ML/LangChain: Fix notebooks by following upstream changes

Author: amotl

topic/machine-learning/llm-langchain/document_loader.ipynb

@@ -159,8 +159,8 @@
     "loader = CrateDBLoader(\n",
     "    'SELECT * FROM mlb_teams_2012 ORDER BY \"Team\" LIMIT 5;',\n",
     "    db=db,\n",
-    "    page_content_columns=[\"Team\"],\n",
-    "    metadata_columns=[\"Payroll (millions)\"],\n",
+    "    page_content_mapper=lambda row: row[\"Team\"],\n",
+    "    metadata_mapper=lambda row: {\"Payroll (millions)\": row[\"Payroll (millions)\"]},\n",
     ")\n",
     "documents = loader.load()"
    ]

topic/machine-learning/llm-langchain/requirements-dev.txt

@@ -1,6 +1,6 @@
 # Real.
 cratedb-toolkit[io]
-pueblo[notebook,testing]==0.0.9
+pueblo[notebook,testing]
 
 # Development.
 # cratedb-toolkit[io] @ git+https://github.com/crate-workbench/cratedb-toolkit.git@main

topic/machine-learning/llm-langchain/requirements.txt

@@ -4,7 +4,7 @@ google-cloud-aiplatform<2
 langchain-google-vertexai<3
 langchain-openai<0.3
 langchain-text-splitters<0.4
-pueblo[cli,nlp]==0.0.9
+pueblo[cli,nlp]>=0.0.10
 pydantic>=2,<3
 pypdf<6
 python-dotenv<2

topic/machine-learning/llm-langchain/vector_search.py

@@ -19,25 +19,24 @@
     # Run program.
     python vector_search.py
 """  # noqa: E501
-from langchain_community.document_loaders import UnstructuredURLLoader
+
 from langchain_community.vectorstores import CrateDBVectorSearch
-from langchain_text_splitters import CharacterTextSplitter
 from langchain_openai import OpenAIEmbeddings
 
 import nltk
+from pueblo.nlp.resource import CachedWebResource
 
 
 def main():
 
     nltk.download("averaged_perceptron_tagger_eng")
     nltk.download("punkt_tab")
 
-    # Load the document, split it into chunks, embed each chunk,
-    # and load it into the vector store.
-    state_of_the_union_url = "https://github.com/langchain-ai/langchain/raw/v0.0.325/docs/docs/modules/state_of_the_union.txt"
-    raw_documents = UnstructuredURLLoader(urls=[state_of_the_union_url]).load()
-    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-    documents = text_splitter.split_documents(raw_documents)
+    # Load a document, and split it into chunks.
+    url = "https://github.com/langchain-ai/langchain/raw/v0.0.325/docs/docs/modules/state_of_the_union.txt"
+    documents = CachedWebResource(url).langchain_documents(chunk_size=1000, chunk_overlap=0)
+
+    # Embed each chunk, and load them into the vector store.
     db = CrateDBVectorSearch.from_documents(documents, OpenAIEmbeddings())
 
     # Invoke a query, and display the first result.