From c151a502a7013d494014aba96a58b9fe515b0d94 Mon Sep 17 00:00:00 2001 From: Arjun Bingly Date: Thu, 21 Mar 2024 14:08:14 -0400 Subject: [PATCH 01/15] Refactor attributes from multivec_retriever for consistency. --- projects/Retriver-GUI/retriever_app.py | 14 +++---- src/grag/components/multivec_retriever.py | 45 +++++++++-------------- src/grag/rag/basic_rag.py | 2 +- src/tests/rag/basic_rag_test.py | 2 +- 4 files changed, 26 insertions(+), 37 deletions(-) diff --git a/projects/Retriver-GUI/retriever_app.py b/projects/Retriver-GUI/retriever_app.py index f55c0c6..9f4198c 100644 --- a/projects/Retriver-GUI/retriever_app.py +++ b/projects/Retriver-GUI/retriever_app.py @@ -46,7 +46,7 @@ def render_search_results(self): st.write(result.metadata) def check_connection(self): - response = self.app.retriever.client.test_connection() + response = self.app.retriever.vectordb.test_connection() if response: return True else: @@ -55,14 +55,14 @@ def check_connection(self): def render_stats(self): st.write(f''' **Chroma Client Details:** \n - Host Address : {self.app.retriever.client.host}:{self.app.retriever.client.port} \n - Collection Name : {self.app.retriever.client.collection_name} \n - Embeddings Type : {self.app.retriever.client.embedding_type} \n - Embeddings Model: {self.app.retriever.client.embedding_model} \n - Number of docs : {self.app.retriever.client.collection.count()} \n + Host Address : {self.app.retriever.vectordb.host}:{self.app.retriever.vectordb.port} \n + Collection Name : {self.app.retriever.vectordb.collection_name} \n + Embeddings Type : {self.app.retriever.vectordb.embedding_type} \n + Embeddings Model: {self.app.retriever.vectordb.embedding_model} \n + Number of docs : {self.app.retriever.vectordb.collection.count()} \n ''') if st.button('Check Connection'): - response = self.app.retriever.client.test_connection() + response = self.app.retriever.vectordb.test_connection() if response: st.write(':green[Connection Active]') else: diff --git a/src/grag/components/multivec_retriever.py b/src/grag/components/multivec_retriever.py index 18ed752..b57a67f 100644 --- a/src/grag/components/multivec_retriever.py +++ b/src/grag/components/multivec_retriever.py @@ -2,9 +2,10 @@ import uuid from typing import List -from grag.components.chroma_client import ChromaClient from grag.components.text_splitter import TextSplitter from grag.components.utils import get_config +from grag.components.vectordb.base import VectorDB +from grag.components.vectordb.chroma_client import ChromaClient from langchain.retrievers.multi_vector import MultiVectorRetriever from langchain.storage import LocalFileStore from langchain_core.documents import Document @@ -20,7 +21,7 @@ class Retriever: Attributes: store_path: Path to the local file store id_key: A key prefix for identifying documents - client: ChromaClient class instance from components.chroma_client + vectordb: ChromaClient class instance from components.client store: langchain.storage.LocalFileStore object, stores the key value pairs of document id and parent file retriever: langchain.retrievers.multi_vector.MultiVectorRetriever class instance, langchain's multi-vector retriever splitter: TextSplitter class instance from components.text_splitter @@ -30,11 +31,11 @@ class Retriever: """ def __init__( - self, - store_path: str = multivec_retriever_conf["store_path"], - id_key: str = multivec_retriever_conf["id_key"], - namespace: str = multivec_retriever_conf["namespace"], - top_k=1, + self, + store_path: str = multivec_retriever_conf["store_path"], + id_key: str = multivec_retriever_conf["id_key"], + namespace: str = multivec_retriever_conf["namespace"], + top_k=1, ): """Args: store_path: Path to the local file store, defaults to argument from config file @@ -45,10 +46,10 @@ def __init__( self.store_path = store_path self.id_key = id_key self.namespace = uuid.UUID(namespace) - self.client = ChromaClient() + self.vectordb: VectorDB = ChromaClient() # TODO - change to init argument self.store = LocalFileStore(self.store_path) self.retriever = MultiVectorRetriever( - vectorstore=self.client.langchain_chroma, + vectorstore=self.vectordb.langchain_client, byte_store=self.store, id_key=self.id_key, ) @@ -113,7 +114,7 @@ def add_docs(self, docs: List[Document]): """ chunks = self.split_docs(docs) doc_ids = self.gen_doc_ids(docs) - self.client.add_docs(chunks) + self.vectordb.add_docs(chunks) self.retriever.docstore.mset(list(zip(doc_ids, docs))) async def aadd_docs(self, docs: List[Document]): @@ -129,11 +130,11 @@ async def aadd_docs(self, docs: List[Document]): """ chunks = self.split_docs(docs) doc_ids = self.gen_doc_ids(docs) - await asyncio.run(self.client.aadd_docs(chunks)) + await asyncio.run(self.vectordb.aadd_docs(chunks)) self.retriever.docstore.mset(list(zip(doc_ids))) def get_chunk(self, query: str, with_score=False, top_k=None): - """Returns the most (cosine) similar chunks from the vector database. + """Returns the most similar chunks from the vector database. Args: query: A query string @@ -144,14 +145,8 @@ def get_chunk(self, query: str, with_score=False, top_k=None): list of Documents """ - if with_score: - return self.client.langchain_chroma.similarity_search_with_relevance_scores( - query=query, **{"k": top_k} if top_k else self.retriever.search_kwargs - ) - else: - return self.client.langchain_chroma.similarity_search( - query=query, **{"k": top_k} if top_k else self.retriever.search_kwargs - ) + _top_k = top_k if top_k else self.retriever.search_kwargs['k'] + return self.vectordb.get_chunk(query=query, top_k=_top_k, with_score=with_score) async def aget_chunk(self, query: str, with_score=False, top_k=None): """Returns the most (cosine) similar chunks from the vector database, asynchronously. @@ -165,14 +160,8 @@ async def aget_chunk(self, query: str, with_score=False, top_k=None): list of Documents """ - if with_score: - return await self.client.langchain_chroma.asimilarity_search_with_relevance_scores( - query=query, **{"k": top_k} if top_k else self.retriever.search_kwargs - ) - else: - return await self.client.langchain_chroma.asimilarity_search( - query=query, **{"k": top_k} if top_k else self.retriever.search_kwargs - ) + _top_k = top_k if top_k else self.retriever.search_kwargs['k'] + return await self.vectordb.aget_chunk(query=query, top_k=_top_k, with_score=with_score) def get_doc(self, query: str): """Returns the parent document of the most (cosine) similar chunk from the vector database. diff --git a/src/grag/rag/basic_rag.py b/src/grag/rag/basic_rag.py index a99ecdd..9589920 100644 --- a/src/grag/rag/basic_rag.py +++ b/src/grag/rag/basic_rag.py @@ -4,7 +4,7 @@ from grag import prompts from grag.components.llm import LLM from grag.components.multivec_retriever import Retriever -from grag.components.prompt import FewShotPrompt, Prompt +from grag.components.prompt import Prompt, FewShotPrompt from grag.components.utils import get_config from importlib_resources import files from langchain_core.documents import Document diff --git a/src/tests/rag/basic_rag_test.py b/src/tests/rag/basic_rag_test.py index 06db25e..2249028 100644 --- a/src/tests/rag/basic_rag_test.py +++ b/src/tests/rag/basic_rag_test.py @@ -1,4 +1,4 @@ -from typing import List, Text +from typing import Text, List from grag.rag.basic_rag import BasicRAG From 5e72ad990c9926d850c00d35d30cb54021e30b86 Mon Sep 17 00:00:00 2001 From: Arjun Bingly Date: Thu, 21 Mar 2024 14:08:38 -0400 Subject: [PATCH 02/15] DeepLake client, vectordb --- pyproject.toml | 1 + src/grag/components/vectordb/__init__.py | 0 src/grag/components/vectordb/base.py | 64 +++++++ src/grag/components/vectordb/chroma_client.py | 170 ++++++++++++++++++ .../components/vectordb/deeplake_client.py | 132 ++++++++++++++ 5 files changed, 367 insertions(+) create mode 100644 src/grag/components/vectordb/__init__.py create mode 100644 src/grag/components/vectordb/base.py create mode 100644 src/grag/components/vectordb/chroma_client.py create mode 100644 src/grag/components/vectordb/deeplake_client.py diff --git a/pyproject.toml b/pyproject.toml index 897ab02..58c2fe0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,6 +42,7 @@ dependencies = [ "huggingface_hub>=0.20.2", "pydantic>=2.5.0", "rouge-score>=0.1.2", + "deeplake>=3.8.27" ] [project.urls] diff --git a/src/grag/components/vectordb/__init__.py b/src/grag/components/vectordb/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/grag/components/vectordb/base.py b/src/grag/components/vectordb/base.py new file mode 100644 index 0000000..67bc5be --- /dev/null +++ b/src/grag/components/vectordb/base.py @@ -0,0 +1,64 @@ +from abc import ABC, abstractmethod +from typing import List + +from langchain_community.vectorstores.utils import filter_complex_metadata +from langchain_core.documents import Document + + +class VectorDB(ABC): + @abstractmethod + def add_docs(self, docs: List[Document], verbose: bool = True): + """Adds documents to the vector database. + + Args: + docs: List of Documents + verbose: Show progress bar + + Returns: + None + """ + ... + + @abstractmethod + async def aadd_docs(self, docs: List[Document], verbose: bool = True): + """Adds documents to the vector database (asynchronous). + + Args: + docs: List of Documents + verbose: Show progress bar + + Returns: + None + """ + ... + + @abstractmethod + def get_chunk(self, query: str, with_score: bool = False, top_k: int = None): + """Returns the most similar chunks from the vector database. + + Args: + query: A query string + with_score: Outputs scores of returned chunks + top_k: Number of top similar chunks to return, if None defaults to self.top_k + + Returns: + list of Documents + """ + ... + + @abstractmethod + async def aget_chunk(self, query: str, with_score: bool = False, top_k: int = None): + """Returns the most similar chunks from the vector database. (asynchronous) + + Args: + query: A query string + with_score: Outputs scores of returned chunks + top_k: Number of top similar chunks to return, if None defaults to self.top_k + + Returns: + list of Documents + """ + ... + + def _filter_metadata(self, docs: List[Document]): + return filter_complex_metadata(docs, allowed_types=self.allowed_metadata_types) diff --git a/src/grag/components/vectordb/chroma_client.py b/src/grag/components/vectordb/chroma_client.py new file mode 100644 index 0000000..53f5547 --- /dev/null +++ b/src/grag/components/vectordb/chroma_client.py @@ -0,0 +1,170 @@ +from typing import List + +import chromadb +from grag.components.embedding import Embedding +from grag.components.utils import get_config +from grag.components.vectordb.base import VectorDB +from langchain_community.vectorstores import Chroma +from langchain_core.documents import Document +from tqdm import tqdm +from tqdm.asyncio import tqdm as atqdm + +chroma_conf = get_config()["chroma"] + + +class ChromaClient(VectorDB): + """A class for connecting to a hosted Chroma Vectorstore collection. + + Attributes: + host : str + IP Address of hosted Chroma Vectorstore + port : str + port address of hosted Chroma Vectorstore + collection_name : str + name of the collection in the Chroma Vectorstore, each ChromaClient connects to a single collection + embedding_type : str + type of embedding used, supported 'sentence-transformers' and 'instructor-embedding' + embedding_model : str + model name of embedding used, should correspond to the embedding_type + embedding_function + a function of the embedding model, derived from the embedding_type and embedding_modelname + client: chromadb.HttpClient + Chroma API for client + collection + Chroma API for the collection + langchain_client: langchain_community.vectorstores.Chroma + LangChain wrapper for Chroma collection + """ + + def __init__( + self, + host=chroma_conf["host"], + port=chroma_conf["port"], + collection_name=chroma_conf["collection_name"], + embedding_type=chroma_conf["embedding_type"], + embedding_model=chroma_conf["embedding_model"], + ): + """Args: + host: IP Address of hosted Chroma Vectorstore, defaults to argument from config file + port: port address of hosted Chroma Vectorstore, defaults to argument from config file + collection_name: name of the collection in the Chroma Vectorstore, defaults to argument from config file + embedding_type: type of embedding used, supported 'sentence-transformers' and 'instructor-embedding', defaults to argument from config file + embedding_model: model name of embedding used, should correspond to the embedding_type, defaults to argument from config file + """ + self.host: str = host + self.port: str = port + self.collection_name: str = collection_name + self.embedding_type: str = embedding_type + self.embedding_model: str = embedding_model + + self.embedding_function = Embedding( + embedding_model=self.embedding_model, embedding_type=self.embedding_type + ).embedding_function + + self.client = chromadb.HttpClient(host=self.host, port=self.port) + self.collection = self.client.get_or_create_collection( + name=self.collection_name + ) + self.langchain_client = Chroma( + client=self.client, + collection_name=self.collection_name, + embedding_function=self.embedding_function, + ) + self.allowed_metadata_types = (str, int, float, bool) + + def test_connection(self, verbose=True): + """Tests connection with Chroma Vectorstore + + Args: + verbose: if True, prints connection status + + Returns: + A random integer if connection is alive else None + """ + response = self.client.heartbeat() + if verbose: + if response: + print(f"Connection to {self.host}/{self.port} is alive..") + else: + print(f"Connection to {self.host}/{self.port} is not alive !!") + return response + + def add_docs(self, docs: List[Document], verbose=True): + """Adds documents to chroma vectorstore + + Args: + docs: List of Documents + verbose: Show progress bar + + Returns: + None + """ + docs = self._filter_metadata(docs) + for doc in ( + tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs + ): + _id = self.langchain_client.add_documents([doc]) + + async def aadd_docs(self, docs: List[Document], verbose=True): + """Asynchronously adds documents to chroma vectorstore + + Args: + docs: List of Documents + verbose: Show progress bar + + Returns: + None + """ + docs = self._filter_metadata(docs) + if verbose: + for doc in atqdm( + docs, + desc=f"Adding documents to {self.collection_name}", + total=len(docs), + ): + await self.langchain_client.aadd_documents([doc]) + else: + for doc in docs: + await self.langchain_client.aadd_documents([doc]) + + def get_chunk(self, query: str, with_score: bool = False, top_k: int = None): + """Returns the most similar chunks from the chroma database. + + Args: + query: A query string + with_score: Outputs scores of returned chunks + top_k: Number of top similar chunks to return, if None defaults to self.top_k + + Returns: + list of Documents + + """ + if with_score: + return self.langchain_client.similarity_search_with_relevance_scores( + query=query, **{"k": top_k} if top_k else 1 + ) + else: + return self.langchain_client.similarity_search( + query=query, **{"k": top_k} if top_k else 1 + ) + + async def aget_chunk(self, query: str, with_score=False, top_k=None): + """Returns the most (cosine) similar chunks from the vector database, asynchronously. + + Args: + query: A query string + with_score: Outputs scores of returned chunks + top_k: Number of top similar chunks to return, if None defaults to self.top_k + + Returns: + list of Documents + + """ + if with_score: + return await self.langchain_client.asimilarity_search_with_relevance_scores( + query=query, **{"k": top_k} if top_k else 1 + ) + else: + return await self.langchain_client.asimilarity_search( + query=query, **{"k": top_k} if top_k else 1 + ) diff --git a/src/grag/components/vectordb/deeplake_client.py b/src/grag/components/vectordb/deeplake_client.py new file mode 100644 index 0000000..75d6058 --- /dev/null +++ b/src/grag/components/vectordb/deeplake_client.py @@ -0,0 +1,132 @@ +from pathlib import Path +from typing import List, Union + +from deeplake.core.vectorstore import VectorStore +from grag.components.embedding import Embedding +from grag.components.utils import get_config +from grag.components.vectordb.base import VectorDB +from langchain_community.vectorstores import DeepLake +from langchain_core.documents import Document +from tqdm import tqdm +from tqdm.asyncio import tqdm as atqdm + +deeplake_conf = get_config()["deeplake"] + + +class DeepLakeClient(VectorDB): + """A class for connecting to a DeepLake Vectorstore + + Attributes: + store_path : str, Path + The path to store the DeepLake vectorstore. + embedding_type : str + type of embedding used, supported 'sentence-transformers' and 'instructor-embedding' + embedding_model : str + model name of embedding used, should correspond to the embedding_type + embedding_function + a function of the embedding model, derived from the embedding_type and embedding_modelname + client: deeplake.core.vectorstore.VectorStore + DeepLake API + collection + Chroma API for the collection + langchain_client: langchain_community.vectorstores.DeepLake + LangChain wrapper for DeepLake API + """ + + def __init__(self, + store_path: Union[str, Path], + embedding_model: str, + embedding_type: str, + ): + self.store_path = Path(store_path) + self.embedding_type: str = embedding_type + self.embedding_model: str = embedding_model + + self.embedding_function = Embedding( + embedding_model=self.embedding_model, embedding_type=self.embedding_type + ).embedding_function + + self.client = VectorStore(path=self.store_path) + self.langchain_client = DeepLake(path=self.store_path, + embedding=self.embedding_function) + self.allowed_metadata_types = (str, int, float, bool) + + def add_docs(self, docs: List[Document], verbose=True): + """Adds documents to deeplake vectorstore + + Args: + docs: List of Documents + verbose: Show progress bar + + Returns: + None + """ + docs = self._filter_metadata(docs) + for doc in ( + tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs + ): + _id = self.langchain_chroma.add_documents([doc]) + + async def aadd_docs(self, docs: List[Document], verbose=True): + """Asynchronously adds documents to chroma vectorstore + + Args: + docs: List of Documents + verbose: Show progress bar + + Returns: + None + """ + docs = self._filter_metadata(docs) + if verbose: + for doc in atqdm( + docs, + desc=f"Adding documents to {self.collection_name}", + total=len(docs), + ): + await self.langchain_deeplake.aadd_documents([doc]) + else: + for doc in docs: + await self.langchain_deeplake.aadd_documents([doc]) + + def get_chunk(self, query: str, with_score: bool = False, top_k: int = None): + """Returns the most similar chunks from the deeplake database. + + Args: + query: A query string + with_score: Outputs scores of returned chunks + top_k: Number of top similar chunks to return, if None defaults to self.top_k + + Returns: + list of Documents + + """ + if with_score: + return self.langchain_client.similarity_search_with_relevance_scores( + query=query, **{"k": top_k} if top_k else 1 + ) + else: + return self.langchain_client.similarity_search( + query=query, **{"k": top_k} if top_k else 1 + ) + + async def aget_chunk(self, query: str, with_score=False, top_k=None): + """Returns the most similar chunks from the deeplake database, asynchronously. + + Args: + query: A query string + with_score: Outputs scores of returned chunks + top_k: Number of top similar chunks to return, if None defaults to self.top_k + + Returns: + list of Documents + + """ + if with_score: + return await self.langchain_client.asimilarity_search_with_relevance_scores( + query=query, **{"k": top_k} if top_k else 1 + ) + else: + return await self.langchain_client.asimilarity_search( + query=query, **{"k": top_k} if top_k else 1 + ) From 820702f3a8dfc9c73d1eaa3251942894e16a42b0 Mon Sep 17 00:00:00 2001 From: Arjun Bingly Date: Thu, 21 Mar 2024 16:35:10 -0400 Subject: [PATCH 03/15] Remove old chroma_client --- src/grag/components/chroma_client.py | 136 --------------------------- 1 file changed, 136 deletions(-) delete mode 100644 src/grag/components/chroma_client.py diff --git a/src/grag/components/chroma_client.py b/src/grag/components/chroma_client.py deleted file mode 100644 index 7efd7c3..0000000 --- a/src/grag/components/chroma_client.py +++ /dev/null @@ -1,136 +0,0 @@ -from typing import List - -import chromadb -from grag.components.embedding import Embedding -from grag.components.utils import get_config -from langchain_community.vectorstores import Chroma -from langchain_community.vectorstores.utils import filter_complex_metadata -from langchain_core.documents import Document -from tqdm import tqdm -from tqdm.asyncio import tqdm as atqdm - -chroma_conf = get_config()["chroma"] - - -class ChromaClient: - """A class for connecting to a hosted Chroma Vectorstore collection. - - Attributes: - host : str - IP Address of hosted Chroma Vectorstore - port : str - port address of hosted Chroma Vectorstore - collection_name : str - name of the collection in the Chroma Vectorstore, each ChromaClient connects to a single collection - embedding_type : str - type of embedding used, supported 'sentence-transformers' and 'instructor-embedding' - embedding_modelname : str - model name of embedding used, should correspond to the embedding_type - embedding_function - a function of the embedding model, derived from the embedding_type and embedding_modelname - chroma_client - Chroma API for client - collection - Chroma API for the collection - langchain_chroma - LangChain wrapper for Chroma collection - """ - - def __init__( - self, - host=chroma_conf["host"], - port=chroma_conf["port"], - collection_name=chroma_conf["collection_name"], - embedding_type=chroma_conf["embedding_type"], - embedding_model=chroma_conf["embedding_model"], - ): - """Args: - host: IP Address of hosted Chroma Vectorstore, defaults to argument from config file - port: port address of hosted Chroma Vectorstore, defaults to argument from config file - collection_name: name of the collection in the Chroma Vectorstore, defaults to argument from config file - embedding_type: type of embedding used, supported 'sentence-transformers' and 'instructor-embedding', defaults to argument from config file - embedding_model: model name of embedding used, should correspond to the embedding_type, defaults to argument from config file - """ - self.host: str = host - self.port: str = port - self.collection_name: str = collection_name - self.embedding_type: str = embedding_type - self.embedding_model: str = embedding_model - - self.embedding_function = Embedding( - embedding_model=self.embedding_model, embedding_type=self.embedding_type - ).embedding_function - - self.chroma_client = chromadb.HttpClient(host=self.host, port=self.port) - self.collection = self.chroma_client.get_or_create_collection( - name=self.collection_name - ) - self.langchain_chroma = Chroma( - client=self.chroma_client, - collection_name=self.collection_name, - embedding_function=self.embedding_function, - ) - self.allowed_metadata_types = (str, int, float, bool) - - def test_connection(self, verbose=True): - """Tests connection with Chroma Vectorstore - - Args: - verbose: if True, prints connection status - - Returns: - A random integer if connection is alive else None - """ - response = self.chroma_client.heartbeat() - if verbose: - if response: - print(f"Connection to {self.host}/{self.port} is alive..") - else: - print(f"Connection to {self.host}/{self.port} is not alive !!") - return response - - async def aadd_docs(self, docs: List[Document], verbose=True): - """Asynchronously adds documents to chroma vectorstore - - Args: - docs: List of Documents - verbose: Show progress bar - - Returns: - None - """ - docs = self._filter_metadata(docs) - # tasks = [self.langchain_chroma.aadd_documents([doc]) for doc in docs] - # if verbose: - # await tqdm_asyncio.gather(*tasks, desc=f'Adding to {self.collection_name}') - # else: - # await asyncio.gather(*tasks) - if verbose: - for doc in atqdm( - docs, - desc=f"Adding documents to {self.collection_name}", - total=len(docs), - ): - await self.langchain_chroma.aadd_documents([doc]) - else: - for doc in docs: - await self.langchain_chroma.aadd_documents([doc]) - - def add_docs(self, docs: List[Document], verbose=True): - """Adds documents to chroma vectorstore - - Args: - docs: List of Documents - verbose: Show progress bar - - Returns: - None - """ - docs = self._filter_metadata(docs) - for doc in ( - tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs - ): - _id = self.langchain_chroma.add_documents([doc]) - - def _filter_metadata(self, docs: List[Document]): - return filter_complex_metadata(docs, allowed_types=self.allowed_metadata_types) From 7729d32647b29cce059d462d891995d00e427038 Mon Sep 17 00:00:00 2001 From: Arjun Bingly Date: Thu, 21 Mar 2024 16:37:14 -0400 Subject: [PATCH 04/15] Bug fix: top_k --- src/grag/components/vectordb/chroma_client.py | 8 ++++---- src/grag/components/vectordb/deeplake_client.py | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/src/grag/components/vectordb/chroma_client.py b/src/grag/components/vectordb/chroma_client.py index 53f5547..3e73b04 100644 --- a/src/grag/components/vectordb/chroma_client.py +++ b/src/grag/components/vectordb/chroma_client.py @@ -141,11 +141,11 @@ def get_chunk(self, query: str, with_score: bool = False, top_k: int = None): """ if with_score: return self.langchain_client.similarity_search_with_relevance_scores( - query=query, **{"k": top_k} if top_k else 1 + query=query, k=top_k if top_k else 1 ) else: return self.langchain_client.similarity_search( - query=query, **{"k": top_k} if top_k else 1 + query=query, k=top_k if top_k else 1 ) async def aget_chunk(self, query: str, with_score=False, top_k=None): @@ -162,9 +162,9 @@ async def aget_chunk(self, query: str, with_score=False, top_k=None): """ if with_score: return await self.langchain_client.asimilarity_search_with_relevance_scores( - query=query, **{"k": top_k} if top_k else 1 + query=query, k=top_k if top_k else 1 ) else: return await self.langchain_client.asimilarity_search( - query=query, **{"k": top_k} if top_k else 1 + query=query, k=top_k if top_k else 1 ) diff --git a/src/grag/components/vectordb/deeplake_client.py b/src/grag/components/vectordb/deeplake_client.py index 75d6058..bb88255 100644 --- a/src/grag/components/vectordb/deeplake_client.py +++ b/src/grag/components/vectordb/deeplake_client.py @@ -103,11 +103,11 @@ def get_chunk(self, query: str, with_score: bool = False, top_k: int = None): """ if with_score: return self.langchain_client.similarity_search_with_relevance_scores( - query=query, **{"k": top_k} if top_k else 1 + query=query, k=top_k if top_k else 1 ) else: return self.langchain_client.similarity_search( - query=query, **{"k": top_k} if top_k else 1 + query=query, k=top_k if top_k else 1 ) async def aget_chunk(self, query: str, with_score=False, top_k=None): @@ -124,9 +124,9 @@ async def aget_chunk(self, query: str, with_score=False, top_k=None): """ if with_score: return await self.langchain_client.asimilarity_search_with_relevance_scores( - query=query, **{"k": top_k} if top_k else 1 + query=query, k=top_k if top_k else 1 ) else: return await self.langchain_client.asimilarity_search( - query=query, **{"k": top_k} if top_k else 1 + query=query, k=top_k if top_k else 1 ) From 2f05d98c37d6358c9140a33c09d78877b845557e Mon Sep 17 00:00:00 2001 From: Arjun Bingly Date: Thu, 21 Mar 2024 16:37:47 -0400 Subject: [PATCH 05/15] Update chroma_client_test --- src/tests/components/vectordb/__init__.py | 0 .../{ => vectordb}/chroma_client_test.py | 79 +++++++++++++++---- 2 files changed, 64 insertions(+), 15 deletions(-) create mode 100644 src/tests/components/vectordb/__init__.py rename src/tests/components/{ => vectordb}/chroma_client_test.py (58%) diff --git a/src/tests/components/vectordb/__init__.py b/src/tests/components/vectordb/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/tests/components/chroma_client_test.py b/src/tests/components/vectordb/chroma_client_test.py similarity index 58% rename from src/tests/components/chroma_client_test.py rename to src/tests/components/vectordb/chroma_client_test.py index 1596dd3..6f0e925 100644 --- a/src/tests/components/chroma_client_test.py +++ b/src/tests/components/vectordb/chroma_client_test.py @@ -1,12 +1,13 @@ import asyncio -from grag.components.chroma_client import ChromaClient +import pytest +from grag.components.vectordb.chroma_client import ChromaClient from langchain_core.documents import Document def test_chroma_connection(): - client = ChromaClient() - response = client.test_connection() + chroma_client = ChromaClient() + response = chroma_client.test_connection() assert isinstance(response, int) @@ -45,13 +46,13 @@ def test_chroma_add_docs(): storm-clouds was split to the blinding zigzag of lightning, and the thunder rolled and boomed, like the Colorado in flood.""", ] - client = ChromaClient(collection_name="test") - if client.collection.count() > 0: - client.chroma_client.delete_collection("test") - client = ChromaClient(collection_name="test") + chroma_client = ChromaClient(collection_name="test") + if chroma_client.collection.count() > 0: + chroma_client.client.delete_collection("test") + chroma_client = ChromaClient(collection_name="test") docs = [Document(page_content=doc) for doc in docs] - client.add_docs(docs) - collection_count = client.collection.count() + chroma_client.add_docs(docs) + collection_count = chroma_client.collection.count() assert collection_count == len(docs) @@ -90,11 +91,59 @@ def test_chroma_aadd_docs(): storm-clouds was split to the blinding zigzag of lightning, and the thunder rolled and boomed, like the Colorado in flood.""", ] - client = ChromaClient(collection_name="test") - if client.collection.count() > 0: - client.chroma_client.delete_collection("test") - client = ChromaClient(collection_name="test") + chroma_client = ChromaClient(collection_name="test") + if chroma_client.collection.count() > 0: + chroma_client.client.delete_collection("test") + chroma_client = ChromaClient(collection_name="test") docs = [Document(page_content=doc) for doc in docs] loop = asyncio.get_event_loop() - loop.run_until_complete(client.aadd_docs(docs)) - assert client.collection.count() == len(docs) + loop.run_until_complete(chroma_client.aadd_docs(docs)) + assert chroma_client.collection.count() == len(docs) + + +chrome_get_chunk_params = [(1, False), (1, True), (2, False), (2, True)] + + +@pytest.mark.parametrize("top_k,with_score", chrome_get_chunk_params) +def test_chroma_get_chunk(top_k, with_score): + query = """Slone and Lucy never rode down so far as the stately monuments, though + these held memories as hauntingly sweet as others were poignantly + bitter. Lucy never rode the King again. But Slone rode him, learned to + love him. And Lucy did not race any more. When Slone tried to stir in + her the old spirit all the response he got was a wistful shake of head + or a laugh that hid the truth or an excuse that the strain on her + ankles from Joel Creech's lasso had never mended. The girl was + unutterably happy, but it was possible that she would never race a + horse again.""" + chroma_client = ChromaClient(collection_name="test") + retrieved_chunks = chroma_client.get_chunk(query=query, top_k=top_k, with_score=with_score) + assert len(retrieved_chunks) == top_k + if with_score: + assert all(isinstance(doc[0], Document) for doc in retrieved_chunks) + assert all(isinstance(doc[1], float) for doc in retrieved_chunks) + else: + assert all(isinstance(doc, Document) for doc in retrieved_chunks) + + +@pytest.mark.parametrize("top_k,with_score", chrome_get_chunk_params) +def test_chroma_aget_chunk(top_k, with_score): + query = """Slone and Lucy never rode down so far as the stately monuments, though + these held memories as hauntingly sweet as others were poignantly + bitter. Lucy never rode the King again. But Slone rode him, learned to + love him. And Lucy did not race any more. When Slone tried to stir in + her the old spirit all the response he got was a wistful shake of head + or a laugh that hid the truth or an excuse that the strain on her + ankles from Joel Creech's lasso had never mended. The girl was + unutterably happy, but it was possible that she would never race a + horse again.""" + chroma_client = ChromaClient(collection_name="test") + loop = asyncio.get_event_loop() + retrieved_chunks = loop.run_until_complete( + chroma_client.aget_chunk(query=query, top_k=top_k, with_score=with_score) + ) + assert len(retrieved_chunks) == top_k + if with_score: + assert all(isinstance(doc[0], Document) for doc in retrieved_chunks) + assert all(isinstance(doc[1], float) for doc in retrieved_chunks) + else: + assert all(isinstance(doc, Document) for doc in retrieved_chunks) From 41b2bcf4c89658ab7bd184864805457bc0752232 Mon Sep 17 00:00:00 2001 From: Arjun Bingly Date: Fri, 22 Mar 2024 16:02:30 -0400 Subject: [PATCH 06/15] Deeplake tests, typing --- src/grag/components/vectordb/base.py | 24 ++- src/grag/components/vectordb/chroma_client.py | 28 +++- .../components/vectordb/deeplake_client.py | 47 +++--- .../components/vectordb/chroma_client_test.py | 15 +- .../vectordb/deeplake_client_test.py | 144 ++++++++++++++++++ 5 files changed, 220 insertions(+), 38 deletions(-) create mode 100644 src/tests/components/vectordb/deeplake_client_test.py diff --git a/src/grag/components/vectordb/base.py b/src/grag/components/vectordb/base.py index 67bc5be..ab63fcb 100644 --- a/src/grag/components/vectordb/base.py +++ b/src/grag/components/vectordb/base.py @@ -1,13 +1,23 @@ from abc import ABC, abstractmethod -from typing import List +from typing import List, Tuple, Union from langchain_community.vectorstores.utils import filter_complex_metadata from langchain_core.documents import Document class VectorDB(ABC): + + @abstractmethod + def __len__(self) -> int: + """Number of chunks in the vector database.""" + ... + + @abstractmethod + def delete(self) -> None: + """Delete all chunks in the vector database.""" + @abstractmethod - def add_docs(self, docs: List[Document], verbose: bool = True): + def add_docs(self, docs: List[Document], verbose: bool = True) -> None: """Adds documents to the vector database. Args: @@ -20,7 +30,7 @@ def add_docs(self, docs: List[Document], verbose: bool = True): ... @abstractmethod - async def aadd_docs(self, docs: List[Document], verbose: bool = True): + async def aadd_docs(self, docs: List[Document], verbose: bool = True) -> None: """Adds documents to the vector database (asynchronous). Args: @@ -33,7 +43,8 @@ async def aadd_docs(self, docs: List[Document], verbose: bool = True): ... @abstractmethod - def get_chunk(self, query: str, with_score: bool = False, top_k: int = None): + def get_chunk(self, query: str, with_score: bool = False, top_k: int = None) -> Union[ + List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the vector database. Args: @@ -47,7 +58,8 @@ def get_chunk(self, query: str, with_score: bool = False, top_k: int = None): ... @abstractmethod - async def aget_chunk(self, query: str, with_score: bool = False, top_k: int = None): + async def aget_chunk(self, query: str, with_score: bool = False, top_k: int = None) -> Union[ + List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the vector database. (asynchronous) Args: @@ -60,5 +72,5 @@ async def aget_chunk(self, query: str, with_score: bool = False, top_k: int = No """ ... - def _filter_metadata(self, docs: List[Document]): + def _filter_metadata(self, docs: List[Document]) -> List[Document]: return filter_complex_metadata(docs, allowed_types=self.allowed_metadata_types) diff --git a/src/grag/components/vectordb/chroma_client.py b/src/grag/components/vectordb/chroma_client.py index 3e73b04..e97323d 100644 --- a/src/grag/components/vectordb/chroma_client.py +++ b/src/grag/components/vectordb/chroma_client.py @@ -1,4 +1,4 @@ -from typing import List +from typing import List, Tuple, Union import chromadb from grag.components.embedding import Embedding @@ -72,7 +72,21 @@ def __init__( ) self.allowed_metadata_types = (str, int, float, bool) - def test_connection(self, verbose=True): + def __len__(self) -> int: + return self.collection.count() + + def delete(self) -> None: + self.client.delete_collection(self.collection_name) + self.collection = self.client.get_or_create_collection( + name=self.collection_name + ) + self.langchain_client = Chroma( + client=self.client, + collection_name=self.collection_name, + embedding_function=self.embedding_function, + ) + + def test_connection(self, verbose=True) -> int: """Tests connection with Chroma Vectorstore Args: @@ -89,7 +103,7 @@ def test_connection(self, verbose=True): print(f"Connection to {self.host}/{self.port} is not alive !!") return response - def add_docs(self, docs: List[Document], verbose=True): + def add_docs(self, docs: List[Document], verbose=True) -> None: """Adds documents to chroma vectorstore Args: @@ -105,7 +119,7 @@ def add_docs(self, docs: List[Document], verbose=True): ): _id = self.langchain_client.add_documents([doc]) - async def aadd_docs(self, docs: List[Document], verbose=True): + async def aadd_docs(self, docs: List[Document], verbose=True) -> None: """Asynchronously adds documents to chroma vectorstore Args: @@ -127,7 +141,8 @@ async def aadd_docs(self, docs: List[Document], verbose=True): for doc in docs: await self.langchain_client.aadd_documents([doc]) - def get_chunk(self, query: str, with_score: bool = False, top_k: int = None): + def get_chunk(self, query: str, with_score: bool = False, top_k: int = None) -> Union[ + List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the chroma database. Args: @@ -148,7 +163,8 @@ def get_chunk(self, query: str, with_score: bool = False, top_k: int = None): query=query, k=top_k if top_k else 1 ) - async def aget_chunk(self, query: str, with_score=False, top_k=None): + async def aget_chunk(self, query: str, with_score=False, top_k=None) -> Union[ + List[Document], List[Tuple[Document, float]]]: """Returns the most (cosine) similar chunks from the vector database, asynchronously. Args: diff --git a/src/grag/components/vectordb/deeplake_client.py b/src/grag/components/vectordb/deeplake_client.py index bb88255..28fc606 100644 --- a/src/grag/components/vectordb/deeplake_client.py +++ b/src/grag/components/vectordb/deeplake_client.py @@ -1,7 +1,6 @@ from pathlib import Path -from typing import List, Union +from typing import List, Tuple, Union -from deeplake.core.vectorstore import VectorStore from grag.components.embedding import Embedding from grag.components.utils import get_config from grag.components.vectordb.base import VectorDB @@ -34,11 +33,15 @@ class DeepLakeClient(VectorDB): """ def __init__(self, - store_path: Union[str, Path], - embedding_model: str, - embedding_type: str, + collection_name: str = deeplake_conf["collection_name"], + store_path: Union[str, Path] = deeplake_conf["store_path"], + embedding_type: str = deeplake_conf["embedding_type"], + embedding_model: str = deeplake_conf["embedding_model"], + read_only: bool = False ): self.store_path = Path(store_path) + self.collection_name = collection_name + self.read_only = read_only self.embedding_type: str = embedding_type self.embedding_model: str = embedding_model @@ -46,12 +49,20 @@ def __init__(self, embedding_model=self.embedding_model, embedding_type=self.embedding_type ).embedding_function - self.client = VectorStore(path=self.store_path) - self.langchain_client = DeepLake(path=self.store_path, - embedding=self.embedding_function) + # self.client = VectorStore(path=self.store_path / self.collection_name) + self.langchain_client = DeepLake(dataset_path=str(self.store_path / self.collection_name), + embedding=self.embedding_function, + read_only=self.read_only) + self.client = self.langchain_client.vectorstore self.allowed_metadata_types = (str, int, float, bool) - def add_docs(self, docs: List[Document], verbose=True): + def __len__(self) -> int: + return self.client.__len__() + + def delete(self) -> None: + self.client.delete(delete_all=True) + + def add_docs(self, docs: List[Document], verbose=True) -> None: """Adds documents to deeplake vectorstore Args: @@ -65,9 +76,9 @@ def add_docs(self, docs: List[Document], verbose=True): for doc in ( tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs ): - _id = self.langchain_chroma.add_documents([doc]) + _id = self.langchain_client.add_documents([doc]) - async def aadd_docs(self, docs: List[Document], verbose=True): + async def aadd_docs(self, docs: List[Document], verbose=True) -> None: """Asynchronously adds documents to chroma vectorstore Args: @@ -84,12 +95,13 @@ async def aadd_docs(self, docs: List[Document], verbose=True): desc=f"Adding documents to {self.collection_name}", total=len(docs), ): - await self.langchain_deeplake.aadd_documents([doc]) + await self.langchain_client.aadd_documents([doc]) else: for doc in docs: - await self.langchain_deeplake.aadd_documents([doc]) + await self.langchain_client.aadd_documents([doc]) - def get_chunk(self, query: str, with_score: bool = False, top_k: int = None): + def get_chunk(self, query: str, with_score: bool = False, top_k: int = None) -> Union[ + List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the deeplake database. Args: @@ -102,7 +114,7 @@ def get_chunk(self, query: str, with_score: bool = False, top_k: int = None): """ if with_score: - return self.langchain_client.similarity_search_with_relevance_scores( + return self.langchain_client.similarity_search_with_score( query=query, k=top_k if top_k else 1 ) else: @@ -110,7 +122,8 @@ def get_chunk(self, query: str, with_score: bool = False, top_k: int = None): query=query, k=top_k if top_k else 1 ) - async def aget_chunk(self, query: str, with_score=False, top_k=None): + async def aget_chunk(self, query: str, with_score=False, top_k=None) -> Union[ + List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the deeplake database, asynchronously. Args: @@ -123,7 +136,7 @@ async def aget_chunk(self, query: str, with_score=False, top_k=None): """ if with_score: - return await self.langchain_client.asimilarity_search_with_relevance_scores( + return await self.langchain_client.asimilarity_search_with_score( query=query, k=top_k if top_k else 1 ) else: diff --git a/src/tests/components/vectordb/chroma_client_test.py b/src/tests/components/vectordb/chroma_client_test.py index 6f0e925..ecffa22 100644 --- a/src/tests/components/vectordb/chroma_client_test.py +++ b/src/tests/components/vectordb/chroma_client_test.py @@ -47,13 +47,11 @@ def test_chroma_add_docs(): thunder rolled and boomed, like the Colorado in flood.""", ] chroma_client = ChromaClient(collection_name="test") - if chroma_client.collection.count() > 0: - chroma_client.client.delete_collection("test") - chroma_client = ChromaClient(collection_name="test") + if len(chroma_client) > 0: + chroma_client.delete() docs = [Document(page_content=doc) for doc in docs] chroma_client.add_docs(docs) - collection_count = chroma_client.collection.count() - assert collection_count == len(docs) + assert len(chroma_client) == len(docs) def test_chroma_aadd_docs(): @@ -92,13 +90,12 @@ def test_chroma_aadd_docs(): thunder rolled and boomed, like the Colorado in flood.""", ] chroma_client = ChromaClient(collection_name="test") - if chroma_client.collection.count() > 0: - chroma_client.client.delete_collection("test") - chroma_client = ChromaClient(collection_name="test") + if len(chroma_client) > 0: + chroma_client.delete() docs = [Document(page_content=doc) for doc in docs] loop = asyncio.get_event_loop() loop.run_until_complete(chroma_client.aadd_docs(docs)) - assert chroma_client.collection.count() == len(docs) + assert len(chroma_client) == len(docs) chrome_get_chunk_params = [(1, False), (1, True), (2, False), (2, True)] diff --git a/src/tests/components/vectordb/deeplake_client_test.py b/src/tests/components/vectordb/deeplake_client_test.py new file mode 100644 index 0000000..921bd18 --- /dev/null +++ b/src/tests/components/vectordb/deeplake_client_test.py @@ -0,0 +1,144 @@ +import asyncio + +import pytest +from grag.components.vectordb.deeplake_client import DeepLakeClient +from langchain_core.documents import Document + + +def test_deeplake_add_docs(): + docs = [ + """And so on this rainbow day, with storms all around them, and blue sky + above, they rode only as far as the valley. But from there, before they + turned to go back, the monuments appeared close, and they loomed + grandly with the background of purple bank and creamy cloud and shafts + of golden lightning. They seemed like sentinels--guardians of a great + and beautiful love born under their lofty heights, in the lonely + silence of day, in the star-thrown shadow of night. They were like that + love. And they held Lucy and Slone, calling every day, giving a + nameless and tranquil content, binding them true to love, true to the + sage and the open, true to that wild upland home.""", + """Slone and Lucy never rode down so far as the stately monuments, though + these held memories as hauntingly sweet as others were poignantly + bitter. Lucy never rode the King again. But Slone rode him, learned to + love him. And Lucy did not race any more. When Slone tried to stir in + her the old spirit all the response he got was a wistful shake of head + or a laugh that hid the truth or an excuse that the strain on her + ankles from Joel Creech's lasso had never mended. The girl was + unutterably happy, but it was possible that she would never race a + horse again.""", + """Bostil wanted to be alone, to welcome the King, to lead him back to the + home corral, perhaps to hide from all eyes the change and the uplift + that would forever keep him from wronging another man. + + The late rains came and like magic, in a few days, the sage grew green + and lustrous and fresh, the gray turning to purple. + + Every morning the sun rose white and hot in a blue and cloudless sky. + And then soon the horizon line showed creamy clouds that rose and + spread and darkened. Every afternoon storms hung along the ramparts and + rainbows curved down beautiful and ethereal. The dim blackness of the + storm-clouds was split to the blinding zigzag of lightning, and the + thunder rolled and boomed, like the Colorado in flood.""", + ] + deeplake_client = DeepLakeClient(collection_name="test") + if len(deeplake_client) > 0: + deeplake_client.delete() + docs = [Document(page_content=doc) for doc in docs] + deeplake_client.add_docs(docs) + assert len(deeplake_client) == len(docs) + del (deeplake_client) + + +def test_chroma_aadd_docs(): + docs = [ + """And so on this rainbow day, with storms all around them, and blue sky + above, they rode only as far as the valley. But from there, before they + turned to go back, the monuments appeared close, and they loomed + grandly with the background of purple bank and creamy cloud and shafts + of golden lightning. They seemed like sentinels--guardians of a great + and beautiful love born under their lofty heights, in the lonely + silence of day, in the star-thrown shadow of night. They were like that + love. And they held Lucy and Slone, calling every day, giving a + nameless and tranquil content, binding them true to love, true to the + sage and the open, true to that wild upland home.""", + """Slone and Lucy never rode down so far as the stately monuments, though + these held memories as hauntingly sweet as others were poignantly + bitter. Lucy never rode the King again. But Slone rode him, learned to + love him. And Lucy did not race any more. When Slone tried to stir in + her the old spirit all the response he got was a wistful shake of head + or a laugh that hid the truth or an excuse that the strain on her + ankles from Joel Creech's lasso had never mended. The girl was + unutterably happy, but it was possible that she would never race a + horse again.""", + """Bostil wanted to be alone, to welcome the King, to lead him back to the + home corral, perhaps to hide from all eyes the change and the uplift + that would forever keep him from wronging another man. + + The late rains came and like magic, in a few days, the sage grew green + and lustrous and fresh, the gray turning to purple. + + Every morning the sun rose white and hot in a blue and cloudless sky. + And then soon the horizon line showed creamy clouds that rose and + spread and darkened. Every afternoon storms hung along the ramparts and + rainbows curved down beautiful and ethereal. The dim blackness of the + storm-clouds was split to the blinding zigzag of lightning, and the + thunder rolled and boomed, like the Colorado in flood.""", + ] + deeplake_client = DeepLakeClient(collection_name="test") + if len(deeplake_client) > 0: + deeplake_client.delete() + docs = [Document(page_content=doc) for doc in docs] + loop = asyncio.get_event_loop() + loop.run_until_complete(deeplake_client.aadd_docs(docs)) + assert len(deeplake_client) == len(docs) + del (deeplake_client) + + +deeplake_get_chunk_params = [(1, False), (1, True), (2, False), (2, True)] + + +@pytest.mark.parametrize("top_k,with_score", deeplake_get_chunk_params) +def test_deeplake_get_chunk(top_k, with_score): + query = """Slone and Lucy never rode down so far as the stately monuments, though + these held memories as hauntingly sweet as others were poignantly + bitter. Lucy never rode the King again. But Slone rode him, learned to + love him. And Lucy did not race any more. When Slone tried to stir in + her the old spirit all the response he got was a wistful shake of head + or a laugh that hid the truth or an excuse that the strain on her + ankles from Joel Creech's lasso had never mended. The girl was + unutterably happy, but it was possible that she would never race a + horse again.""" + deeplake_client = DeepLakeClient(collection_name="test", read_only=True) + retrieved_chunks = deeplake_client.get_chunk(query=query, top_k=top_k, with_score=with_score) + assert len(retrieved_chunks) == top_k + if with_score: + assert all(isinstance(doc[0], Document) for doc in retrieved_chunks) + assert all(isinstance(doc[1], float) for doc in retrieved_chunks) + else: + assert all(isinstance(doc, Document) for doc in retrieved_chunks) + del (deeplake_client) + + +@pytest.mark.parametrize("top_k,with_score", deeplake_get_chunk_params) +def test_deeplake_aget_chunk(top_k, with_score): + query = """Slone and Lucy never rode down so far as the stately monuments, though + these held memories as hauntingly sweet as others were poignantly + bitter. Lucy never rode the King again. But Slone rode him, learned to + love him. And Lucy did not race any more. When Slone tried to stir in + her the old spirit all the response he got was a wistful shake of head + or a laugh that hid the truth or an excuse that the strain on her + ankles from Joel Creech's lasso had never mended. The girl was + unutterably happy, but it was possible that she would never race a + horse again.""" + deeplake_client = DeepLakeClient(collection_name="test", read_only=True) + loop = asyncio.get_event_loop() + retrieved_chunks = loop.run_until_complete( + deeplake_client.aget_chunk(query=query, top_k=top_k, with_score=with_score) + ) + assert len(retrieved_chunks) == top_k + if with_score: + assert all(isinstance(doc[0], Document) for doc in retrieved_chunks) + assert all(isinstance(doc[1], float) for doc in retrieved_chunks) + else: + assert all(isinstance(doc, Document) for doc in retrieved_chunks) + del (deeplake_client) From 698efbd1e0c77a2c7e78be4f6080d96b8a0cd912 Mon Sep 17 00:00:00 2001 From: Arjun Bingly Date: Fri, 22 Mar 2024 18:03:14 -0400 Subject: [PATCH 07/15] Update to remove ruff errors --- pyproject.toml | 4 ++ src/config.ini | 10 ++++- src/grag/components/embedding.py | 8 ++++ src/grag/components/llm.py | 30 +++++++------- src/grag/components/multivec_retriever.py | 38 ++++++++++------- src/grag/components/parse_pdf.py | 36 +++++++++------- src/grag/components/prompt.py | 50 ++++++++++++++++++++++- src/grag/components/text_splitter.py | 22 ++++++++-- src/grag/components/utils.py | 17 ++++++-- 9 files changed, 163 insertions(+), 52 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 897ab02..f7c2d4f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -101,9 +101,13 @@ exclude_lines = [ [tool.ruff] line-length = 88 indent-width = 4 +extend-exclude = ["tests", "others"] [tool.ruff.lint] select = ["E4", "E7", "E9", "F", "I", "D"] +ignore = ["D104"] +exclude = ["__about__.py"] + [tool.ruff.format] quote-style = "double" diff --git a/src/config.ini b/src/config.ini index 452ac04..54990bf 100644 --- a/src/config.ini +++ b/src/config.ini @@ -25,6 +25,14 @@ embedding_model : hkunlp/instructor-xl store_path : ${data:data_path}/vectordb allow_reset : True +[deeplake] +collection_name : arxiv +# embedding_type : sentence-transformers +# embedding_model : "all-mpnet-base-v2" +embedding_type : instructor-embedding +embedding_model : hkunlp/instructor-xl +store_path : ${data:data_path}/vectordb + [text_splitter] chunk_size : 5000 chunk_overlap : 400 @@ -51,4 +59,4 @@ table_as_html : True data_path : ${root:root_path}/data [root] -root_path : /home/ubuntu/volume_2k/Capstone_5 \ No newline at end of file +root_path : /home/ubuntu/CapStone/Capstone_5 diff --git a/src/grag/components/embedding.py b/src/grag/components/embedding.py index 7a9d249..73202e0 100644 --- a/src/grag/components/embedding.py +++ b/src/grag/components/embedding.py @@ -1,3 +1,9 @@ +"""Class for embedding. + +This module provies: +- Embedding +""" + from langchain_community.embeddings import HuggingFaceInstructEmbeddings from langchain_community.embeddings.sentence_transformer import ( SentenceTransformerEmbeddings, @@ -6,6 +12,7 @@ class Embedding: """A class for vector embeddings. + Supports: huggingface sentence transformers -> model_type = 'sentence-transformers' huggingface instructor embeddings -> model_type = 'instructor-embedding' @@ -17,6 +24,7 @@ class Embedding: """ def __init__(self, embedding_type: str, embedding_model: str): + """Initialize the embedding with embedding_type and embedding_model.""" self.embedding_type = embedding_type self.embedding_model = embedding_model match self.embedding_type: diff --git a/src/grag/components/llm.py b/src/grag/components/llm.py index 20db968..23d2a26 100644 --- a/src/grag/components/llm.py +++ b/src/grag/components/llm.py @@ -1,3 +1,4 @@ +"""Class for LLM.""" import os from pathlib import Path @@ -36,20 +37,21 @@ class LLM: """ def __init__( - self, - model_name=llm_conf["model_name"], - device_map=llm_conf["device_map"], - task=llm_conf["task"], - max_new_tokens=llm_conf["max_new_tokens"], - temperature=llm_conf["temperature"], - n_batch=llm_conf["n_batch_gpu_cpp"], - n_ctx=llm_conf["n_ctx_cpp"], - n_gpu_layers=llm_conf["n_gpu_layers_cpp"], - std_out=llm_conf["std_out"], - base_dir=llm_conf["base_dir"], - quantization=llm_conf["quantization"], - pipeline=llm_conf["pipeline"], + self, + model_name=llm_conf["model_name"], + device_map=llm_conf["device_map"], + task=llm_conf["task"], + max_new_tokens=llm_conf["max_new_tokens"], + temperature=llm_conf["temperature"], + n_batch=llm_conf["n_batch_gpu_cpp"], + n_ctx=llm_conf["n_ctx_cpp"], + n_gpu_layers=llm_conf["n_gpu_layers_cpp"], + std_out=llm_conf["std_out"], + base_dir=llm_conf["base_dir"], + quantization=llm_conf["quantization"], + pipeline=llm_conf["pipeline"], ): + """Initialize the LLM class using the given parameters.""" self.base_dir = Path(base_dir) self._model_name = model_name self.quantization = quantization @@ -160,7 +162,7 @@ def llama_cpp(self): return llm def load_model( - self, model_name=None, pipeline=None, quantization=None, is_local=None + self, model_name=None, pipeline=None, quantization=None, is_local=None ): """Loads the model based on the specified pipeline and model name. diff --git a/src/grag/components/multivec_retriever.py b/src/grag/components/multivec_retriever.py index 18ed752..253cb31 100644 --- a/src/grag/components/multivec_retriever.py +++ b/src/grag/components/multivec_retriever.py @@ -1,3 +1,8 @@ +"""Class for retriever. + +This module provides: +- Retriever +""" import asyncio import uuid from typing import List @@ -13,9 +18,11 @@ class Retriever: - """A class for multi vector retriever, it connects to a vector database and a local file store. - It is used to return most similar chunks from a vector store but has the additional funcationality - to return a linked document, chunk, etc. + """A class for multi vector retriever. + + It connects to a vector database and a local file store. + It is used to return most similar chunks from a vector store but has the additional functionality to return a + linked document, chunk, etc. Attributes: store_path: Path to the local file store @@ -30,13 +37,15 @@ class Retriever: """ def __init__( - self, - store_path: str = multivec_retriever_conf["store_path"], - id_key: str = multivec_retriever_conf["id_key"], - namespace: str = multivec_retriever_conf["namespace"], - top_k=1, + self, + store_path: str = multivec_retriever_conf["store_path"], + id_key: str = multivec_retriever_conf["id_key"], + namespace: str = multivec_retriever_conf["namespace"], + top_k=1, ): - """Args: + """Initialize the Retriever. + + Args: store_path: Path to the local file store, defaults to argument from config file id_key: A key prefix for identifying documents, defaults to argument from config file namespace: A namespace for producing unique id, defaults to argument from congig file @@ -58,6 +67,7 @@ def __init__( def id_gen(self, doc: Document) -> str: """Takes a document and returns a unique id (uuid5) using the namespace and document source. + This ensures that a single document always gets the same unique id. Args: @@ -81,7 +91,9 @@ def gen_doc_ids(self, docs: List[Document]) -> List[str]: return [self.id_gen(doc) for doc in docs] def split_docs(self, docs: List[Document]) -> List[Document]: - """Takes a list of documents and splits them into smaller chunks using TextSplitter from compoenents.text_splitter + """Takes a list of documents and splits them into smaller chunks. + + Using TextSplitter from components.text_splitter Also adds the unique parent document id into metadata Args: @@ -101,8 +113,7 @@ def split_docs(self, docs: List[Document]) -> List[Document]: return chunks def add_docs(self, docs: List[Document]): - """Takes a list of documents, splits them using the split_docs method and then adds them into the vector database - and adds the parent document into the file store. + """Adds given documents into the vector database also adds the parent document into the file store. Args: docs: List of langchain_core.documents.Document @@ -117,8 +128,7 @@ def add_docs(self, docs: List[Document]): self.retriever.docstore.mset(list(zip(doc_ids, docs))) async def aadd_docs(self, docs: List[Document]): - """Takes a list of documents, splits them using the split_docs method and then adds them into the vector database - and adds the parent document into the file store. + """Adds given documents into the vector database also adds the parent document into the file store. Args: docs: List of langchain_core.documents.Document diff --git a/src/grag/components/parse_pdf.py b/src/grag/components/parse_pdf.py index d918c93..9ab2205 100644 --- a/src/grag/components/parse_pdf.py +++ b/src/grag/components/parse_pdf.py @@ -1,3 +1,8 @@ +"""Classes for parsing files. + +This module provides: +- ParsePDF +""" from langchain_core.documents import Document from unstructured.partition.pdf import partition_pdf @@ -22,17 +27,17 @@ class ParsePDF: """ def __init__( - self, - single_text_out=parser_conf["single_text_out"], - strategy=parser_conf["strategy"], - infer_table_structure=parser_conf["infer_table_structure"], - extract_images=parser_conf["extract_images"], - image_output_dir=parser_conf["image_output_dir"], - add_captions_to_text=parser_conf["add_captions_to_text"], - add_captions_to_blocks=parser_conf["add_captions_to_blocks"], - table_as_html=parser_conf["table_as_html"], + self, + single_text_out=parser_conf["single_text_out"], + strategy=parser_conf["strategy"], + infer_table_structure=parser_conf["infer_table_structure"], + extract_images=parser_conf["extract_images"], + image_output_dir=parser_conf["image_output_dir"], + add_captions_to_text=parser_conf["add_captions_to_text"], + add_captions_to_blocks=parser_conf["add_captions_to_blocks"], + table_as_html=parser_conf["table_as_html"], ): - # Instantialize instance variables with parameters + """Initialize instance variables with parameters.""" self.strategy = strategy if extract_images: # by default always extract Table self.extract_image_block_types = [ @@ -72,7 +77,8 @@ def partition(self, path: str): def classify(self, partitions): """Classifies the partitioned elements into Text, Tables, and Images list in a dictionary. - Add captions for each element (if available). + + Also adds captions for each element (if available). Parameters: partitions (list): The list of partitioned elements from the PDF document. @@ -88,7 +94,7 @@ def classify(self, partitions): if element.category == "Table": if self.add_captions_to_blocks and i + 1 < len(partitions): if ( - partitions[i + 1].category == "FigureCaption" + partitions[i + 1].category == "FigureCaption" ): # check for caption caption_element = partitions[i + 1] else: @@ -99,7 +105,7 @@ def classify(self, partitions): elif element.category == "Image": if self.add_captions_to_blocks and i + 1 < len(partitions): if ( - partitions[i + 1].category == "FigureCaption" + partitions[i + 1].category == "FigureCaption" ): # check for caption caption_element = partitions[i + 1] else: @@ -117,6 +123,8 @@ def classify(self, partitions): return classified_elements def text_concat(self, elements) -> str: + """Context aware concatenates all elements into a single string.""" + full_text = "" for current_element, next_element in zip(elements, elements[1:]): curr_type = current_element.category next_type = next_element.category @@ -185,7 +193,7 @@ def process_tables(self, elements): if caption_element: if ( - self.add_caption_first + self.add_caption_first ): # if there is a caption, add that before the element content = "\n\n".join([str(caption_element), table_data]) else: diff --git a/src/grag/components/prompt.py b/src/grag/components/prompt.py index ecefa71..86bf8df 100644 --- a/src/grag/components/prompt.py +++ b/src/grag/components/prompt.py @@ -1,3 +1,9 @@ +"""Classes for prompts. + +This module provides: +- Prompt - for generic prompts +- FewShotPrompt - for few-shot prompts +""" import json from pathlib import Path from typing import Any, Dict, List, Optional, Union @@ -13,6 +19,19 @@ class Prompt(BaseModel): + """A class for generic prompts. + + Attributes: + name (str): The prompt name (Optional, defaults to "custom_prompt") + llm_type (str): The type of llm, llama2, etc (Optional, defaults to "None") + task (str): The task (Optional, defaults to QA) + source (str): The source of the prompt (Optional, defaults to "NoSource") + doc_chain (str): The doc chain for the prompt ("stuff", "refine") (Optional, defaults to "stuff") + language (str): The language of the prompt (Optional, defaults to "en") + filepath (str): The filepath of the prompt (Optional) + input_keys (List[str]): The input keys for the prompt + template (str): The template for the prompt + """ name: str = Field(default="custom_prompt") llm_type: str = Field(default="None") task: str = Field(default="QA") @@ -27,6 +46,7 @@ class Prompt(BaseModel): @field_validator("input_keys") @classmethod def validate_input_keys(cls, v) -> List[str]: + """Validate the input_keys field.""" if v is None or v == []: raise ValueError("input_keys cannot be empty") return v @@ -34,6 +54,7 @@ def validate_input_keys(cls, v) -> List[str]: @field_validator("doc_chain") @classmethod def validate_doc_chain(cls, v: str) -> str: + """Validate the doc_chain field.""" if v not in SUPPORTED_DOC_CHAINS: raise ValueError( f"The provided doc_chain, {v} is not supported, supported doc_chains are {SUPPORTED_DOC_CHAINS}" @@ -43,6 +64,7 @@ def validate_doc_chain(cls, v: str) -> str: @field_validator("task") @classmethod def validate_task(cls, v: str) -> str: + """Validate the task field.""" if v not in SUPPORTED_TASKS: raise ValueError( f"The provided task, {v} is not supported, supported tasks are {SUPPORTED_TASKS}" @@ -53,14 +75,16 @@ def validate_task(cls, v: str) -> str: # def load_template(self): # self.prompt = ChatPromptTemplate.from_template(self.template) def __init__(self, **kwargs): + """Initialize the prompt.""" super().__init__(**kwargs) self.prompt = PromptTemplate( input_variables=self.input_keys, template=self.template ) def save( - self, filepath: Union[Path, str, None], overwrite=False + self, filepath: Union[Path, str, None], overwrite=False ) -> Union[None, ValueError]: + """Saves the prompt class into a json file.""" dump = self.model_dump_json(indent=2, exclude_defaults=True, exclude_none=True) if filepath is None: filepath = f"{self.name}.json" @@ -74,17 +98,36 @@ def save( @classmethod def load(cls, filepath: Union[Path, str]): + """Loads a json file and returns a Prompt class.""" with open(f"{filepath}", "r") as f: prompt_json = json.load(f) _prompt = cls(**prompt_json) _prompt.filepath = str(filepath) return _prompt - def format(self, **kwargs): + def format(self, **kwargs) -> str: + """Formats the prompt with provided keys and returns a string.""" return self.prompt.format(**kwargs) class FewShotPrompt(Prompt): + """A class for generic prompts. + + Attributes: + name (str): The prompt name (Optional, defaults to "custom_prompt") (Parent Class) + llm_type (str): The type of llm, llama2, etc (Optional, defaults to "None") (Parent Class) + task (str): The task (Optional, defaults to QA) (Parent Class) + source (str): The source of the prompt (Optional, defaults to "NoSource") (Parent Class) + doc_chain (str): The doc chain for the prompt ("stuff", "refine") (Optional, defaults to "stuff") (Parent Class) + language (str): The language of the prompt (Optional, defaults to "en") (Parent Class) + filepath (str): The filepath of the prompt (Optional) (Parent Class) + input_keys (List[str]): The input keys for the prompt (Parent Class) + input_keys (List[str]): The output keys for the prompt + prefix (str): The template prefix for the prompt + suffix (str): The template suffix for the prompt + example_template (str): The template for formatting the examples + examples (List[Dict[str, Any]]): The list of examples, each example is a dictionary with respective keys + """ output_keys: List[str] examples: List[Dict[str, Any]] prefix: str @@ -95,6 +138,7 @@ class FewShotPrompt(Prompt): ) def __init__(self, **kwargs): + """Initialize the prompt.""" super().__init__(**kwargs) eg_formatter = PromptTemplate( input_vars=self.input_keys + self.output_keys, @@ -111,6 +155,7 @@ def __init__(self, **kwargs): @field_validator("output_keys") @classmethod def validate_output_keys(cls, v) -> List[str]: + """Validate the output_keys field.""" if v is None or v == []: raise ValueError("output_keys cannot be empty") return v @@ -118,6 +163,7 @@ def validate_output_keys(cls, v) -> List[str]: @field_validator("examples") @classmethod def validate_examples(cls, v) -> List[Dict[str, Any]]: + """Validate the examples field.""" if v is None or v == []: raise ValueError("examples cannot be empty") for eg in v: diff --git a/src/grag/components/text_splitter.py b/src/grag/components/text_splitter.py index cff3c7c..a0ecfd9 100644 --- a/src/grag/components/text_splitter.py +++ b/src/grag/components/text_splitter.py @@ -1,3 +1,8 @@ +"""Class for splitting/chunking text. + +This module provides: +- TextSplitter +""" from langchain.text_splitter import RecursiveCharacterTextSplitter from .utils import get_config @@ -7,10 +12,21 @@ # %% class TextSplitter: - def __init__(self): + """Class for recursively chunking text, it prioritizes '/n/n then '/n' and so on. + + Attributes: + chunk_size: maximum size of chunk + chunk_overlap: chunk overlap size + """ + + def __init__(self, + chunk_size: int = text_splitter_conf["chunk_size"], + chunk_overlap: int = text_splitter_conf["chunk_overlap"]): + """Initialize TextSplitter.""" self.text_splitter = RecursiveCharacterTextSplitter( - chunk_size=int(text_splitter_conf["chunk_size"]), - chunk_overlap=int(text_splitter_conf["chunk_overlap"]), + chunk_size=int(chunk_size), + chunk_overlap=int(chunk_overlap), length_function=len, is_separator_regex=False, ) + """Initialize TextSplitter using chunk_size and chunk_overlap""" diff --git a/src/grag/components/utils.py b/src/grag/components/utils.py index cb64258..0233d32 100644 --- a/src/grag/components/utils.py +++ b/src/grag/components/utils.py @@ -1,3 +1,11 @@ +"""Utils functions. + +This module provides: +- stuff_docs: concats langchain documents into string +- load_prompt: loads json prompt to langchain prompt +- find_config_path: finds the path of the 'config.ini' file by traversing up the directory tree from the current path. +- get_config: retrieves and parses the configuration settings from the 'config.ini' file. +""" import json import os import textwrap @@ -10,7 +18,9 @@ def stuff_docs(docs: List[Document]) -> str: - """Args: + r"""Concatenates langchain documents into a string using '\n\n' seperator. + + Args: docs: List of langchain_core.documents.Document Returns: @@ -20,8 +30,7 @@ def stuff_docs(docs: List[Document]) -> str: def reformat_text_with_line_breaks(input_text, max_width=110): - """Reformat the given text to ensure each line does not exceed a specific width, - preserving existing line breaks. + """Reformat the given text to ensure each line does not exceed a specific width, preserving existing line breaks. Args: input_text (str): The text to be reformatted. @@ -62,7 +71,7 @@ def display_llm_output_and_sources(response_from_llm): def load_prompt(json_file: str | os.PathLike, return_input_vars=False): - """Loads a prompt template from json file and returns a langchain ChatPromptTemplate + """Loads a prompt template from json file and returns a langchain ChatPromptTemplate. Args: json_file: path to the prompt template json file. From acd4ba282f6c0dc821b80a4bc23a1a70849349e6 Mon Sep 17 00:00:00 2001 From: Arjun Bingly Date: Fri, 22 Mar 2024 18:20:14 -0400 Subject: [PATCH 08/15] Ruff bugs --- src/grag/rag/basic_rag.py | 44 ++++++++++++++++++++++++++++++++------- 1 file changed, 36 insertions(+), 8 deletions(-) diff --git a/src/grag/rag/basic_rag.py b/src/grag/rag/basic_rag.py index a99ecdd..05a7520 100644 --- a/src/grag/rag/basic_rag.py +++ b/src/grag/rag/basic_rag.py @@ -1,3 +1,8 @@ +"""Class for Basic RAG. + +This module provides: +- BasicRAG +""" import json from typing import List, Union @@ -13,15 +18,27 @@ class BasicRAG: + """Class for Basis RAG. + + Attributes: + model_name (str): Name of the llm model + doc_chain (str): Name of the document chain, ("stuff", "refine"), defaults to "stuff" + task (str): Name of task, defaults to "QA" + llm_kwargs (dict): Keyword arguments for LLM class + retriever_kwargs (dict): Keyword arguments for Retriever class + custom_prompt (Prompt): Prompt, defaults to None + """ + def __init__( - self, - model_name=None, - doc_chain="stuff", - task="QA", - llm_kwargs=None, - retriever_kwargs=None, - custom_prompt: Union[Prompt, FewShotPrompt, None] = None, + self, + model_name=None, + doc_chain="stuff", + task="QA", + llm_kwargs=None, + retriever_kwargs=None, + custom_prompt: Union[Prompt, FewShotPrompt, List[Prompt, FewShotPrompt], None] = None, ): + """Initialize BasicRAG.""" if retriever_kwargs is None: self.retriever = Retriever() else: @@ -54,6 +71,7 @@ def __init__( @property def model_name(self): + """Return the name of the model.""" return self._model_name @model_name.setter @@ -67,6 +85,7 @@ def model_name(self, value): @property def doc_chain(self): + """Returns the doc_chain.""" return self._doc_chain @doc_chain.setter @@ -86,6 +105,7 @@ def doc_chain(self, value): @property def task(self): + """Returns the task.""" return self._task @task.setter @@ -99,6 +119,7 @@ def task(self, value): self.prompt_matcher() def prompt_matcher(self): + """Matches relvant prompt using model, task and doc_chain.""" matcher_path = self.prompt_path.joinpath("matcher.json") with open(f"{matcher_path}", "r") as f: matcher_dict = json.load(f) @@ -122,7 +143,9 @@ def prompt_matcher(self): @staticmethod def stuff_docs(docs: List[Document]) -> str: - """Args: + r"""Concatenates docs into a string seperated by '\n\n'. + + Args: docs: List of langchain_core.documents.Document Returns: @@ -132,6 +155,8 @@ def stuff_docs(docs: List[Document]) -> str: @staticmethod def output_parser(call_func): + """Decorator to format llm output.""" + def output_parser_wrapper(*args, **kwargs): response, sources = call_func(*args, **kwargs) if conf["llm"]["std_out"] == "False": @@ -146,6 +171,7 @@ def output_parser_wrapper(*args, **kwargs): @output_parser def stuff_call(self, query: str): + """Call function for stuff chain.""" retrieved_docs = self.retriever.get_chunk(query) context = self.stuff_docs(retrieved_docs) prompt = self.main_prompt.format(context=context, question=query) @@ -155,6 +181,7 @@ def stuff_call(self, query: str): @output_parser def refine_call(self, query: str): + """Call function for refine chain.""" retrieved_docs = self.retriever.get_chunk(query) sources = [doc.metadata["source"] for doc in retrieved_docs] responses = [] @@ -176,6 +203,7 @@ def refine_call(self, query: str): return responses, sources def __call__(self, query: str): + """Call function for the class.""" if self.doc_chain == "stuff": return self.stuff_call(query) elif self.doc_chain == "refine": From 016011747380dbd57fb45f674447a204eda7efc1 Mon Sep 17 00:00:00 2001 From: arjbingly Date: Fri, 22 Mar 2024 22:23:27 +0000 Subject: [PATCH 09/15] style fixes by ruff --- src/grag/components/multivec_retriever.py | 18 ++++--- src/grag/components/vectordb/base.py | 13 ++--- src/grag/components/vectordb/chroma_client.py | 30 ++++++----- .../components/vectordb/deeplake_client.py | 51 ++++++++++--------- .../components/vectordb/chroma_client_test.py | 4 +- .../vectordb/deeplake_client_test.py | 12 +++-- 6 files changed, 71 insertions(+), 57 deletions(-) diff --git a/src/grag/components/multivec_retriever.py b/src/grag/components/multivec_retriever.py index b57a67f..98b3e6e 100644 --- a/src/grag/components/multivec_retriever.py +++ b/src/grag/components/multivec_retriever.py @@ -31,11 +31,11 @@ class Retriever: """ def __init__( - self, - store_path: str = multivec_retriever_conf["store_path"], - id_key: str = multivec_retriever_conf["id_key"], - namespace: str = multivec_retriever_conf["namespace"], - top_k=1, + self, + store_path: str = multivec_retriever_conf["store_path"], + id_key: str = multivec_retriever_conf["id_key"], + namespace: str = multivec_retriever_conf["namespace"], + top_k=1, ): """Args: store_path: Path to the local file store, defaults to argument from config file @@ -145,7 +145,7 @@ def get_chunk(self, query: str, with_score=False, top_k=None): list of Documents """ - _top_k = top_k if top_k else self.retriever.search_kwargs['k'] + _top_k = top_k if top_k else self.retriever.search_kwargs["k"] return self.vectordb.get_chunk(query=query, top_k=_top_k, with_score=with_score) async def aget_chunk(self, query: str, with_score=False, top_k=None): @@ -160,8 +160,10 @@ async def aget_chunk(self, query: str, with_score=False, top_k=None): list of Documents """ - _top_k = top_k if top_k else self.retriever.search_kwargs['k'] - return await self.vectordb.aget_chunk(query=query, top_k=_top_k, with_score=with_score) + _top_k = top_k if top_k else self.retriever.search_kwargs["k"] + return await self.vectordb.aget_chunk( + query=query, top_k=_top_k, with_score=with_score + ) def get_doc(self, query: str): """Returns the parent document of the most (cosine) similar chunk from the vector database. diff --git a/src/grag/components/vectordb/base.py b/src/grag/components/vectordb/base.py index ab63fcb..c474232 100644 --- a/src/grag/components/vectordb/base.py +++ b/src/grag/components/vectordb/base.py @@ -6,7 +6,6 @@ class VectorDB(ABC): - @abstractmethod def __len__(self) -> int: """Number of chunks in the vector database.""" @@ -19,7 +18,7 @@ def delete(self) -> None: @abstractmethod def add_docs(self, docs: List[Document], verbose: bool = True) -> None: """Adds documents to the vector database. - + Args: docs: List of Documents verbose: Show progress bar @@ -43,8 +42,9 @@ async def aadd_docs(self, docs: List[Document], verbose: bool = True) -> None: ... @abstractmethod - def get_chunk(self, query: str, with_score: bool = False, top_k: int = None) -> Union[ - List[Document], List[Tuple[Document, float]]]: + def get_chunk( + self, query: str, with_score: bool = False, top_k: int = None + ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the vector database. Args: @@ -58,8 +58,9 @@ def get_chunk(self, query: str, with_score: bool = False, top_k: int = None) -> ... @abstractmethod - async def aget_chunk(self, query: str, with_score: bool = False, top_k: int = None) -> Union[ - List[Document], List[Tuple[Document, float]]]: + async def aget_chunk( + self, query: str, with_score: bool = False, top_k: int = None + ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the vector database. (asynchronous) Args: diff --git a/src/grag/components/vectordb/chroma_client.py b/src/grag/components/vectordb/chroma_client.py index e97323d..ef9091f 100644 --- a/src/grag/components/vectordb/chroma_client.py +++ b/src/grag/components/vectordb/chroma_client.py @@ -37,12 +37,12 @@ class ChromaClient(VectorDB): """ def __init__( - self, - host=chroma_conf["host"], - port=chroma_conf["port"], - collection_name=chroma_conf["collection_name"], - embedding_type=chroma_conf["embedding_type"], - embedding_model=chroma_conf["embedding_model"], + self, + host=chroma_conf["host"], + port=chroma_conf["port"], + collection_name=chroma_conf["collection_name"], + embedding_type=chroma_conf["embedding_type"], + embedding_model=chroma_conf["embedding_model"], ): """Args: host: IP Address of hosted Chroma Vectorstore, defaults to argument from config file @@ -115,7 +115,7 @@ def add_docs(self, docs: List[Document], verbose=True) -> None: """ docs = self._filter_metadata(docs) for doc in ( - tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs + tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs ): _id = self.langchain_client.add_documents([doc]) @@ -132,17 +132,18 @@ async def aadd_docs(self, docs: List[Document], verbose=True) -> None: docs = self._filter_metadata(docs) if verbose: for doc in atqdm( - docs, - desc=f"Adding documents to {self.collection_name}", - total=len(docs), + docs, + desc=f"Adding documents to {self.collection_name}", + total=len(docs), ): await self.langchain_client.aadd_documents([doc]) else: for doc in docs: await self.langchain_client.aadd_documents([doc]) - def get_chunk(self, query: str, with_score: bool = False, top_k: int = None) -> Union[ - List[Document], List[Tuple[Document, float]]]: + def get_chunk( + self, query: str, with_score: bool = False, top_k: int = None + ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the chroma database. Args: @@ -163,8 +164,9 @@ def get_chunk(self, query: str, with_score: bool = False, top_k: int = None) -> query=query, k=top_k if top_k else 1 ) - async def aget_chunk(self, query: str, with_score=False, top_k=None) -> Union[ - List[Document], List[Tuple[Document, float]]]: + async def aget_chunk( + self, query: str, with_score=False, top_k=None + ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most (cosine) similar chunks from the vector database, asynchronously. Args: diff --git a/src/grag/components/vectordb/deeplake_client.py b/src/grag/components/vectordb/deeplake_client.py index 28fc606..3a389c3 100644 --- a/src/grag/components/vectordb/deeplake_client.py +++ b/src/grag/components/vectordb/deeplake_client.py @@ -14,7 +14,7 @@ class DeepLakeClient(VectorDB): """A class for connecting to a DeepLake Vectorstore - + Attributes: store_path : str, Path The path to store the DeepLake vectorstore. @@ -32,13 +32,14 @@ class DeepLakeClient(VectorDB): LangChain wrapper for DeepLake API """ - def __init__(self, - collection_name: str = deeplake_conf["collection_name"], - store_path: Union[str, Path] = deeplake_conf["store_path"], - embedding_type: str = deeplake_conf["embedding_type"], - embedding_model: str = deeplake_conf["embedding_model"], - read_only: bool = False - ): + def __init__( + self, + collection_name: str = deeplake_conf["collection_name"], + store_path: Union[str, Path] = deeplake_conf["store_path"], + embedding_type: str = deeplake_conf["embedding_type"], + embedding_model: str = deeplake_conf["embedding_model"], + read_only: bool = False, + ): self.store_path = Path(store_path) self.collection_name = collection_name self.read_only = read_only @@ -50,9 +51,11 @@ def __init__(self, ).embedding_function # self.client = VectorStore(path=self.store_path / self.collection_name) - self.langchain_client = DeepLake(dataset_path=str(self.store_path / self.collection_name), - embedding=self.embedding_function, - read_only=self.read_only) + self.langchain_client = DeepLake( + dataset_path=str(self.store_path / self.collection_name), + embedding=self.embedding_function, + read_only=self.read_only, + ) self.client = self.langchain_client.vectorstore self.allowed_metadata_types = (str, int, float, bool) @@ -64,44 +67,45 @@ def delete(self) -> None: def add_docs(self, docs: List[Document], verbose=True) -> None: """Adds documents to deeplake vectorstore - + Args: docs: List of Documents verbose: Show progress bar - + Returns: None """ docs = self._filter_metadata(docs) for doc in ( - tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs + tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs ): _id = self.langchain_client.add_documents([doc]) async def aadd_docs(self, docs: List[Document], verbose=True) -> None: """Asynchronously adds documents to chroma vectorstore - + Args: docs: List of Documents verbose: Show progress bar - + Returns: None """ docs = self._filter_metadata(docs) if verbose: for doc in atqdm( - docs, - desc=f"Adding documents to {self.collection_name}", - total=len(docs), + docs, + desc=f"Adding documents to {self.collection_name}", + total=len(docs), ): await self.langchain_client.aadd_documents([doc]) else: for doc in docs: await self.langchain_client.aadd_documents([doc]) - def get_chunk(self, query: str, with_score: bool = False, top_k: int = None) -> Union[ - List[Document], List[Tuple[Document, float]]]: + def get_chunk( + self, query: str, with_score: bool = False, top_k: int = None + ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the deeplake database. Args: @@ -122,8 +126,9 @@ def get_chunk(self, query: str, with_score: bool = False, top_k: int = None) -> query=query, k=top_k if top_k else 1 ) - async def aget_chunk(self, query: str, with_score=False, top_k=None) -> Union[ - List[Document], List[Tuple[Document, float]]]: + async def aget_chunk( + self, query: str, with_score=False, top_k=None + ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the deeplake database, asynchronously. Args: diff --git a/src/tests/components/vectordb/chroma_client_test.py b/src/tests/components/vectordb/chroma_client_test.py index ecffa22..c491dfd 100644 --- a/src/tests/components/vectordb/chroma_client_test.py +++ b/src/tests/components/vectordb/chroma_client_test.py @@ -113,7 +113,9 @@ def test_chroma_get_chunk(top_k, with_score): unutterably happy, but it was possible that she would never race a horse again.""" chroma_client = ChromaClient(collection_name="test") - retrieved_chunks = chroma_client.get_chunk(query=query, top_k=top_k, with_score=with_score) + retrieved_chunks = chroma_client.get_chunk( + query=query, top_k=top_k, with_score=with_score + ) assert len(retrieved_chunks) == top_k if with_score: assert all(isinstance(doc[0], Document) for doc in retrieved_chunks) diff --git a/src/tests/components/vectordb/deeplake_client_test.py b/src/tests/components/vectordb/deeplake_client_test.py index 921bd18..cea5e61 100644 --- a/src/tests/components/vectordb/deeplake_client_test.py +++ b/src/tests/components/vectordb/deeplake_client_test.py @@ -46,7 +46,7 @@ def test_deeplake_add_docs(): docs = [Document(page_content=doc) for doc in docs] deeplake_client.add_docs(docs) assert len(deeplake_client) == len(docs) - del (deeplake_client) + del deeplake_client def test_chroma_aadd_docs(): @@ -91,7 +91,7 @@ def test_chroma_aadd_docs(): loop = asyncio.get_event_loop() loop.run_until_complete(deeplake_client.aadd_docs(docs)) assert len(deeplake_client) == len(docs) - del (deeplake_client) + del deeplake_client deeplake_get_chunk_params = [(1, False), (1, True), (2, False), (2, True)] @@ -109,14 +109,16 @@ def test_deeplake_get_chunk(top_k, with_score): unutterably happy, but it was possible that she would never race a horse again.""" deeplake_client = DeepLakeClient(collection_name="test", read_only=True) - retrieved_chunks = deeplake_client.get_chunk(query=query, top_k=top_k, with_score=with_score) + retrieved_chunks = deeplake_client.get_chunk( + query=query, top_k=top_k, with_score=with_score + ) assert len(retrieved_chunks) == top_k if with_score: assert all(isinstance(doc[0], Document) for doc in retrieved_chunks) assert all(isinstance(doc[1], float) for doc in retrieved_chunks) else: assert all(isinstance(doc, Document) for doc in retrieved_chunks) - del (deeplake_client) + del deeplake_client @pytest.mark.parametrize("top_k,with_score", deeplake_get_chunk_params) @@ -141,4 +143,4 @@ def test_deeplake_aget_chunk(top_k, with_score): assert all(isinstance(doc[1], float) for doc in retrieved_chunks) else: assert all(isinstance(doc, Document) for doc in retrieved_chunks) - del (deeplake_client) + del deeplake_client From 2df28d1c7c2cf0b640faddcd796f8888607fc182 Mon Sep 17 00:00:00 2001 From: arjbingly Date: Fri, 22 Mar 2024 22:23:56 +0000 Subject: [PATCH 10/15] style fixes by ruff --- src/grag/components/embedding.py | 2 +- src/grag/components/llm.py | 29 ++++++++++++----------- src/grag/components/multivec_retriever.py | 21 ++++++++-------- src/grag/components/parse_pdf.py | 27 +++++++++++---------- src/grag/components/prompt.py | 7 ++++-- src/grag/components/text_splitter.py | 11 +++++---- src/grag/components/utils.py | 3 ++- src/grag/rag/basic_rag.py | 21 +++++++++------- 8 files changed, 67 insertions(+), 54 deletions(-) diff --git a/src/grag/components/embedding.py b/src/grag/components/embedding.py index 73202e0..eab107f 100644 --- a/src/grag/components/embedding.py +++ b/src/grag/components/embedding.py @@ -12,7 +12,7 @@ class Embedding: """A class for vector embeddings. - + Supports: huggingface sentence transformers -> model_type = 'sentence-transformers' huggingface instructor embeddings -> model_type = 'instructor-embedding' diff --git a/src/grag/components/llm.py b/src/grag/components/llm.py index 23d2a26..6e7296c 100644 --- a/src/grag/components/llm.py +++ b/src/grag/components/llm.py @@ -1,4 +1,5 @@ """Class for LLM.""" + import os from pathlib import Path @@ -37,19 +38,19 @@ class LLM: """ def __init__( - self, - model_name=llm_conf["model_name"], - device_map=llm_conf["device_map"], - task=llm_conf["task"], - max_new_tokens=llm_conf["max_new_tokens"], - temperature=llm_conf["temperature"], - n_batch=llm_conf["n_batch_gpu_cpp"], - n_ctx=llm_conf["n_ctx_cpp"], - n_gpu_layers=llm_conf["n_gpu_layers_cpp"], - std_out=llm_conf["std_out"], - base_dir=llm_conf["base_dir"], - quantization=llm_conf["quantization"], - pipeline=llm_conf["pipeline"], + self, + model_name=llm_conf["model_name"], + device_map=llm_conf["device_map"], + task=llm_conf["task"], + max_new_tokens=llm_conf["max_new_tokens"], + temperature=llm_conf["temperature"], + n_batch=llm_conf["n_batch_gpu_cpp"], + n_ctx=llm_conf["n_ctx_cpp"], + n_gpu_layers=llm_conf["n_gpu_layers_cpp"], + std_out=llm_conf["std_out"], + base_dir=llm_conf["base_dir"], + quantization=llm_conf["quantization"], + pipeline=llm_conf["pipeline"], ): """Initialize the LLM class using the given parameters.""" self.base_dir = Path(base_dir) @@ -162,7 +163,7 @@ def llama_cpp(self): return llm def load_model( - self, model_name=None, pipeline=None, quantization=None, is_local=None + self, model_name=None, pipeline=None, quantization=None, is_local=None ): """Loads the model based on the specified pipeline and model name. diff --git a/src/grag/components/multivec_retriever.py b/src/grag/components/multivec_retriever.py index 253cb31..97684dd 100644 --- a/src/grag/components/multivec_retriever.py +++ b/src/grag/components/multivec_retriever.py @@ -3,6 +3,7 @@ This module provides: - Retriever """ + import asyncio import uuid from typing import List @@ -19,9 +20,9 @@ class Retriever: """A class for multi vector retriever. - + It connects to a vector database and a local file store. - It is used to return most similar chunks from a vector store but has the additional functionality to return a + It is used to return most similar chunks from a vector store but has the additional functionality to return a linked document, chunk, etc. Attributes: @@ -37,14 +38,14 @@ class Retriever: """ def __init__( - self, - store_path: str = multivec_retriever_conf["store_path"], - id_key: str = multivec_retriever_conf["id_key"], - namespace: str = multivec_retriever_conf["namespace"], - top_k=1, + self, + store_path: str = multivec_retriever_conf["store_path"], + id_key: str = multivec_retriever_conf["id_key"], + namespace: str = multivec_retriever_conf["namespace"], + top_k=1, ): """Initialize the Retriever. - + Args: store_path: Path to the local file store, defaults to argument from config file id_key: A key prefix for identifying documents, defaults to argument from config file @@ -67,7 +68,7 @@ def __init__( def id_gen(self, doc: Document) -> str: """Takes a document and returns a unique id (uuid5) using the namespace and document source. - + This ensures that a single document always gets the same unique id. Args: @@ -92,7 +93,7 @@ def gen_doc_ids(self, docs: List[Document]) -> List[str]: def split_docs(self, docs: List[Document]) -> List[Document]: """Takes a list of documents and splits them into smaller chunks. - + Using TextSplitter from components.text_splitter Also adds the unique parent document id into metadata diff --git a/src/grag/components/parse_pdf.py b/src/grag/components/parse_pdf.py index 9ab2205..dc30f8a 100644 --- a/src/grag/components/parse_pdf.py +++ b/src/grag/components/parse_pdf.py @@ -3,6 +3,7 @@ This module provides: - ParsePDF """ + from langchain_core.documents import Document from unstructured.partition.pdf import partition_pdf @@ -27,15 +28,15 @@ class ParsePDF: """ def __init__( - self, - single_text_out=parser_conf["single_text_out"], - strategy=parser_conf["strategy"], - infer_table_structure=parser_conf["infer_table_structure"], - extract_images=parser_conf["extract_images"], - image_output_dir=parser_conf["image_output_dir"], - add_captions_to_text=parser_conf["add_captions_to_text"], - add_captions_to_blocks=parser_conf["add_captions_to_blocks"], - table_as_html=parser_conf["table_as_html"], + self, + single_text_out=parser_conf["single_text_out"], + strategy=parser_conf["strategy"], + infer_table_structure=parser_conf["infer_table_structure"], + extract_images=parser_conf["extract_images"], + image_output_dir=parser_conf["image_output_dir"], + add_captions_to_text=parser_conf["add_captions_to_text"], + add_captions_to_blocks=parser_conf["add_captions_to_blocks"], + table_as_html=parser_conf["table_as_html"], ): """Initialize instance variables with parameters.""" self.strategy = strategy @@ -77,7 +78,7 @@ def partition(self, path: str): def classify(self, partitions): """Classifies the partitioned elements into Text, Tables, and Images list in a dictionary. - + Also adds captions for each element (if available). Parameters: @@ -94,7 +95,7 @@ def classify(self, partitions): if element.category == "Table": if self.add_captions_to_blocks and i + 1 < len(partitions): if ( - partitions[i + 1].category == "FigureCaption" + partitions[i + 1].category == "FigureCaption" ): # check for caption caption_element = partitions[i + 1] else: @@ -105,7 +106,7 @@ def classify(self, partitions): elif element.category == "Image": if self.add_captions_to_blocks and i + 1 < len(partitions): if ( - partitions[i + 1].category == "FigureCaption" + partitions[i + 1].category == "FigureCaption" ): # check for caption caption_element = partitions[i + 1] else: @@ -193,7 +194,7 @@ def process_tables(self, elements): if caption_element: if ( - self.add_caption_first + self.add_caption_first ): # if there is a caption, add that before the element content = "\n\n".join([str(caption_element), table_data]) else: diff --git a/src/grag/components/prompt.py b/src/grag/components/prompt.py index 86bf8df..4364c06 100644 --- a/src/grag/components/prompt.py +++ b/src/grag/components/prompt.py @@ -4,6 +4,7 @@ - Prompt - for generic prompts - FewShotPrompt - for few-shot prompts """ + import json from pathlib import Path from typing import Any, Dict, List, Optional, Union @@ -20,7 +21,7 @@ class Prompt(BaseModel): """A class for generic prompts. - + Attributes: name (str): The prompt name (Optional, defaults to "custom_prompt") llm_type (str): The type of llm, llama2, etc (Optional, defaults to "None") @@ -32,6 +33,7 @@ class Prompt(BaseModel): input_keys (List[str]): The input keys for the prompt template (str): The template for the prompt """ + name: str = Field(default="custom_prompt") llm_type: str = Field(default="None") task: str = Field(default="QA") @@ -82,7 +84,7 @@ def __init__(self, **kwargs): ) def save( - self, filepath: Union[Path, str, None], overwrite=False + self, filepath: Union[Path, str, None], overwrite=False ) -> Union[None, ValueError]: """Saves the prompt class into a json file.""" dump = self.model_dump_json(indent=2, exclude_defaults=True, exclude_none=True) @@ -128,6 +130,7 @@ class FewShotPrompt(Prompt): example_template (str): The template for formatting the examples examples (List[Dict[str, Any]]): The list of examples, each example is a dictionary with respective keys """ + output_keys: List[str] examples: List[Dict[str, Any]] prefix: str diff --git a/src/grag/components/text_splitter.py b/src/grag/components/text_splitter.py index a0ecfd9..d04c9a5 100644 --- a/src/grag/components/text_splitter.py +++ b/src/grag/components/text_splitter.py @@ -3,6 +3,7 @@ This module provides: - TextSplitter """ + from langchain.text_splitter import RecursiveCharacterTextSplitter from .utils import get_config @@ -13,15 +14,17 @@ # %% class TextSplitter: """Class for recursively chunking text, it prioritizes '/n/n then '/n' and so on. - + Attributes: chunk_size: maximum size of chunk chunk_overlap: chunk overlap size """ - def __init__(self, - chunk_size: int = text_splitter_conf["chunk_size"], - chunk_overlap: int = text_splitter_conf["chunk_overlap"]): + def __init__( + self, + chunk_size: int = text_splitter_conf["chunk_size"], + chunk_overlap: int = text_splitter_conf["chunk_overlap"], + ): """Initialize TextSplitter.""" self.text_splitter = RecursiveCharacterTextSplitter( chunk_size=int(chunk_size), diff --git a/src/grag/components/utils.py b/src/grag/components/utils.py index 0233d32..2e34dc9 100644 --- a/src/grag/components/utils.py +++ b/src/grag/components/utils.py @@ -6,6 +6,7 @@ - find_config_path: finds the path of the 'config.ini' file by traversing up the directory tree from the current path. - get_config: retrieves and parses the configuration settings from the 'config.ini' file. """ + import json import os import textwrap @@ -19,7 +20,7 @@ def stuff_docs(docs: List[Document]) -> str: r"""Concatenates langchain documents into a string using '\n\n' seperator. - + Args: docs: List of langchain_core.documents.Document diff --git a/src/grag/rag/basic_rag.py b/src/grag/rag/basic_rag.py index 05a7520..be055ba 100644 --- a/src/grag/rag/basic_rag.py +++ b/src/grag/rag/basic_rag.py @@ -3,6 +3,7 @@ This module provides: - BasicRAG """ + import json from typing import List, Union @@ -19,7 +20,7 @@ class BasicRAG: """Class for Basis RAG. - + Attributes: model_name (str): Name of the llm model doc_chain (str): Name of the document chain, ("stuff", "refine"), defaults to "stuff" @@ -30,13 +31,15 @@ class BasicRAG: """ def __init__( - self, - model_name=None, - doc_chain="stuff", - task="QA", - llm_kwargs=None, - retriever_kwargs=None, - custom_prompt: Union[Prompt, FewShotPrompt, List[Prompt, FewShotPrompt], None] = None, + self, + model_name=None, + doc_chain="stuff", + task="QA", + llm_kwargs=None, + retriever_kwargs=None, + custom_prompt: Union[ + Prompt, FewShotPrompt, List[Prompt, FewShotPrompt], None + ] = None, ): """Initialize BasicRAG.""" if retriever_kwargs is None: @@ -144,7 +147,7 @@ def prompt_matcher(self): @staticmethod def stuff_docs(docs: List[Document]) -> str: r"""Concatenates docs into a string seperated by '\n\n'. - + Args: docs: List of langchain_core.documents.Document From 00e2d6bcd915df648917212e2c6dd01703f20a0e Mon Sep 17 00:00:00 2001 From: Arjun Bingly Date: Sat, 23 Mar 2024 15:31:37 -0400 Subject: [PATCH 11/15] Update embedding docstring --- src/grag/components/embedding.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/grag/components/embedding.py b/src/grag/components/embedding.py index eab107f..eeb0f82 100644 --- a/src/grag/components/embedding.py +++ b/src/grag/components/embedding.py @@ -1,6 +1,6 @@ """Class for embedding. -This module provies: +This module provides: - Embedding """ From 26235f561748e709cc4073cdcefddbcdc0e5e27d Mon Sep 17 00:00:00 2001 From: Arjun Bingly Date: Sat, 23 Mar 2024 15:55:47 -0400 Subject: [PATCH 12/15] Update doc strings. --- src/grag/components/vectordb/base.py | 14 +++++-- src/grag/components/vectordb/chroma_client.py | 41 +++++++++++-------- .../components/vectordb/deeplake_client.py | 39 +++++++++++------- 3 files changed, 60 insertions(+), 34 deletions(-) diff --git a/src/grag/components/vectordb/base.py b/src/grag/components/vectordb/base.py index c474232..1146d77 100644 --- a/src/grag/components/vectordb/base.py +++ b/src/grag/components/vectordb/base.py @@ -1,3 +1,9 @@ +"""Abstract base class for vector database clients. + +This module provides: +- VectorDB +""" + from abc import ABC, abstractmethod from typing import List, Tuple, Union @@ -6,6 +12,8 @@ class VectorDB(ABC): + """Abstract base class for vector database clients.""" + @abstractmethod def __len__(self) -> int: """Number of chunks in the vector database.""" @@ -43,7 +51,7 @@ async def aadd_docs(self, docs: List[Document], verbose: bool = True) -> None: @abstractmethod def get_chunk( - self, query: str, with_score: bool = False, top_k: int = None + self, query: str, with_score: bool = False, top_k: int = None ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the vector database. @@ -59,9 +67,9 @@ def get_chunk( @abstractmethod async def aget_chunk( - self, query: str, with_score: bool = False, top_k: int = None + self, query: str, with_score: bool = False, top_k: int = None ) -> Union[List[Document], List[Tuple[Document, float]]]: - """Returns the most similar chunks from the vector database. (asynchronous) + """Returns the most similar chunks from the vector database (asynchronous). Args: query: A query string diff --git a/src/grag/components/vectordb/chroma_client.py b/src/grag/components/vectordb/chroma_client.py index ef9091f..105882c 100644 --- a/src/grag/components/vectordb/chroma_client.py +++ b/src/grag/components/vectordb/chroma_client.py @@ -1,3 +1,8 @@ +"""Class for Chroma vector database. + +This module provides: +- ChromaClient +""" from typing import List, Tuple, Union import chromadb @@ -37,14 +42,16 @@ class ChromaClient(VectorDB): """ def __init__( - self, - host=chroma_conf["host"], - port=chroma_conf["port"], - collection_name=chroma_conf["collection_name"], - embedding_type=chroma_conf["embedding_type"], - embedding_model=chroma_conf["embedding_model"], + self, + host=chroma_conf["host"], + port=chroma_conf["port"], + collection_name=chroma_conf["collection_name"], + embedding_type=chroma_conf["embedding_type"], + embedding_model=chroma_conf["embedding_model"], ): - """Args: + """Initialize a ChromaClient object. + + Args: host: IP Address of hosted Chroma Vectorstore, defaults to argument from config file port: port address of hosted Chroma Vectorstore, defaults to argument from config file collection_name: name of the collection in the Chroma Vectorstore, defaults to argument from config file @@ -73,9 +80,11 @@ def __init__( self.allowed_metadata_types = (str, int, float, bool) def __len__(self) -> int: + """Count the number of chunks in the database.""" return self.collection.count() def delete(self) -> None: + """Delete all the chunks in the database collection.""" self.client.delete_collection(self.collection_name) self.collection = self.client.get_or_create_collection( name=self.collection_name @@ -87,7 +96,7 @@ def delete(self) -> None: ) def test_connection(self, verbose=True) -> int: - """Tests connection with Chroma Vectorstore + """Tests connection with Chroma Vectorstore. Args: verbose: if True, prints connection status @@ -104,7 +113,7 @@ def test_connection(self, verbose=True) -> int: return response def add_docs(self, docs: List[Document], verbose=True) -> None: - """Adds documents to chroma vectorstore + """Adds documents to chroma vectorstore. Args: docs: List of Documents @@ -115,12 +124,12 @@ def add_docs(self, docs: List[Document], verbose=True) -> None: """ docs = self._filter_metadata(docs) for doc in ( - tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs + tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs ): _id = self.langchain_client.add_documents([doc]) async def aadd_docs(self, docs: List[Document], verbose=True) -> None: - """Asynchronously adds documents to chroma vectorstore + """Asynchronously adds documents to chroma vectorstore. Args: docs: List of Documents @@ -132,9 +141,9 @@ async def aadd_docs(self, docs: List[Document], verbose=True) -> None: docs = self._filter_metadata(docs) if verbose: for doc in atqdm( - docs, - desc=f"Adding documents to {self.collection_name}", - total=len(docs), + docs, + desc=f"Adding documents to {self.collection_name}", + total=len(docs), ): await self.langchain_client.aadd_documents([doc]) else: @@ -142,7 +151,7 @@ async def aadd_docs(self, docs: List[Document], verbose=True) -> None: await self.langchain_client.aadd_documents([doc]) def get_chunk( - self, query: str, with_score: bool = False, top_k: int = None + self, query: str, with_score: bool = False, top_k: int = None ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the chroma database. @@ -165,7 +174,7 @@ def get_chunk( ) async def aget_chunk( - self, query: str, with_score=False, top_k=None + self, query: str, with_score=False, top_k=None ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most (cosine) similar chunks from the vector database, asynchronously. diff --git a/src/grag/components/vectordb/deeplake_client.py b/src/grag/components/vectordb/deeplake_client.py index 3a389c3..2cb0270 100644 --- a/src/grag/components/vectordb/deeplake_client.py +++ b/src/grag/components/vectordb/deeplake_client.py @@ -1,3 +1,9 @@ +"""Class for DeepLake vector database. + +This module provides: +- DeepLakeClient +""" + from pathlib import Path from typing import List, Tuple, Union @@ -13,7 +19,7 @@ class DeepLakeClient(VectorDB): - """A class for connecting to a DeepLake Vectorstore + """A class for connecting to a DeepLake Vectorstore. Attributes: store_path : str, Path @@ -33,13 +39,14 @@ class DeepLakeClient(VectorDB): """ def __init__( - self, - collection_name: str = deeplake_conf["collection_name"], - store_path: Union[str, Path] = deeplake_conf["store_path"], - embedding_type: str = deeplake_conf["embedding_type"], - embedding_model: str = deeplake_conf["embedding_model"], - read_only: bool = False, + self, + collection_name: str = deeplake_conf["collection_name"], + store_path: Union[str, Path] = deeplake_conf["store_path"], + embedding_type: str = deeplake_conf["embedding_type"], + embedding_model: str = deeplake_conf["embedding_model"], + read_only: bool = False, ): + """Initialize DeepLake client object.""" self.store_path = Path(store_path) self.collection_name = collection_name self.read_only = read_only @@ -60,13 +67,15 @@ def __init__( self.allowed_metadata_types = (str, int, float, bool) def __len__(self) -> int: + """Number of chunks in the vector database.""" return self.client.__len__() def delete(self) -> None: + """Delete all chunks in the vector database.""" self.client.delete(delete_all=True) def add_docs(self, docs: List[Document], verbose=True) -> None: - """Adds documents to deeplake vectorstore + """Adds documents to deeplake vectorstore. Args: docs: List of Documents @@ -77,12 +86,12 @@ def add_docs(self, docs: List[Document], verbose=True) -> None: """ docs = self._filter_metadata(docs) for doc in ( - tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs + tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs ): _id = self.langchain_client.add_documents([doc]) async def aadd_docs(self, docs: List[Document], verbose=True) -> None: - """Asynchronously adds documents to chroma vectorstore + """Asynchronously adds documents to chroma vectorstore. Args: docs: List of Documents @@ -94,9 +103,9 @@ async def aadd_docs(self, docs: List[Document], verbose=True) -> None: docs = self._filter_metadata(docs) if verbose: for doc in atqdm( - docs, - desc=f"Adding documents to {self.collection_name}", - total=len(docs), + docs, + desc=f"Adding documents to {self.collection_name}", + total=len(docs), ): await self.langchain_client.aadd_documents([doc]) else: @@ -104,7 +113,7 @@ async def aadd_docs(self, docs: List[Document], verbose=True) -> None: await self.langchain_client.aadd_documents([doc]) def get_chunk( - self, query: str, with_score: bool = False, top_k: int = None + self, query: str, with_score: bool = False, top_k: int = None ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the deeplake database. @@ -127,7 +136,7 @@ def get_chunk( ) async def aget_chunk( - self, query: str, with_score=False, top_k=None + self, query: str, with_score=False, top_k=None ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the deeplake database, asynchronously. From caebf0a3eb19e681c08804e67a3cc6ce8d8ade0a Mon Sep 17 00:00:00 2001 From: Arjun Bingly Date: Sun, 24 Mar 2024 15:29:54 -0400 Subject: [PATCH 13/15] Config changes for deeplake --- src/config.ini | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/src/config.ini b/src/config.ini index 452ac04..eb3cab2 100644 --- a/src/config.ini +++ b/src/config.ini @@ -14,6 +14,12 @@ n_gpu_layers_cpp : -1 std_out : True base_dir : ${root:root_path}/models +[deeplake] +collection_name : arxiv +embedding_type : instructor-embedding +embedding_model : hkunlp/instructor-xl +store_path : ${data:data_path}/vectordb + [chroma] host : localhost port : 8000 @@ -51,4 +57,4 @@ table_as_html : True data_path : ${root:root_path}/data [root] -root_path : /home/ubuntu/volume_2k/Capstone_5 \ No newline at end of file +root_path : /home/ubuntu/volume_2k/Capstone_5 From f94114e4264eabe6952646062a6574cc954693e7 Mon Sep 17 00:00:00 2001 From: Arjun Bingly Date: Sun, 24 Mar 2024 17:50:26 -0400 Subject: [PATCH 14/15] Retriever update --- projects/Basic-RAG/BasicRAG_stuff.py | 8 +- src/grag/components/multivec_retriever.py | 24 +++-- src/grag/rag/basic_rag.py | 28 +++--- .../components/multivec_retriever_test.py | 89 +++++++++++++++++++ 4 files changed, 127 insertions(+), 22 deletions(-) diff --git a/projects/Basic-RAG/BasicRAG_stuff.py b/projects/Basic-RAG/BasicRAG_stuff.py index 4bfafc3..63edeab 100644 --- a/projects/Basic-RAG/BasicRAG_stuff.py +++ b/projects/Basic-RAG/BasicRAG_stuff.py @@ -1,6 +1,10 @@ -from grag.grag.rag import BasicRAG +from grag.components.multivec_retriever import Retriever +from grag.components.vectordb.deeplake_client import DeepLakeClient +from grag.rag.basic_rag import BasicRAG -rag = BasicRAG(doc_chain="stuff") +client = DeepLakeClient(collection_name="test") +retriever = Retriever(vectordb=client) +rag = BasicRAG(doc_chain="stuff", retriever=retriever) if __name__ == "__main__": while True: diff --git a/src/grag/components/multivec_retriever.py b/src/grag/components/multivec_retriever.py index 98b3e6e..b0eeac7 100644 --- a/src/grag/components/multivec_retriever.py +++ b/src/grag/components/multivec_retriever.py @@ -1,11 +1,11 @@ import asyncio import uuid -from typing import List +from typing import Any, Dict, List, Optional from grag.components.text_splitter import TextSplitter from grag.components.utils import get_config from grag.components.vectordb.base import VectorDB -from grag.components.vectordb.chroma_client import ChromaClient +from grag.components.vectordb.deeplake_client import DeepLakeClient from langchain.retrievers.multi_vector import MultiVectorRetriever from langchain.storage import LocalFileStore from langchain_core.documents import Document @@ -31,11 +31,13 @@ class Retriever: """ def __init__( - self, - store_path: str = multivec_retriever_conf["store_path"], - id_key: str = multivec_retriever_conf["id_key"], - namespace: str = multivec_retriever_conf["namespace"], - top_k=1, + self, + vectordb: Optional[VectorDB] = None, + store_path: str = multivec_retriever_conf["store_path"], + id_key: str = multivec_retriever_conf["id_key"], + namespace: str = multivec_retriever_conf["namespace"], + top_k=1, + client_kwargs: Optional[Dict[str, Any]] = None ): """Args: store_path: Path to the local file store, defaults to argument from config file @@ -46,7 +48,13 @@ def __init__( self.store_path = store_path self.id_key = id_key self.namespace = uuid.UUID(namespace) - self.vectordb: VectorDB = ChromaClient() # TODO - change to init argument + if vectordb is None: + if client_kwargs is not None: + self.vectordb = DeepLakeClient(**client_kwargs) + else: + self.vectordb = DeepLakeClient() + else: + self.vectordb = vectordb self.store = LocalFileStore(self.store_path) self.retriever = MultiVectorRetriever( vectorstore=self.vectordb.langchain_client, diff --git a/src/grag/rag/basic_rag.py b/src/grag/rag/basic_rag.py index 9589920..1b344ea 100644 --- a/src/grag/rag/basic_rag.py +++ b/src/grag/rag/basic_rag.py @@ -1,10 +1,10 @@ import json -from typing import List, Union +from typing import List, Optional, Union from grag import prompts from grag.components.llm import LLM from grag.components.multivec_retriever import Retriever -from grag.components.prompt import Prompt, FewShotPrompt +from grag.components.prompt import FewShotPrompt, Prompt from grag.components.utils import get_config from importlib_resources import files from langchain_core.documents import Document @@ -14,18 +14,22 @@ class BasicRAG: def __init__( - self, - model_name=None, - doc_chain="stuff", - task="QA", - llm_kwargs=None, - retriever_kwargs=None, - custom_prompt: Union[Prompt, FewShotPrompt, None] = None, + self, + retriever: Optional[Retriever] = None, + model_name=None, + doc_chain="stuff", + task="QA", + llm_kwargs=None, + retriever_kwargs=None, + custom_prompt: Union[Prompt, FewShotPrompt, None] = None, ): - if retriever_kwargs is None: - self.retriever = Retriever() + if retriever is None: + if retriever_kwargs is None: + self.retriever = Retriever() + else: + self.retriever = Retriever(**retriever_kwargs) else: - self.retriever = Retriever(**retriever_kwargs) + self.retriever = retriever if llm_kwargs is None: self.llm_ = LLM() diff --git a/src/tests/components/multivec_retriever_test.py b/src/tests/components/multivec_retriever_test.py index 3ccb3fb..14dad0b 100644 --- a/src/tests/components/multivec_retriever_test.py +++ b/src/tests/components/multivec_retriever_test.py @@ -1,3 +1,92 @@ +import json + +from grag.components.multivec_retriever import Retriever +from langchain_core.documents import Document + +retriever = Retriever() # pass test collection + +doc = Document(page_content="Hello worlds", metadata={"source": "bars"}) + + +def test_retriver_id_gen(): + doc = Document(page_content="Hello world", metadata={"source": "bar"}) + id_ = retriever.id_gen(doc) + assert isinstance(id, str) + assert len(id_) == 32 + doc.page_content = doc.page_content + 'ABC' + id_1 = retriever.id_gen(doc) + assert id_ == id_1 + doc.metadata["source"] = "bars" + id_1 = retriever.id_gen(doc) + assert id_ != id_1 + + +def test_retriever_gen_doc_ids(): + docs = [Document(page_content="Hello world", metadata={"source": "bar"}), + Document(page_content="Hello", metadata={"source": "foo"})] + ids = retriever.gen_doc_ids(docs) + assert len(ids) == len(docs) + assert all(isinstance(id, str) for id in ids) + + +def test_retriever_split_docs(): + pass + + +def test_retriever_split_docs(): + pass + + +def test_retriever_add_docs(): + # small enough docs to not split. + docs = [Document(page_content= + """And so on this rainbow day, with storms all around them, and blue sky + above, they rode only as far as the valley. But from there, before they + turned to go back, the monuments appeared close, and they loomed + grandly with the background of purple bank and creamy cloud and shafts + of golden lightning. They seemed like sentinels--guardians of a great + and beautiful love born under their lofty heights, in the lonely + silence of day, in the star-thrown shadow of night. They were like that + love. And they held Lucy and Slone, calling every day, giving a + nameless and tranquil content, binding them true to love, true to the + sage and the open, true to that wild upland home""", metadata={"source": "test_doc_1"}), + Document(page_content= + """Slone and Lucy never rode down so far as the stately monuments, though + these held memories as hauntingly sweet as others were poignantly + bitter. Lucy never rode the King again. But Slone rode him, learned to + love him. And Lucy did not race any more. When Slone tried to stir in + her the old spirit all the response he got was a wistful shake of head + or a laugh that hid the truth or an excuse that the strain on her + ankles from Joel Creech's lasso had never mended. The girl was + unutterably happy, but it was possible that she would never race a + horse again.""", metadata={"source": "test_doc_2"}), + Document(page_content= + """Bostil wanted to be alone, to welcome the King, to lead him back to the + home corral, perhaps to hide from all eyes the change and the uplift + that would forever keep him from wronging another man. + + The late rains came and like magic, in a few days, the sage grew green + and lustrous and fresh, the gray turning to purple. + + Every morning the sun rose white and hot in a blue and cloudless sky. + And then soon the horizon line showed creamy clouds that rose and + spread and darkened. Every afternoon storms hung along the ramparts and + rainbows curved down beautiful and ethereal. The dim blackness of the + storm-clouds was split to the blinding zigzag of lightning, and the + thunder rolled and boomed, like the Colorado in flood.""", metadata={"source": "test_doc_3"}) + ] + ids = retriever.gen_doc_ids(docs) + retriever.add_docs(docs) + retrieved = retriever.store.mget(ids) + assert len(retrieved) == len(ids) + for i, doc in enumerate(docs): + retrieved_doc = json.loads(retrieved[i].decode()) + assert doc.metadata == retrieved_doc.metadata + + +def test_retriever_aadd_docs(): + pass + # # add code folder to sys path # import os # from pathlib import Path From 454bb5d4639ea212757307020439df87f0638196 Mon Sep 17 00:00:00 2001 From: arjbingly Date: Sun, 24 Mar 2024 21:57:50 +0000 Subject: [PATCH 15/15] style fixes by ruff --- src/grag/components/multivec_retriever.py | 14 +++++----- src/grag/components/vectordb/base.py | 4 +-- src/grag/components/vectordb/chroma_client.py | 27 ++++++++++--------- .../components/vectordb/deeplake_client.py | 24 ++++++++--------- src/grag/rag/basic_rag.py | 16 +++++------ 5 files changed, 43 insertions(+), 42 deletions(-) diff --git a/src/grag/components/multivec_retriever.py b/src/grag/components/multivec_retriever.py index 9062e69..9fd8664 100644 --- a/src/grag/components/multivec_retriever.py +++ b/src/grag/components/multivec_retriever.py @@ -39,13 +39,13 @@ class Retriever: """ def __init__( - self, - vectordb: Optional[VectorDB] = None, - store_path: str = multivec_retriever_conf["store_path"], - id_key: str = multivec_retriever_conf["id_key"], - namespace: str = multivec_retriever_conf["namespace"], - top_k=1, - client_kwargs: Optional[Dict[str, Any]] = None + self, + vectordb: Optional[VectorDB] = None, + store_path: str = multivec_retriever_conf["store_path"], + id_key: str = multivec_retriever_conf["id_key"], + namespace: str = multivec_retriever_conf["namespace"], + top_k=1, + client_kwargs: Optional[Dict[str, Any]] = None, ): """Initialize the Retriever. diff --git a/src/grag/components/vectordb/base.py b/src/grag/components/vectordb/base.py index 1146d77..b0b0623 100644 --- a/src/grag/components/vectordb/base.py +++ b/src/grag/components/vectordb/base.py @@ -51,7 +51,7 @@ async def aadd_docs(self, docs: List[Document], verbose: bool = True) -> None: @abstractmethod def get_chunk( - self, query: str, with_score: bool = False, top_k: int = None + self, query: str, with_score: bool = False, top_k: int = None ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the vector database. @@ -67,7 +67,7 @@ def get_chunk( @abstractmethod async def aget_chunk( - self, query: str, with_score: bool = False, top_k: int = None + self, query: str, with_score: bool = False, top_k: int = None ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the vector database (asynchronous). diff --git a/src/grag/components/vectordb/chroma_client.py b/src/grag/components/vectordb/chroma_client.py index 105882c..cac8ab3 100644 --- a/src/grag/components/vectordb/chroma_client.py +++ b/src/grag/components/vectordb/chroma_client.py @@ -3,6 +3,7 @@ This module provides: - ChromaClient """ + from typing import List, Tuple, Union import chromadb @@ -42,15 +43,15 @@ class ChromaClient(VectorDB): """ def __init__( - self, - host=chroma_conf["host"], - port=chroma_conf["port"], - collection_name=chroma_conf["collection_name"], - embedding_type=chroma_conf["embedding_type"], - embedding_model=chroma_conf["embedding_model"], + self, + host=chroma_conf["host"], + port=chroma_conf["port"], + collection_name=chroma_conf["collection_name"], + embedding_type=chroma_conf["embedding_type"], + embedding_model=chroma_conf["embedding_model"], ): """Initialize a ChromaClient object. - + Args: host: IP Address of hosted Chroma Vectorstore, defaults to argument from config file port: port address of hosted Chroma Vectorstore, defaults to argument from config file @@ -124,7 +125,7 @@ def add_docs(self, docs: List[Document], verbose=True) -> None: """ docs = self._filter_metadata(docs) for doc in ( - tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs + tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs ): _id = self.langchain_client.add_documents([doc]) @@ -141,9 +142,9 @@ async def aadd_docs(self, docs: List[Document], verbose=True) -> None: docs = self._filter_metadata(docs) if verbose: for doc in atqdm( - docs, - desc=f"Adding documents to {self.collection_name}", - total=len(docs), + docs, + desc=f"Adding documents to {self.collection_name}", + total=len(docs), ): await self.langchain_client.aadd_documents([doc]) else: @@ -151,7 +152,7 @@ async def aadd_docs(self, docs: List[Document], verbose=True) -> None: await self.langchain_client.aadd_documents([doc]) def get_chunk( - self, query: str, with_score: bool = False, top_k: int = None + self, query: str, with_score: bool = False, top_k: int = None ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the chroma database. @@ -174,7 +175,7 @@ def get_chunk( ) async def aget_chunk( - self, query: str, with_score=False, top_k=None + self, query: str, with_score=False, top_k=None ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most (cosine) similar chunks from the vector database, asynchronously. diff --git a/src/grag/components/vectordb/deeplake_client.py b/src/grag/components/vectordb/deeplake_client.py index 2cb0270..f0d5ba5 100644 --- a/src/grag/components/vectordb/deeplake_client.py +++ b/src/grag/components/vectordb/deeplake_client.py @@ -39,12 +39,12 @@ class DeepLakeClient(VectorDB): """ def __init__( - self, - collection_name: str = deeplake_conf["collection_name"], - store_path: Union[str, Path] = deeplake_conf["store_path"], - embedding_type: str = deeplake_conf["embedding_type"], - embedding_model: str = deeplake_conf["embedding_model"], - read_only: bool = False, + self, + collection_name: str = deeplake_conf["collection_name"], + store_path: Union[str, Path] = deeplake_conf["store_path"], + embedding_type: str = deeplake_conf["embedding_type"], + embedding_model: str = deeplake_conf["embedding_model"], + read_only: bool = False, ): """Initialize DeepLake client object.""" self.store_path = Path(store_path) @@ -86,7 +86,7 @@ def add_docs(self, docs: List[Document], verbose=True) -> None: """ docs = self._filter_metadata(docs) for doc in ( - tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs + tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs ): _id = self.langchain_client.add_documents([doc]) @@ -103,9 +103,9 @@ async def aadd_docs(self, docs: List[Document], verbose=True) -> None: docs = self._filter_metadata(docs) if verbose: for doc in atqdm( - docs, - desc=f"Adding documents to {self.collection_name}", - total=len(docs), + docs, + desc=f"Adding documents to {self.collection_name}", + total=len(docs), ): await self.langchain_client.aadd_documents([doc]) else: @@ -113,7 +113,7 @@ async def aadd_docs(self, docs: List[Document], verbose=True) -> None: await self.langchain_client.aadd_documents([doc]) def get_chunk( - self, query: str, with_score: bool = False, top_k: int = None + self, query: str, with_score: bool = False, top_k: int = None ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the deeplake database. @@ -136,7 +136,7 @@ def get_chunk( ) async def aget_chunk( - self, query: str, with_score=False, top_k=None + self, query: str, with_score=False, top_k=None ) -> Union[List[Document], List[Tuple[Document, float]]]: """Returns the most similar chunks from the deeplake database, asynchronously. diff --git a/src/grag/rag/basic_rag.py b/src/grag/rag/basic_rag.py index cdad471..da461b6 100644 --- a/src/grag/rag/basic_rag.py +++ b/src/grag/rag/basic_rag.py @@ -31,14 +31,14 @@ class BasicRAG: """ def __init__( - self, - retriever: Optional[Retriever] = None, - model_name=None, - doc_chain="stuff", - task="QA", - llm_kwargs=None, - retriever_kwargs=None, - custom_prompt: Union[Prompt, FewShotPrompt, None] = None, + self, + retriever: Optional[Retriever] = None, + model_name=None, + doc_chain="stuff", + task="QA", + llm_kwargs=None, + retriever_kwargs=None, + custom_prompt: Union[Prompt, FewShotPrompt, None] = None, ): if retriever is None: if retriever_kwargs is None: