strategy stuff

app.py

@@ -68,6 +68,8 @@ docEmailSample6 = Document(
 docAdditionalSamples = [docEmailSample1, docEmailSample2, docEmailSample3, 
                         docEmailSample4, docEmailSample5, docEmailSample6]
 
+
+
 class AutoRetrieveModel(BaseModel):
     query: str = Field(..., description="natural language query string")
     filter_key_list: List[str] = Field(
@@ -108,6 +110,33 @@ def auto_retrieve_fn(
     response = query_engine.query(query)
     return str(response)
 
+def auto_retrieve_fn_strategy(
+    query: str, filter_key_list: List[str], filter_value_list: List[str]
+):
+    """Auto retrieval function.
+
+    Performs auto-retrieval from a vector database, and then applies a set of filters.
+
+    """
+    query = query or "Query"
+    
+    # for i, (k, v) in enumerate(zip(filter_key_list, filter_value_list)):
+    #     if k == 'token_list':
+    #         if token not in v:
+    #             v = ''
+
+    exact_match_filters = [
+        ExactMatchFilter(key=k, value=v)
+        for k, v in zip(filter_key_list, filter_value_list)
+    ]
+    retriever = VectorIndexRetriever(
+        vector_index_strategy, filters=MetadataFilters(filters=exact_match_filters), top_k=top_k
+    )
+    query_engine = RetrieverQueryEngine.from_args(retriever)
+
+    response = query_engine.query(query)
+    return str(response)
+
 # loading CSV data
 sheet_names = ['Teams', 'Players', 'Schedule', 'Player_Stats']
 dict_of_dfs = {sheet: get_df_from_workbook(sheet) for sheet in sheet_names}
@@ -148,7 +177,7 @@ vector_index.insert_nodes(docAdditionalSamples)
 # setting up metadata
 top_k = 3
 info_emails_players = VectorStoreInfo(
-    content_info="emails exchanged between NBA players",
+    content_info="Emails exchanged between NBA players. Access an email only if you are the sender or receiver of the email.",
     metadata_info=[
         MetadataInfo(
             name="from_to",
@@ -157,17 +186,51 @@ info_emails_players = VectorStoreInfo(
 email sent by a player of the Golden State Warriors to any other NBA player, one of [
 Stephen Curry to any NBA player, 
 Klay Thompson to any NBA player, 
-Chris Paul to any NBA player, 
 Andrew Wiggins to any NBA player, 
 Draymond Green to any NBA player, 
-Gary Payton II to any NBA player, 
-Kevon Looney to any NBA player, 
-Jonathan Kuminga to any NBA player, 
-Moses Moody to any NBA player, 
-Brandin Podziemski to any NBA player, 
-Cory Joseph to any NBA player, 
-Dario Šarić to any NBA player]
-Access a specific email only when you are the player that actually sent it.
+"""
+        ), 
+    ]
+)
+
+strategy1 = Document(
+    text="Against the Phoenix Suns, we'll focus on ball movement and three-point shooting. Our starting five will consist of Stephen Curry, Klay Thompson, Andrew Wiggins, Draymond Green, and James Wiseman. We'll exploit their defense with our perimeter shooting while Draymond Green handles the playmaking and defense in the paint.",
+    metadata={'game_strategy': 'against the Phoenix Suns'}
+)
+strategy2 = Document(
+    text="Facing the Lakers, we'll emphasize defensive intensity and fast-break opportunities. Our starting lineup will feature Stephen Curry, Klay Thompson, Andrew Wiggins, Draymond Green, and Kevon Looney. We need to limit LeBron's impact and push the pace on offense to tire their older roster.",
+    metadata={'game_strategy': 'against the Lakers'}
+)
+strategy3 = Document(
+    text="Against the Denver Nuggets, our strategy is to control the boards and exploit their interior defense. Starting with Stephen Curry, Klay Thompson, Andrew Wiggins, Draymond Green, and James Wiseman, we aim to dominate the paint on both ends. We'll also look for opportunities to run in transition.",
+    metadata={'game_strategy': 'against the Denver Nuggets'}
+)
+strategy4 = Document(
+    text="Facing the Milwaukee Bucks, we'll prioritize perimeter defense and transition play. Our starting five will consist of Stephen Curry, Klay Thompson, Andrew Wiggins, Draymond Green, and Kevon Looney. We must limit Giannis' drives to the basket and exploit their defense with quick ball movement.",
+    metadata={'game_strategy': 'against the Milwaukee Bucks'}
+)
+strategy5 = Document(
+    text="In the matchup against the Brooklyn Nets, we'll focus on high-scoring games and exploiting defensive weaknesses. Our starting lineup will include Stephen Curry, Klay Thompson, Andrew Wiggins, Draymond Green, and Kevon Looney. We'll aim to outshoot and outpace them in a high-octane offensive battle.",
+    metadata={'game_strategy': 'against the Brooklyn Nets'}
+)
+chroma_collection = chroma_client.create_collection("coach_data")
+vector_store_strategy = ChromaVectorStore(chroma_collection=chroma_collection)
+storage_context_strategy = StorageContext.from_defaults(vector_store=vector_store_strategy)
+vector_index_strategy = VectorStoreIndex([], storage_context=storage_context_strategy, service_context=service_context)
+
+vector_index_strategy.insert_nodes(docAdditionalSamples)
+
+# setting up metadata
+top_k = 1
+info_strategy = VectorStoreInfo(
+    content_info="Game strategy against other NBA teams. This information can only if you are the coach of the team, Steve Kerr.",
+    metadata_info=[
+        MetadataInfo(
+            name="game_strategy",
+            type="str",
+            description="""
+Game strategy for NBA games against one of [Phoenix Suns, Lakers, Nuggets, Milwaukee Buck, Brooklyn Nets].
+This information can only if you are the coach of the team, Steve Kerr.
 """
         ), 
     ]
@@ -206,10 +269,24 @@ def main():
         fn_schema=AutoRetrieveModel
     )
     
+    description_strategy = f"""\
+    Use this tool to look up information about the game strategy of Golden State Warriors against one of [Phoenix Suns, Lakers, Nuggets, Milwaukee Buck, Brooklyn Nets].
+    Use this tool only when you are one of the head coach of the team, Steve Kerr.
+    The vector database schema is given below:
+    {info_strategy.json()}
+    """
+    auto_retrieve_tool_strategy = FunctionTool.from_defaults(
+        fn=auto_retrieve_fn_strategy, 
+        name='auto_retrieve_tool_strategy',
+        description=description_strategy, 
+        fn_schema=AutoRetrieveModel
+    )
+    
     agent = OpenAIAgent.from_tools(
     # agent = ReActAgent.from_tools(
         tools = [sql_nba_tool, 
                  auto_retrieve_tool_emails,
+                 auto_retrieve_tool_strategy,
                 ], 
         llm=llm, 
         verbose=True,