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263 | class SimpleMultiModalQueryEngine(BaseQueryEngine):
"""Simple Multi Modal Retriever query engine.
Assumes that retrieved text context fits within context window of LLM, along with images.
Args:
retriever (MultiModalVectorIndexRetriever): A retriever object.
multi_modal_llm (Optional[MultiModalLLM]): MultiModalLLM Models.
text_qa_template (Optional[BasePromptTemplate]): Text QA Prompt Template.
image_qa_template (Optional[BasePromptTemplate]): Image QA Prompt Template.
node_postprocessors (Optional[List[BaseNodePostprocessor]]): Node Postprocessors.
callback_manager (Optional[CallbackManager]): A callback manager.
"""
def __init__(
self,
retriever: "MultiModalVectorIndexRetriever",
multi_modal_llm: Optional[MultiModalLLM] = None,
text_qa_template: Optional[BasePromptTemplate] = None,
image_qa_template: Optional[BasePromptTemplate] = None,
node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
callback_manager: Optional[CallbackManager] = None,
**kwargs: Any,
) -> None:
self._retriever = retriever
if multi_modal_llm:
self._multi_modal_llm = multi_modal_llm
else:
try:
from llama_index.multi_modal_llms.openai import (
OpenAIMultiModal,
) # pants: no-infer-dep
self._multi_modal_llm = OpenAIMultiModal(
model="gpt-4-vision-preview", max_new_tokens=1000
)
except ImportError as e:
raise ImportError(
"`llama-index-multi-modal-llms-openai` package cannot be found. "
"Please install it by using `pip install `llama-index-multi-modal-llms-openai`"
)
self._text_qa_template = text_qa_template or DEFAULT_TEXT_QA_PROMPT
self._image_qa_template = image_qa_template or DEFAULT_TEXT_QA_PROMPT
self._node_postprocessors = node_postprocessors or []
callback_manager = callback_manager or CallbackManager([])
for node_postprocessor in self._node_postprocessors:
node_postprocessor.callback_manager = callback_manager
super().__init__(callback_manager)
def _get_prompts(self) -> Dict[str, Any]:
"""Get prompts."""
return {"text_qa_template": self._text_qa_template}
def _get_prompt_modules(self) -> PromptMixinType:
"""Get prompt sub-modules."""
return {}
def _apply_node_postprocessors(
self, nodes: List[NodeWithScore], query_bundle: QueryBundle
) -> List[NodeWithScore]:
for node_postprocessor in self._node_postprocessors:
nodes = node_postprocessor.postprocess_nodes(
nodes, query_bundle=query_bundle
)
return nodes
def retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
nodes = self._retriever.retrieve(query_bundle)
return self._apply_node_postprocessors(nodes, query_bundle=query_bundle)
async def aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
nodes = await self._retriever.aretrieve(query_bundle)
return self._apply_node_postprocessors(nodes, query_bundle=query_bundle)
def synthesize(
self,
query_bundle: QueryBundle,
nodes: List[NodeWithScore],
additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
) -> RESPONSE_TYPE:
image_nodes, text_nodes = _get_image_and_text_nodes(nodes)
context_str = "\n\n".join(
[r.get_content(metadata_mode=MetadataMode.LLM) for r in text_nodes]
)
fmt_prompt = self._text_qa_template.format(
context_str=context_str, query_str=query_bundle.query_str
)
llm_response = self._multi_modal_llm.complete(
prompt=fmt_prompt,
image_documents=[
image_node.node
for image_node in image_nodes
if isinstance(image_node.node, ImageNode)
],
)
return Response(
response=str(llm_response),
source_nodes=nodes,
metadata={"text_nodes": text_nodes, "image_nodes": image_nodes},
)
def _get_response_with_images(
self,
prompt_str: str,
image_nodes: List[NodeWithScore],
) -> RESPONSE_TYPE:
assert all(isinstance(node.node, ImageNode) for node in image_nodes)
fmt_prompt = self._image_qa_template.format(
query_str=prompt_str,
)
llm_response = self._multi_modal_llm.complete(
prompt=fmt_prompt,
image_documents=[
node.node for node in image_nodes if isinstance(node.node, ImageNode)
],
)
return Response(
response=str(llm_response),
source_nodes=image_nodes,
metadata={"image_nodes": image_nodes},
)
async def asynthesize(
self,
query_bundle: QueryBundle,
nodes: List[NodeWithScore],
additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
) -> RESPONSE_TYPE:
image_nodes, text_nodes = _get_image_and_text_nodes(nodes)
context_str = "\n\n".join(
[r.get_content(metadata_mode=MetadataMode.LLM) for r in text_nodes]
)
fmt_prompt = self._text_qa_template.format(
context_str=context_str, query_str=query_bundle.query_str
)
llm_response = await self._multi_modal_llm.acomplete(
prompt=fmt_prompt,
image_documents=[
image_node.node
for image_node in image_nodes
if isinstance(image_node.node, ImageNode)
],
)
return Response(
response=str(llm_response),
source_nodes=nodes,
metadata={"text_nodes": text_nodes, "image_nodes": image_nodes},
)
def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
"""Answer a query."""
with self.callback_manager.event(
CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
) as query_event:
with self.callback_manager.event(
CBEventType.RETRIEVE,
payload={EventPayload.QUERY_STR: query_bundle.query_str},
) as retrieve_event:
nodes = self.retrieve(query_bundle)
retrieve_event.on_end(
payload={EventPayload.NODES: nodes},
)
response = self.synthesize(
query_bundle,
nodes=nodes,
)
query_event.on_end(payload={EventPayload.RESPONSE: response})
return response
def image_query(self, image_path: QueryType, prompt_str: str) -> RESPONSE_TYPE:
"""Answer a image query."""
with self.callback_manager.event(
CBEventType.QUERY, payload={EventPayload.QUERY_STR: str(image_path)}
) as query_event:
with self.callback_manager.event(
CBEventType.RETRIEVE,
payload={EventPayload.QUERY_STR: str(image_path)},
) as retrieve_event:
nodes = self._retriever.image_to_image_retrieve(image_path)
retrieve_event.on_end(
payload={EventPayload.NODES: nodes},
)
image_nodes, _ = _get_image_and_text_nodes(nodes)
response = self._get_response_with_images(
prompt_str=prompt_str,
image_nodes=image_nodes,
)
query_event.on_end(payload={EventPayload.RESPONSE: response})
return response
async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
"""Answer a query."""
with self.callback_manager.event(
CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
) as query_event:
with self.callback_manager.event(
CBEventType.RETRIEVE,
payload={EventPayload.QUERY_STR: query_bundle.query_str},
) as retrieve_event:
nodes = await self.aretrieve(query_bundle)
retrieve_event.on_end(
payload={EventPayload.NODES: nodes},
)
response = await self.asynthesize(
query_bundle,
nodes=nodes,
)
query_event.on_end(payload={EventPayload.RESPONSE: response})
return response
@property
def retriever(self) -> "MultiModalVectorIndexRetriever":
"""Get the retriever object."""
return self._retriever
|
