[Website] Moving old templates to blog (#7625)

GitOrigin-RevId: b30be25176338eb02bb3f2e923ac13b4c7cc0bc2

Author: olruas

docs/2.developers/4.user-guide/50.llm-xpack/.vectorstore_pipeline/article.py

@@ -175,7 +175,7 @@
 # ### Langchain
 #
 # You can use a Pathway Vector Store in LangChain pipelines with `PathwayVectorClient`
-# and configure a `VectorStoreServer` using LangChain components. For more information see [our article](/developers/templates/langchain-integration) or [LangChain documentation](https://python.langchain.com/v0.1/docs/integrations/vectorstores/pathway/).
+# and configure a `VectorStoreServer` using LangChain components. For more information see [our article](/blog/langchain-integration) or [LangChain documentation](https://python.langchain.com/v0.1/docs/integrations/vectorstores/pathway/).
 #
 
 # %%

docs/2.developers/4.user-guide/50.llm-xpack/10.overview.md

@@ -174,7 +174,7 @@ You can learn more about Vector Store in Pathway in a [dedicated tutorial](/deve
 
 ### Integrating with LlamaIndex and LangChain
 
-Vector Store offer integrations with both LlamaIndex and LangChain. These allow you to incorporate Vector Store Client in your LlamaIndex and LangChain pipelines or use LlamaIndex and LangChain components in the Vector Store. Read more about the integrations in the [article on LlamaIndex](/developers/templates/llamaindex-pathway) and [on LangChain](/developers/templates/langchain-integration).
+Vector Store offer integrations with both LlamaIndex and LangChain. These allow you to incorporate Vector Store Client in your LlamaIndex and LangChain pipelines or use LlamaIndex and LangChain components in the Vector Store. Read more about the integrations in the [article on LlamaIndex](/blog/llamaindex-pathway) and [on LangChain](/blog/langchain-integration).
 
 
 ## Rerankers

docs/2.developers/7.templates/.gemini-multimodal-rag/__init__.py

@@ -103,7 +103,7 @@
 # + [markdown] id="DBo5YKJzKpdR"
 # ## **What's the main difference between LlamaIndex and Pathway?**
 #
-# Pathway offers an indexing solution that always provides the latest information to your LLM application:  Pathway Vector Store preprocesses and indexes your data in real time, always giving up-to-date answers. LlamaIndex is a framework for writing LLM-enabled applications. Pathway and LlamaIndex are best [used together](/developers/templates/llamaindex-pathway). Pathway vector store is natively available in LlamaIndex.
+# Pathway offers an indexing solution that always provides the latest information to your LLM application:  Pathway Vector Store preprocesses and indexes your data in real time, always giving up-to-date answers. LlamaIndex is a framework for writing LLM-enabled applications. Pathway and LlamaIndex are best [used together](/blog/llamaindex-pathway). Pathway vector store is natively available in LlamaIndex.
 
 # + [markdown] id="CzxFvo4S_RIj"
 # ## **Architecture Used for Multimodal RAG for Production Use Cases**

docs/2.developers/7.templates/.gemini-multimodal-rag/article.py

@@ -7,7 +7,7 @@
 #     thumbnailFit: 'contain'
 #     tags: ['showcase', 'llm']
 #     date: '2024-04-23'
-#     related: ['/developers/templates/adaptive-rag', '/developers/templates/llamaindex-pathway']
+#     related: ['/developers/templates/adaptive-rag', '/developers/templates/demo-question-answering']
 # notebook_export_path: notebooks/showcases/mistral_adaptive_rag_question_answering.ipynb
 # author: 'berke'
 # keywords: ['LLM', 'RAG', 'Adaptive RAG', 'prompt engineering', 'explainability', 'mistral', 'ollama', 'private rag', 'local rag', 'ollama rag', 'notebook', 'docker']

docs/2.developers/7.templates/.langchain-integration/.gitignore

@@ -10,7 +10,6 @@ article:
 author: "pathway"
 keywords: ['LLM', 'RAG', 'Adaptive RAG', 'prompt engineering', 'prompt', 'explainability', 'docker']
 docker_github_link: "https://github.com/pathwaycom/llm-app/tree/main/examples/pipelines/adaptive-rag"
-#hide: true
 ---
 
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