diff --git a/posts/get_started/index.html b/posts/get_started/index.html index afca5a9b..4d01a3a6 100644 --- a/posts/get_started/index.html +++ b/posts/get_started/index.html @@ -312,28 +312,21 @@ It shows how to use the system to index some text, and then use the indexed data

Next we'll inject some required config variables:

Set Up Environment Variables

-

First let's make sure to setup the required environment variables:

- +

First let's make sure to setup the required environment variables. For details on these environment variables, and what environment variables are available, see the environment variables documentation.

OpenAI and Azure OpenAI

-

To get started, let's set the base environment variables.

- 
export GRAPHRAG_API_KEY="<api_key>" && \
+  

+To get started, let's set the base environment variables.
+
+```sh
+export GRAPHRAG_API_KEY="<api_key>" && \
 export GRAPHRAG_LLM_MODEL="<chat_completions_model>" && \
 export GRAPHRAG_LLM_MODEL_SUPPORTS_JSON="True" && \
 export GRAPHRAG_EMBEDDING_MODEL="<embeddings_model>" && \
 export GRAPHRAG_INPUT_TYPE="text"

 
-
@@ -341,14 +334,14 @@ It shows how to use the system to index some text, and then use the indexed data

In addition, Azure OpenAI users should set the following env-vars.

- 
export GRAPHRAG_API_BASE="https://<domain>.openai.azure.com" && \
+  
export GRAPHRAG_API_BASE="https://<domain>.openai.azure.com" && \
 export GRAPHRAG_API_VERSION="2024-02-15-preview" && \
 export GRAPHRAG_LLM_API_TYPE = "azure_openai_chat" && \
 export GRAPHRAG_LLM_DEPLOYMENT_NAME="<chat_completions_deployment_name>" && \
 export GRAPHRAG_EMBEDDING_API_TYPE = "azure_openai_embedding" && \
 export GRAPHRAG_EMBEDDING_DEPLOYMENT_NAME="<embeddings_deployment_name>"

 
-
@@ -358,9 +351,9 @@ For more details about using the CLI, refer to the - 
python -m graphrag.index --root ./ragtest
+ 
python -m graphrag.index --root ./ragtest
- @@ -373,24 +366,24 @@ Once the pipeline is complete, you should see a new folder called ./ragtes

Here is an example using Global search to ask a high-level question:

- 
python -m graphrag.query \
+  
python -m graphrag.query \
 --data ./ragtest/output/<timestamp>/artifacts \
 --method global \
 "What are the top themes in this story?"

 
-

Here is an example using Local search to ask a more specific question about a particular character:

- 
python -m graphrag.query \
+  
python -m graphrag.query \
 --data ./ragtest/output/<timestamp>/artifacts \
 --method local \
 "Who is Scrooge, and what are his main relationships?"

 
-