Update python-langchain-pdf-chat Tutorial as per 8.0 Docs Terminology #78
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azaddhirajkumar
commented
Dec 2, 2025
azaddhirajkumar
commented
- Added changes for latest terminology as per 8.0 Docs
- Updated langchain outdated links
Summary of ChangesHello @azaddhirajkumar, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request focuses on modernizing the "Build PDF Chat App with Couchbase Python SDK and LangChain" tutorial. The primary goal is to update the content to reflect the latest Couchbase 8.0 terminology, particularly concerning vector search capabilities, and to ensure all referenced LangChain documentation links are current and accurate. This ensures the tutorial remains relevant and provides correct guidance to users. Highlights
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Code Review
This pull request does a great job of updating the tutorial to align with the new Couchbase 8.0 terminology, specifically renaming the FTS-based vector search feature to "Search Vector Index". The updates to the LangChain documentation links are also a welcome improvement. I've added a few suggestions to enhance terminological consistency and improve phrasing in the modified sections of the document.
| - **Loading and processing PDF documents**: LangChain's [_PDFLoader_](https://docs.langchain.com/oss/python/integrations/document_loaders) is used to load the PDF files and convert them into text documents. | ||
| - **Text splitting**: LangChain's [_RecursiveCharacterTextSplitter_](https://docs.langchain.com/oss/python/integrations/splitters) is used to split the text from the PDF documents into smaller chunks or passages, which are more suitable for embedding and retrieval. | ||
| - **Embedding generation**: LangChain integrates with [various embedding models](https://docs.langchain.com/oss/python/integrations/text_embedding), such as OpenAI's embeddings, to convert the text chunks into embeddings. | ||
| - **Vector store integration**: LangChain provides a [_CouchbaseSearchVectorStore_](https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html#couchbase-search-vector-store) class that seamlessly integrates with Couchbase's Vector Search, allowing the app to store and search through the embeddings and their corresponding text. |
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To maintain consistency with the new terminology introduced in this pull request (e.g., in the title and introduction), it would be clearer to use Search Vector Index here instead of the more generic Vector Search.
| - **Vector store integration**: LangChain provides a [_CouchbaseSearchVectorStore_](https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html#couchbase-search-vector-store) class that seamlessly integrates with Couchbase's Vector Search, allowing the app to store and search through the embeddings and their corresponding text. | |
| - **Vector store integration**: LangChain provides a [_CouchbaseSearchVectorStore_](https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html#couchbase-search-vector-store) class that seamlessly integrates with Couchbase's Search Vector Index, allowing the app to store and search through the embeddings and their corresponding text. |
| - **Chains**: LangChain provides various [chains](https://api.python.langchain.com/en/latest/langchain/chains.html) for different requirements. For using RAG concept, we require _Retrieval Chain_ for Retrieval and _Question Answering Chain_ for Generation part. We also add _Prompts_ that guide the language model's behavior and output. These all are combined to form a single chain which gives output from user questions. | ||
| - **Streaming Output**: LangChain supports [streaming](https://docs.langchain.com/oss/python/langchain/streaming), allowing the app to stream the generated answer to the client in real-time. | ||
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| By combining Vector Search with Couchbase, RAG, and LangChain; the PDF Chat app can efficiently ingest PDF documents, convert their content into searchable embeddings, retrieve relevant information based on user queries and conversation context, and generate context-aware and informative responses using large language models. This approach provides users with a powerful and intuitive way to explore and interact with large PDF files. |
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For consistency with the updated terminology used throughout this tutorial, consider replacing Vector Search with Search Vector Index here.
| By combining Vector Search with Couchbase, RAG, and LangChain; the PDF Chat app can efficiently ingest PDF documents, convert their content into searchable embeddings, retrieve relevant information based on user queries and conversation context, and generate context-aware and informative responses using large language models. This approach provides users with a powerful and intuitive way to explore and interact with large PDF files. | |
| By combining Search Vector Index with Couchbase, RAG, and LangChain; the PDF Chat app can efficiently ingest PDF documents, convert their content into searchable embeddings, retrieve relevant information based on user queries and conversation context, and generate context-aware and informative responses using large language models. This approach provides users with a powerful and intuitive way to explore and interact with large PDF files. |
| ### Create Retriever Chain | ||
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| We also create the [retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/vectorstore) of the couchbase vector store. This retriever will be used to retrieve the previously added documents which are similar to current query. | ||
| We also create the [retriever](https://docs.langchain.com/oss/python/integrations/retrievers) of the couchbase vector store. This retriever will be used to retrieve the previously added documents which are similar to current query. |
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This sentence could be improved for clarity and to follow brand capitalization guidelines. couchbase should be capitalized to Couchbase. Additionally, the phrasing could be more direct and natural.
| We also create the [retriever](https://docs.langchain.com/oss/python/integrations/retrievers) of the couchbase vector store. This retriever will be used to retrieve the previously added documents which are similar to current query. | |
| We also create a [retriever](https://docs.langchain.com/oss/python/integrations/retrievers) for the Couchbase vector store. This retriever is used to retrieve previously added documents that are similar to the current query. |
