Evaluation so far has shown that agents do a reasonable job of emitting
`json` blocks as arguments when cued (instead of typescript), and `json`
permits the `strict=False` flag to permit control characters, which are
likely to appear in the response in particular.
This PR makes this change to the request and response synthesizer
chains, and fixes the temperature to the OpenAI agent in the eval
notebook. It also adds a `raise_error = False` flag in the notebook to
facilitate debugging
This still doesn't handle the following
- non-JSON media types
- anyOf, allOf, oneOf's
And doesn't emit the typescript definitions for referred types yet, but
that can be saved for a separate PR.
Also, we could have better support for Swagger 2.0 specs and OpenAPI
3.0.3 (can use the same lib for the latter) recommend offline conversion
for now.
Seeing a lot of issues in Discord in which the LLM is not using the
correct LIMIT clause for different SQL dialects. ie, it's using `LIMIT`
for mssql instead of `TOP`, or instead of `ROWNUM` for Oracle, etc.
I think this could be due to us specifying the LIMIT statement in the
example rows portion of `table_info`. So the LLM is seeing the `LIMIT`
statement used in the prompt.
Since we can't specify each dialect's method here, I think it's fine to
just replace the `SELECT... LIMIT 3;` statement with `3 rows from
table_name table:`, and wrap everything in a block comment directly
following the `CREATE` statement. The Rajkumar et al paper wrapped the
example rows and `SELECT` statement in a block comment as well anyway.
Thoughts @fpingham?
This PR:
- Increases `qdrant-client` version to 1.0.4
- Introduces custom content and metadata keys (as requested in #1087)
- Moves all the `QdrantClient` parameters into the method parameters to
simplify code completion
Currently, table information is gathered through SQLAlchemy as complete
table DDL and a user-selected number of sample rows from each table.
This PR adds the option to use user-defined table information instead of
automatically collecting it. This will use the provided table
information and fall back to the automatic gathering for tables that the
user didn't provide information for.
Off the top of my head, there are a few cases where this can be quite
useful:
- The first n rows of a table are uninformative, or very similar to one
another. In this case, hand-crafting example rows for a table such that
they provide the good, diverse information can be very helpful. Another
approach we can think about later is getting a random sample of n rows
instead of the first n rows, but there are some performance
considerations that need to be taken there. Even so, hand-crafting the
sample rows is useful and can guarantee the model sees informative data.
- The user doesn't want every column to be available to the model. This
is not an elegant way to fulfill this specific need since the user would
have to provide the table definition instead of a simple list of columns
to include or ignore, but it does work for this purpose.
- For the developers, this makes it a lot easier to compare/benchmark
the performance of different prompting structures for providing table
information in the prompt.
These are cases I've run into myself (particularly cases 1 and 3) and
I've found these changes useful. Personally, I keep custom table info
for a few tables in a yaml file for versioning and easy loading.
Definitely open to other opinions/approaches though!
Currently the chain is getting the column names and types on the one
side and the example rows on the other. It is easier for the llm to read
the table information if the column name and examples are shown together
so that it can easily understand to which columns do the examples refer
to. For an instantiation of this, please refer to the changes in the
`sqlite.ipynb` notebook.
Also changed `eval` for `ast.literal_eval` when interpreting the results
from the sample row query since it is a better practice.
---------
Co-authored-by: Francisco Ingham <>
---------
Co-authored-by: Francisco Ingham <fpingham@gmail.com>
The agents usually benefit from understanding what the data looks like
to be able to filter effectively. Sending just one row in the table info
allows the agent to understand the data before querying and get better
results.
---------
Co-authored-by: Francisco Ingham <>
---------
Co-authored-by: Francisco Ingham <fpingham@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>
