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docs: add Tilores provider and tools (#29244)

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Description: This PR adds documentation for the Tilores provider and
tools.
Issue: closes #26320
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Stefan Berkner 2025-01-23 18:17:59 +01:00 committed by GitHub
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105
docs/docs/integrations/providers/tilores.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tilores\n",
"\n",
"[Tilores](https://tilores.io) is a platform that provides advanced entity resolution solutions for data integration and management. Using cutting-edge algorithms, machine learning, and a user-friendly interfaces, Tilores helps organizations match, resolve, and consolidate data from disparate sources, ensuring high-quality, consistent information."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installation and Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "y8ku6X96sebl"
},
"outputs": [],
"source": [
"%pip install --upgrade tilores-langchain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To access Tilores, you need to [create and configure an instance](https://app.tilores.io). If you prefer to test out Tilores first, you can use the [read-only demo credentials](https://github.com/tilotech/identity-rag-customer-insights-chatbot?tab=readme-ov-file#1-configure-customer-data-access)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"from tilores import TiloresAPI\n",
"\n",
"os.environ[\"TILORES_API_URL\"] = \"<api-url>\"\n",
"os.environ[\"TILORES_TOKEN_URL\"] = \"<token-url>\"\n",
"os.environ[\"TILORES_CLIENT_ID\"] = \"<client-id>\"\n",
"os.environ[\"TILORES_CLIENT_SECRET\"] = \"<client-secret>\"\n",
"\n",
"tilores = TiloresAPI.from_environ()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Please refer to the [Tilores documentation](https://docs.tilotech.io/tilores/publicsaaswalkthrough/) on how to create your own instance."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Toolkits\n",
"\n",
"You can use the [`TiloresTools`](/docs/integrations/tools/tilores) to query data from Tilores:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tilores_langchain import TiloresTools"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "langchain",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.8"
}
},
"nbformat": 4,
"nbformat_minor": 1
}

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{
"cells": [
{
"cell_type": "raw",
"id": "10238e62-3465-4973-9279-606cbb7ccf16",
"metadata": {},
"source": [
"---\n",
"sidebar_label: Tilores\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "a6f91f20",
"metadata": {},
"source": [
"# Tilores\n",
"\n",
"This notebook covers how to get started with the [Tilores](/docs/integrations/providers/tilores) tools.\n",
"For a more complex example you can checkout our [customer insights chatbot example](https://github.com/tilotech/identity-rag-customer-insights-chatbot).\n",
"\n",
"## Overview\n",
"\n",
"### Integration details\n",
"\n",
"| Class | Package | Serializable | JS support | Package latest |\n",
"| :--- | :--- | :---: | :---: | :---: |\n",
"| TiloresTools | [tilores-langchain](https://pypi.org/project/tilores-langchain/) | ❌ | ❌ | ![PyPI - Version](https://img.shields.io/pypi/v/tilores-langchain?style=flat-square&label=%20) |\n",
"\n",
"## Setup\n",
"\n",
"The integration requires the following packages:"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "f85b4089",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install --quiet -U tilores-langchain langchain"
]
},
{
"cell_type": "markdown",
"id": "b15e9266",
"metadata": {},
"source": [
"### Credentials\n",
"\n",
"To access Tilores, you need to [create and configure an instance](https://app.tilores.io). If you prefer to test out Tilores first, you can use the [read-only demo credentials](https://github.com/tilotech/identity-rag-customer-insights-chatbot?tab=readme-ov-file#1-configure-customer-data-access)."
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "e0b178a2-8816-40ca-b57c-ccdd86dde9c9",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"TILORES_API_URL\"] = \"<api-url>\"\n",
"os.environ[\"TILORES_TOKEN_URL\"] = \"<token-url>\"\n",
"os.environ[\"TILORES_CLIENT_ID\"] = \"<client-id>\"\n",
"os.environ[\"TILORES_CLIENT_SECRET\"] = \"<client-secret>\""
]
},
{
"cell_type": "markdown",
"id": "1c97218f-f366-479d-8bf7-fe9f2f6df73f",
"metadata": {},
"source": [
"## Instantiation\n",
"\n",
"Here we show how to instantiate an instance of the Tilores tools:"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "8b3ddfe9-ca79-494c-a7ab-1f56d9407a64",
"metadata": {},
"outputs": [],
"source": [
"from tilores import TiloresAPI\n",
"from tilores_langchain import TiloresTools\n",
"\n",
"tilores = TiloresAPI.from_environ()\n",
"tilores_tools = TiloresTools(tilores)\n",
"search_tool = tilores_tools.search_tool()\n",
"edge_tool = tilores_tools.edge_tool()"
]
},
{
"cell_type": "markdown",
"id": "74147a1a",
"metadata": {},
"source": [
"## Invocation\n",
"\n",
"The parameters for the `tilores_search` tool are dependent on the [configured schema](https://docs.tilotech.io/tilores/schema/) within Tilores. The following examples will use the schema for the demo instance with generated data.\n",
"\n",
"### [Invoke directly with args](/docs/concepts/tools)"
]
},
{
"cell_type": "markdown",
"id": "010aea95",
"metadata": {},
"source": [
"The following example searches for a person called Sophie Müller in Berlin. The Tilores data contains multiple such persons and returns their known email addresses and phone numbers."
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "65310a8b-eb0c-4d9e-a618-4f4abe2414fc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of entities: 3\n",
"Number of records: 3\n",
"Email Addresses: ['s.mueller@newcompany.de', 'sophie.mueller@email.de']\n",
"Phone Numbers: ['30987654', '30987654', '30987654']\n",
"Number of records: 5\n",
"Email Addresses: ['mueller.sophie@uni-berlin.de', 'sophie.m@newshipping.de', 's.mueller@newfinance.de']\n",
"Phone Numbers: ['30135792', '30135792']\n",
"Number of records: 2\n",
"Email Addresses: ['s.mueller@company.de']\n",
"Phone Numbers: ['30123456', '30123456']\n"
]
}
],
"source": [
"result = search_tool.invoke(\n",
" {\n",
" \"searchParams\": {\n",
" \"name\": \"Sophie Müller\",\n",
" \"city\": \"Berlin\",\n",
" },\n",
" \"recordFieldsToQuery\": {\n",
" \"email\": True,\n",
" \"phone\": True,\n",
" },\n",
" }\n",
")\n",
"print(\"Number of entities:\", len(result[\"data\"][\"search\"][\"entities\"]))\n",
"for entity in result[\"data\"][\"search\"][\"entities\"]:\n",
" print(\"Number of records:\", len(entity[\"records\"]))\n",
" print(\n",
" \"Email Addresses:\",\n",
" [record[\"email\"] for record in entity[\"records\"] if record.get(\"email\")],\n",
" )\n",
" print(\n",
" \"Phone Numbers:\",\n",
" [record[\"phone\"] for record in entity[\"records\"] if record.get(\"phone\")],\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "ab5c77ef",
"metadata": {},
"source": [
"If we're interested how the records from the first entity are related, we can use the edge_tool. Note that the Tilores entity resolution engine figured out the relation between those records automatically. Please refer to the [edge documentation](https://docs.tilotech.io/tilores/rules/#edges) for more details."
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "430e425c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of edges: 7\n",
"Edges: ['e1f2g3h4-i5j6-k7l8-m9n0-o1p2q3r4s5t6:f2g3h4i5-j6k7-l8m9-n0o1-p2q3r4s5t6u7:L1', 'e1f2g3h4-i5j6-k7l8-m9n0-o1p2q3r4s5t6:g3h4i5j6-k7l8-m9n0-o1p2-q3r4s5t6u7v8:L4', 'e1f2g3h4-i5j6-k7l8-m9n0-o1p2q3r4s5t6:f2g3h4i5-j6k7-l8m9-n0o1-p2q3r4s5t6u7:L2', 'f2g3h4i5-j6k7-l8m9-n0o1-p2q3r4s5t6u7:g3h4i5j6-k7l8-m9n0-o1p2-q3r4s5t6u7v8:L1', 'f2g3h4i5-j6k7-l8m9-n0o1-p2q3r4s5t6u7:g3h4i5j6-k7l8-m9n0-o1p2-q3r4s5t6u7v8:L4', 'e1f2g3h4-i5j6-k7l8-m9n0-o1p2q3r4s5t6:g3h4i5j6-k7l8-m9n0-o1p2-q3r4s5t6u7v8:L1', 'e1f2g3h4-i5j6-k7l8-m9n0-o1p2q3r4s5t6:f2g3h4i5-j6k7-l8m9-n0o1-p2q3r4s5t6u7:L4']\n"
]
}
],
"source": [
"edge_result = edge_tool.invoke(\n",
" {\"entityID\": result[\"data\"][\"search\"][\"entities\"][0][\"id\"]}\n",
")\n",
"edges = edge_result[\"data\"][\"entity\"][\"entity\"][\"edges\"]\n",
"print(\"Number of edges:\", len(edges))\n",
"print(\"Edges:\", edges)"
]
},
{
"cell_type": "markdown",
"id": "d6e73897",
"metadata": {},
"source": [
"### [Invoke with ToolCall](/docs/concepts/tools)\n",
"\n",
"We can also invoke the tool with a model-generated ToolCall, in which case a ToolMessage will be returned:"
]
},
{
"cell_type": "code",
"execution_count": 77,
"id": "f90e33a7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ToolMessage(content='{\"data\": {\"search\": {\"entities\": [{\"id\": \"9601cf3b-e85f-46ab-aaa8-ffb8b46f1c5b\", \"hits\": {\"c3d4e5f6-g7h8-i9j0-k1l2-m3n4o5p6q7r8\": [\"L1\"]}, \"records\": [{\"email\": \"\", \"phone\": \"30123456\"}, {\"email\": \"s.mueller@company.de\", \"phone\": \"30123456\"}]}, {\"id\": \"03da2e11-0aa2-4d17-8aaa-7b32c52decd9\", \"hits\": {\"e1f2g3h4-i5j6-k7l8-m9n0-o1p2q3r4s5t6\": [\"L1\"], \"g3h4i5j6-k7l8-m9n0-o1p2-q3r4s5t6u7v8\": [\"L1\"]}, \"records\": [{\"email\": \"s.mueller@newcompany.de\", \"phone\": \"30987654\"}, {\"email\": \"\", \"phone\": \"30987654\"}, {\"email\": \"sophie.mueller@email.de\", \"phone\": \"30987654\"}]}, {\"id\": \"4d896fb5-0d08-4212-a043-b5deb0347106\", \"hits\": {\"j6k7l8m9-n0o1-p2q3-r4s5-t6u7v8w9x0y1\": [\"L1\"], \"l8m9n0o1-p2q3-r4s5-t6u7-v8w9x0y1z2a3\": [\"L1\"], \"m9n0o1p2-q3r4-s5t6-u7v8-w9x0y1z2a3b4\": [\"L1\"], \"n0o1p2q3-r4s5-t6u7-v8w9-x0y1z2a3b4c5\": [\"L1\"]}, \"records\": [{\"email\": \"mueller.sophie@uni-berlin.de\", \"phone\": \"\"}, {\"email\": \"sophie.m@newshipping.de\", \"phone\": \"\"}, {\"email\": \"\", \"phone\": \"30135792\"}, {\"email\": \"\", \"phone\": \"\"}, {\"email\": \"s.mueller@newfinance.de\", \"phone\": \"30135792\"}]}]}}}', name='tilores_search', tool_call_id='1')"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This is usually generated by a model, but we'll create a tool call directly for demo purposes.\n",
"model_generated_tool_call = {\n",
" \"args\": {\n",
" \"searchParams\": {\n",
" \"name\": \"Sophie Müller\",\n",
" \"city\": \"Berlin\",\n",
" },\n",
" \"recordFieldsToQuery\": {\n",
" \"email\": True,\n",
" \"phone\": True,\n",
" },\n",
" },\n",
" \"id\": \"1\",\n",
" \"name\": search_tool.name,\n",
" \"type\": \"tool_call\",\n",
"}\n",
"search_tool.invoke(model_generated_tool_call)"
]
},
{
"cell_type": "markdown",
"id": "659f9fbd-6fcf-445f-aa8c-72d8e60154bd",
"metadata": {},
"source": [
"## Chaining\n",
"\n",
"We can use our tool in a chain by first binding it to a [tool-calling model](/docs/how_to/tool_calling/) and then calling it:\n",
"\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af3123ad-7a02-40e5-b58e-7d56e23e5830",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"# !pip install -qU langchain langchain-openai\n",
"from langchain.chat_models import init_chat_model\n",
"\n",
"llm = init_chat_model(model=\"gpt-4o\", model_provider=\"openai\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fdbf35b5-3aaf-4947-9ec6-48c21533fb95",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnableConfig, chain\n",
"\n",
"prompt = ChatPromptTemplate(\n",
" [\n",
" (\"system\", \"You are a helpful assistant.\"),\n",
" (\"human\", \"{user_input}\"),\n",
" (\"placeholder\", \"{messages}\"),\n",
" ]\n",
")\n",
"\n",
"# specifying tool_choice will force the model to call this tool.\n",
"llm_with_tools = llm.bind_tools([search_tool], tool_choice=search_tool.name)\n",
"\n",
"llm_chain = prompt | llm_with_tools\n",
"\n",
"\n",
"@chain\n",
"def tool_chain(user_input: str, config: RunnableConfig):\n",
" input_ = {\"user_input\": user_input}\n",
" ai_msg = llm_chain.invoke(input_, config=config)\n",
" tool_msgs = search_tool.batch(ai_msg.tool_calls, config=config)\n",
" return llm_chain.invoke({**input_, \"messages\": [ai_msg, *tool_msgs]}, config=config)\n",
"\n",
"\n",
"tool_chain.invoke(\"Tell me the email addresses from Sophie Müller from Berlin.\")"
]
},
{
"cell_type": "markdown",
"id": "4ac8146c",
"metadata": {},
"source": [
"## API reference\n",
"\n",
"For detailed documentation of all Tilores features and configurations head to the official documentation: https://docs.tilotech.io/tilores/"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "langchain",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

5
libs/packages.yml
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@ -346,6 +346,11 @@ packages:
path: .
repo: MehdiZare/langchain-fmp-data
downloads: 0
- name: tilores-langchain
path: .
repo: tilotech/tilores-langchain
provider_page: tilores
downloads: 0
- name: langchain-pipeshift
path: .
repo: pipeshift-org/langchain-pipeshift