# Lint sphinx documentation and fix broken links
This PR lints multiple warnings shown in generation of the project
documentation (using "make docs_linkcheck" and "make docs_build").
Additionally documentation internal links to (now?) non-existent files
are modified to point to existing documents as it seemed the new correct
target.
The documentation is not updated content wise.
There are no source code changes.
Fixes # (issue)
- broken documentation links to other files within the project
- sphinx formatting (linting)
## Before submitting
No source code changes, so no new tests added.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Docs: compound ecosystem and integrations
**Problem statement:** We have a big overlap between the
References/Integrations and Ecosystem/LongChain Ecosystem pages. It
confuses users. It creates a situation when new integration is added
only on one of these pages, which creates even more confusion.
- removed References/Integrations page (but move all its information
into the individual integration pages - in the next PR).
- renamed Ecosystem/LongChain Ecosystem into Integrations/Integrations.
I like the Ecosystem term. It is more generic and semantically richer
than the Integration term. But it mentally overloads users. The
`integration` term is more concrete.
UPDATE: after discussion, the Ecosystem is the term.
Ecosystem/Integrations is the page (in place of Ecosystem/LongChain
Ecosystem).
As a result, a user gets a single place to start with the individual
integration.
This reverts commit 5111bec540.
This PR introduced a bug in the async API (the `url` param isn't bound);
it also didn't update the synchronous API correctly, which makes it
error-prone (the behavior of the async and sync endpoints would be
different)
### Description
This PR adds a wrapper which adds support for the OpenSearch vector
database. Using opensearch-py client we are ingesting the embeddings of
given text into opensearch cluster using Bulk API. We can perform the
`similarity_search` on the index using the 3 popular searching methods
of OpenSearch k-NN plugin:
- `Approximate k-NN Search` use approximate nearest neighbor (ANN)
algorithms from the [nmslib](https://github.com/nmslib/nmslib),
[faiss](https://github.com/facebookresearch/faiss), and
[Lucene](https://lucene.apache.org/) libraries to power k-NN search.
- `Script Scoring` extends OpenSearch’s script scoring functionality to
execute a brute force, exact k-NN search.
- `Painless Scripting` adds the distance functions as painless
extensions that can be used in more complex combinations. Also, supports
brute force, exact k-NN search like Script Scoring.
### Issues Resolved
https://github.com/hwchase17/langchain/issues/1054
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Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
This is a work in progress PR to track my progres.
## TODO:
- [x] Get results using the specifed searx host
- [x] Prioritize returning an `answer` or results otherwise
- [ ] expose the field `infobox` when available
- [ ] expose `score` of result to help agent's decision
- [ ] expose the `suggestions` field to agents so they could try new
queries if no results are found with the orignial query ?
- [ ] Dynamic tool description for agents ?
- Searx offers many engines and a search syntax that agents can take
advantage of. It would be nice to generate a dynamic Tool description so
that it can be used many times as a tool but for different purposes.
- [x] Limit number of results
- [ ] Implement paging
- [x] Miror the usage of the Google Search tool
- [x] easy selection of search engines
- [x] Documentation
- [ ] update HowTo guide notebook on Search Tools
- [ ] Handle async
- [ ] Tests
### Add examples / documentation on possible uses with
- [ ] getting factual answers with `!wiki` option and `infoboxes`
- [ ] getting `suggestions`
- [ ] getting `corrections`
---------
Co-authored-by: blob42 <spike@w530>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
