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changeset 4:0e33c65f1904

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playing with extractors
author drewp@bigasterisk.com
date Sat, 06 Jul 2024 16:42:36 -0700
parents ba1ce5921a4b
children 0d520a077228
files extract_pdf.py pdm.lock pyproject.toml query.py search/extract_pdf.py search/query.py webvtt_1.py
diffstat 7 files changed, 1024 insertions(+), 92 deletions(-) [+]
line wrap: on
line diff
--- a/extract_pdf.py	Wed Jul 03 20:20:18 2024 -0700
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,31 +0,0 @@
-import re
-from pathlib import Path
-from typing import Iterable
-
-import nltk
-from pdfminer.converter import PDFPageAggregator
-from pdfminer.layout import LAParams, LTTextBox
-from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager
-from pdfminer.pdfpage import PDFPage
-
-
-def phrasesFromFile(p: Path) -> Iterable:
-    fp = open(p, 'rb')
-    rsrcmgr = PDFResourceManager()
-    laparams = LAParams()
-    device = PDFPageAggregator(rsrcmgr, laparams=laparams)
-    interpreter = PDFPageInterpreter(rsrcmgr, device)
-    pages = PDFPage.get_pages(fp)
-
-    for page in pages:
-        interpreter.process_page(page)
-        layout = device.get_result()
-        for lobj in layout:
-            if isinstance(lobj, LTTextBox):
-                text = lobj.get_text()
-                for sentence in nltk.sent_tokenize(text):
-                    sentence = re.sub(r'\s+', ' ', sentence).strip()
-                    if len(sentence) < 5:
-                        continue
-
-                    yield list(map(int, lobj.bbox)), sentence
--- a/pdm.lock	Wed Jul 03 20:20:18 2024 -0700
+++ b/pdm.lock	Sat Jul 06 16:42:36 2024 -0700
@@ -5,7 +5,7 @@
 groups = ["default"]
 strategy = ["cross_platform", "inherit_metadata"]
 lock_version = "4.4.1"
-content_hash = "sha256:61f034c69e8959c0e345276733b3e11679a2d6c4f8b4d80e6323d00d0baaae0e"
+content_hash = "sha256:914f437191f234f800e6f04c0fedb35ceea1ea96f0c2b60406a111aa691ab8df"
 
 [[package]]
 name = "absl-py"
@@ -19,6 +19,93 @@
 ]
 
 [[package]]
+name = "aiohttp"
+version = "3.9.5"
+requires_python = ">=3.8"
+summary = "Async http client/server framework (asyncio)"
+groups = ["default"]
+dependencies = [
+    "aiosignal>=1.1.2",
+    "attrs>=17.3.0",
+    "frozenlist>=1.1.1",
+    "multidict<7.0,>=4.5",
+    "yarl<2.0,>=1.0",
+]
+files = [
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+    {file = "aiohttp-3.9.5-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:393c7aba2b55559ef7ab791c94b44f7482a07bf7640d17b341b79081f5e5cd1a"},
+    {file = "aiohttp-3.9.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:c671dc117c2c21a1ca10c116cfcd6e3e44da7fcde37bf83b2be485ab377b25da"},
+    {file = "aiohttp-3.9.5-cp312-cp312-win32.whl", hash = "sha256:5a7ee16aab26e76add4afc45e8f8206c95d1d75540f1039b84a03c3b3800dd59"},
+    {file = "aiohttp-3.9.5-cp312-cp312-win_amd64.whl", hash = "sha256:5ca51eadbd67045396bc92a4345d1790b7301c14d1848feaac1d6a6c9289e888"},
+    {file = "aiohttp-3.9.5.tar.gz", hash = "sha256:edea7d15772ceeb29db4aff55e482d4bcfb6ae160ce144f2682de02f6d693551"},
+]
+
+[[package]]
+name = "aiosignal"
+version = "1.3.1"
+requires_python = ">=3.7"
+summary = "aiosignal: a list of registered asynchronous callbacks"
+groups = ["default"]
+dependencies = [
+    "frozenlist>=1.1.0",
+]
+files = [
+    {file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"},
+    {file = "aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc"},
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+requires_python = ">=3.8"
+summary = "Reusable constraint types to use with typing.Annotated"
+groups = ["default"]
+files = [
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
+[[package]]
+name = "anyio"
+version = "4.4.0"
+requires_python = ">=3.8"
+summary = "High level compatibility layer for multiple asynchronous event loop implementations"
+groups = ["default"]
+dependencies = [
+    "idna>=2.8",
+    "sniffio>=1.1",
+]
+files = [
+    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
+    {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
+]
+
+[[package]]
 name = "asttokens"
 version = "2.4.1"
 summary = "Annotate AST trees with source code positions"
@@ -32,6 +119,17 @@
 ]
 
 [[package]]
+name = "attrs"
+version = "23.2.0"
+requires_python = ">=3.7"
+summary = "Classes Without Boilerplate"
+groups = ["default"]
+files = [
+    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
+    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
+]
+
+[[package]]
 name = "certifi"
 version = "2024.7.4"
 requires_python = ">=3.6"
@@ -155,6 +253,32 @@
 ]
 
 [[package]]
+name = "dnspython"
+version = "2.6.1"
+requires_python = ">=3.8"
+summary = "DNS toolkit"
+groups = ["default"]
+files = [
+    {file = "dnspython-2.6.1-py3-none-any.whl", hash = "sha256:5ef3b9680161f6fa89daf8ad451b5f1a33b18ae8a1c6778cdf4b43f08c0a6e50"},
+    {file = "dnspython-2.6.1.tar.gz", hash = "sha256:e8f0f9c23a7b7cb99ded64e6c3a6f3e701d78f50c55e002b839dea7225cff7cc"},
+]
+
+[[package]]
+name = "email-validator"
+version = "2.2.0"
+requires_python = ">=3.8"
+summary = "A robust email address syntax and deliverability validation library."
+groups = ["default"]
+dependencies = [
+    "dnspython>=2.0.0",
+    "idna>=2.0.0",
+]
+files = [
+    {file = "email_validator-2.2.0-py3-none-any.whl", hash = "sha256:561977c2d73ce3611850a06fa56b414621e0c8faa9d66f2611407d87465da631"},
+    {file = "email_validator-2.2.0.tar.gz", hash = "sha256:cb690f344c617a714f22e66ae771445a1ceb46821152df8e165c5f9a364582b7"},
+]
+
+[[package]]
 name = "environs"
 version = "9.5.0"
 requires_python = ">=3.6"
@@ -229,6 +353,44 @@
 ]
 
 [[package]]
+name = "fastapi"
+version = "0.111.0"
+requires_python = ">=3.8"
+summary = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
+groups = ["default"]
+dependencies = [
+    "email-validator>=2.0.0",
+    "fastapi-cli>=0.0.2",
+    "httpx>=0.23.0",
+    "jinja2>=2.11.2",
+    "orjson>=3.2.1",
+    "pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4",
+    "python-multipart>=0.0.7",
+    "starlette<0.38.0,>=0.37.2",
+    "typing-extensions>=4.8.0",
+    "ujson!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0,>=4.0.1",
+    "uvicorn[standard]>=0.12.0",
+]
+files = [
+    {file = "fastapi-0.111.0-py3-none-any.whl", hash = "sha256:97ecbf994be0bcbdadedf88c3150252bed7b2087075ac99735403b1b76cc8fc0"},
+    {file = "fastapi-0.111.0.tar.gz", hash = "sha256:b9db9dd147c91cb8b769f7183535773d8741dd46f9dc6676cd82eab510228cd7"},
+]
+
+[[package]]
+name = "fastapi-cli"
+version = "0.0.4"
+requires_python = ">=3.8"
+summary = "Run and manage FastAPI apps from the command line with FastAPI CLI. 🚀"
+groups = ["default"]
+dependencies = [
+    "typer>=0.12.3",
+]
+files = [
+    {file = "fastapi_cli-0.0.4-py3-none-any.whl", hash = "sha256:a2552f3a7ae64058cdbb530be6fa6dbfc975dc165e4fa66d224c3d396e25e809"},
+    {file = "fastapi_cli-0.0.4.tar.gz", hash = "sha256:e2e9ffaffc1f7767f488d6da34b6f5a377751c996f397902eb6abb99a67bde32"},
+]
+
+[[package]]
 name = "filelock"
 version = "3.15.4"
 requires_python = ">=3.8"
@@ -274,6 +436,47 @@
 ]
 
 [[package]]
+name = "frozenlist"
+version = "1.4.1"
+requires_python = ">=3.8"
+summary = "A list-like structure which implements collections.abc.MutableSequence"
+groups = ["default"]
+files = [
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 ]
 
 [[package]]
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+version = "1.9.4"
+requires_python = ">=3.7"
+summary = "Yet another URL library"
+groups = ["default"]
+dependencies = [
+    "idna>=2.0",
+    "multidict>=4.0",
+]
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+    {file = "yarl-1.9.4-py3-none-any.whl", hash = "sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad"},
+    {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
+]
+
+[[package]]
 name = "zipp"
 version = "3.19.2"
 requires_python = ">=3.8"
--- a/pyproject.toml	Wed Jul 03 20:20:18 2024 -0700
+++ b/pyproject.toml	Sat Jul 06 16:42:36 2024 -0700
@@ -13,6 +13,8 @@
     "ipdb>=0.13.13",
     "flax>=0.8.5",
     "nltk>=3.8.1",
+    "langchain>=0.2.6",
+    "fastapi>=0.111.0",
 ]
 requires-python = ">=3.11"
 readme = "README.md"
--- a/query.py	Wed Jul 03 20:20:18 2024 -0700
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,58 +0,0 @@
-import json
-import sys
-from pathlib import Path
-
-from tqdm import tqdm
-
-from pymilvus import MilvusClient
-from milvus_model.dense.onnx import OnnxEmbeddingFunction
-
-from extract_pdf import phrasesFromFile
-
-
-def rebuild(client, embedding_fn, dim):
-    client.drop_collection(collection_name="demo_collection")
-    if not client.has_collection(collection_name="demo_collection"):
-        client.create_collection(
-            collection_name="demo_collection",
-            dimension=dim,
-        )
-
-    docs = []
-    for i, (bbox, phrase) in tqdm(enumerate(
-            phrasesFromFile(
-                Path("data") /
-                "Meetings2226Minutes_20240702182359526 (1).pdf"))):
-        [vector] = embedding_fn.encode_documents([phrase])
-        doc = {
-            "id": i,
-            "vector": vector,
-            "text": phrase,
-            "bbox": json.dumps(bbox),
-        }
-        docs.append(doc)
-    res = client.insert(collection_name="demo_collection", data=docs)
-    print('insert:', res)
-
-
-def search(q, embedding_fn, client):
-    query_vectors = embedding_fn.encode_queries([q])
-
-    [query_result] = client.search(
-        collection_name="demo_collection",
-        data=query_vectors,
-        limit=5,
-        output_fields=["text"],
-    )
-    query_result.sort(key=lambda x: x["distance"], reverse=True)
-
-    for row in query_result:
-        print(f'{row["distance"]:.6f} {row["entity"]["text"]}')
-
-
-q, = sys.argv[1:]
-
-embedding_fn = OnnxEmbeddingFunction(model_name="GPTCache/paraphrase-albert-onnx")
-client = MilvusClient("milvus_demo.db")
-rebuild(client, embedding_fn, dim=embedding_fn.dim)
-search(q, embedding_fn, client)
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/search/extract_pdf.py	Sat Jul 06 16:42:36 2024 -0700
@@ -0,0 +1,33 @@
+import re
+from pathlib import Path
+from typing import Iterable
+
+import nltk
+from pdfminer.converter import PDFPageAggregator
+from pdfminer.layout import LAParams, LTTextBox
+from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager
+from pdfminer.pdfpage import PDFPage
+
+
+def phrasesFromFile(p: Path) -> Iterable:
+    fp = open(p, 'rb')
+    rsrcmgr = PDFResourceManager()
+    laparams = LAParams()
+    device = PDFPageAggregator(rsrcmgr, laparams=laparams)
+    interpreter = PDFPageInterpreter(rsrcmgr, device)
+    pages = PDFPage.get_pages(fp)
+
+    for page in pages:
+        interpreter.process_page(page)
+        layout = device.get_result()
+        for lobj in layout:
+            if isinstance(lobj, LTTextBox):
+                text = lobj.get_text()
+                for sentence in nltk.sent_tokenize(text):
+                    sentence = re.sub(r'\s+', ' ', sentence).strip()
+                    if len(sentence) < 5:
+                        continue
+                    if not re.search(r'\w\w\w\w\w', sentence):
+                        continue
+
+                    yield list(map(int, lobj.bbox)), sentence
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/search/query.py	Sat Jul 06 16:42:36 2024 -0700
@@ -0,0 +1,70 @@
+import json
+import sys
+from pathlib import Path
+
+from tqdm import tqdm
+
+from pymilvus import MilvusClient
+from milvus_model.dense.onnx import OnnxEmbeddingFunction
+
+from extract_pdf import phrasesFromFile
+
+from fastapi import FastAPI
+
+def rebuild(client, embedding_fn, dim):
+    client.drop_collection(collection_name="demo_collection")
+    if not client.has_collection(collection_name="demo_collection"):
+        client.create_collection(
+            collection_name="demo_collection",
+            dimension=dim,
+        )
+
+    docs = []
+    for i, (bbox, phrase) in tqdm(enumerate(
+            phrasesFromFile(
+                Path("data") /
+                "Meetings2226Minutes_20240702182359526 (1).pdf")),
+                desc="rebuilding",
+                unit=' phrase'):
+        [vector] = embedding_fn.encode_documents([phrase])
+        doc = {
+            "id": i,
+            "vector": vector,
+            "text": phrase,
+            "bbox": json.dumps(bbox),
+        }
+        docs.append(doc)
+    res = client.insert(collection_name="demo_collection", data=docs)
+    print('insert:', res['insert_count'])
+
+
+def search(q, embedding_fn, client):
+    query_vectors = embedding_fn.encode_queries([q])
+
+    [query_result] = client.search(
+        collection_name="demo_collection",
+        data=query_vectors,
+        limit=5,
+        output_fields=["text"],
+    )
+    query_result.sort(key=lambda x: x["distance"], reverse=True)
+
+    for row in query_result:
+        print(f'{row["distance"]:.6f} {row["entity"]["text"]}')
+
+
+# q, = sys.argv[1:]
+
+# https://huggingface.co/models?pipeline_tag=feature-extraction&library=onnx&sort=trending
+# embedding_fn = OnnxEmbeddingFunction(model_name="jinaai/jina-embeddings-v2-base-en")
+# client = MilvusClient("milvus_demo.db")
+# rebuild(client, embedding_fn, dim=embedding_fn.dim)
+# search(q, embedding_fn, client)
+
+app = FastAPI()
+
+
+@app.get("/sco/query")
+def read_query1(q: str|None):
+    print(f'1 {q=}')
+    return {"Hello": "World"}
--- a/webvtt_1.py	Wed Jul 03 20:20:18 2024 -0700
+++ b/webvtt_1.py	Sat Jul 06 16:42:36 2024 -0700
@@ -15,6 +15,7 @@
 #     print(caption.end)
 #     print(caption.text)
 
+##https://stackoverflow.com/questions/51784232/how-do-i-convert-the-webvtt-format-to-plain-text
 transcript = ""
 
 lines = []
@@ -32,10 +33,18 @@
     transcript += " " + line
     previous = line
 
-print(transcript)
+#print(transcript)
 
 # text = lobj.get_text()
 #                 for sentence in nltk.sent_tokenize(text):
 #                     sentence = re.sub(r'\s+', ' ', sentence).strip()
 #                     if len(sentence) < 5:
-#                         continue
\ No newline at end of file
+#                         continue
+
+from langchain import RecursiveCharacterTextSplitter ;
+
+const text = transcript
+const splitter = new RecursiveCharacterTextSplitter({
+  chunkSize: 10,
+  chunkOverlap: 1,
+});
\ No newline at end of file