edited utils

src/metadata_chatbot/agents/agentic_graph.py

@@ -7,6 +7,7 @@
 from langchain_core.tools import tool
 from aind_data_access_api.document_db_ssh import DocumentDbSSHClient, DocumentDbSSHCredentials
 from langchain.agents import AgentExecutor, create_tool_calling_agent
+from aind_data_access_api.document_db import MetadataDbClient
 
 logging.basicConfig(filename='agentic_graph.log', level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', filemode="w")
 
@@ -32,9 +33,19 @@ class RouteQuery(BaseModel):
 
 
 # Queries that require surveying the entire database (like count based questions)
-credentials = DocumentDbSSHCredentials()
-credentials.database = "metadata_vector_index"
-credentials.collection = "curated_assets"
+# credentials = DocumentDbSSHCredentials()
+# credentials.database = "metadata_vector_index"
+# credentials.collection = "curated_assets"
+API_GATEWAY_HOST = "api.allenneuraldynamics-test.org"
+DATABASE = "metadata_vector_index"
+COLLECTION = "bigger_LANGCHAIN_curated_chunks"
+
+docdb_api_client = MetadataDbClient(
+   host=API_GATEWAY_HOST,
+   database=DATABASE,
+   collection=COLLECTION,
+)
+
 @tool
 def aggregation_retrieval(agg_pipeline: list) -> list:
     """Given a MongoDB query and list of projections, this function retrieves and returns the 
@@ -52,12 +63,11 @@ def aggregation_retrieval(agg_pipeline: list) -> list:
     list
         List of retrieved documents
     """
-    with DocumentDbSSHClient(credentials=credentials) as doc_db_client:
 
-        result = list(doc_db_client.collection.aggregate(
-            pipeline=agg_pipeline
-        ))
-        return result
+    result = docdb_api_client.aggregate_docdb_records(
+        pipeline=agg_pipeline
+    )
+    return result
 
 tools = [aggregation_retrieval]
 prompt = hub.pull("eden19/entire_db_retrieval")

src/metadata_chatbot/agents/async_workflow.py

@@ -6,8 +6,8 @@
 from langgraph.checkpoint.memory import MemorySaver
 
 
-sys.path.append(os.path.abspath("C:/Users/sreya.kumar/Documents/GitHub/metadata-chatbot"))
-from metadata_chatbot.utils import ResourceManager
+# sys.path.append(os.path.abspath("C:/Users/sreya.kumar/Documents/GitHub/metadata-chatbot"))
+# from metadata_chatbot.utils import ResourceManager
 from aind_data_access_api.document_db import MetadataDbClient
 
 from metadata_chatbot.agents.docdb_retriever import DocDBRetriever
@@ -205,18 +205,18 @@ async def generate_async(state):
 
 async_app = async_workflow.compile()
 
-async def main():
-    query = "Can you give me a timeline of events for subject 675387?"
-    inputs = {"query": query}
-    result = async_app.astream(inputs)
+# async def main():
+#     query = "Can you give me a timeline of events for subject 675387?"
+#     inputs = {"query": query}
+#     result = async_app.astream(inputs)
 
-    value = None
-    async for output in result:
-        for key, value in output.items():
-            logging.info(f"Currently on node '{key}':")
+#     value = None
+#     async for output in result:
+#         for key, value in output.items():
+#             logging.info(f"Currently on node '{key}':")
 
-    if value:
-        print(value['generation'])
+#     if value:
+#         print(value['generation'])
 
-#Run the async function
-asyncio.run(main())
+# #Run the async function
+# asyncio.run(main())

src/metadata_chatbot/agents/workflow.py

@@ -5,8 +5,8 @@
 from langgraph.checkpoint.memory import MemorySaver
 
 
-sys.path.append(os.path.abspath("C:/Users/sreya.kumar/Documents/GitHub/metadata-chatbot"))
-from metadata_chatbot.utils import ResourceManager
+# sys.path.append(os.path.abspath("C:/Users/sreya.kumar/Documents/GitHub/metadata-chatbot"))
+# from metadata_chatbot.utils import ResourceManager
 
 from metadata_chatbot.agents.docdb_retriever import DocDBRetriever
 from metadata_chatbot.agents.agentic_graph import datasource_router, db_surveyor, query_grader, filter_generation_chain, doc_grader, rag_chain