From 3320e1ae17411430c031fabccc0d69e9eb8cf59a Mon Sep 17 00:00:00 2001
From: Erni Durdevic <erni.durdevic@databricks.com>
Date: Tue, 8 Oct 2024 20:50:51 +0200
Subject: [PATCH] Removed default agent config file

---
 .../agents/generated_configs/agent.yaml       | 40 -------------------
 1 file changed, 40 deletions(-)
 delete mode 100644 agent_app_sample_code/agents/generated_configs/agent.yaml

diff --git a/agent_app_sample_code/agents/generated_configs/agent.yaml b/agent_app_sample_code/agents/generated_configs/agent.yaml
deleted file mode 100644
index e09fa95..0000000
--- a/agent_app_sample_code/agents/generated_configs/agent.yaml
+++ /dev/null
@@ -1,40 +0,0 @@
-input_example:
-  messages:
-  - content: What is RAG?
-    role: user
-llm_config:
-  llm_endpoint_name: databricks-meta-llama-3-1-70b-instruct
-  llm_parameters:
-    max_tokens: 1500
-    temperature: 0.01
-  llm_system_prompt_template: You are a helpful assistant that answers questions by
-    calling tools.  Provide responses ONLY based on the information from tools that
-    are explictly specified to you.  If you do not have a relevant tool for a question,
-    respond with 'Sorry, I'm not trained to answer that question'.
-retriever_tool:
-  chunk_template: 'Passage text: {chunk_text}
-
-    Passage metadata: {metadata}
-
-
-    '
-  parameters:
-    num_results: 5
-    query_type: ann
-  prompt_template: 'Use the following pieces of retrieved context to answer the question.
-
-    Only use the passages from context that are relevant to the query to answer the
-    question, ignore the irrelevant passages.  When responding, cite your source,
-    referring to the passage by the columns in the passage''s metadata.
-
-
-    Context: {context}'
-  tool_description_prompt: Search for documents that are relevant to a user's query
-    about the [REPLACE WITH DESCRIPTION OF YOUR DOCS].
-  vector_search_index: prithvikannan_catalog.cookbook.my_agent_app_chunked_docs_index
-  vector_search_schema:
-    additional_metadata_columns: []
-    chunk_text: content_chunked
-    document_uri: doc_uri
-    primary_key: chunk_id
-  vector_search_threshold: 0.1