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TigerGraph is a natively distributed and high-performance graph database. The storage of data in a graph format of vertices and edges leads to rich relationships, ideal for grouding LLM responses.
A big example of the TigerGraph and LangChain integration presented here.

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Installation and Setup

Follow instructions how to connect to the TigerGraph database. Install the Python SDK: 
pip install pyTigerGraph

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Example

To utilize the TigerGraph InquiryAI functionality, you can import TigerGraph from langchain_community.graphs. 
import pyTigerGraph as tg

conn = tg.TigerGraphConnection(host="DATABASE_HOST_HERE", graphname="GRAPH_NAME_HERE", username="USERNAME_HERE", password="PASSWORD_HERE")

### ==== CONFIGURE INQUIRYAI HOST ====
conn.ai.configureInquiryAIHost("INQUIRYAI_HOST_HERE")

from langchain_community.graphs import TigerGraph

graph = TigerGraph(conn)
result = graph.query("How many servers are there?")
print(result)
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