| # TigerGraph | |
| >[TigerGraph](https://www.tigergraph.com/tigergraph-db/) 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](https://github.com/tigergraph/graph-ml-notebooks/blob/main/applications/large_language_models/TigerGraph_LangChain_Demo.ipynb). | |
| ## Installation and Setup | |
| Follow instructions [how to connect to the `TigerGraph` database](https://docs.tigergraph.com/pytigergraph/current/getting-started/connection). | |
| Install the Python SDK: | |
| ```bash | |
| pip install pyTigerGraph | |
| ``` | |
| ## Example | |
| To utilize the `TigerGraph InquiryAI` functionality, you can import `TigerGraph` from `langchain_community.graphs`. | |
| ```python | |
| 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) | |
| ``` | |