`NotebookLoader.load()` loads the `.ipynb` notebook file into a
`Document` object.
**Parameters**:
* `include_outputs` (bool): whether to include cell outputs in the
resulting document (default is False).
* `max_output_length` (int): the maximum number of characters to include
from each cell output (default is 10).
* `remove_newline` (bool): whether to remove newline characters from the
cell sources and outputs (default is False).
* `traceback` (bool): whether to include full traceback (default is
False).
The current prompt specifically instructs the LLM to use the `LIMIT`
clause. This will cause issues with MS SQL Server, which uses `SELECT
TOP` instead of `LIMIT`. The generated SQL will use `LIMIT`; the
instruction to "always limit... using the LIMIT clause" seems to
override the "create a syntactically correct mssql query to run"
portion. Reported here:
https://github.com/hwchase17/langchain/issues/1103#issuecomment-1441144224
I don't have access to a SQL Server instance to test, but removing that
part of the prompt in OpenAI Playground results in the correct `SELECT
TOP` syntax, whereas keeping it in results in the `LIMIT` clause, even
when instructing it to generate syntactically correct mssql. It's also
still correctly using `LIMIT` in my MariaDB database. I think in this
case we can assume that the model will select the appropriate method
based on the dialect specified.
In general, it would be nice to be able to test a suite of SQL dialects
for things like dialect-specific syntax and other issues we've run into
in the past, but I'm not quite sure how to best approach that yet.
When I try to import the Class HuggingFaceEndpoint I get an Import
Error: cannot import name 'HuggingFaceEndpoint' from 'langchain'.
(langchain version 0.0.88)
These two imports work fine: from langchain import HuggingFacePipeline
and from langchain import HuggingFaceHub.
So I corrected the import statement in the example. There is probably a
better solution to this, but this fixes the Error for me.
It is useful to be able to specify `verbose` or `memory` while still
keeping the chain's overall structure.
---------
Co-authored-by: Francisco Ingham <>
Added a GitBook document loader. It lets you both, (1) fetch text from
any single GitBook page, or (2) fetch all relative paths and return
their respective content in Documents.
I've modified the `scrape` method in the `WebBaseLoader` to accept
custom web paths if given, but happy to remove it and move that logic
into the `GitbookLoader` itself.
### 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
---------
Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
In the similarity search, the pinecone namespace is not used, which
makes the bot return _I don't know_ where the embeddings are stored in
the pinecone namespace. Now we can query by passing the namespace
optionally.
```result = qa({"question": query, "chat_history": chat_history, "namespace":"01gshyhjcfgkq1q5wxjtm17gjh"})```
This approach has several advantages:
* it improves the readability of the code
* removes incompatibilities between SQL dialects
* fixes a bug with `datetime` values in rows and `ast.literal_eval`
Huge thanks and credits to @jzluo for finding the weaknesses in the
current approach and for the thoughtful discussion on the best way to
implement this.
---------
Co-authored-by: Francisco Ingham <>
Co-authored-by: Jon Luo <20971593+jzluo@users.noreply.github.com>
Implementation fails if there are not enough documents. Added the same
check as used for similarity search.
Current implementation raises
```
File ".venv/lib/python3.9/site-packages/langchain/vectorstores/faiss.py", line 160, in max_marginal_relevance_search
_id = self.index_to_docstore_id[i]
KeyError: -1
```
Harrison Chase