Update prompts

- Swap to RunnableWithMemory
- Add verbosity flag

src/reviewllama/cli.py

@@ -69,27 +69,24 @@ Examples:
         default=(
             "You are a PR review assistant in charge of software quality control. "
             "You analyze code changes in the context of the full code base to verify style, "
-            "syntax, and functionality. Your response should contain exactly 3 suggestions. "
+            "syntax, and functionality. You respond with suggestions to improve code quality "
-            "Each suggestion must be in the following format:\n"
+            "You only provide sugestions when you find a flaw in the code otherwise you say that "
-            "```diff\n"
+            "no issues were found. Each suggestion should reference the old code and the new "
-            "-<old code>\n"
+            "suggested code."
-            "+<new code>\n"
+            "Do not provide an analysis of the code and do not summarize suggestions. "
-            "```\n"
+            "Answer as briefly as possible and return only the suggestions in the requested format "
-            "Reason: <explanation>\n\n"
+            "with bullet points and no extra text. Provide examples when appropriate."
-            "Here are two examples of the required format:\n"
-            "```diff\n"
-            "-somvr = 2 + 2\n"
-            "+somevar = 2 + 2\n"
-            "```\n"
-            "Reason: somvr is likely a typo, try replacing with somevar\n\n"
-            "```diff\n"
-            "-add_two_numbers(\"1\", \"2\")\n"
-            "+add_two_numbers(1,2)\n"
-            "```\n"
-            "Reason: add_two_numbers requires numeric values and does not accept strings"
         ),
         help="Base branch to compare against (default: %(default)s)",
     )
+
+    parser.add_argument(
+        "-v",
+        "--verbose",
+        action="store_true",
+        default=False,
+        help="Enable verbose output, including messages sent to the LLM.",
+    )
     return parser
 
 

src/reviewllama/configs.py

@@ -13,7 +13,7 @@ class OllamaConfig:
     base_url: str
     system_prompt: str
     # TODO: Update this to be a passed in value
-    temperature: float = field(default=0.7)
+    temperature: float = field(default=0.0)
 
 
 @dataclass(frozen=True)
@@ -23,13 +23,14 @@ class ReviewConfig:
     paths: List[Path]
     ollama: OllamaConfig
     base_branch: str
+    verbose: bool
 
 
 def create_ollama_config(
     model: str,
     server_url: str,
     system_prompt: str,
-    temperature=0.7,
+    temperature=0.0,
     embedding_model="nomic-embed-text",
 ) -> OllamaConfig:
     """Create OllamaConfig with validated parameters."""
@@ -43,19 +44,24 @@ def create_ollama_config(
 
 
 def create_review_config(
-    paths: List[Path], ollama_config: OllamaConfig, base_branch: str
+    paths: List[Path], ollama_config: OllamaConfig, base_branch: str, verbose
 ) -> ReviewConfig:
     """Create complete ReviewConfig from validated components."""
-    return ReviewConfig(paths=paths, ollama=ollama_config, base_branch=base_branch)
+    return ReviewConfig(
+        paths=paths, ollama=ollama_config, base_branch=base_branch, verbose=verbose
+    )
 
 
-def namespace_to_config(
+def namespace_to_config(namespace: argparse.Namespace):
-        namespace: argparse.Namespace
-):
     """Transform argparse namespace into ReviewConfig."""
     paths = [Path(path_str) for path_str in namespace.paths]
     ollama_config = OllamaConfig(
-        chat_model=namespace.model, base_url=namespace.server_url, system_prompt=namespace.system_prompt, embedding_model=namespace.embedding_model
+        chat_model=namespace.model,
+        base_url=namespace.server_url,
+        system_prompt=namespace.system_prompt,
+        embedding_model=namespace.embedding_model,
     )
 
-    return create_review_config(paths, ollama_config, namespace.base_branch)
+    return create_review_config(
+        paths, ollama_config, namespace.base_branch, namespace.verbose
+    )

src/reviewllama/llm.py

@@ -1,9 +1,8 @@
 from dataclasses import dataclass
 from typing import Any
 
-from langchain.memory import ConversationBufferMemory
+from langchain_core.messages import BaseMessage
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.prompts import ChatPromptTemplate
-from langchain.schema import BaseMessage
 from langchain_core.runnables import RunnableLambda
 from langchain_core.runnables.base import RunnableSerializable
 from langchain_core.runnables.passthrough import RunnablePassthrough
@@ -17,20 +16,13 @@ from .configs import OllamaConfig
 
 @dataclass(frozen=True)
 class ChatClient:
-    chain: RunnableSerializable[dict[str, Any], BaseMessage]
+    chain: RunnableSerializable
-    memory: ConversationBufferMemory
-
-    def get_last_response_or_none(self):
-        try:
-            return self.memory.chat_memory.messages[-1]
-        except IndexError:
-            return None
 
 
 def create_chat_chain(
     config: OllamaConfig,
 ) -> RunnableSerializable[dict[str, Any], BaseMessage]:
-    """Create the chat chain for use by the code."""
+    """Create the base chat chain for use by the code."""
     llm = ChatOllama(
         model=config.chat_model,
         base_url=config.base_url,
@@ -40,28 +32,16 @@ def create_chat_chain(
     prompt = ChatPromptTemplate.from_messages(
         [
             ("system", config.system_prompt),
-            MessagesPlaceholder("chat_history"),
             ("human", "Context\n{context}\n\nQuestion:\n{input}"),
         ]
     )
 
-    def get_chat_history(inputs: dict) -> list:
-        """Extract chat history from memory object."""
-        try:
-            return inputs["memory"].chat_memory.messages
-        except AttributeError:
-            return []
-
     def get_context(inputs: dict) -> str:
         """Extract the RAG context from the input object"""
-        try:
+        return inputs.get("context", "")
-            return inputs["context"]
-        except AttributeError:
-            return ""
 
     return (
         RunnablePassthrough.assign(
-            chat_history=RunnableLambda(get_chat_history),
             context=RunnableLambda(get_context),
         )
         | prompt
@@ -69,32 +49,28 @@ def create_chat_chain(
     )
 
 
-def create_memory():
-    return ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-
-
 def create_chat_client(config: OllamaConfig):
+    base_chain = create_chat_chain(config)
+
     return ChatClient(
-        chain=create_chat_chain(config),
+        chain=base_chain,
-        memory=create_memory(),
     )
 
 
 def chat_with_client(
-    client: ChatClient, message: str, retriever: VectorStoreRetriever | None = None
+    client: ChatClient,
-) -> ChatClient:
+    message: str,
-
+    retriever: VectorStoreRetriever | None = None,
+    verbose: bool = False,
+) -> str:
+    """Chat with the client and return the response content."""
     if retriever:
         context = get_context_from_store(message, retriever)
     else:
         context = ""
 
     response = client.chain.invoke(
-        {"input": message, "memory": client.memory, "context": context}
+        {"input": message, "context": context},
     )
 
-    memory = client.memory
+    return response.content
-    memory.chat_memory.add_user_message(message)
-    memory.chat_memory.add_ai_message(response.content)
-
-    return ChatClient(chain=client.chain, memory=memory)

src/reviewllama/reviewllama.py

@@ -19,7 +19,7 @@ def run_reviewllama(config: ReviewConfig):
         retriever = create_and_log_vector_store_retriever(analysis.repo, config.ollama)
 
         for diff in analysis.diffs:
-            chat_client = get_suggestions(diff, retriever, chat_client)
+            get_suggestions(diff, retriever, chat_client, config.verbose)
 
 
 def create_and_log_chat_client(config: OllamaConfig) -> ChatClient:
@@ -44,20 +44,16 @@ def create_and_log_vector_store_retriever(
 
 
 def get_suggestions(
-    diff: GitDiff, retriever: VectorStoreRetriever, chat_client: ChatClient
+    diff: GitDiff,
-) -> ChatClient:
+    retriever: VectorStoreRetriever,
-    new_client = chat_with_client(chat_client, craft_message(diff), retriever)
+    chat_client: ChatClient,
-    log_info(str(new_client.get_last_response_or_none().content))
+    verbose: bool,
-    return new_client
+):
+    response = chat_with_client(chat_client, craft_message(diff), retriever, verbose)
+    log_info(response)
 
 
 def craft_message(diff) -> str:
     return (
-        "Review the following code changes and make up to three suggestions on "
-        "how to improve it. If the code is sufficiently simple or accurate then say "
-        "no suggestions can be found. Important issues you should consider are consistent "
-        "style, introduction of syntax errors, and potentially breaking changes in "
-        "interfaces/APIs that aren't properly handled.\n\n"
-        f"The original code:\n```\n{diff.old_content}\n```\n"
         f"The new code:\n```\n{diff.new_content}```"
     )

tests/test_llm.py

@@ -17,11 +17,10 @@ def test_chat_client(ollama_config, chat_client):
     if not is_ollama_available(ollama_config):
         pytest.skip("Local Ollama server is not available")
 
-    chat_client = chat_with_client(
+    response = chat_with_client(
         chat_client, "Tell me your name and introduce yourself briefly"
     )
-    response = chat_client.get_last_response_or_none()
 
     assert response is not None
-    assert len(response.content) > 0
+    assert len(response) > 0
-    assert "gemma" in response.content.lower()
+    assert "gemma" in response.lower()