add stock market notebook / library.json

llama_hub/llama_packs/library.json

@@ -257,5 +257,10 @@
     "id": "llama_packs/multi_tenancy_rag",
     "author": "ravi03071991",
     "keywords": ["multi-tenancy", "multi", "tenancy", "rag"]
+  },
+  "StockMarketDataQueryEnginePack": {
+    "id": "llama_packs/stock_market_data_query_engine",
+    "author": "anoopshrma",
+    "keywords": ["stock", "market", "data", "query", "engine"]
   }
 }

llama_hub/llama_packs/stock_market_data_query_engine/base.py

@@ -1,4 +1,4 @@
-from typing import List, Dict, Any
+from typing import List, Dict, Any, Optional
 
 from llama_index.llama_pack.base import BaseLlamaPack
 from llama_index.schema import IndexNode
@@ -7,6 +7,9 @@
 from llama_index.retrievers import RecursiveRetriever
 from llama_index.query_engine import RetrieverQueryEngine
 from llama_index.response_synthesizers import get_response_synthesizer
+from llama_index.llms.llm import LLM
+from llama_index.llms import OpenAI
+from llama_index.service_context import ServiceContext
 
 
 class StockMarketDataQueryEnginePack(BaseLlamaPack):
@@ -15,6 +18,7 @@ class StockMarketDataQueryEnginePack(BaseLlamaPack):
     def __init__(
         self,
         tickers: List[str],
+        llm: Optional[LLM] = None,
         **kwargs: Any,
     ):
         self.tickers = tickers
@@ -39,8 +43,11 @@ def __init__(
             stocks_market_data.append(hist)
         self.stocks_market_data = stocks_market_data
 
+        service_context = ServiceContext.from_defaults(llm=llm or OpenAI(model="gpt-4"))
+
         df_price_query_engines = [
-            PandasQueryEngine(stock) for stock in stocks_market_data
+            PandasQueryEngine(stock, service_context=service_context)
+            for stock in stocks_market_data
         ]
 
         summaries = [f"{ticker} historical market data" for ticker in tickers]
@@ -55,7 +62,9 @@ def __init__(
             for idx, df_engine in enumerate(df_price_query_engines)
         }
 
-        stock_price_vector_index = VectorStoreIndex(df_price_nodes)
+        stock_price_vector_index = VectorStoreIndex(
+            df_price_nodes, service_context=service_context
+        )
         stock_price_vector_retriever = stock_price_vector_index.as_retriever(
             similarity_top_k=1
         )
@@ -69,7 +78,8 @@ def __init__(
 
         stock_price_response_synthesizer = get_response_synthesizer(
             # service_context=service_context,
-            response_mode="compact"
+            response_mode="compact",
+            service_context=service_context,
         )
 
         stock_price_query_engine = RetrieverQueryEngine.from_args(