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Add pdf、docx parser and image、video to text function for knowledge

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wugren
2023-12-05 18:08:32 +08:00
parent 66e407add5
commit 5f451107c9
3 changed files with 164 additions and 17 deletions
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rootfs/agents/TextSummary/agent.py
+1 -1
View File
@@ -20,7 +20,7 @@ class TextSummaryAgent(CustomAIAgent):
chunks = split_text(msg.body, separators=["\n\n", "\n"], chunk_size=4000, chunk_overlap=200, length_function=len)
prompt = AgentPrompt()
prompt.system_message = "Your job is to generate a summary based on the input."
prompt.system_message = {"role":"system","content":"Your job is to generate a summary based on the input."}
if len(chunks) == 1:
prompt.append(AgentPrompt(chunks[0]))
resp = await self.do_llm_complection(prompt)
rootfs/knowledge_pipelines/Mia/input.py
+146 -2
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@@ -1,10 +1,23 @@
import copy
import os
from typing import List
import aiofiles
import chardet
import logging
import string
from knowledge import ImageObjectBuilder, DocumentObjectBuilder, KnowledgePipelineEnvironment, KnowledgePipelineJournal
from aios_kernel.storage import AIStorage
import docx2txt
from PyPDF2 import PdfReader
from aios import KnowledgePipelineEnvironment, ImageObjectBuilder, DocumentObjectBuilder, KnowledgeStore, RichTextObject
from aios.agent.agent_base import AgentPrompt
from aios.frame.compute_kernel import ComputeKernel
from aios.knowledge.data.writer import split_text
from aios.proto.compute_task import ComputeTaskResult, ComputeTaskResultCode
from aios.storage.storage import AIStorage
from aios.utils import video_utils, image_utils
class KnowledgeDirSource:
def __init__(self, env: KnowledgePipelineEnvironment, config):
@@ -66,3 +79,134 @@ class KnowledgeDirSource:
def init(env: KnowledgePipelineEnvironment, params: dict) -> KnowledgeDirSource:
return KnowledgeDirSource(env, params)
async def image_to_text(images: List[str]) -> str:
msg_prompt = AgentPrompt()
image_prompt = "What's in this image?"
content = [{"type": "text", "text": image_prompt}]
content.extend([{"type": "image_url", "image_url": {"url": image_utils.to_base64(image)}} for image in images])
msg_prompt.messages = [{"role": "user", "content": content}]
resp: ComputeTaskResult = await (ComputeKernel.get_instance()
.do_llm_completion(prompt=msg_prompt,
resp_mode="text",
mode_name="gpt-4-vision-preview",
max_token=4000,
inner_functions=None,
timeout=None))
if resp.result_code != ComputeTaskResultCode.OK:
raise Exception(f"image_to_text error: {resp.result_code} msg:{resp.error_str}")
return resp.result_str
async def video_to_text(video: str) -> str:
prompt = "These pictures are key frames extracted from the video. Please describe the content of the video based on these key frames."
frames = video_utils.extract_frames(video, (1024, 1024))
msg_prompt = AgentPrompt()
content = [{"type": "text", "text": prompt}]
content.extend([{"type": "image_url", "image_url": {"url": frame}} for frame in frames])
msg_prompt.messages = [{"role": "user", "content": content}]
resp: ComputeTaskResult = await (ComputeKernel.get_instance()
.do_llm_completion(prompt=msg_prompt,
resp_mode="text",
mode_name="gpt-4-vision-preview",
max_token=4000,
inner_functions=None,
timeout=None))
if resp.result_code != ComputeTaskResultCode.OK:
raise Exception(f"video_to_text error: {resp.result_code} msg:{resp.error_str}")
return resp.result_str
async def summary_document(text: str, separators: List[str]=["\n\n", "\n"]) -> str:
chunks = split_text(text, separators=separators, chunk_size=4000, chunk_overlap=200, length_function=len)
prompt = AgentPrompt()
prompt.system_message = {"role":"system","content":"Your job is to generate a summary based on the input."}
if len(chunks) == 1:
prompt.append(AgentPrompt(chunks[0]))
resp = await (ComputeKernel.get_instance()
.do_llm_completion(prompt=prompt,
resp_mode="text",
mode_name="gpt-4-1106-preview",
max_token=4000,
inner_functions=None,
timeout=None))
if resp.result_code != ComputeTaskResultCode.OK:
raise Exception(f"summary_document error: {resp.result_code} msg:{resp.error_str}")
return resp.result_str
segments = []
for i, chunk in enumerate(chunks):
seg_prompt = copy.deepcopy(prompt)
seg_prompt.append(AgentPrompt(chunk))
resp = await (ComputeKernel.get_instance()
.do_llm_completion(prompt=seg_prompt,
resp_mode="text",
mode_name="gpt-4-1106-preview",
max_token=4000,
inner_functions=None,
timeout=None))
if resp.result_code != ComputeTaskResultCode.OK:
raise Exception(f"summary_document error: {resp.result_code} msg:{resp.error_str}")
segments.append(resp.result_str)
segments_str = "\n".join(segments)
prompt.append(AgentPrompt(f"Please combine the summaries of the following paragraphs into one complete summary:\n{segments_str}"))
resp = await (ComputeKernel.get_instance()
.do_llm_completion(prompt=prompt,
resp_mode="text",
mode_name="gpt-4-1106-preview",
max_token=4000,
inner_functions=None,
timeout=None))
if resp.result_code != ComputeTaskResultCode.OK:
raise Exception(f"summary_document error: {resp.result_code} msg:{resp.error_str}")
return resp.result_str
def pdf_to_rich_text_object(pdf: str, store: KnowledgeStore) -> RichTextObject:
base_name = os.path.basename(pdf)
cache_path = os.path.join(AIStorage.get_instance().get_myai_dir(), "knowledge", "doc_cache", base_name)
if not os.path.exists(cache_path):
os.makedirs(cache_path)
reader = PdfReader(pdf)
rich_text = RichTextObject()
page_texts = []
image_count = 0
for page in reader.pages:
text = page.extract_text()
page_texts.append(text)
for image in page.images:
image_path = os.path.join(cache_path, f"{image_count}_{image.name}")
with open(image_path, "wb") as f:
f.write(image.data)
image_object = ImageObjectBuilder({}, {}, image_path).build(store)
rich_text.add_image(image_object)
document = DocumentObjectBuilder({}, {}, "".join(page_texts)).build(store)
rich_text.add_document(document)
return rich_text
def doc_to_rich_text_object(doc: str, store: KnowledgeStore) -> RichTextObject:
base_name = os.path.basename(doc)
cache_path = os.path.join(AIStorage.get_instance().get_myai_dir(), "knowledge", "doc_cache", base_name)
if not os.path.exists(cache_path):
os.makedirs(cache_path)
text = docx2txt.process(doc, cache_path)
rich_text = RichTextObject()
for image in os.listdir(cache_path):
image_path = os.path.join(cache_path, image)
image_object = ImageObjectBuilder({}, {}, image_path).build(store)
rich_text.add_image(image_object)
document = DocumentObjectBuilder({}, {}, text).build(store)
rich_text.add_document(document)
return rich_text
src/aios/knowledge/object/object_id.py
+3
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@@ -67,3 +67,6 @@ class ObjectID: # pylint: disable=too-few-public-methods
def __eq__(self, other) -> bool:
return self.value == other.value
def __hash__(self):
return hash(self.value)