diff --git a/.libraries-whitelist.txt b/.libraries-whitelist.txt index 06516cf0..708fc338 100644 --- a/.libraries-whitelist.txt +++ b/.libraries-whitelist.txt @@ -1,3 +1,4 @@ pkg_resources tiktoken chardet +chroma-hnswlib \ No newline at end of file diff --git a/.license-whitelist.txt b/.license-whitelist.txt index e4952ec0..b01f07f7 100644 --- a/.license-whitelist.txt +++ b/.license-whitelist.txt @@ -6,6 +6,7 @@ Apache Software License, BSD License Apache Software License, MIT License Apache-2 Apache License, Version 2 +Apache License v2.0 BSD BSD License BSD License, Apache Software License diff --git a/packages/ragbits-document-search/examples/chromadb_example.py b/packages/ragbits-document-search/examples/chromadb_example.py new file mode 100644 index 00000000..d2a78097 --- /dev/null +++ b/packages/ragbits-document-search/examples/chromadb_example.py @@ -0,0 +1,46 @@ +# /// script +# requires-python = ">=3.10" +# dependencies = [ +# "ragbits-document-search", +# "ragbits[litellm]", +# ] +# /// +import asyncio + +import chromadb + +from ragbits.core.embeddings.litellm import LiteLLMEmbeddings +from ragbits.document_search import DocumentSearch +from ragbits.document_search.documents.document import DocumentMeta +from ragbits.document_search.vector_store.chromadb_store import ChromaDBStore + +documents = [ + DocumentMeta.create_text_document_from_literal("RIP boiled water. You will be mist."), + DocumentMeta.create_text_document_from_literal( + "Why programmers don't like to swim? Because they're scared of the floating points." + ), +] + + +async def main(): + """Run the example.""" + + chroma_client = chromadb.PersistentClient(path="chroma") + embedding_client = LiteLLMEmbeddings() + + vector_store = ChromaDBStore( + index_name="jokes", + chroma_client=chroma_client, + embedding_function=embedding_client, + ) + document_search = DocumentSearch(embedder=vector_store.embedding_function, vector_store=vector_store) + + for document in documents: + await document_search.ingest_document(document) + + results = await document_search.search("I'm boiling my water and I need a joke") + print(results) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/packages/ragbits-document-search/pyproject.toml b/packages/ragbits-document-search/pyproject.toml index 6820f0bf..199f958b 100644 --- a/packages/ragbits-document-search/pyproject.toml +++ b/packages/ragbits-document-search/pyproject.toml @@ -38,6 +38,9 @@ dependencies = [ ] [project.optional-dependencies] +chromadb = [ + "chromadb~=0.4.24", +] gcs = [ "gcloud-aio-storage~=9.3.0" ] diff --git a/packages/ragbits-document-search/src/ragbits/document_search/_main.py b/packages/ragbits-document-search/src/ragbits/document_search/_main.py index 9034fb3a..0659dd40 100644 --- a/packages/ragbits-document-search/src/ragbits/document_search/_main.py +++ b/packages/ragbits-document-search/src/ragbits/document_search/_main.py @@ -53,14 +53,14 @@ async def search(self, query: str) -> list[Element]: A list of chunks. """ queries = self.query_rephraser.rephrase(query) - chunks = [] + elements = [] for rephrased_query in queries: search_vector = await self.embedder.embed_text([rephrased_query]) # TODO: search parameters should be configurable entries = await self.vector_store.retrieve(search_vector[0], k=1) - chunks.extend([Element.from_vector_db_entry(entry) for entry in entries]) + elements.extend([Element.from_vector_db_entry(entry) for entry in entries]) - return self.reranker.rerank(chunks) + return self.reranker.rerank(elements) async def ingest_document(self, document: DocumentMeta) -> None: """ diff --git a/packages/ragbits-document-search/src/ragbits/document_search/vector_store/chromadb_store.py b/packages/ragbits-document-search/src/ragbits/document_search/vector_store/chromadb_store.py new file mode 100644 index 00000000..685950be --- /dev/null +++ b/packages/ragbits-document-search/src/ragbits/document_search/vector_store/chromadb_store.py @@ -0,0 +1,204 @@ +import json +from copy import deepcopy +from hashlib import sha256 +from typing import List, Literal, Optional, Union + +try: + import chromadb + + HAS_CHROMADB = True +except ImportError: + HAS_CHROMADB = False + +from ragbits.core.embeddings.base import Embeddings +from ragbits.document_search.vector_store.base import VectorStore +from ragbits.document_search.vector_store.in_memory import VectorDBEntry + + +class ChromaDBStore(VectorStore): + """Class that stores text embeddings using [Chroma](https://docs.trychroma.com/)""" + + def __init__( + self, + index_name: str, + chroma_client: chromadb.ClientAPI, + embedding_function: Union[Embeddings, chromadb.EmbeddingFunction], + max_distance: Optional[float] = None, + distance_method: Literal["l2", "ip", "cosine"] = "l2", + ): + """ + Initializes the ChromaDBStore with the given parameters. + + Args: + index_name: The name of the index. + chroma_client: The ChromaDB client. + embedding_function: The embedding function. + max_distance: The maximum distance for similarity. + distance_method: The distance method to use. + """ + if not HAS_CHROMADB: + raise ImportError("Install the 'ragbits-document-search[chromadb]' extra to use LiteLLM embeddings models") + + super().__init__() + self._index_name = index_name + self._chroma_client = chroma_client + self._embedding_function = embedding_function + self._max_distance = max_distance + self._metadata = {"hnsw:space": distance_method} + self._collection = self._get_chroma_collection() + + def _get_chroma_collection(self) -> chromadb.Collection: + """ + Based on the selected embedding_function, chooses how to retrieve the ChromaDB collection. + If the collection doesn't exist, it creates one. + + Returns: + Retrieved collection + """ + if isinstance(self._embedding_function, Embeddings): + return self._chroma_client.get_or_create_collection(name=self._index_name, metadata=self._metadata) + + return self._chroma_client.get_or_create_collection( + name=self._index_name, + metadata=self._metadata, + embedding_function=self._embedding_function, + ) + + def _return_best_match(self, retrieved: dict) -> Optional[str]: + """ + Based on the retrieved data, returns the best match or None if no match is found. + + Args: + Retrieved data, with a column-first format + + Returns: + The best match or None if no match is found + """ + if self._max_distance is None or retrieved["distances"][0][0] <= self._max_distance: + return retrieved["documents"][0][0] + + return None + + def _process_db_entry(self, entry: VectorDBEntry) -> tuple[str, list[float], str, dict]: + doc_id = sha256(entry.key.encode("utf-8")).hexdigest() + embedding = entry.vector + text = entry.metadata["content"] + + metadata = deepcopy(entry.metadata) + metadata["document"]["source"]["path"] = str(metadata["document"]["source"]["path"]) + metadata["key"] = entry.key + metadata = {key: json.dumps(val) if isinstance(val, dict) else val for key, val in metadata.items()} + + return doc_id, embedding, text, metadata + + def _process_metadata(self, metadata: dict) -> dict[str, Union[str, int, float, bool]]: + """ + Processes the metadata dictionary by parsing JSON strings if applicable. + + Args: + metadata: A dictionary containing metadata where values may be JSON strings. + + Returns: + A dictionary with the same keys as the input, where JSON strings are parsed + into their respective Python data types. + """ + return {key: json.loads(val) if self._is_json(val) else val for key, val in metadata.items()} + + def _is_json(self, myjson: str) -> bool: + """ + Check if the provided string is a valid JSON. + + Args: + myjson: The string to be checked. + + Returns: + True if the string is a valid JSON, False otherwise. + """ + try: + if isinstance(myjson, str): + json.loads(myjson) + return True + return False + except ValueError: + return False + + @property + def embedding_function(self) -> Union[Embeddings, chromadb.EmbeddingFunction]: + """ + Returns the embedding function. + + Returns: + The embedding function. + """ + return self._embedding_function + + async def store(self, entries: List[VectorDBEntry]) -> None: + """ + Stores entries in the ChromaDB collection. + + Args: + entries: The entries to store. + """ + collection = self._get_chroma_collection() + + entries_processed = list(map(self._process_db_entry, entries)) + ids, embeddings, texts, metadatas = map(list, zip(*entries_processed)) + + collection.add(ids=ids, embeddings=embeddings, documents=texts, metadatas=metadatas) + + async def retrieve(self, vector: List[float], k: int = 5) -> List[VectorDBEntry]: + """ + Retrieves entries from the ChromaDB collection. + + Args: + vector: The vector to query. + k: The number of entries to retrieve. + + Returns: + The retrieved entries. + """ + collection = self._get_chroma_collection() + query_result = collection.query(query_embeddings=[vector], n_results=k) + + db_entries = [] + for meta in query_result.get("metadatas"): + db_entry = VectorDBEntry( + key=meta[0].get("key"), + vector=vector, + metadata=self._process_metadata(meta[0]), + ) + + db_entries.append(db_entry) + + return db_entries + + async def find_similar(self, text: str) -> Optional[str]: + """ + Finds the most similar text in the chroma collection or returns None if the most similar text + has distance bigger than `self.max_distance`. + + Args: + text: The text to find similar to. + + Returns: + The most similar text or None if no similar text is found. + """ + + collection = self._get_chroma_collection() + + if isinstance(self._embedding_function, Embeddings): + embedding = await self._embedding_function.embed_text([text]) + retrieved = collection.query(query_embeddings=embedding, n_results=1) + else: + retrieved = collection.query(query_texts=[text], n_results=1) + + return self._return_best_match(retrieved) + + def __repr__(self) -> str: + """ + Returns the string representation of the object. + + Returns: + The string representation of the object. + """ + return f"{self.__class__.__name__}(index_name={self._index_name})" diff --git a/packages/ragbits-document-search/tests/unit/test_chromadb_store.py b/packages/ragbits-document-search/tests/unit/test_chromadb_store.py new file mode 100644 index 00000000..fd86dde0 --- /dev/null +++ b/packages/ragbits-document-search/tests/unit/test_chromadb_store.py @@ -0,0 +1,193 @@ +from hashlib import sha256 +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from ragbits.core.embeddings.base import Embeddings +from ragbits.document_search.vector_store.chromadb_store import ChromaDBStore, VectorDBEntry + + +@pytest.fixture +def mock_chroma_client(): + return MagicMock() + + +@pytest.fixture +def mock_embedding_function(): + return AsyncMock() + + +@pytest.fixture +def mock_chromadb_store(mock_chroma_client, mock_embedding_function): + return ChromaDBStore( + index_name="test_index", + chroma_client=mock_chroma_client, + embedding_function=mock_embedding_function, + ) + + +class MockEmbeddings(Embeddings): + async def embed_text(self, text): + return [[0.4, 0.5, 0.6]] + + def __call__(self, input): + return self.embed_text(input) + + +@pytest.fixture +def custom_embedding_function(): + return MockEmbeddings() + + +@pytest.fixture +def mock_chromadb_store_with_custom_embedding_function(mock_chroma_client, custom_embedding_function): + return ChromaDBStore( + index_name="test_index", + chroma_client=mock_chroma_client, + embedding_function=custom_embedding_function, + ) + + +@pytest.fixture +def mock_vector_db_entry(): + return VectorDBEntry( + key="test_key", + vector=[0.1, 0.2, 0.3], + metadata={ + "content": "test content", + "document": {"title": "test title", "source": {"path": "/test/path"}, "document_type": "test_type"}, + }, + ) + + +def test_chromadbstore_init_import_error(): + with patch("ragbits.document_search.vector_store.chromadb_store.HAS_CHROMADB", False): + with pytest.raises(ImportError): + ChromaDBStore(index_name="test_index", chroma_client=MagicMock(), embedding_function=MagicMock()) + + +def test_get_chroma_collection(mock_chromadb_store): + _ = mock_chromadb_store._get_chroma_collection() + assert mock_chromadb_store._chroma_client.get_or_create_collection.called + + +def test_get_chroma_collection_with_custom_embedding_function( + custom_embedding_function, mock_chromadb_store_with_custom_embedding_function, mock_chroma_client +): + mock_chroma_client.get_or_create_collection.assert_called_once_with( + name="test_index", + metadata={"hnsw:space": "l2"}, + ) + + +async def test_stores_entries_correctly(mock_chromadb_store): + data = [ + VectorDBEntry( + key="test_key", + vector=[0.1, 0.2, 0.3], + metadata={ + "content": "test content", + "document": {"title": "test title", "source": {"path": "/test/path"}, "document_type": "test_type"}, + }, + ) + ] + await mock_chromadb_store.store(data) + mock_chromadb_store._chroma_client.get_or_create_collection().add.assert_called_once() + + +def test_process_db_entry(mock_chromadb_store, mock_vector_db_entry): + id, embedding, text, metadata = mock_chromadb_store._process_db_entry(mock_vector_db_entry) + print(f"metadata: {metadata}, type: {type(metadata)}") + + assert id == sha256(b"test_key").hexdigest() + assert embedding == [0.1, 0.2, 0.3] + assert text == "test content" + assert ( + metadata["document"] + == '{"title": "test title", "source": {"path": "/test/path"}, "document_type": "test_type"}' + ) + assert metadata["key"] == "test_key" + + +async def test_store(mock_chromadb_store, mock_vector_db_entry): + await mock_chromadb_store.store([mock_vector_db_entry]) + assert mock_chromadb_store._chroma_client.get_or_create_collection().add.called + + +async def test_retrieves_entries_correctly(mock_chromadb_store): + vector = [0.1, 0.2, 0.3] + mock_collection = mock_chromadb_store._get_chroma_collection() + mock_collection.query.return_value = { + "documents": [["test content"]], + "metadatas": [ + [ + { + "key": "test_key", + "content": "test content", + "document": {"title": "test title", "source": {"path": "/test/path"}, "document_type": "test_type"}, + } + ] + ], + } + entries = await mock_chromadb_store.retrieve(vector) + assert len(entries) == 1 + assert entries[0].metadata["content"] == "test content" + assert entries[0].metadata["document"]["title"] == "test title" + + +async def test_handles_empty_retrieve(mock_chromadb_store): + vector = [0.1, 0.2, 0.3] + mock_collection = mock_chromadb_store._get_chroma_collection() + mock_collection.query.return_value = {"documents": [], "metadatas": []} + entries = await mock_chromadb_store.retrieve(vector) + assert len(entries) == 0 + + +async def test_find_similar(mock_chromadb_store, mock_embedding_function): + mock_embedding_function.embed_text.return_value = [[0.1, 0.2, 0.3]] + mock_chromadb_store._embedding_function = mock_embedding_function + mock_chromadb_store._chroma_client.get_or_create_collection().query.return_value = { + "documents": [["test content"]], + "distances": [[0.1]], + } + result = await mock_chromadb_store.find_similar("test text") + assert result == "test content" + + +async def test_find_similar_with_custom_embeddings(mock_chromadb_store, custom_embedding_function): + mock_chromadb_store._embedding_function = custom_embedding_function + mock_chromadb_store._chroma_client.get_or_create_collection().query.return_value = { + "documents": [["test content"]], + "distances": [[0.1]], + } + result = await mock_chromadb_store.find_similar("test text") + assert result == "test content" + + +def test_repr(mock_chromadb_store): + assert repr(mock_chromadb_store) == "ChromaDBStore(index_name=test_index)" + + +@pytest.mark.parametrize( + "retrieved, max_distance, expected", + [ + ({"distances": [[0.1]], "documents": [["test content"]]}, None, "test content"), + ({"distances": [[0.1]], "documents": [["test content"]]}, 0.2, "test content"), + ({"distances": [[0.3]], "documents": [["test content"]]}, 0.2, None), + ], +) +def test_return_best_match(mock_chromadb_store, retrieved, max_distance, expected): + mock_chromadb_store._max_distance = max_distance + result = mock_chromadb_store._return_best_match(retrieved) + assert result == expected + + +def test_is_json_valid_string(mock_chromadb_store): + # Arrange + valid_json_string = '{"key": "value"}' + + # Act + result = mock_chromadb_store._is_json(valid_json_string) + + # Assert + assert result is True diff --git a/pyproject.toml b/pyproject.toml index c77cbf2a..e2680984 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ requires-python = ">=3.10" dependencies = [ "ragbits[litellm,local]", "ragbits-dev-kit", - "ragbits-document-search[gcs]", + "ragbits-document-search[chromadb,gcs]", "ragbits-cli" ] diff --git a/uv.lock b/uv.lock index 78b25599..e28c2360 100644 --- a/uv.lock +++ b/uv.lock @@ -147,6 +147,18 @@ wheels = [ { url = 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