mirror of
https://github.com/james-m-jordan/morphik-core.git
synced 2025-05-09 19:32:38 +00:00
249 lines
8.6 KiB
Python
249 lines
8.6 KiB
Python
import json
|
|
from datetime import datetime, UTC
|
|
from typing import Any, Dict, List, Optional
|
|
from fastapi import FastAPI, Form, HTTPException, Depends, Header, UploadFile
|
|
from fastapi.middleware.cors import CORSMiddleware
|
|
import jwt
|
|
import logging
|
|
from core.completion.openai_completion import OpenAICompletionModel
|
|
from core.embedding.ollama_embedding_model import OllamaEmbeddingModel
|
|
from core.models.request import (
|
|
IngestTextRequest,
|
|
RetrieveRequest,
|
|
CompletionQueryRequest,
|
|
)
|
|
from core.models.documents import Document, DocumentResult, ChunkResult
|
|
from core.models.auth import AuthContext, EntityType
|
|
from core.parser.combined_parser import CombinedParser
|
|
from core.completion.base_completion import CompletionResponse
|
|
from core.parser.unstructured_parser import UnstructuredAPIParser
|
|
from core.services.document_service import DocumentService
|
|
from core.config import get_settings
|
|
from core.database.mongo_database import MongoDatabase
|
|
from core.vector_store.mongo_vector_store import MongoDBAtlasVectorStore
|
|
from core.storage.s3_storage import S3Storage
|
|
from core.embedding.openai_embedding_model import OpenAIEmbeddingModel
|
|
from core.completion.ollama_completion import OllamaCompletionModel
|
|
|
|
# Initialize FastAPI app
|
|
app = FastAPI(title="DataBridge API")
|
|
logger = logging.getLogger(__name__)
|
|
|
|
# Add CORS middleware
|
|
app.add_middleware(
|
|
CORSMiddleware,
|
|
allow_origins=["*"],
|
|
allow_credentials=True,
|
|
allow_methods=["*"],
|
|
allow_headers=["*"],
|
|
)
|
|
|
|
# Initialize service
|
|
settings = get_settings()
|
|
|
|
# Initialize database
|
|
match settings.DATABASE_PROVIDER:
|
|
case "mongodb":
|
|
database = MongoDatabase(
|
|
uri=settings.MONGODB_URI,
|
|
db_name=settings.DATABRIDGE_DB,
|
|
collection_name=settings.DOCUMENTS_COLLECTION,
|
|
)
|
|
case _:
|
|
raise ValueError(f"Unsupported database provider: {settings.DATABASE_PROVIDER}")
|
|
|
|
# Initialize vector store
|
|
match settings.VECTOR_STORE_PROVIDER:
|
|
case "mongodb":
|
|
vector_store = MongoDBAtlasVectorStore(
|
|
uri=settings.MONGODB_URI,
|
|
database_name=settings.DATABRIDGE_DB,
|
|
collection_name=settings.CHUNKS_COLLECTION,
|
|
index_name=settings.VECTOR_INDEX_NAME,
|
|
)
|
|
case _:
|
|
raise ValueError(f"Unsupported vector store provider: {settings.VECTOR_STORE_PROVIDER}")
|
|
|
|
# Initialize storage
|
|
match settings.STORAGE_PROVIDER:
|
|
case "aws-s3":
|
|
storage = S3Storage(
|
|
aws_access_key=settings.AWS_ACCESS_KEY,
|
|
aws_secret_key=settings.AWS_SECRET_ACCESS_KEY,
|
|
region_name=settings.AWS_REGION,
|
|
default_bucket=settings.S3_BUCKET,
|
|
)
|
|
case _:
|
|
raise ValueError(f"Unsupported storage provider: {settings.STORAGE_PROVIDER}")
|
|
|
|
# Initialize parser
|
|
match settings.PARSER_PROVIDER:
|
|
case "combined":
|
|
parser = CombinedParser(
|
|
unstructured_api_key=settings.UNSTRUCTURED_API_KEY,
|
|
assemblyai_api_key=settings.ASSEMBLYAI_API_KEY,
|
|
chunk_size=settings.CHUNK_SIZE,
|
|
chunk_overlap=settings.CHUNK_OVERLAP,
|
|
frame_sample_rate=settings.FRAME_SAMPLE_RATE,
|
|
)
|
|
case "unstructured":
|
|
parser = UnstructuredAPIParser(
|
|
unstructured_api_key=settings.UNSTRUCTURED_API_KEY,
|
|
chunk_size=settings.CHUNK_SIZE,
|
|
chunk_overlap=settings.CHUNK_OVERLAP,
|
|
)
|
|
case _:
|
|
raise ValueError(f"Unsupported parser provider: {settings.PARSER_PROVIDER}")
|
|
|
|
# Initialize embedding model
|
|
match settings.EMBEDDING_PROVIDER:
|
|
case "openai":
|
|
embedding_model = OpenAIEmbeddingModel(
|
|
api_key=settings.OPENAI_API_KEY,
|
|
model_name=settings.EMBEDDING_MODEL,
|
|
)
|
|
case "ollama":
|
|
embedding_model = OllamaEmbeddingModel(
|
|
model_name=settings.EMBEDDING_MODEL,
|
|
base_url=settings.OLLAMA_BASE_URL,
|
|
)
|
|
case _:
|
|
raise ValueError(f"Unsupported embedding provider: {settings.EMBEDDING_PROVIDER}")
|
|
|
|
# Initialize completion model
|
|
match settings.COMPLETION_PROVIDER:
|
|
case "ollama":
|
|
completion_model = OllamaCompletionModel(
|
|
model_name=settings.COMPLETION_MODEL,
|
|
base_url=settings.OLLAMA_BASE_URL,
|
|
)
|
|
case "openai":
|
|
completion_model = OpenAICompletionModel(
|
|
model_name=settings.COMPLETION_MODEL,
|
|
)
|
|
case _:
|
|
raise ValueError(f"Unsupported completion provider: {settings.COMPLETION_PROVIDER}")
|
|
|
|
# Initialize document service with configured components
|
|
document_service = DocumentService(
|
|
database=database,
|
|
vector_store=vector_store,
|
|
storage=storage,
|
|
parser=parser,
|
|
embedding_model=embedding_model,
|
|
completion_model=completion_model,
|
|
)
|
|
|
|
|
|
async def verify_token(authorization: str = Header(None)) -> AuthContext:
|
|
"""Verify JWT Bearer token."""
|
|
if not authorization:
|
|
raise HTTPException(
|
|
status_code=401,
|
|
detail="Missing authorization header",
|
|
headers={"WWW-Authenticate": "Bearer"},
|
|
)
|
|
try:
|
|
if not authorization.startswith("Bearer "):
|
|
raise HTTPException(status_code=401, detail="Invalid authorization header")
|
|
|
|
token = authorization[7:] # Remove "Bearer "
|
|
payload = jwt.decode(token, settings.JWT_SECRET_KEY, algorithms=[settings.JWT_ALGORITHM])
|
|
|
|
if datetime.fromtimestamp(payload["exp"], UTC) < datetime.now(UTC):
|
|
raise HTTPException(status_code=401, detail="Token expired")
|
|
|
|
return AuthContext(
|
|
entity_type=EntityType(payload["type"]),
|
|
entity_id=payload["entity_id"],
|
|
app_id=payload.get("app_id"),
|
|
permissions=set(payload.get("permissions", ["read"])),
|
|
)
|
|
except jwt.InvalidTokenError as e:
|
|
raise HTTPException(status_code=401, detail=str(e))
|
|
|
|
|
|
@app.post("/ingest/text", response_model=Document)
|
|
async def ingest_text(
|
|
request: IngestTextRequest, auth: AuthContext = Depends(verify_token)
|
|
) -> Document:
|
|
"""Ingest a text document."""
|
|
try:
|
|
return await document_service.ingest_text(request, auth)
|
|
except PermissionError as e:
|
|
raise HTTPException(status_code=403, detail=str(e))
|
|
|
|
|
|
@app.post("/ingest/file", response_model=Document)
|
|
async def ingest_file(
|
|
file: UploadFile,
|
|
metadata: str = Form("{}"), # JSON string of metadata
|
|
auth: AuthContext = Depends(verify_token),
|
|
) -> Document:
|
|
"""Ingest a file document."""
|
|
try:
|
|
metadata_dict = json.loads(metadata)
|
|
doc = await document_service.ingest_file(file, metadata_dict, auth)
|
|
return doc # TODO: Might be lighter on network to just send the document ID.
|
|
except PermissionError as e:
|
|
raise HTTPException(status_code=403, detail=str(e))
|
|
except json.JSONDecodeError:
|
|
raise HTTPException(400, "Invalid metadata JSON")
|
|
|
|
|
|
@app.post("/retrieve/chunks", response_model=List[ChunkResult])
|
|
async def retrieve_chunks(request: RetrieveRequest, auth: AuthContext = Depends(verify_token)):
|
|
"""Retrieve relevant chunks."""
|
|
return await document_service.retrieve_chunks(
|
|
request.query, auth, request.filters, request.k, request.min_score
|
|
)
|
|
|
|
|
|
@app.post("/retrieve/docs", response_model=List[DocumentResult])
|
|
async def retrieve_documents(request: RetrieveRequest, auth: AuthContext = Depends(verify_token)):
|
|
"""Retrieve relevant documents."""
|
|
return await document_service.retrieve_docs(
|
|
request.query, auth, request.filters, request.k, request.min_score
|
|
)
|
|
|
|
|
|
@app.post("/query", response_model=CompletionResponse)
|
|
async def query_completion(
|
|
request: CompletionQueryRequest, auth: AuthContext = Depends(verify_token)
|
|
):
|
|
"""Generate completion using relevant chunks as context."""
|
|
return await document_service.query(
|
|
request.query,
|
|
auth,
|
|
request.filters,
|
|
request.k,
|
|
request.min_score,
|
|
request.max_tokens,
|
|
request.temperature,
|
|
)
|
|
|
|
|
|
@app.get("/documents", response_model=List[Document])
|
|
async def list_documents(
|
|
auth: AuthContext = Depends(verify_token),
|
|
skip: int = 0,
|
|
limit: int = 100,
|
|
filters: Optional[Dict[str, Any]] = None,
|
|
):
|
|
"""List accessible documents."""
|
|
return await document_service.db.get_documents(auth, skip, limit, filters)
|
|
|
|
|
|
@app.get("/documents/{document_id}", response_model=Document)
|
|
async def get_document(document_id: str, auth: AuthContext = Depends(verify_token)):
|
|
"""Get document by ID."""
|
|
try:
|
|
doc = await document_service.db.get_document(document_id, auth)
|
|
logger.info(f"Found document: {doc}")
|
|
if not doc:
|
|
raise HTTPException(status_code=404, detail="Document not found")
|
|
return doc
|
|
except HTTPException as e:
|
|
logger.error(f"Error getting document: {e}")
|
|
raise e
|