import asyncio import os import shutil from argparse import ArgumentParser from glob import iglob from pathlib import Path from typing import Any, Callable, Dict, Optional, Union, cast from llama_index.legacy import ( Response, ServiceContext, SimpleDirectoryReader, VectorStoreIndex, ) from llama_index.legacy.bridge.pydantic import BaseModel, Field, validator from llama_index.legacy.chat_engine import CondenseQuestionChatEngine from llama_index.legacy.core.response.schema import RESPONSE_TYPE, StreamingResponse from llama_index.legacy.embeddings.base import BaseEmbedding from llama_index.legacy.ingestion import IngestionPipeline from llama_index.legacy.llms import LLM, OpenAI from llama_index.legacy.query_engine import CustomQueryEngine from llama_index.legacy.query_pipeline import FnComponent from llama_index.legacy.query_pipeline.query import QueryPipeline from llama_index.legacy.readers.base import BaseReader from llama_index.legacy.response_synthesizers import CompactAndRefine from llama_index.legacy.utils import get_cache_dir RAG_HISTORY_FILE_NAME = "files_history.txt" def default_ragcli_persist_dir() -> str: return str(Path(get_cache_dir()) / "rag_cli") def query_input(query_str: Optional[str] = None) -> str: return query_str or "" class QueryPipelineQueryEngine(CustomQueryEngine): query_pipeline: QueryPipeline = Field( description="Query Pipeline to use for Q&A.", ) def custom_query(self, query_str: str) -> RESPONSE_TYPE: return self.query_pipeline.run(query_str=query_str) async def acustom_query(self, query_str: str) -> RESPONSE_TYPE: return await self.query_pipeline.arun(query_str=query_str) class RagCLI(BaseModel): """ CLI tool for chatting with output of a IngestionPipeline via a QueryPipeline. """ ingestion_pipeline: IngestionPipeline = Field( description="Ingestion pipeline to run for RAG ingestion." ) verbose: bool = Field( description="Whether to print out verbose information during execution.", default=False, ) persist_dir: str = Field( description="Directory to persist ingestion pipeline.", default_factory=default_ragcli_persist_dir, ) llm: LLM = Field( description="Language model to use for response generation.", default_factory=lambda: OpenAI(model="gpt-3.5-turbo", streaming=True), ) query_pipeline: Optional[QueryPipeline] = Field( description="Query Pipeline to use for Q&A.", default=None, ) chat_engine: Optional[CondenseQuestionChatEngine] = Field( description="Chat engine to use for chatting.", default_factory=None, ) file_extractor: Optional[Dict[str, BaseReader]] = Field( description="File extractor to use for extracting text from files.", default=None, ) class Config: arbitrary_types_allowed = True @validator("query_pipeline", always=True) def query_pipeline_from_ingestion_pipeline( cls, query_pipeline: Any, values: Dict[str, Any] ) -> Optional[QueryPipeline]: """ If query_pipeline is not provided, create one from ingestion_pipeline. """ if query_pipeline is not None: return query_pipeline ingestion_pipeline = cast(IngestionPipeline, values["ingestion_pipeline"]) if ingestion_pipeline.vector_store is None: return None verbose = cast(bool, values["verbose"]) query_component = FnComponent( fn=query_input, output_key="output", req_params={"query_str"} ) llm = cast(LLM, values["llm"]) # get embed_model from transformations if possible embed_model = None if ingestion_pipeline.transformations is not None: for transformation in ingestion_pipeline.transformations: if isinstance(transformation, BaseEmbedding): embed_model = transformation break service_context = ServiceContext.from_defaults( llm=llm, embed_model=embed_model or "default" ) retriever = VectorStoreIndex.from_vector_store( ingestion_pipeline.vector_store, service_context=service_context ).as_retriever(similarity_top_k=8) response_synthesizer = CompactAndRefine( service_context=service_context, streaming=True, verbose=verbose ) # define query pipeline query_pipeline = QueryPipeline(verbose=verbose) query_pipeline.add_modules( { "query": query_component, "retriever": retriever, "summarizer": response_synthesizer, } ) query_pipeline.add_link("query", "retriever") query_pipeline.add_link("retriever", "summarizer", dest_key="nodes") query_pipeline.add_link("query", "summarizer", dest_key="query_str") return query_pipeline @validator("chat_engine", always=True) def chat_engine_from_query_pipeline( cls, chat_engine: Any, values: Dict[str, Any] ) -> Optional[CondenseQuestionChatEngine]: """ If chat_engine is not provided, create one from query_pipeline. """ if chat_engine is not None: return chat_engine if values.get("query_pipeline", None) is None: values["query_pipeline"] = cls.query_pipeline_from_ingestion_pipeline( query_pipeline=None, values=values ) query_pipeline = cast(QueryPipeline, values["query_pipeline"]) if query_pipeline is None: return None query_engine = QueryPipelineQueryEngine(query_pipeline=query_pipeline) # type: ignore verbose = cast(bool, values["verbose"]) llm = cast(LLM, values["llm"]) return CondenseQuestionChatEngine.from_defaults( query_engine=query_engine, llm=llm, verbose=verbose ) async def handle_cli( self, files: Optional[str] = None, question: Optional[str] = None, chat: bool = False, verbose: bool = False, clear: bool = False, create_llama: bool = False, **kwargs: Dict[str, Any], ) -> None: """ Entrypoint for local document RAG CLI tool. """ if clear: # delete self.persist_dir directory including all subdirectories and files if os.path.exists(self.persist_dir): # Ask for confirmation response = input( f"Are you sure you want to delete data within {self.persist_dir}? [y/N] " ) if response.strip().lower() != "y": print("Aborted.") return os.system(f"rm -rf {self.persist_dir}") print(f"Successfully cleared {self.persist_dir}") self.verbose = verbose ingestion_pipeline = cast(IngestionPipeline, self.ingestion_pipeline) if self.verbose: print("Saving/Loading from persist_dir: ", self.persist_dir) if files is not None: documents = [] for _file in iglob(files, recursive=True): _file = os.path.abspath(_file) if os.path.isdir(_file): reader = SimpleDirectoryReader( input_dir=_file, filename_as_id=True, file_extractor=self.file_extractor, ) else: reader = SimpleDirectoryReader( input_files=[_file], filename_as_id=True, file_extractor=self.file_extractor, ) documents.extend(reader.load_data(show_progress=verbose)) await ingestion_pipeline.arun(show_progress=verbose, documents=documents) ingestion_pipeline.persist(persist_dir=self.persist_dir) # Append the `--files` argument to the history file with open(f"{self.persist_dir}/{RAG_HISTORY_FILE_NAME}", "a") as f: f.write(files + "\n") if create_llama: if shutil.which("npx") is None: print( "`npx` is not installed. Please install it by calling `npm install -g npx`" ) else: history_file_path = Path(f"{self.persist_dir}/{RAG_HISTORY_FILE_NAME}") if not history_file_path.exists(): print( "No data has been ingested, " "please specify `--files` to create llama dataset." ) else: with open(history_file_path) as f: stored_paths = {line.strip() for line in f if line.strip()} if len(stored_paths) == 0: print( "No data has been ingested, " "please specify `--files` to create llama dataset." ) elif len(stored_paths) > 1: print( "Multiple files or folders were ingested, which is not supported by create-llama. " "Please call `llamaindex-cli rag --clear` to clear the cache first, " "then call `llamaindex-cli rag --files` again with a single folder or file" ) else: path = stored_paths.pop() if "*" in path: print( "Glob pattern is not supported by create-llama. " "Please call `llamaindex-cli rag --clear` to clear the cache first, " "then call `llamaindex-cli rag --files` again with a single folder or file." ) elif not os.path.exists(path): print( f"The path {path} does not exist. " "Please call `llamaindex-cli rag --clear` to clear the cache first, " "then call `llamaindex-cli rag --files` again with a single folder or file." ) else: print(f"Calling create-llama using data from {path} ...") command_args = [ "npx", "create-llama@latest", "--frontend", "--template", "streaming", "--framework", "fastapi", "--ui", "shadcn", "--vector-db", "none", "--engine", "context", f"--files {path}", ] os.system(" ".join(command_args)) if question is not None: await self.handle_question(question) if chat: await self.start_chat_repl() async def handle_question(self, question: str) -> None: if self.query_pipeline is None: raise ValueError("query_pipeline is not defined.") query_pipeline = cast(QueryPipeline, self.query_pipeline) query_pipeline.verbose = self.verbose chat_engine = cast(CondenseQuestionChatEngine, self.chat_engine) response = chat_engine.chat(question) if isinstance(response, StreamingResponse): response.print_response_stream() else: response = cast(Response, response) print(response) async def start_chat_repl(self) -> None: """ Start a REPL for chatting with the agent. """ if self.query_pipeline is None: raise ValueError("query_pipeline is not defined.") chat_engine = cast(CondenseQuestionChatEngine, self.chat_engine) chat_engine.streaming_chat_repl() @classmethod def add_parser_args( cls, parser: Union[ArgumentParser, Any], instance_generator: Callable[[], "RagCLI"], ) -> None: parser.add_argument( "-q", "--question", type=str, help="The question you want to ask.", required=False, ) parser.add_argument( "-f", "--files", type=str, help=( "The name of the file or directory you want to ask a question about," 'such as "file.pdf".' ), ) parser.add_argument( "-c", "--chat", help="If flag is present, opens a chat REPL.", action="store_true", ) parser.add_argument( "-v", "--verbose", help="Whether to print out verbose information during execution.", action="store_true", ) parser.add_argument( "--clear", help="Clears out all currently embedded data.", action="store_true", ) parser.add_argument( "--create-llama", help="Create a LlamaIndex application with your embedded data.", required=False, action="store_true", ) parser.set_defaults( func=lambda args: asyncio.run(instance_generator().handle_cli(**vars(args))) ) def cli(self) -> None: """ Entrypoint for CLI tool. """ parser = ArgumentParser(description="LlamaIndex RAG Q&A tool.") subparsers = parser.add_subparsers( title="commands", dest="command", required=True ) llamarag_parser = subparsers.add_parser( "rag", help="Ask a question to a document / a directory of documents." ) self.add_parser_args(llamarag_parser, lambda: self) # Parse the command-line arguments args = parser.parse_args() # Call the appropriate function based on the command args.func(args)