- allows to build tools and dynamically inject extra searxh suffix in
the query
example: search.run("python library", query_suffix="site:github.com")
resulting query: "python library site:github.com"
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>
Follow-up of @hinthornw's PR:
- Migrate the Tool abstraction to a separate file (`BaseTool`).
- `Tool` implementation of `BaseTool` takes in function and coroutine to
more easily maintain backwards compatibility
- Add a Toolkit abstraction that can own the generation of tools around
a shared concept or state
---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Francisco Ingham <fpingham@gmail.com>
Co-authored-by: Dhruv Anand <105786647+dhruv-anand-aintech@users.noreply.github.com>
Co-authored-by: cragwolfe <cragcw@gmail.com>
Co-authored-by: Anton Troynikov <atroyn@users.noreply.github.com>
Co-authored-by: Oliver Klingefjord <oliver@klingefjord.com>
Co-authored-by: William Fu-Hinthorn <whinthorn@Williams-MBP-3.attlocal.net>
Co-authored-by: Bruno Bornsztein <bruno.bornsztein@gmail.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>
Pydantic validation breaks tests for example (`test_qdrant.py`) because
fake embeddings contain an integer.
This PR casts the embeddings array to all floats.
Now the `qdrant` test passes, `poetry run pytest
tests/integration_tests/vectorstores/test_qdrant.py`
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
```
#1081 introduced a method to get DDL (table definitions) in a manner
specific to sqlite3, thus breaking compatibility with other non-sqlite3
databases. This uses the sqlite3 command if the detected dialect is
sqlite, and otherwise uses the standard SQL `SHOW CREATE TABLE`. This
should fix#1103.
blob42
Harrison Chase
Matt Robinson
Konstantin Hebenstreit
Francisco Ingham
Anton Troynikov
Dennis Antela Martinez
William FH
Naveen Tatikonda
Andrew White
Zach Schillaci
blob42
Nan Wang
Nick
Ji
CG80499
Ankush Gola
Sam Hogan
kekayan
Noah Gundotra
Tom Bocklisch
Jon Luo