diff --git a/.changeset/plenty-beers-cheer.md b/.changeset/plenty-beers-cheer.md
new file mode 100644
index 0000000000000000000000000000000000000000..da3dba2718fe23ce6419f48335ddccb872fefa24
--- /dev/null
+++ b/.changeset/plenty-beers-cheer.md
@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Use LlamaCloud pipeline for data ingestion (private file uploads and generate script)
diff --git a/helpers/index.ts b/helpers/index.ts
index 91eed694dcf07a254c43314d75a8bdca9c0e70ee..6ff16e93d3d03d8483fe4f4ff85ce1e7c8dc09cd 100644
--- a/helpers/index.ts
+++ b/helpers/index.ts
@@ -142,12 +142,15 @@ export const installTemplate = async (
if (props.framework === "fastapi") {
await installPythonTemplate(props);
- // write loaders configuration (currently Python only)
- await writeLoadersConfig(
- props.root,
- props.dataSources,
- props.useLlamaParse,
- );
+ if (props.vectorDb !== "llamacloud") {
+ // write loaders configuration (currently Python only)
+ // not needed for LlamaCloud as it has its own loaders
+ await writeLoadersConfig(
+ props.root,
+ props.dataSources,
+ props.useLlamaParse,
+ );
+ }
} else {
await installTSTemplate(props);
}
diff --git a/helpers/python.ts b/helpers/python.ts
index 7aac6c2f4644a86c7ada98e99bcaa05f2f295368..445f2f9c34f42d6d987d378bb82e3877dfc732ff 100644
--- a/helpers/python.ts
+++ b/helpers/python.ts
@@ -350,12 +350,15 @@ export const installPythonTemplate = async ({
cwd: path.join(compPath, "vectordbs", "python", vectorDb ?? "none"),
});
- // Copy all loaders to enginePath
- const loaderPath = path.join(enginePath, "loaders");
- await copy("**", loaderPath, {
- parents: true,
- cwd: path.join(compPath, "loaders", "python"),
- });
+ if (vectorDb !== "llamacloud") {
+ // Copy all loaders to enginePath
+ // Not needed for LlamaCloud as it has its own loaders
+ const loaderPath = path.join(enginePath, "loaders");
+ await copy("**", loaderPath, {
+ parents: true,
+ cwd: path.join(compPath, "loaders", "python"),
+ });
+ }
// Copy settings.py to app
await copy("**", path.join(root, "app"), {
diff --git a/templates/components/sample-projects/llamapack/pyproject.toml b/templates/components/sample-projects/llamapack/pyproject.toml
index 4bd28bd809d189b8f6d505459c0ab14a2a2f0b7f..3ac40927a1497004db22407ecf7b8df957a1f22d 100644
--- a/templates/components/sample-projects/llamapack/pyproject.toml
+++ b/templates/components/sample-projects/llamapack/pyproject.toml
@@ -6,7 +6,7 @@ authors = ["Marcus Schiesser <mail@marcusschiesser.de>"]
readme = "README.md"
[tool.poetry.dependencies]
-python = "^3.11,<3.12"
+python = "^3.11,<4.0"
llama-index = "^0.10.6"
llama-index-readers-file = "^0.1.3"
python-dotenv = "^1.0.0"
diff --git a/templates/components/vectordbs/python/llamacloud/generate.py b/templates/components/vectordbs/python/llamacloud/generate.py
index 8bcf60689e0ce85214106bbeda1ba3fe4692174e..7ddfb7e8f09d69b7aea837437233c5e3a6b14f3c 100644
--- a/templates/components/vectordbs/python/llamacloud/generate.py
+++ b/templates/components/vectordbs/python/llamacloud/generate.py
@@ -1,48 +1,46 @@
from dotenv import load_dotenv
+from app.engine.index import get_index
+
load_dotenv()
-import os
import logging
-from app.settings import init_settings
-from app.engine.loaders import get_documents
-from llama_index.indices.managed.llama_cloud import LlamaCloudIndex
-
+from llama_index.core.readers import SimpleDirectoryReader
+from app.engine.service import LLamaCloudFileService
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()
def generate_datasource():
- init_settings()
logger.info("Generate index for the provided data")
- name = os.getenv("LLAMA_CLOUD_INDEX_NAME")
- project_name = os.getenv("LLAMA_CLOUD_PROJECT_NAME")
- api_key = os.getenv("LLAMA_CLOUD_API_KEY")
- base_url = os.getenv("LLAMA_CLOUD_BASE_URL")
- organization_id = os.getenv("LLAMA_CLOUD_ORGANIZATION_ID")
-
- if name is None or project_name is None or api_key is None:
- raise ValueError(
- "Please set LLAMA_CLOUD_INDEX_NAME, LLAMA_CLOUD_PROJECT_NAME and LLAMA_CLOUD_API_KEY"
- " to your environment variables or config them in .env file"
- )
-
- documents = get_documents()
-
- # Set private=false to mark the document as public (required for filtering)
- for doc in documents:
- doc.metadata["private"] = "false"
-
- LlamaCloudIndex.from_documents(
- documents=documents,
- name=name,
- project_name=project_name,
- api_key=api_key,
- base_url=base_url,
- organization_id=organization_id
+ index = get_index()
+ project_id = index._get_project_id()
+ pipeline_id = index._get_pipeline_id()
+
+ # use SimpleDirectoryReader to retrieve the files to process
+ reader = SimpleDirectoryReader(
+ "data",
+ recursive=True,
)
+ files_to_process = reader.input_files
+
+ # add each file to the LlamaCloud pipeline
+ for input_file in files_to_process:
+ with open(input_file, "rb") as f:
+ logger.info(
+ f"Adding file {input_file} to pipeline {index.name} in project {index.project_name}"
+ )
+ LLamaCloudFileService.add_file_to_pipeline(
+ project_id,
+ pipeline_id,
+ f,
+ custom_metadata={
+ # Set private=false to mark the document as public (required for filtering)
+ "private": "false",
+ },
+ )
logger.info("Finished generating the index")
diff --git a/templates/components/vectordbs/python/llamacloud/index.py b/templates/components/vectordbs/python/llamacloud/index.py
index e54e8ca9df634913a21df411aef29c09d88f22dd..0a4ba7951bbe3b28d98f9348658ce3a965ef7d6c 100644
--- a/templates/components/vectordbs/python/llamacloud/index.py
+++ b/templates/components/vectordbs/python/llamacloud/index.py
@@ -1,10 +1,20 @@
import logging
import os
from llama_index.indices.managed.llama_cloud import LlamaCloudIndex
-
+from llama_index.core.ingestion.api_utils import (
+ get_client as llama_cloud_get_client,
+)
logger = logging.getLogger("uvicorn")
+
+def get_client():
+ return llama_cloud_get_client(
+ os.getenv("LLAMA_CLOUD_API_KEY"),
+ os.getenv("LLAMA_CLOUD_BASE_URL"),
+ )
+
+
def get_index(params=None):
configParams = params or {}
pipelineConfig = configParams.get("llamaCloudPipeline", {})
@@ -25,7 +35,7 @@ def get_index(params=None):
project_name=project_name,
api_key=api_key,
base_url=base_url,
- organization_id=organization_id
+ organization_id=organization_id,
)
return index
diff --git a/templates/components/vectordbs/python/llamacloud/query_filter.py b/templates/components/vectordbs/python/llamacloud/query_filter.py
index 9c59735a6786b42302fbfec01b0d936c152c672b..b9a5ca405bde97e392ada198e1ba5944a7f0888e 100644
--- a/templates/components/vectordbs/python/llamacloud/query_filter.py
+++ b/templates/components/vectordbs/python/llamacloud/query_filter.py
@@ -12,7 +12,7 @@ def generate_filters(doc_ids):
operator="nin", # type: ignore
)
selected_doc_filter = MetadataFilter(
- key="doc_id",
+ key="file_id", # Note: LLamaCloud uses "file_id" to reference private document ids as "doc_id" is a restricted field in LlamaCloud
value=doc_ids,
operator="in", # type: ignore
)
diff --git a/templates/components/vectordbs/python/llamacloud/service.py b/templates/components/vectordbs/python/llamacloud/service.py
new file mode 100644
index 0000000000000000000000000000000000000000..68216f98e2fc59ad88bdcec6578a14bccbd30995
--- /dev/null
+++ b/templates/components/vectordbs/python/llamacloud/service.py
@@ -0,0 +1,173 @@
+from io import BytesIO
+import logging
+import os
+import time
+from typing import Any, Dict, List, Optional, Set, Tuple, Union
+import typing
+
+from fastapi import BackgroundTasks
+from llama_cloud import ManagedIngestionStatus, PipelineFileCreateCustomMetadataValue
+from pydantic import BaseModel
+import requests
+from app.api.routers.models import SourceNodes
+from app.engine.index import get_client
+from llama_index.core.schema import NodeWithScore
+
+
+logger = logging.getLogger("uvicorn")
+
+
+class LlamaCloudFile(BaseModel):
+ file_name: str
+ pipeline_id: str
+
+ def __eq__(self, other):
+ if not isinstance(other, LlamaCloudFile):
+ return NotImplemented
+ return (
+ self.file_name == other.file_name and self.pipeline_id == other.pipeline_id
+ )
+
+ def __hash__(self):
+ return hash((self.file_name, self.pipeline_id))
+
+
+class LLamaCloudFileService:
+ LOCAL_STORE_PATH = "output/llamacloud"
+ DOWNLOAD_FILE_NAME_TPL = "{pipeline_id}${filename}"
+
+ @classmethod
+ def get_all_projects_with_pipelines(cls) -> List[Dict[str, Any]]:
+ try:
+ client = get_client()
+ projects = client.projects.list_projects()
+ pipelines = client.pipelines.search_pipelines()
+ return [
+ {
+ **(project.dict()),
+ "pipelines": [
+ {"id": p.id, "name": p.name}
+ for p in pipelines
+ if p.project_id == project.id
+ ],
+ }
+ for project in projects
+ ]
+ except Exception as error:
+ logger.error(f"Error listing projects and pipelines: {error}")
+ return []
+
+ @classmethod
+ def add_file_to_pipeline(
+ cls,
+ project_id: str,
+ pipeline_id: str,
+ upload_file: Union[typing.IO, Tuple[str, BytesIO]],
+ custom_metadata: Optional[Dict[str, PipelineFileCreateCustomMetadataValue]],
+ ) -> str:
+ client = get_client()
+ file = client.files.upload_file(project_id=project_id, upload_file=upload_file)
+ files = [
+ {
+ "file_id": file.id,
+ "custom_metadata": {"file_id": file.id, **(custom_metadata or {})},
+ }
+ ]
+ files = client.pipelines.add_files_to_pipeline(pipeline_id, request=files)
+
+ # Wait 2s for the file to be processed
+ max_attempts = 20
+ attempt = 0
+ while attempt < max_attempts:
+ result = client.pipelines.get_pipeline_file_status(pipeline_id, file.id)
+ if result.status == ManagedIngestionStatus.ERROR:
+ raise Exception(f"File processing failed: {str(result)}")
+ if result.status == ManagedIngestionStatus.SUCCESS:
+ # File is ingested - return the file id
+ return file.id
+ attempt += 1
+ time.sleep(0.1) # Sleep for 100ms
+ raise Exception(
+ f"File processing did not complete after {max_attempts} attempts."
+ )
+
+ @classmethod
+ def download_pipeline_file(
+ cls,
+ file: LlamaCloudFile,
+ force_download: bool = False,
+ ):
+ client = get_client()
+ file_name = file.file_name
+ pipeline_id = file.pipeline_id
+
+ # Check is the file already exists
+ downloaded_file_path = cls._get_file_path(file_name, pipeline_id)
+ if os.path.exists(downloaded_file_path) and not force_download:
+ logger.debug(f"File {file_name} already exists in local storage")
+ return
+ try:
+ logger.info(f"Downloading file {file_name} for pipeline {pipeline_id}")
+ files = client.pipelines.list_pipeline_files(pipeline_id)
+ if not files or not isinstance(files, list):
+ raise Exception("No files found in LlamaCloud")
+ for file_entry in files:
+ if file_entry.name == file_name:
+ file_id = file_entry.file_id
+ project_id = file_entry.project_id
+ file_detail = client.files.read_file_content(
+ file_id, project_id=project_id
+ )
+ cls._download_file(file_detail.url, downloaded_file_path)
+ break
+ except Exception as error:
+ logger.info(f"Error fetching file from LlamaCloud: {error}")
+
+ @classmethod
+ def download_files_from_nodes(
+ cls, nodes: List[NodeWithScore], background_tasks: BackgroundTasks
+ ):
+ files = cls._get_files_to_download(nodes)
+ for file in files:
+ logger.info(f"Adding download of {file.file_name} to background tasks")
+ background_tasks.add_task(
+ LLamaCloudFileService.download_pipeline_file, file
+ )
+
+ @classmethod
+ def _get_files_to_download(cls, nodes: List[NodeWithScore]) -> Set[LlamaCloudFile]:
+ source_nodes = SourceNodes.from_source_nodes(nodes)
+ llama_cloud_files = [
+ LlamaCloudFile(
+ file_name=node.metadata.get("file_name"),
+ pipeline_id=node.metadata.get("pipeline_id"),
+ )
+ for node in source_nodes
+ if (
+ node.metadata.get("pipeline_id") is not None
+ and node.metadata.get("file_name") is not None
+ )
+ ]
+ # Remove duplicates and return
+ return set(llama_cloud_files)
+
+ @classmethod
+ def _get_file_name(cls, name: str, pipeline_id: str) -> str:
+ return cls.DOWNLOAD_FILE_NAME_TPL.format(pipeline_id=pipeline_id, filename=name)
+
+ @classmethod
+ def _get_file_path(cls, name: str, pipeline_id: str) -> str:
+ return os.path.join(cls.LOCAL_STORE_PATH, cls._get_file_name(name, pipeline_id))
+
+ @classmethod
+ def _download_file(cls, url: str, local_file_path: str):
+ logger.info(f"Saving file to {local_file_path}")
+ # Create directory if it doesn't exist
+ os.makedirs(cls.LOCAL_STORE_PATH, exist_ok=True)
+ # Download the file
+ with requests.get(url, stream=True) as r:
+ r.raise_for_status()
+ with open(local_file_path, "wb") as f:
+ for chunk in r.iter_content(chunk_size=8192):
+ f.write(chunk)
+ logger.info("File downloaded successfully")
diff --git a/templates/types/extractor/fastapi/pyproject.toml b/templates/types/extractor/fastapi/pyproject.toml
index b5bdea0f80eb4918d3ef9b9c7279f156142038d9..37e2df2598c4b9bc9b438a5f51ae7f7759e70b3a 100644
--- a/templates/types/extractor/fastapi/pyproject.toml
+++ b/templates/types/extractor/fastapi/pyproject.toml
@@ -9,7 +9,7 @@ readme = "README.md"
generate = "app.engine.generate:generate_datasource"
[tool.poetry.dependencies]
-python = "^3.11,<3.12"
+python = "^3.11,<4.0"
fastapi = "^0.109.1"
uvicorn = { extras = ["standard"], version = "^0.23.2" }
python-dotenv = "^1.0.0"
diff --git a/templates/types/streaming/express/src/controllers/chat-upload.controller.ts b/templates/types/streaming/express/src/controllers/chat-upload.controller.ts
index 781cb6350cb26bc62026b08ace2df4efaf4e8080..f97234e5e22bfa489b9ca561d3f876bedd231cd0 100644
--- a/templates/types/streaming/express/src/controllers/chat-upload.controller.ts
+++ b/templates/types/streaming/express/src/controllers/chat-upload.controller.ts
@@ -3,12 +3,12 @@ import { getDataSource } from "./engine";
import { uploadDocument } from "./llamaindex/documents/upload";
export const chatUpload = async (req: Request, res: Response) => {
- const { base64 }: { base64: string } = req.body;
+ const { base64, params }: { base64: string; params?: any } = req.body;
if (!base64) {
return res.status(400).json({
error: "base64 is required in the request body",
});
}
- const index = await getDataSource();
+ const index = await getDataSource(params);
return res.status(200).json(await uploadDocument(index, base64));
};
diff --git a/templates/types/streaming/fastapi/app/api/routers/chat.py b/templates/types/streaming/fastapi/app/api/routers/chat.py
index 11a3453328469a6444bee858e9e9aafad0ba6e86..ace2a3b874d2ab8efd36c5fee821bd19b8da84fc 100644
--- a/templates/types/streaming/fastapi/app/api/routers/chat.py
+++ b/templates/types/streaming/fastapi/app/api/routers/chat.py
@@ -13,7 +13,6 @@ from app.api.routers.models import (
SourceNodes,
)
from app.api.routers.vercel_response import VercelStreamResponse
-from app.api.services.llama_cloud import LLamaCloudFileService
from app.engine import get_chat_engine
from app.engine.query_filter import generate_filters
@@ -22,27 +21,12 @@ chat_router = r = APIRouter()
logger = logging.getLogger("uvicorn")
-def process_response_nodes(
- nodes: List[NodeWithScore],
- background_tasks: BackgroundTasks,
-):
- """
- Start background tasks on the source nodes if needed.
- """
- files_to_download = SourceNodes.get_download_files(nodes)
- for file in files_to_download:
- background_tasks.add_task(
- LLamaCloudFileService.download_llamacloud_pipeline_file, file
- )
-
-
# streaming endpoint - delete if not needed
@r.post("")
async def chat(
request: Request,
data: ChatData,
background_tasks: BackgroundTasks,
- chat_engine: BaseChatEngine = Depends(get_chat_engine),
):
try:
last_message_content = data.get_last_message_content()
@@ -85,3 +69,17 @@ async def chat_request(
result=Message(role=MessageRole.ASSISTANT, content=response.response),
nodes=SourceNodes.from_source_nodes(response.source_nodes),
)
+
+
+def process_response_nodes(
+ nodes: List[NodeWithScore],
+ background_tasks: BackgroundTasks,
+):
+ try:
+ # Start background tasks to download documents from LlamaCloud if needed
+ from app.engine.service import LLamaCloudFileService
+
+ LLamaCloudFileService.download_files_from_nodes(nodes, background_tasks)
+ except ImportError:
+ logger.debug("LlamaCloud is not configured. Skipping post processing of nodes")
+ pass
diff --git a/templates/types/streaming/fastapi/app/api/routers/chat_config.py b/templates/types/streaming/fastapi/app/api/routers/chat_config.py
index ebd51ca46b27c70d9af2f9e1280b46aaf7f50ac0..8d926e50bab944d9baff2ae2cb681ab6d48d4cef 100644
--- a/templates/types/streaming/fastapi/app/api/routers/chat_config.py
+++ b/templates/types/streaming/fastapi/app/api/routers/chat_config.py
@@ -1,12 +1,15 @@
+import logging
import os
from fastapi import APIRouter
from app.api.routers.models import ChatConfig
-from app.api.services.llama_cloud import LLamaCloudFileService
+
config_router = r = APIRouter()
+logger = logging.getLogger("uvicorn")
+
@r.get("")
async def chat_config() -> ChatConfig:
@@ -17,21 +20,29 @@ async def chat_config() -> ChatConfig:
return ChatConfig(starter_questions=starter_questions)
-@r.get("/llamacloud")
-async def chat_llama_cloud_config():
- projects = LLamaCloudFileService.get_all_projects_with_pipelines()
- pipeline = os.getenv("LLAMA_CLOUD_INDEX_NAME")
- project = os.getenv("LLAMA_CLOUD_PROJECT_NAME")
- pipeline_config = (
- pipeline
- and project
- and {
- "pipeline": pipeline,
- "project": project,
+try:
+ from app.engine.service import LLamaCloudFileService
+
+ logger.info("LlamaCloud is configured. Adding /config/llamacloud route.")
+
+ @r.get("/llamacloud")
+ async def chat_llama_cloud_config():
+ projects = LLamaCloudFileService.get_all_projects_with_pipelines()
+ pipeline = os.getenv("LLAMA_CLOUD_INDEX_NAME")
+ project = os.getenv("LLAMA_CLOUD_PROJECT_NAME")
+ pipeline_config = None
+ if pipeline and project:
+ pipeline_config = {
+ "pipeline": pipeline,
+ "project": project,
+ }
+ return {
+ "projects": projects,
+ "pipeline": pipeline_config,
}
- or None
+
+except ImportError:
+ logger.debug(
+ "LlamaCloud is not configured. Skipping adding /config/llamacloud route."
)
- return {
- "projects": projects,
- "pipeline": pipeline_config,
- }
+ pass
diff --git a/templates/types/streaming/fastapi/app/api/routers/models.py b/templates/types/streaming/fastapi/app/api/routers/models.py
index c9ea1adb772c7c61b7d1ecc9f8b7480d69ac55ec..3d790adb9659be0f0538e67f561cbd051465b36a 100644
--- a/templates/types/streaming/fastapi/app/api/routers/models.py
+++ b/templates/types/streaming/fastapi/app/api/routers/models.py
@@ -147,21 +147,6 @@ class ChatData(BaseModel):
return list(set(document_ids))
-class LlamaCloudFile(BaseModel):
- file_name: str
- pipeline_id: str
-
- def __eq__(self, other):
- if not isinstance(other, LlamaCloudFile):
- return NotImplemented
- return (
- self.file_name == other.file_name and self.pipeline_id == other.pipeline_id
- )
-
- def __hash__(self):
- return hash((self.file_name, self.pipeline_id))
-
-
class SourceNodes(BaseModel):
id: str
metadata: Dict[str, Any]
@@ -193,8 +178,8 @@ class SourceNodes(BaseModel):
if file_name and url_prefix:
# file_name exists and file server is configured
pipeline_id = metadata.get("pipeline_id")
- if pipeline_id and metadata.get("private") is None:
- # file is from LlamaCloud and was not ingested locally
+ if pipeline_id:
+ # file is from LlamaCloud
file_name = f"{pipeline_id}${file_name}"
return f"{url_prefix}/output/llamacloud/{file_name}"
is_private = metadata.get("private", "false") == "true"
@@ -209,25 +194,6 @@ class SourceNodes(BaseModel):
def from_source_nodes(cls, source_nodes: List[NodeWithScore]):
return [cls.from_source_node(node) for node in source_nodes]
- @staticmethod
- def get_download_files(nodes: List[NodeWithScore]) -> Set[LlamaCloudFile]:
- source_nodes = SourceNodes.from_source_nodes(nodes)
- llama_cloud_files = [
- LlamaCloudFile(
- file_name=node.metadata.get("file_name"),
- pipeline_id=node.metadata.get("pipeline_id"),
- )
- for node in source_nodes
- if (
- node.metadata.get("private")
- is None # Only download files are from LlamaCloud and were not ingested locally
- and node.metadata.get("pipeline_id") is not None
- and node.metadata.get("file_name") is not None
- )
- ]
- # Remove duplicates and return
- return set(llama_cloud_files)
-
class Result(BaseModel):
result: Message
diff --git a/templates/types/streaming/fastapi/app/api/routers/upload.py b/templates/types/streaming/fastapi/app/api/routers/upload.py
index 94f3ce7d3e059eeb7964be60f9b4c544e911c8ae..dcb3a08dc8a5323d0b48174db6ed50e8cebda76a 100644
--- a/templates/types/streaming/fastapi/app/api/routers/upload.py
+++ b/templates/types/streaming/fastapi/app/api/routers/upload.py
@@ -1,5 +1,5 @@
import logging
-from typing import List
+from typing import List, Any
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
@@ -13,13 +13,15 @@ logger = logging.getLogger("uvicorn")
class FileUploadRequest(BaseModel):
base64: str
+ filename: str
+ params: Any
@r.post("")
def upload_file(request: FileUploadRequest) -> List[str]:
try:
logger.info("Processing file")
- return PrivateFileService.process_file(request.base64)
+ return PrivateFileService.process_file(request.filename, request.base64, request.params)
except Exception as e:
logger.error(f"Error processing file: {e}", exc_info=True)
raise HTTPException(status_code=500, detail="Error processing file")
diff --git a/templates/types/streaming/fastapi/app/api/services/file.py b/templates/types/streaming/fastapi/app/api/services/file.py
index a478570a00d3b8d2dbfd3a5fe36e0debea44ddc3..20356113f609f3795a83a2351e536d8a294b697e 100644
--- a/templates/types/streaming/fastapi/app/api/services/file.py
+++ b/templates/types/streaming/fastapi/app/api/services/file.py
@@ -1,19 +1,20 @@
import base64
import mimetypes
import os
+from io import BytesIO
from pathlib import Path
-from typing import Dict, List
+import time
+from typing import Any, Dict, List, Tuple
from uuid import uuid4
+
from app.engine.index import get_index
from llama_index.core import VectorStoreIndex
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.readers.file.base import (
_try_loading_included_file_formats as get_file_loaders_map,
)
-from llama_index.core.readers.file.base import (
- default_file_metadata_func,
-)
+from llama_index.core.readers.file.base import default_file_metadata_func
from llama_index.core.schema import Document
from llama_index.indices.managed.llama_cloud.base import LlamaCloudIndex
from llama_index.readers.file import FlatReader
@@ -41,7 +42,7 @@ class PrivateFileService:
PRIVATE_STORE_PATH = "output/uploaded"
@staticmethod
- def preprocess_base64_file(base64_content: str) -> tuple:
+ def preprocess_base64_file(base64_content: str) -> Tuple[bytes, str | None]:
header, data = base64_content.split(",", 1)
mime_type = header.split(";")[0].split(":", 1)[1]
extension = mimetypes.guess_extension(mime_type)
@@ -78,25 +79,34 @@ class PrivateFileService:
return documents
@staticmethod
- def process_file(base64_content: str) -> List[str]:
+ def process_file(file_name: str, base64_content: str, params: Any) -> List[str]:
file_data, extension = PrivateFileService.preprocess_base64_file(base64_content)
- documents = PrivateFileService.store_and_parse_file(file_data, extension)
-
- # Only process nodes, no store the index
- pipeline = IngestionPipeline()
- nodes = pipeline.run(documents=documents)
# Add the nodes to the index and persist it
- current_index = get_index()
+ current_index = get_index(params)
# Insert the documents into the index
if isinstance(current_index, LlamaCloudIndex):
- # LlamaCloudIndex is a managed index so we don't need to process the nodes
- # just insert the documents
- for doc in documents:
- current_index.insert(doc)
+ from app.engine.service import LLamaCloudFileService
+
+ project_id = current_index._get_project_id()
+ pipeline_id = current_index._get_pipeline_id()
+ # LlamaCloudIndex is a managed index so we can directly use the files
+ upload_file = (file_name, BytesIO(file_data))
+ return [
+ LLamaCloudFileService.add_file_to_pipeline(
+ project_id,
+ pipeline_id,
+ upload_file,
+ custom_metadata={
+ # Set private=true to mark the document as private user docs (required for filtering)
+ "private": "true",
+ },
+ )
+ ]
else:
- # Only process nodes, no store the index
+ # First process documents into nodes
+ documents = PrivateFileService.store_and_parse_file(file_data, extension)
pipeline = IngestionPipeline()
nodes = pipeline.run(documents=documents)
@@ -109,5 +119,5 @@ class PrivateFileService:
persist_dir=os.environ.get("STORAGE_DIR", "storage")
)
- # Return the document ids
- return [doc.doc_id for doc in documents]
+ # Return the document ids
+ return [doc.doc_id for doc in documents]
diff --git a/templates/types/streaming/fastapi/app/api/services/llama_cloud.py b/templates/types/streaming/fastapi/app/api/services/llama_cloud.py
deleted file mode 100644
index 852ae7cee693b81ad9cb94b7c34252e7a5153279..0000000000000000000000000000000000000000
--- a/templates/types/streaming/fastapi/app/api/services/llama_cloud.py
+++ /dev/null
@@ -1,114 +0,0 @@
-import logging
-import os
-from typing import Any, Dict, List, Optional
-
-import requests
-from app.api.routers.models import LlamaCloudFile
-
-logger = logging.getLogger("uvicorn")
-
-
-class LLamaCloudFileService:
- LLAMA_CLOUD_URL = "https://cloud.llamaindex.ai/api/v1"
- LOCAL_STORE_PATH = "output/llamacloud"
-
- DOWNLOAD_FILE_NAME_TPL = "{pipeline_id}${filename}"
-
- @classmethod
- def get_all_projects(cls) -> List[Dict[str, Any]]:
- url = f"{cls.LLAMA_CLOUD_URL}/projects"
- return cls._make_request(url)
-
- @classmethod
- def get_all_pipelines(cls) -> List[Dict[str, Any]]:
- url = f"{cls.LLAMA_CLOUD_URL}/pipelines"
- return cls._make_request(url)
-
- @classmethod
- def get_all_projects_with_pipelines(cls) -> List[Dict[str, Any]]:
- try:
- projects = cls.get_all_projects()
- pipelines = cls.get_all_pipelines()
- return [
- {
- **project,
- "pipelines": [p for p in pipelines if p["project_id"] == project["id"]],
- }
- for project in projects
- ]
- except Exception as error:
- logger.error(f"Error listing projects and pipelines: {error}")
- return []
-
- @classmethod
- def _get_files(cls, pipeline_id: str) -> List[Dict[str, Any]]:
- url = f"{cls.LLAMA_CLOUD_URL}/pipelines/{pipeline_id}/files"
- return cls._make_request(url)
-
- @classmethod
- def _get_file_detail(cls, project_id: str, file_id: str) -> Dict[str, Any]:
- url = f"{cls.LLAMA_CLOUD_URL}/files/{file_id}/content?project_id={project_id}"
- return cls._make_request(url)
-
- @classmethod
- def _download_file(cls, url: str, local_file_path: str):
- logger.info(f"Downloading file to {local_file_path}")
- # Create directory if it doesn't exist
- os.makedirs(cls.LOCAL_STORE_PATH, exist_ok=True)
- # Download the file
- with requests.get(url, stream=True) as r:
- r.raise_for_status()
- with open(local_file_path, "wb") as f:
- for chunk in r.iter_content(chunk_size=8192):
- f.write(chunk)
- logger.info("File downloaded successfully")
-
- @classmethod
- def download_llamacloud_pipeline_file(
- cls,
- file: LlamaCloudFile,
- force_download: bool = False,
- ):
- file_name = file.file_name
- pipeline_id = file.pipeline_id
-
- # Check is the file already exists
- downloaded_file_path = cls.get_file_path(file_name, pipeline_id)
- if os.path.exists(downloaded_file_path) and not force_download:
- logger.debug(f"File {file_name} already exists in local storage")
- return
- try:
- logger.info(f"Downloading file {file_name} for pipeline {pipeline_id}")
- files = cls._get_files(pipeline_id)
- if not files or not isinstance(files, list):
- raise Exception("No files found in LlamaCloud")
- for file_entry in files:
- if file_entry["name"] == file_name:
- file_id = file_entry["file_id"]
- project_id = file_entry["project_id"]
- file_detail = cls._get_file_detail(project_id, file_id)
- cls._download_file(file_detail["url"], downloaded_file_path)
- break
- except Exception as error:
- logger.info(f"Error fetching file from LlamaCloud: {error}")
-
- @classmethod
- def get_file_name(cls, name: str, pipeline_id: str) -> str:
- return cls.DOWNLOAD_FILE_NAME_TPL.format(pipeline_id=pipeline_id, filename=name)
-
- @classmethod
- def get_file_path(cls, name: str, pipeline_id: str) -> str:
- return os.path.join(cls.LOCAL_STORE_PATH, cls.get_file_name(name, pipeline_id))
-
- @staticmethod
- def _make_request(
- url: str, data=None, headers: Optional[Dict] = None, method: str = "get"
- ):
- if headers is None:
- headers = {
- "Accept": "application/json",
- "Authorization": f'Bearer {os.getenv("LLAMA_CLOUD_API_KEY")}',
- }
- response = requests.request(method, url, headers=headers, data=data)
- response.raise_for_status()
- return response.json()
diff --git a/templates/types/streaming/fastapi/pyproject.toml b/templates/types/streaming/fastapi/pyproject.toml
index e897faffccb5f931390245f6c626bd9b10177a0a..f622b1d8c96eaf4bb4effc940133efc598945614 100644
--- a/templates/types/streaming/fastapi/pyproject.toml
+++ b/templates/types/streaming/fastapi/pyproject.toml
@@ -9,7 +9,7 @@ readme = "README.md"
generate = "app.engine.generate:generate_datasource"
[tool.poetry.dependencies]
-python = "^3.11,<3.12"
+python = "^3.11,<4.0"
fastapi = "^0.109.1"
uvicorn = { extras = ["standard"], version = "^0.23.2" }
python-dotenv = "^1.0.0"
diff --git a/templates/types/streaming/nextjs/app/api/chat/upload/route.ts b/templates/types/streaming/nextjs/app/api/chat/upload/route.ts
index 08b1d41961f154650fff1bc41d3779702b6fc9ed..d5aeb505c50393e6cdd7a4d5b5f1cd01d2af6f74 100644
--- a/templates/types/streaming/nextjs/app/api/chat/upload/route.ts
+++ b/templates/types/streaming/nextjs/app/api/chat/upload/route.ts
@@ -10,14 +10,15 @@ export const dynamic = "force-dynamic";
export async function POST(request: NextRequest) {
try {
- const { base64 }: { base64: string } = await request.json();
+ const { base64, params }: { base64: string; params?: any } =
+ await request.json();
if (!base64) {
return NextResponse.json(
{ error: "base64 is required in the request body" },
{ status: 400 },
);
}
- const index = await getDataSource();
+ const index = await getDataSource(params);
if (!index) {
throw new Error(
`StorageContext is empty - call 'npm run generate' to generate the storage first`,
diff --git a/templates/types/streaming/nextjs/app/components/chat-section.tsx b/templates/types/streaming/nextjs/app/components/chat-section.tsx
index 619ab9a421e6f8ff8428d650f5853e1b5e266d0d..7fcf188d75e41c4f17419389259bf133da2ed954 100644
--- a/templates/types/streaming/nextjs/app/components/chat-section.tsx
+++ b/templates/types/streaming/nextjs/app/components/chat-section.tsx
@@ -48,6 +48,7 @@ export default function ChatSection() {
messages={messages}
append={append}
setInput={setInput}
+ requestParams={{ params: requestData }}
setRequestData={setRequestData}
/>
</div>
diff --git a/templates/types/streaming/nextjs/app/components/ui/chat/chat-input.tsx b/templates/types/streaming/nextjs/app/components/ui/chat/chat-input.tsx
index 3f75820570d24932383e2c81549fffadaba9b405..9d1cb44eca85fa5ad99f321822bfde83996944ef 100644
--- a/templates/types/streaming/nextjs/app/components/ui/chat/chat-input.tsx
+++ b/templates/types/streaming/nextjs/app/components/ui/chat/chat-input.tsx
@@ -71,7 +71,8 @@ export default function ChatInput(
await uploadFile(file, props.requestParams);
props.onFileUpload?.(file);
} catch (error: any) {
- props.onFileError?.(error.message);
+ const onFileUploadError = props.onFileError || window.alert;
+ onFileUploadError(error.message);
}
};
diff --git a/templates/types/streaming/nextjs/app/components/ui/chat/hooks/use-file.ts b/templates/types/streaming/nextjs/app/components/ui/chat/hooks/use-file.ts
index 2c2c34bc82e6e068c9eef99dca04720db409100b..cc49169ac62747f1a6c135a6dc9774314dabb313 100644
--- a/templates/types/streaming/nextjs/app/components/ui/chat/hooks/use-file.ts
+++ b/templates/types/streaming/nextjs/app/components/ui/chat/hooks/use-file.ts
@@ -49,9 +49,10 @@ export function useFile() {
};
const uploadContent = async (
- base64: string,
+ file: File,
requestParams: any = {},
): Promise<string[]> => {
+ const base64 = await readContent({ file, asUrl: true });
const uploadAPI = `${backend}/api/chat/upload`;
const response = await fetch(uploadAPI, {
method: "POST",
@@ -59,8 +60,9 @@ export function useFile() {
"Content-Type": "application/json",
},
body: JSON.stringify({
- base64,
...requestParams,
+ base64,
+ filename: file.name,
}),
});
if (!response.ok) throw new Error("Failed to upload document.");
@@ -128,8 +130,7 @@ export function useFile() {
});
}
default: {
- const base64 = await readContent({ file, asUrl: true });
- const ids = await uploadContent(base64, requestParams);
+ const ids = await uploadContent(file, requestParams);
return addDoc({
...newDoc,
content: {