From 19f11c6837f4436a16ec569bfd8a0cea808eb208 Mon Sep 17 00:00:00 2001 From: Adrian Lyjak Date: Thu, 19 Jun 2025 23:53:13 -0400 Subject: [PATCH 1/2] Add basic mcp server --- examples/mcp/.python-version | 1 + examples/mcp/README.md | 21 + examples/mcp/llama_deploy.yaml | 24 + examples/mcp/pyproject.toml | 20 + examples/mcp/src/mcp/__init__.py | 9 + examples/mcp/src/mcp/workflows.py | 170 + examples/mcp/uv.lock | 2316 ++++++++++++ examples/python_dependencies/deployment.yaml | 2 +- examples/python_dependencies/workflow.py | 37 +- llama_deploy/apiserver/app.py | 3 + llama_deploy/apiserver/routers/mcp.py | 44 + llama_deploy/apiserver/server.py | 5 +- pyproject.toml | 5 +- uv.lock | 3473 +++++++++--------- 14 files changed, 4405 insertions(+), 1725 deletions(-) create mode 100644 examples/mcp/.python-version create mode 100644 examples/mcp/README.md create mode 100644 examples/mcp/llama_deploy.yaml create mode 100644 examples/mcp/pyproject.toml create mode 100644 examples/mcp/src/mcp/__init__.py create mode 100644 examples/mcp/src/mcp/workflows.py create mode 100644 examples/mcp/uv.lock create mode 100644 llama_deploy/apiserver/routers/mcp.py diff --git a/examples/mcp/.python-version b/examples/mcp/.python-version new file mode 100644 index 00000000..24ee5b1b --- /dev/null +++ b/examples/mcp/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/examples/mcp/README.md b/examples/mcp/README.md new file mode 100644 index 00000000..df04c7eb --- /dev/null +++ b/examples/mcp/README.md @@ -0,0 +1,21 @@ +Deployed workflows also publish the workflows as MCP tools. You can use this example as a scrapable knowledge store + +Start the store: + +From the root directory, run this to start the server: + +```sh +LLAMA_DEPLOY_APISERVER_RC_PATH=$PWD/examples/mcp uv run uvicorn llama_deploy.apiserver.app:app +``` + +You can then configure your client to connect to the server. For example cursor: + +```json +{ + "mcpServers": { + "llama-deploy": { + "url": "http://localhost:8000/mcp" + } + } +} +``` diff --git a/examples/mcp/llama_deploy.yaml b/examples/mcp/llama_deploy.yaml new file mode 100644 index 00000000..9579a167 --- /dev/null +++ b/examples/mcp/llama_deploy.yaml @@ -0,0 +1,24 @@ +name: dependencies + +control-plane: + port: 8000 + +default-service: scrape_page + +services: + scrape_page: + name: scrape_page + source: + type: local + name: . + path: src.mcp:scraper_workflow + python-dependencies: + - "." + query_knowledge_base: + name: query_knowledge_base + source: + type: local + name: . + path: src.mcp:search_workflow + python-dependencies: + - "." diff --git a/examples/mcp/pyproject.toml b/examples/mcp/pyproject.toml new file mode 100644 index 00000000..33c442ad --- /dev/null +++ b/examples/mcp/pyproject.toml @@ -0,0 +1,20 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "mcp" +version = "0.1.0" +description = "Web scraper with local vector store MCP server" +readme = "README.md" +authors = [{name = "Adrian Lyjak", email = "adrianlyjak@gmail.com"}] +requires-python = ">=3.13" +dependencies = [ + "llama-index>=0.11.0", + "llama-index-embeddings-huggingface>=0.3.0", + "llama-index-vector-stores-qdrant>=0.3.0", + "llama-index-readers-web>=0.2.0" +] + +[project.scripts] +mcp = "mcp:main" diff --git a/examples/mcp/src/mcp/__init__.py b/examples/mcp/src/mcp/__init__.py new file mode 100644 index 00000000..d307e12c --- /dev/null +++ b/examples/mcp/src/mcp/__init__.py @@ -0,0 +1,9 @@ +"""Web scraper and vector search workflows.""" + +from .workflows import scraper_workflow, search_workflow + +__all__ = ["scraper_workflow", "search_workflow"] + + +def main() -> None: + print("Hello from mcp!") diff --git a/examples/mcp/src/mcp/workflows.py b/examples/mcp/src/mcp/workflows.py new file mode 100644 index 00000000..4bb161fb --- /dev/null +++ b/examples/mcp/src/mcp/workflows.py @@ -0,0 +1,170 @@ +"""Web scraper workflows with local vector storage.""" + +import functools + +from pathlib import Path +from typing import Any, Dict, List, Optional + +from llama_index.core import VectorStoreIndex +from llama_index.core.node_parser import SentenceSplitter +from llama_index.core.workflow import ( + Context, + StartEvent, + StopEvent, + Workflow, + step, +) +from llama_index.embeddings.huggingface import HuggingFaceEmbedding +from llama_index.readers.web import SimpleWebPageReader +from llama_index.vector_stores.qdrant import QdrantVectorStore +from qdrant_client import QdrantClient +from qdrant_client.http.models import Distance, VectorParams + +QDRANT_DATA_DIR = Path("./data") +EMBEDDINGS_MODEL = "sentence-transformers/all-MiniLM-L6-v2" +EMBEDDINGS_MODEL_DIMENSION = 384 +COLLECTION_NAME = "knowledge_base" +CHUNK_SIZE = 1000 +CHUNK_OVERLAP = 200 + + +# Events +class ScrapeEvent(StartEvent): + url: str + + +class SearchEvent(StartEvent): + query: str + top_k: int = 5 + include_sources: bool = True + + +class ScrapeResult(StopEvent): + success: bool + url: str + chunks_created: int + error: Optional[str] = None + + +class SearchResult(StopEvent): + results: List[Dict[str, Any]] + query: str + + +@functools.lru_cache(maxsize=1) +def get_embeddings_model() -> HuggingFaceEmbedding: + """Get the embeddings model.""" + return HuggingFaceEmbedding(model_name=EMBEDDINGS_MODEL) + + +@functools.lru_cache(maxsize=1) +def get_qdrant_client() -> QdrantClient: + """Get or create Qdrant client and collection.""" + client = QdrantClient(path=str(QDRANT_DATA_DIR / "qdrant")) + + # Create collection if it doesn't exist + collections = client.get_collections().collections + collection_names = [c.name for c in collections] + + if COLLECTION_NAME not in collection_names: + client.create_collection( + collection_name=COLLECTION_NAME, + vectors_config=VectorParams( + size=EMBEDDINGS_MODEL_DIMENSION, + distance=Distance.COSINE, + ), + ) + + return client + + +@functools.lru_cache(maxsize=1) +def get_vector_store_index() -> VectorStoreIndex: + """Get or create vector store.""" + client = get_qdrant_client() + vector_store = QdrantVectorStore(client=client, collection_name=COLLECTION_NAME) + return VectorStoreIndex.from_vector_store( + vector_store=vector_store, embed_model=get_embeddings_model() + ) + + +class WebScraperWorkflow(Workflow): + """Scrapes a given URL and adds it to the knowledge base.""" + + @step + async def scrape_and_index(self, ctx: Context, ev: ScrapeEvent) -> ScrapeResult: + """Scrape webpage and add to vector store.""" + try: + url = ev.url + + reader = SimpleWebPageReader(html_to_text=True) + documents = reader.load_data([url]) + text_splitter = SentenceSplitter( + chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP + ) + nodes = text_splitter.get_nodes_from_documents(documents) + index = get_vector_store_index() + + # Add nodes to index + index.insert_nodes(nodes) + + return ScrapeResult( + success=True, + url=url, + chunks_created=len(nodes), + ) + + except Exception as e: + return ScrapeResult( + success=False, + url=url, + chunks_created=0, + error=str(e), + ) + + +class VectorSearchWorkflow(Workflow): + """Workflow to search the vector store.""" + + @step + async def search_knowledge_base( + self, ctx: Context, ev: SearchEvent + ) -> SearchResult: + """Search the vector knowledge base.""" + try: + query = ev.query + top_k = ev.top_k + include_sources = ev.include_sources + client = get_qdrant_client() + + vector_store = QdrantVectorStore( + client=client, collection_name=COLLECTION_NAME + ) + index = VectorStoreIndex.from_vector_store( + vector_store=vector_store, embed_model=get_embeddings_model() + ) + retriever = index.as_retriever(similarity_top_k=top_k) + nodes = retriever.retrieve(query) + results = [] + for node in nodes: + result = { + "text": node.text, + "score": node.score, + } + + if include_sources and node.metadata: + result["metadata"] = node.metadata + if "url" in node.metadata: 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pyfiglet import Figlet from workflows import Workflow, step from workflows.events import StartEvent, StopEvent +class CreateMessage(StartEvent): + message: str = Field( + description="A custom message to be displayed in the ascii art" + ) + font: str = Field( + default="blocky", + description="The figlet ascii art font to use for the message. Some popular options are: blocky, standard, small, big, digital, banner, mini, etc.", + ) + animal: str = Field( + default="daemon", + description="The animal/character to use for the ascii art. Options are: daemon, dragonandcow, stegosaurus, turtle, tux, vader, turkey, ghostbusters, surgery, kiss", + ) + + +class MessageCreated(StopEvent): + rendered_message: str + + # create a dummy workflow class EchoWorkflow(Workflow): """A dummy workflow with only one step sending back the input given.""" @step() - async def run_step(self, ev: StartEvent) -> StopEvent: - msg = str(ev.get("message", "")) - font = str(ev.get("font", "blocky")) + async def run_step(self, ev: CreateMessage) -> MessageCreated: + msg = str(ev.message) + font = str(ev.font) + animal = str(ev.animal) + fortune_msg = fortune() f = Figlet(font=font) ascii_art_message = f.renderText(msg) - ascii_art_message += cow.Stegosaurus().milk(fortune_msg) - return StopEvent(result=ascii_art_message) + + # Get the selected cow character dynamically + selected_cow = cow.get_cow(animal) + ascii_art_message += selected_cow().milk(fortune_msg) + + return MessageCreated(rendered_message=ascii_art_message) echo_workflow = EchoWorkflow() async def main(): - print(await echo_workflow.run(message="Hello!")) + print(await echo_workflow.run(message="Hello!", animal="daemon")) if __name__ == "__main__": diff --git a/llama_deploy/apiserver/app.py b/llama_deploy/apiserver/app.py index 112d0c70..907bbec2 100644 --- a/llama_deploy/apiserver/app.py +++ b/llama_deploy/apiserver/app.py @@ -6,6 +6,8 @@ from fastapi.requests import Request from fastapi.responses import JSONResponse +from llama_deploy.apiserver.routers.mcp import mcp_app + from .routers import deployments_router, status_router from .server import lifespan from .settings import settings @@ -15,6 +17,7 @@ app = FastAPI(lifespan=lifespan) +app.mount("/", mcp_app.streamable_http_app()) # Setup tracing configure_tracing(settings) diff --git a/llama_deploy/apiserver/routers/mcp.py b/llama_deploy/apiserver/routers/mcp.py new file mode 100644 index 00000000..8e4560e8 --- /dev/null +++ b/llama_deploy/apiserver/routers/mcp.py @@ -0,0 +1,44 @@ +import json +from contextlib import asynccontextmanager +from typing import Any, AsyncIterator + +from mcp.server.fastmcp import FastMCP + +from llama_index.core.workflow import StartEvent, StopEvent +from workflows import Workflow + +from llama_deploy.apiserver.server import manager + + +@asynccontextmanager +async def lifespan(app: FastMCP) -> AsyncIterator[None]: + deployments = list(manager._deployments.values()) + services: list[tuple[str, Workflow]] = [ + (workflow_name, workflow) + for deployment in deployments + for workflow_name, workflow in deployment._workflow_services.items() + ] + + for workflow_name, workflow in services: + start: type[StartEvent] = workflow._start_event_class + stop: type[StopEvent] = workflow._stop_event_class + + async def tool( + event: dict[str, Any], + workflow: Workflow = workflow, + start: type[StartEvent] = start, + ) -> dict[str, Any]: + handler = workflow.run(start_event=start.model_validate(event)) + result = await handler + return result.model_dump() + + tool.__name__ = workflow_name + description = workflow.__class__.__doc__ or "" + description += "\n\nInput:\n" + json.dumps(start.model_json_schema(), indent=2) + description += "\n\nOutput:\n" + json.dumps(stop.model_json_schema(), indent=2) + app.add_tool(tool, workflow_name, description=description) + + yield None + + +mcp_app = FastMCP(lifespan=lifespan, stateless_http=True, json_response=True) diff --git a/llama_deploy/apiserver/server.py b/llama_deploy/apiserver/server.py index 3bfa3f9b..75eb27ff 100644 --- a/llama_deploy/apiserver/server.py +++ b/llama_deploy/apiserver/server.py @@ -16,6 +16,8 @@ @asynccontextmanager async def lifespan(app: FastAPI) -> AsyncGenerator[None, Any]: + from .routers.mcp import mcp_app + apiserver_state.state("starting") manager.set_deployments_path(settings.deployments_path) @@ -39,7 +41,8 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, Any]: logger.error(f"Failed to deploy {yaml_file}: {str(e)}") apiserver_state.state("running") - yield + async with mcp_app.session_manager.run(): + yield t.cancel() diff --git a/pyproject.toml b/pyproject.toml index b223da01..4427a544 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,7 +41,7 @@ dependencies = [ "pydantic!=2.10", "pydantic-settings>=2.0,<3.0", "PyYAML>6", - "uvicorn[standard]>=0.12.0", + "uvicorn[standard]>=0.34.3", "gitpython>=3.1.43,<4", "python-multipart>=0.0.18,<0.0.19", "typing_extensions>=4.0.0,<5", @@ -52,7 +52,8 @@ dependencies = [ "rich>=13.9.4,<14", "brotli>=1.1.0", "websockets>=15.0.1", - "llama-index-workflows>=0.2.1" + "llama-index-workflows>=0.2.1", + "mcp>=1.9.4" ] [project.optional-dependencies] diff --git a/uv.lock b/uv.lock index 1a78ba89..0e238c3c 100644 --- a/uv.lock +++ b/uv.lock @@ -16,18 +16,18 @@ dependencies = [ { name = "exceptiongroup" }, { name = "yarl" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/48/00/5391405f15e85bd6cb859186dbe04d99186ca29410a7cdc52848b55a1d72/aio_pika-9.5.5.tar.gz", hash = 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Optional, Set +from pydantic import BaseModel +import requests +from bs4 import BeautifulSoup from llama_index.core import VectorStoreIndex from llama_index.core.node_parser import SentenceSplitter from llama_index.core.workflow import ( @@ -43,11 +46,21 @@ class ScrapeResult(StopEvent): success: bool url: str chunks_created: int + pages_scraped: int = 1 + urls_processed: List[str] = [] + errors: List[str] = [] error: Optional[str] = None +class SearchResultDetails(BaseModel): + text: str + score: float + metadata: Dict[str, Any] + source_url: Optional[str] = None + + class SearchResult(StopEvent): - results: List[Dict[str, Any]] + results: List[SearchResultDetails] query: str @@ -88,39 +101,169 @@ def get_vector_store_index() -> VectorStoreIndex: ) -class WebScraperWorkflow(Workflow): - """Scrapes a given URL and adds it to the knowledge base.""" - - @step - async def scrape_and_index(self, ctx: Context, ev: ScrapeEvent) -> ScrapeResult: - """Scrape webpage and add to vector store.""" - try: - url = ev.url +def get_domain(url: str) -> str: + """Extract domain from URL.""" + return urlparse(url).netloc + + +def get_links_from_page(url: str) -> List[str]: + """Extract all links from a webpage.""" + try: + response = requests.get(url, timeout=10) + response.raise_for_status() + soup = BeautifulSoup(response.content, "html.parser") + + links = [] + for link in soup.find_all("a", href=True): + href = link["href"] + # Convert relative URLs to absolute + absolute_url = urljoin(url, href) + links.append(absolute_url) + + return links + except Exception: + return [] + + +def is_valid_crawl_url(url: str, base_domain: str) -> bool: + """Check if URL is valid for crawling (same domain, not a file, etc).""" + try: + parsed = urlparse(url) + + # Must be same domain + if parsed.netloc != base_domain: + return False + + # Skip common file extensions and fragments + path = parsed.path.lower() + skip_extensions = { + ".pdf", + ".jpg", + ".jpeg", + ".png", + ".gif", + ".zip", + ".doc", + ".docx", + } + if any(path.endswith(ext) for ext in skip_extensions): + return False + + # Skip fragments and query params for deduplication + if parsed.fragment: + return False + + return True + except Exception: + return False + + +def is_url_already_processed(url: str) -> bool: + """Check if a URL has already been processed and stored in the vector store.""" + try: + from qdrant_client.models import Filter, FieldCondition, MatchValue + + client = get_qdrant_client() + + # Search for points with this URL in metadata using proper Qdrant filter syntax + result = client.scroll( + collection_name=COLLECTION_NAME, + scroll_filter=Filter( + must=[FieldCondition(key="url", match=MatchValue(value=url))] + ), + limit=1, # We only need to know if any exist + with_payload=True, + ) - reader = SimpleWebPageReader(html_to_text=True) - documents = reader.load_data([url]) - text_splitter = SentenceSplitter( - chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP - ) - nodes = text_splitter.get_nodes_from_documents(documents) - index = get_vector_store_index() + # If we found any points with this URL, it's already processed + return len(result[0]) > 0 + except Exception as e: + # If there's an error (e.g., collection doesn't exist yet), assume not processed + print(f"Error checking URL {url}: {e}") + return False - # Add nodes to index - index.insert_nodes(nodes) - return ScrapeResult( - success=True, - url=url, - chunks_created=len(nodes), - ) +class WebScraperWorkflow(Workflow): + """Scrapes a given URL and adds it to the knowledge base. Always scrapes max 20 pages at depth 2 using breadth-first traversal.""" - except Exception as e: - return ScrapeResult( - success=False, - url=url, - chunks_created=0, - error=str(e), - ) + @step + async def scrape_and_index(self, ctx: Context, ev: ScrapeEvent) -> ScrapeResult: + """Scrape webpage(s) and add to vector store.""" + root_url = ev.url + max_depth = 2 # Fixed depth + max_pages = 20 # Fixed page limit + + # Recursive crawling with breadth-first traversal + base_domain = get_domain(root_url) + visited_urls: Set[str] = set() + urls_to_process = [(root_url, 0)] # (url, depth) + pages_scraped = 0 + total_chunks = 0 + errors = [] + processed_urls = [] + + reader = SimpleWebPageReader(html_to_text=True) + text_splitter = SentenceSplitter( + chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP + ) + index = get_vector_store_index() + + while urls_to_process and pages_scraped < max_pages: + current_url, depth = urls_to_process.pop(0) + + # Skip if already visited in this session + if current_url in visited_urls: + continue + + # Skip if too deep + if depth > max_depth: + continue + + # Skip if already processed globally + if is_url_already_processed(current_url): + visited_urls.add(current_url) + continue + + visited_urls.add(current_url) + + try: + # Scrape current page + documents = reader.load_data([current_url]) + nodes = text_splitter.get_nodes_from_documents(documents) + + # Add URL metadata to nodes + for node in nodes: + node.metadata["url"] = current_url + + # Add to index + index.insert_nodes(nodes) + + pages_scraped += 1 + total_chunks += len(nodes) + processed_urls.append(current_url) + + # Get links for next level if we haven't reached max depth + if depth < max_depth: + links = get_links_from_page(current_url) + for link in links: + if ( + is_valid_crawl_url(link, base_domain) + and link not in visited_urls + ): + urls_to_process.append((link, depth + 1)) + + except Exception as e: + errors.append(f"Error processing {current_url}: {str(e)}") + continue + + return ScrapeResult( + success=True, + url=root_url, + chunks_created=total_chunks, + pages_scraped=pages_scraped, + urls_processed=processed_urls, + errors=errors, + ) class VectorSearchWorkflow(Workflow): @@ -134,7 +277,6 @@ async def search_knowledge_base( try: query = ev.query top_k = ev.top_k - include_sources = ev.include_sources client = get_qdrant_client() vector_store = QdrantVectorStore( @@ -145,17 +287,14 @@ async def search_knowledge_base( ) retriever = index.as_retriever(similarity_top_k=top_k) nodes = retriever.retrieve(query) - results = [] + results: List[SearchResultDetails] = [] for node in nodes: - result = { - "text": node.text, - "score": node.score, - } - - if include_sources and node.metadata: - result["metadata"] = node.metadata - if "url" in node.metadata: - result["source_url"] = node.metadata["url"] + result = SearchResultDetails( + text=node.text, + score=node.score, + metadata=node.metadata, + source_url=node.metadata.get("url"), + ) results.append(result) diff --git a/llama_deploy/apiserver/app.py b/llama_deploy/apiserver/app.py index 907bbec2..86a6d547 100644 --- a/llama_deploy/apiserver/app.py +++ b/llama_deploy/apiserver/app.py @@ -17,7 +17,7 @@ app = FastAPI(lifespan=lifespan) -app.mount("/", mcp_app.streamable_http_app()) +app.mount("/", mcp_app.sse_app()) # Setup tracing configure_tracing(settings) diff --git a/llama_deploy/apiserver/routers/mcp.py b/llama_deploy/apiserver/routers/mcp.py index 8e4560e8..35b8ccb5 100644 --- a/llama_deploy/apiserver/routers/mcp.py +++ b/llama_deploy/apiserver/routers/mcp.py @@ -23,16 +23,17 @@ async def lifespan(app: FastMCP) -> AsyncIterator[None]: start: type[StartEvent] = workflow._start_event_class stop: type[StopEvent] = workflow._stop_event_class - async def tool( - event: dict[str, Any], - workflow: Workflow = workflow, - start: type[StartEvent] = start, - ) -> dict[str, Any]: - handler = workflow.run(start_event=start.model_validate(event)) - result = await handler - return result.model_dump() - - tool.__name__ = workflow_name + def make_tool(workflow: Workflow, start: type[StartEvent]) -> Any: + async def tool(event: dict[str, Any]) -> dict[str, Any]: + handler = workflow.run(start_event=start.model_validate(event)) + result = await handler + return result.model_dump() + + tool.__name__ = workflow_name + + return tool + + tool = make_tool(workflow, start) description = workflow.__class__.__doc__ or "" description += "\n\nInput:\n" + json.dumps(start.model_json_schema(), indent=2) description += "\n\nOutput:\n" + json.dumps(stop.model_json_schema(), indent=2) @@ -41,4 +42,4 @@ async def tool( yield None -mcp_app = FastMCP(lifespan=lifespan, stateless_http=True, json_response=True) +mcp_app: FastMCP = FastMCP(lifespan=lifespan, stateless_http=True, json_response=True) diff --git a/llama_deploy/apiserver/server.py b/llama_deploy/apiserver/server.py index 75eb27ff..3bfa3f9b 100644 --- a/llama_deploy/apiserver/server.py +++ b/llama_deploy/apiserver/server.py @@ -16,8 +16,6 @@ @asynccontextmanager async def lifespan(app: FastAPI) -> AsyncGenerator[None, Any]: - from .routers.mcp import mcp_app - apiserver_state.state("starting") manager.set_deployments_path(settings.deployments_path) @@ -41,8 +39,7 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, Any]: logger.error(f"Failed to deploy {yaml_file}: {str(e)}") apiserver_state.state("running") - async with mcp_app.session_manager.run(): - yield + yield t.cancel()