code quality fix

.github/media/workflows/quality.yaml

@@ -0,0 +1,33 @@
+name: 
+
+on: push
+
+jobs:
+  black:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+    - name: Install deps
+      uses: knowsuchagency/poetry-install@v1
+      env:
+        POETRY_VIRTUALENVS_CREATE: false
+    - name: Run black check
+      run: python3 -m black --check .
+  flake8:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+    - name: Install deps
+      uses: knowsuchagency/poetry-install@v1
+      env:
+        POETRY_VIRTUALENVS_CREATE: false
+    - name: Run flake8 check
+      run: python3 -m flake8 --count .

.pre-commit-config.yaml

@@ -0,0 +1,53 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v2.4.0
+  hooks:
+  - id: check-ast
+  - id: trailing-whitespace
+  - id: check-toml
+  - id: end-of-file-fixer
+
+- repo: https://github.com/asottile/add-trailing-comma
+  rev: v2.1.0
+  hooks:
+  - id: add-trailing-comma
+
+- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
+  rev: v2.1.0
+  hooks:
+  - id: pretty-format-yaml
+    args:
+    - --autofix
+    - --preserve-quotes
+    - --indent=2
+
+- repo: local
+  hooks:
+  - id: autoflake
+    name: autoflake
+    entry: poetry run autoflake
+    language: system
+    types: [python]
+    args: [--in-place, --remove-all-unused-imports, --remove-duplicate-keys]
+
+  - id: black
+    name: Format with Black
+    entry: poetry run black
+    language: system
+    types: [python]
+
+  - id: isort
+    name: isort
+    entry: poetry run isort
+    language: system
+    types: [python]
+
+  - id: flake8
+    name: Check with Flake8
+    entry: poetry run flake8
+    language: system
+    pass_filenames: false
+    types: [python]
+    args: [--count, .]

bot.py

@@ -83,44 +83,52 @@ def on_llm_new_token(self, token: str, **kwargs) -> None:
 
 
 def chat_input():
-    user_input = st.chat_input("What coding issue can I help you resolve today?")
-
-    if user_input:
-        with st.chat_message("user"):
-            st.write(user_input)
-        with st.chat_message("assistant"):
-            st.caption(f"RAG: {name}")
-            stream_handler = StreamHandler(st.empty())
-            result = output_function(
-                {"question": user_input, "chat_history": []}, callbacks=[stream_handler]
-            )["answer"]
-            output = result
-            st.session_state[f"user_input"].append(user_input)
-            st.session_state[f"generated"].append(output)
-            st.session_state[f"rag_mode"].append(name)
+    if not (
+        user_input := st.chat_input(
+            "What coding issue can I help you resolve today?"
+        )
+    ):
+        return
+    with st.chat_message("user"):
+        st.write(user_input)
+    with st.chat_message("assistant"):
+        _extracted_from_chat_input_(user_input)
+
+
+# TODO Rename this here and in `chat_input`
+def _extracted_from_chat_input_(user_input):
+    st.caption(f"RAG: {name}")
+    stream_handler = StreamHandler(st.empty())
+    result = output_function(
+        {"question": user_input, "chat_history": []}, callbacks=[stream_handler]
+    )["answer"]
+    output = result
+    st.session_state["user_input"].append(user_input)
+    st.session_state["generated"].append(output)
+    st.session_state["rag_mode"].append(name)
 
 
 def display_chat():
     # Session state
     if "generated" not in st.session_state:
-        st.session_state[f"generated"] = []
+        st.session_state["generated"] = []
 
     if "user_input" not in st.session_state:
-        st.session_state[f"user_input"] = []
+        st.session_state["user_input"] = []
 
     if "rag_mode" not in st.session_state:
-        st.session_state[f"rag_mode"] = []
+        st.session_state["rag_mode"] = []
 
-    if st.session_state[f"generated"]:
-        size = len(st.session_state[f"generated"])
+    if st.session_state["generated"]:
+        size = len(st.session_state["generated"])
         # Display only the last three exchanges
         for i in range(max(size - 3, 0), size):
             with st.chat_message("user"):
-                st.write(st.session_state[f"user_input"][i])
+                st.write(st.session_state["user_input"][i])
 
             with st.chat_message("assistant"):
-                st.caption(f"RAG: {st.session_state[f'rag_mode'][i]}")
-                st.write(st.session_state[f"generated"][i])
+                st.caption(f"RAG: {st.session_state['rag_mode'][i]}")
+                st.write(st.session_state["generated"][i])
 
         with st.expander("Not finding what you're looking for?"):
             st.write(
@@ -142,9 +150,9 @@ def mode_select() -> str:
 
 
 name = mode_select()
-if name == "LLM only" or name == "Disabled":
+if name in ["LLM only", "Disabled"]:
     output_function = llm_chain
-elif name == "Vector + Graph" or name == "Enabled":
+elif name in ["Vector + Graph", "Enabled"]:
     output_function = rag_chain
 
 
@@ -153,9 +161,10 @@ def generate_ticket():
     records = neo4j_graph.query(
         "MATCH (q:Question) RETURN q.title AS title, q.body AS body ORDER BY q.score DESC LIMIT 3"
     )
-    questions = []
-    for i, question in enumerate(records, start=1):
-        questions.append((question["title"], question["body"]))
+    questions = [
+        (question["title"], question["body"])
+        for i, question in enumerate(records, start=1)
+    ]
     # Ask LLM to generate new question in the same style
     questions_prompt = ""
     for i, question in enumerate(questions, start=1):
@@ -182,7 +191,7 @@ def generate_ticket():
     system_prompt = SystemMessagePromptTemplate.from_template(
         gen_system_template, template_format="jinja2"
     )
-    q_prompt = st.session_state[f"user_input"][-1]
+    q_prompt = st.session_state["user_input"][-1]
     chat_prompt = ChatPromptTemplate.from_messages(
         [
             system_prompt,
@@ -215,7 +224,7 @@ def close_sidebar():
     st.session_state.open_sidebar = False
 
 
-if not "open_sidebar" in st.session_state:
+if "open_sidebar" not in st.session_state:
     st.session_state.open_sidebar = False
 if st.session_state.open_sidebar:
     new_title, new_question = generate_ticket()

chains.py

@@ -13,7 +13,9 @@
 from utils import BaseLogger
 
 
-def load_embedding_model(embedding_model_name: str, logger=BaseLogger(), config={}):
+def load_embedding_model(embedding_model_name: str, logger=BaseLogger(), config=None):
+    if config is None:
+        config = {}
     if embedding_model_name == "ollama":
         embeddings = OllamaEmbeddings(
             base_url=config["ollama_base_url"], model="llama2"
@@ -33,7 +35,9 @@ def load_embedding_model(embedding_model_name: str, logger=BaseLogger(), config=
     return embeddings, dimension
 
 
-def load_llm(llm_name: str, logger=BaseLogger(), config={}):
+def load_llm(llm_name: str, logger=BaseLogger(), config=None):
+    if config is None:
+        config = {}
     if llm_name == "gpt-4":
         logger.info("LLM: Using GPT-4")
         return ChatOpenAI(temperature=0, model_name="gpt-4", streaming=True)
@@ -140,10 +144,9 @@ def configure_qa_rag_chain(llm, embeddings, embeddings_store_url, username, pass
     """,
     )
 
-    kg_qa = RetrievalQAWithSourcesChain(
+    return RetrievalQAWithSourcesChain(
         combine_documents_chain=qa_chain,
         retriever=kg.as_retriever(search_kwargs={"k": 2}),
         reduce_k_below_max_tokens=False,
         max_tokens_limit=3375,
     )
-    return kg_qa

loader.py

@@ -97,10 +97,9 @@ def insert_so_data(data: dict) -> None:
 
 # Streamlit
 def get_tag() -> str:
-    input_text = st.text_input(
+    return st.text_input(
         "Which tag questions do you want to import?", value="neo4j"
     )
-    return input_text
 
 
 def get_pages():

pdf_bot.py

@@ -57,10 +57,7 @@ def main():
     if pdf is not None:
         pdf_reader = PdfReader(pdf)
 
-        text = ""
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-
+        text = "".join(page.extract_text() for page in pdf_reader.pages)
         # langchain_textspliter
         text_splitter = RecursiveCharacterTextSplitter(
             chunk_size=1000, chunk_overlap=200, length_function=len
@@ -83,10 +80,9 @@ def main():
             llm=llm, chain_type="stuff", retriever=vectorstore.as_retriever()
         )
 
-        # Accept user questions/query
-        query = st.text_input("Ask questions about related your upload pdf file")
-
-        if query:
+        if query := st.text_input(
+            "Ask questions about related your upload pdf file"
+        ):
             stream_handler = StreamHandler(st.empty())
             qa.run(query, callbacks=[stream_handler])
 

utils.py

@@ -1,3 +1,4 @@
+import contextlib
 class BaseLogger:
     def __init__(self) -> None:
         self.info = print
@@ -28,15 +29,11 @@ def extract_title_and_question(input_string):
 
 def create_vector_index(driver, dimension: int) -> None:
     index_query = "CALL db.index.vector.createNodeIndex('stackoverflow', 'Question', 'embedding', $dimension, 'cosine')"
-    try:
+    with contextlib.suppress(Exception):
         driver.query(index_query, {"dimension": dimension})
-    except:  # Already exists
-        pass
     index_query = "CALL db.index.vector.createNodeIndex('top_answers', 'Answer', 'embedding', $dimension, 'cosine')"
-    try:
+    with contextlib.suppress(Exception):
         driver.query(index_query, {"dimension": dimension})
-    except:  # Already exists
-        pass
 
 
 def create_constraints(driver):