diff --git a/README.md b/README.md index da9695e7c..53fc7a815 100644 --- a/README.md +++ b/README.md @@ -35,6 +35,7 @@ This README provides detailed information on how to set up, develop, and deploy - [Setup](#setup) - [General Environment Variables](#general-environment-variables) - [Choosing a Vector Database](#choosing-a-vector-database) + - [Chroma](#chroma) - [Pinecone](#pinecone) - [Weaviate](#weaviate) - [Zilliz](#zilliz) @@ -73,7 +74,7 @@ Follow these steps to quickly set up and run the ChatGPT Retrieval Plugin: export OPENAI_API_KEY= # Optional environment variables used when running Azure OpenAI - export OPENAI_API_BASE=https://.openai.azure.com/ + export OPENAI_API_BASE=https://.openai.azure.com/ export OPENAI_API_TYPE=azure export OPENAI_EMBEDDINGMODEL_DEPLOYMENTID= export OPENAI_METADATA_EXTRACTIONMODEL_DEPLOYMENTID= @@ -239,27 +240,29 @@ The API requires the following environment variables to work: | Name | Required | Description | | ---------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| `DATASTORE` | Yes | This specifies the vector database provider you want to use to store and query embeddings. You can choose from `pinecone`, `weaviate`, `zilliz`, `milvus`, `qdrant`, or `redis`. | +| `DATASTORE` | Yes | This specifies the vector database provider you want to use to store and query embeddings. You can choose from `chroma`, `pinecone`, `weaviate`, `zilliz`, `milvus`, `qdrant`, or `redis`. | | `BEARER_TOKEN` | Yes | This is a secret token that you need to authenticate your requests to the API. You can generate one using any tool or method you prefer, such as [jwt.io](https://jwt.io/). | | `OPENAI_API_KEY` | Yes | This is your OpenAI API key that you need to generate embeddings using the `text-embedding-ada-002` model. You can get an API key by creating an account on [OpenAI](https://openai.com/). | - ### Using the plugin with Azure OpenAI The Azure Open AI uses URLs that are specific to your resource and references models not by model name but by the deployment id. As a result, you need to set additional environment variables for this case. -In addition to the OPENAI_API_BASE (your specific URL) and OPENAI_API_TYPE (azure), you should also set OPENAI_EMBEDDINGMODEL_DEPLOYMENTID which specifies the model to use for getting embeddings on upsert and query. For this, we recommend deploying text-embedding-ada-002 model and using the deployment name here. +In addition to the OPENAI_API_BASE (your specific URL) and OPENAI_API_TYPE (azure), you should also set OPENAI_EMBEDDINGMODEL_DEPLOYMENTID which specifies the model to use for getting embeddings on upsert and query. For this, we recommend deploying text-embedding-ada-002 model and using the deployment name here. -If you wish to use the data preparation scripts, you will also need to set OPENAI_METADATA_EXTRACTIONMODEL_DEPLOYMENTID, used for metadata extraction and +If you wish to use the data preparation scripts, you will also need to set OPENAI_METADATA_EXTRACTIONMODEL_DEPLOYMENTID, used for metadata extraction and OPENAI_COMPLETIONMODEL_DEPLOYMENTID, used for PII handling. - ### Choosing a Vector Database The plugin supports several vector database providers, each with different features, performance, and pricing. Depending on which one you choose, you will need to use a different Dockerfile and set different environment variables. The following sections provide brief introductions to each vector database provider. For more detailed instructions on setting up and using each vector database provider, please refer to the respective documentation in the `/docs/providers//setup.md` file ([folders here](/docs/providers)). +#### Chroma + +[Chroma](https://trychroma.com) is an AI-native open-source embedding database designed to make getting started as easy as possible. Chroma runs in-memory, or in a client-server setup. It supports metadata and keyword filtering out of the box. For detailed instructions, refer to [`/docs/providers/chroma/setup.md`](/docs/providers/chroma/setup.md). + #### Pinecone [Pinecone](https://www.pinecone.io) is a managed vector database designed for speed, scale, and rapid deployment to production. It supports hybrid search and is currently the only datastore to natively support SPLADE sparse vectors. For detailed setup instructions, refer to [`/docs/providers/pinecone/setup.md`](/docs/providers/pinecone/setup.md). @@ -367,7 +370,13 @@ Before deploying your app, you might want to remove unused dependencies from you Once you have deployed your app, consider uploading an initial batch of documents using one of [these scripts](/scripts) or by calling the `/upsert` endpoint. -Here are detailed deployment instructions for various platforms: +- **Chroma:** Remove `pinecone-client`, `weaviate-client`, `pymilvus`, `qdrant-client`, and `redis`. +- **Pinecone:** Remove `chromadb`, `weaviate-client`, `pymilvus`, `qdrant-client`, and `redis`. +- **Weaviate:** Remove `chromadb`, `pinecone-client`, `pymilvus`, `qdrant-client`, and `redis`. +- **Zilliz:** Remove `chromadb`, `pinecone-client`, `weaviate-client`, `qdrant-client`, and `redis`. +- **Milvus:** Remove `chromadb`, `pinecone-client`, `weaviate-client`, `qdrant-client`, and `redis`. +- **Qdrant:** Remove `chromadb`, `pinecone-client`, `weaviate-client`, `pymilvus`, and `redis`. +- **Redis:** Remove `chromadb`, `pinecone-client`, `weaviate-client`, `pymilvus`, and `qdrant-client`. - [Deploying to Fly.io](/docs/deployment/flyio.md) - [Deploying to Heroku](/docs/deployment/heroku.md) diff --git a/datastore/factory.py b/datastore/factory.py index 48cd7c40f..e6eb48edf 100644 --- a/datastore/factory.py +++ b/datastore/factory.py @@ -7,6 +7,10 @@ async def get_datastore() -> DataStore: assert datastore is not None match datastore: + case "chroma": + from datastore.providers.chroma_datastore import ChromaDataStore + + return ChromaDataStore() case "llama": from datastore.providers.llama_datastore import LlamaDataStore return LlamaDataStore() diff --git a/datastore/providers/chroma_datastore.py b/datastore/providers/chroma_datastore.py new file mode 100644 index 000000000..0db5cc291 --- /dev/null +++ b/datastore/providers/chroma_datastore.py @@ -0,0 +1,249 @@ +""" +Chroma datastore support for the ChatGPT retrieval plugin. + +Consult the Chroma docs and GitHub repo for more information: +- https://docs.trychroma.com/usage-guide?lang=py +- https://github.com/chroma-core/chroma +- https://www.trychroma.com/ +""" + +import os +from datetime import datetime +from typing import Dict, List, Optional + +import chromadb + +from datastore.datastore import DataStore +from models.models import ( + DocumentChunk, + DocumentChunkMetadata, + DocumentChunkWithScore, + DocumentMetadataFilter, + QueryResult, + QueryWithEmbedding, + Source, +) +from services.chunks import get_document_chunks + +CHROMA_IN_MEMORY = os.environ.get("CHROMA_IN_MEMORY", "True") +CHROMA_PERSISTENCE_DIR = os.environ.get("CHROMA_PERSISTENCE_DIR", "openai") +CHROMA_HOST = os.environ.get("CHROMA_HOST", "http://127.0.0.1") +CHROMA_PORT = os.environ.get("CHROMA_PORT", "8000") +CHROMA_COLLECTION = os.environ.get("CHROMA_COLLECTION", "openaiembeddings") + + +class ChromaDataStore(DataStore): + def __init__( + self, + in_memory: bool = CHROMA_IN_MEMORY, + persistence_dir: Optional[str] = CHROMA_PERSISTENCE_DIR, + collection_name: str = CHROMA_COLLECTION, + host: str = CHROMA_HOST, + port: str = CHROMA_PORT, + client: Optional[chromadb.Client] = None, + ): + if client: + self._client = client + else: + if in_memory: + settings = ( + chromadb.config.Settings( + chroma_db_impl="duckdb+parquet", + persist_directory=persistence_dir, + ) + if persistence_dir + else chromadb.config.Settings() + ) + + self._client = chromadb.Client(settings=settings) + else: + self._client = chromadb.Client( + settings=chromadb.config.Settings( + chroma_api_impl="rest", + chroma_server_host=host, + chroma_server_http_port=port, + ) + ) + self._collection = self._client.get_or_create_collection( + name=collection_name, + embedding_function=None, + ) + + async def upsert( + self, documents: List[DocumentChunk], chunk_token_size: Optional[int] = None + ) -> List[str]: + """ + Takes in a list of documents and inserts them into the database. If an id already exists, the document is updated. + Return a list of document ids. + """ + + chunks = get_document_chunks(documents, chunk_token_size) + + # Chroma has a true upsert, so we don't need to delete first + return await self._upsert(chunks) + + async def _upsert(self, chunks: Dict[str, List[DocumentChunk]]) -> List[str]: + """ + Takes in a list of list of document chunks and inserts them into the database. + Return a list of document ids. + """ + + self._collection.upsert( + ids=[chunk.id for chunk_list in chunks.values() for chunk in chunk_list], + embeddings=[ + chunk.embedding + for chunk_list in chunks.values() + for chunk in chunk_list + ], + documents=[ + chunk.text for chunk_list in chunks.values() for chunk in chunk_list + ], + metadatas=[ + self._process_metadata_for_storage(chunk.metadata) + for chunk_list in chunks.values() + for chunk in chunk_list + ], + ) + return list(chunks.keys()) + + def _where_from_query_filter(self, query_filter: DocumentMetadataFilter) -> Dict: + output = { + k: v + for (k, v) in query_filter.dict().items() + if v is not None and k != "start_date" and k != "end_date" and k != "source" + } + if query_filter.source: + output["source"] = query_filter.source.value + if query_filter.start_date and query_filter.end_date: + output["$and"] = [ + { + "created_at": { + "$gte": int( + datetime.fromisoformat(query_filter.start_date).timestamp() + ) + } + }, + { + "created_at": { + "$lte": int( + datetime.fromisoformat(query_filter.end_date).timestamp() + ) + } + }, + ] + elif query_filter.start_date: + output["created_at"] = { + "$gte": int(datetime.fromisoformat(query_filter.start_date).timestamp()) + } + elif query_filter.end_date: + output["created_at"] = { + "$lte": int(datetime.fromisoformat(query_filter.end_date).timestamp()) + } + + return output + + def _process_metadata_for_storage(self, metadata: DocumentChunkMetadata) -> Dict: + stored_metadata = {} + if metadata.source: + stored_metadata["source"] = metadata.source.value + if metadata.source_id: + stored_metadata["source_id"] = metadata.source_id + if metadata.url: + stored_metadata["url"] = metadata.url + if metadata.created_at: + stored_metadata["created_at"] = int( + datetime.fromisoformat(metadata.created_at).timestamp() + ) + if metadata.author: + stored_metadata["author"] = metadata.author + if metadata.document_id: + stored_metadata["document_id"] = metadata.document_id + + return stored_metadata + + def _process_metadata_from_storage(self, metadata: Dict) -> DocumentChunkMetadata: + return DocumentChunkMetadata( + source=Source(metadata["source"]) if "source" in metadata else None, + source_id=metadata.get("source_id", None), + url=metadata.get("url", None), + created_at=datetime.fromtimestamp(metadata["created_at"]).isoformat() + if "created_at" in metadata + else None, + author=metadata.get("author", None), + document_id=metadata.get("document_id", None), + ) + + async def _query(self, queries: List[QueryWithEmbedding]) -> List[QueryResult]: + """ + Takes in a list of queries with embeddings and filters and returns a list of query results with matching document chunks and scores. + """ + results = [ + self._collection.query( + query_embeddings=[query.embedding], + include=["documents", "distances", "metadatas"], # embeddings + n_results=min(query.top_k, self._collection.count()), + where=( + self._where_from_query_filter(query.filter) if query.filter else {} + ), + ) + for query in queries + ] + + output = [] + for query, result in zip(queries, results): + inner_results = [] + (ids,) = result["ids"] + # (embeddings,) = result["embeddings"] + (documents,) = result["documents"] + (metadatas,) = result["metadatas"] + (distances,) = result["distances"] + for id_, text, metadata, distance in zip( + ids, + documents, + metadatas, + distances, # embeddings (https://github.com/openai/chatgpt-retrieval-plugin/pull/59#discussion_r1154985153) + ): + inner_results.append( + DocumentChunkWithScore( + id=id_, + text=text, + metadata=self._process_metadata_from_storage(metadata), + # embedding=embedding, + score=distance, + ) + ) + output.append(QueryResult(query=query.query, results=inner_results)) + + return output + + async def delete( + self, + ids: Optional[List[str]] = None, + filter: Optional[DocumentMetadataFilter] = None, + delete_all: Optional[bool] = None, + ) -> bool: + """ + Removes vectors by ids, filter, or everything in the datastore. + Multiple parameters can be used at once. + Returns whether the operation was successful. + """ + if delete_all: + self._collection.delete() + return True + + if ids and len(ids) > 0: + if len(ids) > 1: + where_clause = {"$or": [{"document_id": id_} for id_ in ids]} + else: + (id_,) = ids + where_clause = {"document_id": id_} + + if filter: + where_clause = { + "$and": [self._where_from_query_filter(filter), where_clause] + } + elif filter: + where_clause = self._where_from_query_filter(filter) + + self._collection.delete(where=where_clause) + return True diff --git a/docs/providers/chroma/setup.md b/docs/providers/chroma/setup.md new file mode 100644 index 000000000..2822b0454 --- /dev/null +++ b/docs/providers/chroma/setup.md @@ -0,0 +1,29 @@ +[Chroma](https://trychroma.com) is an AI-native open-source embedding database designed to make it easy to work with embeddings. Chroma runs in-memory, or in a client-server setup. + +Install Chroma by running `pip install chromadb`. Once installed, the core API consists of four essential commands for creating collections, adding embeddings, documents, and metadata, and querying embeddings to find similar documents. Get started with Chroma by visiting the [Getting Started](https://docs.trychroma.com) page on their documentation website, or explore the open-source code on their [GitHub repository](https://github.com/chroma-core/chroma). + +**Chroma Environment Variables** + +To set up Chroma and start using it as your vector database provider, you need to define some environment variables to connect to your Chroma instance. + +**Chroma Datastore Environment Variables** + +Chroma runs _in-memory_ by default, with local persistence. It can also run in [self-hosted](https://docs.trychroma.com/usage-guide#running-chroma-in-clientserver-mode) client-server mode, with a fully managed hosted version coming soon. + +| Name | Required | Description | Default | +| ------------------------ | -------- | -------------------------------------------------------------------------------------------------- | ---------------- | +| `DATASTORE` | Yes | Datastore name. Set this to `chroma` | | +| `BEARER_TOKEN` | Yes | Your secret token for authenticating requests to the API | | +| `OPENAI_API_KEY` | Yes | Your OpenAI API key for generating embeddings | | +| `CHROMA_COLLECTION` | Optional | Your chosen Chroma collection name to store your embeddings | openaiembeddings | +| `CHROMA_IN_MEMORY` | Optional | If set to `True`, ignore `CHROMA_HOST` and `CHROMA_PORT` and just use an in-memory Chroma instance | `True` | +| `CHROMA_PERSISTENCE_DIR` | Optional | If set, and `CHROMA_IN_MEMORY` is set, persist to and load from this directory. | `openai` | + +To run Chroma in self-hosted client-server mode, st the following variables: + +| Name | Required | Description | Default | +| ------------- | -------- | --------------------------------------------------- | ------------------ | +| `CHROMA_HOST` | Optional | Your Chroma instance host address (see notes below) | `http://127.0.0.1` | +| `CHROMA_PORT` | Optional | Your Chroma port number | `8000` | + +> For **self-hosted instances**, if your instance is not at 127.0.0.1:8000, set `CHROMA_HOST` and `CHROMA_PORT` accordingly. 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weaviate-client = "^3.12.0" pymilvus = "^2.2.2" diff --git a/tests/datastore/providers/chroma/test_chroma_datastore.py b/tests/datastore/providers/chroma/test_chroma_datastore.py new file mode 100644 index 000000000..2820af1f6 --- /dev/null +++ b/tests/datastore/providers/chroma/test_chroma_datastore.py @@ -0,0 +1,293 @@ +import shutil +from typing import Dict, List +import pytest +import random + +from datastore.providers.chroma_datastore import ChromaDataStore +from models.models import ( + DocumentChunk, + DocumentChunkMetadata, + DocumentMetadataFilter, + QueryWithEmbedding, + Source, +) + +TEST_PERSISTENCE_DIR = "chroma_test_datastore" +COLLECTION_NAME = "documents" + + +def ephemeral_chroma_datastore() -> ChromaDataStore: + # Initalize an ephemeral in-memory ChromaDB instance + return ChromaDataStore( + collection_name=COLLECTION_NAME, in_memory=True, persistence_dir=None + ) + + +def persisted_chroma_datastore() -> ChromaDataStore: + # Initialize an in-memory ChromaDB instance with persistence + return ChromaDataStore( + collection_name=COLLECTION_NAME, + in_memory=True, + persistence_dir=TEST_PERSISTENCE_DIR, + ) + + +def get_chroma_datastore() -> ChromaDataStore: + yield ephemeral_chroma_datastore() + yield persisted_chroma_datastore() + # Delete the persistence directory after the test + + +@pytest.fixture(autouse=True) +def cleanup(): + yield + shutil.rmtree(TEST_PERSISTENCE_DIR, ignore_errors=True) + + +# Seed for deterministic testing +random.seed(0) + + +def create_embedding(dim: int) -> List[float]: + return [random.random() for _ in range(dim)] + + +# Data fixtures +TEST_EMBEDDING_DIM = 5 +N_TEST_CHUNKS = 5 + + +@pytest.fixture +def initial_document_chunks() -> Dict[str, List[DocumentChunk]]: + first_doc_chunks = [ + DocumentChunk( + id=f"first-doc-{i}", + text=f"Lorem ipsum {i}", + metadata=DocumentChunkMetadata(), + embedding=create_embedding(TEST_EMBEDDING_DIM), + ) + for i in range(N_TEST_CHUNKS) + ] + return { + "first-doc": first_doc_chunks, + } + + +@pytest.fixture +def document_chunks(initial_document_chunks) -> Dict[str, List[DocumentChunk]]: + doc_chunks = initial_document_chunks + + for k, v in doc_chunks.items(): + for chunk in v: + chunk.metadata = DocumentChunkMetadata( + source=Source.email, created_at="2023-04-03", document_id="first-doc" + ) + chunk.embedding = create_embedding(TEST_EMBEDDING_DIM) + + doc_chunks["second-doc"] = [ + DocumentChunk( + id=f"second-doc-{i}", + text=f"Dolor sit amet {i}", + metadata=DocumentChunkMetadata( + created_at="2023-04-04", document_id="second-doc" + ), + embedding=create_embedding(TEST_EMBEDDING_DIM), + ) + for i in range(N_TEST_CHUNKS) + ] + + return doc_chunks + + +@pytest.mark.asyncio +async def test_add_chunks(document_chunks: Dict[str, List[DocumentChunk]]): + for datastore in get_chroma_datastore(): + await datastore.delete(delete_all=True) + assert datastore._collection.count() == 0 + + print(document_chunks) + + assert await datastore._upsert(document_chunks) == list(document_chunks.keys()) + assert datastore._collection.count() == sum( + len(v) for v in document_chunks.values() + ) + + +@pytest.mark.asyncio +async def test_upsert( + initial_document_chunks: Dict[str, List[DocumentChunk]], + document_chunks: Dict[str, List[DocumentChunk]], +): + for datastore in get_chroma_datastore(): + await datastore.delete(delete_all=True) + + assert await datastore._upsert(initial_document_chunks) == list( + initial_document_chunks.keys() + ) + assert datastore._collection.count() == sum( + len(v) for v in initial_document_chunks.values() + ) + + assert await datastore._upsert(document_chunks) == list(document_chunks.keys()) + assert datastore._collection.count() == sum( + len(v) for v in document_chunks.values() + ) + + +@pytest.mark.asyncio +async def test_add_and_query_all(document_chunks): + for datastore in get_chroma_datastore(): + await datastore.delete(delete_all=True) + + await datastore._upsert(document_chunks) == list(document_chunks.keys()) + + query = QueryWithEmbedding( + query="", + embedding=create_embedding(TEST_EMBEDDING_DIM), + top_k=10, + ) + query_results = await datastore._query(queries=[query]) + assert 1 == len(query_results) + assert 10 == len(query_results[0].results) + + +@pytest.mark.asyncio +async def test_query_accuracy(document_chunks): + for _, v in document_chunks.items(): + for chunk in v: + print(f"id: {chunk.id} emb: {chunk.embedding}") + + def add_noise_to_embedding(embedding: List[float], eps: float = 0) -> List[float]: + return [x + eps * (1.0 - 2 * random.random()) for x in embedding] + + for datastore in get_chroma_datastore(): + await datastore.delete(delete_all=True) + + print(datastore._collection.get(include=["embeddings"])) + + res = await datastore._upsert(document_chunks) + + res = datastore._collection.get(include=["embeddings"]) + for id, emb in zip(res["ids"], res["embeddings"]): + print(f"id: {id} emb: {emb}") + + for _, v in document_chunks.items(): + for chunk in v: + print(f"chunk: {chunk}") + query = QueryWithEmbedding( + query="", + embedding=add_noise_to_embedding(chunk.embedding), + top_k=1, + ) + query_results = await datastore._query(queries=[query]) + print(query_results) + assert query_results[0].results[0].id == chunk.id + + +@pytest.mark.asyncio +async def test_query_filter_by_id(document_chunks): + for datastore in get_chroma_datastore(): + await datastore.delete(delete_all=True) + + await datastore._upsert(document_chunks) + + for doc_id, chunks in document_chunks.items(): + query = QueryWithEmbedding( + query="", + embedding=chunks[0].embedding, + top_k=N_TEST_CHUNKS, + filter=DocumentMetadataFilter(document_id=doc_id), + ) + query_results = await datastore._query(queries=[query]) + # Assert that all document chunks are returned + assert len(query_results[0].results) == len(chunks) + assert all( + [ + result.id in [chunk.id for chunk in chunks] + for result in query_results[0].results + ] + ) + + +@pytest.mark.asyncio +async def test_query_filter_by_date(document_chunks): + for datastore in get_chroma_datastore(): + await datastore.delete(delete_all=True) + + await datastore._upsert(document_chunks) + + # Filter by dates for only the first document + query = QueryWithEmbedding( + query="", + embedding=document_chunks["first-doc"][0].embedding, + top_k=N_TEST_CHUNKS, + filter=DocumentMetadataFilter( + start_date="2023-04-03", end_date="2023-04-03" + ), + ) + + query_results = await datastore._query(queries=[query]) + + # Assert that only the first document is returned + assert len(query_results[0].results) == len(document_chunks["first-doc"]) + assert all( + [ + result.id in [chunk.id for chunk in document_chunks["first-doc"]] + for result in query_results[0].results + ] + ) + + # Filter for the entire date span + query = QueryWithEmbedding( + query="", + embedding=document_chunks["first-doc"][0].embedding, + top_k=N_TEST_CHUNKS * len(document_chunks), + filter=DocumentMetadataFilter( + start_date="2023-04-03", end_date="2023-04-04" + ), + ) + + query_results = await datastore._query(queries=[query]) + + # Assert that both documents are returned + assert len(query_results[0].results) == len(document_chunks["first-doc"]) + len( + document_chunks["second-doc"] + ) + assert all( + [ + result.id + in [chunk.id for chunk in document_chunks["first-doc"]] + + [chunk.id for chunk in document_chunks["second-doc"]] + for result in query_results[0].results + ] + ) + + +@pytest.mark.asyncio +async def test_delete_by_id(document_chunks): + for datastore in get_chroma_datastore(): + await datastore.delete(delete_all=True) + + await datastore._upsert(document_chunks) + + # Delete the first document + await datastore.delete(ids=["first-doc"]) + + # Assert that the first document is deleted + query = QueryWithEmbedding( + query="", + embedding=document_chunks["first-doc"][0].embedding, + top_k=N_TEST_CHUNKS, + ) + query_results = await datastore._query(queries=[query]) + + # Assert that only the second document is still there + query_results = await datastore._query(queries=[query]) + assert len(query_results[0].results) == len(document_chunks["second-doc"]) + + assert all( + [ + result.id in [chunk.id for chunk in document_chunks["second-doc"]] + for result in query_results[0].results + ] + )