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

rootfs/agents/TextSummary/agent.py

@@ -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

@@ -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

@@ -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)