- quickly run or exec_run commands with sane defaults
- wip image templates with parameters for common docker images
- shell escaping logic
- capture stdout+stderr for exec commands
- added minimal testing
nitpicking but just thought i'd add this typo which I found when going
through the How-to 😄 (unless it was intentional) also, it's amazing that
you added ReAct to LangChain!
Checking if weaviate similarity_search kwargs contains "certainty" and
use it accordingly. The minimal level of certainty must be a float, and
it is computed by normalized distance.
While using a `SQLiteCache`, if there are duplicate `(prompt, llm, idx)`
tuples passed to
[`update_cache()`](c5dd491a21/langchain/llms/base.py (L39)),
then an `IntegrityError` is thrown. This can happen when there are
duplicated prompts within the same batch.
This PR changes the SQLAlchemy `session.add()` to a `session.merge()` in
`cache.py`, [following the solution from this SO
thread](https://stackoverflow.com/questions/10322514/dealing-with-duplicate-primary-keys-on-insert-in-sqlalchemy-declarative-style).
I believe this fixes#983, but not entirely sure since that also
involves async
Here's a minimal example of the error:
```python
from pathlib import Path
import langchain
from langchain.cache import SQLiteCache
llm = langchain.OpenAI(model_name="text-ada-001", openai_api_key=Path("/.openai_api_key").read_text().strip())
langchain.llm_cache = SQLiteCache("test_cache.db")
llm.generate(['a'] * 5)
```
```
> IntegrityError: (sqlite3.IntegrityError) UNIQUE constraint failed: full_llm_cache.prompt, full_llm_cache.llm, full_llm_cache.idx
[SQL: INSERT INTO full_llm_cache (prompt, llm, idx, response) VALUES (?, ?, ?, ?)]
[parameters: ('a', "[('_type', 'openai'), ('best_of', 1), ('frequency_penalty', 0), ('logit_bias', {}), ('max_tokens', 256), ('model_name', 'text-ada-001'), ('n', 1), ('presence_penalty', 0), ('request_timeout', None), ('stop', None), ('temperature', 0.7), ('top_p', 1)]", 0, '\n\nA is for air.\n\nA is for atmosphere.')]
(Background on this error at: https://sqlalche.me/e/14/gkpj)
```
After the change, we now have the following
```python
class Output:
def __init__(self, text):
self.text = text
# make dummy data
cache = SQLiteCache("test_cache_2.db")
cache.update(prompt="prompt_0", llm_string="llm_0", return_val=[Output("text_0")])
cache.engine.execute("SELECT * FROM full_llm_cache").fetchall()
# output
> [('prompt_0', 'llm_0', 0, 'text_0')]
```
```python
# update data, before change this would have thrown an `IntegrityError`
cache.update(prompt="prompt_0", llm_string="llm_0", return_val=[Output("text_0_new")])
cache.engine.execute("SELECT * FROM full_llm_cache").fetchall()
# output
> [('prompt_0', 'llm_0', 0, 'text_0_new')]
```
Thanks for all your hard work!
I noticed a small typo in the bash util doc so here's a quick update.
Additionally, my formatter caught some spacing in the `.md` as well.
Happy to revert that if it's an issue.
The main change is just
```
- A common use case this is for letting it interact with your local file system.
+ A common use case for this is letting the LLM interact with your local file system.
```
## Testing
`make docs_build` succeeds locally and the changes show as expected ✌️
<img width="704" alt="image"
src="https://user-images.githubusercontent.com/17773666/221376160-e99e59a6-b318-49d1-a1d7-89f5c17cdab4.png">
I've added a simple
[CoNLL-U](https://universaldependencies.org/format.html) document
loader. CoNLL-U is a common format for NLP tasks and is used, for
example, in the Universal Dependencies treebank corpora. The loader
reads a single file in standard CoNLL-U format and returns a document.
### Summary
Adds a document loader for MS Word Documents. Works with both `.docx`
and `.doc` files as longer as the user has installed
`unstructured>=0.4.11`.
### Testing
The follow workflow test the loader for both `.doc` and `.docx` files
using example docs from the `unstructured` repo.
#### `.docx`
```python
from langchain.document_loaders import UnstructuredWordDocumentLoader
filename = "../unstructured/example-docs/fake.docx"
loader = UnstructuredWordDocumentLoader(filename)
loader.load()
```
#### `.doc`
```python
from langchain.document_loaders import UnstructuredWordDocumentLoader
filename = "../unstructured/example-docs/fake.doc"
loader = UnstructuredWordDocumentLoader(filename)
loader.load()
```
`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).
### Summary
Updates the docs to remove the `nltk` download steps from
`unstructured`. As of `unstructured` `0.4.14`, this is handled
automatically in the relevant modules within `unstructured`.
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.
Link for easier navigation (it's not immediately clear where to find
more info on SimpleSequentialChain (3 clicks away)
---------
Co-authored-by: Larry Fisherman <l4rryfisherman@protonmail.com>
With the current method used to get the SQL table info, sqlite internal
schema tables are being included and are not being handled correctly by
sqlalchemy because the columns have no types. This is easy to see with
the Chinook database:
```python
db = SQLDatabase.from_uri("sqlite:///Chinook.db")
print(db.table_info)
```
```python
...
sqlalchemy.exc.CompileError: (in table 'sqlite_sequence', column 'name'): Can't generate DDL for NullType(); did you forget to specify a type on this Column?
```
SQLAlchemy 2.0 [ignores these by
default](63d90b0f44/lib/sqlalchemy/dialects/sqlite/base.py (L856-L880)):
63d90b0f44/lib/sqlalchemy/dialects/sqlite/base.py (L2096-L2123)
blob42
Harrison Chase
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