> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # Tilores integration > Integrate with the Tilores tool using LangChain Python. This notebook covers how to get started with the [Tilores](/oss/python/integrations/providers/tilores) tools. For a more complex example you can checkout our [customer insights chatbot example](https://github.com/tilotech/identity-rag-customer-insights-chatbot). ## Overview ### Integration details | Class | Package | Serializable | JS support | Version | | :------------- | :----------------------------------------------------------------- | :----------: | :--------: | :---------------------------------------------------------------------------------------------: | | `TiloresTools` | [`tilores-langchain`](https://pypi.org/project/tilores-langchain/) | ❌ | ❌ | ![PyPI - Version](https://img.shields.io/pypi/v/tilores-langchain?style=flat-square\&label=%20) | ## Setup The integration requires the following packages: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install --quiet -U tilores-langchain langchain ``` ### Credentials 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). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os os.environ["TILORES_API_URL"] = "" os.environ["TILORES_TOKEN_URL"] = "" os.environ["TILORES_CLIENT_ID"] = "" os.environ["TILORES_CLIENT_SECRET"] = "" ``` ## Instantiation Here we show how to instantiate an instance of the Tilores tools: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from tilores import TiloresAPI from tilores_langchain import TiloresTools tilores = TiloresAPI.from_environ() tilores_tools = TiloresTools(tilores) search_tool = tilores_tools.search_tool() edge_tool = tilores_tools.edge_tool() ``` ## Invocation 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. ### [Invoke directly with args](/oss/python/langchain/tools) 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. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} result = search_tool.invoke( { "searchParams": { "name": "Sophie Müller", "city": "Berlin", }, "recordFieldsToQuery": { "email": True, "phone": True, }, } ) print("Number of entities:", len(result["data"]["search"]["entities"])) for entity in result["data"]["search"]["entities"]: print("Number of records:", len(entity["records"])) print( "Email Addresses:", [record["email"] for record in entity["records"] if record.get("email")], ) print( "Phone Numbers:", [record["phone"] for record in entity["records"] if record.get("phone")], ) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} Number of entities: 3 Number of records: 3 Email Addresses: ['s.mueller@newcompany.de', 'sophie.mueller@email.de'] Phone Numbers: ['30987654', '30987654', '30987654'] Number of records: 5 Email Addresses: ['mueller.sophie@uni-berlin.de', 'sophie.m@newshipping.de', 's.mueller@newfinance.de'] Phone Numbers: ['30135792', '30135792'] Number of records: 2 Email Addresses: ['s.mueller@company.de'] Phone Numbers: ['30123456', '30123456'] ``` 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. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} edge_result = edge_tool.invoke( {"entityID": result["data"]["search"]["entities"][0]["id"]} ) edges = edge_result["data"]["entity"]["entity"]["edges"] print("Number of edges:", len(edges)) print("Edges:", edges) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} Number of edges: 7 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'] ``` ### [Invoke with ToolCall](/oss/python/langchain/tools) We can also invoke the tool with a model-generated ToolCall, in which case a ToolMessage will be returned: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # This is usually generated by a model, but we'll create a tool call directly for demo purposes. model_generated_tool_call = { "args": { "searchParams": { "name": "Sophie Müller", "city": "Berlin", }, "recordFieldsToQuery": { "email": True, "phone": True, }, }, "id": "1", "name": search_tool.name, "type": "tool_call", } search_tool.invoke(model_generated_tool_call) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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') ``` ## Chaining We can use our tool in a chain by first binding it to a [tool-calling model](/oss/python/langchain/tools/) and then calling it: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # | output: false # | echo: false # !pip install -qU langchain langchain-openai from langchain.chat_models import init_chat_model model = init_chat_model(model="gpt-5.5", model_provider="openai") ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnableConfig, chain prompt = ChatPromptTemplate( [ ("system", "You are a helpful assistant."), ("human", "{user_input}"), ("placeholder", "{messages}"), ] ) # specifying tool_choice will force the model to call this tool. model_with_tools = model.bind_tools([search_tool], tool_choice=search_tool.name) model_chain = prompt | model_with_tools @chain def tool_chain(user_input: str, config: RunnableConfig): input_ = {"user_input": user_input} ai_msg = model_chain.invoke(input_, config=config) tool_msgs = search_tool.batch(ai_msg.tool_calls, config=config) return model_chain.invoke({**input_, "messages": [ai_msg, *tool_msgs]}, config=config) tool_chain.invoke("Tell me the email addresses from Sophie Müller from Berlin.") ``` *** ## API reference For detailed documentation of all Tilores features and configurations head to the official documentation: [docs.tilotech.io/tilores/](https://docs.tilotech.io/tilores/) ***
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers. [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/tools/tilores.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).