feat: kms-migration

core/quivr_core/files/file.py

@@ -112,9 +112,9 @@ def __init__(
         id: UUID,
         original_filename: str,
         path: Path,
-        brain_id: UUID,
         file_sha1: str,
         file_extension: FileExtension | str,
+        brain_id: UUID | None = None,
         file_size: int | None = None,
         metadata: dict[str, Any] | None = None,
     ) -> None:

core/quivr_core/processor/implementations/megaparse_processor.py

@@ -3,6 +3,7 @@
 import tiktoken
 from langchain_core.documents import Document
 from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter
+from megaparse import MegaParse
 
 from quivr_core.config import MegaparseConfig
 from quivr_core.files.file import QuivrFile
@@ -55,14 +56,11 @@ def processor_metadata(self):
         }
 
     async def process_file_inner(self, file: QuivrFile) -> list[Document]:
-        # mega_parse = MegaParse(file_path=file.path, config=self.megaparse_config)  # type: ignore
-        # document: Document = await mega_parse.aload()
-        # if len(document.page_content) > self.splitter_config.chunk_size:
-        #     docs = self.text_splitter.split_documents([document])
-        #     for doc in docs:
-        #         # if "Production Fonts (maximum)" in doc.page_content:
-        #         #    print('Doc: ', doc.page_content)
-        #         doc.metadata = {"chunk_size": len(self.enc.encode(doc.page_content))}
-        #     return docs
-        # return [document]
-        return []
+        mega_parse = MegaParse(file_path=file.path, config=self.megaparse_config)  # type: ignore
+        document: Document = await mega_parse.aload()
+        if len(document.page_content) > self.splitter_config.chunk_size:
+            docs = self.text_splitter.split_documents([document])
+            for doc in docs:
+                doc.metadata = {"chunk_size": len(self.enc.encode(doc.page_content))}
+            return docs
+        return [document]

core/quivr_core/processor/implementations/tika_processor.py

@@ -1,4 +1,5 @@
 import logging
+import os
 from typing import AsyncIterable
 
 import httpx
@@ -28,7 +29,7 @@ class TikaProcessor(ProcessorBase):
 
     def __init__(
         self,
-        tika_url: str = "http://localhost:9998/tika",
+        tika_url: str = os.getenv("TIKA_SERVER_URL", "http://localhost:9998/tika"),
         splitter: TextSplitter | None = None,
         splitter_config: SplitterConfig = SplitterConfig(),
         timeout: float = 5.0,

core/quivr_core/processor/processor_base.py

@@ -2,7 +2,6 @@
 from abc import ABC, abstractmethod
 from importlib.metadata import PackageNotFoundError, version
 from typing import Any
-from uuid import uuid4
 
 from langchain_core.documents import Document
 
@@ -43,7 +42,6 @@ async def process_file(self, file: QuivrFile) -> list[Document]:
                 "utf-8"
             )
             doc.metadata = {
-                "id": uuid4(),
                 "chunk_index": idx,
                 "quivr_core_version": qvr_version,
                 **file.metadata,

core/quivr_core/processor/registry.py

@@ -117,13 +117,13 @@ def defaults_to_proc_entries(
 
     # TODO(@aminediro): Megaparse should register itself
     # Append Megaparse
-    _append_proc_mapping(
-        mapping=base_processors,
-        file_ext=FileExtension.pdf,
-        cls_mod="quivr_core.processor.implementations.megaparse_processor.MegaparseProcessor",
-        errtxt=f"can't import MegaparseProcessor. Please install quivr-core[{ext_str}] to access MegaparseProcessor",
-        priority=None,
-    )
+    # _append_proc_mapping(
+    #     mapping=base_processors,
+    #     file_ext=FileExtension.pdf,
+    #     cls_mod="quivr_core.processor.implementations.megaparse_processor.MegaparseProcessor",
+    #     errtxt=f"can't import MegaparseProcessor. Please install quivr-core[{ext_str}] to access MegaparseProcessor",
+    #     priority=None,
+    # )
     return base_processors
 
 

core/quivr_core/rag/entities/models.py

@@ -39,10 +39,11 @@ class ChatMessage(BaseModelV1):
 
 
 class KnowledgeStatus(str, Enum):
-    PROCESSING = "PROCESSING"
-    UPLOADED = "UPLOADED"
     ERROR = "ERROR"
     RESERVED = "RESERVED"
+    PROCESSING = "PROCESSING"
+    PROCESSED = "PROCESSED"
+    UPLOADED = "UPLOADED"
 
 
 class Source(BaseModel):

core/quivr_core/rag/quivr_rag_langgraph.py

@@ -1,41 +1,38 @@
+import asyncio
 import logging
 from typing import (
     Annotated,
+    Any,
     AsyncGenerator,
+    Dict,
     List,
     Optional,
     Sequence,
     Tuple,
-    TypedDict,
-    Dict,
-    Any,
     Type,
+    TypedDict,
 )
 from uuid import uuid4
-import asyncio
 
-# TODO(@aminediro): this is the only dependency to langchain package, we should remove it
+import openai
 from langchain.retrievers import ContextualCompressionRetriever
 from langchain_cohere import CohereRerank
 from langchain_community.document_compressors import JinaRerank
 from langchain_core.callbacks import Callbacks
 from langchain_core.documents import BaseDocumentCompressor, Document
 from langchain_core.messages import BaseMessage
 from langchain_core.messages.ai import AIMessageChunk
-from langchain_core.vectorstores import VectorStore
 from langchain_core.prompts.base import BasePromptTemplate
-from langgraph.graph import START, END, StateGraph
+from langchain_core.vectorstores import VectorStore
+from langgraph.graph import END, START, StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.types import Send
-
-
 from pydantic import BaseModel, Field
-import openai
 
-from quivr_core.rag.entities.chat import ChatHistory
-from quivr_core.rag.entities.config import DefaultRerankers, NodeConfig, RetrievalConfig
 from quivr_core.llm import LLMEndpoint
 from quivr_core.llm_tools.llm_tools import LLMToolFactory
+from quivr_core.rag.entities.chat import ChatHistory
+from quivr_core.rag.entities.config import DefaultRerankers, NodeConfig, RetrievalConfig
 from quivr_core.rag.entities.models import (
     ParsedRAGChunkResponse,
     QuivrKnowledge,