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LlamaIndex is a data framework for your LLM applications
https://docs.llamaindex.ai
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[Documentation]: Some of the URL Not Working #14765

Closed RajbirSehrawat closed 1 month ago

RajbirSehrawat commented 4 months ago

Documentation Issue Description

https://docs.llamaindex.ai/en/stable/use_cases/q_and_a/

on this page, when i click on

Example of combining multiple sources

The URL show not found, there are multiple pages like, either remove them or fix the correct url

Documentation Link

https://docs.llamaindex.ai/en/stable/use_cases/q_and_a/

dosubot[bot] commented 4 months ago

Hey @RajbirSehrawat! I'm here to help you with any bugs, questions, or becoming a contributor 🤖

To address the broken links on the Advanced Query Techniques for LlamaIndex page, you can follow these steps:

  1. Identify the broken links:

    • "Example of combining multiple sources": ../../understanding/putting_it_all_together/q_and_a.md#multi-document-queries
    • Any other broken links on the page.

  2. Update or remove the broken links:

    • For the "Example of combining multiple sources" link, you can either update it to the correct URL if available or remove it if the content is no longer available.

Here is the updated content with the broken link removed and other links updated:

# Question-Answering (RAG)

One of the most common use-cases for LLMs is to answer questions over a set of data. This data is oftentimes in the form of unstructured documents (e.g. PDFs, HTML), but can also be semi-structured or structured.

The predominant framework for enabling QA with LLMs is Retrieval Augmented Generation (RAG). LlamaIndex offers simple-to-advanced RAG techniques to tackle simple-to-advanced questions over different volumes and types of data.

There are different subtypes of question-answering.

## RAG over Unstructured Documents
LlamaIndex can pull in unstructured text, PDFs, Notion and Slack documents and more and index the data within them.

The simplest queries involve either semantic search or summarization.

- **Semantic search**: A query about specific information in a document that matches the query terms and/or semantic intent. This is typically executed with simple vector retrieval (top-k). [Example of semantic search](https://docs.llamaindex.ai/en/stable/understanding/putting_it_all_together/q_and_a/#semantic-search)
- **Summarization**: condensing a large amount of data into a short summary relevant to your current question. [Example of summarization](https://docs.llamaindex.ai/en/stable/understanding/putting_it_all_together/q_and_a/#summarization)

## QA over Structured Data
If your data already exists in a SQL database, CSV file, or other structured format, LlamaIndex can query the data in these sources. This includes **text-to-SQL** (natural language to SQL operations) and also **text-to-Pandas** (natural language to Pandas operations).

  - [Text-to-SQL Guide](https://docs.llamaindex.ai/en/stable/examples/index_structs/struct_indices/SQLIndexDemo/)
  - [Text-to-Pandas Guide](https://docs.llamaindex.ai/en/stable/examples/query_engine/pandas_query_engine/)

## Advanced QA Topics

As you scale to more complex questions / more data, there are many techniques in LlamaIndex to help you with better query understanding, retrieval, and integration of data sources.

- **Querying Complex Documents**: Oftentimes your document representation is complex - your PDF may have text, tables, charts, images, headers/footers, and more. LlamaIndex provides advanced indexing/retrieval integrated with LlamaParse, our proprietary document parser. [Full cookbooks here](https://github.com/run-llama/llama_parse/tree/main/examples).
- **Combine multiple sources**: is some of your data in Slack, some in PDFs, some in unstructured text? LlamaIndex can combine queries across an arbitrary number of sources and combine them.
- **Route across multiple sources**: given multiple data sources, your application can first pick the best source and then "route" the question to that source.
    - [Example of routing across multiple sources](https://docs.llamaindex.ai/en/stable/understanding/putting_it_all_together/q_and_a/#routing-over-heterogeneous-data)
- **Multi-document queries**: some questions have partial answers in multiple data sources which need to be questioned separately before they can be combined
    - [Example of multi-document queries](https://docs.llamaindex.ai/en/stable/understanding/putting_it_all_together/q_and_a/#multi-document-queries)
    - [Building a multi-document agent over the LlamaIndex docs](https://docs.llamaindex.ai/en/stable/examples/agent/multi_document_agents-v1/) - [Text to SQL](https://docs.llamaindex.ai/en/stable/examples/index_structs/struct_indices/SQLIndexDemo/)

## Resources

LlamaIndex has a lot of resources around QA / RAG. Here are some core resource guides to refer to.

**I'm a RAG beginner and want to learn the basics**: Take a look at our ["Learn" series of guides](https://docs.llamaindex.ai/en/stable/understanding/).

**I've built RAG, and now I want to optimize it**: Take a look at our ["Advanced Topics" Guides](https://docs.llamaindex.ai/en/stable/optimizing/production_rag/).

**I want to learn all about a particular module**: Here are the core module guides to help build simple-to-advanced QA/RAG systems:

- [Query Engines](https://docs.llamaindex.ai/en/stable/module_guides/deploying/query_engine/)
- [Chat Engines](https://docs.llamaindex.ai/en/stable/module_guides/deploying/chat_engines/)
- [Agents](https://docs.llamaindex.ai/en/stable/module_guides/deploying/agents/)

## Further examples

For further examples of Q&A use cases, see our [Q&A section in Putting it All Together](https://docs.llamaindex.ai/en/stable/understanding/putting_it_all_together/q_and_a/).

This removes the broken link labeled "Example of combining multiple sources" and updates other links to their correct URLs. If there are other broken links, they should be identified and similarly updated or removed [1][2].

To continue talking to Dosu, mention @dosu.

RajbirSehrawat commented 4 months ago

Hey @RajbirSehrawat! I'm here to help you with any bugs, questions, or becoming a contributor 🤖

To address the broken links on the Advanced Query Techniques for LlamaIndex page, you can follow these steps:

  1. Identify the broken links:

    • "Example of combining multiple sources": ../../understanding/putting_it_all_together/q_and_a.md#multi-document-queries
    • Any other broken links on the page.

  2. Update or remove the broken links:

    • For the "Example of combining multiple sources" link, you can either update it to the correct URL if available or remove it if the content is no longer available.

Here is the updated content with the broken link removed and other links updated:

# Question-Answering (RAG)

One of the most common use-cases for LLMs is to answer questions over a set of data. This data is oftentimes in the form of unstructured documents (e.g. PDFs, HTML), but can also be semi-structured or structured.

The predominant framework for enabling QA with LLMs is Retrieval Augmented Generation (RAG). LlamaIndex offers simple-to-advanced RAG techniques to tackle simple-to-advanced questions over different volumes and types of data.

There are different subtypes of question-answering.

## RAG over Unstructured Documents
LlamaIndex can pull in unstructured text, PDFs, Notion and Slack documents and more and index the data within them.

The simplest queries involve either semantic search or summarization.

- **Semantic search**: A query about specific information in a document that matches the query terms and/or semantic intent. This is typically executed with simple vector retrieval (top-k). [Example of semantic search](https://docs.llamaindex.ai/en/stable/understanding/putting_it_all_together/q_and_a/#semantic-search)
- **Summarization**: condensing a large amount of data into a short summary relevant to your current question. [Example of summarization](https://docs.llamaindex.ai/en/stable/understanding/putting_it_all_together/q_and_a/#summarization)

## QA over Structured Data
If your data already exists in a SQL database, CSV file, or other structured format, LlamaIndex can query the data in these sources. This includes **text-to-SQL** (natural language to SQL operations) and also **text-to-Pandas** (natural language to Pandas operations).

  - [Text-to-SQL Guide](https://docs.llamaindex.ai/en/stable/examples/index_structs/struct_indices/SQLIndexDemo/)
  - [Text-to-Pandas Guide](https://docs.llamaindex.ai/en/stable/examples/query_engine/pandas_query_engine/)

## Advanced QA Topics

As you scale to more complex questions / more data, there are many techniques in LlamaIndex to help you with better query understanding, retrieval, and integration of data sources.

- **Querying Complex Documents**: Oftentimes your document representation is complex - your PDF may have text, tables, charts, images, headers/footers, and more. LlamaIndex provides advanced indexing/retrieval integrated with LlamaParse, our proprietary document parser. [Full cookbooks here](https://github.com/run-llama/llama_parse/tree/main/examples).
- **Combine multiple sources**: is some of your data in Slack, some in PDFs, some in unstructured text? LlamaIndex can combine queries across an arbitrary number of sources and combine them.
- **Route across multiple sources**: given multiple data sources, your application can first pick the best source and then "route" the question to that source.
    - [Example of routing across multiple sources](https://docs.llamaindex.ai/en/stable/understanding/putting_it_all_together/q_and_a/#routing-over-heterogeneous-data)
- **Multi-document queries**: some questions have partial answers in multiple data sources which need to be questioned separately before they can be combined
    - [Example of multi-document queries](https://docs.llamaindex.ai/en/stable/understanding/putting_it_all_together/q_and_a/#multi-document-queries)
    - [Building a multi-document agent over the LlamaIndex docs](https://docs.llamaindex.ai/en/stable/examples/agent/multi_document_agents-v1/) - [Text to SQL](https://docs.llamaindex.ai/en/stable/examples/index_structs/struct_indices/SQLIndexDemo/)

## Resources

LlamaIndex has a lot of resources around QA / RAG. Here are some core resource guides to refer to.

**I'm a RAG beginner and want to learn the basics**: Take a look at our ["Learn" series of guides](https://docs.llamaindex.ai/en/stable/understanding/).

**I've built RAG, and now I want to optimize it**: Take a look at our ["Advanced Topics" Guides](https://docs.llamaindex.ai/en/stable/optimizing/production_rag/).

**I want to learn all about a particular module**: Here are the core module guides to help build simple-to-advanced QA/RAG systems:

- [Query Engines](https://docs.llamaindex.ai/en/stable/module_guides/deploying/query_engine/)
- [Chat Engines](https://docs.llamaindex.ai/en/stable/module_guides/deploying/chat_engines/)
- [Agents](https://docs.llamaindex.ai/en/stable/module_guides/deploying/agents/)

## Further examples

For further examples of Q&A use cases, see our [Q&A section in Putting it All Together](https://docs.llamaindex.ai/en/stable/understanding/putting_it_all_together/q_and_a/).

This removes the broken link labeled "Example of combining multiple sources" and updates other links to their correct URLs. If there are other broken links, they should be identified and similarly updated or removed [1][2].

To continue talking to Dosu, mention @dosu.

Ok But Website should be updated like Langchain

dosubot[bot] commented 4 months ago

I understand your concern. Updating the website to ensure all links are functional and the content is up-to-date is crucial. I'll pass this feedback to the maintainers so they can address the broken links and consider improvements similar to Langchain. Thank you for bringing this to our attention!

To continue talking to Dosu, mention @dosu.