commit e0d0962fc5124f2b57d0f8a58674dccc3fba54fc Author: Alex Selimov Date: Fri Mar 21 10:09:07 2025 -0400 Initial commit diff --git a/README.md b/README.md new file mode 100644 index 0000000..0f0e761 --- /dev/null +++ b/README.md @@ -0,0 +1,6 @@ +# code_rag + +This is intended to be a fully local Retrieval Augmented Generation tool for software development. +This will be loadable as a neovim plugin, where we retrieve the current workspace files and add them to a persistent chroma vector store. +This tool will expose an Ollama API to query locally running Ollama models with prompts augmented by relevant context from the current code. + diff --git a/document_tracker.json b/document_tracker.json new file mode 100644 index 0000000..89eadad --- /dev/null +++ b/document_tracker.json @@ -0,0 +1,97 @@ +{ + "/tmp/tmpje6soo0x/documents/python_info.txt": { + "chunk_ids": [ + "c0b04224-f72e-4f6e-bef1-067f1cf5c716" + ] + }, + "/tmp/tmpy2siaefv/documents/python_info.txt": { + "chunk_ids": [ + 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100644 index 0000000..d930f3d Binary files /dev/null and b/src/code_rag/__pycache__/__init__.cpython-313.pyc differ diff --git a/src/code_rag/__pycache__/doc_tracker.cpython-313.pyc b/src/code_rag/__pycache__/doc_tracker.cpython-313.pyc new file mode 100644 index 0000000..60978c7 Binary files /dev/null and b/src/code_rag/__pycache__/doc_tracker.cpython-313.pyc differ diff --git a/src/code_rag/__pycache__/rag.cpython-313.pyc b/src/code_rag/__pycache__/rag.cpython-313.pyc new file mode 100644 index 0000000..490123b Binary files /dev/null and b/src/code_rag/__pycache__/rag.cpython-313.pyc differ diff --git a/src/code_rag/doc_tracker.py b/src/code_rag/doc_tracker.py new file mode 100644 index 0000000..1c51445 --- /dev/null +++ b/src/code_rag/doc_tracker.py @@ -0,0 +1,174 @@ +import os +import hashlib +import json +from datetime import datetime +import time + + +class DocMetaData: + """ + Class that stores document meta data. Using this so we know that the MetaData is always + initialized and can avoid a lot of checking whether keys exist or not + """ + + def __init__(self, mod_time, hash, last_updated, chunk_ids): + self.mod_time = mod_time + self.hash = hash + self.last_updated = last_updated + self.chunk_ids = chunk_ids + + def __eq__(self, other): + if isinstance(other, DocMetaData): + return ( + self.mod_time == other.mod_time + and self.hash == other.hash + and self.last_updated == other.last_updated + and self.chunk_ids == other.chunk_ids + ) + return NotImplemented + + def update_chunks(self, chunks): + self.chunk_ids = chunks + + def to_dict(self): + return { + "mod_time": self.mod_time, + "hash": self.hash, + "last_updated": self.last_updated, + "chunk_ids": self.chunk_ids, + } + + @classmethod + def from_file(cls, file_path): + return cls( + os.path.getmtime(file_path), calculate_file_hash(file_path), time.time(), [] + ) + + @classmethod + def from_dict(cls, input_dict): + return cls( + input_dict["mod_time"], + input_dict["hash"], + input_dict["last_updated"], + input_dict["chunk_ids"], + ) + + +class DocumentTracker: + """ + Tracks document changes using file hashes, modification times, and chunk IDs + """ + + def __init__(self, tracking_file): + self.tracking_file = tracking_file + self.doc_info = self._load_tracking_data() + + def _load_tracking_data(self): + """Load existing tracking data if available""" + doc_info = dict() + if os.path.exists(self.tracking_file): + with open(self.tracking_file, "r") as f: + serialized = json.load(f) + for k, v in serialized.items(): + doc_info[k] = DocMetaData.from_dict(v) + + return doc_info + + def _save_tracking_data(self): + """Save tracking data to file""" + output = dict() + for k, v in self.doc_info.items(): + output[k] = v.to_dict() + with open(self.tracking_file, "w") as f: + json.dump(output, f, indent=2) + + def get_changed_files(self, directory, file_extension=".txt"): + """ + Detect new, modified, and deleted files + Returns: dict with 'new', 'modified', and 'deleted' lists + """ + current_file_mod_times = {} + for root, _, files in os.walk(directory): + for file in files: + if file.endswith(file_extension): + file_path = os.path.join(root, file) + mod_time = os.path.getmtime(file_path) + current_file_mod_times[file_path] = mod_time + + new_files = [] + modified_files = [] + + # Check for new or modified files + for file_path, mod_time in current_file_mod_times.items(): + if file_path not in self.doc_info: + new_files.append(file_path) + elif mod_time > self.doc_info[file_path].mod_time: + # Check if content actually changed using hash + current_hash = calculate_file_hash(file_path) + if current_hash != self.doc_info[file_path].hash: + modified_files.append(file_path) + self.doc_info[file_path].hash = current_hash + self.doc_info[file_path].mod_time = mod_time + + # Check for deleted files + deleted_files = [f for f in self.doc_info if f not in current_file_mod_times] + + # Update tracking information for new files + for file_path in new_files: + self.doc_info[file_path] = DocMetaData( + current_file_mod_times[file_path], + calculate_file_hash(file_path), + time.time(), + [], # Will store IDs of chunks derived from this document + ) + + # Update last_updated for modified files + for file_path in modified_files: + self.doc_info[file_path].last_updated = datetime.now().isoformat() + + self._save_tracking_data() + + return {"new": new_files, "modified": modified_files, "deleted": deleted_files} + + def update_chunk_mappings(self, file_path, chunk_ids): + """Store the chunk IDs associated with a document""" + if file_path not in self.doc_info: + self.doc_info[file_path] = DocMetaData.from_file(file_path) + + self.doc_info[file_path].chunk_ids = chunk_ids + self._save_tracking_data() + + def get_chunks_to_delete(self, deleted_files): + """Get all chunk IDs associated with deleted files""" + chunks_to_delete = [] + for file_path in deleted_files: + if file_path in self.doc_info and self.doc_info[file_path].chunk_ids: + chunks_to_delete.extend(self.doc_info[file_path].chunk_ids) + # Remove the file from tracking after processing + del self.doc_info[file_path] + + self._save_tracking_data() + return chunks_to_delete + + def get_chunks_for_modified_files(self, modified_files): + """Get chunk IDs for modified files that need to be deleted before re-indexing""" + chunks_to_delete = [] + for file_path in modified_files: + if file_path in self.doc_info and self.doc_info[file_path].chunk_ids: + chunks_to_delete.extend(self.doc_info[file_path].chunk_ids) + # Clear the chunk IDs (will be updated with new ones) + self.doc_info[file_path].chunk_ids = [] + + self._save_tracking_data() + return chunks_to_delete + + +def calculate_file_hash(file_path): + """Calculate MD5 hash of file contents""" + hasher = hashlib.md5() + with open(file_path, "rb") as f: + buf = f.read(65536) + while len(buf) > 0: + hasher.update(buf) + buf = f.read(65536) + return hasher.hexdigest() diff --git a/src/code_rag/rag.py b/src/code_rag/rag.py new file mode 100644 index 0000000..74dd36c --- /dev/null +++ b/src/code_rag/rag.py @@ -0,0 +1,228 @@ +import os +import uuid +from langchain_community.document_loaders import DirectoryLoader, TextLoader +from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_ollama import OllamaEmbeddings +from langchain_community.vectorstores import Chroma +from langchain_community.llms import Ollama +from langchain.chains import RetrievalQA +from langchain.prompts import PromptTemplate + +from code_rag.doc_tracker import DocumentTracker + + +class RAG: + def __init__(self, docs_dir, db_dir, tracker_file): + self.docs_dir = docs_dir + self.db_dir = db_dir + self.tracker = DocumentTracker(tracker_file) + + def process_documents(self, files, text_splitter): + """Process document files into chunks with tracking metadata""" + all_chunks = [] + file_chunk_map = {} + + for file_path in files: + # Load the document + loader = TextLoader(file_path) + documents = loader.load() + + # Add source metadata + for doc in documents: + doc.metadata["source"] = file_path + doc.metadata["source_id"] = file_path # For easier identification + + # Split the document + chunks = text_splitter.split_documents(documents) + + # Generate and track chunk IDs + chunk_ids = [] + for chunk in chunks: + chunk_id = str(uuid.uuid4()) + chunk.metadata["chunk_id"] = chunk_id + chunk_ids.append(chunk_id) + + # Store chunk mappings if tracker is provided + if self.tracker: + self.tracker.update_chunk_mappings(file_path, chunk_ids) + + file_chunk_map[file_path] = chunks + all_chunks.extend(chunks) + + return all_chunks, file_chunk_map + + def create_vector_db(self, extension=".txt", force_refresh=False): + """ + Create or update a vector database, with complete handling of changes + """ + # Text splitter configuration + text_splitter = RecursiveCharacterTextSplitter( + chunk_size=1000, chunk_overlap=200 + ) + + # Create embeddings + embeddings = OllamaEmbeddings(model="nomic-embed-text") + + if force_refresh: + print("Force refresh: Processing all documents") + # Load all documents + loader = DirectoryLoader(self.docs_dir, glob=f"**/*{extension}") + all_documents = loader.load() + + # Add unique IDs to each document + for doc in all_documents: + doc.metadata["source"] = os.path.abspath(doc.metadata["source"]) + doc.metadata["source_id"] = doc.metadata["source"] + + # Split documents + chunks = text_splitter.split_documents(all_documents) + + # Add chunk IDs and update tracker + file_chunk_map = {} + for chunk in chunks: + chunk_id = str(uuid.uuid4()) + chunk.metadata["chunk_id"] = chunk_id + + source = chunk.metadata["source"] + if source not in file_chunk_map: + file_chunk_map[source] = [] + + file_chunk_map[source].append(chunk_id) + + # Update tracker with chunk mappings + for file_path, chunk_ids in file_chunk_map.items(): + self.tracker.update_chunk_mappings(file_path, chunk_ids) + + print( + f"Processing {len(all_documents)} documents with {len(chunks)} chunks" + ) + + # Create new vector store + vectorstore = Chroma.from_documents( + documents=chunks, embedding=embeddings, persist_directory=self.db_dir + ) + vectorstore.persist() + print(f"Created new vector database at {self.db_dir}") + + return vectorstore + + # Get changes since last update + changed_files = self.tracker.get_changed_files(self.docs_dir) + + if not any(changed_files.values()): + print("No document changes detected") + # Load existing vector store if available + if os.path.exists(self.db_dir): + return Chroma( + persist_directory=self.db_dir, embedding_function=embeddings + ) + else: + print("No vector database exists. Creating from all documents...") + return self.create_vector_db(force_refresh=True) + + # Process changes + print( + f"Changes detected - New: {len(changed_files['new'])}, Modified: {len(changed_files['modified'])}, Deleted: {len(changed_files['deleted'])}" + ) + + # Load existing vector store if it exists + if os.path.exists(self.db_dir): + vectorstore = Chroma( + persist_directory=self.db_dir, embedding_function=embeddings + ) + + # 1. Handle deleted documents + if changed_files["deleted"]: + chunks_to_delete = self.tracker.get_chunks_to_delete( + changed_files["deleted"] + ) + if chunks_to_delete: + print( + f"Removing {len(chunks_to_delete)} chunks from deleted documents" + ) + # Delete the chunks from vector store + vectorstore._collection.delete( + where={"chunk_id": {"$in": chunks_to_delete}} + ) + + # 2. Handle modified documents (delete old chunks first) + chunks_to_delete_modified = self.tracker.get_chunks_for_modified_files( + changed_files["modified"] + ) + if chunks_to_delete_modified: + print( + f"Removing {len(chunks_to_delete_modified)} chunks from modified documents" + ) + vectorstore._collection.delete( + where={"chunk_id": {"$in": chunks_to_delete_modified}} + ) + + # 3. Process new and modified documents + files_to_process = changed_files["new"] + changed_files["modified"] + if files_to_process: + chunks, _ = self.process_documents( + files_to_process, text_splitter, self.tracker + ) + print(f"Adding {len(chunks)} new chunks to the vector store") + vectorstore.add_documents(chunks) + else: + # If no existing DB, create from all documents + print("No existing vector database. Creating from all documents...") + return self.create_vector_db(force_refresh=True) + + # Persist changes + vectorstore.persist() + print(f"Vector database updated at {self.db_dir}") + + return vectorstore + + def setup_rag(self, model_name="llama3"): + """ + Set up the RAG system with an existing vector database + """ + # Load the embeddings + embeddings = OllamaEmbeddings(model="nomic-embed-text") + + # Load the vector store + vectorstore = Chroma( + persist_directory=self.db_dir, embedding_function=embeddings + ) + + # Create a retriever + retriever = vectorstore.as_retriever(search_kwargs={"k": 4}) + + # Set up the LLM + llm = Ollama(model=model_name) + + # 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 the RAG chain + rag_chain = RetrievalQA.from_chain_type( + llm=llm, + chain_type="stuff", + retriever=retriever, + chain_type_kwargs={"prompt": prompt}, + ) + + return rag_chain + + def query_rag(self, rag_chain, query): + """ + Query the RAG system + """ + response = rag_chain.invoke({"query": query}) + return response["result"] diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/__pycache__/__init__.cpython-313.pyc b/tests/__pycache__/__init__.cpython-313.pyc new file mode 100644 index 0000000..0046516 Binary files /dev/null and b/tests/__pycache__/__init__.cpython-313.pyc differ diff --git a/tests/__pycache__/fixtures.cpython-313.pyc b/tests/__pycache__/fixtures.cpython-313.pyc new file mode 100644 index 0000000..6ab0623 Binary files /dev/null and b/tests/__pycache__/fixtures.cpython-313.pyc differ diff --git a/tests/__pycache__/test_doc_tracker.cpython-313-pytest-8.3.5.pyc b/tests/__pycache__/test_doc_tracker.cpython-313-pytest-8.3.5.pyc new file mode 100644 index 0000000..78d2814 Binary files /dev/null and b/tests/__pycache__/test_doc_tracker.cpython-313-pytest-8.3.5.pyc differ diff --git a/tests/__pycache__/test_rag.cpython-313-pytest-8.3.5.pyc b/tests/__pycache__/test_rag.cpython-313-pytest-8.3.5.pyc new file mode 100644 index 0000000..9ce9147 Binary files /dev/null and b/tests/__pycache__/test_rag.cpython-313-pytest-8.3.5.pyc differ diff --git a/tests/__pycache__/utility.cpython-313.pyc b/tests/__pycache__/utility.cpython-313.pyc new file mode 100644 index 0000000..c57fb38 Binary files /dev/null and b/tests/__pycache__/utility.cpython-313.pyc differ diff --git a/tests/fixtures.py b/tests/fixtures.py new file mode 100644 index 0000000..edb68a6 --- /dev/null +++ b/tests/fixtures.py @@ -0,0 +1,66 @@ +import pytest +import shutil +import tempfile +import os + + +@pytest.fixture +def temp_dir(): + """Create a temporary directory for test files""" + temp_dir = tempfile.mkdtemp() + yield temp_dir + shutil.rmtree(temp_dir) + + +@pytest.fixture +def docs_dir(temp_dir): + """Create a temporary documents directory""" + docs_dir = os.path.join(temp_dir, "documents") + os.makedirs(docs_dir) + yield docs_dir + + +@pytest.fixture +def db_dir(temp_dir): + """Create a temporary vector database directory""" + db_dir = os.path.join(temp_dir, "vector_db") + os.makedirs(db_dir) + yield db_dir + + +@pytest.fixture +def tracker_file(temp_dir): + """Create a temporary tracker file""" + tracker_path = os.path.join(temp_dir, "test_tracker.json") + yield tracker_path + # Clean up after tests + if os.path.exists(tracker_path): + os.remove(tracker_path) + + +@pytest.fixture +def sample_docs(docs_dir): + """Create sample text documents for testing""" + # Create a few sample documents + doc1_path = os.path.join(docs_dir, "doc1.txt") + doc2_path = os.path.join(docs_dir, "doc2.txt") + doc3_path = os.path.join(docs_dir, "doc3.txt") + + with open(doc1_path, "w") as f: + f.write("This is a sample document about artificial intelligence. " * 10) + + with open(doc2_path, "w") as f: + f.write("This document discusses machine learning concepts. " * 10) + + with open(doc3_path, "w") as f: + f.write("Natural language processing is a field of AI. " * 10) + + return [doc1_path, doc2_path, doc3_path] + + +@pytest.fixture +def text_splitter(): + """Create a mock text splitter""" + from langchain.text_splitter import RecursiveCharacterTextSplitter + + return RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=20) diff --git a/tests/test_doc_tracker.py b/tests/test_doc_tracker.py new file mode 100644 index 0000000..03f053b --- /dev/null +++ b/tests/test_doc_tracker.py @@ -0,0 +1,157 @@ +import os +import time +import pytest + +from .fixtures import * +from code_rag.doc_tracker import DocMetaData, DocumentTracker, calculate_file_hash + + +def doc_infos_are_equal(left, right): + """Check to see if two doc_infos are the same""" + for k, v in left.items(): + try: + print(v.to_dict(), right[k].to_dict(), v.to_dict() == right[k].to_dict()) + if v != right[k]: + return False + except KeyError: + return False + return True + + +@pytest.fixture +def document_tracker(tracker_file): + """Create a DocumentTracker instance""" + return DocumentTracker(tracking_file=tracker_file) + + +# Tests for DocumentTracker +def test_init_new_tracker(tracker_file): + """Test creating a new tracker""" + tracker = DocumentTracker(tracking_file=tracker_file) + assert tracker.doc_info == {} + assert not os.path.exists(tracker_file) + + +def test_save_and_load_tracking_data(document_tracker, tracker_file): + """Test saving and loading tracking data""" + # Add some data + update_time = time.time() + document_tracker.doc_info = { + "test.txt": DocMetaData(123456, "abcdef", update_time, ["1", "2"]) + } + document_tracker._save_tracking_data() + + # Check file exists + assert os.path.exists(tracker_file) + + # Create a new tracker that should load the data + new_tracker = DocumentTracker(tracking_file=tracker_file) + assert doc_infos_are_equal( + new_tracker.doc_info, + {"test.txt": DocMetaData(123456, "abcdef", update_time, ["1", "2"])}, + ) + + +def test_calculate_file_hash(document_tracker, sample_docs): + """Test hash calculation for a file""" + file_path = sample_docs[0] + hash1 = calculate_file_hash(file_path) + + # Same content should yield same hash + hash2 = calculate_file_hash(file_path) + assert hash1 == hash2 + + # Different content should yield different hash + with open(file_path, "a") as f: + f.write("Additional content") + + hash3 = calculate_file_hash(file_path) + assert hash1 != hash3 + + +def test_get_changed_files_new(document_tracker, docs_dir, sample_docs): + """Test detecting new files""" + changes = document_tracker.get_changed_files(docs_dir) + assert set(changes["new"]) == set(sample_docs) + assert changes["modified"] == [] + assert changes["deleted"] == [] + + # Verify tracking was updated + for file_path in sample_docs: + assert file_path in document_tracker.doc_info + assert document_tracker.doc_info[file_path].chunk_ids == [] + + +def test_get_changed_files_modified(document_tracker, docs_dir, sample_docs): + """Test detecting modified files""" + # First scan to establish tracking + document_tracker.get_changed_files(docs_dir) + + # Modify a file and wait to ensure timestamp difference + time.sleep(0.1) + with open(sample_docs[0], "a") as f: + f.write("Modified content") + + # Detect changes + changes = document_tracker.get_changed_files(docs_dir) + assert changes["new"] == [] + assert changes["modified"] == [sample_docs[0]] + assert changes["deleted"] == [] + + +def test_get_changed_files_deleted(document_tracker, docs_dir, sample_docs): + """Test detecting deleted files""" + # First scan to establish tracking + document_tracker.get_changed_files(docs_dir) + + # Delete a file + os.remove(sample_docs[0]) + + # Detect changes + changes = document_tracker.get_changed_files(docs_dir) + assert changes["new"] == [] + assert changes["modified"] == [] + assert changes["deleted"] == [sample_docs[0]] + + +def test_update_chunk_mappings(document_tracker, sample_docs): + """Test updating chunk mappings""" + file_path = sample_docs[0] + chunk_ids = ["chunk1", "chunk2", "chunk3"] + + # First make sure the file is tracked + document_tracker.doc_info[file_path] = DocMetaData( + 123, + "abc", + "2023-01-01", + [], + ) + + # Update chunk mappings + document_tracker.update_chunk_mappings(file_path, chunk_ids) + assert document_tracker.doc_info[file_path].chunk_ids == chunk_ids + + +def test_get_chunks_to_delete(document_tracker): + """Test getting chunks to delete for deleted files""" + # Setup tracking data + document_tracker.doc_info = { + "file1.txt": DocMetaData(0, "abc", 0, ["chunk1", "chunk2"]), + "file2.txt": DocMetaData(0, "abc", 0, ["chunk3", "chunk4"]), + "file3.txt": DocMetaData(0, "abc", 0, ["chunk5"]), + } + + # Test with one deleted file + chunks = document_tracker.get_chunks_to_delete(["file1.txt"]) + assert set(chunks) == {"chunk1", "chunk2"} + + # Verify file was removed from tracking + assert "file1.txt" not in document_tracker.doc_info + + # Test with multiple deleted files + chunks = document_tracker.get_chunks_to_delete(["file2.txt", "file3.txt"]) + assert set(chunks) == {"chunk3", "chunk4", "chunk5"} + + # Verify files were removed from tracking + assert "file2.txt" not in document_tracker.doc_info + assert "file3.txt" not in document_tracker.doc_info diff --git a/tests/test_rag.py b/tests/test_rag.py new file mode 100644 index 0000000..6c09d49 --- /dev/null +++ b/tests/test_rag.py @@ -0,0 +1,124 @@ +import os +import pytest + +from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_ollama import OllamaEmbeddings +from langchain_community.vectorstores import Chroma +from .fixtures import * +from .utility import * +from code_rag.rag import RAG +from code_rag.doc_tracker import DocumentTracker + + +@pytest.fixture +def rag_pipeline(docs_dir, db_dir, tracker_file): + """Create a RAG instance""" + return RAG(docs_dir, db_dir, tracker_file) + + +# Tests for document processing +def test_process_documents(tracker_file, docs_dir, db_dir, sample_docs, rag_pipeline): + """Test processing documents into chunks with tracking""" + rag_pipeline = RAG(docs_dir, db_dir, tracker_file) + files = [ + os.path.join(rag_pipeline.docs_dir, "doc1.txt"), + os.path.join(rag_pipeline.docs_dir, "doc2.txt"), + ] + + text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) + + chunks, file_chunk_map = rag_pipeline.process_documents(files, text_splitter) + + # Verify chunks were created + assert len(chunks) >= 2 # At least one chunk per document + tracker = rag_pipeline.tracker + # Verify chunk IDs were tracked + for file_path in files: + assert file_path in tracker.doc_info + assert "chunk_ids" in tracker.doc_info[file_path] + assert len(tracker.doc_info[file_path]["chunk_ids"]) > 0 + + # Verify metadata in chunks + for chunk in chunks: + assert "source" in chunk.metadata + assert "chunk_id" in chunk.metadata + assert chunk.metadata["source"] in files + + +@pytest.mark.skipif( + not shutil.which("ollama"), reason="Ollama not installed or not in PATH" +) +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 initial vector database + vectorstore = rag_pipeline.create_vector_db(force_refresh=True) + + # Verify it was created + assert os.path.exists(rag_pipeline.db_dir) + assert vectorstore is not None + # Check the database has content + embeddings = OllamaEmbeddings(model="nomic-embed-text") + loaded_db = Chroma( + persist_directory=rag_pipeline.db_dir, embedding_function=embeddings + ) + assert loaded_db._collection.count() > 0 + + +@pytest.mark.skipif( + not shutil.which("ollama"), reason="Ollama not installed or not in PATH" +) +def test_update_vector_db_with_changes(docs_dir, db_dir, tracker_file, sample_docs): + """Test updating a vector database with document changes""" + rag_pipeline = RAG(docs_dir, db_dir, tracker_file) + # Create initial vector database + rag_pipeline.create_vector_db(force_refresh=True) + + # Get initial count + embeddings = OllamaEmbeddings(model="nomic-embed-text") + initial_db = Chroma( + persist_directory=rag_pipeline.db_dir, embedding_function=embeddings + ) + initial_count = initial_db._collection.count() + + # Make changes to documents + # Add a new document + create_test_document( + docs_dir, "newdoc.txt", "This is a brand new document for testing." + ) + + # Update the vector database + rag_pipeline.create_vector_db() + + # Check the database has been updated + updated_db = Chroma( + persist_directory=rag_pipeline.db_dir, embedding_function=embeddings + ) + assert updated_db._collection.count() > initial_count + + +# Final integration test - full RAG pipeline +@pytest.mark.skipif( + not shutil.which("ollama"), reason="Ollama not installed or not in PATH" +) +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 a specific document with known content + test_content = "Python is a high-level programming language known for its readability and versatility." + create_test_document(rag_pipeline.docs_dir, "python_info.txt", test_content) + + # Create vector database + rag_pipeline.create_vector_db(force_refresh=True) + + # Set up RAG + rag_chain = rag_pipeline.setup_rag(model_name="llama3.2") + + # Query the system + query = "What is Python?" + response = rag_pipeline.query_rag(rag_chain, query) + + # Check if response contains relevant information + # This is a soft test since the exact response will depend on the LLM + assert response.strip() != "" + assert "programming" in response.lower() or "language" in response.lower() diff --git a/tests/utility.py b/tests/utility.py new file mode 100644 index 0000000..5d647e4 --- /dev/null +++ b/tests/utility.py @@ -0,0 +1,26 @@ +import time +import os + + +def create_test_document(base_path, filename, content): + """Create a test document with specified content""" + file_path = os.path.join(base_path, filename) + # Create directory if it doesn't exist (for subdirectories) + os.makedirs(os.path.dirname(file_path), exist_ok=True) + with open(file_path, "w") as f: + f.write(content) + return file_path + + +def modify_test_document(file_path, new_content): + """Modify an existing test document""" + with open(file_path, "w") as f: + f.write(new_content) + # Ensure modification time changes + time.sleep(0.1) + + +def delete_test_document(file_path): + """Delete a test document""" + if os.path.exists(file_path): + os.remove(file_path)