Update lambda configuration and chunk size

FloRul · Feb 16, 2024 · 5a7ee3d · 5a7ee3d
1 parent c1955b0
commit 5a7ee3d
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Showing 3 changed files with 8 additions and 5 deletions.
diff --git a/lambdas/inference/lambda.tf b/lambdas/inference/lambda.tf
@@ -29,10 +29,10 @@ module "lambda_function_container_image" {
     ENABLE_RETRIEVAL              = 1
     MEMORY_LAMBDA_NAME            = var.memory_lambda_name
     DYNAMO_TABLE                  = var.dynamo_history_table_name
-    TOP_K                         = 50
-    TEMPERATURE                   = 0.5
+    TOP_K                         = 10
+    TEMPERATURE                   = 0.1
     TOP_P                         = 0.99
-    RELEVANCE_THRESHOLD           = 0.65
+    RELEVANCE_THRESHOLD           = 0.67
     MODEL_ID                      = "anthropic.claude-instant-v1"
     EMBEDDING_COLLECTION_NAME     = var.embedding_collection_name
     SYSTEM_PROMPT                 = "Answer in four to five sentences maximum.Answer in french."

diff --git a/lambdas/ingestion/lambda.tf b/lambdas/ingestion/lambda.tf
@@ -23,7 +23,8 @@ module "lambda_function_container_image" {
     PGVECTOR_DATABASE             = var.pg_vector_database
     PGVECTOR_USER                 = var.pg_vector_user
     PGVECTOR_PASSWORD_SECRET_NAME = var.pg_vector_password_secret_name
-    USE_TEXTRACT                  = 0
+    CHUNK_SIZE                    = 256
+    CHUNK_OVERLAP                 = 20
   }
   policy_statements = {
     log_group = {

diff --git a/lambdas/ingestion/src/index.py b/lambdas/ingestion/src/index.py
@@ -31,6 +31,8 @@ def get_secret():
 PGVECTOR_PORT = int(os.environ.get("PGVECTOR_PORT", 5432))
 PGVECTOR_DATABASE = os.environ.get("PGVECTOR_DATABASE", "postgres")
 PGVECTOR_USER = os.environ.get("PGVECTOR_USER", "postgres")
+CHUNK_SIZE = int(os.environ.get("CHUNK_SIZE", 256))
+CHUNK_OVERLAP = int(os.environ.get("CHUNK_OVERLAP", 20))
 PGVECTOR_PASSWORD = get_secret()
 
 
@@ -100,7 +102,7 @@ def extract_pdf_content(file_path, file_name):
 
     loader = PyPDFLoader(file_path)
     docs = loader.load_and_split(
-        text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
+        text_splitter=RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)
     )
     created_at = datetime.datetime.now().isoformat()
     for doc in docs: