Merge remote-tracking branch 'origin/chore-update-registry1-weekly-bu…

…ndle-0.13.0' into 835-upgrade-vllm-for-gptq-bfloat16-inferencing

src/leapfrogai_api/backend/composer.py

@@ -78,12 +78,25 @@ async def create_chat_messages(
         thread: Thread,
         additional_instructions: str | None,
         tool_resources: BetaThreadToolResources | None = None,
-    ) -> tuple[list[ChatMessage], list[str]]:
+    ) -> tuple[list[ChatMessage], SearchResponse]:
+        """Create chat message list for consumption by the LLM backend.
+
+        Args:
+            request (RunCreateParamsRequest): The request object.
+            session (Session): The database session.
+            thread (Thread): The thread object.
+            additional_instructions (str | None): Additional instructions.
+            tool_resources (BetaThreadToolResources | None): The tool resources.
+
+        Returns:
+            tuple[list[ChatMessage], SearchResponse]: The chat messages and any RAG responses.
+        """
         # Get existing messages
         thread_messages: list[Message] = await self.list_messages(thread.id, session)
+        rag_responses: SearchResponse = SearchResponse(data=[])
 
         if len(thread_messages) == 0:
-            return [], []
+            return [], rag_responses
 
         def sort_by_created_at(msg: Message):
             return msg.created_at
@@ -125,7 +138,6 @@ def sort_by_created_at(msg: Message):
         chat_messages.extend(chat_thread_messages)
 
         # 4 - The RAG results are appended behind the user's query
-        file_ids: set[str] = set()
         if request.can_use_rag(tool_resources) and chat_thread_messages:
             rag_message: str = "Here are relevant docs needed to reply:\n"
 
@@ -138,22 +150,22 @@ def sort_by_created_at(msg: Message):
             vector_store_ids: list[str] = cast(list[str], file_search.vector_store_ids)
 
             for vector_store_id in vector_store_ids:
-                rag_responses: SearchResponse = await query_service.query_rag(
+                rag_responses = await query_service.query_rag(
                     query=query_message.content_as_str(),
                     vector_store_id=vector_store_id,
                 )
+
                 # Insert the RAG response messages just before the user's query
                 for rag_response in rag_responses.data:
-                    file_ids.add(rag_response.file_id)
                     response_with_instructions: str = f"{rag_response.content}"
                     rag_message += f"{response_with_instructions}\n"
 
             chat_messages.insert(
                 len(chat_messages) - 1,  # Insert right before the user message
                 ChatMessage(role="user", content=rag_message),
-            )  # TODO: Should this go in user or something else like function?
+            )
 
-        return chat_messages, list(file_ids)
+        return chat_messages, rag_responses
 
     async def generate_message_for_thread(
         self,
@@ -182,7 +194,7 @@ async def generate_message_for_thread(
             else:
                 tool_resources = None
 
-        chat_messages, file_ids = await self.create_chat_messages(
+        chat_messages, rag_responses = await self.create_chat_messages(
             request, session, thread, additional_instructions, tool_resources
         )
 
@@ -204,13 +216,15 @@ async def generate_message_for_thread(
 
         choice: ChatChoice = cast(ChatChoice, chat_response.choices[0])
 
-        message = from_text_to_message(choice.message.content_as_str(), file_ids)
+        message: Message = from_text_to_message(
+            text=choice.message.content_as_str(), search_responses=rag_responses
+        )
 
         create_message_request = CreateMessageRequest(
             role=message.role,
             content=message.content,
             attachments=message.attachments,
-            metadata=message.metadata.__dict__ if message.metadata else None,
+            metadata=vars(message.metadata),
         )
 
         await create_message_request.create_message(
@@ -249,7 +263,7 @@ async def stream_generate_message_for_thread(
             else:
                 tool_resources = None
 
-        chat_messages, file_ids = await self.create_chat_messages(
+        chat_messages, rag_responses = await self.create_chat_messages(
             request, session, thread, additional_instructions, tool_resources
         )
 
@@ -274,13 +288,15 @@ async def stream_generate_message_for_thread(
             yield "\n\n"
 
         # Create an empty message
-        new_message: Message = from_text_to_message("", [])
+        new_message: Message = from_text_to_message(
+            text="", search_responses=SearchResponse(data=[])
+        )
 
         create_message_request = CreateMessageRequest(
             role=new_message.role,
             content=new_message.content,
             attachments=new_message.attachments,
-            metadata=new_message.metadata.__dict__ if new_message.metadata else None,
+            metadata=vars(new_message.metadata),
         )
 
         new_message = await create_message_request.create_message(
@@ -319,7 +335,9 @@ async def stream_generate_message_for_thread(
             yield "\n\n"
             index += 1
 
-        new_message.content = from_text_to_message(response, file_ids).content
+        new_message.content = from_text_to_message(
+            text=response, search_responses=rag_responses
+        ).content
         new_message.created_at = int(time.time())
 
         crud_message = CRUDMessage(db=session)

src/leapfrogai_api/backend/converters.py

@@ -4,6 +4,7 @@
 from openai.types.beta import AssistantStreamEvent
 from openai.types.beta.assistant_stream_event import ThreadMessageDelta
 from openai.types.beta.threads.file_citation_annotation import FileCitation
+from openai.types.beta.threads.file_path_annotation import FilePathAnnotation
 from openai.types.beta.threads import (
     MessageContentPartParam,
     MessageContent,
@@ -17,6 +18,9 @@
     FileCitationAnnotation,
 )
 
+from leapfrogai_api.typedef.vectorstores.search_types import SearchResponse
+from leapfrogai_api.typedef.common import MetadataObject
+
 
 def from_assistant_stream_event_to_str(stream_event: AssistantStreamEvent):
     return f"event: {stream_event.event}\ndata: {stream_event.data.model_dump_json()}"
@@ -44,24 +48,41 @@ def from_content_param_to_content(
         )
 
 
-def from_text_to_message(text: str, file_ids: list[str]) -> Message:
-    all_file_ids: str = ""
+def from_text_to_message(text: str, search_responses: SearchResponse | None) -> Message:
+    """Loads text and RAG search responses into a Message object
 
-    for file_id in file_ids:
-        all_file_ids += f" [{file_id}]"
+    Args:
+        text: The text to load into the message
+        search_responses: The RAG search responses to load into the message
 
-    message_content: TextContentBlock = TextContentBlock(
-        text=Text(
-            annotations=[
+    Returns:
+        The OpenAI compliant Message object
+    """
+
+    all_file_ids: str = ""
+    all_vector_ids: list[str] = []
+    annotations: list[FileCitationAnnotation | FilePathAnnotation] = []
+
+    if search_responses:
+        for search_response in search_responses.data:
+            all_file_ids += f"[{search_response.file_id}]"
+            all_vector_ids.append(search_response.id)
+            file_name = search_response.metadata.get("source", "source")
+            annotations.append(
                 FileCitationAnnotation(
-                    text=f"[{file_id}]",
-                    file_citation=FileCitation(file_id=file_id, quote=""),
+                    text=f"【4:0†{file_name}】",  # TODO: What should these numbers be? https://github.com/defenseunicorns/leapfrogai/issues/1110
+                    file_citation=FileCitation(
+                        file_id=search_response.file_id, quote=search_response.content
+                    ),
                     start_index=0,
                     end_index=0,
                     type="file_citation",
                 )
-                for file_id in file_ids
-            ],
+            )
+
+    message_content: TextContentBlock = TextContentBlock(
+        text=Text(
+            annotations=annotations,
             value=text + all_file_ids,
         ),
         type="text",
@@ -75,7 +96,9 @@ def from_text_to_message(text: str, file_ids: list[str]) -> Message:
         thread_id="",
         content=[message_content],
         role="assistant",
-        metadata=None,
+        metadata=MetadataObject(
+            vector_ids=all_vector_ids.__str__(),
+        ),
     )
 
     return new_message

src/leapfrogai_api/backend/rag/index.py

@@ -81,6 +81,8 @@ async def index_file(self, vector_store_id: str, file_id: str) -> VectorStoreFil
             temp_file.write(file_bytes)
             temp_file.seek(0)
             documents = await load_file(temp_file.name)
+            for document in documents:
+                document.metadata["source"] = file_object.filename
             chunks = await split(documents)
 
             if len(chunks) == 0:

src/leapfrogai_api/data/crud_vector_content.py

@@ -1,20 +1,11 @@
 """CRUD Operations for VectorStore."""
 
-from pydantic import BaseModel
 from supabase import AClient as AsyncClient
 from leapfrogai_api.data.crud_base import get_user_id
 import ast
 from leapfrogai_api.typedef.vectorstores import SearchItem, SearchResponse
 from leapfrogai_api.backend.constants import TOP_K
-
-
-class Vector(BaseModel):
-    id: str = ""
-    vector_store_id: str
-    file_id: str
-    content: str
-    metadata: dict
-    embedding: list[float]
+from leapfrogai_api.typedef.vectorstores import Vector
 
 
 class CRUDVectorContent:
@@ -65,6 +56,30 @@ async def add_vectors(self, object_: list[Vector]) -> list[Vector]:
         except Exception as e:
             raise e
 
+    async def get_vector(self, vector_id: str) -> Vector:
+        """Get a vector by its ID."""
+        data, _count = (
+            await self.db.table(self.table_name)
+            .select("*")
+            .eq("id", vector_id)
+            .single()
+            .execute()
+        )
+
+        _, response = data
+
+        if isinstance(response["embedding"], str):
+            response["embedding"] = self.string_to_float_list(response["embedding"])
+
+        return Vector(
+            id=response["id"],
+            vector_store_id=response["vector_store_id"],
+            file_id=response["file_id"],
+            content=response["content"],
+            metadata=response["metadata"],
+            embedding=response["embedding"],
+        )
+
     async def delete_vectors(self, vector_store_id: str, file_id: str) -> bool:
         """Delete a vector store file by its ID."""
         data, _count = (

src/leapfrogai_api/routers/leapfrogai/vector_stores.py

@@ -4,6 +4,7 @@
 from leapfrogai_api.backend.rag.query import QueryService
 from leapfrogai_api.typedef.vectorstores import SearchResponse
 from leapfrogai_api.routers.supabase_session import Session
+from leapfrogai_api.data.crud_vector_content import CRUDVectorContent, Vector
 from leapfrogai_api.backend.constants import TOP_K
 
 router = APIRouter(
@@ -36,3 +37,24 @@ async def search(
         vector_store_id=vector_store_id,
         k=k,
     )
+
+
+@router.get("/vector/{vector_id}")
+async def get_vector(
+    session: Session,
+    vector_id: str,
+) -> Vector:
+    """
+    Get a specfic vector by its ID.
+
+    Args:
+        session (Session): The database session.
+        vector_id (str): The ID of the vector.
+
+    Returns:
+        Vector: The vector object.
+    """
+    crud_vector_content = CRUDVectorContent(db=session)
+    vector = await crud_vector_content.get_vector(vector_id=vector_id)
+
+    return vector

src/leapfrogai_api/typedef/__init__.py

@@ -1 +1,4 @@
-from .common import Usage as Usage
+from .common import (
+    Usage as Usage,
+    MetadataObject as MetadataObject,
+)

src/leapfrogai_api/typedef/common.py

@@ -2,6 +2,17 @@
 from leapfrogai_api.backend.constants import DEFAULT_MAX_COMPLETION_TOKENS
 
 
+class MetadataObject:
+    """A metadata object that can be serialized back to a dict."""
+
+    def __init__(self, **kwargs):
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+    def __getattr__(self, key):
+        return self.__dict__.get(key)
+
+
 class Usage(BaseModel):
     """Usage object."""
 

src/leapfrogai_api/typedef/vectorstores/__init__.py

@@ -7,6 +7,7 @@
     ListVectorStoresResponse as ListVectorStoresResponse,
 )
 from .search_types import (
+    Vector as Vector,
     SearchItem as SearchItem,
     SearchResponse as SearchResponse,
 )

src/leapfrogai_api/typedef/vectorstores/search_types.py

@@ -1,6 +1,15 @@
 from pydantic import BaseModel, Field
 
 
+class Vector(BaseModel):
+    id: str = ""
+    vector_store_id: str
+    file_id: str
+    content: str
+    metadata: dict
+    embedding: list[float]
+
+
 class SearchItem(BaseModel):
     """Object representing a single item in a search result."""
 

src/leapfrogai_ui/src/app.css

@@ -9,6 +9,17 @@
   scrollbar-color: #4b5563 #1f2937;
 }
 
+/* Override TailwindCSS default Preflight styles for lists in messages */
+#message-content-container {
+  ul {
+    margin: revert;
+    padding: revert;
+    li {
+      list-style: square;
+    }
+  }
+}
+
 /*TODO - can we get rid of some of these?*/
 @layer utilities {
   .content {

src/leapfrogai_ui/src/lib/components/Message.svelte

@@ -178,14 +178,16 @@
             {#if message.role !== 'user' && !messageText}
               <MessagePendingSkeleton size="sm" class="mt-4" darkColor="bg-gray-500" />
             {:else}
-              <!--eslint-disable-next-line svelte/no-at-html-tags -- We use DomPurity to sanitize the code snippet-->
-              {@html DOMPurify.sanitize(md.render(messageText), {
-                CUSTOM_ELEMENT_HANDLING: {
-                  tagNameCheck: /^code-block$/,
-                  attributeNameCheck: /^(code|language)$/,
-                  allowCustomizedBuiltInElements: false
-                }
-              })}
+              <div id="message-content-container">
+                <!--eslint-disable-next-line svelte/no-at-html-tags -- We use DomPurity to sanitize the code snippet-->
+                {@html DOMPurify.sanitize(md.render(messageText), {
+                  CUSTOM_ELEMENT_HANDLING: {
+                    tagNameCheck: /^code-block$/,
+                    attributeNameCheck: /^(code|language)$/,
+                    allowCustomizedBuiltInElements: false
+                  }
+                })}
+              </div>
               <div class="flex flex-col items-start">
                 {#each getCitations(message, $page.data.files) as { component: Component, props }}
                   <svelte:component this={Component} {...props} />