diff --git a/chatbot/bot/conversation/conversation_retrieval.py b/chatbot/bot/conversation/conversation_retrieval.py index d3ab7b3..4b2f6e7 100644 --- a/chatbot/bot/conversation/conversation_retrieval.py +++ b/chatbot/bot/conversation/conversation_retrieval.py @@ -1,8 +1,8 @@ from asyncio import get_event_loop from typing import Any, List, Tuple +from entities.document import Document from helpers.log import get_logger -from langchain_core.documents import Document from bot.client.lama_cpp_client import LamaCppClient from bot.conversation.ctx_strategy import AsyncTreeSummarizationStrategy, BaseSynthesisStrategy diff --git a/chatbot/bot/conversation/ctx_strategy.py b/chatbot/bot/conversation/ctx_strategy.py index 942d36e..472eb14 100644 --- a/chatbot/bot/conversation/ctx_strategy.py +++ b/chatbot/bot/conversation/ctx_strategy.py @@ -3,8 +3,8 @@ from typing import Any, List, Union import nest_asyncio +from entities.document import Document from helpers.log import get_logger -from langchain_core.documents import Document from bot.client.lama_cpp_client import LamaCppClient diff --git a/chatbot/bot/memory/embedder.py b/chatbot/bot/memory/embedder.py index ac8fa50..e0b4e50 100644 --- a/chatbot/bot/memory/embedder.py +++ b/chatbot/bot/memory/embedder.py @@ -1,16 +1,80 @@ -from abc import ABC +from abc import ABC, abstractmethod from typing import Any -from langchain.embeddings import HuggingFaceEmbeddings - class Embedder(ABC): - embedder: Any + @abstractmethod + def embed_documents(self, texts: list[str]) -> list[list[float]]: + """Embed search docs.""" + + @abstractmethod + def embed_query(self, text: str) -> list[float]: + """Embed query text.""" + + +class HuggingFaceEmbedder(Embedder): + """HuggingFace sentence_transformers embedding models. + + To use, you should have the ``sentence_transformers`` python package installed. + """ + + client: Any #: :meta private: + model_name: str = "all-MiniLM-L6-v2" + """Model name to use.""" + cache_folder: str | None = None + """Path to store models. + Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable.""" + model_kwargs: dict[str, Any] = {} + """Keyword arguments to pass to the model.""" + encode_kwargs: dict[str, Any] = {} + """Keyword arguments to pass when calling the `encode` method of the model.""" + multi_process: bool = False + """Run encode() on multiple GPUs.""" + + def __init__(self, **kwargs: Any): + """Initialize the sentence_transformer.""" + super().__init__(**kwargs) + try: + import sentence_transformers + + except ImportError as exc: + raise ImportError( + "Could not import sentence_transformers python package. " + "Please install it with `pip install sentence-transformers`." + ) from exc + + self.client = sentence_transformers.SentenceTransformer( + self.model_name, cache_folder=self.cache_folder, **self.model_kwargs + ) + + def embed_documents(self, texts: list[str]) -> list[list[float]]: + """Compute doc embeddings using a HuggingFace transformer model. + + Args: + texts: The list of texts to embed. + + Returns: + List of embeddings, one for each text. + """ + import sentence_transformers + + texts = list(map(lambda x: x.replace("\n", " "), texts)) + if self.multi_process: + pool = self.client.start_multi_process_pool() + embeddings = self.client.encode_multi_process(texts, pool) + sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool) + else: + embeddings = self.client.encode(texts, **self.encode_kwargs) + + return embeddings.tolist() - def get_embedding(self): - return self.embedder + def embed_query(self, text: str) -> list[float]: + """Compute query embeddings using a HuggingFace transformer model. + Args: + text: The text to embed. -class EmbedderHuggingFace(Embedder): - def __init__(self, model_name: str = "all-MiniLM-L6-v2"): - self.embedder = HuggingFaceEmbeddings(model_name=model_name) + Returns: + Embeddings for the text. + """ + return self.embed_documents([text])[0] diff --git a/chatbot/bot/memory/vector_memory.py b/chatbot/bot/memory/vector_memory.py index 671f3f8..176060d 100644 --- a/chatbot/bot/memory/vector_memory.py +++ b/chatbot/bot/memory/vector_memory.py @@ -1,9 +1,9 @@ from typing import Any, Dict, List, Tuple from cleantext import clean +from entities.document import Document from helpers.log import get_logger -from langchain.vectorstores import Chroma -from langchain_core.documents import Document +from vector_database.chroma import Chroma logger = get_logger(__name__) @@ -99,10 +99,9 @@ def similarity_search( def create_memory_index(embedding: Any, chunks: List, vector_store_path: str): texts = [clean(doc.page_content, no_emoji=True) for doc in chunks] metadatas = [doc.metadata for doc in chunks] - memory_index = Chroma.from_texts( + Chroma.from_texts( texts=texts, embedding=embedding, metadatas=metadatas, persist_directory=vector_store_path, ) - memory_index.persist() diff --git a/chatbot/cli/rag_chatbot.py b/chatbot/cli/rag_chatbot.py index 24224d3..a6b4583 100644 --- a/chatbot/cli/rag_chatbot.py +++ b/chatbot/cli/rag_chatbot.py @@ -6,7 +6,7 @@ from bot.client.lama_cpp_client import LamaCppClient from bot.conversation.conversation_retrieval import ConversationRetrieval from bot.conversation.ctx_strategy import get_ctx_synthesis_strategies, get_ctx_synthesis_strategy -from bot.memory.embedder import EmbedderHuggingFace +from bot.memory.embedder import HuggingFaceEmbedder from bot.memory.vector_memory import VectorMemory from bot.model.model_settings import get_model_setting, get_models from helpers.log import get_logger @@ -135,7 +135,7 @@ def main(parameters): conversation = ConversationRetrieval(llm) - embedding = EmbedderHuggingFace().get_embedding() + embedding = HuggingFaceEmbedder() index = VectorMemory(vector_store_path=str(vector_store_path), embedding=embedding) loop(conversation, synthesis_strategy, index, parameters) diff --git a/chatbot/document_loader/format.py b/chatbot/document_loader/format.py new file mode 100644 index 0000000..3f11415 --- /dev/null +++ b/chatbot/document_loader/format.py @@ -0,0 +1,70 @@ +from enum import Enum + + +class Format(Enum): + MARKDOWN = "markdown" + HTML = "html" + + +SUPPORTED_FORMATS = { + Format.MARKDOWN.value: [ + # First, try to split along Markdown headings (starting with level 2) + "\n#{1,6} ", + # Note the alternative syntax for headings (below) is not handled here + # Heading level 2 + # --------------- + # End of code block + "```\n", + # Horizontal lines + "\n\\*\\*\\*+\n", + "\n---+\n", + "\n___+\n", + # Note that this splitter doesn't handle horizontal lines defined + # by *three or more* of ***, ---, or ___, but this is not handled + "\n\n", + "\n", + " ", + "", + ], + Format.HTML.value: [ + # First, try to split along HTML tags + " list[str]: - if format == Format.MARKDOWN: - return [ - # First, try to split along Markdown headings (starting with level 2) - "\n#{1,6} ", - # Note the alternative syntax for headings (below) is not handled here - # Heading level 2 - # --------------- - # End of code block - "```\n", - # Horizontal lines - "\n\\*\\*\\*+\n", - "\n---+\n", - "\n___+\n", - # Note that this splitter doesn't handle horizontal lines defined - # by *three or more* of ***, ---, or ___, but this is not handled - "\n\n", - "\n", - " ", - "", - ] - elif format == Format.HTML: - return [ - # First, try to split along HTML tags - " list[str]: # We now want to combine these smaller pieces into medium size # chunks to send to the LLM. separator_len = self._length_function(separator) - - docs = [] - current_doc: list[str] = [] + docs, current_doc = [], [] total = 0 + for d in splits: _len = self._length_function(d) if total + _len + (separator_len if len(current_doc) > 0 else 0) > self._chunk_size: @@ -281,7 +219,7 @@ def _split_text_with_regex(text: str, separator: str, keep_separator: bool) -> l return [s for s in splits if s != ""] -def create_recursive_text_splitter(format: Format, **kwargs: Any) -> RecursiveCharacterTextSplitter: +def create_recursive_text_splitter(format: str, **kwargs: Any) -> RecursiveCharacterTextSplitter: """ Factory function to create a RecursiveCharacterTextSplitter instance based on the specified format. @@ -292,5 +230,5 @@ def create_recursive_text_splitter(format: Format, **kwargs: Any) -> RecursiveCh Returns: An instance of RecursiveCharacterTextSplitter configured with the appropriate separators. """ - separators = __get_separators(format) + separators = get_separators(format) return RecursiveCharacterTextSplitter(separators=separators, **kwargs) diff --git a/chatbot/memory_builder.py b/chatbot/memory_builder.py index 8c2cc34..45e1a06 100644 --- a/chatbot/memory_builder.py +++ b/chatbot/memory_builder.py @@ -3,7 +3,7 @@ from pathlib import Path from typing import List -from bot.memory.embedder import EmbedderHuggingFace +from bot.memory.embedder import HuggingFaceEmbedder from bot.memory.vector_memory import VectorMemory from document_loader.loader import DirectoryLoader from document_loader.text_splitter import Format, create_recursive_text_splitter @@ -62,7 +62,7 @@ def build_memory_index(docs_path: Path, vector_store_path: str, chunk_size: int, logger.info(f"Number of generated chunks: {len(chunks)}") logger.info("Creating memory index...") - embedding = EmbedderHuggingFace().get_embedding() + embedding = HuggingFaceEmbedder() VectorMemory.create_memory_index(embedding, chunks, vector_store_path) logger.info("Memory Index has been created successfully!") diff --git a/chatbot/rag_chatbot_app.py b/chatbot/rag_chatbot_app.py index 71bffc3..de9a61e 100644 --- a/chatbot/rag_chatbot_app.py +++ b/chatbot/rag_chatbot_app.py @@ -11,7 +11,7 @@ get_ctx_synthesis_strategies, get_ctx_synthesis_strategy, ) -from bot.memory.embedder import EmbedderHuggingFace +from bot.memory.embedder import HuggingFaceEmbedder from bot.memory.vector_memory import VectorMemory from bot.model.model_settings import get_model_setting, get_models from helpers.log import get_logger @@ -51,7 +51,7 @@ def load_index(vector_store_path: Path) -> VectorMemory: Returns: VectorMemory: An instance of the VectorMemory class with the loaded index. """ - embedding = EmbedderHuggingFace().get_embedding() + embedding = HuggingFaceEmbedder() index = VectorMemory(vector_store_path=str(vector_store_path), embedding=embedding) return index diff --git a/chatbot/vector_database/chroma.py b/chatbot/vector_database/chroma.py new file mode 100644 index 0000000..690212c --- /dev/null +++ b/chatbot/vector_database/chroma.py @@ -0,0 +1,506 @@ +import logging +import math +import uuid +from typing import Any, Callable, Iterable + +import chromadb +import chromadb.config +from bot.memory.embedder import HuggingFaceEmbedder +from chromadb.api.types import ID, OneOrMany, Where, WhereDocument +from entities.document import Document + +logger = logging.getLogger(__name__) + + +def _results_to_docs(results: Any) -> list[Document]: + return [doc for doc, _ in _results_to_docs_and_scores(results)] + + +def _results_to_docs_and_scores(results: Any) -> list[tuple[Document, float]]: + return [ + # TODO: Chroma can do batch querying, + # we shouldn't hard code to the 1st result + (Document(page_content=result[0], metadata=result[1] or {}), result[2]) + for result in zip( + results["documents"][0], + results["metadatas"][0], + results["distances"][0], + ) + ] + + +class Chroma: + def __init__( + self, + embedding_function: HuggingFaceEmbedder | None = None, + persist_directory: str | None = None, + client_settings: chromadb.config.Settings | None = None, + collection_name: str = "default", + collection_metadata: dict | None = None, + client: chromadb.Client = None, + relevance_score_fn: Callable[[float], float] | None = None, + ) -> None: + """Initialize with a Chroma client.""" + + if client is not None: + self._client_settings = client_settings + self._client = client + self._persist_directory = persist_directory + else: + if client_settings: + # If client_settings is provided with persist_directory specified, + # then it is "in-memory and persisting to disk" mode. + client_settings.persist_directory = persist_directory or client_settings.persist_directory + _client_settings = client_settings + elif persist_directory: + # Maintain backwards compatibility with chromadb < 0.4.0 + _client_settings = chromadb.config.Settings(is_persistent=True) + _client_settings.persist_directory = persist_directory + else: + _client_settings = chromadb.config.Settings() + self._client_settings = _client_settings + self._client = chromadb.Client(_client_settings) + self._persist_directory = _client_settings.persist_directory or persist_directory + + self._embedding_function = embedding_function + self._collection = self._client.get_or_create_collection( + name=collection_name, + embedding_function=None, + metadata=collection_metadata, + ) + self.override_relevance_score_fn = relevance_score_fn + + @property + def embeddings(self) -> HuggingFaceEmbedder | None: + return self._embedding_function + + def __query_collection( + self, + query_texts: list[str] | None = None, + query_embeddings: list[list[float]] | None = None, + n_results: int = 4, + where: dict[str, str] | None = None, + where_document: dict[str, str] | None = None, + **kwargs: Any, + ): + """Query the chroma collection.""" + + return self._collection.query( + query_texts=query_texts, + query_embeddings=query_embeddings, + n_results=n_results, + where=where, + where_document=where_document, + **kwargs, + ) + + def add_texts( + self, + texts: Iterable[str], + metadatas: list[dict] | None = None, + ids: list[str] | None = None, + ) -> list[str]: + """Run more texts through the embeddings and add to the vectorstore. + + Args: + texts (Iterable[str]): Texts to add to the vectorstore. + metadatas (Optional[List[dict]], optional): Optional list of metadatas. + ids (Optional[List[str]], optional): Optional list of IDs. + + Returns: + List[str]: List of IDs of the added texts. + """ + # TODO: Handle the case where the user doesn't provide ids on the Collection + if ids is None: + ids = [str(uuid.uuid1()) for _ in texts] + embeddings = None + texts = list(texts) + if self._embedding_function is not None: + embeddings = self._embedding_function.embed_documents(texts) + if metadatas: + # fill metadatas with empty dicts if somebody + # did not specify metadata for all texts + length_diff = len(texts) - len(metadatas) + if length_diff: + metadatas = metadatas + [{}] * length_diff + empty_ids = [] + non_empty_ids = [] + for idx, m in enumerate(metadatas): + if m: + non_empty_ids.append(idx) + else: + empty_ids.append(idx) + if non_empty_ids: + metadatas = [metadatas[idx] for idx in non_empty_ids] + texts_with_metadatas = [texts[idx] for idx in non_empty_ids] + embeddings_with_metadatas = [embeddings[idx] for idx in non_empty_ids] if embeddings else None + ids_with_metadata = [ids[idx] for idx in non_empty_ids] + try: + self._collection.upsert( + metadatas=metadatas, + embeddings=embeddings_with_metadatas, + documents=texts_with_metadatas, + ids=ids_with_metadata, + ) + except ValueError as e: + if "Expected metadata value to be" in str(e): + msg = "Try filtering complex metadata from the document." + raise ValueError(e.args[0] + "\n\n" + msg) + else: + raise e + if empty_ids: + texts_without_metadatas = [texts[j] for j in empty_ids] + embeddings_without_metadatas = [embeddings[j] for j in empty_ids] if embeddings else None + ids_without_metadatas = [ids[j] for j in empty_ids] + self._collection.upsert( + embeddings=embeddings_without_metadatas, + documents=texts_without_metadatas, + ids=ids_without_metadatas, + ) + else: + self._collection.upsert( + embeddings=embeddings, + documents=texts, + ids=ids, + ) + return ids + + def similarity_search( + self, + query: str, + k: int = 4, + filter: dict[str, str] | None = None, + **kwargs: Any, + ) -> list[Document]: + """Run similarity search with Chroma. + + Args: + query (str): Query text to search for. + k (int): Number of results to return. Defaults to 4. + filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. + + Returns: + List[Document]: List of documents most similar to the query text. + """ + docs_and_scores = self.similarity_search_with_score(query, k, filter=filter) + return [doc for doc, _ in docs_and_scores] + + def similarity_search_with_score( + self, + query: str, + k: int = 4, + filter: dict[str, str] | None = None, + where_document: dict[str, str] | None = None, + **kwargs: Any, + ) -> list[tuple[Document, float]]: + """Run similarity search with Chroma with distance. + + Args: + query (str): Query text to search for. + k (int): Number of results to return. Defaults to 4. + filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. + + Returns: + List[Tuple[Document, float]]: List of documents most similar to + the query text and cosine distance in float for each. + Lower score represents more similarity. + """ + if self._embedding_function is None: + results = self.__query_collection( + query_texts=[query], + n_results=k, + where=filter, + where_document=where_document, + ) + else: + query_embedding = self._embedding_function.embed_query(query) + results = self.__query_collection( + query_embeddings=[query_embedding], + n_results=k, + where=filter, + where_document=where_document, + ) + + return _results_to_docs_and_scores(results) + + @staticmethod + def _cosine_relevance_score_fn(distance: float) -> float: + """Normalize the distance to a score on a scale [0, 1].""" + + return 1.0 - distance + + @staticmethod + def _euclidean_relevance_score_fn(distance: float) -> float: + """Return a similarity score on a scale [0, 1].""" + # The 'correct' relevance function + # may differ depending on a few things, including: + # - the distance / similarity metric used by the VectorStore + # - the scale of your embeddings (OpenAI's are unit normed. Many + # others are not!) + # - embedding dimensionality + # - etc. + # This function converts the euclidean norm of normalized embeddings + # (0 is most similar, sqrt(2) most dissimilar) + # to a similarity function (0 to 1) + return 1.0 - distance / math.sqrt(2) + + @staticmethod + def _max_inner_product_relevance_score_fn(distance: float) -> float: + """Normalize the distance to a score on a scale [0, 1].""" + if distance > 0: + return 1.0 - distance + + return -1.0 * distance + + def _select_relevance_score_fn(self) -> Callable[[float], float]: + """ + The 'correct' relevance function + may differ depending on a few things, including: + - the distance / similarity metric used by the VectorStore + - the scale of your embeddings (OpenAI's are unit normed. Many others are not!) + - embedding dimensionality + - etc. + """ + if self.override_relevance_score_fn: + return self.override_relevance_score_fn + + distance = "l2" + distance_key = "hnsw:space" + metadata = self._collection.metadata + + if metadata and distance_key in metadata: + distance = metadata[distance_key] + + if distance == "cosine": + return self._cosine_relevance_score_fn + elif distance == "l2": + return self._euclidean_relevance_score_fn + elif distance == "ip": + return self._max_inner_product_relevance_score_fn + else: + raise ValueError( + "No supported normalization function" + f" for distance metric of type: {distance}." + "Consider providing relevance_score_fn to Chroma constructor." + ) + + def _similarity_search_with_relevance_scores( + self, + query: str, + k: int = 4, + **kwargs: Any, + ) -> list[tuple[Document, float]]: + """ + Default similarity search with relevance scores. Modify if necessary + in subclass. + Return docs and relevance scores in the range [0, 1]. + + 0 is dissimilar, 1 is most similar. + + Args: + query: input text + k: Number of Documents to return. Defaults to 4. + **kwargs: kwargs to be passed to similarity search. Should include: + score_threshold: Optional, a floating point value between 0 to 1 to + filter the resulting set of retrieved docs + + Returns: + List of Tuples of (doc, similarity_score) + """ + relevance_score_fn = self._select_relevance_score_fn() + docs_and_scores = self.similarity_search_with_score(query, k, **kwargs) + return [(doc, relevance_score_fn(score)) for doc, score in docs_and_scores] + + def similarity_search_with_relevance_scores( + self, + query: str, + k: int = 4, + **kwargs: Any, + ) -> list[tuple[Document, float]]: + """Return docs and relevance scores in the range [0, 1]. + + 0 is dissimilar, 1 is most similar. + + Args: + query: input text + k: Number of Documents to return. Defaults to 4. + **kwargs: kwargs to be passed to similarity search. Should include: + score_threshold: Optional, a floating point value between 0 to 1 to + filter the resulting set of retrieved docs + + Returns: + List of Tuples of (doc, similarity_score) + """ + score_threshold = kwargs.pop("score_threshold", None) + + docs_and_similarities = self._similarity_search_with_relevance_scores(query, k=k, **kwargs) + if any(similarity < 0.0 or similarity > 1.0 for _, similarity in docs_and_similarities): + logger.warning("Relevance scores must be between" f" 0 and 1, got {docs_and_similarities}") + + if score_threshold is not None: + docs_and_similarities = [ + (doc, similarity) for doc, similarity in docs_and_similarities if similarity >= score_threshold + ] + if len(docs_and_similarities) == 0: + logger.warning( + "No relevant docs were retrieved using the relevance score" f" threshold {score_threshold}" + ) + return docs_and_similarities + + def delete_collection(self) -> None: + """Delete the collection.""" + self._client.delete_collection(self._collection.name) + + def get( + self, + ids: OneOrMany[ID] | None = None, + where: Where | None = None, + limit: int | None = None, + offset: int | None = None, + where_document: WhereDocument | None = None, + include: list[str] | None = None, + ) -> dict[str, Any]: + """Gets the collection. + + Args: + ids: The ids of the embeddings to get. Optional. + where: A Where type dict used to filter results by. + E.g. `{"color" : "red", "price": 4.20}`. Optional. + limit: The number of documents to return. Optional. + offset: The offset to start returning results from. + Useful for paging results with limit. Optional. + where_document: A WhereDocument type dict used to filter by the documents. + E.g. `{$contains: "hello"}`. Optional. + include: A list of what to include in the results. + Can contain `"embeddings"`, `"metadatas"`, `"documents"`. + Ids are always included. + Defaults to `["metadatas", "documents"]`. Optional. + """ + kwargs = { + "ids": ids, + "where": where, + "limit": limit, + "offset": offset, + "where_document": where_document, + } + + if include is not None: + kwargs["include"] = include + + return self._collection.get(**kwargs) + + def update_document(self, document_id: str, document: Document) -> None: + """Update a document in the collection. + + Args: + document_id (str): ID of the document to update. + document (Document): Document to update. + """ + return self.update_documents([document_id], [document]) + + def update_documents(self, ids: list[str], documents: list[Document]) -> None: + """Update a document in the collection. + + Args: + ids (List[str]): List of ids of the document to update. + documents (List[Document]): List of documents to update. + """ + text = [document.page_content for document in documents] + metadata = [document.metadata for document in documents] + if self._embedding_function is None: + raise ValueError("For update, you must specify an embedding function on creation.") + embeddings = self._embedding_function.embed_documents(text) + + if hasattr(self._collection._client, "max_batch_size"): # for Chroma 0.4.10 and above + from chromadb.utils.batch_utils import create_batches + + for batch in create_batches( + api=self._collection._client, + ids=ids, + metadatas=metadata, + documents=text, + embeddings=embeddings, + ): + self._collection.update( + ids=batch[0], + embeddings=batch[1], + documents=batch[3], + metadatas=batch[2], + ) + else: + self._collection.update( + ids=ids, + embeddings=embeddings, + documents=text, + metadatas=metadata, + ) + + @classmethod + def from_texts( + cls, + texts: list[str], + embedding: HuggingFaceEmbedder | None = None, + metadatas: list[dict] | None = None, + ids: list[str] | None = None, + collection_name: str = "default", + persist_directory: str | None = None, + client_settings: chromadb.config.Settings | None = None, + client=None, + collection_metadata: dict | None = None, + **kwargs: Any, + ): + """Create a Chroma vectorstore from a raw documents. + + If a persist_directory is specified, the collection will be persisted there. + Otherwise, the data will be ephemeral in-memory. + + Args: + texts (List[str]): List of texts to add to the collection. + collection_name (str): Name of the collection to create. + persist_directory (Optional[str]): Directory to persist the collection. + embedding (Optional[Embeddings]): Embedding function. Defaults to None. + metadatas (Optional[List[dict]]): List of metadatas. Defaults to None. + ids (Optional[List[str]]): List of document IDs. Defaults to None. + client_settings (Optional[chromadb.config.Settings]): Chroma client settings + collection_metadata (Optional[Dict]): Collection configurations. + Defaults to None. + + Returns: + Chroma: Chroma vectorstore. + """ + chroma_collection = cls( + collection_name=collection_name, + embedding_function=embedding, + persist_directory=persist_directory, + client_settings=client_settings, + client=client, + collection_metadata=collection_metadata, + **kwargs, + ) + if ids is None: + ids = [str(uuid.uuid1()) for _ in texts] + if hasattr(chroma_collection._client, "max_batch_size"): # for Chroma 0.4.10 and above + from chromadb.utils.batch_utils import create_batches + + for batch in create_batches( + api=chroma_collection._client, + ids=ids, + metadatas=metadatas, + documents=texts, + ): + chroma_collection.add_texts( + texts=batch[3] if batch[3] else [], + metadatas=batch[2] if batch[2] else None, + ids=batch[0], + ) + else: + chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids) + return chroma_collection + + def delete(self, ids: list[str] | None = None, **kwargs: Any) -> None: + """Delete by vector IDs. + + Args: + ids: List of ids to delete. + """ + self._collection.delete(ids=ids) diff --git a/experiments/exp_gpt4all/model.py b/experiments/exp_gpt4all/model.py deleted file mode 100644 index e91f36f..0000000 --- a/experiments/exp_gpt4all/model.py +++ /dev/null @@ -1,117 +0,0 @@ -import dataclasses -import os -from abc import ABC -from dataclasses import dataclass -from typing import Optional - -import requests -from tqdm import tqdm - -# Check https://github.com/nomic-ai/gpt4all for the latest models. - - -@dataclass -class GenerateConfig: - max_tokens: int = 200 - temp: float = 0.7 - top_k: int = 40 - top_p: float = 0.4 - repeat_penalty: float = 1.18 - repeat_last_n: int = 64 - n_batch: int = 8 - n_predict: Optional[int] = None - streaming: bool = False - - -def get_generate_config(): - return dataclasses.asdict(GenerateConfig()) - - -class ModelSettings(ABC): - url: str - name: str - system_template: str - prompt_template: str - device = "cpu" - - -class WizardSettings(ModelSettings): - url = "https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGUF/resolve/main/wizardlm-13b-v1.2.Q4_0.gguf" - name = "wizardlm-13b-v1.2.Q4_0.gguf" - system_template = ( - "A chat between a curious user and an artificial intelligence assistant. " - "The assistant gives helpful, detailed, and polite answers to the user's questions." - ) - prompt_template = "USER: {0}\nASSISTANT: " - - -class MistralSettings(ModelSettings): - url = "https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GGUF/resolve/main/mistral-7b-openorca.Q4_0.gguf" - name = "mistral-7b-openorca.Q4_0.gguf" - system_template = ( - "A chat between a curious user and an artificial intelligence assistant. " - "The assistant gives helpful, detailed, and polite answers to the user's questions." - ) - # https://github.com/nomic-ai/gpt4all/issues/1614 - prompt_template = """### Human:\n{0}\n### Assistant:""" - # Nomic Vulkan support for Q4_0, Q6 quantizations in GGUF. - device = "gpu" - - -SUPPORTED_MODELS = {"wizard": WizardSettings, "mistral": MistralSettings} - - -def get_models(): - return list(SUPPORTED_MODELS.keys()) - - -def get_model_setting(model_name: str): - model_settings = SUPPORTED_MODELS.get(model_name) - - # validate input - if model_settings is None: - raise KeyError(model_name + " is a not supported model") - - return model_settings - - -def auto_download(model_settings: ModelSettings, download_path: str) -> None: - """ - Downloads a model file based on the provided name and saves it to the specified path. - - Args: - model_settings (ModelSettings): The settings of the model to download. - download_path (str): The path where the downloaded model file will be saved. - - Returns: - None - - Raises: - Any exceptions raised during the download process will be caught and printed, but not re-raised. - - This function fetches model settings using the provided name, including the model's URL, and then downloads - the model file from the URL. The download is done in chunks, and a progress bar is displayed to visualize - the download process. - - """ - model_name = model_settings.name - url = model_settings.url - - if not os.path.exists(download_path): - # send a GET request to the URL to download the file. - # Stream it while downloading, since the file is large - - try: - response = requests.get(url, stream=True) - # open the file in binary mode and write the contents of the response - # in chunks. - with open(download_path, "wb") as f: - for chunk in tqdm(response.iter_content(chunk_size=8912)): - if chunk: - f.write(chunk) - - except Exception as e: - print(f"=> Download Failed. Error: {e}") - return - - print(f"=> Model: {model_name} downloaded successfully 🥳") diff --git a/experiments/exp_gpt4all/native_gpt4all.py b/experiments/exp_gpt4all/native_gpt4all.py deleted file mode 100644 index e76b3cd..0000000 --- a/experiments/exp_gpt4all/native_gpt4all.py +++ /dev/null @@ -1,36 +0,0 @@ -import time -from pathlib import Path - -from gpt4all import GPT4All -from model import auto_download, get_generate_config, get_model_setting - -root_folder = Path(__file__).resolve().parent.parent.parent -model_folder = root_folder / "models" -Path(model_folder).parent.mkdir(parents=True, exist_ok=True) - -model_settings = get_model_setting("mistral") -config = get_generate_config() -model_path = model_folder / model_settings.name - -auto_download(model_settings, model_path) - -model = GPT4All( - model_name=model_settings.name, - model_path=model_folder, - device=model_settings.device, - allow_download=False, - verbose=False, -) - -system_template = model_settings.system_template -prompt_template = model_settings.prompt_template - -with model.chat_session(system_template, prompt_template): - start_time = time.time() - - question = "Create a regex to extract dates from logs in Python" - output = model.generate(prompt=question, **config) - print(output) - - took = time.time() - start_time - print(f"--- Took {took:.2f} seconds ---") diff --git a/experiments/explore_memory.py b/experiments/explore_memory.py index e45422f..ca37962 100644 --- a/experiments/explore_memory.py +++ b/experiments/explore_memory.py @@ -1,10 +1,10 @@ from pathlib import Path import chromadb -from bot.memory.embedder import EmbedderHuggingFace +from bot.memory.embedder import HuggingFaceEmbedder from bot.memory.vector_memory import VectorMemory from helpers.prettier import prettify_source -from langchain_community.vectorstores.chroma import Chroma +from vector_database.chroma import Chroma if __name__ == "__main__": root_folder = Path(__file__).resolve().parent.parent @@ -13,7 +13,7 @@ # Contains an extract of things the user said in the past; episodic_vector_store_path = root_folder / "vector_store" / "episodic_index" - embedding = EmbedderHuggingFace().get_embedding() + embedding = HuggingFaceEmbedder() index = VectorMemory(vector_store_path=str(declarative_vector_store_path), embedding=embedding) # query = "" diff --git a/poetry.lock b/poetry.lock index 02fc5ff..0ae8006 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,115 +1,5 @@ # This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand. -[[package]] -name = "aiohttp" -version = "3.9.1" -description = "Async http client/server framework (asyncio)" -optional = false -python-versions = ">=3.8" -files = [ - {file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e1f80197f8b0b846a8d5cf7b7ec6084493950d0882cc5537fb7b96a69e3c8590"}, - {file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c72444d17777865734aa1a4d167794c34b63e5883abb90356a0364a28904e6c0"}, - {file = "aiohttp-3.9.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9b05d5cbe9dafcdc733262c3a99ccf63d2f7ce02543620d2bd8db4d4f7a22f83"}, - 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