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3ecdea8be4
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>
44 lines
2.1 KiB
Markdown
44 lines
2.1 KiB
Markdown
# Key Concepts
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## Text Splitter
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This class is responsible for splitting long pieces of text into smaller components.
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It contains different ways for splitting text (on characters, using Spacy, etc)
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as well as different ways for measuring length (token based, character based, etc).
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## Embeddings
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These classes are very similar to the LLM classes in that they are wrappers around models,
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but rather than return a string they return an embedding (list of floats). These are particularly useful when
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implementing semantic search functionality. They expose separate methods for embedding queries versus embedding documents.
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## Vectorstores
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These are datastores that store embeddings of documents in vector form.
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They expose a method for passing in a string and finding similar documents.
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## Python REPL
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Sometimes, for complex calculations, rather than have an LLM generate the answer directly,
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it can be better to have the LLM generate code to calculate the answer, and then run that code to get the answer.
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In order to easily do that, we provide a simple Python REPL to execute commands in.
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This interface will only return things that are printed -
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therefore, if you want to use it to calculate an answer, make sure to have it print out the answer.
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## Bash
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It can often be useful to have an LLM generate bash commands, and then run them.
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A common use case this is for letting it interact with your local file system.
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We provide an easy component to execute bash commands.
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## Requests Wrapper
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The web contains a lot of information that LLMs do not have access to.
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In order to easily let LLMs interact with that information,
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we provide a wrapper around the Python Requests module that takes in a URL and fetches data from that URL.
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## Google Search
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This uses the official Google Search API to look up information on the web.
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## SerpAPI
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This uses SerpAPI, a third party search API engine, to interact with Google Search.
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## Searx Search
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This uses the Searx (SearxNG fork) meta search engine API to lookup information
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on the web. It supports 139 search engines and is easy to self-host
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which makes it a good choice for privacy-conscious users.
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