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https://github.com/tilotech/tilores-langchain
This repository provides the building blocks for integrating LangChain, LangGraph, and the Tilores entity resolution system.
https://github.com/tilotech/tilores-langchain
agentic-rag entity-resolution identity-resolution langchain
Last synced: 18 days ago
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This repository provides the building blocks for integrating LangChain, LangGraph, and the Tilores entity resolution system.
- Host: GitHub
- URL: https://github.com/tilotech/tilores-langchain
- Owner: tilotech
- License: mit
- Created: 2024-11-07T14:28:01.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-21T13:52:19.000Z (about 1 month ago)
- Last Synced: 2024-12-02T18:01:24.891Z (20 days ago)
- Topics: agentic-rag, entity-resolution, identity-resolution, langchain
- Language: Python
- Homepage: htttps://tilores.io/RAG
- Size: 35.2 KB
- Stars: 3
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# LangChain Using Tilores
This repository provides the building blocks for integrating LangChain, LangGraph, and the
[Tilores entity resolution system](https://docs.tilotech.io/tilores/).
Developers can use these tools to create powerful systems that leverage entity resolution
for record retrieval, search, and entity resolution.
## Examples
* **Human-in-the-Loop Chat:** [`examples/chat`](https://github.com/tilotech/tilores-langchain/tree/main/examples/chat)
This example demonstrates how to build a chat application using Chainlit and LangGraph to explore a Tilores instance through natural language. It guides users through search functionality and explains the search results.
* **Basic Usage:** [`examples/basic`](https://github.com/tilotech/tilores-langchain/tree/main/examples/basic)
This example shows how to use tools with an LLM model in a basic setup.
## Usage
```python
from tilores import TiloresAPI
from tilores_langchain import TiloresTools
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
# Initialize the Tilores API.
tilores = TiloresAPI.from_environ()
# TiloresTools helps you build typed tools from a specific Tilores instance, typed according to
# the schema of the instance.
tilores_tools = TiloresTools(tilores)
# Setup a LLM model for inference bound with a set of tools.
tools = [tilores_tools.search_tool]
tools_dict = {tool.name: tool for tool in tools}
model = ChatOpenAI(temperature=0, streaming=True, model_name="gpt-4o")
model = model.bind_tools(tools)
# The basic loop works like this, that a list of messages is passed to the LLM
messages = [
HumanMessage("Find me an entity by the first name Emma, surname Schulz, born on 1988-03-12")
]
ai_message = model.invoke(messages)
messages.append(ai_message)
# And for each AiMessage, you must check if it wants to invoke tools.
for tool_call in ai_message.tool_calls:
# Perform the tool call and append the ToolMessage to the list of messages
selected_tool = tools_dict[tool_call['name']]
tool_message = selected_tool.invoke(tool_call)
messages.append(tool_message)
# Then continue the basic loop by invoking the LLM with the current state, passing the list of messages.
ai_response = model.invoke(messages)
print(ai_response.content)
```
```console
$ cd examples/basic/
$ pip install -r requirements.txt
$ python llm_with_tools.py
I found multiple records for an entity with the first name Emma, surname Schulz, born on 1988-03-12. Here are the details:
1. **Record ID:** cc001001-0006-4000-c000-000000000006
- **First Name:** Emma
- **Last Name:** Schulz
- **Date of Birth:** 1988-03-12
2. **Record ID:** cc001001-0002-4000-c000-000000000002
- **First Name:** Emma
- **Last Name:** Schulz
- **Date of Birth:** 1988-03-12
[... snip ...]
If you need more specific information or further assistance, please let me know!
```
## Provided tools
- [x] `tilores_search`