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Vectorstore Agent Reference

Here we handle the application of vectorstore services to retrieval-augmented generation tasks by embedding documents and connections/management of vectorstore services and semantic search.

Vectorstore Implementation

Module for handling document embedding, storage and retrieval.

This module provides classes for splitting documents into chunks, embedding them using various LLM providers (OpenAI, Xinference, Ollama), storing them in vector databases, and retrieving relevant passages through similarity search.

DocumentEmbedder

Handle retrieval-augmented generation (RAG) functionality of BioChatter.

This class is responsible for: - Splitting text documents into manageable chunks - Embedding these chunks using various LLM providers - Storing embeddings in vector databases - Performing similarity searches for retrieval

Source code in biochatter/vectorstore.py 
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class DocumentEmbedder:
    """Handle retrieval-augmented generation (RAG) functionality of BioChatter.

    This class is responsible for:
    - Splitting text documents into manageable chunks
    - Embedding these chunks using various LLM providers
    - Storing embeddings in vector databases
    - Performing similarity searches for retrieval
    """

    def __init__(
        self,
        used: bool = False,
        online: bool = False,
        chunk_size: int = 1000,
        chunk_overlap: int = 0,
        split_by_characters: bool = True,
        separators: list | None = None,
        n_results: int = 3,
        model: str | None = "text-embedding-ada-002",
        vector_db_vendor: str | None = None,
        connection_args: dict | None = None,
        embedding_collection_name: str | None = None,
        metadata_collection_name: str | None = None,
        base_url: str | None = None,
        embeddings: OpenAIEmbeddings | XinferenceEmbeddings | OllamaEmbeddings | AzureOpenAIEmbeddings | None = None,
        documentids_workspace: list[str] | None = None,
    ) -> None:
        r"""Initialize the DocumentEmbedder with the specified configuration.

        Args:
        ----
            used (bool, optional): whether RAG has been used (frontend setting).
                Defaults to False.

            online (bool, optional): whether we are running the frontend online.
                Defaults to False.

            chunk_size (int, optional): size of chunks to split text into.
                Defaults to 1000.

            chunk_overlap (int, optional): overlap between chunks.
                Defaults to 0.

            split_by_characters (bool, optional): whether to split by characters
                or tokens. Defaults to True.

            separators (Optional[list], optional): list of separators to use
                when splitting by characters. Defaults to [" ", ",", "\n"].

            n_results (int, optional): number of results to return from
                similarity search. Defaults to 3.

            model (Optional[str], optional): name of model to use for
                embeddings. Defaults to 'text-embedding-ada-002'.

            vector_db_vendor (Optional[str], optional): name of vector database
                to use. Defaults to Milvus.

            connection_args (Optional[dict], optional): arguments to pass to
                vector database connection. Defaults to None.

            base_url (Optional[str], optional): base url of OpenAI API.

            embeddings (Optional[OpenAIEmbeddings | XinferenceEmbeddings],
                optional): Embeddings object to use. Defaults to OpenAI.

            documentids_workspace (Optional[List[str]], optional): a list of
                document IDs that defines the scope within which RAG operations
                (remove, similarity search, and get all) occur. Defaults to
                None, which means the operations will be performed across all
                documents in the database.

        """
        self.used = used
        self.online = online
        self.chunk_size = chunk_size
        self.chunk_overlap = chunk_overlap
        self.separators = separators or [" ", ",", "\n"]
        self.n_results = n_results
        self.split_by_characters = split_by_characters
        self.model_name = model

        # TODO API Key handling to central config?
        if base_url:
            openai.api_base = base_url

        self.embeddings = embeddings

        # connection arguments
        self.connection_args = connection_args or {
            "host": "127.0.0.1",
            "port": "19530",
        }
        self.embedding_collection_name = embedding_collection_name
        self.metadata_collection_name = metadata_collection_name
        self.documentids_workspace = documentids_workspace

        # TODO: vector db selection
        self.vector_db_vendor = vector_db_vendor or "milvus"
        # instantiate VectorDatabaseHost
        self.database_host = None
        self._init_database_host()

    def _set_embeddings(
        self,
        embeddings: (
            OpenAIEmbeddings
            | XinferenceEmbeddings
            | OllamaEmbeddings
            | AzureOpenAIEmbeddings
        ),
    ) -> None:
        print("setting embedder")
        self.embeddings = embeddings

    def _init_database_host(self) -> None:
        if self.vector_db_vendor == "milvus":
            self.database_host = VectorDatabaseAgentMilvus(
                embedding_func=self.embeddings,
                connection_args=self.connection_args,
                embedding_collection_name=self.embedding_collection_name,
                metadata_collection_name=self.metadata_collection_name,
            )
        else:
            raise NotImplementedError(self.vector_db_vendor)

    def set_chunk_size(self, chunk_size: int) -> None:
        """Set the chunk size for the text splitter."""
        self.chunk_size = chunk_size

    def set_chunk_overlap(self, chunk_overlap: int) -> None:
        """Set the chunk overlap for the text splitter."""
        self.chunk_overlap = chunk_overlap

    def set_separators(self, separators: list) -> None:
        """Set the separators for the text splitter."""
        self.separators = separators

    def _characters_splitter(self) -> RecursiveCharacterTextSplitter:
        return RecursiveCharacterTextSplitter(
            chunk_size=self.chunk_size,
            chunk_overlap=self.chunk_overlap,
            separators=self.separators,
        )

    def _tokens_splitter(self) -> RecursiveCharacterTextSplitter:
        DEFAULT_OPENAI_MODEL = "gpt-3.5-turbo"
        HUGGINGFACE_MODELS = ["bigscience/bloom"]
        if self.model_name and self.model_name in HUGGINGFACE_MODELS:
            tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
            return RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
                tokenizer,
                chunk_size=self.chunk_size,
                chunk_overlap=self.chunk_overlap,
                separators=self.separators,
            )

        return RecursiveCharacterTextSplitter.from_tiktoken_encoder(
            encoding_name="",
            model_name=(DEFAULT_OPENAI_MODEL if not self.model_name else self.model_name),
            chunk_size=self.chunk_size,
            chunk_overlap=self.chunk_overlap,
            separators=self.separators,
        )

    def _text_splitter(self) -> RecursiveCharacterTextSplitter:
        return self._characters_splitter() if self.split_by_characters else self._tokens_splitter()

    def save_document(self, doc: list[Document]) -> str:
        """Save a list of documents to the vector database.

        Args:
            doc (List[Document]): document content, read with `DocumentReader`
                functions `load_document()`, `document_from_pdf()`, or
                `document_from_txt()`

        Returns:
            str: document id, which can be used to remove an uploaded document
                with `remove_document()`

        """
        splitted = self._split_document(doc)
        return self._store_embeddings(splitted)

    def _split_document(self, document: list[Document]) -> list[Document]:
        """Split a document into chunks."""
        text_splitter = self._text_splitter()
        return text_splitter.split_documents(document)

    def _store_embeddings(self, doc: list[Document]) -> str:
        """Store embeddings for a list of documents."""
        return self.database_host.store_embeddings(documents=doc)

    def connect(self) -> None:
        """Connect to the vector database."""
        self.database_host.connect()

    def get_all_documents(self) -> list[dict]:
        """Get all documents from the vector database."""
        return self.database_host.get_all_documents(
            doc_ids=self.documentids_workspace,
        )

    def remove_document(self, doc_id: str) -> None:
        """Remove a document from the vector database."""
        return self.database_host.remove_document(
            doc_id,
            self.documentids_workspace,
        )

__init__(used=False, online=False, chunk_size=1000, chunk_overlap=0, split_by_characters=True, separators=None, n_results=3, model='text-embedding-ada-002', vector_db_vendor=None, connection_args=None, embedding_collection_name=None, metadata_collection_name=None, base_url=None, embeddings=None, documentids_workspace=None)

Initialize the DocumentEmbedder with the specified configuration.

used (bool, optional): whether RAG has been used (frontend setting).
    Defaults to False.

online (bool, optional): whether we are running the frontend online.
    Defaults to False.

chunk_size (int, optional): size of chunks to split text into.
    Defaults to 1000.

chunk_overlap (int, optional): overlap between chunks.
    Defaults to 0.

split_by_characters (bool, optional): whether to split by characters
    or tokens. Defaults to True.

separators (Optional[list], optional): list of separators to use
    when splitting by characters. Defaults to [" ", ",", "\n"].

n_results (int, optional): number of results to return from
    similarity search. Defaults to 3.

model (Optional[str], optional): name of model to use for
    embeddings. Defaults to 'text-embedding-ada-002'.

vector_db_vendor (Optional[str], optional): name of vector database
    to use. Defaults to Milvus.

connection_args (Optional[dict], optional): arguments to pass to
    vector database connection. Defaults to None.

base_url (Optional[str], optional): base url of OpenAI API.

embeddings (Optional[OpenAIEmbeddings | XinferenceEmbeddings],
    optional): Embeddings object to use. Defaults to OpenAI.

documentids_workspace (Optional[List[str]], optional): a list of
    document IDs that defines the scope within which RAG operations
    (remove, similarity search, and get all) occur. Defaults to
    None, which means the operations will be performed across all
    documents in the database.
Source code in biochatter/vectorstore.py 
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def __init__(
    self,
    used: bool = False,
    online: bool = False,
    chunk_size: int = 1000,
    chunk_overlap: int = 0,
    split_by_characters: bool = True,
    separators: list | None = None,
    n_results: int = 3,
    model: str | None = "text-embedding-ada-002",
    vector_db_vendor: str | None = None,
    connection_args: dict | None = None,
    embedding_collection_name: str | None = None,
    metadata_collection_name: str | None = None,
    base_url: str | None = None,
    embeddings: OpenAIEmbeddings | XinferenceEmbeddings | OllamaEmbeddings | AzureOpenAIEmbeddings | None = None,
    documentids_workspace: list[str] | None = None,
) -> None:
    r"""Initialize the DocumentEmbedder with the specified configuration.

    Args:
    ----
        used (bool, optional): whether RAG has been used (frontend setting).
            Defaults to False.

        online (bool, optional): whether we are running the frontend online.
            Defaults to False.

        chunk_size (int, optional): size of chunks to split text into.
            Defaults to 1000.

        chunk_overlap (int, optional): overlap between chunks.
            Defaults to 0.

        split_by_characters (bool, optional): whether to split by characters
            or tokens. Defaults to True.

        separators (Optional[list], optional): list of separators to use
            when splitting by characters. Defaults to [" ", ",", "\n"].

        n_results (int, optional): number of results to return from
            similarity search. Defaults to 3.

        model (Optional[str], optional): name of model to use for
            embeddings. Defaults to 'text-embedding-ada-002'.

        vector_db_vendor (Optional[str], optional): name of vector database
            to use. Defaults to Milvus.

        connection_args (Optional[dict], optional): arguments to pass to
            vector database connection. Defaults to None.

        base_url (Optional[str], optional): base url of OpenAI API.

        embeddings (Optional[OpenAIEmbeddings | XinferenceEmbeddings],
            optional): Embeddings object to use. Defaults to OpenAI.

        documentids_workspace (Optional[List[str]], optional): a list of
            document IDs that defines the scope within which RAG operations
            (remove, similarity search, and get all) occur. Defaults to
            None, which means the operations will be performed across all
            documents in the database.

    """
    self.used = used
    self.online = online
    self.chunk_size = chunk_size
    self.chunk_overlap = chunk_overlap
    self.separators = separators or [" ", ",", "\n"]
    self.n_results = n_results
    self.split_by_characters = split_by_characters
    self.model_name = model

    # TODO API Key handling to central config?
    if base_url:
        openai.api_base = base_url

    self.embeddings = embeddings

    # connection arguments
    self.connection_args = connection_args or {
        "host": "127.0.0.1",
        "port": "19530",
    }
    self.embedding_collection_name = embedding_collection_name
    self.metadata_collection_name = metadata_collection_name
    self.documentids_workspace = documentids_workspace

    # TODO: vector db selection
    self.vector_db_vendor = vector_db_vendor or "milvus"
    # instantiate VectorDatabaseHost
    self.database_host = None
    self._init_database_host()

connect()

Connect to the vector database.

Source code in biochatter/vectorstore.py 
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def connect(self) -> None:
    """Connect to the vector database."""
    self.database_host.connect()

get_all_documents()

Get all documents from the vector database.

Source code in biochatter/vectorstore.py 
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def get_all_documents(self) -> list[dict]:
    """Get all documents from the vector database."""
    return self.database_host.get_all_documents(
        doc_ids=self.documentids_workspace,
    )

remove_document(doc_id)

Remove a document from the vector database.

Source code in biochatter/vectorstore.py 
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def remove_document(self, doc_id: str) -> None:
    """Remove a document from the vector database."""
    return self.database_host.remove_document(
        doc_id,
        self.documentids_workspace,
    )

save_document(doc)

Save a list of documents to the vector database.

Parameters:

Name Type Description Default
doc List[Document]

document content, read with DocumentReader functions load_document(), document_from_pdf(), or document_from_txt()

 required

Returns:

Name Type Description
str str

document id, which can be used to remove an uploaded document with remove_document()
Source code in biochatter/vectorstore.py 
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def save_document(self, doc: list[Document]) -> str:
    """Save a list of documents to the vector database.

    Args:
        doc (List[Document]): document content, read with `DocumentReader`
            functions `load_document()`, `document_from_pdf()`, or
            `document_from_txt()`

    Returns:
        str: document id, which can be used to remove an uploaded document
            with `remove_document()`

    """
    splitted = self._split_document(doc)
    return self._store_embeddings(splitted)

set_chunk_overlap(chunk_overlap)

Set the chunk overlap for the text splitter.

Source code in biochatter/vectorstore.py 
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def set_chunk_overlap(self, chunk_overlap: int) -> None:
    """Set the chunk overlap for the text splitter."""
    self.chunk_overlap = chunk_overlap

set_chunk_size(chunk_size)

Set the chunk size for the text splitter.

Source code in biochatter/vectorstore.py 
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def set_chunk_size(self, chunk_size: int) -> None:
    """Set the chunk size for the text splitter."""
    self.chunk_size = chunk_size

set_separators(separators)

Set the separators for the text splitter.

Source code in biochatter/vectorstore.py 
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def set_separators(self, separators: list) -> None:
    """Set the separators for the text splitter."""
    self.separators = separators

DocumentReader

Class for reading documents from various sources.

Source code in biochatter/vectorstore.py 
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class DocumentReader:
    """Class for reading documents from various sources."""

    def load_document(self, path: str) -> list[Document]:
        """Load a document from a path; accept txt and pdf files.

        Txt files are loaded as-is, pdf files are converted to text using
        `fitz`.

        Args:
        ----
            path (str): path to document

        Returns:
        -------
            List[Document]: list of documents

        Raises:
        ------
            ValueError: If file extension is not supported

        """
        if path.endswith(".txt"):
            loader = TextLoader(path)
            return loader.load()

        if path.endswith(".pdf"):
            doc = fitz.open(path)
            text = ""
            for page in doc:
                text += page.get_text()

            meta = {k: v for k, v in doc.metadata.items() if v}
            meta.update({"source": path})

            return [
                Document(
                    page_content=text,
                    metadata=meta,
                ),
            ]

        err_msg = f"Unsupported file extension in {path}. File must be .txt or .pdf"
        raise ValueError(err_msg)

    def document_from_pdf(self, pdf: bytes) -> list[Document]:
        """Return a list of Documents from a pdf file byte representation.

        Receive a byte representation of a pdf file and return a list of
        Documents with metadata.

        Args:
        ----
            pdf (bytes): byte representation of pdf file

        Returns:
        -------
            List[Document]: list of documents

        """
        doc = fitz.open(stream=pdf, filetype="pdf")
        text = ""
        for page in doc:
            text += page.get_text()

        meta = {k: v for k, v in doc.metadata.items() if v}
        meta.update({"source": "pdf"})

        return [
            Document(
                page_content=text,
                metadata=meta,
            ),
        ]

    def document_from_txt(self, txt: bytes) -> list[Document]:
        """Return a list of Documents from a txt file byte representation.

        Receive a byte representation of a txt file and return a list of
        Documents with metadata.

        Args:
        ----
            txt (bytes): byte representation of txt file

        Returns:
        -------
            List[Document]: list of documents

        """
        meta = {"source": "txt"}
        return [
            Document(
                page_content=txt,
                metadata=meta,
            ),
        ]

document_from_pdf(pdf)

Return a list of Documents from a pdf file byte representation.

Receive a byte representation of a pdf file and return a list of Documents with metadata.

pdf (bytes): byte representation of pdf file

List[Document]: list of documents
Source code in biochatter/vectorstore.py 
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def document_from_pdf(self, pdf: bytes) -> list[Document]:
    """Return a list of Documents from a pdf file byte representation.

    Receive a byte representation of a pdf file and return a list of
    Documents with metadata.

    Args:
    ----
        pdf (bytes): byte representation of pdf file

    Returns:
    -------
        List[Document]: list of documents

    """
    doc = fitz.open(stream=pdf, filetype="pdf")
    text = ""
    for page in doc:
        text += page.get_text()

    meta = {k: v for k, v in doc.metadata.items() if v}
    meta.update({"source": "pdf"})

    return [
        Document(
            page_content=text,
            metadata=meta,
        ),
    ]

document_from_txt(txt)

Return a list of Documents from a txt file byte representation.

Receive a byte representation of a txt file and return a list of Documents with metadata.

txt (bytes): byte representation of txt file

List[Document]: list of documents
Source code in biochatter/vectorstore.py 
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def document_from_txt(self, txt: bytes) -> list[Document]:
    """Return a list of Documents from a txt file byte representation.

    Receive a byte representation of a txt file and return a list of
    Documents with metadata.

    Args:
    ----
        txt (bytes): byte representation of txt file

    Returns:
    -------
        List[Document]: list of documents

    """
    meta = {"source": "txt"}
    return [
        Document(
            page_content=txt,
            metadata=meta,
        ),
    ]

load_document(path)

Load a document from a path; accept txt and pdf files.

Txt files are loaded as-is, pdf files are converted to text using fitz.

path (str): path to document

List[Document]: list of documents

ValueError: If file extension is not supported
Source code in biochatter/vectorstore.py 
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def load_document(self, path: str) -> list[Document]:
    """Load a document from a path; accept txt and pdf files.

    Txt files are loaded as-is, pdf files are converted to text using
    `fitz`.

    Args:
    ----
        path (str): path to document

    Returns:
    -------
        List[Document]: list of documents

    Raises:
    ------
        ValueError: If file extension is not supported

    """
    if path.endswith(".txt"):
        loader = TextLoader(path)
        return loader.load()

    if path.endswith(".pdf"):
        doc = fitz.open(path)
        text = ""
        for page in doc:
            text += page.get_text()

        meta = {k: v for k, v in doc.metadata.items() if v}
        meta.update({"source": path})

        return [
            Document(
                page_content=text,
                metadata=meta,
            ),
        ]

    err_msg = f"Unsupported file extension in {path}. File must be .txt or .pdf"
    raise ValueError(err_msg)

OllamaDocumentEmbedder

Bases: DocumentEmbedder

Extension of the DocumentEmbedder class to Ollama.

Source code in biochatter/vectorstore.py 
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class OllamaDocumentEmbedder(DocumentEmbedder):
    """Extension of the DocumentEmbedder class to Ollama."""

    def __init__(
        self,
        used: bool = False,
        chunk_size: int = 1000,
        chunk_overlap: int = 0,
        split_by_characters: bool = True,
        separators: list | None = None,
        n_results: int = 3,
        model: str | None = "nomic-embed-text",
        vector_db_vendor: str | None = None,
        connection_args: dict | None = None,
        embedding_collection_name: str | None = None,
        metadata_collection_name: str | None = None,
        base_url: str | None = None,
        documentids_workspace: list[str] | None = None,
    ) -> None:
        """Initialize with the specified configuration.

        Args:
        ----
            used (bool, optional): whether RAG has been used (frontend setting).

            chunk_size (int, optional): size of chunks to split text into.

            chunk_overlap (int, optional): overlap between chunks.

            split_by_characters (bool, optional): whether to split by characters
                or tokens.

            separators (Optional[list], optional): list of separators to use
                when splitting by characters.

            n_results (int, optional): number of results to return from
                similarity search.

            model (Optional[str], optional): name of model to use for
                embeddings. Can be "auto" to use the first available model.

            vector_db_vendor (Optional[str], optional): name of vector database
                to use.

            connection_args (Optional[dict], optional): arguments to pass to
                vector database connection.

            embedding_collection_name (Optional[str], optional): name of
                collection to store embeddings in.

            metadata_collection_name (Optional[str], optional): name of
                collection to store metadata in.

            base_url (Optional[str], optional): base url of Xinference API.

            documentids_workspace (Optional[List[str]], optional): a list of
                document IDs that defines the scope within which RAG operations
                (remove, similarity search, and get all) occur. Defaults to
                None, which means the operations will be performed across all
                documents in the database.

        """
        from langchain_community.embeddings import OllamaEmbeddings

        self.model_name = model

        super().__init__(
            used=used,
            online=True,
            chunk_size=chunk_size,
            chunk_overlap=chunk_overlap,
            split_by_characters=split_by_characters,
            separators=separators,
            n_results=n_results,
            model=model,
            vector_db_vendor=vector_db_vendor,
            connection_args=connection_args,
            embedding_collection_name=embedding_collection_name,
            metadata_collection_name=metadata_collection_name,
            base_url=base_url,
            embeddings=OllamaEmbeddings(
                base_url=base_url,
                model=self.model_name,
            ),
            documentids_workspace=documentids_workspace,
        )

__init__(used=False, chunk_size=1000, chunk_overlap=0, split_by_characters=True, separators=None, n_results=3, model='nomic-embed-text', vector_db_vendor=None, connection_args=None, embedding_collection_name=None, metadata_collection_name=None, base_url=None, documentids_workspace=None)

Initialize with the specified configuration.

used (bool, optional): whether RAG has been used (frontend setting).

chunk_size (int, optional): size of chunks to split text into.

chunk_overlap (int, optional): overlap between chunks.

split_by_characters (bool, optional): whether to split by characters
    or tokens.

separators (Optional[list], optional): list of separators to use
    when splitting by characters.

n_results (int, optional): number of results to return from
    similarity search.

model (Optional[str], optional): name of model to use for
    embeddings. Can be "auto" to use the first available model.

vector_db_vendor (Optional[str], optional): name of vector database
    to use.

connection_args (Optional[dict], optional): arguments to pass to
    vector database connection.

embedding_collection_name (Optional[str], optional): name of
    collection to store embeddings in.

metadata_collection_name (Optional[str], optional): name of
    collection to store metadata in.

base_url (Optional[str], optional): base url of Xinference API.

documentids_workspace (Optional[List[str]], optional): a list of
    document IDs that defines the scope within which RAG operations
    (remove, similarity search, and get all) occur. Defaults to
    None, which means the operations will be performed across all
    documents in the database.
Source code in biochatter/vectorstore.py 
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def __init__(
    self,
    used: bool = False,
    chunk_size: int = 1000,
    chunk_overlap: int = 0,
    split_by_characters: bool = True,
    separators: list | None = None,
    n_results: int = 3,
    model: str | None = "nomic-embed-text",
    vector_db_vendor: str | None = None,
    connection_args: dict | None = None,
    embedding_collection_name: str | None = None,
    metadata_collection_name: str | None = None,
    base_url: str | None = None,
    documentids_workspace: list[str] | None = None,
) -> None:
    """Initialize with the specified configuration.

    Args:
    ----
        used (bool, optional): whether RAG has been used (frontend setting).

        chunk_size (int, optional): size of chunks to split text into.

        chunk_overlap (int, optional): overlap between chunks.

        split_by_characters (bool, optional): whether to split by characters
            or tokens.

        separators (Optional[list], optional): list of separators to use
            when splitting by characters.

        n_results (int, optional): number of results to return from
            similarity search.

        model (Optional[str], optional): name of model to use for
            embeddings. Can be "auto" to use the first available model.

        vector_db_vendor (Optional[str], optional): name of vector database
            to use.

        connection_args (Optional[dict], optional): arguments to pass to
            vector database connection.

        embedding_collection_name (Optional[str], optional): name of
            collection to store embeddings in.

        metadata_collection_name (Optional[str], optional): name of
            collection to store metadata in.

        base_url (Optional[str], optional): base url of Xinference API.

        documentids_workspace (Optional[List[str]], optional): a list of
            document IDs that defines the scope within which RAG operations
            (remove, similarity search, and get all) occur. Defaults to
            None, which means the operations will be performed across all
            documents in the database.

    """
    from langchain_community.embeddings import OllamaEmbeddings

    self.model_name = model

    super().__init__(
        used=used,
        online=True,
        chunk_size=chunk_size,
        chunk_overlap=chunk_overlap,
        split_by_characters=split_by_characters,
        separators=separators,
        n_results=n_results,
        model=model,
        vector_db_vendor=vector_db_vendor,
        connection_args=connection_args,
        embedding_collection_name=embedding_collection_name,
        metadata_collection_name=metadata_collection_name,
        base_url=base_url,
        embeddings=OllamaEmbeddings(
            base_url=base_url,
            model=self.model_name,
        ),
        documentids_workspace=documentids_workspace,
    )

XinferenceDocumentEmbedder

Bases: DocumentEmbedder

Extension of the DocumentEmbedder class to Xinference.

Source code in biochatter/vectorstore.py 
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class XinferenceDocumentEmbedder(DocumentEmbedder):
    """Extension of the DocumentEmbedder class to Xinference."""

    def __init__(
        self,
        used: bool = False,
        chunk_size: int = 1000,
        chunk_overlap: int = 0,
        split_by_characters: bool = True,
        separators: list | None = None,
        n_results: int = 3,
        model: str | None = "auto",
        vector_db_vendor: str | None = None,
        connection_args: dict | None = None,
        embedding_collection_name: str | None = None,
        metadata_collection_name: str | None = None,
        base_url: str | None = None,
        documentids_workspace: list[str] | None = None,
    ) -> None:
        """Initialize with the specified configuration.

        Args:
        ----
            used (bool, optional): whether RAG has been used (frontend setting).

            chunk_size (int, optional): size of chunks to split text into.

            chunk_overlap (int, optional): overlap between chunks.

            split_by_characters (bool, optional): whether to split by characters
                or tokens.

            separators (Optional[list], optional): list of separators to use
                when splitting by characters.

            n_results (int, optional): number of results to return from
                similarity search.

            model (Optional[str], optional): name of model to use for
                embeddings. Can be "auto" to use the first available model.

            vector_db_vendor (Optional[str], optional): name of vector database
                to use.

            connection_args (Optional[dict], optional): arguments to pass to
                vector database connection.

            embedding_collection_name (Optional[str], optional): name of
                collection to store embeddings in.

            metadata_collection_name (Optional[str], optional): name of
                collection to store metadata in.

            base_url (Optional[str], optional): base url of Xinference API.

            documentids_workspace (Optional[List[str]], optional): a list of
                document IDs that defines the scope within which RAG operations
                (remove, similarity search, and get all) occur. Defaults to
                None, which means the operations will be performed across all
                documents in the database.

        """
        from xinference.client import Client

        self.model_name = model
        self.client = Client(base_url=base_url)
        self.models = {}
        self.load_models()

        if self.model_name is None or self.model_name == "auto":
            self.model_name = self.list_models_by_type("embedding")[0]
        self.model_uid = self.models[self.model_name]["id"]

        super().__init__(
            used=used,
            online=True,
            chunk_size=chunk_size,
            chunk_overlap=chunk_overlap,
            split_by_characters=split_by_characters,
            separators=separators,
            n_results=n_results,
            model=model,
            vector_db_vendor=vector_db_vendor,
            connection_args=connection_args,
            embedding_collection_name=embedding_collection_name,
            metadata_collection_name=metadata_collection_name,
            base_url=base_url,
            embeddings=XinferenceEmbeddings(
                server_url=base_url,
                model_uid=self.model_uid,
            ),
            documentids_workspace=documentids_workspace,
        )

    def load_models(self) -> None:
        """Get all models that are currently available.

        Connect to the Xinference server and write the running models to
        `self.models`.
        """
        for _id, model in self.client.list_models().items():
            model["id"] = _id
            self.models[model["model_name"]] = model

    def list_models_by_type(self, model_type: str) -> list[str]:
        """Return all models of a certain type.

        Connect to the Xinference server and return all models of a certain
        type.

        Args:
        ----
            model_type (str): type of model to list (e.g. "embedding", "chat")

        Returns:
        -------
            List[str]: list of model names

        """
        names = []
        for name, model in self.models.items():
            if "model_ability" in model:
                if model_type in model["model_ability"]:
                    names.append(name)
            elif model["model_type"] == model_type:
                names.append(name)
        return names

__init__(used=False, chunk_size=1000, chunk_overlap=0, split_by_characters=True, separators=None, n_results=3, model='auto', vector_db_vendor=None, connection_args=None, embedding_collection_name=None, metadata_collection_name=None, base_url=None, documentids_workspace=None)

Initialize with the specified configuration.

used (bool, optional): whether RAG has been used (frontend setting).

chunk_size (int, optional): size of chunks to split text into.

chunk_overlap (int, optional): overlap between chunks.

split_by_characters (bool, optional): whether to split by characters
    or tokens.

separators (Optional[list], optional): list of separators to use
    when splitting by characters.

n_results (int, optional): number of results to return from
    similarity search.

model (Optional[str], optional): name of model to use for
    embeddings. Can be "auto" to use the first available model.

vector_db_vendor (Optional[str], optional): name of vector database
    to use.

connection_args (Optional[dict], optional): arguments to pass to
    vector database connection.

embedding_collection_name (Optional[str], optional): name of
    collection to store embeddings in.

metadata_collection_name (Optional[str], optional): name of
    collection to store metadata in.

base_url (Optional[str], optional): base url of Xinference API.

documentids_workspace (Optional[List[str]], optional): a list of
    document IDs that defines the scope within which RAG operations
    (remove, similarity search, and get all) occur. Defaults to
    None, which means the operations will be performed across all
    documents in the database.
Source code in biochatter/vectorstore.py 
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def __init__(
    self,
    used: bool = False,
    chunk_size: int = 1000,
    chunk_overlap: int = 0,
    split_by_characters: bool = True,
    separators: list | None = None,
    n_results: int = 3,
    model: str | None = "auto",
    vector_db_vendor: str | None = None,
    connection_args: dict | None = None,
    embedding_collection_name: str | None = None,
    metadata_collection_name: str | None = None,
    base_url: str | None = None,
    documentids_workspace: list[str] | None = None,
) -> None:
    """Initialize with the specified configuration.

    Args:
    ----
        used (bool, optional): whether RAG has been used (frontend setting).

        chunk_size (int, optional): size of chunks to split text into.

        chunk_overlap (int, optional): overlap between chunks.

        split_by_characters (bool, optional): whether to split by characters
            or tokens.

        separators (Optional[list], optional): list of separators to use
            when splitting by characters.

        n_results (int, optional): number of results to return from
            similarity search.

        model (Optional[str], optional): name of model to use for
            embeddings. Can be "auto" to use the first available model.

        vector_db_vendor (Optional[str], optional): name of vector database
            to use.

        connection_args (Optional[dict], optional): arguments to pass to
            vector database connection.

        embedding_collection_name (Optional[str], optional): name of
            collection to store embeddings in.

        metadata_collection_name (Optional[str], optional): name of
            collection to store metadata in.

        base_url (Optional[str], optional): base url of Xinference API.

        documentids_workspace (Optional[List[str]], optional): a list of
            document IDs that defines the scope within which RAG operations
            (remove, similarity search, and get all) occur. Defaults to
            None, which means the operations will be performed across all
            documents in the database.

    """
    from xinference.client import Client

    self.model_name = model
    self.client = Client(base_url=base_url)
    self.models = {}
    self.load_models()

    if self.model_name is None or self.model_name == "auto":
        self.model_name = self.list_models_by_type("embedding")[0]
    self.model_uid = self.models[self.model_name]["id"]

    super().__init__(
        used=used,
        online=True,
        chunk_size=chunk_size,
        chunk_overlap=chunk_overlap,
        split_by_characters=split_by_characters,
        separators=separators,
        n_results=n_results,
        model=model,
        vector_db_vendor=vector_db_vendor,
        connection_args=connection_args,
        embedding_collection_name=embedding_collection_name,
        metadata_collection_name=metadata_collection_name,
        base_url=base_url,
        embeddings=XinferenceEmbeddings(
            server_url=base_url,
            model_uid=self.model_uid,
        ),
        documentids_workspace=documentids_workspace,
    )

list_models_by_type(model_type)

Return all models of a certain type.

Connect to the Xinference server and return all models of a certain type.

model_type (str): type of model to list (e.g. "embedding", "chat")

List[str]: list of model names
Source code in biochatter/vectorstore.py 
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def list_models_by_type(self, model_type: str) -> list[str]:
    """Return all models of a certain type.

    Connect to the Xinference server and return all models of a certain
    type.

    Args:
    ----
        model_type (str): type of model to list (e.g. "embedding", "chat")

    Returns:
    -------
        List[str]: list of model names

    """
    names = []
    for name, model in self.models.items():
        if "model_ability" in model:
            if model_type in model["model_ability"]:
                names.append(name)
        elif model["model_type"] == model_type:
            names.append(name)
    return names

load_models()

Get all models that are currently available.

Connect to the Xinference server and write the running models to self.models.

Source code in biochatter/vectorstore.py 
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def load_models(self) -> None:
    """Get all models that are currently available.

    Connect to the Xinference server and write the running models to
    `self.models`.
    """
    for _id, model in self.client.list_models().items():
        model["id"] = _id
        self.models[model["model_name"]] = model

Vectorstore Agent

VectorDatabaseAgentMilvus

The VectorDatabaseAgentMilvus class manages vector databases in a connected host database. It manages an embedding collection _col_embeddings:langchain.vectorstores.Milvus, which is the main information on the embedded text fragments and the basis for similarity search, and a metadata collection _col_metadata:pymilvus.Collection, which stores the metadata of the embedded text fragments. A typical workflow includes the following operations:

  1. connect to a host using connect()
  2. get all documents in the active database using get_all_documents()
  3. save a number of fragments, usually from a specific document, using store_embeddings()
  4. do similarity search among all fragments of the currently active database using similarity_search()
  5. remove a document from the currently active database using remove_document()
Source code in biochatter/vectorstore_agent.py 
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class VectorDatabaseAgentMilvus:
    """The VectorDatabaseAgentMilvus class manages vector databases in a connected
    host database. It manages an embedding collection
    `_col_embeddings:langchain.vectorstores.Milvus`, which is the main
    information on the embedded text fragments and the basis for similarity
    search, and a metadata collection `_col_metadata:pymilvus.Collection`, which
    stores the metadata of the embedded text fragments. A typical workflow
    includes the following operations:

    1. connect to a host using `connect()`
    2. get all documents in the active database using `get_all_documents()`
    3. save a number of fragments, usually from a specific document, using
        `store_embeddings()`
    4. do similarity search among all fragments of the currently active database
        using `similarity_search()`
    5. remove a document from the currently active database using
        `remove_document()`
    """

    def __init__(
        self,
        embedding_func: OpenAIEmbeddings,
        connection_args: dict | None = None,
        embedding_collection_name: str | None = None,
        metadata_collection_name: str | None = None,
    ):
        """Args:
        ----
            embedding_func OpenAIEmbeddings: Function used to embed the text

            connection_args Optional dict: args to connect Vector Database

            embedding_collection_name Optional str: exposed for test

            metadata_collection_name Optional str: exposed for test

        """
        self._embedding_func = embedding_func
        self._col_embeddings: Milvus | None = None
        self._col_metadata: Collection | None = None
        self._connection_args = validate_connection_args(connection_args)
        self._embedding_name = embedding_collection_name or DOCUMENT_EMBEDDINGS_COLLECTION_NAME
        self._metadata_name = metadata_collection_name or DOCUMENT_METADATA_COLLECTION_NAME

    def connect(self) -> None:
        """Connect to a host and read two document collections (the default names
        are `DocumentEmbeddings` and `DocumentMetadata`) in the currently active
        database (default database name is `default`); if those document
        collections don't exist, create the two collections.
        """
        self._connect(**self._connection_args)
        self._init_host()

    def _connect(self, host: str, port: str, user: str, password: str) -> None:
        self.alias = self._create_connection_alias(host, port, user, password)

    def _init_host(self) -> None:
        """Initialize host. Will read/create document collection inside currently
        active database.
        """
        self._create_collections()

    def _create_connection_alias(
        self,
        host: str,
        port: str,
        user: str,
        password: str,
    ) -> str:
        """Connect to host and create a connection alias for metadata collection
        using a random uuid.

        Args:
        ----
            host (str): host ip address
            port (str): host port

        Returns:
        -------
            str: connection alias

        """
        alias = uuid.uuid4().hex
        try:
            connections.connect(
                host=host,
                port=port,
                user=user,
                password=password,
                alias=alias,
            )
            logger.debug(f"Created new connection using: {alias}")
            return alias
        except MilvusException as e:
            logger.error(f"Failed to create  new connection using: {alias}")
            raise e

    def _create_collections(self) -> None:
        """Create or load the embedding and metadata collections from the currently
        active database.
        """
        embedding_exists = utility.has_collection(
            self._embedding_name,
            using=self.alias,
        )
        meta_exists = utility.has_collection(
            self._metadata_name,
            using=self.alias,
        )

        if embedding_exists:
            self._load_embeddings_collection()
        else:
            self._create_embeddings_collection()

        if meta_exists:
            self._load_metadata_collection()
        else:
            self._create_metadata_collection()

        self._create_metadata_collection_index()
        self._col_metadata.load()

    def _load_embeddings_collection(self) -> None:
        """Load embeddings collection from currently active database."""
        try:
            self._col_embeddings = Milvus(
                embedding_function=self._embedding_func,
                collection_name=self._embedding_name,
                connection_args=self._connection_args,
            )
        except MilvusException as e:
            logger.error(
                f"Failed to load embeddings collection {self._embedding_name}.",
            )
            raise e

    def _create_embeddings_collection(self) -> None:
        """Create embedding collection.
        All fields: "meta_id", "vector"
        """
        try:
            self._col_embeddings = Milvus(
                embedding_function=self._embedding_func,
                collection_name=self._embedding_name,
                connection_args=self._connection_args,
            )
        except MilvusException as e:
            logger.error(
                f"Failed to create embeddings collection {self._embedding_name}",
            )
            raise e

    def _load_metadata_collection(self) -> None:
        """Load metadata collection from currently active database."""
        self._col_metadata = Collection(
            self._metadata_name,
            using=self.alias,
        )
        self._col_metadata.load()

    def _create_metadata_collection(self) -> None:
        """Create metadata collection.

        All fields: "id", "name", "author", "title", "format", "subject",
        "creator", "producer", "creationDate", "modDate", "source", "embedding",
        "isDeleted".

        As the vector database requires a vector field, we will create a fake
        vector "embedding". The field "isDeleted" is used to specify if the
        document is deleted.
        """
        MAX_LENGTH = 10000
        doc_id = FieldSchema(
            name="id",
            dtype=DataType.INT64,
            is_primary=True,
            auto_id=True,
        )
        doc_name = FieldSchema(
            name="name",
            dtype=DataType.VARCHAR,
            max_length=MAX_LENGTH,
        )
        doc_author = FieldSchema(
            name="author",
            dtype=DataType.VARCHAR,
            max_length=MAX_LENGTH,
        )
        doc_title = FieldSchema(
            name="title",
            dtype=DataType.VARCHAR,
            max_length=MAX_LENGTH,
        )
        doc_format = FieldSchema(
            name="format",
            dtype=DataType.VARCHAR,
            max_length=255,
        )
        doc_subject = FieldSchema(
            name="subject",
            dtype=DataType.VARCHAR,
            max_length=MAX_LENGTH,
        )
        doc_creator = FieldSchema(
            name="creator",
            dtype=DataType.VARCHAR,
            max_length=MAX_LENGTH,
        )
        doc_producer = FieldSchema(
            name="producer",
            dtype=DataType.VARCHAR,
            max_length=MAX_LENGTH,
        )
        doc_creationDate = FieldSchema(
            name="creationDate",
            dtype=DataType.VARCHAR,
            max_length=1024,
        )
        doc_modDate = FieldSchema(
            name="modDate",
            dtype=DataType.VARCHAR,
            max_length=1024,
        )
        doc_source = FieldSchema(
            name="source",
            dtype=DataType.VARCHAR,
            max_length=MAX_LENGTH,
        )
        embedding = FieldSchema(
            name="embedding",
            dtype=DataType.FLOAT_VECTOR,
            dim=METADATA_VECTOR_DIM,
        )
        isDeleted = FieldSchema(
            name="isDeleted",
            dtype=DataType.BOOL,
        )
        fields = [
            doc_id,
            doc_name,
            doc_author,
            doc_title,
            doc_format,
            doc_subject,
            doc_creator,
            doc_producer,
            doc_creationDate,
            doc_modDate,
            doc_source,
            embedding,
            isDeleted,
        ]
        schema = CollectionSchema(fields=fields)
        try:
            self._col_metadata = Collection(
                name=self._metadata_name,
                schema=schema,
                using=self.alias,
            )
        except MilvusException as e:
            logger.error(f"Failed to create collection {self._metadata_name}")
            raise e

    def _create_metadata_collection_index(self) -> None:
        """Create index for metadata collection in currently active database."""
        if not isinstance(self._col_metadata, Collection) or len(self._col_metadata.indexes) > 0:
            return

        index_params = {
            "metric_type": "L2",
            "index_type": "HNSW",
            "params": {"M": 8, "efConstruction": 64},
        }

        try:
            self._col_metadata.create_index(
                field_name="embedding",
                index_params=index_params,
                using=self.alias,
            )
        except MilvusException as e:
            logger.error(
                "Failed to create index for meta collection " f"{self._metadata_name}.",
            )
            raise e

    def _insert_data(self, documents: list[Document]) -> str:
        """Insert documents into the currently active database.

        Args:
        ----
            documents (List[Documents]): documents array, usually from
                DocumentReader.load_document, DocumentReader.document_from_pdf,
                DocumentReader.document_from_txt

        Returns:
        -------
            str: document id

        """
        if len(documents) == 0:
            return None
        metadata = [documents[0].metadata]
        aligned_metadata = align_metadata(metadata)
        try:
            result = self._col_metadata.insert(aligned_metadata)
            meta_id = str(result.primary_keys[0])
            self._col_metadata.flush()
        except MilvusException as e:
            logger.error("Failed to insert meta data")
            raise e
        aligned_docs = align_embeddings(documents, meta_id)
        try:
            # As we passed collection_name, documents will be added to existed collection
            self._col_embeddings = Milvus.from_documents(
                embedding=self._embedding_func,
                collection_name=self._embedding_name,
                connection_args=self._connection_args,
                documents=aligned_docs,
            )
        except MilvusException as e:
            logger.error(
                "Failed to insert data to embedding collection " f"{self._embedding_name}.",
            )
            raise e
        return meta_id

    def store_embeddings(self, documents: list[Document]) -> str:
        """Store documents in the currently active database.

        Args:
        ----
            documents (List[Documents]): documents array, usually from
                DocumentReader.load_document, DocumentReader.document_from_pdf,
                DocumentReader.document_from_txt

        Returns:
        -------
            str: document id

        """
        if len(documents) == 0:
            return None
        return self._insert_data(documents)

    def _build_embedding_search_expression(
        self,
        meta_ids: list[dict],
    ) -> str | None:
        """Build search expression for embedding collection. The generated
        expression follows the pattern: "meta_id in [{id1}, {id2}, ...]

        Args:
        ----
            meta_ids: the array of metadata id in metadata collection

        Returns:
        -------
            str: search expression or None

        """
        if len(meta_ids) == 0:
            return "meta_id in []"
        built_expr = """meta_id in ["""
        for item in meta_ids:
            id = f'"{item["id"]}",'
            built_expr += id
        built_expr = built_expr[:-1]
        built_expr += """]"""
        return built_expr

    def _join_embedding_and_metadata_results(
        self,
        result_embedding: list[Document],
        result_meta: list[dict],
    ) -> list[Document]:
        """Join the search results of embedding collection and results of metadata.

        Args:
        ----
            result_embedding (List[Document]): search result of embedding
                collection

            result_meta (List[Dict]): search result of metadata collection

        Returns:
        -------
            List[Document]: combined results like
                [{page_content: str, metadata: {...}}]

        """

        def _find_metadata_by_id(
            metadata: list[dict],
            id: str,
        ) -> dict | None:
            for d in metadata:
                if str(d["id"]) == id:
                    return d
            return None

        joined_docs = []
        for res in result_embedding:
            found = _find_metadata_by_id(result_meta, res.metadata["meta_id"])
            if found is None:  # discard
                logger.error(
                    f"Failed to join meta_id {res.metadata['meta_id']}",
                )
                continue
            joined_docs.append(
                Document(page_content=res.page_content, metadata=found),
            )
        return joined_docs

    @staticmethod
    def _build_meta_col_query_expr_for_all_documents(
        doc_ids: list[str] | None = None,
    ) -> str:
        """Build metadata collection query expression to obtain all documents.

        Args:
        ----
            doc_ids: the list of document ids (metadata ids), if thie argument is None,
                     that is, the query is to get all undeleted documents in metadata collection.
                     Otherwise, the query is to getr all undeleted documents form provided doc_ids

        Returns:
        -------
            query: str

        """
        expr = f"id in {doc_ids} and isDeleted == false" if doc_ids is not None else "isDeleted == false"
        return expr.replace('"', "").replace("'", "")

    def similarity_search(
        self,
        query: str,
        k: int = 3,
        doc_ids: list[str] | None = None,
    ) -> list[Document]:
        """Perform similarity search insider the currently active database
        according to the input query.

        This method will:
        1. get all non-deleted meta_id and build to search expression for
            the currently active embedding collection
        2. do similarity search in the embedding collection
        3. combine metadata and embeddings

        Args:
        ----
            query (str): query string

            k (int): the number of results to return

            doc_ids (Optional[list[str]]): the list of document ids, do
                similarity search across the specified documents

        Returns:
        -------
            List[Document]: search results

        """
        result_metadata = []
        expr = VectorDatabaseAgentMilvus._build_meta_col_query_expr_for_all_documents(
            doc_ids,
        )
        result_metadata = self._col_metadata.query(
            expr=expr,
            output_fields=METADATA_FIELDS,
        )
        expr = self._build_embedding_search_expression(result_metadata)
        result_embedding = self._col_embeddings.similarity_search(
            query=query,
            k=k,
            expr=expr,
        )
        return self._join_embedding_and_metadata_results(
            result_embedding,
            result_metadata,
        )

    def remove_document(
        self,
        doc_id: str,
        doc_ids: list[str] | None = None,
    ) -> bool:
        """Remove the document include meta data and its embeddings.

        Args:
        ----
            doc_id (str): the document to be deleted

            doc_ids (Optional[list[str]]): the list of document ids, defines
                documents scope within which remove operation occurs.

        Returns:
        -------
            bool: True if the document is deleted, False otherwise

        """
        if not self._col_metadata:
            return False
        if doc_ids is not None and (len(doc_ids) == 0 or (len(doc_ids) > 0 and doc_id not in doc_ids)):
            return False
        try:
            expr = f"id in [{doc_id}]"
            res = self._col_metadata.query(
                expr=expr,
                output_fields=METADATA_FIELDS,
            )
            if len(res) == 0:
                return False
            del_res = self._col_metadata.delete(expr)
            self._col_metadata.flush()

            res = self._col_embeddings.col.query(f'meta_id in ["{doc_id}"]')
            if len(res) == 0:
                return True
            ids = [item["pk"] for item in res]
            embedding_expr = f"pk in {ids}"
            del_res = self._col_embeddings.col.delete(expr=embedding_expr)
            self._col_embeddings.col.flush()
            return True
        except MilvusException as e:
            logger.error(e)
            raise e

    def get_all_documents(
        self,
        doc_ids: list[str] | None = None,
    ) -> list[dict]:
        """Get all non-deleted documents from the currently active database.

        Args:
        ----
            doc_ids (List[str] optional): the list of document ids, defines
                documents scope within which the operation of obtaining all
                documents occurs

        Returns:
        -------
            List[Dict]: the metadata of all non-deleted documents in the form
                [{{id}, {author}, {source}, ...}]

        """
        try:
            expr = VectorDatabaseAgentMilvus._build_meta_col_query_expr_for_all_documents(
                doc_ids,
            )
            result_metadata = self._col_metadata.query(
                expr=expr,
                output_fields=METADATA_FIELDS,
            )
            return result_metadata
        except MilvusException as e:
            logger.error(e)
            raise e

    def get_description(self, doc_ids: list[str] | None = None):
        def get_name(meta: dict[str, str]):
            name_col = ["title", "name", "subject", "source"]
            for col in name_col:
                if meta[col] is not None and len(meta[col]) > 0:
                    return meta[col]
            return ""

        expr = VectorDatabaseAgentMilvus._build_meta_col_query_expr_for_all_documents(
            doc_ids,
        )
        result = self._col_metadata.query(
            expr=expr,
            output_fields=METADATA_FIELDS,
        )
        names = list(map(get_name, result))
        names_set = set(names)
        desc = f"This vector store contains the following articles: {names_set}"
        return desc[:MAX_AGENT_DESC_LENGTH]

__init__(embedding_func, connection_args=None, embedding_collection_name=None, metadata_collection_name=None) 

embedding_func OpenAIEmbeddings: Function used to embed the text

connection_args Optional dict: args to connect Vector Database

embedding_collection_name Optional str: exposed for test

metadata_collection_name Optional str: exposed for test
Source code in biochatter/vectorstore_agent.py 
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def __init__(
    self,
    embedding_func: OpenAIEmbeddings,
    connection_args: dict | None = None,
    embedding_collection_name: str | None = None,
    metadata_collection_name: str | None = None,
):
    """Args:
    ----
        embedding_func OpenAIEmbeddings: Function used to embed the text

        connection_args Optional dict: args to connect Vector Database

        embedding_collection_name Optional str: exposed for test

        metadata_collection_name Optional str: exposed for test

    """
    self._embedding_func = embedding_func
    self._col_embeddings: Milvus | None = None
    self._col_metadata: Collection | None = None
    self._connection_args = validate_connection_args(connection_args)
    self._embedding_name = embedding_collection_name or DOCUMENT_EMBEDDINGS_COLLECTION_NAME
    self._metadata_name = metadata_collection_name or DOCUMENT_METADATA_COLLECTION_NAME

connect()

Connect to a host and read two document collections (the default names are DocumentEmbeddings and DocumentMetadata) in the currently active database (default database name is default); if those document collections don't exist, create the two collections.

Source code in biochatter/vectorstore_agent.py 
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def connect(self) -> None:
    """Connect to a host and read two document collections (the default names
    are `DocumentEmbeddings` and `DocumentMetadata`) in the currently active
    database (default database name is `default`); if those document
    collections don't exist, create the two collections.
    """
    self._connect(**self._connection_args)
    self._init_host()

get_all_documents(doc_ids=None)

Get all non-deleted documents from the currently active database.

doc_ids (List[str] optional): the list of document ids, defines
    documents scope within which the operation of obtaining all
    documents occurs

List[Dict]: the metadata of all non-deleted documents in the form
    [{{id}, {author}, {source}, ...}]
Source code in biochatter/vectorstore_agent.py 
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def get_all_documents(
    self,
    doc_ids: list[str] | None = None,
) -> list[dict]:
    """Get all non-deleted documents from the currently active database.

    Args:
    ----
        doc_ids (List[str] optional): the list of document ids, defines
            documents scope within which the operation of obtaining all
            documents occurs

    Returns:
    -------
        List[Dict]: the metadata of all non-deleted documents in the form
            [{{id}, {author}, {source}, ...}]

    """
    try:
        expr = VectorDatabaseAgentMilvus._build_meta_col_query_expr_for_all_documents(
            doc_ids,
        )
        result_metadata = self._col_metadata.query(
            expr=expr,
            output_fields=METADATA_FIELDS,
        )
        return result_metadata
    except MilvusException as e:
        logger.error(e)
        raise e

remove_document(doc_id, doc_ids=None)

Remove the document include meta data and its embeddings.

doc_id (str): the document to be deleted

doc_ids (Optional[list[str]]): the list of document ids, defines
    documents scope within which remove operation occurs.

bool: True if the document is deleted, False otherwise
Source code in biochatter/vectorstore_agent.py 
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def remove_document(
    self,
    doc_id: str,
    doc_ids: list[str] | None = None,
) -> bool:
    """Remove the document include meta data and its embeddings.

    Args:
    ----
        doc_id (str): the document to be deleted

        doc_ids (Optional[list[str]]): the list of document ids, defines
            documents scope within which remove operation occurs.

    Returns:
    -------
        bool: True if the document is deleted, False otherwise

    """
    if not self._col_metadata:
        return False
    if doc_ids is not None and (len(doc_ids) == 0 or (len(doc_ids) > 0 and doc_id not in doc_ids)):
        return False
    try:
        expr = f"id in [{doc_id}]"
        res = self._col_metadata.query(
            expr=expr,
            output_fields=METADATA_FIELDS,
        )
        if len(res) == 0:
            return False
        del_res = self._col_metadata.delete(expr)
        self._col_metadata.flush()

        res = self._col_embeddings.col.query(f'meta_id in ["{doc_id}"]')
        if len(res) == 0:
            return True
        ids = [item["pk"] for item in res]
        embedding_expr = f"pk in {ids}"
        del_res = self._col_embeddings.col.delete(expr=embedding_expr)
        self._col_embeddings.col.flush()
        return True
    except MilvusException as e:
        logger.error(e)
        raise e

Perform similarity search insider the currently active database according to the input query.

This method will: 1. get all non-deleted meta_id and build to search expression for the currently active embedding collection 2. do similarity search in the embedding collection 3. combine metadata and embeddings

query (str): query string

k (int): the number of results to return

doc_ids (Optional[list[str]]): the list of document ids, do
    similarity search across the specified documents

List[Document]: search results
Source code in biochatter/vectorstore_agent.py 
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def similarity_search(
    self,
    query: str,
    k: int = 3,
    doc_ids: list[str] | None = None,
) -> list[Document]:
    """Perform similarity search insider the currently active database
    according to the input query.

    This method will:
    1. get all non-deleted meta_id and build to search expression for
        the currently active embedding collection
    2. do similarity search in the embedding collection
    3. combine metadata and embeddings

    Args:
    ----
        query (str): query string

        k (int): the number of results to return

        doc_ids (Optional[list[str]]): the list of document ids, do
            similarity search across the specified documents

    Returns:
    -------
        List[Document]: search results

    """
    result_metadata = []
    expr = VectorDatabaseAgentMilvus._build_meta_col_query_expr_for_all_documents(
        doc_ids,
    )
    result_metadata = self._col_metadata.query(
        expr=expr,
        output_fields=METADATA_FIELDS,
    )
    expr = self._build_embedding_search_expression(result_metadata)
    result_embedding = self._col_embeddings.similarity_search(
        query=query,
        k=k,
        expr=expr,
    )
    return self._join_embedding_and_metadata_results(
        result_embedding,
        result_metadata,
    )

store_embeddings(documents)

Store documents in the currently active database.

documents (List[Documents]): documents array, usually from
    DocumentReader.load_document, DocumentReader.document_from_pdf,
    DocumentReader.document_from_txt

str: document id
Source code in biochatter/vectorstore_agent.py 
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def store_embeddings(self, documents: list[Document]) -> str:
    """Store documents in the currently active database.

    Args:
    ----
        documents (List[Documents]): documents array, usually from
            DocumentReader.load_document, DocumentReader.document_from_pdf,
            DocumentReader.document_from_txt

    Returns:
    -------
        str: document id

    """
    if len(documents) == 0:
        return None
    return self._insert_data(documents)

align_embeddings(docs, meta_id)

Ensure that the metadata id is present in each document.

docs (List[Document]): List of documents

meta_id (int): Metadata id to assign to the documents

List[Document]: List of documents, with each document having a metadata
    id.
Source code in biochatter/vectorstore_agent.py 
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def align_embeddings(docs: list[Document], meta_id: int) -> list[Document]:
    """Ensure that the metadata id is present in each document.

    Args:
    ----
        docs (List[Document]): List of documents

        meta_id (int): Metadata id to assign to the documents

    Returns:
    -------
        List[Document]: List of documents, with each document having a metadata
            id.

    """
    ret = []
    for doc in docs:
        ret.append(
            Document(
                page_content=doc.page_content,
                metadata={"meta_id": meta_id},
            ),
        )
    return ret

align_metadata(metadata, isDeleted=False)

Ensure that specific metadata fields are present; if not provided, fill with "unknown". Also, add a random vector to each metadata item to simulate an embedding.

metadata (List[Dict]): List of metadata items

isDeleted (Optional[bool], optional): Whether the document is deleted.
    Defaults to False.

List[List]: List of metadata items, with each item being a list of
    metadata fields.
Source code in biochatter/vectorstore_agent.py 
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def align_metadata(
    metadata: list[dict],
    isDeleted: bool | None = False,
) -> list[list]:
    """Ensure that specific metadata fields are present; if not provided, fill with
    "unknown". Also, add a random vector to each metadata item to simulate an
    embedding.

    Args:
    ----
        metadata (List[Dict]): List of metadata items

        isDeleted (Optional[bool], optional): Whether the document is deleted.
            Defaults to False.

    Returns:
    -------
        List[List]: List of metadata items, with each item being a list of
            metadata fields.

    """
    ret = []
    fields = METADATA_FIELDS.copy()
    fields.pop(0)
    for ix, k in enumerate(fields):
        ret.append([item[k] if k in item else "unknown" for item in metadata])

    ret.append(
        [[random.random() for _ in range(METADATA_VECTOR_DIM)] for _ in range(len(metadata))],
    )
    ret.append([isDeleted for _ in metadata])
    return ret