Konko fix dependency

docs/docs_skeleton/docs/modules/model_io/output_parsers/index.mdx

@@ -12,7 +12,7 @@ Output parsers are classes that help structure language model responses. There a
 
 And then one optional one:
 
-- "Parse with prompt": A method which takes in a string (assumed to be the response from a language model) and a prompt (assumed to the prompt that generated such a response) and parses it into some structure. The prompt is largely provided in the event the OutputParser wants to retry or fix the output in some way, and needs information from the prompt to do so.
+- "Parse with prompt": A method which takes in a string (assumed to be the response from a language model) and a prompt (assumed to be the prompt that generated such a response) and parses it into some structure. The prompt is largely provided in the event the OutputParser wants to retry or fix the output in some way, and needs information from the prompt to do so.
 
 ## Get started
 

docs/docs_skeleton/docs/use_cases/question_answering/_category_.yml

@@ -1 +1,2 @@
 position: 0
+collapsed: false

docs/docs_skeleton/docs/use_cases/web_scraping/index.mdx

@@ -1,5 +0,0 @@
-# Web Scraping
-
-Web scraping has historically been a challenging endeavor due to the ever-changing nature of website structures, making it tedious for developers to maintain their scraping scripts. Traditional methods often rely on specific HTML tags and patterns which, when altered, can disrupt data extraction processes.
-
-Enter the LLM-based method for parsing HTML: By leveraging the capabilities of LLMs, and especially OpenAI Functions in LangChain's extraction chain, developers can instruct the model to extract only the desired data in a specified format. This method not only streamlines the extraction process but also significantly reduces the time spent on manual debugging and script modifications. Its adaptability means that even if websites undergo significant design changes, the extraction remains consistent and robust. This level of resilience translates to reduced maintenance efforts, cost savings, and ensures a higher quality of extracted data. Compared to its predecessors, the LLM-based approach wins out in the web scraping domain by transforming a historically cumbersome task into a more automated and efficient process.

docs/docs_skeleton/vercel.json

@@ -1076,6 +1076,10 @@
       "source": "/docs/modules/agents/tools/integrations/zapier",
       "destination": "/docs/integrations/tools/zapier"
     },
+    {
+      "source": "/docs/integrations/tools/sqlite",
+      "destination": "/docs/use_cases/sql/sqlite"
+    },
     {
       "source": "/en/latest/modules/callbacks/filecallbackhandler.html",
       "destination": "/docs/modules/callbacks/how_to/filecallbackhandler"
@@ -2216,6 +2220,10 @@
       "source": "/docs/modules/data_connection/text_embedding/integrations/tensorflowhub",
       "destination": "/docs/integrations/text_embedding/tensorflowhub"
     },
+    {
+      "source": "/docs/integrations/text_embedding/Awa",
+      "destination": "/docs/integrations/text_embedding/awadb"
+    },
     {
       "source": "/en/latest/modules/indexes/vectorstores/examples/analyticdb.html",
       "destination": "/docs/integrations/vectorstores/analyticdb"
@@ -3178,7 +3186,11 @@
     },
     {
       "source": "/en/latest/use_cases/tabular.html",
-      "destination": "/docs/use_cases/tabular"
+      "destination": "/docs/use_cases/qa_structured"
+    },
+    {
+      "source": "/docs/use_cases/sql(/?)",
+      "destination": "/docs/use_cases/qa_structured/sql"
     },
     {
       "source": "/en/latest/youtube.html",
@@ -3370,7 +3382,7 @@
     },
     {
       "source": "/docs/modules/chains/popular/sqlite",
-      "destination": "/docs/use_cases/tabular/sqlite"
+      "destination": "/docs/use_cases/qa_structured/sql"
     },
     {
       "source": "/docs/modules/chains/popular/openai_functions",
@@ -3582,7 +3594,7 @@
     },
     {
       "source": "/docs/modules/chains/additional/elasticsearch_database",
-      "destination": "/docs/use_cases/tabular/elasticsearch_database"
+      "destination": "/docs/use_cases/qa_structured/integrations/elasticsearch"
     },
     {
       "source": "/docs/modules/chains/additional/tagging",