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]]
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-version = "3.9.1"
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-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-
[[package]]
name = "zipp"
version = "3.17.0"
@@ -5430,4 +4633,4 @@ cuda-acceleration = ["nvidia-cublas-cu12", "nvidia-cuda-cupti-cu12", "nvidia-cud
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<3.11"
-content-hash = "5470c4ff87ad2976569a6e6b4044e9737c26a2f6efb408f68c7fe72af857c2e5"
+content-hash = "bcacb82af60624e2e1fad7628d199477f73d533b8b60b57f487f031e4039700f"
diff --git a/pyproject.toml b/pyproject.toml
index 5d4e79a..0e504cc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,15 +12,12 @@ torch = [
{ version = "~=2.1.2", source = "PyPI", platform = "darwin" }
]
sentence-transformers = { version = "~=2.2.2" }
+sentencepiece = "~=0.1.99"
tqdm = "~=4.65.0"
requests = "~=2.31.0"
numpy = "~=1.24.2"
-sentencepiece = "~=0.1.99"
-langchain = "~=0.0.341"
-pyllamacpp = "==1.0.7"
-gpt4all = "==2.0.2"
streamlit = "~=1.29.0"
-unstructured = "==0.7.7"
+unstructured = "~=0.7.7"
chromadb = "~=0.4.18"
transformers = "~=4.33.0"
rich = "~=13.4.2"
@@ -59,7 +56,7 @@ pytest-cov = "~=4.0.0"
pytest-mock = "~=3.10.0"
pytest-asyncio = "~=0.23.6"
pre-commit = "~=3.6.0"
-ruff = "~=0.1.9"
+ruff = "~=0.6.4"
httpx = "~=0.23.3"
[[tool.poetry.source]]