Add await for each component run example to make it explicit that it'…

…s an async method (#143)

docs/source/user_guide_kg_builder.rst

@@ -76,7 +76,7 @@ This package currently supports text extraction from PDFs:
     from neo4j_graphrag.experimental.components.pdf_loader import PdfLoader
 
     loader = PdfLoader()
-    loader.run(path=Path("my_file.pdf"))
+    await loader.run(path=Path("my_file.pdf"))
 
 To implement your own loader, use the `DataLoader` interface:
 
@@ -115,7 +115,7 @@ Wrappers for LangChain and LlamaIndex text splitters are included in this packag
     splitter = LangChainTextSplitterAdapter(
         CharacterTextSplitter(chunk_size=4000, chunk_overlap=200, separator=".")
     )
-    splitter.run(text="Hello World. Life is beautiful.")
+    await splitter.run(text="Hello World. Life is beautiful.")
 
 
 Also see :ref:`langchaintextsplitteradapter` and :ref:`llamaindextextsplitteradapter`.
@@ -155,7 +155,7 @@ Example usage:
     from neo4j_graphrag.experimental.components.embedder import TextChunkEmbedder
     from neo4j_graphrag.embeddings.openai import OpenAIEmbeddings
     text_chunk_embedder = TextChunkEmbedder(embedder=OpenAIEmbeddings())
-    text_chunk_embedder.run(text_chunks=TextChunks(chunks=[TextChunk(text="my_text")]))
+    await text_chunk_embedder.run(text_chunks=TextChunks(chunks=[TextChunk(text="my_text")]))
 
 .. note::
 
@@ -257,6 +257,8 @@ It can be used in this way:
             },
         )
     )
+    await extractor.run(chunks=TextChunks(chunks=[TextChunk(text="some text")]))
+
 
 .. warning::
 
@@ -322,12 +324,12 @@ The default prompt uses the :ref:`erextractiontemplate`. It is possible to provi
 
     extractor = LLMEntityRelationExtractor(
         llm=....,
-        prompt="this is my prompt",
+        prompt="Extract entities from {text}",
     )
 
 The following variables can be used in the prompt:
 
-- `text` (str): the text to be analyzed.
+- `text` (str): the text to be analyzed (mandatory).
 - `schema` (str): the graph schema to be used.
 - `examples` (str): examples for few-shot learning.
 
@@ -351,17 +353,17 @@ If more customization is needed, it is possible to subclass the `EntityRelationE
 
     class MyExtractor(EntityRelationExtractor):
 
-    @validate_call
-    async def run(self, chunks: TextChunks, **kwargs: Any) -> Neo4jGraph:
-        return Neo4jGraph(
-            nodes=[
-                Neo4jNode(id="0", label="Person", properties={"name": "A. Einstein"}),
-                Neo4jNode(id="1", label="Concept", properties={"name": "Theory of relativity"}),
-            ],
-            relationships=[
-                Neo4jRelationship(type="PROPOSED_BY", start_node_id="1", end_node_id="0", properties={"year": 1915})
-            ],
-        )
+        @validate_call
+        async def run(self, chunks: TextChunks, **kwargs: Any) -> Neo4jGraph:
+            return Neo4jGraph(
+                nodes=[
+                    Neo4jNode(id="0", label="Person", properties={"name": "A. Einstein"}),
+                    Neo4jNode(id="1", label="Concept", properties={"name": "Theory of relativity"}),
+                ],
+                relationships=[
+                    Neo4jRelationship(type="PROPOSED_BY", start_node_id="1", end_node_id="0", properties={"year": 1915})
+                ],
+            )
 
 
 See :ref:`entityrelationextractor`.
@@ -385,7 +387,7 @@ to a Neo4j database:
     ) as driver:
         writer = Neo4jWriter(driver)
         graph = Neo4jGraph(nodes=[], relationships=[])
-        asyncio.run(writer.run())
+        await writer.run(graph)
 
 See :ref:`neo4jgraph` for the description of the input type.