pyproject.toml

@@ -24,6 +24,9 @@ ollama = "*"
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.3.5"
 
+[tool.poetry.scripts]
+code-rag = "code_rag.cli:main"
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"

src/code_rag/cli.py

@@ -0,0 +1,165 @@
+#!/usr/bin/env python3
+
+import os
+import sys
+import argparse
+from code_rag.rag import RAG
+
+def stream_output(response_iter):
+    """Stream the response to console, handling each token"""
+    try:
+        for chunk in response_iter:
+            print(chunk, end="", flush=True)
+        print()  # New line at end
+    except KeyboardInterrupt:
+        print("\nStreaming interrupted by user")
+
+
+def interactive_chat(rag_pipeline, rag_chain, no_stream=False):
+    """Run an interactive chat session"""
+    print(
+        "\nEnter your questions about the codebase. Type 'exit', 'quit', or press Ctrl+C to end the session.\n"
+    )
+
+    try:
+        while True:
+            try:
+                query = input("\nQuestion: ").strip()
+
+                if query.lower() in ["exit", "quit", ""]:
+                    print("\nEnding chat session.")
+                    break
+
+                print("\nResponse:")
+                if no_stream:
+                    response = rag_pipeline.query_rag(rag_chain, query, stream=False)
+                    print(response)
+                else:
+                    response_iter = rag_pipeline.query_rag(
+                        rag_chain, query, stream=True
+                    )
+                    stream_output(response_iter)
+
+            except KeyboardInterrupt:
+                print("\n\nEnding chat session.")
+                break
+            except EOFError:
+                break
+    except KeyboardInterrupt:
+        print("\nChat session interrupted.")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Code RAG - Query your codebase using natural language"
+    )
+    parser.add_argument(
+        "docs_dir", help="Directory containing the documents to process", type=str
+    )
+    parser.add_argument(
+        "query",
+        help="Initial query about your codebase (optional in interactive mode)",
+        nargs="?",
+        default=None,
+        type=str,
+    )
+    parser.add_argument(
+        "--db-dir",
+        help="Directory to store the vector database (default: .code_rag_db)",
+        default=os.path.expanduser("~/.code_rag_db"),
+        type=str,
+    )
+    parser.add_argument(
+        "--tracker-file",
+        help="File to track document changes (default: .code_rag_tracker.json)",
+        default=os.path.expanduser("~/.code_rag_tracker.json"),
+        type=str,
+    )
+    parser.add_argument(
+        "--force-refresh",
+        help="Force refresh of the vector database",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--ollama-url",
+        help="URL for the Ollama server (default: 127.0.0.1)",
+        default="127.0.0.1",
+        type=str,
+    )
+    parser.add_argument(
+        "--embedding-model",
+        help="Model to use for embeddings (default: nomic-embed-text)",
+        default="nomic-embed-text",
+        type=str,
+    )
+    parser.add_argument(
+        "--llm-model",
+        help="Model to use for text generation (default: llama3.2)",
+        default="llama3.2",
+        type=str,
+    )
+    parser.add_argument(
+        "--no-stream", help="Disable streaming output", action="store_true"
+    )
+    parser.add_argument(
+        "--no-interactive",
+        help="Run in non-interactive mode (answer single query and exit)",
+        action="store_true",
+    )
+
+    args = parser.parse_args()
+
+    if args.no_interactive and not args.query:
+        parser.error("Query is required in non-interactive mode")
+
+    # Create RAG pipeline
+    rag_pipeline = RAG(
+        docs_dir=args.docs_dir,
+        db_dir=args.db_dir,
+        tracker_file=args.tracker_file,
+        ollama_url=args.ollama_url,
+        embedding_model=args.embedding_model,
+        llm_model=args.llm_model,
+    )
+
+    # Create or update vector database
+    print(f"Processing documents in {args.docs_dir}...")
+    print(f"Using models: embedding={args.embedding_model}, llm={args.llm_model}")
+    print(f"Ollama server: {args.ollama_url}")
+    vectorstore = rag_pipeline.create_vector_db(force_refresh=args.force_refresh)
+
+    # Set up RAG chain
+    print("Setting up RAG pipeline...")
+    rag_chain = rag_pipeline.setup_rag()
+
+    if args.no_interactive:
+        # Single query mode
+        print(f"\nQuery: {args.query}")
+        print("\nResponse:")
+        if args.no_stream:
+            response = rag_pipeline.query_rag(rag_chain, args.query, stream=False)
+            stream_output(response)
+        else:
+            response_iter = rag_pipeline.query_rag(rag_chain, args.query, stream=True)
+            stream_output(response_iter)
+    else:
+        # Interactive mode
+        if args.query:
+            # Handle initial query if provided
+            print(f"\nQuery: {args.query}")
+            print("\nResponse:")
+            if args.no_stream:
+                response = rag_pipeline.query_rag(rag_chain, args.query, stream=False)
+                stream_output(response)
+            else:
+                response_iter = rag_pipeline.query_rag(
+                    rag_chain, args.query, stream=True
+                )
+                stream_output(response_iter)
+
+        # Start interactive chat
+        interactive_chat(rag_pipeline, rag_chain, args.no_stream)
+
+
+if __name__ == "__main__":
+    main()

src/code_rag/rag.py

@@ -1,15 +1,24 @@
 import os
-import uuid
 import glob
-from langchain_community.document_loaders import DirectoryLoader, TextLoader
+import uuid
+from langchain.schema import Document
 from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import TextLoader
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.chat_history import BaseChatMessageHistory
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.chains import create_history_aware_retriever, create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_community.chat_message_histories import ChatMessageHistory
+from langchain_core.chat_history import BaseChatMessageHistory
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain_chroma import Chroma
-from langchain.chains import RetrievalQA
-from langchain.prompts import PromptTemplate
-from langchain_core.documents import Document
+import time
 
-from code_rag.doc_tracker import DocumentTracker
-from code_rag.ollama_wrapper import OllamaWrapper
+from .doc_tracker import DocumentTracker
+from .ollama_wrapper import OllamaWrapper
 
 
 class RAG:
@@ -28,6 +37,7 @@ class RAG:
         self.ollama = OllamaWrapper(
             ollama_url, embedding_model=embedding_model, llm_model=llm_model
         )
+        self.session_id = time.time()
 
     def process_documents(self, files, text_splitter):
         """Process document files into chunks with tracking metadata"""
@@ -84,9 +94,7 @@ class RAG:
         )
 
         # Create embeddings
-        print("Before embedding")
         embeddings = self.ollama.embeddings
-        print("after embedding")
 
         # Load or create vector store
         if os.path.exists(self.db_dir) and not force_refresh:
@@ -161,55 +169,117 @@ class RAG:
         return vectorstore
 
     def setup_rag(self):
-        """
-        Set up the RAG system with an existing vector database
-        """
-        # Load the embeddings
-        embeddings = self.ollama.embeddings
-
-        # Load the vector store
+        """Set up the RAG pipeline"""
+        # Create vector store
         vectorstore = Chroma(
-            persist_directory=self.db_dir, embedding_function=embeddings
+            persist_directory=self.db_dir, embedding_function=self.ollama.embeddings
         )
 
-        # Create a retriever
-        retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
-
-        # Set up the LLM
-        llm = self.ollama.llm
-
-        # Create a custom prompt template
-        template = """
-        Answer the question based on the context provided. If you don't know the answer,
-        just say you don't know. Don't try to make up an answer.
-
-        Context: {context}
-
-        Question: {question}
-
-        Answer:
-        """
-
-        prompt = PromptTemplate(
-            input_variables=["context", "question"], template=template
+        # Create retriever
+        retriever = vectorstore.as_retriever(
+            search_type="similarity", search_kwargs={"k": 4}
         )
 
-        # Create the RAG chain
-        rag_chain = RetrievalQA.from_chain_type(
-            llm=llm,
-            chain_type="stuff",
-            retriever=retriever,
-            chain_type_kwargs={"prompt": prompt},
+        # Create chat history buffer
+        self.chat_history = []
+
+        ### Contextualize question ###
+        contextualize_q_system_prompt = (
+            "Given a chat history and the latest user question "
+            "which might reference context in the chat history, "
+            "formulate a standalone question which can be understood "
+            "without the chat history. Do NOT answer the question, "
+            "just reformulate it if needed and otherwise return it as is."
+        )
+        contextualize_q_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", contextualize_q_system_prompt),
+                MessagesPlaceholder("chat_history"),
+                ("human", "{input}"),
+            ]
+        )
+        history_aware_retriever = create_history_aware_retriever(
+            self.ollama.llm, retriever, contextualize_q_prompt
         )
 
-        return rag_chain
+        ### Answer question ###
+        system_prompt = (
+            "You are an expert at analyzing code and documentation. "
+            "Use the following pieces of context to answer the question at the end."
+            "If you don't know the answer, just say that you don't know, "
+            "don't try to make up an answer"
+            "\n\n"
+            "{context}\n\n"
+            "Answer in a clear and concise manner. "
+            "If you're referring to code, use markdown formatting."
+        )
+        qa_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", system_prompt),
+                MessagesPlaceholder("chat_history"),
+                ("human", "{input}"),
+            ]
+        )
+        question_answer_chain = create_stuff_documents_chain(self.ollama.llm, qa_prompt)
 
-    def query_rag(self, rag_chain, query):
+        rag_chain = create_retrieval_chain(
+            history_aware_retriever, question_answer_chain
+        )
+
+        ### Statefully manage chat history ###
+        self.store = {}
+
+        def get_session_history(session_id: str) -> BaseChatMessageHistory:
+            if session_id not in self.store:
+                self.store[session_id] = ChatMessageHistory()
+            return self.store[session_id]
+
+        return RunnableWithMessageHistory(
+            rag_chain,
+            get_session_history,
+            input_messages_key="input",
+            history_messages_key="chat_history",
+            output_messages_key="answer",
+        )
+
+    def query_rag(self, rag_chain, query, stream=False):
         """
         Query the RAG system
+
+        Args:
+            rag_chain: The RAG chain to use
+            query: Query string
+            stream: If True, stream the response
         """
-        response = rag_chain.invoke({"query": query})
-        return response["result"]
+        if stream:
+            response = rag_chain.stream(
+                {"input": query},
+                config={"configurable": {"session_id": self.session_id}},
+            )
+            # Store in chat history after getting full response
+            full_response = ""
+            for chunk in response:
+                # Extract only the LLM answer chunk
+                if "answer" in chunk:
+                    chunk_text = (
+                        chunk["answer"].content
+                        if hasattr(chunk["answer"], "content")
+                        else str(chunk["answer"])
+                    )
+                    full_response += chunk_text
+                    yield chunk_text
+        else:
+            response = rag_chain.invoke(
+                {"input": query},
+                config={"configurable": {"session_id": self.session_id}},
+            )
+            # Extract only the LLM answer from the response
+            result = (
+                response["answer"].content
+                if hasattr(response["answer"], "content")
+                else str(response["answer"])
+            )
+            return result
 
 
 def default_file_extensions():
@@ -217,11 +287,6 @@ def default_file_extensions():
     return [
         # Python
         ".py",
-        ".pyi",
-        ".pyx",
-        ".pyc",
-        ".pyd",
-        ".pyw",
         # C/C++
         ".c",
         ".cpp",
@@ -235,14 +300,8 @@ def default_file_extensions():
         ".ipp",
         # Rust
         ".rs",
-        ".rlib",
-        ".rmeta",
         # Java
         ".java",
-        ".jsp",
-        ".jav",
-        ".jar",
-        ".class",
         ".kt",
         ".kts",
         ".groovy",
@@ -250,9 +309,6 @@ def default_file_extensions():
         ".html",
         ".htm",
         ".css",
-        ".scss",
-        ".sass",
-        ".less",
         ".js",
         ".jsx",
         ".ts",

tests/test_rag.py

@@ -51,16 +51,16 @@ def test_process_documents(tracker_file, docs_dir, db_dir, sample_docs, rag_pipe
 def test_create_vector_db(docs_dir, db_dir, tracker_file, sample_docs):
     """Test creating a vector database"""
     rag_pipeline = RAG(docs_dir, db_dir, tracker_file)
-    
+
     # Create files with different extensions
     files = {
         "test.py": "def hello():\n    print('Hello, World!')",
         "main.cpp": "#include <iostream>\nint main() { std::cout << 'Hello'; return 0; }",
         "lib.rs": "fn main() { println!('Hello from Rust!'); }",
         "config.toml": "[package]\nname = 'test'",
-        "doc.md": "# Documentation\nThis is a test file."
+        "doc.md": "# Documentation\nThis is a test file.",
     }
-    
+
     for filename, content in files.items():
         filepath = os.path.join(docs_dir, filename)
         with open(filepath, "w") as f:
@@ -72,14 +72,15 @@ def test_create_vector_db(docs_dir, db_dir, tracker_file, sample_docs):
     # Verify it was created
     assert os.path.exists(rag_pipeline.db_dir)
     assert vectorstore is not None
-    
+
     # Check the database has content from all file types
     loaded_db = Chroma(
-        persist_directory=rag_pipeline.db_dir, embedding_function=rag_pipeline.ollama.embeddings
+        persist_directory=rag_pipeline.db_dir,
+        embedding_function=rag_pipeline.ollama.embeddings,
     )
     # Should have content from all files
     assert loaded_db._collection.count() > 0
-    
+
     # Verify each file type is included
     docs = loaded_db._collection.get()
     sources = {os.path.basename(m["source"]) for m in docs["metadatas"]}
@@ -94,11 +95,12 @@ def test_update_vector_db_with_changes(docs_dir, db_dir, tracker_file, sample_do
     """Test updating a vector database with document changes"""
     rag_pipeline = RAG(docs_dir, db_dir, tracker_file)
     # Create initial vector database with only Python files
-    vectorstore = rag_pipeline.create_vector_db(extensions=['.py'], force_refresh=True)
+    vectorstore = rag_pipeline.create_vector_db(extensions=[".py"], force_refresh=True)
 
     # Get initial count
     initial_db = Chroma(
-        persist_directory=rag_pipeline.db_dir, embedding_function=rag_pipeline.ollama.embeddings
+        persist_directory=rag_pipeline.db_dir,
+        embedding_function=rag_pipeline.ollama.embeddings,
     )
     initial_count = initial_db._collection.count()
 
@@ -107,9 +109,9 @@ def test_update_vector_db_with_changes(docs_dir, db_dir, tracker_file, sample_do
     new_files = {
         "newdoc.cpp": "#include <iostream>\nint main() { return 0; }",
         "lib.rs": "fn main() { println!('Hello'); }",
-        "config.toml": "[package]\nname = 'test'"
+        "config.toml": "[package]\nname = 'test'",
     }
-    
+
     for filename, content in new_files.items():
         filepath = os.path.join(docs_dir, filename)
         with open(filepath, "w") as f:
@@ -120,10 +122,11 @@ def test_update_vector_db_with_changes(docs_dir, db_dir, tracker_file, sample_do
 
     # Check the database has been updated
     updated_db = Chroma(
-        persist_directory=rag_pipeline.db_dir, embedding_function=rag_pipeline.ollama.embeddings
+        persist_directory=rag_pipeline.db_dir,
+        embedding_function=rag_pipeline.ollama.embeddings,
     )
     assert updated_db._collection.count() > initial_count
-    
+
     # Verify new files are included
     docs = updated_db._collection.get()
     sources = {os.path.basename(m["source"]) for m in docs["metadatas"]}
@@ -137,16 +140,16 @@ def test_update_vector_db_with_changes(docs_dir, db_dir, tracker_file, sample_do
 def test_full_rag_pipeline(docs_dir, db_dir, tracker_file, sample_docs):
     """Test the entire RAG pipeline from document processing to querying"""
     rag_pipeline = RAG(docs_dir, db_dir, tracker_file)
-    
+
     # Create documents with mixed content types
     test_files = {
         "python_info.py": """# Python Information
 def describe_python():
     \"\"\"Python is a high-level programming language known for its readability and versatility.\"\"\"
     pass""",
-        "readme.md": "# Python\nPython is a popular programming language used in web development, data science, and automation."
+        "readme.md": "# Python\nPython is a popular programming language used in web development, data science, and automation.",
     }
-    
+
     for filename, content in test_files.items():
         filepath = os.path.join(rag_pipeline.docs_dir, filename)
         with open(filepath, "w") as f:
@@ -160,7 +163,10 @@ def describe_python():
 
     # Query the system
     query = "What is Python?"
-    response = rag_pipeline.query_rag(rag_chain, query)
+    try:
+        response = next(rag_pipeline.query_rag(rag_chain, query))
+    except StopIteration as ex:
+        response = ex.value
 
     # Check if response contains relevant information
     # This is a soft test since the exact response will depend on the LLM