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Vectorstore module

Here we handle the application of vectorstore services to retrieval-augmented generation tasks by embedding documents.

DocumentEmbedder

Source code in biochatter/vectorstore.py 
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class DocumentEmbedder:
    def __init__(
        self,
        used: bool = False,
        online: bool = False,
        chunk_size: int = 1000,
        chunk_overlap: int = 0,
        split_by_characters: bool = True,
        separators: Optional[list] = None,
        n_results: int = 3,
        model: Optional[str] = "text-embedding-ada-002",
        vector_db_vendor: Optional[str] = None,
        connection_args: Optional[dict] = None,
        embedding_collection_name: Optional[str] = None,
        metadata_collection_name: Optional[str] = None,
        api_key: Optional[str] = None,
        is_azure: Optional[bool] = False,
        azure_deployment: Optional[str] = None,
        azure_endpoint: Optional[str] = None,
        base_url: Optional[str] = None,
        embeddings: Optional[OpenAIEmbeddings | XinferenceEmbeddings] = None,
        documentids_workspace: Optional[list[str]] = None,
    ) -> None:
        """
        Class that handles the retrieval-augmented generation (RAG) functionality
        of BioChatter. It splits text into chunks, embeds them, and stores them in
        a vector database. It can then be used to do similarity search on the
        database.

        Args:

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

            online (bool, optional): whether we are running ChatGSE 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.

            api_key (Optional[str], optional): OpenAI API key. 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.

            is_azure (Optional[bool], optional): if we are using Azure
            azure_deployment (Optional[str], optional): Azure embeddings model deployment,
                should work with azure_endpoint when is_azure is True
            azure_endpoint (Optional[str], optional): Azure endpoint, should work with
                azure_deployment when is_azure is True

        """
        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

        if embeddings:
            self.embeddings = embeddings
        else:
            if not self.online:
                self.embeddings = (
                    OpenAIEmbeddings(openai_api_key=api_key, model=model)
                    if not is_azure
                    else AzureOpenAIEmbeddings(
                        api_key=api_key,
                        azure_deployment=azure_deployment,
                        azure_endpoint=azure_endpoint,
                        model=model,
                    )
                )
            else:
                self.embeddings = None

        # 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):
        print("setting embedder")
        self.embeddings = embeddings

    def _init_database_host(self):
        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_siue(self, chunk_size: int) -> None:
        self.chunk_size = chunk_size

    def set_chunk_overlap(self, chunk_overlap: int) -> None:
        self.chunk_overlap = chunk_overlap

    def set_separators(self, separators: list) -> None:
        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,
            )
        else:
            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:
        """
        This function saves document to the vector database
        Args:
            doc List[Document]: document content, read with DocumentReader load_document(),
                or document_from_pdf(), 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]:
        text_splitter = self._text_splitter()
        return text_splitter.split_documents(document)

    def _store_embeddings(self, doc: list[Document]) -> str:
        return self.database_host.store_embeddings(documents=doc)

    def connect(self) -> None:
        self.database_host.connect()

    def get_all_documents(self) -> list[dict]:
        return self.database_host.get_all_documents(
            doc_ids=self.documentids_workspace
        )

    def remove_document(self, doc_id: str) -> None:
        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, api_key=None, is_azure=False, azure_deployment=None, azure_endpoint=None, base_url=None, embeddings=None, documentids_workspace=None) 

    Class that handles the retrieval-augmented generation (RAG) functionality
    of BioChatter. It splits text into chunks, embeds them, and stores them in
    a vector database. It can then be used to do similarity search on the
    database.

    Args:

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

        online (bool, optional): whether we are running ChatGSE 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_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.

        api_key (Optional[str], optional): OpenAI API key. 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.

        is_azure (Optional[bool], optional): if we are using Azure
        azure_deployment (Optional[str], optional): Azure embeddings model deployment,
            should work with azure_endpoint when is_azure is True
        azure_endpoint (Optional[str], optional): Azure endpoint, should work with
            azure_deployment when is_azure is True
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: Optional[list] = None,
    n_results: int = 3,
    model: Optional[str] = "text-embedding-ada-002",
    vector_db_vendor: Optional[str] = None,
    connection_args: Optional[dict] = None,
    embedding_collection_name: Optional[str] = None,
    metadata_collection_name: Optional[str] = None,
    api_key: Optional[str] = None,
    is_azure: Optional[bool] = False,
    azure_deployment: Optional[str] = None,
    azure_endpoint: Optional[str] = None,
    base_url: Optional[str] = None,
    embeddings: Optional[OpenAIEmbeddings | XinferenceEmbeddings] = None,
    documentids_workspace: Optional[list[str]] = None,
) -> None:
    """
    Class that handles the retrieval-augmented generation (RAG) functionality
    of BioChatter. It splits text into chunks, embeds them, and stores them in
    a vector database. It can then be used to do similarity search on the
    database.

    Args:

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

        online (bool, optional): whether we are running ChatGSE 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.

        api_key (Optional[str], optional): OpenAI API key. 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.

        is_azure (Optional[bool], optional): if we are using Azure
        azure_deployment (Optional[str], optional): Azure embeddings model deployment,
            should work with azure_endpoint when is_azure is True
        azure_endpoint (Optional[str], optional): Azure endpoint, should work with
            azure_deployment when is_azure is True

    """
    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

    if embeddings:
        self.embeddings = embeddings
    else:
        if not self.online:
            self.embeddings = (
                OpenAIEmbeddings(openai_api_key=api_key, model=model)
                if not is_azure
                else AzureOpenAIEmbeddings(
                    api_key=api_key,
                    azure_deployment=azure_deployment,
                    azure_endpoint=azure_endpoint,
                    model=model,
                )
            )
        else:
            self.embeddings = None

    # 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()

save_document(doc)

This function saves document to the vector database Args: doc List[Document]: document content, read with DocumentReader load_document(), or document_from_pdf(), document_from_txt() Returns: 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:
    """
    This function saves document to the vector database
    Args:
        doc List[Document]: document content, read with DocumentReader load_document(),
            or document_from_pdf(), 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)

DocumentReader

Source code in biochatter/vectorstore.py 
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class DocumentReader:
    def load_document(self, path: str) -> list[Document]:
        """
        Loads a document from a path; accepts 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
        """
        if path.endswith(".txt"):
            loader = TextLoader(path)
            return loader.load()

        elif 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,
                )
            ]

    def document_from_pdf(self, pdf: bytes) -> list[Document]:
        """
        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]:
        """
        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)

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

Parameters:

Name Type Description Default
pdf bytes

byte representation of pdf file

 required

Returns:

Type Description
list[Document]

List[Document]: list of documents
Source code in biochatter/vectorstore.py 
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def document_from_pdf(self, pdf: bytes) -> list[Document]:
    """
    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)

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

Parameters:

Name Type Description Default
txt bytes

byte representation of txt file

 required

Returns:

Type Description
list[Document]

List[Document]: list of documents
Source code in biochatter/vectorstore.py 
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def document_from_txt(self, txt: bytes) -> list[Document]:
    """
    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)

Loads a document from a path; accepts txt and pdf files. Txt files are loaded as-is, pdf files are converted to text using fitz.

Parameters:

Name Type Description Default
path str

path to document

 required

Returns:

Type Description
list[Document]

List[Document]: list of documents
Source code in biochatter/vectorstore.py 
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def load_document(self, path: str) -> list[Document]:
    """
    Loads a document from a path; accepts 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
    """
    if path.endswith(".txt"):
        loader = TextLoader(path)
        return loader.load()

    elif 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,
            )
        ]

XinferenceDocumentEmbedder

Bases: DocumentEmbedder

Source code in biochatter/vectorstore.py 
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class XinferenceDocumentEmbedder(DocumentEmbedder):
    def __init__(
        self,
        used: bool = False,
        chunk_size: int = 1000,
        chunk_overlap: int = 0,
        split_by_characters: bool = True,
        separators: Optional[list] = None,
        n_results: int = 3,
        model: Optional[str] = "auto",
        vector_db_vendor: Optional[str] = None,
        connection_args: Optional[dict] = None,
        embedding_collection_name: Optional[str] = None,
        metadata_collection_name: Optional[str] = None,
        api_key: Optional[str] = "none",
        base_url: Optional[str] = None,
        documentids_workspace: Optional[list[str]] = None,
    ):
        """
        Extension of the DocumentEmbedder class that uses Xinference for
        embeddings.

        Args:

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

            api_key (Optional[str], optional): Xinference API key.

            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,
            api_key=api_key,
            base_url=base_url,
            embeddings=XinferenceEmbeddings(
                server_url=base_url, model_uid=self.model_uid
            ),
            documentids_workspace=documentids_workspace,
        )

    def load_models(self):
        """
        Return all models that are currently available on the Xinference server.

        Returns:
            dict: dict of 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, type: str):
        """
        Return all models of a certain type that are currently available on the
        Xinference server.

        Args:
            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 type in model["model_ability"]:
                    names.append(name)
            elif model["model_type"] == 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, api_key='none', base_url=None, documentids_workspace=None)

Extension of the DocumentEmbedder class that uses Xinference for embeddings.

Args:

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

api_key (Optional[str], optional): Xinference API key.

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: Optional[list] = None,
    n_results: int = 3,
    model: Optional[str] = "auto",
    vector_db_vendor: Optional[str] = None,
    connection_args: Optional[dict] = None,
    embedding_collection_name: Optional[str] = None,
    metadata_collection_name: Optional[str] = None,
    api_key: Optional[str] = "none",
    base_url: Optional[str] = None,
    documentids_workspace: Optional[list[str]] = None,
):
    """
    Extension of the DocumentEmbedder class that uses Xinference for
    embeddings.

    Args:

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

        api_key (Optional[str], optional): Xinference API key.

        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,
        api_key=api_key,
        base_url=base_url,
        embeddings=XinferenceEmbeddings(
            server_url=base_url, model_uid=self.model_uid
        ),
        documentids_workspace=documentids_workspace,
    )

list_models_by_type(type)

Return all models of a certain type that are currently available on the Xinference server.

Parameters:

Name Type Description Default
type str

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

 required

Returns:

Type Description

List[str]: list of model names
Source code in biochatter/vectorstore.py 
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def list_models_by_type(self, type: str):
    """
    Return all models of a certain type that are currently available on the
    Xinference server.

    Args:
        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 type in model["model_ability"]:
                names.append(name)
        elif model["model_type"] == type:
            names.append(name)
    return names

load_models()

Return all models that are currently available on the Xinference server.

Returns:

Name Type Description
dict

dict of models
Source code in biochatter/vectorstore.py 
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def load_models(self):
    """
    Return all models that are currently available on the Xinference server.

    Returns:
        dict: dict of models
    """
    for id, model in self.client.list_models().items():
        model["id"] = id
        self.models[model["model_name"]] = model