SYMBOL INDEX (197 symbols across 26 files) FILE: app/main/main.ts function handleSetTitle (line 25) | function handleSetTitle(event: any, title: string) { class ServerManager (line 34) | class ServerManager { method findOpenPort (line 38) | private findOpenPort(startingPort: number): Promise { method runPythonServer (line 54) | private runPythonServer(port: number): any { method start (line 74) | start(model: string): Promise { method stop (line 119) | stop(): void { method click (line 343) | click() { FILE: app/main/renderer.d.ts type Window (line 4) | interface Window { FILE: app/src/AppProvider.tsx function StoreProvider (line 16) | function StoreProvider({ FILE: app/src/app/layout.tsx function RootLayout (line 16) | function RootLayout({ FILE: app/src/app/page.tsx function Home (line 35) | function Home() { FILE: app/src/app/settings/page.tsx type SETTINGS (line 28) | enum SETTINGS { function SettingsOption (line 33) | function SettingsOption({ function GeneralSettings (line 71) | function GeneralSettings() { function PromptSettings (line 128) | function PromptSettings() { function Settings (line 176) | function Settings() { FILE: app/src/components/ui/button.tsx type ButtonProps (line 40) | interface ButtonProps FILE: app/src/components/ui/input.tsx type InputProps (line 6) | interface InputProps extends React.InputHTMLAttributes {} FILE: app/src/components/ui/textarea.tsx type TextareaProps (line 6) | interface TextareaProps extends React.TextareaHTMLAttributes(value: T): T | undefined { function convertToNiceShortcut (line 33) | function convertToNiceShortcut(shortcut: string) { function useKeyboardShortcut (line 40) | function useKeyboardShortcut() { FILE: app/src/lib/store.ts type AppStore (line 45) | type AppStore = ReturnType; type RootState (line 47) | type RootState = ReturnType; type AppDispatch (line 48) | type AppDispatch = AppStore['dispatch']; FILE: app/src/lib/utils.ts function cn (line 9) | function cn(...inputs: ClassValue[]) { FILE: server/convert.py function configure_parser (line 6) | def configure_parser() -> argparse.ArgumentParser: FILE: server/models/base.py class BaseModelArgs (line 6) | class BaseModelArgs: method from_dict (line 8) | def from_dict(cls, params): FILE: server/models/bert.py class ModelArgs (line 13) | class ModelArgs(BaseModelArgs): function apply_chunking_to_forward (line 40) | def apply_chunking_to_forward( class BertEmbeddings (line 122) | class BertEmbeddings(nn.Module): method __init__ (line 125) | def __init__(self, config): method __call__ (line 144) | def __call__( class BertSelfAttention (line 191) | class BertSelfAttention(nn.Module): method __init__ (line 192) | def __init__(self, config, position_embedding_type=None): method transpose_for_scores (line 220) | def transpose_for_scores(self, x: mx.array) -> mx.array: method __call__ (line 226) | def __call__( class BertSelfOutput (line 342) | class BertSelfOutput(nn.Module): method __init__ (line 343) | def __init__(self, config): method __call__ (line 350) | def __call__(self, hidden_states: mx.array, input_tensor: mx.array) ->... class BertAttention (line 357) | class BertAttention(nn.Module): method __init__ (line 358) | def __init__(self, config, position_embedding_type=None): method __call__ (line 365) | def __call__( class BertIntermediate (line 390) | class BertIntermediate(nn.Module): method __init__ (line 391) | def __init__(self, config): method __call__ (line 396) | def __call__(self, hidden_states: mx.array) -> mx.array: class BertOutput (line 402) | class BertOutput(nn.Module): method __init__ (line 403) | def __init__(self, config): method __call__ (line 410) | def __call__(self, hidden_states: mx.array, input_tensor: mx.array) ->... class BertLayer (line 417) | class BertLayer(nn.Module): method __init__ (line 418) | def __init__(self, config): method __call__ (line 434) | def __call__( method feed_forward_chunk (line 504) | def feed_forward_chunk(self, attention_output): class BertEncoder (line 510) | class BertEncoder(nn.Module): method __init__ (line 511) | def __init__(self, config): method __call__ (line 519) | def __call__( class BertPooler (line 603) | class BertPooler(nn.Module): method __init__ (line 604) | def __init__(self, config): method __call__ (line 609) | def __call__(self, hidden_states: mx.array) -> mx.array: class BertModel (line 618) | class BertModel(nn.Module): method __init__ (line 631) | def __init__(self, config: ModelArgs, add_pooling_layer=True): method get_input_embeddings (line 639) | def get_input_embeddings(self): method set_input_embeddings (line 642) | def set_input_embeddings(self, value): method get_extended_attention_mask (line 645) | def get_extended_attention_mask( method get_head_mask (line 690) | def get_head_mask( method __call__ (line 718) | def __call__( class Model (line 847) | class Model(nn.Module): method __init__ (line 848) | def __init__(self, args: ModelArgs): method __call__ (line 853) | def __call__( method sanitize (line 861) | def sanitize(weights): method layers (line 868) | def layers(self): FILE: server/models/gemma.py class ModelArgs (line 12) | class ModelArgs(BaseModelArgs): function rms_norm (line 27) | def rms_norm(x, weight, eps): class RMSNorm (line 33) | class RMSNorm(nn.Module): method __init__ (line 34) | def __init__(self, dims: int, eps: float = 1e-5): method __call__ (line 39) | def __call__(self, x): class Attention (line 43) | class Attention(nn.Module): method __init__ (line 44) | def __init__(self, args: ModelArgs): method __call__ (line 67) | def __call__( class MLP (line 104) | class MLP(nn.Module): method __init__ (line 105) | def __init__(self, dim, hidden_dim): method __call__ (line 111) | def __call__(self, x) -> mx.array: class TransformerBlock (line 115) | class TransformerBlock(nn.Module): method __init__ (line 116) | def __init__(self, args: ModelArgs): method __call__ (line 126) | def __call__( class GemmaModel (line 139) | class GemmaModel(nn.Module): method __init__ (line 140) | def __init__(self, args: ModelArgs): method __call__ (line 152) | def __call__( class Model (line 174) | class Model(nn.Module): method __init__ (line 175) | def __init__(self, args: ModelArgs): method __call__ (line 180) | def __call__( method layers (line 190) | def layers(self): FILE: server/models/layers.py function rms_norm (line 8) | def rms_norm(x, weight, eps): class RMSNorm (line 14) | class RMSNorm(nn.Module): method __init__ (line 15) | def __init__(self, dims: int, eps: float = 1e-5): method __call__ (line 20) | def __call__(self, x): function ln_norm (line 25) | def ln_norm(x, eps, weight=None, bias=None): class LayerNorm (line 35) | class LayerNorm(nn.Module): method __init__ (line 36) | def __init__(self, dims: int, eps: float = 1e-5, affine: bool = True): method _extra_repr (line 44) | def _extra_repr(self): method __call__ (line 47) | def __call__(self, x: mx.array) -> mx.array: FILE: server/models/llama.py class ModelArgs (line 12) | class ModelArgs(BaseModelArgs): method __post_init__ (line 25) | def __post_init__(self): class Attention (line 38) | class Attention(nn.Module): method __init__ (line 39) | def __init__(self, args: ModelArgs): method __call__ (line 68) | def __call__( class MLP (line 105) | class MLP(nn.Module): method __init__ (line 106) | def __init__(self, dim, hidden_dim): method __call__ (line 112) | def __call__(self, x) -> mx.array: class TransformerBlock (line 116) | class TransformerBlock(nn.Module): method __init__ (line 117) | def __init__(self, args: ModelArgs): method __call__ (line 127) | def __call__( class LlamaModel (line 140) | class LlamaModel(nn.Module): method __init__ (line 141) | def __init__(self, args: ModelArgs): method __call__ (line 153) | def __call__( class Model (line 174) | class Model(nn.Module): method __init__ (line 175) | def __init__(self, args: ModelArgs): method __call__ (line 181) | def __call__( method sanitize (line 190) | def sanitize(weights): method layers (line 197) | def layers(self): FILE: server/retriever/document.py class Document (line 4) | class Document(): method __init__ (line 15) | def __init__(self, page_content: str, metadata: Optional[dict] = None,... FILE: server/retriever/embeddings.py class Embeddings (line 12) | class Embeddings(ABC): method embed_documents (line 16) | def embed_documents(self, texts: List[str]) -> List[List[float]]: method embed_query (line 20) | def embed_query(self, text: str) -> List[float]: class E5Embeddings (line 24) | class E5Embeddings(Embeddings): method __init__ (line 29) | def __init__(self, hf_path: str = 'intfloat/multilingual-e5-small', qu... method _average_pool (line 35) | def _average_pool(self, last_hidden_states: mx.array, method embed_documents (line 41) | def embed_documents(self, texts: List[str], batch_size: int = 8) -> Li... method embed_query (line 49) | def embed_query(self, texts: Any, batch: bool = False) -> List[Any]: class ChatEmbeddings (line 66) | class ChatEmbeddings(Embeddings): method __init__ (line 71) | def __init__(self, model: nn.Module, tokenizer: PreTrainedTokenizer): method embed_documents (line 75) | def embed_documents(self, texts: List[str]) -> List[List[float]]: method embed_query (line 78) | def embed_query(self, text: str) -> List[float]: FILE: server/retriever/loader.py function directory_loader (line 9) | def directory_loader(directory: Optional[str] = None) -> Optional[List[D... FILE: server/retriever/splitter.py function _split_text_with_regex (line 16) | def _split_text_with_regex( class TextSplitter (line 36) | class TextSplitter(ABC): method __init__ (line 39) | def __init__( method split_text (line 70) | def split_text(self, text: str) -> List[str]: method create_documents (line 73) | def create_documents( method split_documents (line 93) | def split_documents(self, documents: Iterable[Document]) -> List[Docum... method _join_docs (line 101) | def _join_docs(self, docs: List[str], separator: str) -> Optional[str]: method _merge_splits (line 110) | def _merge_splits(self, splits: Iterable[str], separator: str) -> List... class RecursiveCharacterTextSplitter (line 152) | class RecursiveCharacterTextSplitter(TextSplitter): method __init__ (line 159) | def __init__( method _split_text (line 171) | def _split_text(self, text: str, separators: List[str]) -> List[str]: method split_text (line 212) | def split_text(self, text: str) -> List[str]: FILE: server/retriever/vectorstore.py function _results_to_docs (line 30) | def _results_to_docs(results: Any) -> List[Document]: function _results_to_docs_and_scores (line 34) | def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, fl... function xor_args (line 47) | def xor_args(*arg_groups: Tuple[str, ...]) -> Callable: function cosine_similarity (line 75) | def cosine_similarity( function maximal_marginal_relevance (line 86) | def maximal_marginal_relevance( class Chroma (line 121) | class Chroma(): method __init__ (line 128) | def __init__( method embeddings (line 185) | def embeddings(self) -> Optional[Embeddings]: method __query_collection (line 189) | def __query_collection( method add_texts (line 208) | def add_texts( method similarity_search (line 288) | def similarity_search( method similarity_search_with_score (line 310) | def similarity_search_with_score( method max_marginal_relevance_search_by_vector (line 350) | def max_marginal_relevance_search_by_vector( method max_marginal_relevance_search (line 399) | def max_marginal_relevance_search( method delete_collection (line 442) | def delete_collection(self) -> None: method get (line 446) | def get( method update_document (line 484) | def update_document(self, document_id: str, document: Document) -> None: method update_documents (line 493) | def update_documents(self, ids: List[str], documents: List[Document]) ... method from_texts (line 533) | def from_texts( method from_documents (line 596) | def from_documents( method delete (line 641) | def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None: FILE: server/server.py function load_model (line 26) | def load_model(model_path: str, adapter_file: Optional[str] = None): function index_directory (line 40) | def index_directory(directory: str, use_embedding: bool = True): function create_response (line 58) | def create_response(chat_id, prompt, tokens, text): function format_messages (line 85) | def format_messages(messages: List[Dict], indexed_files: Optional[str], ... class APIHandler (line 117) | class APIHandler(BaseHTTPRequestHandler): method _set_headers (line 118) | def _set_headers(self, status_code=200): method do_OPTIONS (line 126) | def do_OPTIONS(self): method do_POST (line 129) | def do_POST(self): method index (line 185) | def index(self, body): method init (line 190) | def init(self, body): method query (line 195) | def query(self, body): function run (line 256) | def run(host: str, port: int, server_class=HTTPServer, handler_class=API... function main (line 263) | def main(): FILE: server/utils.py function _get_classes (line 34) | def _get_classes(config: dict): function get_model_path (line 56) | def get_model_path(path_or_hf_repo: str) -> Path: function apply_repetition_penalty (line 84) | def apply_repetition_penalty(logits: mx.array, generated_tokens: Any, pe... function generate_step (line 108) | def generate_step( function generate (line 196) | def generate( function load_model (line 282) | def load_model(model_path: Path, lazy: bool = False) -> nn.Module: function load (line 353) | def load( function fetch_from_hub (line 390) | def fetch_from_hub( function make_shards (line 401) | def make_shards(weights: dict, max_file_size_gb: int = MAX_FILE_SIZE_GB)... function upload_to_hub (line 425) | def upload_to_hub(path: str, upload_repo: str, hf_path: str): function save_weights (line 471) | def save_weights( function quantize_model (line 523) | def quantize_model( function get_mlx_path (line 550) | def get_mlx_path(hf_path: str, quantize: bool = False) -> str: function convert (line 556) | def convert(