Add num_workers to raptor pack SummaryModule init, in RaptorPack (#12106)

* Add num_workers to raptor pack SummaryModule init

* Update poetry version of package

* Fix spelling mistake

llama-index-packs/llama-index-packs-raptor/README.md

@@ -76,3 +76,30 @@ retriever = RaptorRetriever([], ..., vector_store=vector_store)
 ```
 
 Check out the [notebook here for complete details!](https://github.com/run-llama/llama_index/blob/main/llama-index-packs/llama-index-packs-raptor/examples/raptor.ipynb).
+
+## Configure Summary Module
+
+Using the SummaryModule you can configure how the Raptor Pack does summaries and how many workers are applied to summaries.
+
+You can configure the LLM.
+
+You can configure summary_prompt. This will change the prompt sent to your LLM to summarize you docs.
+
+You can configure num_workers, which will influence the number of workers or rather async semaphores allowing more summaries to process simulatneously.
+This might affect openai or other LLm provider API limits, be aware.
+
+```python
+from llama_index.packs.raptor.base import SummaryModule
+from llama_index.packs.raptor import RaptorRetriever
+
+summary_prompt = "As a professional summarizer, create a concise and comprehensive summary of the provided text, be it an article, post, conversation, or passage with as much detail as possible."
+
+# Adding SummaryModule you can configure the summary prompt and number of workers doing summaries.
+summary_module = SummaryModule(
+    llm=llm, summary_prompt=summary_prompt, num_workers=16
+)
+
+pack = RaptorPack(
+    documents, llm=llm, embed_model=embed_model, summary_module=summary_module
+)
+```

llama-index-packs/llama-index-packs-raptor/llama_index/packs/raptor/base.py

@@ -59,13 +59,18 @@ class SummaryModule(BaseModel):
         arbitrary_types_allowed = True
 
     def __init__(
-        self, llm: Optional[LLM] = None, summary_prompt: str = DEFAULT_SUMMARY_PROMPT
+        self,
+        llm: Optional[LLM] = None,
+        summary_prompt: str = DEFAULT_SUMMARY_PROMPT,
+        num_workers: int = 4,
     ) -> None:
         response_synthesizer = get_response_synthesizer(
             response_mode="tree_summarize", use_async=True, llm=llm
         )
         super().__init__(
-            response_synthesizer=response_synthesizer, summary_prompt=summary_prompt
+            response_synthesizer=response_synthesizer,
+            summary_prompt=summary_prompt,
+            num_workers=num_workers,
         )
 
     async def generate_summaries(

llama-index-packs/llama-index-packs-raptor/pyproject.toml

@@ -31,7 +31,7 @@ license = "MIT"
 name = "llama-index-packs-raptor"
 packages = [{include = "llama_index/"}]
 readme = "README.md"
-version = "0.1.2"
+version = "0.1.3"
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"