This PR updates the usage instructions for PromptLayerOpenAI in
Langchain's documentation. The updated instructions provide more detail
and conform better to the style of other LLM integration documentation
pages.
No code changes were made in this PR, only improvements to the
documentation. This update will make it easier for users to understand
how to use `PromptLayerOpenAI`
### Summary
Adds tracked metadata from `unstructured` elements to the document
metadata when `UnstructuredFileLoader` is used in `"elements"` mode.
Tracked metadata is available in `unstructured>=0.4.9`, but the code is
written for backward compatibility with older `unstructured` versions.
### Testing
Before running, make sure to upgrade to `unstructured==0.4.9`. In the
code snippet below, you should see `page_number`, `filename`, and
`category` in the metadata for each document. `doc[0]` should have
`page_number: 1` and `doc[-1]` should have `page_number: 2`. The example
document is `layout-parser-paper-fast.pdf` from the [`unstructured`
sample
docs](https://github.com/Unstructured-IO/unstructured/tree/main/example-docs).
```python
from langchain.document_loaders import UnstructuredFileLoader
loader = UnstructuredFileLoader(file_path=f"layout-parser-paper-fast.pdf", mode="elements")
docs = loader.load()
```
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>
This PR adds persistence to the Chroma vector store.
Users can supply a `persist_directory` with any of the `Chroma` creation
methods. If supplied, the store will be automatically persisted at that
directory.
If a user creates a new `Chroma` instance with the same persistence
directory, it will get loaded up automatically. If they use `from_texts`
or `from_documents` in this way, the documents will be loaded into the
existing store.
There is the chance of some funky behavior if the user passes a
different embedding function from the one used to create the collection
- we will make this easier in future updates. For now, we log a warning.
Chroma is a simple to use, open-source, zero-config, zero setup
vectorstore.
Simply `pip install chromadb`, and you're good to go.
Out-of-the-box Chroma is suitable for most LangChain workloads, but is
highly flexible. I tested to 1M embs on my M1 mac, with out issues and
reasonably fast query times.
Look out for future releases as we integrate more Chroma features with
LangChain!
The provided example uses the default `max_length` of `20` tokens, which
leads to the example generation getting cut off. 20 tokens is way too
short to show CoT reasoning, so I boosted it to `64`.
Without knowing HF's API well, it can be hard to figure out just where
those `model_kwargs` come from, and `max_length` is a super critical
one.
Co-authored-by: Andrew White <white.d.andrew@gmail.com>
Co-authored-by: Harrison Chase <harrisonchase@Harrisons-MBP.attlocal.net>
Co-authored-by: Peng Qu <82029664+pengqu123@users.noreply.github.com>
Sometimes, the docs may be empty. For example for the text =
soup.find_all("main", {"id": "main-content"}) was an empty list. To
cater to these edge cases, the clean function needs to be checked if it
is empty or not.
