Merge branch 'main' into cdb-fix

Author: corradodebari

app/requirements.txt

@@ -20,9 +20,9 @@ langchain-cohere==0.3.1
 langchain-community==0.3.7
 langchain-huggingface==0.1.2
 langchain-ollama==0.2.0
-langchain-openai==0.2.8
-langgraph==0.2.48
-llama_index==0.11.23
+langchain-openai==0.2.9
+langgraph==0.2.53
+llama_index==0.12.1
 lxml==5.3.0
 matplotlib==3.9.2
 oci>=2.0.0

app/src/modules/utilities.py

@@ -235,19 +235,24 @@ def init_vs(db_conn, embedding_function, store_table, distance_metric):
 
 def get_vs_table(model, chunk_size, chunk_overlap, distance_metric, embed_alias=None):
     """Return the concatenated VS Table name and comment"""
-    chunk_overlap_ceil = math.ceil(chunk_overlap)
-    table_string = f"{model}_{chunk_size}_{chunk_overlap_ceil}_{distance_metric}"
-    if embed_alias:
-        table_string = f"{embed_alias}_{table_string}"
-    store_table = re.sub(r"\W", "_", table_string.upper())
-    store_comment = (
-        f'{{"alias": "{embed_alias}",'
-        f'"model": "{model}",'
-        f'"chunk_size": {chunk_size},'
-        f'"chunk_overlap": {chunk_overlap_ceil},'
-        f'"distance_metric": "{distance_metric}"}}'
-    )
-    logger.info("Vector Store Table: %s; Comment: %s", store_table, store_comment)
+    store_table = None
+    store_comment = None
+    try:
+        chunk_overlap_ceil = math.ceil(chunk_overlap)
+        table_string = f"{model}_{chunk_size}_{chunk_overlap_ceil}_{distance_metric}"
+        if embed_alias:
+            table_string = f"{embed_alias}_{table_string}"
+        store_table = re.sub(r"\W", "_", table_string.upper())
+        store_comment = (
+            f'{{"alias": "{embed_alias}",'
+            f'"model": "{model}",'
+            f'"chunk_size": {chunk_size},'
+            f'"chunk_overlap": {chunk_overlap_ceil},'
+            f'"distance_metric": "{distance_metric}"}}'
+        )
+        logger.info("Vector Store Table: %s; Comment: %s", store_table, store_comment)
+    except TypeError:
+        logger.fatal("Not all required values provided to get Vector Store Table name.")
     return store_table, store_comment
 
 
@@ -332,7 +337,8 @@ def json_to_doc(file: str):
     # Build the Index
     logger.info("Creating index on: %s", store_table)
     try:
-        LangchainVS.create_index(db_conn, vectorstore)
+        params = {"idx_name": f"{store_table}_HNSW_IDX", "idx_type": "HNSW"}
+        LangchainVS.create_index(db_conn, vectorstore, params)
     except Exception as ex:
         logger.error("Unable to create vector index: %s", ex)
 
@@ -370,7 +376,6 @@ def get_vs_tables(conn, enabled_embed):
 
     return json.dumps(output, indent=4)
 
-
 ###############################################################################
 # Oracle Cloud Infrastructure
 ###############################################################################

docs/content/walkthrough/_index.md

@@ -114,7 +114,7 @@ To start Oracle Database 23ai Free:
 
    alter session set container=FREEPDB1;
 
-   CREATE USER "WALKTHROUGH" IDENTIFIED BY ORA_41_M_SANDBOX
+   CREATE USER "WALKTHROUGH" IDENTIFIED BY OrA_41_M_SANDBOX
        DEFAULT TABLESPACE "USERS"
        TEMPORARY TABLESPACE "TEMP";
    GRANT "DB_DEVELOPER_ROLE" TO "WALKTHROUGH";
@@ -215,7 +215,7 @@ To configure the On-Premises Embedding Model, navigate back to the _Configuratio
 To configure Oracle Database 23ai Free, navigate to the _Configuration -> Database_ screen:
 
 1. Enter the Database Username: `WALKTHROUGH`
-1. Enter the Database Password for `WALKTHROUGH`: `ORA_41_M_SANDBOX`
+1. Enter the Database Password for `WALKTHROUGH`: `OrA_41_M_SANDBOX`
 1. Enter the Database Connection String: `//localhost:1521/FREEPDB1`
 1. Save
 
@@ -256,7 +256,7 @@ From the command line:
 1. Connect to the Oracle Database 23ai Database:
 
    ```bash
-   podman exec -it oaim-db sqlplus 'WALKTHROUGH/ORA_41_M_SANDBOX@FREEPDB1'
+   podman exec -it oaim-db sqlplus 'WALKTHROUGH/OrA_41_M_SANDBOX@FREEPDB1'
    ```
 
 1. Query the Vector Store: