From 9f0ad037da9347ddeb2b171aec2cf48a7eff7753 Mon Sep 17 00:00:00 2001 From: Brent Salisbury Date: Wed, 30 Oct 2024 12:13:23 -0400 Subject: [PATCH] Demo: RAG service Adds APIs for a basic RAG service. Signed-off-by: Brent Salisbury --- docling_serve/app.py | 283 ++++- poetry.lock | 2654 +++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 10 + 3 files changed, 2893 insertions(+), 54 deletions(-) diff --git a/docling_serve/app.py b/docling_serve/app.py index 0d8cd95..c05c1ba 100644 --- a/docling_serve/app.py +++ b/docling_serve/app.py @@ -1,8 +1,10 @@ import base64 +import os from contextlib import asynccontextmanager +from enum import Enum from io import BytesIO from pathlib import Path -from typing import Any, Dict, Union +from typing import Any, Dict, Iterable, List, Union import httpx from docling.datamodel.base_models import ( @@ -12,11 +14,19 @@ ) from docling.datamodel.document import ConversionResult, DocumentConversionInput from docling.document_converter import DocumentConverter -from fastapi import FastAPI, HTTPException +from fastapi import FastAPI, HTTPException, File, UploadFile from pydantic import BaseModel from docling_serve.settings import Settings +from llama_index.core.readers.base import BasePydanticReader +from llama_index.core.schema import Document as LIDocument +from llama_index.core.node_parser import MarkdownNodeParser +from llama_index.embeddings.huggingface import HuggingFaceEmbedding +from llama_index.vector_stores.milvus import MilvusVectorStore +from llama_index.core import StorageContext, VectorStoreIndex +from llama_index.llms.openai import OpenAI +from pymilvus import connections, Collection, utility class HttpSource(BaseModel): url: str @@ -45,8 +55,58 @@ class ConvertDocumentResponse(BaseModel): ] +# New models for RAG functionality +class CreateCollectionRequest(BaseModel): + collection_name: str + + +class QueryRequest(BaseModel): + question: str + + +class DocumentMetadata(BaseModel): + dl_doc_hash: str + + models = {} +# Setting TOKENIZERS_PARALLELISM to avoid warnings +os.environ["TOKENIZERS_PARALLELISM"] = "false" +MILVUS_LOCAL_FILE_PATH = "/Users/brent/code/docling/rag-svc-pr/docling-serve" + + +class DoclingPDFReader(BasePydanticReader): + class ParseType(str, Enum): + MARKDOWN = "markdown" + # JSON = "json" + + parse_type: ParseType = ParseType.MARKDOWN + + def lazy_load_data( + self, file_path: Union[str, List[str]] + ) -> Iterable[LIDocument]: + file_paths = file_path if isinstance(file_path, list) else [file_path] + converter = DocumentConverter() + for source in file_paths: + dl_doc = converter.convert_single(source).output + if self.parse_type == self.ParseType.MARKDOWN: + text = dl_doc.export_to_markdown() + else: + raise RuntimeError( + f"Unexpected parse type encountered: {self.parse_type}" + ) + excl_metadata_keys = ["dl_doc_hash"] + li_doc = LIDocument( + doc_id=dl_doc.file_info.document_hash, + text=text, + excluded_embed_metadata_keys=excl_metadata_keys, + excluded_llm_metadata_keys=excl_metadata_keys, + ) + li_doc.metadata = DocumentMetadata( + dl_doc_hash=dl_doc.file_info.document_hash, + ).model_dump() + yield li_doc + @asynccontextmanager async def lifespan(app: FastAPI): @@ -56,6 +116,34 @@ async def lifespan(app: FastAPI): pipeline_options.do_ocr = settings.do_ocr pipeline_options.do_table_structure = settings.do_table_structure models["converter"] = DocumentConverter(pipeline_options=pipeline_options) + + # Embedding model + embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5") + models["embed_model"] = embed_model + + # Calculate the dimension once and store it + sample_embedding = embed_model.get_text_embedding("sample text") + models["embed_dim"] = len(sample_embedding) + + # Use OpenAI API for the Instructlab endpoint + models["llm"] = OpenAI( + api_key="dummy-key", + api_base="http://127.0.0.1:8080/v1", + model="merlinite-7b-lab-Q4_K_M.gguf", + max_tokens=1500, + temperature=0.1, + ) + + # Node Parser + models["node_parser"] = MarkdownNodeParser() + + # Check if database exists, if not, create it + if not os.path.exists(MILVUS_LOCAL_FILE_PATH): + os.makedirs(MILVUS_LOCAL_FILE_PATH) + print(f"Created Milvus storage directory at {MILVUS_LOCAL_FILE_PATH}") + else: + print(f"Milvus storage directory exists at {MILVUS_LOCAL_FILE_PATH}") + yield models.clear() @@ -71,7 +159,6 @@ async def lifespan(app: FastAPI): def convert_pdf_document( body: ConvertDocumentRequest, ) -> ConvertDocumentResponse: - filename: str buf: BytesIO @@ -83,7 +170,7 @@ def convert_pdf_document( buf = BytesIO(http_res.content) filename = Path( body.http_source.url - ).name # TODO: use better way to detect filename, e.g. from Content-Disposition + ).name docs_input = DocumentConversionInput.from_streams( [DocumentStream(filename=filename, stream=buf)] @@ -94,3 +181,191 @@ def convert_pdf_document( raise HTTPException(status_code=500, detail={"errors": result.errors}) return ConvertDocumentResponse(content_md=result.render_as_markdown()) + + +@app.get("/collections") +def list_collections(): + try: + connections.connect(uri=f"{MILVUS_LOCAL_FILE_PATH}/milvus_llamaindex.db") + + # List all collections + collections = utility.list_collections() + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + return {"collections": collections} + + +@app.delete("/collections/{collection_name}") +def delete_collection(collection_name: str): + # Connect to Milvus using the local file path + connections.connect(uri=f"{MILVUS_LOCAL_FILE_PATH}/milvus_llamaindex.db") + + # Check if the collection exists + if collection_name not in utility.list_collections(): + raise HTTPException(status_code=404, detail=f"Collection '{collection_name}' does not exist.") + + try: + # Drop the collection + utility.drop_collection(collection_name) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + return {"message": f"Collection '{collection_name}' deleted successfully"} + + +@app.post("/collections/{collection_name}/documents/file") +async def ingest_document_file( + collection_name: str, + file: UploadFile = File(...) +): + # Handle the file upload + file_content = await file.read() + buf = BytesIO(file_content) + filename = file.filename + + # Convert the document and ingest into the vector DB + return await process_and_ingest_document(collection_name, filename, buf) + + +@app.post("/collections/{collection_name}/documents/url") +async def ingest_document_http( + collection_name: str, + body: ConvertDocumentHttpSourceRequest +): + # Fetch the document from the URL + http_res = httpx.get(body.http_source.url, headers=body.http_source.headers) + if http_res.status_code != 200: + raise HTTPException( + status_code=400, + detail=f"Failed to fetch document from URL: {body.http_source.url}", + ) + buf = BytesIO(http_res.content) + filename = Path(body.http_source.url).name + + # Convert the document and ingest into the vector DB + return await process_and_ingest_document(collection_name, filename, buf) + + +async def process_and_ingest_document(collection_name: str, filename: str, buf: BytesIO): + # Convert the document to markdown using the converter + converter = models.get("converter") + docs_input = DocumentConversionInput.from_streams( + [DocumentStream(filename=filename, stream=buf)] + ) + result: ConversionResult = next(converter.convert(docs_input), None) + + if result is None or result.status != ConversionStatus.SUCCESS: + raise HTTPException(status_code=500, detail={"errors": result.errors}) + + # Get the markdown content + markdown_content = result.render_as_markdown() + + # Create a LlamaIndex document + li_doc = LIDocument( + doc_id=result.output.file_info.document_hash, + text=markdown_content, + excluded_embed_metadata_keys=["dl_doc_hash"], + excluded_llm_metadata_keys=["dl_doc_hash"], + ) + li_doc.metadata = DocumentMetadata( + dl_doc_hash=result.output.file_info.document_hash, + ).model_dump() + + # Transformations + node_parser = models.get("node_parser") + transformations = [node_parser] + + # Embed model + embed_model = models.get("embed_model") + + # Index name and parameters (must match the ones used during collection creation) + index_name = "default_index" + index_params = { + "metric_type": "IP", + "index_type": "HNSW", + "params": {"M": 8, "efConstruction": 64}, + } + + # Initialize the vector store for the collection + vector_store = MilvusVectorStore( + collection_name=collection_name, + dim=len(embed_model.get_text_embedding("hi")), + index_params=index_params, + index_name=index_name, + overwrite=False, # Do not overwrite existing collection + + ) + + # Create storage context + storage_context = StorageContext.from_defaults(vector_store=vector_store) + + # Create or update the index + index = VectorStoreIndex.from_documents( + documents=[li_doc], + embed_model=embed_model, + storage_context=storage_context, + transformations=transformations, + ) + + # Return success + return {"message": "Document ingested successfully into collection"} + + +@app.post("/collections/{collection_name}/query") +def query_collection(collection_name: str, body: QueryRequest): + question = body.question + + # Embed model + embed_model = models.get("embed_model") + + # Use the Instructlab API initialized earlier + llm = models.get("llm") + + # Initialize the vector store for the collection + vector_store = MilvusVectorStore( + collection_name=collection_name, + ) + + # Check if the collection exists + if collection_name not in vector_store.client.list_collections(): + raise HTTPException( + status_code=400, + detail=f"Collection '{collection_name}' does not exist.", + ) + + # Check if _collection is loaded, otherwise load it + if not hasattr(vector_store, '_collection') or vector_store._collection is None: + vector_store._collection = Collection(collection_name, using=vector_store.client._using) + + # Load the index + index = VectorStoreIndex.from_vector_store( + vector_store=vector_store, + embed_model=embed_model, + ) + + # Create query engine + query_engine = index.as_query_engine(llm=llm) + + # Form the prompt and perform the query + prompt = f"Question: {question}\nProvide a detailed answer based on the document." + query_res = query_engine.query(prompt) + + response = { + "query": question, + "answer": query_res.response.strip(), + "sources": [{"text": node.node.get_text(), "metadata": node.node.metadata} for node in query_res.source_nodes] + } + + # Print the response to the console + print("Query:", response["query"]) + print("Answer:", response["answer"]) + + # Print sources and metadata + if "sources" in response: + print("Sources:") + for source in response["sources"]: + print(f"Text: {source['text']}") + print(f"Metadata: {source['metadata']}") + + return response diff --git a/poetry.lock b/poetry.lock index 3ab9903..74a0698 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,142 @@ # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +[[package]] +name = "aiohappyeyeballs" +version = "2.4.3" +description = "Happy Eyeballs for asyncio" +optional = false +python-versions = ">=3.8" +files = [ + {file = 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