containing the result of the operation An http Response containing the result of the operation
""" """
api_url = self._default_node + self._cache_url + "/" + name api_url = self._default_node + self._cache_url + "/" + name
response = requests.post( response = self._h2c.post(
api_url, api_url,
config, content=config,
headers={"Content-Type": "application/json"}, headers={"Content-Type": "application/json"},
timeout=REST_TIMEOUT, timeout=REST_TIMEOUT,
) )
return response return response
def schema_delete(self, name: str) -> requests.Response: def schema_delete(self, name: str) -> Response:
"""Delete a schema """Delete a schema
Args: Args:
name(str): name of the schema. name(str): name of the schema.
@ -531,10 +651,10 @@ class Infinispan:
An http Response containing the result of the operation An http Response containing the result of the operation
""" """
api_url = self._default_node + self._schema_url + "/" + name api_url = self._default_node + self._schema_url + "/" + name
response = requests.delete(api_url, timeout=REST_TIMEOUT) response = self._h2c.delete(api_url, timeout=REST_TIMEOUT)
return response return response
def cache_delete(self, name: str) -> requests.Response: def cache_delete(self, name: str) -> Response:
"""Delete a cache """Delete a cache
Args: Args:
name(str): name of the cache. name(str): name of the cache.
@ -542,10 +662,10 @@ class Infinispan:
An http Response containing the result of the operation An http Response containing the result of the operation
""" """
api_url = self._default_node + self._cache_url + "/" + name api_url = self._default_node + self._cache_url + "/" + name
response = requests.delete(api_url, timeout=REST_TIMEOUT) response = self._h2c.delete(api_url, timeout=REST_TIMEOUT)
return response return response
def cache_clear(self, cache_name: str) -> requests.Response: def cache_clear(self, cache_name: str) -> Response:
"""Clear a cache """Clear a cache
Args: Args:
cache_name(str): name of the cache. cache_name(str): name of the cache.
@ -555,7 +675,7 @@ class Infinispan:
api_url = ( api_url = (
self._default_node + self._cache_url + "/" + cache_name + "?action=clear" self._default_node + self._cache_url + "/" + cache_name + "?action=clear"
) )
response = requests.post(api_url, timeout=REST_TIMEOUT) response = self._h2c.post(api_url, timeout=REST_TIMEOUT)
return response return response
def cache_exists(self, cache_name: str) -> bool: def cache_exists(self, cache_name: str) -> bool:
@ -570,18 +690,17 @@ class Infinispan:
) )
return self.resource_exists(api_url) return self.resource_exists(api_url)
@staticmethod def resource_exists(self, api_url: str) -> bool:
def resource_exists(api_url: str) -> bool:
"""Check if a resource exists """Check if a resource exists
Args: Args:
api_url(str): url of the resource. api_url(str): url of the resource.
Returns: Returns:
true if resource exists true if resource exists
""" """
response = requests.head(api_url, timeout=REST_TIMEOUT) response = self._h2c.head(api_url, timeout=REST_TIMEOUT)
return response.ok return response.status_code == self.Codes.OK
def index_clear(self, cache_name: str) -> requests.Response: def index_clear(self, cache_name: str) -> Response:
"""Clear an index on a cache """Clear an index on a cache
Args: Args:
cache_name(str): name of the cache. cache_name(str): name of the cache.
@ -595,9 +714,9 @@ class Infinispan:
+ cache_name + cache_name
+ "/search/indexes?action=clear" + "/search/indexes?action=clear"
) )
return requests.post(api_url, timeout=REST_TIMEOUT) return self._h2c.post(api_url, timeout=REST_TIMEOUT)
def index_reindex(self, cache_name: str) -> requests.Response: def index_reindex(self, cache_name: str) -> Response:
"""Rebuild index on a cache """Rebuild index on a cache
Args: Args:
cache_name(str): name of the cache. cache_name(str): name of the cache.
@ -611,4 +730,4 @@ class Infinispan:
+ cache_name + cache_name
+ "/search/indexes?action=reindex" + "/search/indexes?action=reindex"
) )
return requests.post(api_url, timeout=REST_TIMEOUT) return self._h2c.post(api_url, timeout=REST_TIMEOUT)

7
libs/community/langchain_community/vectorstores/myscale.py
View File

@ -193,6 +193,13 @@ class MyScale(VectorStore):
password=self.config.password, password=self.config.password,
**kwargs, **kwargs,
) )
try:
self.client.command("SET allow_experimental_json_type=1")
except Exception as _:
logger.debug(
f"Clickhouse version={self.client.server_version} - "
"There is no allow_experimental_json_type parameter."
)
self.client.command("SET allow_experimental_object_type=1") self.client.command("SET allow_experimental_object_type=1")
self.client.command(schema_) self.client.command(schema_)

4
libs/community/langchain_community/vectorstores/neo4j_vector.py
View File

@ -623,7 +623,7 @@ class Neo4jVector(VectorStore):
params = params or {} params = params or {}
try: try:
data, _, _ = self._driver.execute_query( data, _, _ = self._driver.execute_query(
query, database=self._database, parameters_=params query, database_=self._database, parameters_=params
) )
return [r.data() for r in data] return [r.data() for r in data]
except Neo4jError as e: except Neo4jError as e:
@ -646,7 +646,7 @@ class Neo4jVector(VectorStore):
): ):
raise raise
# Fallback to allow implicit transactions # Fallback to allow implicit transactions
with self._driver.session() as session: with self._driver.session(database=self._database) as session:
data = session.run(Query(text=query), params) data = session.run(Query(text=query), params)
return [r.data() for r in data] return [r.data() for r in data]

1133
libs/community/poetry.lock generated
View File

File diff suppressed because it is too large Load Diff

8
libs/community/pyproject.toml
View File

@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry] [tool.poetry]
name = "langchain-community" name = "langchain-community"
version = "0.3.1" version = "0.3.2"
description = "Community contributed LangChain integrations." description = "Community contributed LangChain integrations."
authors = [] authors = []
license = "MIT" license = "MIT"
@ -33,13 +33,13 @@ ignore-words-list = "momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogy
[tool.poetry.dependencies] [tool.poetry.dependencies]
python = ">=3.9,<4.0" python = ">=3.9,<4.0"
langchain-core = "^0.3.6" langchain-core = "^0.3.10"
langchain = "^0.3.1" langchain = "^0.3.3"
SQLAlchemy = ">=1.4,<3" SQLAlchemy = ">=1.4,<3"
requests = "^2" requests = "^2"
PyYAML = ">=5.3" PyYAML = ">=5.3"
aiohttp = "^3.8.3" aiohttp = "^3.8.3"
tenacity = "^8.1.0,!=8.4.0" tenacity = ">=8.1.0,!=8.4.0,<10"
dataclasses-json = ">= 0.5.7, < 0.7" dataclasses-json = ">= 0.5.7, < 0.7"
pydantic-settings = "^2.4.0" pydantic-settings = "^2.4.0"
langsmith = "^0.1.125" langsmith = "^0.1.125"

4
libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan.sh Executable file
View File

@ -0,0 +1,4 @@
#/bin/sh
cd infinispan
docker compose up

2
libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/groups.properties Normal file
View File

@ -0,0 +1,2 @@
#Fri May 03 10:19:58 CEST 2024
user=ADMIN,admin

62
libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/infinispan.xml Normal file
View File

@ -0,0 +1,62 @@
<infinispan
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="urn:infinispan:config:15.0 https://infinispan.org/schemas/infinispan-config-15.0.xsd
urn:infinispan:server:15.0 https://infinispan.org/schemas/infinispan-server-15.0.xsd"
xmlns="urn:infinispan:config:15.0"
xmlns:server="urn:infinispan:server:15.0">
<cache-container name="default" statistics="true">
<transport cluster="${infinispan.cluster.name:cluster}" stack="${infinispan.cluster.stack:tcp}" node-name="${infinispan.node.name:}"/>
</cache-container>
<server xmlns="urn:infinispan:server:15.0">
<interfaces>
<interface name="public">
<inet-address value="${infinispan.bind.address:127.0.0.1}"/>
</interface>
</interfaces>
<socket-bindings default-interface="public" port-offset="${infinispan.socket.binding.port-offset:0}">
<socket-binding name="default" port="${infinispan.bind.port:11222}"/>
<socket-binding name="authenticated" port="11232"/>
<socket-binding name="auth-tls" port="11242"/>
</socket-bindings>
<security>
<credential-stores>
<credential-store name="credentials" path="credentials.pfx">
<clear-text-credential clear-text="secret"/>
</credential-store>
</credential-stores>
<security-realms>
<security-realm name="default">
<properties-realm groups-attribute="Roles">
<user-properties path="/user-config/users.properties"/>
<group-properties path="/user-config/groups.properties"/>
</properties-realm>
</security-realm>
<security-realm name="tls">
<!-- Uncomment to enable TLS on the realm -->
<server-identities>
<ssl>
<keystore path="application.keystore"
password="password" alias="server"
generate-self-signed-certificate-host="localhost"/>
</ssl>
</server-identities>
<properties-realm groups-attribute="Roles">
<user-properties path="/user-config/users.properties"/>
<group-properties path="/user-config/groups.properties"/>
</properties-realm>
</security-realm>
</security-realms>
</security>
<endpoints>
<endpoint socket-binding="default"/>
<endpoint socket-binding="authenticated" security-realm="default"/>
<endpoint socket-binding="auth-tls" security-realm="tls"/>
</endpoints>
</server>
</infinispan>

4
libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/users.properties Normal file
View File

@ -0,0 +1,4 @@
#$REALM_NAME=default$
#$ALGORITHM=encrypted$
#Fri May 03 10:19:58 CEST 2024
user=scram-sha-1\:BYGcIAws2gznU/kpezoSb1VQNVd+YMX9r+9SAINFoZtPHaHTAQ\=\=;scram-sha-256\:BYGcIAwRiWiD+8f7dyQEs1Wsum/64MOcjGJ2UcmZFQB6DZJqwRDJ4NrvII4NttmxlA\=\=;scram-sha-384\:BYGcIAz+Eud65N8GWK4TMwhSCZpeE5EFSdynywdryQj3ZwBEgv+KF8hRUuGxiq3EyRxsby6w7DHK3CICGZLsPrM\=;scram-sha-512\:BYGcIAwWxVY9DHn42kHydivyU3s9LSPmyfPPJkIFYyt/XsMASFHGoy5rzk4ahX4HjpJgb+NjdCwhGfi33CY0azUIrn439s62Yg5mq9i+ISto;digest-md5\:AgR1c2VyB2RlZmF1bHSYYyzPjRDR7MhrsdFSK03P;digest-sha\:AgR1c2VyB2RlZmF1bHTga5gDNnNYh7/2HqhBVOdUHjBzhw\=\=;digest-sha-256\:AgR1c2VyB2RlZmF1bHTig5qZQIxqtJBTUp3EMh5UIFoS4qOhz9Uk5aOW9ZKCfw\=\=;digest-sha-384\:AgR1c2VyB2RlZmF1bHT01pAN/pRMLS5afm4Q9S0kuLlA0NokuP8F0AISTwXCb1E8RMsFHlBVPOa5rC6Nyso\=;digest-sha-512\:AgR1c2VyB2RlZmF1bHTi+cHn1Ez2Ze41CvPXb9eP/7JmRys7m1f5qPMQWhAmDOuuUXNWEG4yKSI9k2EZgQvMKTd5hDbR24ul1BsYP8X5;

16
libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/docker-compose.yaml Normal file
View File

@ -0,0 +1,16 @@
version: "3.7"
services:
infinispan:
image: quay.io/infinispan/server:15.0
ports:
- '11222:11222'
- '11232:11232'
- '11242:11242'
deploy:
resources:
limits:
memory: 25Gb
volumes:
- ./conf:/user-config
command: -c /user-config/infinispan.xml

147
libs/community/tests/integration_tests/vectorstores/test_cassandra.py
View File

@ -17,6 +17,17 @@ from tests.integration_tests.vectorstores.fake_embeddings import (
) )
def _strip_docs(documents: List[Document]) -> List[Document]:
return [_strip_doc(doc) for doc in documents]
def _strip_doc(document: Document) -> Document:
return Document(
page_content=document.page_content,
metadata=document.metadata,
)
def _vectorstore_from_texts( def _vectorstore_from_texts(
texts: List[str], texts: List[str],
metadatas: Optional[List[dict]] = None, metadatas: Optional[List[dict]] = None,
@ -110,9 +121,9 @@ async def test_cassandra() -> None:
texts = ["foo", "bar", "baz"] texts = ["foo", "bar", "baz"]
docsearch = _vectorstore_from_texts(texts) docsearch = _vectorstore_from_texts(texts)
output = docsearch.similarity_search("foo", k=1) output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")] assert _strip_docs(output) == _strip_docs([Document(page_content="foo")])
output = await docsearch.asimilarity_search("foo", k=1) output = await docsearch.asimilarity_search("foo", k=1)
assert output == [Document(page_content="foo")] assert _strip_docs(output) == _strip_docs([Document(page_content="foo")])
async def test_cassandra_with_score() -> None: async def test_cassandra_with_score() -> None:
@ -130,13 +141,13 @@ async def test_cassandra_with_score() -> None:
output = docsearch.similarity_search_with_score("foo", k=3) output = docsearch.similarity_search_with_score("foo", k=3)
docs = [o[0] for o in output] docs = [o[0] for o in output]
scores = [o[1] for o in output] scores = [o[1] for o in output]
assert docs == expected_docs assert _strip_docs(docs) == _strip_docs(expected_docs)
assert scores[0] > scores[1] > scores[2] assert scores[0] > scores[1] > scores[2]
output = await docsearch.asimilarity_search_with_score("foo", k=3) output = await docsearch.asimilarity_search_with_score("foo", k=3)
docs = [o[0] for o in output] docs = [o[0] for o in output]
scores = [o[1] for o in output] scores = [o[1] for o in output]
assert docs == expected_docs assert _strip_docs(docs) == _strip_docs(expected_docs)
assert scores[0] > scores[1] > scores[2] assert scores[0] > scores[1] > scores[2]
@ -239,7 +250,7 @@ async def test_cassandra_no_drop_async() -> None:
def test_cassandra_delete() -> None: def test_cassandra_delete() -> None:
"""Test delete methods from vector store.""" """Test delete methods from vector store."""
texts = ["foo", "bar", "baz", "gni"] texts = ["foo", "bar", "baz", "gni"]
metadatas = [{"page": i} for i in range(len(texts))] metadatas = [{"page": i, "mod2": i % 2} for i in range(len(texts))]
docsearch = _vectorstore_from_texts([], metadatas=metadatas) docsearch = _vectorstore_from_texts([], metadatas=metadatas)
ids = docsearch.add_texts(texts, metadatas) ids = docsearch.add_texts(texts, metadatas)
@ -263,11 +274,21 @@ def test_cassandra_delete() -> None:
output = docsearch.similarity_search("foo", k=10) output = docsearch.similarity_search("foo", k=10)
assert len(output) == 0 assert len(output) == 0
docsearch.add_texts(texts, metadatas)
num_deleted = docsearch.delete_by_metadata_filter({"mod2": 0}, batch_size=1)
assert num_deleted == 2
output = docsearch.similarity_search("foo", k=10)
assert len(output) == 2
docsearch.clear()
with pytest.raises(ValueError):
docsearch.delete_by_metadata_filter({})
async def test_cassandra_adelete() -> None: async def test_cassandra_adelete() -> None:
"""Test delete methods from vector store.""" """Test delete methods from vector store."""
texts = ["foo", "bar", "baz", "gni"] texts = ["foo", "bar", "baz", "gni"]
metadatas = [{"page": i} for i in range(len(texts))] metadatas = [{"page": i, "mod2": i % 2} for i in range(len(texts))]
docsearch = await _vectorstore_from_texts_async([], metadatas=metadatas) docsearch = await _vectorstore_from_texts_async([], metadatas=metadatas)
ids = await docsearch.aadd_texts(texts, metadatas) ids = await docsearch.aadd_texts(texts, metadatas)
@ -291,6 +312,16 @@ async def test_cassandra_adelete() -> None:
output = docsearch.similarity_search("foo", k=10) output = docsearch.similarity_search("foo", k=10)
assert len(output) == 0 assert len(output) == 0
await docsearch.aadd_texts(texts, metadatas)
num_deleted = await docsearch.adelete_by_metadata_filter({"mod2": 0}, batch_size=1)
assert num_deleted == 2
output = await docsearch.asimilarity_search("foo", k=10)
assert len(output) == 2
await docsearch.aclear()
with pytest.raises(ValueError):
await docsearch.adelete_by_metadata_filter({})
def test_cassandra_metadata_indexing() -> None: def test_cassandra_metadata_indexing() -> None:
"""Test comparing metadata indexing policies.""" """Test comparing metadata indexing policies."""
@ -316,3 +347,107 @@ def test_cassandra_metadata_indexing() -> None:
with pytest.raises(ValueError): with pytest.raises(ValueError):
# "Non-indexed metadata fields cannot be used in queries." # "Non-indexed metadata fields cannot be used in queries."
vstore_f1.similarity_search("bar", filter={"field2": "b"}, k=2) vstore_f1.similarity_search("bar", filter={"field2": "b"}, k=2)
def test_cassandra_replace_metadata() -> None:
"""Test of replacing metadata."""
N_DOCS = 100
REPLACE_RATIO = 2 # one in ... will have replaced metadata
BATCH_SIZE = 3
vstore_f1 = _vectorstore_from_texts(
texts=[],
metadata_indexing=("allowlist", ["field1", "field2"]),
table_name="vector_test_table_indexing",
)
orig_documents = [
Document(
page_content=f"doc_{doc_i}",
id=f"doc_id_{doc_i}",
metadata={"field1": f"f1_{doc_i}", "otherf": "pre"},
)
for doc_i in range(N_DOCS)
]
vstore_f1.add_documents(orig_documents)
ids_to_replace = [
f"doc_id_{doc_i}" for doc_i in range(N_DOCS) if doc_i % REPLACE_RATIO == 0
]
# various kinds of replacement at play here:
def _make_new_md(mode: int, doc_id: str) -> dict[str, str]:
if mode == 0:
return {}
elif mode == 1:
return {"field2": f"NEW_{doc_id}"}
elif mode == 2:
return {"field2": f"NEW_{doc_id}", "ofherf2": "post"}
else:
return {"ofherf2": "post"}
ids_to_new_md = {
doc_id: _make_new_md(rep_i % 4, doc_id)
for rep_i, doc_id in enumerate(ids_to_replace)
}
vstore_f1.replace_metadata(ids_to_new_md, batch_size=BATCH_SIZE)
# thorough check
expected_id_to_metadata: dict[str, dict] = {
**{(document.id or ""): document.metadata for document in orig_documents},
**ids_to_new_md,
}
for hit in vstore_f1.similarity_search("doc", k=N_DOCS + 1):
assert hit.id is not None
assert hit.metadata == expected_id_to_metadata[hit.id]
async def test_cassandra_areplace_metadata() -> None:
"""Test of replacing metadata."""
N_DOCS = 100
REPLACE_RATIO = 2 # one in ... will have replaced metadata
BATCH_SIZE = 3
vstore_f1 = _vectorstore_from_texts(
texts=[],
metadata_indexing=("allowlist", ["field1", "field2"]),
table_name="vector_test_table_indexing",
)
orig_documents = [
Document(
page_content=f"doc_{doc_i}",
id=f"doc_id_{doc_i}",
metadata={"field1": f"f1_{doc_i}", "otherf": "pre"},
)
for doc_i in range(N_DOCS)
]
await vstore_f1.aadd_documents(orig_documents)
ids_to_replace = [
f"doc_id_{doc_i}" for doc_i in range(N_DOCS) if doc_i % REPLACE_RATIO == 0
]
# various kinds of replacement at play here:
def _make_new_md(mode: int, doc_id: str) -> dict[str, str]:
if mode == 0:
return {}
elif mode == 1:
return {"field2": f"NEW_{doc_id}"}
elif mode == 2:
return {"field2": f"NEW_{doc_id}", "ofherf2": "post"}
else:
return {"ofherf2": "post"}
ids_to_new_md = {
doc_id: _make_new_md(rep_i % 4, doc_id)
for rep_i, doc_id in enumerate(ids_to_replace)
}
await vstore_f1.areplace_metadata(ids_to_new_md, concurrency=BATCH_SIZE)
# thorough check
expected_id_to_metadata: dict[str, dict] = {
**{(document.id or ""): document.metadata for document in orig_documents},
**ids_to_new_md,
}
for hit in await vstore_f1.asimilarity_search("doc", k=N_DOCS + 1):
assert hit.id is not None
assert hit.metadata == expected_id_to_metadata[hit.id]

109
libs/community/tests/integration_tests/vectorstores/test_infinispanvs.py
View File

@ -1,7 +1,9 @@
"""Test Infinispan functionality.""" """Test Infinispan functionality."""
import warnings
from typing import Any, List, Optional from typing import Any, List, Optional
import httpx
import pytest import pytest
from langchain_core.documents import Document from langchain_core.documents import Document
@ -11,9 +13,18 @@ from tests.integration_tests.vectorstores.fake_embeddings import (
fake_texts, fake_texts,
) )
"""
cd tests/integration_tests/vectorstores/docker-compose
./infinispan.sh
def _infinispan_setup_noautoconf() -> None: Current Infinispan implementation relies on httpx: `pip install "httpx[http2]"`
ispnvs = InfinispanVS(auto_config=False) if not installed. HTTP/2 is enable by default, if it's not
wanted use `pip install "httpx"`.
"""
def _infinispan_setup_noautoconf(**kwargs: Any) -> None:
ispnvs = InfinispanVS(http2=_hasHttp2(), auto_config=False, **kwargs)
ispnvs.cache_delete() ispnvs.cache_delete()
ispnvs.schema_delete() ispnvs.schema_delete()
proto = """ proto = """
@ -54,64 +65,104 @@ def _infinispanvs_from_texts(
ids=ids, ids=ids,
clear_old=clear_old, clear_old=clear_old,
auto_config=auto_config, auto_config=auto_config,
http2=_hasHttp2(),
**kwargs, **kwargs,
) )
def _hasHttp2() -> bool:
try:
httpx.Client(http2=True)
return True
except Exception:
return False
@pytest.mark.parametrize("autoconfig", [False, True]) @pytest.mark.parametrize("autoconfig", [False, True])
@pytest.mark.parametrize(
"conn_opts",
[
{},
{
"user": "user",
"password": "password",
"hosts": ["localhost:11232"],
"schema": "http",
},
{
"user": "user",
"password": "password",
"hosts": ["localhost:11242"],
"schema": "https",
"verify": False,
},
],
)
class TestBasic: class TestBasic:
def test_infinispan(self, autoconfig: bool) -> None: def test_infinispan(self, autoconfig: bool, conn_opts: dict) -> None:
"""Test end to end construction and search.""" """Test end to end construction and search."""
if not autoconfig: if not autoconfig:
_infinispan_setup_noautoconf() _infinispan_setup_noautoconf(**conn_opts)
docsearch = _infinispanvs_from_texts(auto_config=autoconfig) docsearch = _infinispanvs_from_texts(auto_config=autoconfig, **conn_opts)
output = docsearch.similarity_search("foo", k=1) output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")] assert output == [Document(page_content="foo")]
def test_infinispan_with_metadata(self, autoconfig: bool) -> None: def test_infinispan_with_auth(self, autoconfig: bool, conn_opts: dict) -> None:
"""Test end to end construction and search."""
if not autoconfig:
_infinispan_setup_noautoconf(**conn_opts)
docsearch = _infinispanvs_from_texts(auto_config=autoconfig, **conn_opts)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
def test_infinispan_with_metadata(self, autoconfig: bool, conn_opts: dict) -> None:
"""Test with metadata""" """Test with metadata"""
if not autoconfig: if not autoconfig:
_infinispan_setup_noautoconf() _infinispan_setup_noautoconf(**conn_opts)
meta = [] meta = []
for _ in range(len(fake_texts)): for _ in range(len(fake_texts)):
meta.append({"label": "test"}) meta.append({"label": "test"})
docsearch = _infinispanvs_from_texts(metadatas=meta, auto_config=autoconfig) docsearch = _infinispanvs_from_texts(
metadatas=meta, auto_config=autoconfig, **conn_opts
)
output = docsearch.similarity_search("foo", k=1) output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"label": "test"})] assert output == [Document(page_content="foo", metadata={"label": "test"})]
def test_infinispan_with_metadata_with_output_fields( def test_infinispan_with_metadata_with_output_fields(
self, autoconfig: bool self, autoconfig: bool, conn_opts: dict
) -> None: ) -> None:
"""Test with metadata""" """Test with metadata"""
if not autoconfig: if not autoconfig:
_infinispan_setup_noautoconf() _infinispan_setup_noautoconf(**conn_opts)
metadatas = [ metadatas = [
{"page": i, "label": "label" + str(i)} for i in range(len(fake_texts)) {"page": i, "label": "label" + str(i)} for i in range(len(fake_texts))
] ]
c = {"output_fields": ["label", "page", "text"]} c = {"output_fields": ["label", "page", "text"]}
docsearch = _infinispanvs_from_texts( docsearch = _infinispanvs_from_texts(
metadatas=metadatas, configuration=c, auto_config=autoconfig metadatas=metadatas, configuration=c, auto_config=autoconfig, **conn_opts
) )
output = docsearch.similarity_search("foo", k=1) output = docsearch.similarity_search("foo", k=1)
assert output == [ assert output == [
Document(page_content="foo", metadata={"label": "label0", "page": 0}) Document(page_content="foo", metadata={"label": "label0", "page": 0})
] ]
def test_infinispanvs_with_id(self, autoconfig: bool) -> None: def test_infinispanvs_with_id(self, autoconfig: bool, conn_opts: dict) -> None:
"""Test with ids""" """Test with ids"""
ids = ["id_" + str(i) for i in range(len(fake_texts))] ids = ["id_" + str(i) for i in range(len(fake_texts))]
docsearch = _infinispanvs_from_texts(ids=ids, auto_config=autoconfig) docsearch = _infinispanvs_from_texts(
ids=ids, auto_config=autoconfig, **conn_opts
)
output = docsearch.similarity_search("foo", k=1) output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")] assert output == [Document(page_content="foo")]
def test_infinispan_with_score(self, autoconfig: bool) -> None: def test_infinispan_with_score(self, autoconfig: bool, conn_opts: dict) -> None:
"""Test end to end construction and search with scores and IDs.""" """Test end to end construction and search with scores and IDs."""
if not autoconfig: if not autoconfig:
_infinispan_setup_noautoconf() _infinispan_setup_noautoconf(**conn_opts)
texts = ["foo", "bar", "baz"] texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))] metadatas = [{"page": i} for i in range(len(texts))]
docsearch = _infinispanvs_from_texts( docsearch = _infinispanvs_from_texts(
metadatas=metadatas, auto_config=autoconfig metadatas=metadatas, auto_config=autoconfig, **conn_opts
) )
output = docsearch.similarity_search_with_score("foo", k=3) output = docsearch.similarity_search_with_score("foo", k=3)
docs = [o[0] for o in output] docs = [o[0] for o in output]
@ -123,14 +174,14 @@ class TestBasic:
] ]
assert scores[0] >= scores[1] >= scores[2] assert scores[0] >= scores[1] >= scores[2]
def test_infinispan_add_texts(self, autoconfig: bool) -> None: def test_infinispan_add_texts(self, autoconfig: bool, conn_opts: dict) -> None:
"""Test end to end construction and MRR search.""" """Test end to end construction and MRR search."""
if not autoconfig: if not autoconfig:
_infinispan_setup_noautoconf() _infinispan_setup_noautoconf(**conn_opts)
texts = ["foo", "bar", "baz"] texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))] metadatas = [{"page": i} for i in range(len(texts))]
docsearch = _infinispanvs_from_texts( docsearch = _infinispanvs_from_texts(
metadatas=metadatas, auto_config=autoconfig metadatas=metadatas, auto_config=autoconfig, **conn_opts
) )
docsearch.add_texts(texts, metadatas) docsearch.add_texts(texts, metadatas)
@ -138,19 +189,22 @@ class TestBasic:
output = docsearch.similarity_search("foo", k=10) output = docsearch.similarity_search("foo", k=10)
assert len(output) == 6 assert len(output) == 6
def test_infinispan_no_clear_old(self, autoconfig: bool) -> None: def test_infinispan_no_clear_old(self, autoconfig: bool, conn_opts: dict) -> None:
"""Test end to end construction and MRR search.""" """Test end to end construction and MRR search."""
if not autoconfig: if not autoconfig:
_infinispan_setup_noautoconf() _infinispan_setup_noautoconf(**conn_opts)
texts = ["foo", "bar", "baz"] texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))] metadatas = [{"page": i} for i in range(len(texts))]
docsearch = _infinispanvs_from_texts( docsearch = _infinispanvs_from_texts(
metadatas=metadatas, auto_config=autoconfig metadatas=metadatas, auto_config=autoconfig, **conn_opts
) )
del docsearch del docsearch
try: try:
docsearch = _infinispanvs_from_texts( docsearch = _infinispanvs_from_texts(
metadatas=metadatas, clear_old=False, auto_config=autoconfig metadatas=metadatas,
clear_old=False,
auto_config=autoconfig,
**conn_opts,
) )
except AssertionError: except AssertionError:
if autoconfig: if autoconfig:
@ -159,3 +213,12 @@ class TestBasic:
raise raise
output = docsearch.similarity_search("foo", k=10) output = docsearch.similarity_search("foo", k=10)
assert len(output) == 6 assert len(output) == 6
class TestHttp2:
def test_http2(self) -> None:
try:
httpx.Client(http2=True)
except Exception:
warnings.warn('pip install "httpx[http2]" if you need HTTP/2')
pass

10
libs/core/langchain_core/rate_limiters.py
View File

@ -248,8 +248,14 @@ class InMemoryRateLimiter(BaseRateLimiter):
if not blocking: if not blocking:
return self._consume() return self._consume()
while not self._consume(): # noqa: ASYNC110 while not self._consume():
await asyncio.sleep(self.check_every_n_seconds) # This code ignores the ASYNC110 warning which is a false positive in this
# case.
# There is no external actor that can mark that the Event is done
# since the tokens are managed by the rate limiter itself.
# It needs to wake up to re-fill the tokens.
# https://docs.astral.sh/ruff/rules/async-busy-wait/
await asyncio.sleep(self.check_every_n_seconds) # ruff: noqa: ASYNC110
return True return True

404
libs/core/poetry.lock generated
View File

@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]] [[package]]
name = "annotated-types" name = "annotated-types"
@ -902,13 +902,13 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
[[package]] [[package]]
name = "jsonschema-specifications" name = "jsonschema-specifications"
version = "2023.12.1" version = "2024.10.1"
description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry" description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.9"
files = [ files = [
{file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"}, {file = "jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf"},
{file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"}, {file = "jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272"},
] ]
[package.dependencies] [package.dependencies]
@ -1215,13 +1215,13 @@ url = "../text-splitters"
[[package]] [[package]]
name = "langsmith" name = "langsmith"
version = "0.1.129" version = "0.1.132"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "langsmith-0.1.129-py3-none-any.whl", hash = "sha256:31393fbbb17d6be5b99b9b22d530450094fab23c6c37281a6a6efb2143d05347"}, {file = "langsmith-0.1.132-py3-none-any.whl", hash = "sha256:2320894203675c1c292b818cbecf68b69e47a9f7814d4e950237d1faaafd5dee"},
{file = "langsmith-0.1.129.tar.gz", hash = "sha256:6c3ba66471bef41b9f87da247cc0b493268b3f54656f73648a256a205261b6a0"}, {file = "langsmith-0.1.132.tar.gz", hash = "sha256:007b8fac469138abdba89db931900a26c5d316640e27ff4660d28c92a766aae1"},
] ]
[package.dependencies] [package.dependencies]
@ -1232,74 +1232,76 @@ pydantic = [
{version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""}, {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
] ]
requests = ">=2,<3" requests = ">=2,<3"
requests-toolbelt = ">=1.0.0,<2.0.0"
[[package]] [[package]]
name = "markupsafe" name = "markupsafe"
version = "2.1.5" version = "3.0.1"
description = "Safely add untrusted strings to HTML/XML markup." description = "Safely add untrusted strings to HTML/XML markup."
optional = false optional = false
python-versions = ">=3.7" python-versions = ">=3.9"
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@ -2710,13 +2736,13 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
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files = [ files = [
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{file = "syrupy-4.7.1.tar.gz", hash = "sha256:f9d4485f3f27d0e5df6ed299cac6fa32eb40a441915d988e82be5a4bdda335c8"}, {file = "syrupy-4.7.2.tar.gz", hash = "sha256:ea45e099f242de1bb53018c238f408a5bb6c82007bc687aefcbeaa0e1c2e935a"},
] ]
[package.dependencies] [package.dependencies]
@ -2724,13 +2750,13 @@ pytest = ">=7.0.0,<9.0.0"
[[package]] [[package]]
name = "tenacity" name = "tenacity"
version = "8.5.0" version = "9.0.0"
description = "Retry code until it succeeds" description = "Retry code until it succeeds"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "tenacity-8.5.0-py3-none-any.whl", hash = "sha256:b594c2a5945830c267ce6b79a166228323ed52718f30302c1359836112346687"}, {file = "tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539"},
{file = "tenacity-8.5.0.tar.gz", hash = "sha256:8bc6c0c8a09b31e6cad13c47afbed1a567518250a9a171418582ed8d9c20ca78"}, {file = "tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b"},
] ]
[package.extras] [package.extras]
@ -2849,13 +2875,13 @@ files = [
[[package]] [[package]]
name = "types-python-dateutil" name = "types-python-dateutil"
version = "2.9.0.20240906" version = "2.9.0.20241003"
description = "Typing stubs for python-dateutil" description = "Typing stubs for python-dateutil"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "types-python-dateutil-2.9.0.20240906.tar.gz", hash = "sha256:9706c3b68284c25adffc47319ecc7947e5bb86b3773f843c73906fd598bc176e"}, {file = "types-python-dateutil-2.9.0.20241003.tar.gz", hash = "sha256:58cb85449b2a56d6684e41aeefb4c4280631246a0da1a719bdbe6f3fb0317446"},
{file = "types_python_dateutil-2.9.0.20240906-py3-none-any.whl", hash = "sha256:27c8cc2d058ccb14946eebcaaa503088f4f6dbc4fb6093d3d456a49aef2753f6"}, {file = "types_python_dateutil-2.9.0.20241003-py3-none-any.whl", hash = "sha256:250e1d8e80e7bbc3a6c99b907762711d1a1cdd00e978ad39cb5940f6f0a87f3d"},
] ]
[[package]] [[package]]
@ -3053,4 +3079,4 @@ type = ["pytest-mypy"]
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = ">=3.9,<4.0" python-versions = ">=3.9,<4.0"
content-hash = "d226029b416404cd24944878e0381319620636d4b4ccdee5646406e025ed79c4" content-hash = "553cec5042402574196ae21460a8f8915ddcd9c0190196bd9a666ef8c3a112a8"

10
libs/core/pyproject.toml
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@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry] [tool.poetry]
name = "langchain-core" name = "langchain-core"
version = "0.3.9" version = "0.3.10"
description = "Building applications with LLMs through composability" description = "Building applications with LLMs through composability"
authors = [] authors = []
license = "MIT" license = "MIT"
@ -28,7 +28,7 @@ target-version = "py39"
[tool.poetry.dependencies] [tool.poetry.dependencies]
python = ">=3.9,<4.0" python = ">=3.9,<4.0"
langsmith = "^0.1.125" langsmith = "^0.1.125"
tenacity = "^8.1.0,!=8.4.0" tenacity = ">=8.1.0,!=8.4.0,<10.0.0"
jsonpatch = "^1.33" jsonpatch = "^1.33"
PyYAML = ">=5.3" PyYAML = ">=5.3"
packaging = ">=23.2,<25" packaging = ">=23.2,<25"
@ -119,6 +119,7 @@ classmethod-decorators = [ "classmethod", "langchain_core.utils.pydantic.pre_ini
[tool.poetry.group.lint.dependencies] [tool.poetry.group.lint.dependencies]
ruff = "^0.5" ruff = "^0.5"
[tool.poetry.group.typing.dependencies] [tool.poetry.group.typing.dependencies]
mypy = ">=1.10,<1.11" mypy = ">=1.10,<1.11"
types-pyyaml = "^6.0.12.2" types-pyyaml = "^6.0.12.2"
@ -126,11 +127,13 @@ types-requests = "^2.28.11.5"
types-jinja2 = "^2.11.9" types-jinja2 = "^2.11.9"
simsimd = "^5.0.0" simsimd = "^5.0.0"
[tool.poetry.group.dev.dependencies] [tool.poetry.group.dev.dependencies]
jupyter = "^1.0.0" jupyter = "^1.0.0"
setuptools = "^67.6.1" setuptools = "^67.6.1"
grandalf = "^0.8" grandalf = "^0.8"
[tool.poetry.group.test.dependencies] [tool.poetry.group.test.dependencies]
pytest = "^7.3.0" pytest = "^7.3.0"
freezegun = "^1.2.2" freezegun = "^1.2.2"
@ -149,12 +152,15 @@ python = "<3.12"
version = "^1.26.0" version = "^1.26.0"
python = ">=3.12" python = ">=3.12"
[tool.poetry.group.test_integration.dependencies] [tool.poetry.group.test_integration.dependencies]
[tool.poetry.group.typing.dependencies.langchain-text-splitters] [tool.poetry.group.typing.dependencies.langchain-text-splitters]
path = "../text-splitters" path = "../text-splitters"
develop = true develop = true
[tool.poetry.group.test.dependencies.langchain-standard-tests] [tool.poetry.group.test.dependencies.langchain-standard-tests]
path = "../standard-tests" path = "../standard-tests"
develop = true develop = true

22
libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr
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905
libs/langchain/poetry.lock generated
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6
libs/langchain/pyproject.toml
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@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry] [tool.poetry]
name = "langchain" name = "langchain"
version = "0.3.2" version = "0.3.3"
description = "Building applications with LLMs through composability" description = "Building applications with LLMs through composability"
authors = [] authors = []
license = "MIT" license = "MIT"
@ -33,7 +33,7 @@ langchain-server = "langchain.server:main"
[tool.poetry.dependencies] [tool.poetry.dependencies]
python = ">=3.9,<4.0" python = ">=3.9,<4.0"
langchain-core = "^0.3.8" langchain-core = "^0.3.10"
langchain-text-splitters = "^0.3.0" langchain-text-splitters = "^0.3.0"
langsmith = "^0.1.17" langsmith = "^0.1.17"
pydantic = "^2.7.4" pydantic = "^2.7.4"
@ -41,7 +41,7 @@ SQLAlchemy = ">=1.4,<3"
requests = "^2" requests = "^2"
PyYAML = ">=5.3" PyYAML = ">=5.3"
aiohttp = "^3.8.3" aiohttp = "^3.8.3"
tenacity = "^8.1.0,!=8.4.0" tenacity = ">=8.1.0,!=8.4.0,<10"
[[tool.poetry.dependencies.numpy]] [[tool.poetry.dependencies.numpy]]
version = "^1" version = "^1"
python = "<3.12" python = "<3.12"

1359
libs/partners/chroma/poetry.lock generated
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4
libs/partners/chroma/pyproject.toml
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@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry] [tool.poetry]
name = "langchain-chroma" name = "langchain-chroma"
version = "0.1.4" version = "0.1.5"
description = "An integration package connecting Chroma and LangChain" description = "An integration package connecting Chroma and LangChain"
authors = [] authors = []
readme = "README.md" readme = "README.md"
@ -47,7 +47,7 @@ addopts = " --strict-markers --strict-config --durations=5"
markers = [ "requires: mark tests as requiring a specific library", "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them",] markers = [ "requires: mark tests as requiring a specific library", "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them",]
[tool.poetry.dependencies.chromadb] [tool.poetry.dependencies.chromadb]
version = ">=0.4.0,<0.6.0,!=0.5.4,!=0.5.5" version = ">=0.4.0,<0.6.0,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,!=0.5.10,!=0.5.11,!=0.5.12"
[tool.poetry.dependencies.fastapi] [tool.poetry.dependencies.fastapi]
version = ">=0.95.2,<1" version = ">=0.95.2,<1"

9
libs/partners/openai/langchain_openai/chat_models/base.py
View File

@ -480,6 +480,15 @@ class BaseChatOpenAI(BaseChatModel):
values = _build_model_kwargs(values, all_required_field_names) values = _build_model_kwargs(values, all_required_field_names)
return values return values
@model_validator(mode="before")
@classmethod
def validate_temperature(cls, values: Dict[str, Any]) -> Any:
"""Currently o1 models only allow temperature=1."""
model = values.get("model_name") or values.get("model") or ""
if model.startswith("o1") and "temperature" not in values:
values["temperature"] = 1
return values
@model_validator(mode="after") @model_validator(mode="after")
def validate_environment(self) -> Self: def validate_environment(self) -> Self:
"""Validate that api key and python package exists in environment.""" """Validate that api key and python package exists in environment."""

7
libs/partners/openai/tests/unit_tests/chat_models/test_base.py
View File

@ -35,6 +35,13 @@ def test_openai_model_param() -> None:
assert llm.model_name == "foo" assert llm.model_name == "foo"
def test_openai_o1_temperature() -> None:
llm = ChatOpenAI(model="o1-preview")
assert llm.temperature == 1
llm = ChatOpenAI(model_name="o1-mini") # type: ignore[call-arg]
assert llm.temperature == 1
def test_function_message_dict_to_function_message() -> None: def test_function_message_dict_to_function_message() -> None:
content = json.dumps({"result": "Example #1"}) content = json.dumps({"result": "Example #1"})
name = "test_function" name = "test_function"