get basic e2e example running

.gitignore

@@ -1,4 +1,5 @@
 .mypy_cache
 build
 env
-*.egg-info
+*.egg-info
+__pycache__

hello_wordsmith/hello_wordsmith.py

@@ -0,0 +1,80 @@
+#!/path/to/your/virtualenv/bin/python
+import os
+from llama_index.core.ingestion import IngestionPipeline
+from llama_index.core.query_pipeline import QueryPipeline
+from llama_index.core.storage.docstore import SimpleDocumentStore
+from llama_index.core import SimpleDirectoryReader
+from llama_index.cli.rag import RagCLI
+from llama_index.llms.openai import OpenAI
+from llama_index.vector_stores.chroma import ChromaVectorStore
+import chromadb
+
+
+# optional, set any API keys your script may need (perhaps using python-dotenv library instead)
+# os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+from llama_index.core import VectorStoreIndex, StorageContext
+
+chroma_client = chromadb.EphemeralClient()
+chroma_collection = chroma_client.create_collection("wordsmith")
+vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
+storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+
+package_directory = os.path.dirname(os.path.abspath(__file__))
+dataset_path = os.path.join(package_directory, 'public_wordsmith_dataset')
+reader = SimpleDirectoryReader(input_dir=dataset_path)
+docs = reader.load_data()
+index = VectorStoreIndex.from_documents(
+    docs, storage_context=storage_context
+)
+
+# docstore = SimpleDocumentStore()
+
+llm = OpenAI(api_key=os.environ["OPENAI_API_KEY"], model="gpt-4")
+
+custom_ingestion_pipeline = IngestionPipeline(
+    vector_store=vector_store,
+)
+
+from llama_index.core import PromptTemplate
+
+
+prompt_str = "Please generate related movies to {query_str}"
+prompt_tmpl = PromptTemplate(prompt_str)
+query_pipeline = QueryPipeline(verbose=True)
+
+from llama_index.core.response_synthesizers import TreeSummarize
+from llama_index.core.query_pipeline import InputComponent
+
+# construct vector store and customize storage context
+
+retriever = index.as_retriever(similarity_top_k=5)
+summarizer = TreeSummarize(llm=llm)
+query_pipeline.add_modules(
+    {
+        "input": InputComponent(),
+        "retriever": retriever,
+        "summarizer": summarizer,
+    }
+)
+query_pipeline.add_link("input", "retriever")
+query_pipeline.add_link("input", "summarizer", dest_key="query_str")
+query_pipeline.add_link("retriever", "summarizer", dest_key="nodes")
+
+# you can optionally specify your own custom readers to support additional file types.
+# file_extractor = {".html": ...}
+
+rag_cli_instance = RagCLI(
+    ingestion_pipeline=custom_ingestion_pipeline,
+    llm=llm,
+    query_pipeline=query_pipeline
+)
+
+
+def main():
+    rag_cli_instance.cli()
+
+
+if __name__ == "__main__":
+    main()

hello_wordsmith/public_wordsmith_dataset/info.txt

@@ -0,0 +1,4 @@
+Wordsmith is a company based in Scotland. 
+Gigz is CTO.
+Kostis and Derek are ENG. 
+It's pretty lit.

setup.py

@@ -23,5 +23,10 @@ setup(
         'Programming Language :: Python :: 3',
         'Programming Language :: Python :: 3.7',
     ],
+    entry_points={
+        'console_scripts': [
+            'hello-wordsmith=hello_wordsmith.hello_wordsmith:main',
+        ],
+    },
     python_requires='>=3.6',
 )