# Zep Retriever - Vector Search Over Chat History with the Zep Long-term
Memory Service
More on Zep: https://github.com/getzep/zep
Note: This PR is related to and relies on
https://github.com/hwchase17/langchain/pull/4834. I did not want to
modify the `pyproject.toml` file to add the `zep-python` dependency a
second time.
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
# docs: updated `Supabase` notebook
- the title of the notebook was inconsistent (included redundant
"Vectorstore"). Removed this "Vectorstore"
- added `Postgress` to the title. It is important. The `Postgres` name
is much more popular than `Supabase`.
- added description for the `Postrgress`
- added more info to the `Supabase` description
# Update GPT4ALL integration
GPT4ALL have completely changed their bindings. They use a bit odd
implementation that doesn't fit well into base.py and it will probably
be changed again, so it's a temporary solution.
Fixes#3839, #4628
# Docs: compound ecosystem and integrations
**Problem statement:** We have a big overlap between the
References/Integrations and Ecosystem/LongChain Ecosystem pages. It
confuses users. It creates a situation when new integration is added
only on one of these pages, which creates even more confusion.
- removed References/Integrations page (but move all its information
into the individual integration pages - in the next PR).
- renamed Ecosystem/LongChain Ecosystem into Integrations/Integrations.
I like the Ecosystem term. It is more generic and semantically richer
than the Integration term. But it mentally overloads users. The
`integration` term is more concrete.
UPDATE: after discussion, the Ecosystem is the term.
Ecosystem/Integrations is the page (in place of Ecosystem/LongChain
Ecosystem).
As a result, a user gets a single place to start with the individual
integration.
# Fix bilibili api import error
bilibili-api package is depracated and there is no sync module.
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Fixes#2673#2724
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
## Who can review?
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@vowelparrot @liaokongVFX
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# TextLoader auto detect encoding and enhanced exception handling
- Add an option to enable encoding detection on `TextLoader`.
- The detection is done using `chardet`
- The loading is done by trying all detected encodings by order of
confidence or raise an exception otherwise.
### New Dependencies:
- `chardet`
Fixes#4479
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
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- @eyurtsev
---------
Co-authored-by: blob42 <spike@w530>
# Load specific file types from Google Drive (issue #4878)
Add the possibility to define what file types you want to load from
Google Drive.
```
loader = GoogleDriveLoader(
folder_id="1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5",
file_types=["document", "pdf"]
recursive=False
)
```
Fixes ##4878
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
DataLoaders
- @eyurtsev
Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord:
RicChilligerDude#7589
---------
Co-authored-by: UmerHA <40663591+UmerHA@users.noreply.github.com>
#docs: text splitters improvements
Changes are only in the Jupyter notebooks.
- added links to the source packages and a short description of these
packages
- removed " Text Splitters" suffixes from the TOC elements (they made
the list of the text splitters messy)
- moved text splitters, based on the length function into a separate
list. They can be mixed with any classes from the "Text Splitters", so
it is a different classification.
## Who can review?
@hwchase17 - project lead
@eyurtsev
@vowelparrot
NOTE: please, check out the results of the `Python code` text splitter
example (text_splitters/examples/python.ipynb). It looks suboptimal.
# Added another helpful way for developers who want to set OpenAI API
Key dynamically
Previous methods like exporting environment variables are good for
project-wide settings.
But many use cases need to assign API keys dynamically, recently.
```python
from langchain.llms import OpenAI
llm = OpenAI(openai_api_key="OPENAI_API_KEY")
```
## Before submitting
```bash
export OPENAI_API_KEY="..."
```
Or,
```python
import os
os.environ["OPENAI_API_KEY"] = "..."
```
<hr>
Thank you.
Cheers,
Bongsang
# Documentation for Azure OpenAI embeddings model
- OPENAI_API_VERSION environment variable is needed for the endpoint
- The constructor does not work with model, it works with deployment.
I fixed it in the notebook.
(This is my first contribution)
## Who can review?
@hwchase17
@agola
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Docs: improvements in the `retrievers/examples/` notebooks
Its primary purpose is to make the Jupyter notebook examples
**consistent** and more suitable for first-time viewers.
- add links to the integration source (if applicable) with a short
description of this source;
- removed `_retriever` suffix from the file names (where it existed) for
consistency;
- removed ` retriever` from the notebook title (where it existed) for
consistency;
- added code to install necessary Python package(s);
- added code to set up the necessary API Key.
- very small fixes in notebooks from other folders (for consistency):
- docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb
- docs/modules/indexes/vectorstores/examples/pinecone.ipynb
- docs/modules/models/llms/integrations/cohere.ipynb
- fixed misspelling in langchain/retrievers/time_weighted_retriever.py
comment (sorry, about this change in a .py file )
## Who can review
@dev2049
# Fixed typos (issues #4818 & #4668 & more typos)
- At some places, it said `model = ChatOpenAI(model='gpt-3.5-turbo')`
but should be `model = ChatOpenAI(model_name='gpt-3.5-turbo')`
- Fixes some other typos
Fixes#4818, #4668
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @vowelparrot
# Removed usage of deprecated methods
Replaced `SQLDatabaseChain` deprecated direct initialisation with
`from_llm` method
## Who can review?
@hwchase17
@agola11
---------
Co-authored-by: imeckr <chandanroutray2012@gmail.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
- Installation of non-colab packages
- Get API keys
# Added dependencies to make notebook executable on hosted notebooks
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@hwchase17
@vowelparrot
- Installation of non-colab packages
- Get API keys
- Get rid of warnings
# Cleanup and added dependencies to make notebook executable on hosted
notebooks
@hwchase17
@vowelparrot
The current example in
https://python.langchain.com/en/latest/modules/agents/plan_and_execute.html
has inconsistent reasoning step (observing 28 years and thinking it's 26
years):
```
Observation: 28 years
Thought:Based on my search, Gigi Hadid's current age is 26 years old.
Action:
{
"action": "Final Answer",
"action_input": "Gigi Hadid's current age is 26 years old."
}
```
Guessing this is model noise. Rerunning seems to give correct answer of
28 years.
# Fix Telegram API loader + add tests.
I was testing this integration and it was broken with next error:
```python
message_threads = loader._get_message_threads(df)
KeyError: False
```
Also, this particular loader didn't have any tests / related group in
poetry, so I added those as well.
@hwchase17 / @eyurtsev please take a look on this fix PR.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Cassandra support for chat history
### Description
- Store chat messages in cassandra
### Dependency
- cassandra-driver - Python Module
## Before submitting
- Added Integration Test
## Who can review?
@hwchase17
@agola11
# Your PR Title (What it does)
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Fixes # (issue)
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Co-authored-by: Jinto Jose <129657162+jj701@users.noreply.github.com>
# Add GraphQL Query Support
This PR introduces a GraphQL API Wrapper tool that allows LLM agents to
query GraphQL databases. The tool utilizes the httpx and gql Python
packages to interact with GraphQL APIs and provides a simple interface
for running queries with LLM agents.
@vowelparrot
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
### Adds a document loader for Docugami
Specifically:
1. Adds a data loader that talks to the [Docugami](http://docugami.com)
API to download processed documents as semantic XML
2. Parses the semantic XML into chunks, with additional metadata
capturing chunk semantics
3. Adds a detailed notebook showing how you can use additional metadata
returned by Docugami for techniques like the [self-querying
retriever](https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query_retriever.html)
4. Adds an integration test, and related documentation
Here is an example of a result that is not possible without the
capabilities added by Docugami (from the notebook):
<img width="1585" alt="image"
src="https://github.com/hwchase17/langchain/assets/749277/bb6c1ce3-13dc-4349-a53b-de16681fdd5b">
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
Co-authored-by: Taqi Jaffri <tjaffri@gmail.com>
[OpenWeatherMapAPIWrapper](f70e18a5b3/docs/modules/agents/tools/examples/openweathermap.ipynb)
works wonderfully, but the _tool_ itself can't be used in master branch.
- added OpenWeatherMap **tool** to the public api, to be loadable with
`load_tools` by using "openweathermap-api" tool name (that name is used
in the existing
[docs](aff33d52c5/docs/modules/agents/tools/getting_started.md),
at the bottom of the page)
- updated OpenWeatherMap tool's **description** to make the input format
match what the API expects (e.g. `London,GB` instead of `'London,GB'`)
- added [ecosystem documentation page for
OpenWeatherMap](f9c41594fe/docs/ecosystem/openweathermap.md)
- added tool usage example to [OpenWeatherMap's
notebook](f9c41594fe/docs/modules/agents/tools/examples/openweathermap.ipynb)
Let me know if there's something I missed or something needs to be
updated! Or feel free to make edits yourself if that makes it easier for
you 🙂
[RELLM](https://github.com/r2d4/rellm) is a library that wraps local
HuggingFace pipeline models for structured decoding.
RELLM works by generating tokens one at a time. At each step, it masks
tokens that don't conform to the provided partial regular expression.
[JSONFormer](https://github.com/1rgs/jsonformer) is a bit different, where it sequentially adds the keys then decodes each value directly
