diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index d8b7843fe..fa95ea76f 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -118,53 +118,6 @@ jobs: --force_pr_creation \ --disable_telemetry - rag-test: - # disabled because this currently takes too long - if: false - runs-on: ubuntu-latest - steps: - - name: Checkout code - uses: actions/checkout@v3 - with: - ref: ${{ github.head_ref }} - - - uses: jwalton/gh-find-current-pr@master - id: findPr - - - name: Set up python - id: setup-python - uses: actions/setup-python@v5 - with: - python-version: '3.9' - - - name: Install Poetry - uses: snok/install-poetry@v1 - with: - virtualenvs-create: true - virtualenvs-in-project: true - installer-parallel: true - - - name: Load cached venv - id: cached-poetry-dependencies - uses: actions/cache@v4 - with: - path: ./.venv - key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('./poetry.lock') }}-${{ github.job }} - - - name: Install dependencies - if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' - run: poetry install --no-interaction --only main --extras rag - - - name: Resolve issue - run: | - source .venv/bin/activate - patchwork ResolveIssue --log debug \ - --patched_api_key=${{ secrets.PATCHED_API_KEY }} \ - --github_api_key=${{ secrets.SCM_GITHUB_KEY }} \ - --issue_url=https://github.com/patched-codes/patchwork/issues/1129 \ - --disable_telemetry - --max_llm_calls=10 - main-test: runs-on: ubuntu-latest steps: diff --git a/patchwork/common/utils/dependency.py b/patchwork/common/utils/dependency.py index 040822136..27b89bfed 100644 --- a/patchwork/common/utils/dependency.py +++ b/patchwork/common/utils/dependency.py @@ -2,7 +2,6 @@ from functools import lru_cache __DEPENDENCY_GROUPS = { - "rag": ["chromadb"], "security": ["semgrep", "depscan"], "notification": ["slack_sdk"], } @@ -22,9 +21,5 @@ def import_with_dependency_group(name): raise ImportError(error_msg) -def chromadb(): - return import_with_dependency_group("chromadb") - - def slack_sdk(): return import_with_dependency_group("slack_sdk") diff --git a/patchwork/common/utils/utils.py b/patchwork/common/utils/utils.py index bd1d50082..d28bfb21f 100644 --- a/patchwork/common/utils/utils.py +++ b/patchwork/common/utils/utils.py @@ -15,9 +15,7 @@ from git import Head, Repo from typing_extensions import Any, Callable, Counter -from patchwork.common.utils.dependency import chromadb from patchwork.logger import logger -from patchwork.managed_files import HOME_FOLDER _CLEANUP_FILES: set[Path] = set() _NEWLINES = {"\n", "\r\n", "\r"} @@ -129,66 +127,6 @@ def count_openai_tokens(code: str): return len(_ENCODING.encode(code)) -def get_vector_db_path() -> str: - CHROMA_DB_PATH = HOME_FOLDER / "chroma.db" - if CHROMA_DB_PATH: - return str(CHROMA_DB_PATH) - else: - return ".chroma.db" - - -def openai_embedding_model( - inputs: dict, -) -> "chromadb.api.types.EmbeddingFunction"["chromadb.api.types.Documents"] | None: - model = inputs.get(openai_embedding_model.__name__) - if model is None: - return None - - api_key = inputs.get("openai_api_key") - if api_key is None: - raise ValueError("Missing required input data: 'openai_api_key'") - - return chromadb().utils.embedding_functions.OpenAIEmbeddingFunction( - api_key=api_key, - model_name=model, - ) - - -def huggingface_embedding_model( - inputs: dict, -) -> "chromadb.api.types.EmbeddingFunction"["chromadb.api.types.Documents"] | None: - model = inputs.get(huggingface_embedding_model.__name__) - if model is None: - return None - - api_key = inputs.get("openai_api_key") or inputs.get("huggingface_api_key") - if api_key is None: - raise ValueError("Missing required input data: 'openai_api_key' or 'huggingface_api_key'") - - return chromadb().utils.embedding_functions.HuggingFaceEmbeddingFunction( - api_key=api_key, - model_name=model, - ) - - -_EMBEDDING_FUNCS = [openai_embedding_model, huggingface_embedding_model] - -_EMBEDDING_TO_API_KEY_NAME: dict[ - str, Callable[[dict], "chromadb.api.type.EmbeddingFunction"["chromadb.api.types.Documents"] | None] -] = {func.__name__: func for func in _EMBEDDING_FUNCS} - - -def get_embedding_function(inputs: dict) -> "chromadb.api.types.EmbeddingFunction"["chromadb.api.types.Documents"]: - embedding_function = next( - (func(inputs) for input_key, func in _EMBEDDING_TO_API_KEY_NAME.items() if input_key in inputs.keys()), - chromadb().utils.embedding_functions.SentenceTransformerEmbeddingFunction(), - ) - if embedding_function is None: - raise ValueError(f"Missing required input data: one of {_EMBEDDING_TO_API_KEY_NAME.keys()}") - - return embedding_function - - def get_current_branch(repo: Repo) -> Head: remote = repo.remote("origin") if repo.head.is_detached: diff --git a/patchwork/steps/GenerateCodeRepositoryEmbeddings/GenerateCodeRepositoryEmbeddings.py b/patchwork/steps/GenerateCodeRepositoryEmbeddings/GenerateCodeRepositoryEmbeddings.py deleted file mode 100644 index 519e60791..000000000 --- a/patchwork/steps/GenerateCodeRepositoryEmbeddings/GenerateCodeRepositoryEmbeddings.py +++ /dev/null @@ -1,141 +0,0 @@ -from __future__ import annotations - -import hashlib -import time -from itertools import islice -from pathlib import Path - -import git -from typing_extensions import Iterable - -from patchwork.common.utils.dependency import chromadb -from patchwork.common.utils.utils import get_vector_db_path, open_with_chardet -from patchwork.logger import logger -from patchwork.step import Step -from patchwork.steps.GenerateCodeRepositoryEmbeddings.filter_lists import ( - _DIRECTORY_BLACKLIST, - _EXTENSION_WHITELIST, -) -from patchwork.steps.GenerateEmbeddings.GenerateEmbeddings import GenerateEmbeddings - - -def filter_files(files: Iterable[str]) -> set[str]: - rv = set() - for file in files: - file_path = Path(file) - if any(directory in file_path.parts for directory in _DIRECTORY_BLACKLIST): - continue - rv.add(file) - - return rv - - -def batch(iterable, n=1): - l = len(iterable) - for ndx in range(0, l, n): - yield islice(iterable, ndx, min(ndx + n, l)) - - -def hash_text(text: str | list[str]) -> str: - full_text = text if isinstance(text, str) else "".join(text) - return hashlib.sha256(full_text.encode()).hexdigest() - - -class GenerateCodeRepositoryEmbeddings(Step): - required_keys = {} - - def __init__(self, inputs: dict): - super().__init__(inputs) - if not all(key in inputs.keys() for key in self.required_keys): - raise ValueError(f'Missing required data: "{self.required_keys}"') - - self.client = chromadb().PersistentClient(path=get_vector_db_path()) - self.disable_cache = inputs.get("disable_cache", False) - self.inputs = inputs - - def run(self) -> dict: - cwd = Path.cwd() - base_embedding_name = cwd.name - embedding_name = base_embedding_name - - files = set() - for ext in _EXTENSION_WHITELIST: - found_files = {str(file.relative_to(cwd)) for file in cwd.glob(f"**/*{ext}")} - found_files = filter_files(found_files) - files.update(found_files) - - try: - repo = git.Repo(cwd, search_parent_directories=True) - base_embedding_name = Path(repo.working_dir).name - commit_hash = repo.head.reference.commit.hexsha - embedding_name = f"{base_embedding_name}_{commit_hash}" - - ignored_files = set() - for batched_files in batch(files, 10): - ignored_files.update(repo.ignored(*batched_files)) - files = files - ignored_files - except git.InvalidGitRepositoryError: - pass - - reference_collection = None - is_exact_collection = False - try: - reference_collection = self.client.get_collection(embedding_name) - is_exact_collection = True - except ValueError as e: - for collection in self.client.list_collections(): - if not collection.name.startswith(base_embedding_name): - continue - reference_collection = collection - break - - documents = [] - found_reference_ids = set() - for file in files: - try: - with open_with_chardet(file, "r") as fp: - text = fp.read() - except Exception as e: - logger.warning(f"Error reading file {file}: {e}") - - if len(text.strip()) == 0: - continue - - text_hash = hash_text(text) - - documents_to_add = [ - dict( - id=file, - document=text, - hash=text_hash, - created_at=int(time.time()), - path=file, - ) - ] - if reference_collection is not None and not self.disable_cache: - result = reference_collection.get( - where={"$and": [{"hash": text_hash}, {"path": file}]}, include=["metadatas", "embeddings"] - ) - if len(result["ids"]) > 0: - documents_to_add = [] - for embedding_id, embedding, metadata in zip( - result["ids"], result["embeddings"], result["metadatas"] - ): - original_metadata = { - key: value for key, value in metadata.items() if key not in ["id", "embedding"] - } - original_metadata["path"] = file - found_reference_ids.add(embedding_id) - documents_to_add.append(dict(id=embedding_id, embedding=embedding, **original_metadata)) - - documents.extend(documents_to_add) - - if is_exact_collection and reference_collection is not None and len(found_reference_ids) > 0: - unused_ids = set(reference_collection.get(include=[])["ids"]) - found_reference_ids - if len(unused_ids) > 0: - reference_collection.delete(ids=list(unused_ids)) - - self.inputs.update(dict(embedding_name=embedding_name, documents=documents)) - outputs = GenerateEmbeddings(self.inputs).run() - self.inputs.update(outputs) - return self.inputs diff --git a/patchwork/steps/GenerateCodeRepositoryEmbeddings/README.md b/patchwork/steps/GenerateCodeRepositoryEmbeddings/README.md deleted file mode 100644 index 0f5f02d04..000000000 --- a/patchwork/steps/GenerateCodeRepositoryEmbeddings/README.md +++ /dev/null @@ -1,21 +0,0 @@ -# Code Documentation - -## Inputs - -- The code takes inputs in the form of a dictionary passed to the `__init__` method of the `GenerateCodeRepositoryEmbeddings` class. - -### Description -- The code is responsible for generating embeddings for code files in a code repository. -- It uses the `git` Python package to interact with the Git repository where the code files are stored. -- The code provides a function `filter_files` that takes an iterable of file paths and filters out files based on directory blacklists. -- The code filters out specific file types based on a whitelist and ignores certain directories based on a blacklist. -- The `hash_text` function generates a SHA-1 hash for the text content of code files. -- The `GenerateCodeRepositoryEmbeddings` class manages the process of generating embeddings for the code repository. -- It fetches code files, reads their content, generates hashes, and interacts with the ChromaDB database to store embeddings and related metadata. -- The results are then passed to the `GenerateEmbeddings` class for further processing. -- The code includes a function `batch` that slices an iterable into batches of a specific size. - -## Outputs -- The `GenerateCodeRepositoryEmbeddings` class generates embeddings for code repositories, processes files, handles ignored files, interacts with a database, and eventually runs a separate `GenerateEmbeddings` step with updated inputs. -- The `run` method of the `GenerateCodeRepositoryEmbeddings` class returns a dictionary containing the results of generating embeddings for a code repository. - diff --git a/patchwork/steps/GenerateCodeRepositoryEmbeddings/__init__.py b/patchwork/steps/GenerateCodeRepositoryEmbeddings/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/patchwork/steps/GenerateCodeRepositoryEmbeddings/filter_lists.py b/patchwork/steps/GenerateCodeRepositoryEmbeddings/filter_lists.py deleted file mode 100644 index 46b59b501..000000000 --- a/patchwork/steps/GenerateCodeRepositoryEmbeddings/filter_lists.py +++ /dev/null @@ -1,99 +0,0 @@ -_EXTENSION_WHITELIST = [ - # JavaScript - ".js", - ".ts", - ".jsx", - ".tsx", - # Python - ".py", - # Java - ".java", - # C# - ".cs", - ".csx", - # C++ - ".cpp", - ".hpp", - ".cxx", - ".hxx", - # C - ".c", - ".h", - # PHP - ".php", - ".php3", - ".php4", - ".php5", - # Go - ".go", - # HTML - ".html", - # OCaml - ".ml", - ".mli", - # F# - ".fs", - ".fsi", - # Haskell - ".hs", - ".lhs", - # Kotlin - ".kt", - ".kts", - # Ruby - ".rb", - # Swift - ".swift", - # Rust - ".rs", - # Scala - ".scala", - # TypeScript - ".ts", - # Visual Basic.NET - ".vb", - ".vbs", - # Perl - ".pl", - ".pm", - # R - ".r", - # SQL - ".sql", - # TypeScript React - ".tsx", - # Julia - ".jl", - # Lua - ".lua", - # MATLAB - ".m", - # Pascal - ".pas", - # PowerShell - ".ps1", - # Racket - ".rkt", - # Shell - ".sh", - # SQL - ".sql", - # Tcl - ".tcl", - # Visual Basic - ".vb", - # XML - ".xml", - # YAML - ".yaml", - # Zig - ".zig", -] - -_DIRECTORY_BLACKLIST = [ - "test", - "tests", - "___init__.py", - "___main__.py", - "__pycache__", -] diff --git a/patchwork/steps/GenerateCodeRepositoryEmbeddings/typed.py b/patchwork/steps/GenerateCodeRepositoryEmbeddings/typed.py deleted file mode 100644 index 129a52cd0..000000000 --- a/patchwork/steps/GenerateCodeRepositoryEmbeddings/typed.py +++ /dev/null @@ -1,12 +0,0 @@ -from typing_extensions import Annotated, TypedDict - -from patchwork.common.utils.step_typing import StepTypeConfig - - -class GenerateCodeRepositoryEmbeddingsInputs(TypedDict, total=False): - disable_cache: Annotated[bool, StepTypeConfig(is_config=True)] - - -class GenerateCodeRepositoryEmbeddingsOutputs(TypedDict): - embedding_name: str - documents: str diff --git a/patchwork/steps/GenerateEmbeddings/GenerateEmbeddings.py b/patchwork/steps/GenerateEmbeddings/GenerateEmbeddings.py deleted file mode 100644 index 76fcb517f..000000000 --- a/patchwork/steps/GenerateEmbeddings/GenerateEmbeddings.py +++ /dev/null @@ -1,93 +0,0 @@ -from __future__ import annotations - -import uuid - -from typing_extensions import Any - -from patchwork.common.utils.dependency import chromadb -from patchwork.common.utils.utils import get_embedding_function, get_vector_db_path -from patchwork.step import Step - - -def filter_by_extension(file, extensions): - return any(file.endswith(ext) for ext in extensions) - - -def split_text(document_text: str, chunk_size: int, overlap: int) -> list[str]: - char_length = len(document_text) - chunks = [] - for i in range(0, char_length, chunk_size - overlap): - chunk = "".join(document_text[i : i + chunk_size]) - if chunk == "": - continue - - chunks.append(chunk) - - return chunks - - -def delete_collection(client, collection_name): - for collection in client.list_collections(): - if collection.name == collection_name: - client.delete_collection(collection_name) - break - - -class GenerateEmbeddings(Step): - required_keys = {"embedding_name", "documents"} - - def __init__(self, inputs: dict): - super().__init__(inputs) - if not all(key in inputs.keys() for key in self.required_keys): - raise ValueError(f'Missing required data: "{self.required_keys}"') - - client = chromadb().PersistentClient(path=get_vector_db_path()) - - if inputs.get("disable_cache", False): - delete_collection(client, inputs["embedding_name"]) - - embedding_function = get_embedding_function(inputs) - self.collection = client.get_or_create_collection( - inputs["embedding_name"], embedding_function=embedding_function, metadata={"hnsw:space": "cosine"} - ) - self.documents: list[dict[str, Any]] = inputs["documents"] - - self.chunk_size = inputs.get("chunk_size", 4000) - self.overlap_size = inputs.get("overlap_size", 2000) - - def run(self) -> dict: - document_ids = [] - documents = [] - document_metadatas = [] - - embedding_ids = [] - embeddings = [] - embedding_metadatas = [] - for document in self.documents: - document_text = document.get("document") - embedding = document.get("embedding") - - if document_text is not None: - doc_id = str(document.get("id")) - document_texts = split_text(document_text, self.chunk_size, self.overlap_size) - for i, document_text in enumerate(document_texts): - document_ids.append(str(uuid.uuid4())) - documents.append(document_text) - - metadata = {key: value for key, value in document.items() if key not in ["id", "document"]} - metadata["original_document"] = document_text - metadata["original_id"] = doc_id - document_metadatas.append(metadata) - elif embeddings is not None: - embedding_ids.append(str(document.get("id"))) - embeddings.append(embedding) - - metadata = {key: value for key, value in document.items() if key not in ["embedding"]} - embedding_metadatas.append(metadata) - - if len(document_ids) > 0: - self.collection.upsert(ids=document_ids, documents=documents, metadatas=document_metadatas) - if len(embedding_ids) > 0: - self.collection.upsert(ids=embedding_ids, embeddings=embeddings, metadatas=embedding_metadatas) - - return dict() diff --git a/patchwork/steps/GenerateEmbeddings/README.md b/patchwork/steps/GenerateEmbeddings/README.md deleted file mode 100644 index 66c267c7f..000000000 --- a/patchwork/steps/GenerateEmbeddings/README.md +++ /dev/null @@ -1,19 +0,0 @@ -# Summary of `GenerateEmbeddings.py` - -## Inputs: -- The code imports necessary modules and functions. -- Defines a function `filter_by_extension` to filter files by their extensions. -- Defines a function `split_text` to split a document text into chunks. -- Creates a class `GenerateEmbeddings` that inherits from `Step`. - - Constructor `__init__` initializes the class instance with required data. - - Method `run` processes documents for embedding generation. - -## Outputs: -- The `GenerateEmbeddings` class processes document texts and embeddings, generates embeddings, and saves them to a database collection. -- Returns an empty dictionary. - -## Usage: -1. Import the `GenerateEmbeddings` class from the module. -2. Create an instance of `GenerateEmbeddings` with the required input dictionary. -3. Call the `run` method to generate embeddings for the provided documents and store them in the database collection. -4. Receive the output dictionary indicating the completion of the embeddings generation process. \ No newline at end of file diff --git a/patchwork/steps/GenerateEmbeddings/__init__.py b/patchwork/steps/GenerateEmbeddings/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/patchwork/steps/GenerateEmbeddings/typed.py b/patchwork/steps/GenerateEmbeddings/typed.py deleted file mode 100644 index 71d002060..000000000 --- a/patchwork/steps/GenerateEmbeddings/typed.py +++ /dev/null @@ -1,16 +0,0 @@ -from typing_extensions import Annotated, Any, Dict, List, TypedDict - -from patchwork.common.utils.step_typing import StepTypeConfig - - -class __GenerateEmbeddingsRequiredInputs(TypedDict): - embedding_name: Annotated[str, StepTypeConfig(is_config=True)] - documents: List[Dict[str, Any]] - - -class GenerateEmbeddingsInputs(__GenerateEmbeddingsRequiredInputs, total=False): - disable_cache: Annotated[bool, StepTypeConfig(is_config=True)] - - -class GenerateEmbeddingsOutputs(TypedDict): - pass diff --git a/patchwork/steps/QueryEmbeddings/QueryEmbeddings.py b/patchwork/steps/QueryEmbeddings/QueryEmbeddings.py deleted file mode 100644 index 2d8e49809..000000000 --- a/patchwork/steps/QueryEmbeddings/QueryEmbeddings.py +++ /dev/null @@ -1,66 +0,0 @@ -from __future__ import annotations - -from patchwork.common.utils.dependency import chromadb -from patchwork.common.utils.utils import ( - count_openai_tokens, - get_embedding_function, - get_vector_db_path, -) -from patchwork.step import Step - - -class QueryEmbeddings(Step): - required_keys = {"embedding_name", "texts"} - - def __init__(self, inputs: dict): - super().__init__(inputs) - if not all(key in inputs.keys() for key in self.required_keys): - raise ValueError(f'Missing required data: "{self.required_keys}"') - - client = chromadb().PersistentClient(path=get_vector_db_path()) - embedding_function = get_embedding_function(inputs) - self.collection = client.get_collection(name=inputs["embedding_name"], embedding_function=embedding_function) - - self.texts: list[str] = inputs["texts"] - self.top_k = inputs.get("top_k", 10) - self.token_limit = inputs.get("token_limit", 4096) - - def run(self): - results = self.collection.query( - query_texts=self.texts, n_results=self.top_k, include=["metadatas", "distances"] - ) - - token_count = 0 - embedding_results_by_id = {} - for i in range(len(results["ids"])): - for j in range(len(results["ids"][i])): - metadata = results["metadatas"][i][j] - distance = results["distances"][i][j] - - if metadata["original_document"] is None: - continue - if metadata["original_id"] in embedding_results_by_id: - if distance < embedding_results_by_id[metadata["original_id"]]["distance"]: - embedding_results_by_id[metadata["original_id"]]["distance"] = distance - continue - - token_count += count_openai_tokens(metadata["original_document"]) - if token_count > self.token_limit: - break - - original_metadatas = { - key: value - for key, value in metadata.items() - if key not in ["id", "document", "distance", "original_document", "original_id"] - } - embedding_results_by_id[metadata["original_id"]] = dict( - id=metadata["original_id"], - document=metadata["original_document"], - distance=distance, - **original_metadatas, - ) - - if token_count > self.token_limit: - break - - return dict(embedding_results=list(sorted(embedding_results_by_id.values(), key=lambda x: x["distance"]))) diff --git a/patchwork/steps/QueryEmbeddings/README.md b/patchwork/steps/QueryEmbeddings/README.md deleted file mode 100644 index 019e1d35b..000000000 --- a/patchwork/steps/QueryEmbeddings/README.md +++ /dev/null @@ -1,23 +0,0 @@ -# Documentation for `QueryEmbeddings.py` - -## Inputs -- Module imports: - - `chromadb` - - `get_embedding_function` from `patchwork.common.utils.utils` - - `get_vector_db_path` from `patchwork.common.utils.utils` -- Classes: - - `QueryEmbeddings` (extending `Step` class) -- Attributes: - - `required_keys` set to `{"embedding_name", "texts"}` -- Methods: - - `__init__` method taking `inputs` dict as a parameter to initialize the class instance. - - `run` method to execute the functionality of querying embeddings. - -## Outputs -- A dictionary containing the embedded results of the queried texts. The output includes: - - `embedding_results` key with a value being a sorted list of embedding results by distance. - -## Usage -The `QueryEmbeddings` class is designed to query embeddings for a list of texts using a specified embedding function and a given embedding name. -- Users can pass the required inputs to the `__init__` method to create an instance of `QueryEmbeddings`. -- The `run` method executes the query process and returns the dictionary with the embedding results sorted by distance, respecting the specified token limit. \ No newline at end of file diff --git a/patchwork/steps/QueryEmbeddings/__init__.py b/patchwork/steps/QueryEmbeddings/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/patchwork/steps/QueryEmbeddings/typed.py b/patchwork/steps/QueryEmbeddings/typed.py deleted file mode 100644 index 511760288..000000000 --- a/patchwork/steps/QueryEmbeddings/typed.py +++ /dev/null @@ -1,17 +0,0 @@ -from typing_extensions import Annotated, Any, Dict, List, TypedDict - -from patchwork.common.utils.step_typing import StepTypeConfig - - -class __QueryEmbeddingsRequiredInputs(TypedDict): - embedding_name: str - texts: List[str] - - -class QueryEmbeddingsInputs(__QueryEmbeddingsRequiredInputs, total=False): - top_k: Annotated[int, StepTypeConfig(is_config=True)] - token_limit: Annotated[int, StepTypeConfig(is_config=True)] - - -class QueryEmbeddingsOutputs(TypedDict): - embedding_results: List[Dict[str, Any]] diff --git a/patchwork/steps/__init__.py b/patchwork/steps/__init__.py index 30c1e6b0f..354c366f8 100644 --- a/patchwork/steps/__init__.py +++ b/patchwork/steps/__init__.py @@ -26,10 +26,6 @@ ) from patchwork.steps.FilterBySimilarity.FilterBySimilarity import FilterBySimilarity from patchwork.steps.FixIssue.FixIssue import FixIssue -from patchwork.steps.GenerateCodeRepositoryEmbeddings.GenerateCodeRepositoryEmbeddings import ( - GenerateCodeRepositoryEmbeddings, -) -from patchwork.steps.GenerateEmbeddings.GenerateEmbeddings import GenerateEmbeddings from patchwork.steps.GetTypescriptTypeInfo.GetTypescriptTypeInfo import ( GetTypescriptTypeInfo, ) @@ -40,7 +36,6 @@ from patchwork.steps.PR.PR import PR from patchwork.steps.PreparePR.PreparePR import PreparePR from patchwork.steps.PreparePrompt.PreparePrompt import PreparePrompt -from patchwork.steps.QueryEmbeddings.QueryEmbeddings import QueryEmbeddings from patchwork.steps.ReadEmail.ReadEmail import ReadEmail from patchwork.steps.ReadFile.ReadFile import ReadFile from patchwork.steps.ReadIssues.ReadIssues import ReadIssues @@ -84,8 +79,6 @@ "ExtractPackageManagerFile", "FilterBySimilarity", "FixIssue", - "GenerateCodeRepositoryEmbeddings", - "GenerateEmbeddings", "LLM", "ModifyCode", "ModifyCodePB", @@ -94,7 +87,6 @@ "PreparePR", "PreparePrompt", "PRPB", - "QueryEmbeddings", "ReadEmail", "ReadFile", "ReadIssues", diff --git a/poetry.lock b/poetry.lock index a38f0aa5d..28cc8e905 100644 --- a/poetry.lock +++ b/poetry.lock @@ -24,92 +24,92 @@ files = [ 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{file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"}, - {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"}, -] - -[package.extras] -docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"] -optional = ["python-socks", "wsaccel"] -test = ["websockets"] - [[package]] name = "websockets" version = "14.2" @@ -7324,12 +5909,11 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", type = ["pytest-mypy"] [extras] -all = ["chromadb", "orjson", "owasp-depscan", "semgrep", "sentence-transformers", "slack-sdk", "torch"] +all = ["owasp-depscan", "semgrep", "slack-sdk"] notifications = ["slack-sdk"] -rag = ["chromadb", "orjson", "sentence-transformers", "torch"] security = ["owasp-depscan", "semgrep"] [metadata] lock-version = "2.0" python-versions = "^3.9" -content-hash = "2a8a11d54ca4549e1f1b9ed9d872f0c469e8ddf590122e5f10441e9a01cd60cc" +content-hash = "e59cc30b3f1e66a386737e5b21d0cca162215711188207764eb6d294a6435a3f" diff --git a/pyproject.toml b/pyproject.toml index 8710ea9b9..d4bba681d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -72,26 +72,14 @@ scipy = [ semgrep = { version = "^1.51.0", optional = true } owasp-depscan = { version = "5.2.15", optional = true } -# rag -chromadb = { version = "~0.4.24", optional = true } -sentence-transformers = { version = "~2.7.0", optional = true } - -# rag - pinning transitive dependencies -opentelemetry-exporter-otlp-proto-grpc = { version = "1.25.0", optional = true } -onnxruntime = { version = "1.19.2", optional = true } -torch = { version = "~2.5.1", optional = true } -orjson = { version = "~3.9.15", optional = true } - # notifications slack-sdk = { version = "~3.28.0", optional = true } [tool.poetry.extras] security = ["semgrep", "owasp-depscan"] -rag = ["chromadb", "sentence-transformers", "torch", "orjson"] notifications = ["slack-sdk"] all = [ "semgrep", "owasp-depscan", - "chromadb", "sentence-transformers", "torch", "orjson", "slack-sdk", ] diff --git a/tests/steps/test_GenerateEmbeddings.py b/tests/steps/test_GenerateEmbeddings.py deleted file mode 100644 index 03f3e2b6e..000000000 --- a/tests/steps/test_GenerateEmbeddings.py +++ /dev/null @@ -1,44 +0,0 @@ -import pytest - -from patchwork.steps.GenerateEmbeddings.GenerateEmbeddings import ( - GenerateEmbeddings, - filter_by_extension, -) - - -def test_filter_by_extension(tmp_path): - assert filter_by_extension("example.txt", [".txt"]) - assert not filter_by_extension("example.txt", [".pdf"]) - - -def test_generate_embeddings_init(): - inputs = {"embedding_name": "test", "documents": [{"document": "test document"}]} - step = GenerateEmbeddings(inputs) - assert step.collection is not None - assert step.documents == inputs["documents"] - - -def test_generate_embeddings_run(): - inputs = {"embedding_name": "test", "documents": [{"document": "test document"}]} - step = GenerateEmbeddings(inputs) - result = step.run() - assert result == {} - - -def test_generate_embeddings_init_required_keys_missing(): - inputs = {"documents": [{"document": "test document"}]} - with pytest.raises(ValueError): - GenerateEmbeddings(inputs) - - -def test_generate_embeddings_init_embedding_name_missing(): - inputs = {"embedding_name": "test"} - with pytest.raises(ValueError): - GenerateEmbeddings(inputs) - - -def test_generate_embeddings_run(): - inputs = {"embedding_name": "test", "documents": [{"document": "test document"}]} - step = GenerateEmbeddings(inputs) - result = step.run() - assert result == {} diff --git a/tests/steps/test_QueryEmbeddings.py b/tests/steps/test_QueryEmbeddings.py deleted file mode 100644 index 9f8af3355..000000000 --- a/tests/steps/test_QueryEmbeddings.py +++ /dev/null @@ -1,58 +0,0 @@ -import uuid - -import chromadb -import pytest - -from patchwork.common.utils.utils import get_vector_db_path -from patchwork.steps.QueryEmbeddings.QueryEmbeddings import QueryEmbeddings - - -@pytest.fixture -def setup_collection(): - _TEST_COLLECTION = "test" - - client = chromadb.PersistentClient(path=get_vector_db_path()) - collection = client.get_or_create_collection(_TEST_COLLECTION) - collection.upsert( - ids=[str(uuid.uuid1()), str(uuid.uuid1()), str(uuid.uuid1())], - documents=["text1", "text2", "text3"], - metadatas=[ - {"original_id": "1", "original_document": "text1"}, - {"original_id": "2", "original_document": "text2"}, - {"original_id": "3", "original_document": "text3"}, - ], - ) - yield collection - client.delete_collection(_TEST_COLLECTION) - - -def test_required_keys(setup_collection): - # Test that the required keys are checked - inputs = {"embedding_name": setup_collection.name, "texts": ["text1", "text2"]} - query_embeddings = QueryEmbeddings(inputs) - - -def test_missing_required_key(): - # Test that a ValueError is raised when a required key is missing - inputs = {"texts": ["text1", "text2"]} - with pytest.raises(ValueError): - QueryEmbeddings(inputs) - - -def test_query_results(setup_collection): - # Test that the query results are processed correctly - collection = setup_collection - inputs = {"embedding_name": collection.name, "texts": ["text1", "text2"]} - query_embeddings = QueryEmbeddings(inputs) - results = query_embeddings.run() - assert isinstance(results, dict) - assert "embedding_results" in results - - -@pytest.mark.parametrize("top_k", [1, 2, 3]) -def test_top_k(setup_collection, top_k): - # Test that the token limit is enforced - inputs = {"embedding_name": setup_collection.name, "texts": ["text1"], "top_k": top_k} - query_embeddings = QueryEmbeddings(inputs) - results = query_embeddings.run() - assert len(results["embedding_results"]) == top_k