From 4f14742233cb3baeafae889efa61929863ff8e03 Mon Sep 17 00:00:00 2001 From: mrT23 Date: Thu, 24 Oct 2024 21:38:31 +0300 Subject: [PATCH 1/5] Refactor PR help message tool to use full documentation content for answering questions and update relevant section handling in prompts --- pr_agent/settings/pr_help_prompts.toml | 20 +++--- pr_agent/tools/pr_help_message.py | 89 +++++++++++++++----------- 2 files changed, 65 insertions(+), 44 deletions(-) diff --git a/pr_agent/settings/pr_help_prompts.toml b/pr_agent/settings/pr_help_prompts.toml index b537b7b74..4ff6cf6dd 100644 --- a/pr_agent/settings/pr_help_prompts.toml +++ b/pr_agent/settings/pr_help_prompts.toml @@ -1,20 +1,23 @@ [pr_help_prompts] system="""You are Doc-helper, a language models designed to answer questions about a documentation website for an open-soure project called "PR-Agent" (recently renamed to "Qodo Merge"). -You will recieve a question, and a list of snippets that were collected for a documentation site using RAG as the retrieval method. -Your goal is to provide the best answer to the question using the snippets provided. +You will recieve a question, and the full documentation website content. +Your goal is to provide the best answer to the question using the documentation provided. Additional instructions: - Try to be short and concise in your answers. Give examples if needed. -- It is possible some of the snippets may not be relevant to the question. In that case, you should ignore them and focus on the ones that are relevant. - The main tools of PR-Agent are 'describe', 'review', 'improve'. If there is ambiguity to which tool the user is referring to, prioritize snippets of these tools over others. The output must be a YAML object equivalent to type $DocHelper, according to the following Pydantic definitions: ===== +class relevant_section(BaseModel): + file_name: str = Field(description="The name of the relevant file") + relevant_section_header_string: str = Field(description="Exact text of the relevant section heading") + class DocHelper(BaseModel): user_question: str = Field(description="The user's question") response: str = Field(description="The response to the user's question") - relevant_snippets: List[int] = Field(description="One-based index of the relevant snippets in the list of snippets provided. Order the by relevance, with the most relevant first. If a snippet was not relevant, do not include it in the list.") + relevant_sections: List[relevant_section] = Field(description="A list of the relevant markdown sections in the documentation that answer the user's question, ordered by importance") ===== @@ -24,10 +27,11 @@ user_question: | ... response: | ... -relevant_snippets: -- 2 -- 1 -- 4 +relevant_sections: +- file_name: "src/file1.py" + relevant_section_header_string: | + ... +- ... """ user="""\ diff --git a/pr_agent/tools/pr_help_message.py b/pr_agent/tools/pr_help_message.py index 5c909ea68..b2933632e 100644 --- a/pr_agent/tools/pr_help_message.py +++ b/pr_agent/tools/pr_help_message.py @@ -4,6 +4,7 @@ import tempfile import copy from functools import partial +from pathlib import Path from jinja2 import Environment, StrictUndefined @@ -157,38 +158,58 @@ async def run(self): get_logger().error("The `Help` tool chat feature requires an OpenAI API key for calculating embeddings") return - # Initialize embeddings - from langchain_openai import OpenAIEmbeddings - embeddings = OpenAIEmbeddings(model="text-embedding-3-small", - api_key=get_settings().openai.key) - - # Get similar snippets via similarity search - if get_settings().pr_help.force_local_db: - sim_results = self.get_sim_results_from_local_db(embeddings) - elif get_settings().get('pinecone.api_key'): - sim_results = self.get_sim_results_from_pinecone_db(embeddings) - else: - sim_results = self.get_sim_results_from_s3_db(embeddings) - if not sim_results: - get_logger().info("Failed to load the S3 index. Loading the local index...") - sim_results = self.get_sim_results_from_local_db(embeddings) - if not sim_results: - get_logger().error("Failed to retrieve similar snippets. Exiting...") - return - - # Prepare relevant snippets - relevant_pages_full, relevant_snippets_full_header, relevant_snippets_str =\ - await self.prepare_relevant_snippets(sim_results) - self.vars['snippets'] = relevant_snippets_str.strip() + # current path + docs_path= Path(__file__).parent.parent.parent/'docs'/'docs' + # get all the 'md' files inside docs_path and its subdirectories + md_files = list(docs_path.glob('**/*.md')) + folders_to_exclude =['/finetuning_benchmark/'] + files_to_exclude = ['EXAMPLE_BEST_PRACTICE.md','compression_strategy.md'] + md_files = [file for file in md_files if not any(folder in str(file) for folder in folders_to_exclude) and not any(file.name == file_to_exclude for file_to_exclude in files_to_exclude)] + # # calculate the token count of all the md files + # token_count = 0 + # for file in md_files: + # with open(file, 'r') as f: + # token_count += self.token_handler.count_tokens(f.read()) + + docs_prompt ="" + for file in md_files: + with open(file, 'r') as f: + file_path = str(file).replace(str(docs_path), '') + docs_prompt += f"==file name:==\n\n{file_path}\n\n==file content:==\n\n{f.read()}\n=========\n\n" + + self.vars['snippets'] = docs_prompt.strip() + # # Initialize embeddings + # from langchain_openai import OpenAIEmbeddings + # embeddings = OpenAIEmbeddings(model="text-embedding-3-small", + # api_key=get_settings().openai.key) + # + # # Get similar snippets via similarity search + # if get_settings().pr_help.force_local_db: + # sim_results = self.get_sim_results_from_local_db(embeddings) + # elif get_settings().get('pinecone.api_key'): + # sim_results = self.get_sim_results_from_pinecone_db(embeddings) + # else: + # sim_results = self.get_sim_results_from_s3_db(embeddings) + # if not sim_results: + # get_logger().info("Failed to load the S3 index. Loading the local index...") + # sim_results = self.get_sim_results_from_local_db(embeddings) + # if not sim_results: + # get_logger().error("Failed to retrieve similar snippets. Exiting...") + # return + + # # Prepare relevant snippets + # relevant_pages_full, relevant_snippets_full_header, relevant_snippets_str =\ + # await self.prepare_relevant_snippets(sim_results) + # self.vars['snippets'] = relevant_snippets_str.strip() # run the AI model response = await retry_with_fallback_models(self._prepare_prediction, model_type=ModelType.REGULAR) response_yaml = load_yaml(response) response_str = response_yaml.get('response') - relevant_snippets_numbers = response_yaml.get('relevant_snippets') + relevant_sections = response_yaml.get('relevant_sections') - if not relevant_snippets_numbers: - get_logger().info(f"Could not find relevant snippets for the question: {self.question_str}") + if not relevant_sections: + get_logger().info(f"Could not find relevant answer for the question: {self.question_str}") if get_settings().config.publish_output: answer_str = f"### Question: \n{self.question_str}\n\n" answer_str += f"### Answer:\n\n" @@ -202,16 +223,12 @@ async def run(self): answer_str += f"### Question: \n{self.question_str}\n\n" answer_str += f"### Answer:\n{response_str.strip()}\n\n" answer_str += f"#### Relevant Sources:\n\n" - paged_published = [] - for page in relevant_snippets_numbers: - page = int(page - 1) - if page < len(relevant_pages_full) and page >= 0: - if relevant_pages_full[page] in paged_published: - continue - link = f"{relevant_pages_full[page]}{relevant_snippets_full_header[page]}" - # answer_str += f"> - [{relevant_pages_full[page]}]({link})\n" - answer_str += f"> - {link}\n" - paged_published.append(relevant_pages_full[page]) + base_path = "https://qodo-merge-docs.qodo.ai/" + for section in relevant_sections: + file = section.get('file_name').strip().removesuffix('.md') + markdown_header = section['relevant_section_header_string'].strip().strip('#').strip().lower().replace(' ', '-') + answer_str += f"> - {base_path}{file}#{markdown_header}\n" + # publish the answer if get_settings().config.publish_output: From 9786499fa6e7050895b807a771844673df528681 Mon Sep 17 00:00:00 2001 From: mrT23 Date: Thu, 24 Oct 2024 22:01:40 +0300 Subject: [PATCH 2/5] Refactor PR help message tool to use full documentation content for answering questions and update relevant section handling in prompts --- docs/chroma_db.zip | Bin 2163500 -> 0 bytes pr_agent/settings/pr_help_prompts.toml | 6 +- pr_agent/tools/pr_help_message.py | 136 +++++-------------------- requirements.txt | 6 -- 4 files changed, 27 insertions(+), 121 deletions(-) delete mode 100644 docs/chroma_db.zip diff --git a/docs/chroma_db.zip b/docs/chroma_db.zip deleted file mode 100644 index 2726a919a41edab574aa584287bc34306c77e826..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2163500 zcmaHS1F$X4(&e#@du-dbZQHi(du-oh+qP}nw(Ys!Kl8u;y@`p*=-QoGYt`z#D>8Og zbw|rf0fRsR{A1?|tQ!C2{GSgf0Biswb4NQH13hCyIu&I|0ATRJ8`FQ9i#s#`AjlIS z000QeU$*>TjepJ%|D`b%u}K(}0UVrfo~(>~bN?yM>JCVSJTeLNS4q 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Your goal is to provide the best answer to the question using the documentation provided. Additional instructions: -- Try to be short and concise in your answers. Give examples if needed. +- Try to be short and concise in your answers. Try to give examples if needed. - The main tools of PR-Agent are 'describe', 'review', 'improve'. If there is ambiguity to which tool the user is referring to, prioritize snippets of these tools over others. @@ -17,7 +17,7 @@ class relevant_section(BaseModel): class DocHelper(BaseModel): user_question: str = Field(description="The user's question") response: str = Field(description="The response to the user's question") - relevant_sections: List[relevant_section] = Field(description="A list of the relevant markdown sections in the documentation that answer the user's question, ordered by importance") + relevant_sections: List[relevant_section] = Field(description="A list of the relevant markdown sections in the documentation that answer the user's question, ordered by importance (most relevant first)") ===== @@ -41,7 +41,7 @@ User's Question: ===== -Relevant doc snippets retrieved: +Documentation website content: ===== {{ snippets|trim }} ===== diff --git a/pr_agent/tools/pr_help_message.py b/pr_agent/tools/pr_help_message.py index b2933632e..cd2da713b 100644 --- a/pr_agent/tools/pr_help_message.py +++ b/pr_agent/tools/pr_help_message.py @@ -8,11 +8,12 @@ from jinja2 import Environment, StrictUndefined +from pr_agent.algo import MAX_TOKENS from pr_agent.algo.ai_handlers.base_ai_handler import BaseAiHandler from pr_agent.algo.ai_handlers.litellm_ai_handler import LiteLLMAIHandler from pr_agent.algo.pr_processing import retry_with_fallback_models from pr_agent.algo.token_handler import TokenHandler -from pr_agent.algo.utils import ModelType, load_yaml +from pr_agent.algo.utils import ModelType, load_yaml, clip_tokens from pr_agent.config_loader import get_settings from pr_agent.git_providers import get_git_provider, GithubProvider, BitbucketServerProvider, \ get_git_provider_with_context @@ -68,83 +69,6 @@ def parse_args(self, args): question_str = "" return question_str - def get_sim_results_from_s3_db(self, embeddings): - get_logger().info("Loading the S3 index...") - sim_results = [] - try: - from langchain_chroma import Chroma - from urllib import request - with tempfile.TemporaryDirectory() as temp_dir: - # Define the local file path within the temporary directory - local_file_path = os.path.join(temp_dir, 'chroma_db.zip') - - bucket = 'pr-agent' - file_name = 'chroma_db.zip' - s3_url = f'https://{bucket}.s3.amazonaws.com/{file_name}' - request.urlretrieve(s3_url, local_file_path) - - # # Download the file from S3 to the temporary directory - # s3 = boto3.client('s3') - # s3.download_file(bucket, file_name, local_file_path) - - # Extract the contents of the zip file - with zipfile.ZipFile(local_file_path, 'r') as zip_ref: - zip_ref.extractall(temp_dir) - - vectorstore = Chroma(persist_directory=temp_dir + "/chroma_db", - embedding_function=embeddings) - sim_results = vectorstore.similarity_search_with_score(self.question_str, k=self.num_retrieved_snippets) - except Exception as e: - get_logger().error(f"Error while getting sim from S3: {e}", - artifact={"traceback": traceback.format_exc()}) - return sim_results - - def get_sim_results_from_local_db(self, embeddings): - get_logger().info("Loading the local index...") - sim_results = [] - try: - from langchain_chroma import Chroma - get_logger().info("Loading the Chroma index...") - db_path = "./docs/chroma_db.zip" - if not os.path.exists(db_path): - db_path= "/app/docs/chroma_db.zip" - if not os.path.exists(db_path): - get_logger().error("Local db not found") - return sim_results - with tempfile.TemporaryDirectory() as temp_dir: - - # Extract the ZIP file - with zipfile.ZipFile(db_path, 'r') as zip_ref: - zip_ref.extractall(temp_dir) - - vectorstore = Chroma(persist_directory=temp_dir + "/chroma_db", - embedding_function=embeddings) - - # Do similarity search - sim_results = vectorstore.similarity_search_with_score(self.question_str, k=self.num_retrieved_snippets) - except Exception as e: - get_logger().error(f"Error while getting sim from local db: {e}", - artifact={"traceback": traceback.format_exc()}) - return sim_results - - def get_sim_results_from_pinecone_db(self, embeddings): - get_logger().info("Loading the Pinecone index...") - sim_results = [] - try: - from langchain_pinecone import PineconeVectorStore - INDEX_NAME = "pr-agent-docs" - vectorstore = PineconeVectorStore( - index_name=INDEX_NAME, embedding=embeddings, - pinecone_api_key=get_settings().pinecone.api_key - ) - - # Do similarity search - sim_results = vectorstore.similarity_search_with_score(self.question_str, k=self.num_retrieved_snippets) - except Exception as e: - get_logger().error(f"Error while getting sim from Pinecone db: {e}", - artifact={"traceback": traceback.format_exc()}) - return sim_results - async def run(self): try: if self.question_str: @@ -159,48 +83,36 @@ async def run(self): return # current path - docs_path= Path(__file__).parent.parent.parent/'docs'/'docs' + docs_path= Path(__file__).parent.parent.parent / 'docs' / 'docs' # get all the 'md' files inside docs_path and its subdirectories md_files = list(docs_path.glob('**/*.md')) - folders_to_exclude =['/finetuning_benchmark/'] - files_to_exclude = ['EXAMPLE_BEST_PRACTICE.md','compression_strategy.md'] + folders_to_exclude = ['/finetuning_benchmark/'] + files_to_exclude = ['EXAMPLE_BEST_PRACTICE.md', 'compression_strategy.md', '/docs/overview/index.md'] md_files = [file for file in md_files if not any(folder in str(file) for folder in folders_to_exclude) and not any(file.name == file_to_exclude for file_to_exclude in files_to_exclude)] - # # calculate the token count of all the md files - # token_count = 0 - # for file in md_files: - # with open(file, 'r') as f: - # token_count += self.token_handler.count_tokens(f.read()) - docs_prompt ="" + # sort the 'md_files' so that 'priority_files' will be at the top + priority_files_strings = ['/docs/index.md', '/usage-guide', 'tools/describe.md', 'tools/review.md', + 'tools/improve.md', '/faq'] + md_files_priority = [file for file in md_files if + any(priority_string in str(file) for priority_string in priority_files_strings)] + md_files_not_priority = [file for file in md_files if file not in md_files_priority] + md_files = md_files_priority + md_files_not_priority + + docs_prompt = "" for file in md_files: with open(file, 'r') as f: file_path = str(file).replace(str(docs_path), '') - docs_prompt += f"==file name:==\n\n{file_path}\n\n==file content:==\n\n{f.read()}\n=========\n\n" - + docs_prompt += f"==file name:==\n\n{file_path}\n\n==file content:==\n\n{f.read().strip()}\n=========\n\n" + token_count = self.token_handler.count_tokens(docs_prompt) + get_logger().debug(f"Token count of full documentation website: {token_count}") + + model = get_settings().config.model + max_tokens_full = MAX_TOKENS[model] # note - here we take the actual max tokens, without any reductions. we do aim to get the full documentation website in the prompt + delta_output = 2000 + if token_count > max_tokens_full - delta_output: + get_logger().info(f"Token count {token_count} exceeds the limit {max_tokens_full - delta_output}. Skipping the PR Help message.") + docs_prompt = clip_tokens(docs_prompt, max_tokens_full - delta_output) self.vars['snippets'] = docs_prompt.strip() - # # Initialize embeddings - # from langchain_openai import OpenAIEmbeddings - # embeddings = OpenAIEmbeddings(model="text-embedding-3-small", - # api_key=get_settings().openai.key) - # - # # Get similar snippets via similarity search - # if get_settings().pr_help.force_local_db: - # sim_results = self.get_sim_results_from_local_db(embeddings) - # elif get_settings().get('pinecone.api_key'): - # sim_results = self.get_sim_results_from_pinecone_db(embeddings) - # else: - # sim_results = self.get_sim_results_from_s3_db(embeddings) - # if not sim_results: - # get_logger().info("Failed to load the S3 index. Loading the local index...") - # sim_results = self.get_sim_results_from_local_db(embeddings) - # if not sim_results: - # get_logger().error("Failed to retrieve similar snippets. Exiting...") - # return - - # # Prepare relevant snippets - # relevant_pages_full, relevant_snippets_full_header, relevant_snippets_str =\ - # await self.prepare_relevant_snippets(sim_results) - # self.vars['snippets'] = relevant_snippets_str.strip() # run the AI model response = await retry_with_fallback_models(self._prepare_prediction, model_type=ModelType.REGULAR) diff --git a/requirements.txt b/requirements.txt index 152e6a5b5..a1003e6c5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -28,12 +28,6 @@ gunicorn==22.0.0 pytest-cov==5.0.0 pydantic==2.8.2 html2text==2024.2.26 -# help bot -langchain==0.3.0 -langchain-openai==0.2.0 -langchain-pinecone==0.2.0 -langchain-chroma==0.1.4 -chromadb==0.5.7 # Uncomment the following lines to enable the 'similar issue' tool # pinecone-client # pinecone-datasets @ git+https://github.com/mrT23/pinecone-datasets.git@main From 204a0a7912c21457bf06d2d34e266ff9228579a7 Mon Sep 17 00:00:00 2001 From: mrT23 Date: Thu, 24 Oct 2024 22:03:44 +0300 Subject: [PATCH 3/5] s --- docker/Dockerfile | 1 - pr_agent/tools/pr_help_message.py | 6 +----- 2 files changed, 1 insertion(+), 6 deletions(-) diff --git a/docker/Dockerfile b/docker/Dockerfile index 07f74cb52..601e16ecc 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -1,7 +1,6 @@ FROM python:3.12.3 AS base WORKDIR /app -ADD docs/chroma_db.zip /app/docs/chroma_db.zip ADD pyproject.toml . ADD requirements.txt . RUN pip install . && rm pyproject.toml requirements.txt diff --git a/pr_agent/tools/pr_help_message.py b/pr_agent/tools/pr_help_message.py index cd2da713b..3989df06e 100644 --- a/pr_agent/tools/pr_help_message.py +++ b/pr_agent/tools/pr_help_message.py @@ -1,7 +1,3 @@ -import os -import traceback -import zipfile -import tempfile import copy from functools import partial from pathlib import Path @@ -15,7 +11,7 @@ from pr_agent.algo.token_handler import TokenHandler from pr_agent.algo.utils import ModelType, load_yaml, clip_tokens from pr_agent.config_loader import get_settings -from pr_agent.git_providers import get_git_provider, GithubProvider, BitbucketServerProvider, \ +from pr_agent.git_providers import GithubProvider, BitbucketServerProvider, \ get_git_provider_with_context from pr_agent.log import get_logger From 2e34d7a05afd7315ad47abaa5ead41a721abec46 Mon Sep 17 00:00:00 2001 From: mrT23 Date: Thu, 24 Oct 2024 22:05:25 +0300 Subject: [PATCH 4/5] s --- pr_agent/tools/pr_help_message.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/pr_agent/tools/pr_help_message.py b/pr_agent/tools/pr_help_message.py index 3989df06e..a99b20e9b 100644 --- a/pr_agent/tools/pr_help_message.py +++ b/pr_agent/tools/pr_help_message.py @@ -83,7 +83,7 @@ async def run(self): # get all the 'md' files inside docs_path and its subdirectories md_files = list(docs_path.glob('**/*.md')) folders_to_exclude = ['/finetuning_benchmark/'] - files_to_exclude = ['EXAMPLE_BEST_PRACTICE.md', 'compression_strategy.md', '/docs/overview/index.md'] + files_to_exclude = {'EXAMPLE_BEST_PRACTICE.md', 'compression_strategy.md', '/docs/overview/index.md'} md_files = [file for file in md_files if not any(folder in str(file) for folder in folders_to_exclude) and not any(file.name == file_to_exclude for file_to_exclude in files_to_exclude)] # sort the 'md_files' so that 'priority_files' will be at the top @@ -96,9 +96,12 @@ async def run(self): docs_prompt = "" for file in md_files: - with open(file, 'r') as f: - file_path = str(file).replace(str(docs_path), '') - docs_prompt += f"==file name:==\n\n{file_path}\n\n==file content:==\n\n{f.read().strip()}\n=========\n\n" + try: + with open(file, 'r') as f: + file_path = str(file).replace(str(docs_path), '') + docs_prompt += f"==file name:==\n\n{file_path}\n\n==file content:==\n\n{f.read().strip()}\n=========\n\n" + except Exception as e: + get_logger().error(f"Error while reading the file {file}: {e}") token_count = self.token_handler.count_tokens(docs_prompt) get_logger().debug(f"Token count of full documentation website: {token_count}") From 3b47c75c3250f8a7c73c06c333f4a773a2d973c4 Mon Sep 17 00:00:00 2001 From: mrT23 Date: Fri, 25 Oct 2024 12:27:59 +0300 Subject: [PATCH 5/5] Improve handling of empty markdown headers in PR help message tool and update prompt instructions in pr_help_prompts.toml --- pr_agent/settings/pr_help_prompts.toml | 3 ++- pr_agent/tools/pr_help_message.py | 9 ++++++--- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/pr_agent/settings/pr_help_prompts.toml b/pr_agent/settings/pr_help_prompts.toml index cc3b5153f..1911db9c6 100644 --- a/pr_agent/settings/pr_help_prompts.toml +++ b/pr_agent/settings/pr_help_prompts.toml @@ -6,13 +6,14 @@ Your goal is to provide the best answer to the question using the documentation Additional instructions: - Try to be short and concise in your answers. Try to give examples if needed. - The main tools of PR-Agent are 'describe', 'review', 'improve'. If there is ambiguity to which tool the user is referring to, prioritize snippets of these tools over others. +- If the question has ambiguity and can relate to different tools or platfroms, provide the best answer possible based on what is available, but also state in your answer what additional information would be needed to give a more accurate answer. The output must be a YAML object equivalent to type $DocHelper, according to the following Pydantic definitions: ===== class relevant_section(BaseModel): file_name: str = Field(description="The name of the relevant file") - relevant_section_header_string: str = Field(description="Exact text of the relevant section heading") + relevant_section_header_string: str = Field(description="From the relevant file, exact text of the relevant section heading. If no markdown heading is relevant, return empty string") class DocHelper(BaseModel): user_question: str = Field(description="The user's question") diff --git a/pr_agent/tools/pr_help_message.py b/pr_agent/tools/pr_help_message.py index a99b20e9b..f35b81734 100644 --- a/pr_agent/tools/pr_help_message.py +++ b/pr_agent/tools/pr_help_message.py @@ -99,7 +99,7 @@ async def run(self): try: with open(file, 'r') as f: file_path = str(file).replace(str(docs_path), '') - docs_prompt += f"==file name:==\n\n{file_path}\n\n==file content:==\n\n{f.read().strip()}\n=========\n\n" + docs_prompt += f"\n==file name==\n\n{file_path}\n\n==file content==\n\n{f.read().strip()}\n=========\n\n" except Exception as e: get_logger().error(f"Error while reading the file {file}: {e}") token_count = self.token_handler.count_tokens(docs_prompt) @@ -137,8 +137,11 @@ async def run(self): base_path = "https://qodo-merge-docs.qodo.ai/" for section in relevant_sections: file = section.get('file_name').strip().removesuffix('.md') - markdown_header = section['relevant_section_header_string'].strip().strip('#').strip().lower().replace(' ', '-') - answer_str += f"> - {base_path}{file}#{markdown_header}\n" + if str(section['relevant_section_header_string']).strip(): + markdown_header = section['relevant_section_header_string'].strip().strip('#').strip().lower().replace(' ', '-') + answer_str += f"> - {base_path}{file}#{markdown_header}\n" + else: + answer_str += f"> - {base_path}{file}\n" # publish the answer