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.
Fix KeyError 'items' when no result found.
## Problem
When no result found for a query, google search crashed with `KeyError
'items'`.
## Solution
I added a check for an empty response before accessing the 'items' key.
It will handle the case correctly.
## Other
my twitter: yakigac
(I don't mind even if you don't mention me for this PR. But just because
last time my real name was shout out :) )
### Summary
Adds support for older `.ppt` file in the PowerPoint loader.
### Testing
The following should work on `unstructured==0.4.11` using the example
docs from the `unstructured` repo.
```python
from langchain.document_loaders import UnstructuredPowerPointLoader
filename = "../unstructured/example-docs/fake-power-point.pptx"
loader = UnstructuredPowerPointLoader(filename)
loader.load()
filename = "../unstructured/example-docs/fake-power-point.ppt"
loader = UnstructuredPowerPointLoader(filename)
loader.load()
```
Now downgrade `unstructured` to version `0.4.10`. The following should
work:
```python
from langchain.document_loaders import UnstructuredPowerPointLoader
filename = "../unstructured/example-docs/fake-power-point.pptx"
loader = UnstructuredPowerPointLoader(filename)
loader.load()
```
and the following should give you a `ValueError` and invite you to
upgrade `unstructured`.
```python
from langchain.document_loaders import UnstructuredPowerPointLoader
filename = "../unstructured/example-docs/fake-power-point.ppt"
loader = UnstructuredPowerPointLoader(filename)
loader.load()
```
https://github.com/hwchase17/langchain/issues/1100
When faiss data and doc.index are created in past versions, error occurs
that say there was no attribute. So I put hasattr in the check as a
simple solution.
However, increasing the number of such checks is not good for
conservatism, so I think there is a better solution.
Also, the code for the batch process was left out, so I put it back in.
This import works fine:
```python
from langchain import Anthropic
```
This import does not:
```python
from langchain import AI21
```
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ImportError: cannot import name 'AI21' from 'langchain' (/opt/anaconda3/envs/fed_nlp/lib/python3.9/site-packages/langchain/__init__.py)
```
I think there is a slight documentation inconsistency here:
https://langchain.readthedocs.io/en/latest/reference/modules/llms.html
This PR starts to solve that. Should all the import examples be
`from langchain.llms import X` instead of `from langchain import X`?
The #1088 introduced a bug in Qdrant integration. That PR reverts those
changes and provides class attributes to ensure consistent payload keys.
In addition to that, an exception will be thrown if any of texts is None
(that could have been an issue reported in #1087)
