Hi @rlancemartin, @eyurtsev!
- Description: Adding HNSW extension support for Postgres. Similar to
pgvector vectorstore, with 3 differences
1. it uses HNSW extension for exact and ANN searches,
2. Vectors are of type array of real
3. Only supports L2
- Dependencies: [HNSW](https://github.com/knizhnik/hnsw) extension for
Postgres
- Example:
```python
db = HNSWVectoreStore.from_documents(
embedding=embeddings,
documents=docs,
collection_name=collection_name,
connection_string=connection_string
)
query = "What did the president say about Ketanji Brown Jackson"
docs_with_score: List[Tuple[Document, float]] =
db.similarity_search_with_score(query)
```
The example notebook is in the PR too.
Documentation update for [Jina
ecosystem](https://python.langchain.com/docs/ecosystem/integrations/jina)
and `langchain-serve` in the deployments section to latest features.
@hwchase17
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[Apache HugeGraph](https://github.com/apache/incubator-hugegraph) is a
convenient, efficient, and adaptable graph database, compatible with the
Apache TinkerPop3 framework and the Gremlin query language.
In this PR, the HugeGraph and HugeGraphQAChain provide the same
functionality as the existing integration with Neo4j and enables query
generation and question answering over HugeGraph database. The
difference is that the graph query language supported by HugeGraph is
not cypher but another very popular graph query language
[Gremlin](https://tinkerpop.apache.org/gremlin.html).
A notebook example and a simple test case have also been added.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR introduces a new Mendable UI tailored to a better search
experience.
We're more closely integrating our traditional search with our AI
generation.
With this change, you won't have to tab back and forth between the
mendable bot and the keyword search. Both types of search are handled in
the same bar. This should make the docs easier to navigate. while still
letting users get code generations or AI-summarized answers if they so
wish. Also, it should reduce the cost.
Would love to hear your feedback :)
Cc: @dev2049 @hwchase17
- Description: Added a new SpacyEmbeddings class for generating
embeddings using the Spacy library.
- Issue: Sentencebert/Bert/Spacy/Doc2vec embedding support #6952
- Dependencies: This change requires the Spacy library and the
'en_core_web_sm' Spacy model.
- Tag maintainer: @dev2049
- Twitter handle: N/A
This change includes a new SpacyEmbeddings class, but does not include a
test or an example notebook.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**:
The JSON Lines format is used by some services such as OpenAI and
HuggingFace. It's also a convenient alternative to CSV.
This PR adds JSON Lines support to `JSONLoader` and also updates related
tests.
**Tag maintainer**: @rlancemartin, @eyurtsev.
PS I was not able to build docs locally so didn't update related
section.
Update to Vectara integration
- By user request added "add_files" to take advantage of Vectara
capabilities to process files on the backend, without the need for
separate loading of documents and chunking in the chain.
- Updated vectara.ipynb example notebook to be broader and added testing
of add_file()
@hwchase17 - project lead
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
Retrying with the same improvements as in #6772, this time trying not to
mess up with branches.
@rlancemartin doing a fresh new PR from a branch with a new name. This
should do. Thank you for your help!
---------
Co-authored-by: Jonathan Ellis <jbellis@datastax.com>
Co-authored-by: rlm <pexpresss31@gmail.com>
### Summary
Updates `UnstructuredEmailLoader` so that it can process attachments in
addition to the e-mail content. The loader will process attachments if
the `process_attachments` kwarg is passed when the loader is
instantiated.
### Testing
```python
file_path = "fake-email-attachment.eml"
loader = UnstructuredEmailLoader(
file_path, mode="elements", process_attachments=True
)
docs = loader.load()
docs[-1]
```
### Reviewers
- @rlancemartin
- @eyurtsev
- @hwchase17
Handle the new retriever events in a way that (I think) is entirely
backwards compatible? Needs more testing for some of the chain changes
and all.
This creates an entire new run type, however. We could also just treat
this as an event within a chain run presumably (same with memory)
Adds a subclass initializer that upgrades old retriever implementations
to the new schema, along with tests to ensure they work.
First commit doesn't upgrade any of our retriever implementations (to
show that we can pass the tests along with additional ones testing the
upgrade logic).
Second commit upgrades the known universe of retrievers in langchain.
- [X] Add callback handling methods for retriever start/end/error (open
to renaming to 'retrieval' if you want that)
- [X] Update BaseRetriever schema to support callbacks
- [X] Tests for upgrading old "v1" retrievers for backwards
compatibility
- [X] Update existing retriever implementations to implement the new
interface
- [X] Update calls within chains to .{a]get_relevant_documents to pass
the child callback manager
- [X] Update the notebooks/docs to reflect the new interface
- [X] Test notebooks thoroughly
Not handled:
- Memory pass throughs: retrieval memory doesn't have a parent callback
manager passed through the method
---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
Description: `all_metadatas` was not defined, `OpenAIEmbeddings` was not
imported,
Issue: #6723 the issue # it fixes (if applicable),
Dependencies: lark,
Tag maintainer: @vowelparrot , @dev2049
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
# Description
This PR makes it possible to use named vectors from Qdrant in Langchain.
That was requested multiple times, as people want to reuse externally
created collections in Langchain. It doesn't change anything for the
existing applications. The changes were covered with some integration
tests and included in the docs.
## Example
```python
Qdrant.from_documents(
docs,
embeddings,
location=":memory:",
collection_name="my_documents",
vector_name="custom_vector",
)
```
### Issue: #2594
Tagging @rlancemartin & @eyurtsev. I'd appreciate your review.
### Scientific Article PDF Parsing via Grobid
`Description:`
This change adds the GrobidParser class, which uses the Grobid library
to parse scientific articles into a universal XML format containing the
article title, references, sections, section text etc. The GrobidParser
uses a local Grobid server to return PDFs document as XML and parses the
XML to optionally produce documents of individual sentences or of whole
paragraphs. Metadata includes the text, paragraph number, pdf relative
bboxes, pages (text may overlap over two pages), section title
(Introduction, Methodology etc), section_number (i.e 1.1, 2.3), the
title of the paper and finally the file path.
Grobid parsing is useful beyond standard pdf parsing as it accurately
outputs sections and paragraphs within them. This allows for
post-fitering of results for specific sections i.e. limiting results to
the methodology section or results. While sections are split via
headings, ideally they could be classified specifically into
introduction, methodology, results, discussion, conclusion. I'm
currently experimenting with chatgpt-3.5 for this function, which could
later be implemented as a textsplitter.
`Dependencies:`
For use, the grobid repo must be cloned and Java must be installed, for
colab this is:
```
!apt-get install -y openjdk-11-jdk -q
!update-alternatives --set java /usr/lib/jvm/java-11-openjdk-amd64/bin/java
!git clone https://github.com/kermitt2/grobid.git
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-11-openjdk-amd64"
os.chdir('grobid')
!./gradlew clean install
```
Once installed the server is ran on localhost:8070 via
```
get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')
```
@rlancemartin, @eyurtsev
Twitter Handle: @Corranmac
Grobid Demo Notebook is
[here](https://colab.research.google.com/drive/1X-St_mQRmmm8YWtct_tcJNtoktbdGBmd?usp=sharing).
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
### Overview
This PR aims at building on #4378, expanding the capabilities and
building on top of the `cassIO` library to interface with the database
(as opposed to using the core drivers directly).
Usage of `cassIO` (a library abstracting Cassandra access for
ML/GenAI-specific purposes) is already established since #6426 was
merged, so no new dependencies are introduced.
In the same spirit, we try to uniform the interface for using Cassandra
instances throughout LangChain: all our appreciation of the work by
@jj701 notwithstanding, who paved the way for this incremental work
(thank you!), we identified a few reasons for changing the way a
`CassandraChatMessageHistory` is instantiated. Advocating a syntax
change is something we don't take lighthearted way, so we add some
explanations about this below.
Additionally, this PR expands on integration testing, enables use of
Cassandra's native Time-to-Live (TTL) features and improves the phrasing
around the notebook example and the short "integrations" documentation
paragraph.
We would kindly request @hwchase to review (since this is an elaboration
and proposed improvement of #4378 who had the same reviewer).
### About the __init__ breaking changes
There are
[many](https://docs.datastax.com/en/developer/python-driver/3.28/api/cassandra/cluster/)
options when creating the `Cluster` object, and new ones might be added
at any time. Choosing some of them and exposing them as `__init__`
parameters `CassandraChatMessageHistory` will prove to be insufficient
for at least some users.
On the other hand, working through `kwargs` or adding a long, long list
of arguments to `__init__` is not a desirable option either. For this
reason, (as done in #6426), we propose that whoever instantiates the
Chat Message History class provide a Cassandra `Session` object, ready
to use. This also enables easier injection of mocks and usage of
Cassandra-compatible connections (such as those to the cloud database
DataStax Astra DB, obtained with a different set of init parameters than
`contact_points` and `port`).
We feel that a breaking change might still be acceptable since LangChain
is at `0.*`. However, while maintaining that the approach we propose
will be more flexible in the future, room could be made for a
"compatibility layer" that respects the current init method. Honestly,
we would to that only if there are strong reasons for it, as that would
entail an additional maintenance burden.
### Other changes
We propose to remove the keyspace creation from the class code for two
reasons: first, production Cassandra instances often employ RBAC so that
the database user reading/writing from tables does not necessarily (and
generally shouldn't) have permission to create keyspaces, and second
that programmatic keyspace creation is not a best practice (it should be
done more or less manually, with extra care about schema mismatched
among nodes, etc). Removing this (usually unnecessary) operation from
the `__init__` path would also improve initialization performance
(shorter time).
We suggest, likewise, to remove the `__del__` method (which would close
the database connection), for the following reason: it is the
recommended best practice to create a single Cassandra `Session` object
throughout an application (it is a resource-heavy object capable to
handle concurrency internally), so in case Cassandra is used in other
ways by the app there is the risk of truncating the connection for all
usages when the history instance is destroyed. Moreover, the `Session`
object, in typical applications, is best left to garbage-collect itself
automatically.
As mentioned above, we defer the actual database I/O to the `cassIO`
library, which is designed to encode practices optimized for LLM
applications (among other) without the need to expose LangChain
developers to the internals of CQL (Cassandra Query Language). CassIO is
already employed by the LangChain's Vector Store support for Cassandra.
We added a few more connection options in the companion notebook example
(most notably, Astra DB) to encourage usage by anyone who cannot run
their own Cassandra cluster.
We surface the `ttl_seconds` option for automatic handling of an
expiration time to chat history messages, a likely useful feature given
that very old messages generally may lose their importance.
We elaborated a bit more on the integration testing (Time-to-live,
separation of "session ids", ...).
### Remarks from linter & co.
We reinstated `cassio` as a dependency both in the "optional" group and
in the "integration testing" group of `pyproject.toml`. This might not
be the right thing do to, in which case the author of this PR offer his
apologies (lack of confidence with Poetry - happy to be pointed in the
right direction, though!).
During linter tests, we were hit by some errors which appear unrelated
to the code in the PR. We left them here and report on them here for
awareness:
```
langchain/vectorstores/mongodb_atlas.py:137: error: Argument 1 to "insert_many" of "Collection" has incompatible type "List[Dict[str, Sequence[object]]]"; expected "Iterable[Union[MongoDBDocumentType, RawBSONDocument]]" [arg-type]
langchain/vectorstores/mongodb_atlas.py:186: error: Argument 1 to "aggregate" of "Collection" has incompatible type "List[object]"; expected "Sequence[Mapping[str, Any]]" [arg-type]
langchain/vectorstores/qdrant.py:16: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:19: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:20: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:22: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:23: error: Name "grpc" is not defined [name-defined]
```
In the same spirit, we observe that to even get `import langchain` run,
it seems that a `pip install bs4` is missing from the minimal package
installation path.
Thank you!
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Description: Adds a brief example of using an OAuth access token with
the Zapier wrapper. Also links to the Zapier documentation to learn more
about OAuth flows.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
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<!-- Remove if not applicable -->
### Summary
This PR adds a LarkSuite (FeiShu) document loader.
> [LarkSuite](https://www.larksuite.com/) is an enterprise collaboration
platform developed by ByteDance.
### Tests
- an integration test case is added
- an example notebook showing usage is added. [Notebook
preview](https://github.com/yaohui-wyh/langchain/blob/master/docs/extras/modules/data_connection/document_loaders/integrations/larksuite.ipynb)
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### Who can review?
- PTAL @eyurtsev @hwchase17
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---------
Co-authored-by: Yaohui Wang <wangyaohui.01@bytedance.com>
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<!-- Remove if not applicable -->
- add tencent cos directory and file support for document-loader
#### Before submitting
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#### Who can review?
@eyurtsev
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Distance-based vector database retrieval embeds (represents) queries in
high-dimensional space and finds similar embedded documents based on
"distance". But, retrieval may produce difference results with subtle
changes in query wording or if the embeddings do not capture the
semantics of the data well. Prompt engineering / tuning is sometimes
done to manually address these problems, but can be tedious.
The `MultiQueryRetriever` automates the process of prompt tuning by
using an LLM to generate multiple queries from different perspectives
for a given user input query. For each query, it retrieves a set of
relevant documents and takes the unique union across all queries to get
a larger set of potentially relevant documents. By generating multiple
perspectives on the same question, the `MultiQueryRetriever` might be
able to overcome some of the limitations of the distance-based retrieval
and get a richer set of results.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Proxies are helpful, especially when you start querying against more
anti-bot websites.
[Proxy
services](https://developers.oxylabs.io/advanced-proxy-solutions/web-unblocker/making-requests)
(of which there are many) and `requests` make it easy to rotate IPs to
prevent banning by just passing along a simple dict to `requests`.
CC @rlancemartin, @eyurtsev
### Summary
The Unstructured API will soon begin requiring API keys. This PR updates
the Unstructured integrations docs with instructions on how to generate
Unstructured API keys.
### Reviewers
@rlancemartin
@eyurtsev
@hwchase17
#### Summary
A new approach to loading source code is implemented:
Each top-level function and class in the code is loaded into separate
documents. Then, an additional document is created with the top-level
code, but without the already loaded functions and classes.
This could improve the accuracy of QA chains over source code.
For instance, having this script:
```
class MyClass:
def __init__(self, name):
self.name = name
def greet(self):
print(f"Hello, {self.name}!")
def main():
name = input("Enter your name: ")
obj = MyClass(name)
obj.greet()
if __name__ == '__main__':
main()
```
The loader will create three documents with this content:
First document:
```
class MyClass:
def __init__(self, name):
self.name = name
def greet(self):
print(f"Hello, {self.name}!")
```
Second document:
```
def main():
name = input("Enter your name: ")
obj = MyClass(name)
obj.greet()
```
Third document:
```
# Code for: class MyClass:
# Code for: def main():
if __name__ == '__main__':
main()
```
A threshold parameter is added to control whether small scripts are
split in this way or not.
At this moment, only Python and JavaScript are supported. The
appropriate parser is determined by examining the file extension.
#### Tests
This PR adds:
- Unit tests
- Integration tests
#### Dependencies
Only one dependency was added as optional (needed for the JavaScript
parser).
#### Documentation
A notebook is added showing how the loader can be used.
#### Who can review?
@eyurtsev @hwchase17
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
Description: Update documentation to
1) point to updated documentation links at Zapier.com (we've revamped
our help docs and paths), and
2) To provide clarity how to use the wrapper with an access token for
OAuth support
Demo:
Initializing the Zapier Wrapper with an OAuth Access Token
`ZapierNLAWrapper(zapier_nla_oauth_access_token="<redacted>")`
Using LangChain to resolve the current weather in Vancouver BC
leveraging Zapier NLA to lookup weather by coords.
```
> Entering new chain...
I need to use a tool to get the current weather.
Action: The Weather: Get Current Weather
Action Input: Get the current weather for Vancouver BC
Observation: {"coord__lon": -123.1207, "coord__lat": 49.2827, "weather": [{"id": 802, "main": "Clouds", "description": "scattered clouds", "icon": "03d", "icon_url": "http://openweathermap.org/img/wn/03d@2x.png"}], "weather[]icon_url": ["http://openweathermap.org/img/wn/03d@2x.png"], "weather[]icon": ["03d"], "weather[]id": [802], "weather[]description": ["scattered clouds"], "weather[]main": ["Clouds"], "base": "stations", "main__temp": 71.69, "main__feels_like": 71.56, "main__temp_min": 67.64, "main__temp_max": 76.39, "main__pressure": 1015, "main__humidity": 64, "visibility": 10000, "wind__speed": 3, "wind__deg": 155, "wind__gust": 11.01, "clouds__all": 41, "dt": 1687806607, "sys__type": 2, "sys__id": 2011597, "sys__country": "CA", "sys__sunrise": 1687781297, "sys__sunset": 1687839730, "timezone": -25200, "id": 6173331, "name": "Vancouver", "cod": 200, "summary": "scattered clouds", "_zap_search_was_found_status": true}
Thought: I now know the current weather in Vancouver BC.
Final Answer: The current weather in Vancouver BC is scattered clouds with a temperature of 71.69 and wind speed of 3
```
**Description:** Add a documentation page for the Streamlit Callback
Handler integration (#6315)
Notes:
- Implemented as a markdown file instead of a notebook since example
code runs in a Streamlit app (happy to discuss / consider alternatives
now or later)
- Contains an embedded Streamlit app ->
https://mrkl-minimal.streamlit.app/ Currently this app is hosted out of
a Streamlit repo but we're working to migrate the code to a LangChain
owned repo
cc @dev2049 @tconkling
Notebook shows preference scoring between two chains and reports wilson
score interval + p value
I think I'll add the option to insert ground truth labels but doesn't
have to be in this PR
allows for where filtering on collection via get
- Description: aligns langchain chroma vectorstore get with underlying
[chromadb collection
get](https://github.com/chroma-core/chroma/blob/main/chromadb/api/models/Collection.py#L103)
allowing for where filtering, etc.
- Issue: NA
- Dependencies: none
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @pappanaka
#### Background
With the development of [structured
tools](https://blog.langchain.dev/structured-tools/), the LangChain team
expanded the platform's functionality to meet the needs of new
applications. The GMail tool, empowered by structured tools, now
supports multiple arguments and powerful search capabilities,
demonstrating LangChain's ability to interact with dynamic data sources
like email servers.
#### Challenge
The current GMail tool only supports GMail, while users often utilize
other email services like Outlook in Office365. Additionally, the
proposed calendar tool in PR
https://github.com/hwchase17/langchain/pull/652 only works with Google
Calendar, not Outlook.
#### Changes
This PR implements an Office365 integration for LangChain, enabling
seamless email and calendar functionality with a single authentication
process.
#### Future Work
With the core Office365 integration complete, future work could include
integrating other Office365 tools such as Tasks and Address Book.
#### Who can review?
@hwchase17 or @vowelparrot can review this PR
#### Appendix
@janscas, I utilized your [O365](https://github.com/O365/python-o365)
library extensively. Given the rising popularity of LangChain and
similar AI frameworks, the convergence of libraries like O365 and tools
like this one is likely. So, I wanted to keep you updated on our
progress.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
MHTML is a very interesting format since it's used both for emails but
also for archived webpages. Some scraping projects want to store pages
in disk to process them later, mhtml is perfect for that use case.
This is heavily inspired from the beautifulsoup html loader, but
extracting the html part from the mhtml file.
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
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- Description: Fix Typo in LangChain MyScale Integration Doc
@hwchase17
# Add caching to BaseChatModel
Fixes#1644
(Sidenote: While testing, I noticed we have multiple implementations of
Fake LLMs, used for testing. I consolidated them.)
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
Models
- @hwchase17
- @agola11
Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord:
RicChilligerDude#7589
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR adds a new LLM class for the Amazon API Gateway hosted LLM. The
PR also includes example notebooks for using the LLM class in an Agent
chain.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
### Just corrected a small inconsistency on a doc page (not exactly a
typo, per se)
- Description: There was inconsistency due to the use of single quotes
at one place on the [Squential
Chains](https://python.langchain.com/docs/modules/chains/foundational/sequential_chains)
page of the docs,
- Issue: NA,
- Dependencies: NA,
- Tag maintainer: @dev2049,
- Twitter handle: kambleakash0
This link for the notebook of OpenLLM is not migrated to the new format
Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>
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Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>
We may want to process load all URLs under a root directory.
For example, let's look at the [LangChain JS
documentation](https://js.langchain.com/docs/).
This has many interesting child pages that we may want to read in bulk.
Of course, the `WebBaseLoader` can load a list of pages.
But, the challenge is traversing the tree of child pages and actually
assembling that list!
We do this using the `RecusiveUrlLoader`.
This also gives us the flexibility to exclude some children (e.g., the
`api` directory with > 800 child pages).
Many cities have open data portals for events like crime, traffic, etc.
Socrata provides an API for many, including SF (e.g., see
[here](https://dev.socrata.com/foundry/data.sfgov.org/tmnf-yvry)).
This is a new data loader for city data that uses Socrata API.
# Changes
This PR adds [Clarifai](https://www.clarifai.com/) integration to
Langchain. Clarifai is an end-to-end AI Platform. Clarifai offers user
the ability to use many types of LLM (OpenAI, cohere, ect and other open
source models). As well, a clarifai app can be treated as a vector
database to upload and retrieve data. The integrations includes:
- Clarifai LLM integration: Clarifai supports many types of language
model that users can utilize for their application
- Clarifai VectorDB: A Clarifai application can hold data and
embeddings. You can run semantic search with the embeddings
#### Before submitting
- [x] Added integration test for LLM
- [x] Added integration test for VectorDB
- [x] Added notebook for LLM
- [x] Added notebook for VectorDB
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
### Description
We have added a new LLM integration `azureml_endpoint` that allows users
to leverage models from the AzureML platform. Microsoft recently
announced the release of [Azure Foundation
Models](https://learn.microsoft.com/en-us/azure/machine-learning/concept-foundation-models?view=azureml-api-2)
which users can find in the AzureML Model Catalog. The Model Catalog
contains a variety of open source and Hugging Face models that users can
deploy on AzureML. The `azureml_endpoint` allows LangChain users to use
the deployed Azure Foundation Models.
### Dependencies
No added dependencies were required for the change.
### Tests
Integration tests were added in
`tests/integration_tests/llms/test_azureml_endpoint.py`.
### Notebook
A Jupyter notebook demonstrating how to use `azureml_endpoint` was added
to `docs/modules/llms/integrations/azureml_endpoint_example.ipynb`.
### Twitters
[Prakhar Gupta](https://twitter.com/prakhar_in)
[Matthew DeGuzman](https://twitter.com/matthew_d13)
---------
Co-authored-by: Matthew DeGuzman <91019033+matthewdeguzman@users.noreply.github.com>
Co-authored-by: prakharg-msft <75808410+prakharg-msft@users.noreply.github.com>
Everything needed to support sending messages over WhatsApp Business
Platform (GA), Facebook Messenger (Public Beta) and Google Business
Messages (Private Beta) was present. Just added some details on
leveraging it.
Just some grammar fixes: I found "retriver" instead of "retriever" in
several comments across the documentation and in the comments. I fixed
it.
Co-authored-by: andrey.vedishchev <andrey.vedishchev@rgigroup.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
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<!-- Remove if not applicable -->
Fixes # (issue)
#### Before submitting
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Here are some examples to use StarRocks as vectordb
```
from langchain.vectorstores import StarRocks
from langchain.vectorstores.starrocks import StarRocksSettings
embeddings = OpenAIEmbeddings()
# conifgure starrocks settings
settings = StarRocksSettings()
settings.port = 41003
settings.host = '127.0.0.1'
settings.username = 'root'
settings.password = ''
settings.database = 'zya'
# to fill new embeddings
docsearch = StarRocks.from_documents(split_docs, embeddings, config = settings)
# or to use already-built embeddings in database.
docsearch = StarRocks(embeddings, settings)
```
#### Who can review?
Tag maintainers/contributors who might be interested:
@dev2049
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---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
### Integration of Infino with LangChain for Enhanced Observability
This PR aims to integrate [Infino](https://github.com/infinohq/infino),
an open source observability platform written in rust for storing
metrics and logs at scale, with LangChain, providing users with a
streamlined and efficient method of tracking and recording LangChain
experiments. By incorporating Infino into LangChain, users will be able
to gain valuable insights and easily analyze the behavior of their
language models.
#### Please refer to the following files related to integration:
- `InfinoCallbackHandler`: A [callback
handler](https://github.com/naman-modi/langchain/blob/feature/infino-integration/langchain/callbacks/infino_callback.py)
specifically designed for storing chain responses within Infino.
- Example `infino.ipynb` file: A comprehensive notebook named
[infino.ipynb](https://github.com/naman-modi/langchain/blob/feature/infino-integration/docs/extras/modules/callbacks/integrations/infino.ipynb)
has been included to guide users on effectively leveraging Infino for
tracking LangChain requests.
- [Integration
Doc](https://github.com/naman-modi/langchain/blob/feature/infino-integration/docs/extras/ecosystem/integrations/infino.mdx)
for Infino integration.
By integrating Infino, LangChain users will gain access to powerful
visualization and debugging capabilities. Infino enables easy tracking
of inputs, outputs, token usage, execution time of LLMs. This
comprehensive observability ensures a deeper understanding of individual
executions and facilitates effective debugging.
Co-authors: @vinaykakade @savannahar68
---------
Co-authored-by: Vinay Kakade <vinaykakade@gmail.com>
This PR adds Rockset as a vectorstore for langchain.
[Rockset](https://rockset.com/blog/introducing-vector-search-on-rockset/)
is a real time OLAP database which provides a fast and efficient vector
search functionality. Further since it is entirely schemaless, it can
store metadata in separate columns thereby allowing fast metadata
filters during vector similarity search (as opposed to storing the
entire metadata in a single JSON column). It currently supports three
distance functions: `COSINE_SIMILARITY`, `EUCLIDEAN_DISTANCE`, and
`DOT_PRODUCT`.
This PR adds `rockset` client as an optional dependency.
We would love a twitter shoutout, our handle is
https://twitter.com/RocksetCloud
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
This pull request introduces a new feature to the LangChain QA Retrieval
Chains with Structures. The change involves adding a prompt template as
an optional parameter for the RetrievalQA chains that utilize the
recently implemented OpenAI Functions.
The main purpose of this enhancement is to provide users with the
ability to input a more customizable prompt to the chain. By introducing
a prompt template as an optional parameter, users can tailor the prompt
to their specific needs and context, thereby improving the flexibility
and effectiveness of the RetrievalQA chains.
## Changes Made
- Created a new optional parameter, "prompt", for the RetrievalQA with
structure chains.
- Added an example to the RetrievalQA with sources notebook.
My twitter handle is @El_Rey_Zero
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Added the functionality to leverage 3 new Codey models from Vertex AI:
- code-bison - Code generation using the existing LLM integration
- code-gecko - Code completion using the existing LLM integration
- codechat-bison - Code chat using the existing chat_model integration
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
This PR adds `KuzuGraph` and `KuzuQAChain` for interacting with [Kùzu
database](https://github.com/kuzudb/kuzu). Kùzu is an in-process
property graph database management system (GDBMS) built for query speed
and scalability. The `KuzuGraph` and `KuzuQAChain` provide the same
functionality as the existing integration with NebulaGraph and Neo4j and
enables query generation and question answering over Kùzu database.
A notebook example and a simple test case have also been added.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
#### Fix
Added the mention of "store" amongst the tasks that the data connection
module can perform aside from the existing 3 (load, transform and
query). Particularly, this implies the generation of embeddings vectors
and the creation of vector stores.
This addresses #6291 adding support for using Cassandra (and compatible
databases, such as DataStax Astra DB) as a [Vector
Store](https://cwiki.apache.org/confluence/display/CASSANDRA/CEP-30%3A+Approximate+Nearest+Neighbor(ANN)+Vector+Search+via+Storage-Attached+Indexes).
A new class `Cassandra` is introduced, which complies with the contract
and interface for a vector store, along with the corresponding
integration test, a sample notebook and modified dependency toml.
Dependencies: the implementation relies on the library `cassio`, which
simplifies interacting with Cassandra for ML- and LLM-oriented
workloads. CassIO, in turn, uses the `cassandra-driver` low-lever
drivers to communicate with the database. The former is added as
optional dependency (+ in `extended_testing`), the latter was already in
the project.
Integration testing relies on a locally-running instance of Cassandra.
[Here](https://cassio.org/more_info/#use-a-local-vector-capable-cassandra)
a detailed description can be found on how to compile and run it (at the
time of writing the feature has not made it yet to a release).
During development of the integration tests, I added a new "fake
embedding" class for what I consider a more controlled way of testing
the MMR search method. Likewise, I had to amend what looked like a
glitch in the behaviour of `ConsistentFakeEmbeddings` whereby an
`embed_query` call would have bypassed storage of the requested text in
the class cache for use in later repeated invocations.
@dev2049 might be the right person to tag here for a review. Thank you!
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
Hello Folks,
Thanks for creating and maintaining this great project. I'm excited to
submit this PR to add Alibaba Cloud OpenSearch as a new vector store.
OpenSearch is a one-stop platform to develop intelligent search
services. OpenSearch was built based on the large-scale distributed
search engine developed by Alibaba. OpenSearch serves more than 500
business cases in Alibaba Group and thousands of Alibaba Cloud
customers. OpenSearch helps develop search services in different search
scenarios, including e-commerce, O2O, multimedia, the content industry,
communities and forums, and big data query in enterprises.
OpenSearch provides the vector search feature. In specific scenarios,
especially test question search and image search scenarios, you can use
the vector search feature together with the multimodal search feature to
improve the accuracy of search results.
This PR includes:
A AlibabaCloudOpenSearch class that can connect to the Alibaba Cloud
OpenSearch instance.
add embedings and metadata into a opensearch datasource.
querying by squared euclidean and metadata.
integration tests.
ipython notebook and docs.
I have read your contributing guidelines. And I have passed the tests
below
- [x] make format
- [x] make lint
- [x] make coverage
- [x] make test
---------
Co-authored-by: zhaoshengbo <shengbo.zsb@alibaba-inc.com>
1. Introduced new distance strategies support: **DOT_PRODUCT** and
**EUCLIDEAN_DISTANCE** for enhanced flexibility.
2. Implemented a feature to filter results based on metadata fields.
3. Incorporated connection attributes specifying "langchain python sdk"
usage for enhanced traceability and debugging.
4. Expanded the suite of integration tests for improved code
reliability.
5. Updated the existing notebook with the usage example
@dev2049
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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<!-- Remove if not applicable -->
Fixes a link typo from `/-/route` to `/-/routes`.
and change endpoint format
from `f"{self.anyscale_service_url}/{self.anyscale_service_route}"` to
`f"{self.anyscale_service_url}{self.anyscale_service_route}"`
Also adding documentation about the format of the endpoint
#### Before submitting
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fixed several inconsistencies:
- file names and notebook titles should be similar otherwise ToC on the
[retrievers
page](https://python.langchain.com/en/latest/modules/indexes/retrievers.html)
and on the left ToC tab are different. For example, now, `Self-querying
with Chroma` is not correctly alphabetically sorted because its file
named `chroma_self_query.ipynb`
- `Stringing compressors and document transformers...` demoted from `#`
to `##`. Otherwise, it appears in Toc.
- several formatting problems
#### Who can review?
@hwchase17
@dev2049
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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The `CustomOutputParser` needs to throw `OutputParserException` when it
fails to parse the response from the agent, so that the executor can
[catch it and
retry](be9371ca8f/langchain/agents/agent.py (L767))
when `handle_parsing_errors=True`.
<!-- Remove if not applicable -->
#### Who can review?
Tag maintainers/contributors who might be interested: @hwchase17
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#### Description
- Removed two backticks surrounding the phrase "chat messages as"
- This phrase stood out among other formatted words/phrases such as
`prompt`, `role`, `PromptTemplate`, etc., which all seem to have a clear
function.
- `chat messages as`, formatted as such, confused me while reading,
leading me to believe the backticks were misplaced.
#### Who can review?
@hwchase17
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Minor new line character in the markdown.
Also, this option is not yet in the latest version of LangChain
(0.0.190) from Conda. Maybe in the next update.
@eyurtsev
@hwchase17
This PR adds an example of doing question answering over documents using
OpenAI Function Agents.
#### Who can review?
@hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- return raw and full output (but keep run shortcut method functional)
- change output parser to take in generations (good for working with
messages)
- add output parser to base class, always run (default to same as
current)
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
#### Before submitting
Add memory support for `OpenAIFunctionsAgent` like
`StructuredChatAgent`.
#### Who can review?
@hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
To bypass SSL verification errors during fetching, you can include the
`verify=False` parameter. This markdown proves useful, especially for
beginners in the field of web scraping.
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Fixes#6079
#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17
@eyurtsev
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
To bypass SSL verification errors during web scraping, you can include
the ssl_verify=False parameter along with the headers parameter. This
combination of arguments proves useful, especially for beginners in the
field of web scraping.
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Fixes#1829
#### Before submitting
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Hot Fixes for Deep Lake [would highly appreciate expedited review]
* deeplake version was hardcoded and since deeplake upgraded the
integration fails with confusing error
* an additional integration test fixed due to embedding function
* Additionally fixed docs for code understanding links after docs
upgraded
* notebook removal of public parameter to make sure code understanding
notebook works
#### Who can review?
@hwchase17 @dev2049
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
skip building preview of docs for anything branch that doesn't start
with `__docs__`. will eventually update to look at code diff directories
but patching for now
Add oobabooga/text-generation-webui support as an LLM. Currently,
supports using text-generation-webui's non-streaming API interface.
Allows users who already have text-gen running to use the same models
with langchain.
#### Before submitting
Simple usage, similar to existing LLM supported:
```
from langchain.llms import TextGen
llm = TextGen(model_url = "http://localhost:5000")
```
#### Who can review?
@hwchase17 - project lead
---------
Co-authored-by: Hien Ngo <Hien.Ngo@adia.ae>
## DocArray as a Retriever
[DocArray](https://github.com/docarray/docarray) is an open-source tool
for managing your multi-modal data. It offers flexibility to store and
search through your data using various document index backends. This PR
introduces `DocArrayRetriever` - which works with any available backend
and serves as a retriever for Langchain apps.
Also, I added 2 notebooks:
DocArray Backends - intro to all 5 currently supported backends, how to
initialize, index, and use them as a retriever
DocArray Usage - showcasing what additional search parameters you can
pass to create versatile retrievers
Example:
```python
from docarray.index import InMemoryExactNNIndex
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.retrievers import DocArrayRetriever
# define document schema
class MyDoc(BaseDoc):
description: str
description_embedding: NdArray[1536]
embeddings = OpenAIEmbeddings()
# create documents
descriptions = ["description 1", "description 2"]
desc_embeddings = embeddings.embed_documents(texts=descriptions)
docs = DocList[MyDoc](
[
MyDoc(description=desc, description_embedding=embedding)
for desc, embedding in zip(descriptions, desc_embeddings)
]
)
# initialize document index with data
db = InMemoryExactNNIndex[MyDoc](docs)
# create a retriever
retriever = DocArrayRetriever(
index=db,
embeddings=embeddings,
search_field="description_embedding",
content_field="description",
)
# find the relevant document
doc = retriever.get_relevant_documents("action movies")
print(doc)
```
#### Who can review?
@dev2049
---------
Signed-off-by: jupyterjazz <saba.sturua@jina.ai>
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Fixes #
links to prompt templates and example selectors on the
[Prompts](https://python.langchain.com/docs/modules/model_io/prompts/)
page are invalid.
#### Before submitting
Just a small note that I tried to run `make docs_clean` and other
related commands before PR written
[here](https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md#build-documentation-locally),
it gives me an error:
```bash
langchain % make docs_clean
Traceback (most recent call last):
File "/Users/masafumi/Downloads/langchain/.venv/bin/make", line 5, in <module>
from scripts.proto import main
ModuleNotFoundError: No module named 'scripts'
make: *** [docs_clean] Error 1
# Poetry (version 1.5.1)
# Python 3.9.13
```
I couldn't figure out how to fix this, so I didn't run those command.
But links should work.
#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17
Similar issue #6323
Co-authored-by: masafumimori <m.masafumimori@outlook.com>
1. Changed the implementation of add_texts interface for the AwaDB
vector store in order to improve the performance
2. Upgrade the AwaDB from 0.3.2 to 0.3.3
---------
Co-authored-by: vincent <awadb.vincent@gmail.com>
**Short Description**
Added a new argument to AutoGPT class which allows to persist the chat
history to a file.
**Changes**
1. Removed the `self.full_message_history: List[BaseMessage] = []`
2. Replaced it with `chat_history_memory` which can take any subclasses
of `BaseChatMessageHistory`
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
adding new loader for [acreom](https://acreom.com) vaults. It's based on
the Obsidian loader with some additional text processing for acreom
specific markdown elements.
@eyurtsev please take a look!
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
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## Add Solidity programming language support for code splitter.
Twitter: @0xjord4n_
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This adds implementation of MMR search in pinecone; and I have two
semi-related observations about this vector store class:
- Maybe we should also have a
`similarity_search_by_vector_returning_embeddings` like in supabase, but
it's not in the base `VectorStore` class so I didn't implement
- Talking about the base class, there's
`similarity_search_with_relevance_scores`, but in pinecone it is called
`similarity_search_with_score`; maybe we should consider renaming it to
align with other `VectorStore` base and sub classes (or add that as an
alias for backward compatibility)
#### Who can review?
Tag maintainers/contributors who might be interested:
- VectorStores / Retrievers / Memory - @dev2049
# Introduces embaas document extraction api endpoints
In this PR, we add support for embaas document extraction endpoints to
Text Embedding Models (with LLMs, in different PRs coming). We currently
offer the MTEB leaderboard top performers, will continue to add top
embedding models and soon add support for customers to deploy thier own
models. Additional Documentation + Infomation can be found
[here](https://embaas.io).
While developing this integration, I closely followed the patterns
established by other langchain integrations. Nonetheless, if there are
any aspects that require adjustments or if there's a better way to
present a new integration, let me know! :)
Additionally, I fixed some docs in the embeddings integration.
Related PR: #5976
#### Who can review?
DataLoaders
- @eyurtsev
This creates a new kind of text splitter for markdown files.
The user can supply a set of headers that they want to split the file
on.
We define a new text splitter class, `MarkdownHeaderTextSplitter`, that
does a few things:
(1) For each line, it determines the associated set of user-specified
headers
(2) It groups lines with common headers into splits
See notebook for example usage and test cases.
#### What I do
Adding embedding api for
[DashScope](https://help.aliyun.com/product/610100.html), which is the
DAMO Academy's multilingual text unified vector model based on the LLM
base. It caters to multiple mainstream languages worldwide and offers
high-quality vector services, helping developers quickly transform text
data into high-quality vector data. Currently supported languages
include Chinese, English, Spanish, French, Portuguese, Indonesian, and
more.
#### Who can review?
Models
- @hwchase17
- @agola11
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Added description of LangChain Decorators ✨ into the integration section
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#### Before submitting
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Inspired by the filtering capability available in ChromaDB, added the
same functionality to the FAISS vectorestore as well. Since FAISS does
not have an inbuilt method of filtering used the approach suggested in
this [thread](https://github.com/facebookresearch/faiss/issues/1079)
Langchain Issue inspiration:
https://github.com/hwchase17/langchain/issues/4572
- [x] Added filtering capability to semantic similarly and MMR
- [x] Added test cases for filtering in
`tests/integration_tests/vectorstores/test_faiss.py`
#### Who can review?
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VectorStores / Retrievers / Memory
- @dev2049
- @hwchase17
HuggingFace -> Hugging Face
#### Before submitting
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#### Who can review?
This PR updates the Vectara integration (@hwchase17 ):
* Adds reuse of requests.session to imrpove efficiency and speed.
* Utilizes Vectara's low-level API (instead of standard API) to better
match user's specific chunking with LangChain
* Now add_texts puts all the texts into a single Vectara document so
indexing is much faster.
* updated variables names from alpha to lambda_val (to be consistent
with Vectara docs) and added n_context_sentence so it's available to use
if needed.
* Updates to documentation and tests
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Unstructured XML Loader
Adds an `UnstructuredXMLLoader` class for .xml files. Works with
unstructured>=0.6.7. A plain text representation of the text with the
XML tags will be available under the `page_content` attribute in the
doc.
### Testing
```python
from langchain.document_loaders import UnstructuredXMLLoader
loader = UnstructuredXMLLoader(
"example_data/factbook.xml",
)
docs = loader.load()
```
## Who can review?
@hwchase17
@eyurtsev
Added AwaDB vector store, which is a wrapper over the AwaDB, that can be
used as a vector storage and has an efficient similarity search. Added
integration tests for the vector store
Added jupyter notebook with the example
Delete a unneeded empty file and resolve the
conflict(https://github.com/hwchase17/langchain/pull/5886)
Please check, Thanks!
@dev2049
@hwchase17
---------
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---------
Co-authored-by: ljeagle <vincent_jieli@yeah.net>
Co-authored-by: vincent <awadb.vincent@gmail.com>
Based on the inspiration from the SQL chain, the following three
parameters are added to Graph Cypher Chain.
- top_k: Limited the number of results from the database to be used as
context
- return_direct: Return database results without transforming them to
natural language
- return_intermediate_steps: Return intermediate steps
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Fixed a simple typo on
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"One Retriever to merge them all, One Retriever to expose them, One
Retriever to bring them all and in and process them with Document
formatters."
Hi @dev2049! Here bothering people again!
I'm using this simple idea to deal with merging the output of several
retrievers into one.
I'm aware of DocumentCompressorPipeline and
ContextualCompressionRetriever but I don't think they allow us to do
something like this. Also I was getting in trouble to get the pipeline
working too. Please correct me if i'm wrong.
This allow to do some sort of "retrieval" preprocessing and then using
the retrieval with the curated results anywhere you could use a
retriever.
My use case is to generate diff indexes with diff embeddings and sources
for a more colorful results then filtering them with one or many
document formatters.
I saw some people looking for something like this, here:
https://github.com/hwchase17/langchain/issues/3991
and something similar here:
https://github.com/hwchase17/langchain/issues/5555
This is just a proposal I know I'm missing tests , etc. If you think
this is a worth it idea I can work on tests and anything you want to
change.
Let me know!
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR adds the possibility of specifying the endpoint URL to AWS in
the DynamoDBChatMessageHistory, so that it is possible to target not
only the AWS cloud services, but also a local installation.
Specifying the endpoint URL, which is normally not done when addressing
the cloud services, is very helpful when targeting a local instance
(like [Localstack](https://localstack.cloud/)) when running local tests.
Fixes#5835
#### Who can review?
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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#### Add start index to metadata in TextSplitter
- Modified method `create_documents` to track start position of each
chunk
- The `start_index` is included in the metadata if the `add_start_index`
parameter in the class constructor is set to `True`
This enables referencing back to the original document, particularly
useful when a specific chunk is retrieved.
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This PR adds a Baseten integration. I've done my best to follow the
contributor's guidelines and add docs, an example notebook, and an
integration test modeled after similar integrations' test.
Please let me know if there is anything I can do to improve the PR. When
it is merged, please tag https://twitter.com/basetenco and
https://twitter.com/philip_kiely as contributors (the note on the PR
template said to include Twitter accounts)
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---------
Co-authored-by: rlm <pexpresss31@gmail.com>
Fix the document page to open both search and Mendable when pressing
Ctrl+K.
I have changed the shortcut for Mendable to Ctrl+J.
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# Your PR Title (What it does)
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## Before submitting
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## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
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- Added `SingleStoreDB` vector store, which is a wrapper over the
SingleStore DB database, that can be used as a vector storage and has an
efficient similarity search.
- Added integration tests for the vector store
- Added jupyter notebook with the example
@dev2049
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Simply fixing a small typo in the memory page.
Also removed an extra code block at the end of the file.
Along the way, the current outputs seem to have changed in a few places
so left that for posterity, and updated the number of runs which seems
harmless, though I can clean that up if preferred.
This PR adds documentation for Shale Protocol's integration with
LangChain.
[Shale Protocol](https://shaleprotocol.com) provides forever-free
production-ready inference APIs to the open-source community. We have
global data centers and plan to support all major open LLMs (estimated
~1,000 by 2025).
The team consists of software and ML engineers, AI researchers,
designers, and operators across North America and Asia. Combined
together, the team has 50+ years experience in machine learning, cloud
infrastructure, software engineering and product development. Team
members have worked at places like Google and Microsoft.
#### Who can review?
Tag maintainers/contributors who might be interested:
- @hwchase17
- @agola11
---------
Co-authored-by: Karen Sheng <46656667+karensheng@users.noreply.github.com>
### Summary
Adds an `UnstructuredCSVLoader` for loading CSVs. One advantage of using
`UnstructuredCSVLoader` relative to the standard `CSVLoader` is that if
you use `UnstructuredCSVLoader` in `"elements"` mode, an HTML
representation of the table will be available in the metadata.
#### Who can review?
@hwchase17
@eyurtsev
Hi! I just added an example of how to use a custom scraping function
with the sitemap loader. I recently used this feature and had to dig in
the source code to find it. I thought it might be useful to other devs
to have an example in the Jupyter Notebook directly.
I only added the example to the documentation page.
@eyurtsev I was not able to run the lint. Please let me know if I have
to do anything else.
I know this is a very small contribution, but I hope it will be
valuable. My Twitter handle is @web3Dav3.
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---------
Co-authored-by: Yessen Kanapin <yessen@deepinfra.com>
just change "to" to "too" so it matches the above prompt
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Some links were broken from the previous merge. This PR fixes them.
Tested locally.
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Signed-off-by: Kourosh Hakhamaneshi <kourosh@anyscale.com>
This introduces the `YoutubeAudioLoader`, which will load blobs from a
YouTube url and write them. Blobs are then parsed by
`OpenAIWhisperParser()`, as show in this
[PR](https://github.com/hwchase17/langchain/pull/5580), but we extend
the parser to split audio such that each chuck meets the 25MB OpenAI
size limit. As shown in the notebook, this enables a very simple UX:
```
# Transcribe the video to text
loader = GenericLoader(YoutubeAudioLoader([url],save_dir),OpenAIWhisperParser())
docs = loader.load()
```
Tested on full set of Karpathy lecture videos:
```
# Karpathy lecture videos
urls = ["https://youtu.be/VMj-3S1tku0"
"https://youtu.be/PaCmpygFfXo",
"https://youtu.be/TCH_1BHY58I",
"https://youtu.be/P6sfmUTpUmc",
"https://youtu.be/q8SA3rM6ckI",
"https://youtu.be/t3YJ5hKiMQ0",
"https://youtu.be/kCc8FmEb1nY"]
# Directory to save audio files
save_dir = "~/Downloads/YouTube"
# Transcribe the videos to text
loader = GenericLoader(YoutubeAudioLoader(urls,save_dir),OpenAIWhisperParser())
docs = loader.load()
```
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In the [Databricks
integration](https://python.langchain.com/en/latest/integrations/databricks.html)
and [Databricks
LLM](https://python.langchain.com/en/latest/modules/models/llms/integrations/databricks.html),
we suggestted users to set the ENV variable `DATABRICKS_API_TOKEN`.
However, this is inconsistent with the other Databricks library. To make
it consistent, this PR changes the variable from `DATABRICKS_API_TOKEN`
to `DATABRICKS_TOKEN`
After changes, there is no more `DATABRICKS_API_TOKEN` in the doc
```
$ git grep DATABRICKS_API_TOKEN|wc -l
0
$ git grep DATABRICKS_TOKEN|wc -l
8
```
cc @hwchase17 @dev2049 @mengxr since you have reviewed the previous PRs.
# Scores in Vectorestores' Docs Are Explained
Following vectorestores can return scores with similar documents by
using `similarity_search_with_score`:
- chroma
- docarray_hnsw
- docarray_in_memory
- faiss
- myscale
- qdrant
- supabase
- vectara
- weaviate
However, in documents, these scores were either not explained at all or
explained in a way that could lead to misunderstandings (e.g., FAISS).
For instance in FAISS document: if we consider the score returned by the
function as a similarity score, we understand that a document returning
a higher score is more similar to the source document. However, since
the scores returned by the function are distance scores, we should
understand that smaller scores correspond to more similar documents.
For the libraries other than Vectara, I wrote the scores they use by
investigating from the source libraries. Since I couldn't be certain
about the score metric used by Vectara, I didn't make any changes in its
documentation. The links mentioned in Vectara's documentation became
broken due to updates, so I replaced them with working ones.
VectorStores / Retrievers / Memory
- @dev2049
my twitter: [berkedilekoglu](https://twitter.com/berkedilekoglu)
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Added an overview of LangChain modules
Aimed at introducing newcomers to LangChain's main modules :)
Twitter handle is @edrick_dch
## Who can review?
@eyurtsev
Aviary is an open source toolkit for evaluating and deploying open
source LLMs. You can find out more about it on
[http://github.com/ray-project/aviary). You can try it out at
[http://aviary.anyscale.com](aviary.anyscale.com).
This code adds support for Aviary in LangChain. To minimize
dependencies, it connects directly to the HTTP endpoint.
The current implementation is not accelerated and uses the default
implementation of `predict` and `generate`.
It includes a test and a simple example.
@hwchase17 and @agola11 could you have a look at this?
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# OpenAIWhisperParser
This PR creates a new parser, `OpenAIWhisperParser`, that uses the
[OpenAI Whisper
model](https://platform.openai.com/docs/guides/speech-to-text/quickstart)
to perform transcription of audio files to text (`Documents`). Please
see the notebook for usage.
# Token text splitter for sentence transformers
The current TokenTextSplitter only works with OpenAi models via the
`tiktoken` package. This is not clear from the name `TokenTextSplitter`.
In this (first PR) a token based text splitter for sentence transformer
models is added. In the future I think we should work towards injecting
a tokenizer into the TokenTextSplitter to make ti more flexible.
Could perhaps be reviewed by @dev2049
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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Fixes#5638. Retitles "Amazon Bedrock" page to "Bedrock" so that the
Integrations section of the left nav is properly sorted in alphabetical
order.
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# like
[StdoutCallbackHandler](https://github.com/hwchase17/langchain/blob/master/langchain/callbacks/stdout.py),
but writes to a file
When running experiments I have found myself wanting to log the outputs
of my chains in a more lightweight way than using WandB tracing. This PR
contributes a callback handler that writes to file what
`StdoutCallbackHandler` would print.
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## Example Notebook
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See the included `filecallbackhandler.ipynb` notebook for usage. Would
it be better to include this notebook under `modules/callbacks` or under
`integrations/`?
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@agola11
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Created fix for 5475
Currently in PGvector, we do not have any function that returns the
instance of an existing store. The from_documents always adds embeddings
and then returns the store. This fix is to add a function that will
return the instance of an existing store
Also changed the jupyter example for PGVector to show the example of
using the function
<!-- Remove if not applicable -->
Fixes # 5475
#### Before submitting
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#### Who can review?
@dev2049
@hwchase17
Tag maintainers/contributors who might be interested:
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---------
Co-authored-by: rajib76 <rajib76@yahoo.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
This PR corrects a minor typo in the Momento chat message history
notebook and also expands the title from "Momento" to "Momento Chat
History", inline with other chat history storage providers.
#### Before submitting
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#### Who can review?
cc @dev2049 who reviewed the original integration
# Your PR Title (What it does)
Fixes the pgvector python example notebook : one of the variables was
not referencing anything
## Before submitting
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
VectorStores / Retrievers / Memory
- @dev2049
# Make FinalStreamingStdOutCallbackHandler more robust by ignoring new
lines & white spaces
`FinalStreamingStdOutCallbackHandler` doesn't work out of the box with
`ChatOpenAI`, as it tokenized slightly differently than `OpenAI`. The
response of `OpenAI` contains the tokens `["\nFinal", " Answer", ":"]`
while `ChatOpenAI` contains `["Final", " Answer", ":"]`.
This PR make `FinalStreamingStdOutCallbackHandler` more robust by
ignoring new lines & white spaces when determining if the answer prefix
has been reached.
Fixes#5433
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
Tracing / Callbacks
- @agola11
Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord:
RicChilligerDude#7589
# Implements support for Personal Access Token Authentication in the
ConfluenceLoader
Fixes#5191
Implements a new optional parameter for the ConfluenceLoader: `token`.
This allows the use of personal access authentication when using the
on-prem server version of Confluence.
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@eyurtsev @Jflick58
Twitter Handle: felipe_yyc
---------
Co-authored-by: Felipe <feferreira@ea.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# minor refactor of GenerativeAgentMemory
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<!-- Remove if not applicable -->
- refactor `format_memories_detail` to be more reusable
- modified prompts for getting topics for reflection and for generating
insights
- update `characters.ipynb` to reflect changes
## Before submitting
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@hwchase17
@dev2049
# docs: modules pages simplified
Fixied #5627 issue
Merged several repetitive sections in the `modules` pages. Some texts,
that were hard to understand, were also simplified.
## Who can review?
@hwchase17
@dev2049
# Fixed multi input prompt for MapReduceChain
Added `kwargs` support for inner chains of `MapReduceChain` via
`from_params` method
Currently the `from_method` method of intialising `MapReduceChain` chain
doesn't work if prompt has multiple inputs. It happens because it uses
`StuffDocumentsChain` and `MapReduceDocumentsChain` underneath, both of
them require specifying `document_variable_name` if `prompt` of their
`llm_chain` has more than one `input`.
With this PR, I have added support for passing their respective `kwargs`
via the `from_params` method.
## Fixes https://github.com/hwchase17/langchain/issues/4752
## Who can review?
@dev2049 @hwchase17 @agola11
---------
Co-authored-by: imeckr <chandanroutray2012@gmail.com>
# Unstructured Excel Loader
Adds an `UnstructuredExcelLoader` class for `.xlsx` and `.xls` files.
Works with `unstructured>=0.6.7`. A plain text representation of the
Excel file will be available under the `page_content` attribute in the
doc. If you use the loader in `"elements"` mode, an HTML representation
of the Excel file will be available under the `text_as_html` metadata
key. Each sheet in the Excel document is its own document.
### Testing
```python
from langchain.document_loaders import UnstructuredExcelLoader
loader = UnstructuredExcelLoader(
"example_data/stanley-cups.xlsx",
mode="elements"
)
docs = loader.load()
```
## Who can review?
@hwchase17
@eyurtsev
Co-authored-by: Alvaro Bartolome <alvarobartt@gmail.com>
Co-authored-by: Daniel Vila Suero <daniel@argilla.io>
Co-authored-by: Tom Aarsen <37621491+tomaarsen@users.noreply.github.com>
Co-authored-by: Tom Aarsen <Cubiegamedev@gmail.com>
# Create elastic_vector_search.ElasticKnnSearch class
This extends `langchain/vectorstores/elastic_vector_search.py` by adding
a new class `ElasticKnnSearch`
Features:
- Allow creating an index with the `dense_vector` mapping compataible
with kNN search
- Store embeddings in index for use with kNN search (correct mapping
creates HNSW data structure)
- Perform approximate kNN search
- Perform hybrid BM25 (`query{}`) + kNN (`knn{}`) search
- perform knn search by either providing a `query_vector` or passing a
hosted `model_id` to use query_vector_builder to automatically generate
a query_vector at search time
Connection options
- Using `cloud_id` from Elastic Cloud
- Passing elasticsearch client object
search options
- query
- k
- query_vector
- model_id
- size
- source
- knn_boost (hybrid search)
- query_boost (hybrid search)
- fields
This also adds examples to
`docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb`
Fixes # [5346](https://github.com/hwchase17/langchain/issues/5346)
cc: @dev2049
-->
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Lint sphinx documentation and fix broken links
This PR lints multiple warnings shown in generation of the project
documentation (using "make docs_linkcheck" and "make docs_build").
Additionally documentation internal links to (now?) non-existent files
are modified to point to existing documents as it seemed the new correct
target.
The documentation is not updated content wise.
There are no source code changes.
Fixes # (issue)
- broken documentation links to other files within the project
- sphinx formatting (linting)
## Before submitting
No source code changes, so no new tests added.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# docs: `ecosystem_integrations` update 3
Next cycle of updating the `ecosystem/integrations`
* Added an integration `template` file
* Added missed integration files
* Fixed several document_loaders/notebooks
## Who can review?
Is it possible to assign somebody to review PRs on docs? Thanks.
# Fix wrong class instantiation in docs MMR example
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When looking at the Maximal Marginal Relevance ExampleSelector example
at
https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/mmr.html,
I noticed that there seems to be an error. Initially, the
`MaxMarginalRelevanceExampleSelector` class is used as an
`example_selector` argument to the `FewShotPromptTemplate` class. Then,
according to the text, a comparison is made to regular similarity
search. However, the `FewShotPromptTemplate` still uses the
`MaxMarginalRelevanceExampleSelector` class, so the output is the same.
To fix it, I added an instantiation of the
`SemanticSimilarityExampleSelector` class, because this seems to be what
is intended.
## Who can review?
@hwchase17
# Update Unstructured docs to remove the `detectron2` install
instructions
Removes `detectron2` installation instructions from the Unstructured
docs because installing `detectron2` is no longer required for
`unstructured>=0.7.0`. The `detectron2` model now runs using the ONNX
runtime.
## Who can review?
@hwchase17
@eyurtsev
# Add Managed Motorhead
This change enabled MotorheadMemory to utilize Metal's managed version
of Motorhead. We can easily enable this by passing in a `api_key` and
`client_id` in order to hit the managed url and access the memory api on
Metal.
Twitter: [@softboyjimbo](https://twitter.com/softboyjimbo)
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@dev2049 @hwchase17
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# added DeepLearing.AI course link
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
not @hwchase17 - hehe
# Bedrock LLM and Embeddings
This PR adds a new LLM and an Embeddings class for the
[Bedrock](https://aws.amazon.com/bedrock) service. The PR also includes
example notebooks for using the LLM class in a conversation chain and
embeddings usage in creating an embedding for a query and document.
**Note**: AWS is doing a private release of the Bedrock service on
05/31/2023; users need to request access and added to an allowlist in
order to start using the Bedrock models and embeddings. Please use the
[Bedrock Home Page](https://aws.amazon.com/bedrock) to request access
and to learn more about the models available in Bedrock.
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# Support Qdrant filters
Qdrant has an [extensive filtering
system](https://qdrant.tech/documentation/concepts/filtering/) with rich
type support. This PR makes it possible to use the filters in Langchain
by passing an additional param to both the
`similarity_search_with_score` and `similarity_search` methods.
## Who can review?
@dev2049 @hwchase17
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# SQLite-backed Entity Memory
Following the initiative of
https://github.com/hwchase17/langchain/pull/2397 I think it would be
helpful to be able to persist Entity Memory on disk by default
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
This PR adds a new method `from_es_connection` to the
`ElasticsearchEmbeddings` class allowing users to use Elasticsearch
clusters outside of Elastic Cloud.
Users can create an Elasticsearch Client object and pass that to the new
function.
The returned object is identical to the one returned by calling
`from_credentials`
```
# Create Elasticsearch connection
es_connection = Elasticsearch(
hosts=['https://es_cluster_url:port'],
basic_auth=('user', 'password')
)
# Instantiate ElasticsearchEmbeddings using es_connection
embeddings = ElasticsearchEmbeddings.from_es_connection(
model_id,
es_connection,
)
```
I also added examples to the elasticsearch jupyter notebook
Fixes # https://github.com/hwchase17/langchain/issues/5239
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
As the title says, I added more code splitters.
The implementation is trivial, so i don't add separate tests for each
splitter.
Let me know if any concerns.
Fixes # (issue)
https://github.com/hwchase17/langchain/issues/5170
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@eyurtsev @hwchase17
---------
Signed-off-by: byhsu <byhsu@linkedin.com>
Co-authored-by: byhsu <byhsu@linkedin.com>
# Creates GitHubLoader (#5257)
GitHubLoader is a DocumentLoader that loads issues and PRs from GitHub.
Fixes#5257
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Added New Trello loader class and documentation
Simple Loader on top of py-trello wrapper.
With a board name you can pull cards and to do some field parameter
tweaks on load operation.
I included documentation and examples.
Included unit test cases using patch and a fixture for py-trello client
class.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add ToolException that a tool can throw
This is an optional exception that tool throws when execution error
occurs.
When this exception is thrown, the agent will not stop working,but will
handle the exception according to the handle_tool_error variable of the
tool,and the processing result will be returned to the agent as
observation,and printed in pink on the console.It can be used like this:
```python
from langchain.schema import ToolException
from langchain import LLMMathChain, SerpAPIWrapper, OpenAI
from langchain.agents import AgentType, initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.tools import BaseTool, StructuredTool, Tool, tool
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(temperature=0)
llm_math_chain = LLMMathChain(llm=llm, verbose=True)
class Error_tool:
def run(self, s: str):
raise ToolException('The current search tool is not available.')
def handle_tool_error(error) -> str:
return "The following errors occurred during tool execution:"+str(error)
search_tool1 = Error_tool()
search_tool2 = SerpAPIWrapper()
tools = [
Tool.from_function(
func=search_tool1.run,
name="Search_tool1",
description="useful for when you need to answer questions about current events.You should give priority to using it.",
handle_tool_error=handle_tool_error,
),
Tool.from_function(
func=search_tool2.run,
name="Search_tool2",
description="useful for when you need to answer questions about current events",
return_direct=True,
)
]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,
handle_tool_errors=handle_tool_error)
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
```
## Who can review?
- @vowelparrot
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# docs: ecosystem/integrations update
It is the first in a series of `ecosystem/integrations` updates.
The ecosystem/integrations list is missing many integrations.
I'm adding the missing integrations in a consistent format:
1. description of the integrated system
2. `Installation and Setup` section with 'pip install ...`, Key setup,
and other necessary settings
3. Sections like `LLM`, `Text Embedding Models`, `Chat Models`... with
links to correspondent examples and imports of the used classes.
This PR keeps new docs, that are presented in the
`docs/modules/models/text_embedding/examples` but missed in the
`ecosystem/integrations`. The next PRs will cover the next example
sections.
Also updated `integrations.rst`: added the `Dependencies` section with a
link to the packages used in LangChain.
## Who can review?
@hwchase17
@eyurtsev
@dev2049
# docs: ecosystem/integrations update 2
#5219 - part 1
The second part of this update (parts are independent of each other! no
overlap):
- added diffbot.md
- updated confluence.ipynb; added confluence.md
- updated college_confidential.md
- updated openai.md
- added blackboard.md
- added bilibili.md
- added azure_blob_storage.md
- added azlyrics.md
- added aws_s3.md
## Who can review?
@hwchase17@agola11
@agola11
@vowelparrot
@dev2049
# Update llamacpp demonstration notebook
Add instructions to install with BLAS backend, and update the example of
model usage.
Fixes#5071. However, it is more like a prevention of similar issues in
the future, not a fix, since there was no problem in the framework
functionality
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
- @hwchase17
- @agola11
# Fix for `update_document` Function in Chroma
## Summary
This pull request addresses an issue with the `update_document` function
in the Chroma class, as described in
[#5031](https://github.com/hwchase17/langchain/issues/5031#issuecomment-1562577947).
The issue was identified as an `AttributeError` raised when calling
`update_document` due to a missing corresponding method in the
`Collection` object. This fix refactors the `update_document` method in
`Chroma` to correctly interact with the `Collection` object.
## Changes
1. Fixed the `update_document` method in the `Chroma` class to correctly
call methods on the `Collection` object.
2. Added the corresponding test `test_chroma_update_document` in
`tests/integration_tests/vectorstores/test_chroma.py` to reflect the
updated method call.
3. Added an example and explanation of how to use the `update_document`
function in the Jupyter notebook tutorial for Chroma.
## Test Plan
All existing tests pass after this change. In addition, the
`test_chroma_update_document` test case now correctly checks the
functionality of `update_document`, ensuring that the function works as
expected and updates the content of documents correctly.
## Reviewers
@dev2049
This fix will ensure that users are able to use the `update_document`
function as expected, without encountering the previous
`AttributeError`. This will enhance the usability and reliability of the
Chroma class for all users.
Thank you for considering this pull request. I look forward to your
feedback and suggestions.
# Add SKLearnVectorStore
This PR adds SKLearnVectorStore, a simply vector store based on
NearestNeighbors implementations in the scikit-learn package. This
provides a simple drop-in vector store implementation with minimal
dependencies (scikit-learn is typically installed in a data scientist /
ml engineer environment). The vector store can be persisted and loaded
from json, bson and parquet format.
SKLearnVectorStore has soft (dynamic) dependency on the scikit-learn,
numpy and pandas packages. Persisting to bson requires the bson package,
persisting to parquet requires the pyarrow package.
## Before submitting
Integration tests are provided under
`tests/integration_tests/vectorstores/test_sklearn.py`
Sample usage notebook is provided under
`docs/modules/indexes/vectorstores/examples/sklear.ipynb`
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Sample Notebook for DynamoDB Chat Message History
@dev2049
Adding a sample notebook for the DynamoDB Chat Message History class.
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# Add Chainlit to deployment options
Add [Chainlit](https://github.com/Chainlit/chainlit) as deployment
options
Used links to Github examples and Chainlit doc on the LangChain
integration
Co-authored-by: Dan Constantini <danconstantini@Dan-Constantini-MacBook.local>
# docs: improve flow of llm caching notebook
The notebook `llm_caching` demos various caching providers. In the
previous version, there was setup common to all examples but under the
`In Memory Caching` heading.
If a user comes and only wants to try a particular example, they will
run the common setup, then the cells for the specific provider they are
interested in. Then they will get import and variable reference errors.
This commit moves the common setup to the top to avoid this.
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@dev2049
# Better docs for weaviate hybrid search
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Fixes: NA
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
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- @hwchase17
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- @dev2049
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@dev2049
This PR adds LLM wrapper for Databricks. It supports two endpoint types:
* serving endpoint
* cluster driver proxy app
An integration notebook is included to show how it works.
Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Gengliang Wang <gengliang@apache.org>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Fixed typo: 'ouput' to 'output' in all documentation
In this instance, the typo 'ouput' was amended to 'output' in all
occurrences within the documentation. There are no dependencies required
for this change.
# Add Momento as a standard cache and chat message history provider
This PR adds Momento as a standard caching provider. Implements the
interface, adds integration tests, and documentation. We also add
Momento as a chat history message provider along with integration tests,
and documentation.
[Momento](https://www.gomomento.com/) is a fully serverless cache.
Similar to S3 or DynamoDB, it requires zero configuration,
infrastructure management, and is instantly available. Users sign up for
free and get 50GB of data in/out for free every month.
## Before submitting
✅ We have added documentation, notebooks, and integration tests
demonstrating usage.
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Add Multi-CSV/DF support in CSV and DataFrame Toolkits
* CSV and DataFrame toolkits now accept list of CSVs/DFs
* Add default prompts for many dataframes in `pandas_dataframe` toolkit
Fixes#1958
Potentially fixes#4423
## Testing
* Add single and multi-dataframe integration tests for
`pandas_dataframe` toolkit with permutations of `include_df_in_prompt`
* Add single and multi-CSV integration tests for csv toolkit
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Add C Transformers for GGML Models
I created Python bindings for the GGML models:
https://github.com/marella/ctransformers
Currently it supports GPT-2, GPT-J, GPT-NeoX, LLaMA, MPT, etc. See
[Supported
Models](https://github.com/marella/ctransformers#supported-models).
It provides a unified interface for all models:
```python
from langchain.llms import CTransformers
llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2')
print(llm('AI is going to'))
```
It can be used with models hosted on the Hugging Face Hub:
```py
llm = CTransformers(model='marella/gpt-2-ggml')
```
It supports streaming:
```py
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = CTransformers(model='marella/gpt-2-ggml', callbacks=[StreamingStdOutCallbackHandler()])
```
Please see [README](https://github.com/marella/ctransformers#readme) for
more details.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
zep-python's sync methods no longer need an asyncio wrapper. This was
causing issues with FastAPI deployment.
Zep also now supports putting and getting of arbitrary message metadata.
Bump zep-python version to v0.30
Remove nest-asyncio from Zep example notebooks.
Modify tests to include metadata.
---------
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
Co-authored-by: Daniel Chalef <131175+danielchalef@users.noreply.github.com>
For most queries it's the `size` parameter that determines final number
of documents to return. Since our abstractions refer to this as `k`, set
this to be `k` everywhere instead of expecting a separate param. Would
be great to have someone more familiar with OpenSearch validate that
this is reasonable (e.g. that having `size` and what OpenSearch calls
`k` be the same won't lead to any strange behavior). cc @naveentatikonda
Closes#5212
# Resolve error in StructuredOutputParser docs
Documentation for `StructuredOutputParser` currently not reproducible,
that is, `output_parser.parse(output)` raises an error because the LLM
returns a response with an invalid format
```python
_input = prompt.format_prompt(question="what's the capital of france")
output = model(_input.to_string())
output
# ?
#
# ```json
# {
# "answer": "Paris",
# "source": "https://www.worldatlas.com/articles/what-is-the-capital-of-france.html"
# }
# ```
```
Was fixed by adding a question mark to the prompt
# Add QnA with sources example
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Fixes: see
https://stackoverflow.com/questions/76207160/langchain-doesnt-work-with-weaviate-vector-database-getting-valueerror/76210017#76210017
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
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- @agola11
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- @eyurtsev
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- @hwchase17
- @agola11
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- @vowelparrot
VectorStores / Retrievers / Memory
- @dev2049
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@dev2049
# Bibtex integration
Wrap bibtexparser to retrieve a list of docs from a bibtex file.
* Get the metadata from the bibtex entries
* `page_content` get from the local pdf referenced in the `file` field
of the bibtex entry using `pymupdf`
* If no valid pdf file, `page_content` set to the `abstract` field of
the bibtex entry
* Support Zotero flavour using regex to get the file path
* Added usage example in
`docs/modules/indexes/document_loaders/examples/bibtex.ipynb`
---------
Co-authored-by: Sébastien M. Popoff <sebastien.popoff@espci.fr>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
I found an API key for `serpapi_api_key` while reading the docs. It
seems to have been modified very recently. Removed it in this PR
@hwchase17 - project lead
# fix a mistake in concepts.md
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
# Add Joplin document loader
[Joplin](https://joplinapp.org/) is an open source note-taking app.
Joplin has a [REST API](https://joplinapp.org/api/references/rest_api/)
for accessing its local database. The proposed `JoplinLoader` uses the
API to retrieve all notes in the database and their metadata. Joplin
needs to be installed and running locally, and an access token is
required.
- The PR includes an integration test.
- The PR includes an example notebook.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Beam
Calls the Beam API wrapper to deploy and make subsequent calls to an
instance of the gpt2 LLM in a cloud deployment. Requires installation of
the Beam library and registration of Beam Client ID and Client Secret.
Additional calls can then be made through the instance of the large
language model in your code or by calling the Beam API.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Vectara Integration
This PR provides integration with Vectara. Implemented here are:
* langchain/vectorstore/vectara.py
* tests/integration_tests/vectorstores/test_vectara.py
* langchain/retrievers/vectara_retriever.py
And two IPYNB notebooks to do more testing:
* docs/modules/chains/index_examples/vectara_text_generation.ipynb
* docs/modules/indexes/vectorstores/examples/vectara.ipynb
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# DOCS added missed document_loader examples
Added missed examples: `JSON`, `Open Document Format (ODT)`,
`Wikipedia`, `tomarkdown`.
Updated them to a consistent format.
## Who can review?
@hwchase17
@dev2049
# Clarification of the reference to the "get_text_legth" function in
getting_started.md
Reference to the function "get_text_legth" in the documentation did not
make sense. Comment added for clarification.
@hwchase17
# Docs: updated getting_started.md
Just accommodating some unnecessary spaces in the example of "pass few
shot examples to a prompt template".
@vowelparrot
# Add MosaicML inference endpoints
This PR adds support in langchain for MosaicML inference endpoints. We
both serve a select few open source models, and allow customers to
deploy their own models using our inference service. Docs are here
(https://docs.mosaicml.com/en/latest/inference.html), and sign up form
is here (https://forms.mosaicml.com/demo?utm_source=langchain). I'm not
intimately familiar with the details of langchain, or the contribution
process, so please let me know if there is anything that needs fixing or
this is the wrong way to submit a new integration, thanks!
I'm also not sure what the procedure is for integration tests. I have
tested locally with my api key.
## Who can review?
@hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR introduces a new module, `elasticsearch_embeddings.py`, which
provides a wrapper around Elasticsearch embedding models. The new
ElasticsearchEmbeddings class allows users to generate embeddings for
documents and query texts using a [model deployed in an Elasticsearch
cluster](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-model-ref.html#ml-nlp-model-ref-text-embedding).
### Main features:
1. The ElasticsearchEmbeddings class initializes with an Elasticsearch
connection object and a model_id, providing an interface to interact
with the Elasticsearch ML client through
[infer_trained_model](https://elasticsearch-py.readthedocs.io/en/v8.7.0/api.html?highlight=trained%20model%20infer#elasticsearch.client.MlClient.infer_trained_model)
.
2. The `embed_documents()` method generates embeddings for a list of
documents, and the `embed_query()` method generates an embedding for a
single query text.
3. The class supports custom input text field names in case the deployed
model expects a different field name than the default `text_field`.
4. The implementation is compatible with any model deployed in
Elasticsearch that generates embeddings as output.
### Benefits:
1. Simplifies the process of generating embeddings using Elasticsearch
models.
2. Provides a clean and intuitive interface to interact with the
Elasticsearch ML client.
3. Allows users to easily integrate Elasticsearch-generated embeddings.
Related issue https://github.com/hwchase17/langchain/issues/3400
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Fix typo + add wikipedia package installation part in
human_input_llm.ipynb
This PR
1. Fixes typo ("the the human input LLM"),
2. Addes wikipedia package installation part (in accordance with
`WikipediaQueryRun`
[documentation](https://python.langchain.com/en/latest/modules/agents/tools/examples/wikipedia.html))
in `human_input_llm.ipynb`
(`docs/modules/models/llms/examples/human_input_llm.ipynb`)
# Add link to Psychic from document loaders documentation page
In my previous PR I forgot to update `document_loaders.rst` to link to
`psychic.ipynb` to make it discoverable from the main documentation.
# Add AzureCognitiveServicesToolkit to call Azure Cognitive Services
API: achieve some multimodal capabilities
This PR adds a toolkit named AzureCognitiveServicesToolkit which bundles
the following tools:
- AzureCogsImageAnalysisTool: calls Azure Cognitive Services image
analysis API to extract caption, objects, tags, and text from images.
- AzureCogsFormRecognizerTool: calls Azure Cognitive Services form
recognizer API to extract text, tables, and key-value pairs from
documents.
- AzureCogsSpeech2TextTool: calls Azure Cognitive Services speech to
text API to transcribe speech to text.
- AzureCogsText2SpeechTool: calls Azure Cognitive Services text to
speech API to synthesize text to speech.
This toolkit can be used to process image, document, and audio inputs.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add a WhyLabs callback handler
* Adds a simple WhyLabsCallbackHandler
* Add required dependencies as optional
* protect against missing modules with imports
* Add docs/ecosystem basic example
based on initial prototype from @andrewelizondo
> this integration gathers privacy preserving telemetry on text with
whylogs and sends stastical profiles to WhyLabs platform to monitoring
these metrics over time. For more information on what WhyLabs is see:
https://whylabs.ai
After you run the notebook (if you have env variables set for the API
Keys, org_id and dataset_id) you get something like this in WhyLabs:
Co-authored-by: Andre Elizondo <andre@whylabs.ai>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
OpenLM is a zero-dependency OpenAI-compatible LLM provider that can call
different inference endpoints directly via HTTP. It implements the
OpenAI Completion class so that it can be used as a drop-in replacement
for the OpenAI API. This changeset utilizes BaseOpenAI for minimal added
code.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add Mastodon toots loader.
Loader works either with public toots, or Mastodon app credentials. Toot
text and user info is loaded.
I've also added integration test for this new loader as it works with
public data, and a notebook with example output run now.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Improve pinecone hybrid search retriever adding metadata support
I simply remove the hardwiring of metadata to the existing
implementation allowing one to pass `metadatas` attribute to the
constructors and in `get_relevant_documents`. I also add one missing pip
install to the accompanying notebook (I am not adding dependencies, they
were pre-existing).
First contribution, just hoping to help, feel free to critique :)
my twitter username is `@andreliebschner`
While looking at hybrid search I noticed #3043 and #1743. I think the
former can be closed as following the example right now (even prior to
my improvements) works just fine, the latter I think can be also closed
safely, maybe pointing out the relevant classes and example. Should I
reply those issues mentioning someone?
@dev2049, @hwchase17
---------
Co-authored-by: Andreas Liebschner <a.liebschner@shopfully.com>
# docs: `deployments` page moved into `ecosystem/`
The `Deployments` page moved into the `Ecosystem/` group
Small fixes:
- `index` page: fixed order of items in the `Modules` list, in the `Use
Cases` list
- item `References/Installation` was lost in the `index` page (not on
the Navbar!). Restored it.
- added `|` marker in several places.
NOTE: I also thought about moving the `Additional Resources/Gallery`
page into the `Ecosystem` group but decided to leave it unchanged.
Please, advise on this.
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@dev2049
### Submit Multiple Files to the Unstructured API
Enables batching multiple files into a single Unstructured API requests.
Support for requests with multiple files was added to both
`UnstructuredAPIFileLoader` and `UnstructuredAPIFileIOLoader`. Note that
if you submit multiple files in "single" mode, the result will be
concatenated into a single document. We recommend using this feature in
"elements" mode.
### Testing
The following should load both documents, using two of the example docs
from the integration tests folder.
```python
from langchain.document_loaders import UnstructuredAPIFileLoader
file_paths = ["examples/layout-parser-paper.pdf", "examples/whatsapp_chat.txt"]
loader = UnstructuredAPIFileLoader(
file_paths=file_paths,
api_key="FAKE_API_KEY",
strategy="fast",
mode="elements",
)
docs = loader.load()
```
# Corrected Misspelling in agents.rst Documentation
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In the
[documentation](https://python.langchain.com/en/latest/modules/agents.html)
it says "in fact, it is often best to have an Action Agent be in
**change** of the execution for the Plan and Execute agent."
**Suggested Change:** I propose correcting change to charge.
Fix for issue: #5039
# Add documentation for Databricks integration
This is a follow-up of https://github.com/hwchase17/langchain/pull/4702
It documents the details of how to integrate Databricks using langchain.
It also provides examples in a notebook.
## Who can review?
@dev2049 @hwchase17 since you are aware of the context. We will promote
the integration after this doc is ready. Thanks in advance!
# Fixes an annoying typo in docs
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Fixes Annoying typo in docs - "Therefor" -> "Therefore". It's so
annoying to read that I just had to make this PR.
# Streaming only final output of agent (#2483)
As requested in issue #2483, this Callback allows to stream only the
final output of an agent (ie not the intermediate steps).
Fixes#2483
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add self query translator for weaviate vectorstore
Adds support for the EQ comparator and the AND/OR operators.
Co-authored-by: Dominic Chan <dchan@cppib.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
- Higher accuracy on the responses
- New redesigned UI
- Pretty Sources: display the sources by title / sub-section instead of
long URL.
- Fixed Reset Button bugs and some other UI issues
- Other tweaks
# Improve Evernote Document Loader
When exporting from Evernote you may export more than one note.
Currently the Evernote loader concatenates the content of all notes in
the export into a single document and only attaches the name of the
export file as metadata on the document.
This change ensures that each note is loaded as an independent document
and all available metadata on the note e.g. author, title, created,
updated are added as metadata on each document.
It also uses an existing optional dependency of `html2text` instead of
`pypandoc` to remove the need to download the pandoc application via
`download_pandoc()` to be able to use the `pypandoc` python bindings.
Fixes#4493
Co-authored-by: Mike McGarry <mike.mcgarry@finbourne.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Remove autoreload in examples
Remove the `autoreload` in examples since it is not necessary for most
users:
```
%load_ext autoreload,
%autoreload 2
```
# Powerbi API wrapper bug fix + integration tests
- Bug fix by removing `TYPE_CHECKING` in in utilities/powerbi.py
- Added integration test for power bi api in
utilities/test_powerbi_api.py
- Added integration test for power bi agent in
agent/test_powerbi_agent.py
- Edited .env.examples to help set up power bi related environment
variables
- Updated demo notebook with working code in
docs../examples/powerbi.ipynb - AzureOpenAI -> ChatOpenAI
Notes:
Chat models (gpt3.5, gpt4) are much more capable than davinci at writing
DAX queries, so that is important to getting the agent to work properly.
Interestingly, gpt3.5-turbo needed the examples=DEFAULT_FEWSHOT_EXAMPLES
to write consistent DAX queries, so gpt4 seems necessary as the smart
llm.
Fixes#4325
## Before submitting
Azure-core and Azure-identity are necessary dependencies
check integration tests with the following:
`pytest tests/integration_tests/utilities/test_powerbi_api.py`
`pytest tests/integration_tests/agent/test_powerbi_agent.py`
You will need a power bi account with a dataset id + table name in order
to test. See .env.examples for details.
## Who can review?
@hwchase17
@vowelparrot
---------
Co-authored-by: aditya-pethe <adityapethe1@gmail.com>
# Added a YouTube Tutorial
Added a LangChain tutorial playlist aimed at onboarding newcomers to
LangChain and its use cases.
I've shared the video in the #tutorials channel and it seemed to be well
received. I think this could be useful to the greater community.
## Who can review?
@dev2049
This PR adds support for Databricks runtime and Databricks SQL by using
[Databricks SQL Connector for
Python](https://docs.databricks.com/dev-tools/python-sql-connector.html).
As a cloud data platform, accessing Databricks requires a URL as follows
`databricks://token:{api_token}@{hostname}?http_path={http_path}&catalog={catalog}&schema={schema}`.
**The URL is **complicated** and it may take users a while to figure it
out**. Since the fields `api_token`/`hostname`/`http_path` fields are
known in the Databricks notebook, I am proposing a new method
`from_databricks` to simplify the connection to Databricks.
## In Databricks Notebook
After changes, Databricks users only need to specify the `catalog` and
`schema` field when using langchain.
<img width="881" alt="image"
src="https://github.com/hwchase17/langchain/assets/1097932/984b4c57-4c2d-489d-b060-5f4918ef2f37">
## In Jupyter Notebook
The method can be used on the local setup as well:
<img width="678" alt="image"
src="https://github.com/hwchase17/langchain/assets/1097932/142e8805-a6ef-4919-b28e-9796ca31ef19">
# Add Spark SQL support
* Add Spark SQL support. It can connect to Spark via building a
local/remote SparkSession.
* Include a notebook example
I tried some complicated queries (window function, table joins), and the
tool works well.
Compared to the [Spark Dataframe
agent](https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark.html),
this tool is able to generate queries across multiple tables.
---------
# Your PR Title (What it does)
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Fixes # (issue)
## Before submitting
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---------
Co-authored-by: Gengliang Wang <gengliang@apache.org>
Co-authored-by: Mike W <62768671+skcoirz@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: UmerHA <40663591+UmerHA@users.noreply.github.com>
Co-authored-by: 张城铭 <z@hyperf.io>
Co-authored-by: assert <zhangchengming@kkguan.com>
Co-authored-by: blob42 <spike@w530>
Co-authored-by: Yuekai Zhang <zhangyuekai@foxmail.com>
Co-authored-by: Richard He <he.yucheng@outlook.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Co-authored-by: Leonid Ganeline <leo.gan.57@gmail.com>
Co-authored-by: Alexey Nominas <60900649+Chae4ek@users.noreply.github.com>
Co-authored-by: elBarkey <elbarkey@gmail.com>
Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Jeffrey D <1289344+verygoodsoftwarenotvirus@users.noreply.github.com>
Co-authored-by: so2liu <yangliu35@outlook.com>
Co-authored-by: Viswanadh Rayavarapu <44315599+vishwa-rn@users.noreply.github.com>
Co-authored-by: Chakib Ben Ziane <contact@blob42.xyz>
Co-authored-by: Daniel Chalef <131175+danielchalef@users.noreply.github.com>
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
Co-authored-by: Jari Bakken <jari.bakken@gmail.com>
Co-authored-by: escafati <scafatieugenio@gmail.com>
# 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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<!-- Remove if not applicable -->
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?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@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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maintainers/contributors who might be interested:
- @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>
# Update deployments doc with langcorn API server
API server example
```python
from fastapi import FastAPI
from langcorn import create_service
app: FastAPI = create_service(
"examples.ex1:chain",
"examples.ex2:chain",
"examples.ex3:chain",
"examples.ex4:sequential_chain",
"examples.ex5:conversation",
"examples.ex6:conversation_with_summary",
)
```
More examples: https://github.com/msoedov/langcorn/tree/main/examples
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Docs and code review fixes for Docugami DataLoader
1. I noticed a couple of hyperlinks that are not loading in the
langchain docs (I guess need explicit anchor tags). Added those.
2. In code review @eyurtsev had a
[suggestion](https://github.com/hwchase17/langchain/pull/4727#discussion_r1194069347)
to allow string paths. Turns out just updating the type works (I tested
locally with string paths).
# Pre-submission checks
I ran `make lint` and `make tests` successfully.
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
# Fix Homepage Typo
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested... not sure
# 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
# Your PR Title (What it does)
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Fixes # (issue)
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
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# 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)
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
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Co-authored-by: Jinto Jose <129657162+jj701@users.noreply.github.com>
# docs: added `additional_resources` folder
The additional resource files were inside the doc top-level folder,
which polluted the top-level folder.
- added the `additional_resources` folder and moved correspondent files
to this folder;
- fixed a broken link to the "Model comparison" page (model_laboratory
notebook)
- fixed a broken link to one of the YouTube videos (sorry, it is not
directly related to this PR)
## Who can review?
@dev2049
This reverts commit 5111bec540.
This PR introduced a bug in the async API (the `url` param isn't bound);
it also didn't update the synchronous API correctly, which makes it
error-prone (the behavior of the async and sync endpoints would be
different)
- added an official LangChain YouTube channel :)
- added new tutorials and videos (only videos with enough subscriber or
view numbers)
- added a "New video" icon
## Who can review?
@dev2049
# 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>
# glossary.md renamed as concepts.md and moved under the Getting Started
small PR.
`Concepts` looks right to the point. It is moved under Getting Started
(typical place). Previously it was lost in the Additional Resources
section.
## Who can review?
@hwchase17
### 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>
# Added Tutorials section on the top-level of documentation
**Problem Statement**: the Tutorials section in the documentation is
top-priority. Not every project has resources to make tutorials. We have
such a privilege. Community experts created several tutorials on
YouTube.
But the tutorial links are now hidden on the YouTube page and not easily
discovered by first-time visitors.
**PR**: I've created the `Tutorials` page (from the `Additional
Resources/YouTube` page) and moved it to the top level of documentation
in the `Getting Started` section.
## Who can review?
@dev2049
NOTE:
PR checks are randomly failing
3aefaafcdb258819eadf514d81b5b3
[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
**Problem statement:** the
[document_loaders](https://python.langchain.com/en/latest/modules/indexes/document_loaders.html#)
section is too long and hard to comprehend.
**Proposal:** group document_loaders by 3 classes: (see `Files changed`
tab)
UPDATE: I've completely reworked the document_loader classification.
Now this PR changes only one file!
FYI @eyurtsev @hwchase17
[Text Generation
Inference](https://github.com/huggingface/text-generation-inference) is
a Rust, Python and gRPC server for generating text using LLMs.
This pull request add support for self hosted Text Generation Inference
servers.
feature: #4280
---------
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Rebased Mahmedk's PR with the callback refactor and added the example
requested by hwchase plus a couple minor fixes
---------
Co-authored-by: Ahmed K <77802633+mahmedk@users.noreply.github.com>
Co-authored-by: Ahmed K <mda3k27@gmail.com>
Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Corey Zumar <39497902+dbczumar@users.noreply.github.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
We're fans of the LangChain framework thus we wanted to make sure we
provide an easy way for our customers to be able to utilize this
framework for their LLM-powered applications at our platform.
# Add option to `load_huggingface_tool`
Expose a method to load a huggingface Tool from the HF hub
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Thanks to @anna-charlotte and @jupyterjazz for the contribution! Made
few small changes to get it across the finish line
---------
Signed-off-by: anna-charlotte <charlotte.gerhaher@jina.ai>
Signed-off-by: jupyterjazz <saba.sturua@jina.ai>
Co-authored-by: anna-charlotte <charlotte.gerhaher@jina.ai>
Co-authored-by: jupyterjazz <saba.sturua@jina.ai>
Co-authored-by: Saba Sturua <45267439+jupyterjazz@users.noreply.github.com>
# ODF File Loader
Adds a data loader for handling Open Office ODT files. Requires
`unstructured>=0.6.3`.
### Testing
The following should work using the `fake.odt` example doc from the
[`unstructured` repo](https://github.com/Unstructured-IO/unstructured).
```python
from langchain.document_loaders import UnstructuredODTLoader
loader = UnstructuredODTLoader(file_path="fake.odt", mode="elements")
loader.load()
loader = UnstructuredODTLoader(file_path="fake.odt", mode="single")
loader.load()
```
- added `Wikipedia` retriever. It is effectively a wrapper for
`WikipediaAPIWrapper`. It wrapps load() into get_relevant_documents()
- sorted `__all__` in the `retrievers/__init__`
- added integration tests for the WikipediaRetriever
- added an example (as Jupyter notebook) for the WikipediaRetriever
# Minor Wording Documentation Change
```python
agent_chain.run("When's my friend Eric's surname?")
# Answer with 'Zhu'
```
is change to
```python
agent_chain.run("What's my friend Eric's surname?")
# Answer with 'Zhu'
```
I think when is a residual of the old query that was "When’s my friends
Eric`s birthday?".
# Fix grammar in Text Splitters docs
Just a small fix of grammar in the documentation:
"That means there two different axes" -> "That means there are two
different axes"
Related: #4028, I opened a new PR because (1) I was unable to unstage
mistakenly committed files (I'm not familiar with git enough to resolve
this issue), (2) I felt closing the original PR and opening a new PR
would be more appropriate if I changed the class name.
This PR creates HumanInputLLM(HumanLLM in #4028), a simple LLM wrapper
class that returns user input as the response. I also added a simple
Jupyter notebook regarding how and why to use this LLM wrapper. In the
notebook, I went over how to use this LLM wrapper and showed example of
testing `WikipediaQueryRun` using HumanInputLLM.
I believe this LLM wrapper will be useful especially for debugging,
educational or testing purpose.
- Added the `Wikipedia` document loader. It is based on the existing
`unilities/WikipediaAPIWrapper`
- Added a respective ut-s and example notebook
- Sorted list of classes in __init__
- made notebooks consistent: titles, service/format descriptions.
- corrected short names to full names, for example, `Word` -> `Microsoft
Word`
- added missed descriptions
- renamed notebook files to make ToC correctly sorted
This implements a loader of text passages in JSON format. The `jq`
syntax is used to define a schema for accessing the relevant contents
from the JSON file. This requires dependency on the `jq` package:
https://pypi.org/project/jq/.
---------
Signed-off-by: Aivin V. Solatorio <avsolatorio@gmail.com>
In the example for creating a Python REPL tool under the Agent module,
the ".run" was omitted in the example. I believe this is required when
defining a Tool.
In the section `Get Message Completions from a Chat Model` of the quick
start guide, the HumanMessage doesn't need to include `Translate this
sentence from English to French.` when there is a system message.
Simplify HumanMessages in these examples can further demonstrate the
power of LLM.
* implemented arun, results, and aresults. Reuses aiosession if
available.
* helper tools GoogleSerperRun and GoogleSerperResults
* support for Google Images, Places and News (examples given) and
filtering based on time (e.g. past hour)
* updated docs
Google Scholar outputs a nice list of scientific and research articles
that use LangChain.
I added a link to the Google Scholar page to the `gallery` doc page
Single edit to: models/text_embedding/examples/openai.ipynb - Line 88:
changed from: "embeddings = OpenAIEmbeddings(model_name=\"ada\")" to
"embeddings = OpenAIEmbeddings()" as model_name is no longer part of the
OpenAIEmbeddings class.
Seems the pyllamacpp package is no longer the supported bindings from
gpt4all. Tested that this works locally.
Given that the older models weren't very performant, I think it's better
to migrate now without trying to include a lot of try / except blocks
---------
Co-authored-by: Nissan Pow <npow@users.noreply.github.com>
Co-authored-by: Nissan Pow <pownissa@amazon.com>
### Summary
Adds `UnstructuredAPIFileLoaders` and `UnstructuredAPIFIleIOLoaders`
that partition documents through the Unstructured API. Defaults to the
URL for hosted Unstructured API, but can switch to a self hosted or
locally running API using the `url` kwarg. Currently, the Unstructured
API is open and does not require an API, but it will soon. A note was
added about that to the Unstructured ecosystem page.
### Testing
```python
from langchain.document_loaders import UnstructuredAPIFileIOLoader
filename = "fake-email.eml"
with open(filename, "rb") as f:
loader = UnstructuredAPIFileIOLoader(file=f, file_filename=filename)
docs = loader.load()
docs[0]
```
```python
from langchain.document_loaders import UnstructuredAPIFileLoader
filename = "fake-email.eml"
loader = UnstructuredAPIFileLoader(file_path=filename, mode="elements")
docs = loader.load()
docs[0]
```
Modified Modern Treasury and Strip slightly so credentials don't have to
be passed in explicitly. Thanks @mattgmarcus for adding Modern Treasury!
---------
Co-authored-by: Matt Marcus <matt.g.marcus@gmail.com>
Haven't gotten to all of them, but this:
- Updates some of the tools notebooks to actually instantiate a tool
(many just show a 'utility' rather than a tool. More changes to come in
separate PR)
- Move the `Tool` and decorator definitions to `langchain/tools/base.py`
(but still export from `langchain.agents`)
- Add scene explain to the load_tools() function
- Add unit tests for public apis for the langchain.tools and langchain.agents modules
I have added a reddit document loader which fetches the text from the
Posts of Subreddits or Reddit users, using the `praw` Python package. I
have also added an example notebook reddit.ipynb in order to guide users
to use this dataloader.
This code was made in format similar to twiiter document loader. I have
run code formating, linting and also checked the code myself for
different scenarios.
This is my first contribution to an open source project and I am really
excited about this. If you want to suggest some improvements in my code,
I will be happy to do it. :)
Co-authored-by: Taaha Bajwa <taaha.s.bajwa@gmail.com>
This PR includes some minor alignment updates, including:
- metadata object extended to support contractAddress, blockchainType,
and tokenId
- notebook doc better aligned to standard langchain format
- startToken changed from int to str to support multiple hex value types
on the Alchemy API
The updated metadata will look like the below. It's possible for a
single contractAddress to exist across multiple blockchains (e.g.
Ethereum, Polygon, etc.) so it's important to include the
blockchainType.
```
metadata = {"source": self.contract_address,
"blockchain": self.blockchainType,
"tokenId": tokenId}
```
For many applications of LLM agents, the environment is real (internet,
database, REPL, etc). However, we can also define agents to interact in
simulated environments like text-based games. This is an example of how
to create a simple agent-environment interaction loop with
[Gymnasium](https://github.com/Farama-Foundation/Gymnasium) (formerly
[OpenAI Gym](https://github.com/openai/gym)).
- Added links to the vectorstore providers
- Added installation code (it is not clear that we have to go to the
`LangChan Ecosystem` page to get installation instructions.)
Add other File Utilities, include
- List Directory
- Search for file
- Move
- Copy
- Remove file
Bundle as toolkit
Add a notebook that connects to the Chat Agent, which somewhat supports
multi-arg input tools
Update original read/write files to return the original dir paths and
better handle unsupported file paths.
Add unit tests
Adds a PlayWright web browser toolkit with the following tools:
- NavigateTool (navigate_browser) - navigate to a URL
- NavigateBackTool (previous_page) - wait for an element to appear
- ClickTool (click_element) - click on an element (specified by
selector)
- ExtractTextTool (extract_text) - use beautiful soup to extract text
from the current web page
- ExtractHyperlinksTool (extract_hyperlinks) - use beautiful soup to
extract hyperlinks from the current web page
- GetElementsTool (get_elements) - select elements by CSS selector
- CurrentPageTool (current_page) - get the current page URL
This notebook showcases how to implement a multi-agent simulation where
a privileged agent decides who to speak.
This follows the polar opposite selection scheme as [multi-agent
decentralized speaker
selection](https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html).
We show an example of this approach in the context of a fictitious
simulation of a news network. This example will showcase how we can
implement agents that
- think before speaking
- terminate the conversation
Alternate implementation of #3452 that relies on a generic query
constructor chain and language and then has vector store-specific
translation layer. Still refactoring and updating examples but general
structure is there and seems to work s well as #3452 on exampels
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR
* Adds `clear` method for `BaseCache` and implements it for various
caches
* Adds the default `init_func=None` and fixes gptcache integtest
* Since right now integtest is not running in CI, I've verified the
changes by running `docs/modules/models/llms/examples/llm_caching.ipynb`
(until proper e2e integtest is done in CI)
This notebook showcases how to implement a multi-agent simulation
without a fixed schedule for who speaks when. Instead the agents decide
for themselves who speaks. We can implement this by having each agent
bid to speak. Whichever agent's bid is the highest gets to speak.
We will show how to do this in the example below that showcases a
fictitious presidential debate.
It makes sense to use `arxiv` as another source of the documents for
downloading.
- Added the `arxiv` document_loader, based on the
`utilities/arxiv.py:ArxivAPIWrapper`
- added tests
- added an example notebook
- sorted `__all__` in `__init__.py` (otherwise it is hard to find a
class in the very long list)
Tools for Bing, DDG and Google weren't consistent even though the
underlying implementations were.
All three services now have the same tools and implementations to easily
switch and experiment when building chains.
One of our users noticed a bug when calling streaming models. This is
because those models return an iterator. So, I've updated the Replicate
`_call` code to join together the output. The other advantage of this
fix is that if you requested multiple outputs you would get them all –
previously I was just returning output[0].
I also adjusted the demo docs to use dolly, because we're featuring that
model right now and it's always hot, so people won't have to wait for
the model to boot up.
The error that this fixes:
```
> llm = Replicate(model=“replicate/flan-t5-xl:eec2f71c986dfa3b7a5d842d22e1130550f015720966bec48beaae059b19ef4c”)
> llm(“hello”)
> Traceback (most recent call last):
File "/Users/charlieholtz/workspace/dev/python/main.py", line 15, in <module>
print(llm(prompt))
File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/base.py", line 246, in __call__
return self.generate([prompt], stop=stop).generations[0][0].text
File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/base.py", line 140, in generate
raise e
File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/base.py", line 137, in generate
output = self._generate(prompts, stop=stop)
File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/base.py", line 324, in _generate
text = self._call(prompt, stop=stop)
File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/replicate.py", line 108, in _call
return outputs[0]
TypeError: 'generator' object is not subscriptable
```
The sentence transformers was a dup of the HF one.
This is a breaking change (model_name vs. model) for anyone using
`SentenceTransformerEmbeddings(model="some/nondefault/model")`, but
since it was landed only this week it seems better to do this now rather
than doing a wrapper.
This notebook shows how the DialogueAgent and DialogueSimulator class
make it easy to extend the [Two-Player Dungeons & Dragons
example](https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html)
to multiple players.
The main difference between simulating two players and multiple players
is in revising the schedule for when each agent speaks
To this end, we augment DialogueSimulator to take in a custom function
that determines the schedule of which agent speaks. In the example
below, each character speaks in round-robin fashion, with the
storyteller interleaved between each player.
I would like to contribute with a jupyter notebook example
implementation of an AI Sales Agent using `langchain`.
The bot understands the conversation stage (you can define your own
stages fitting your needs)
using two chains:
1. StageAnalyzerChain - takes context and LLM decides what part of sales
conversation is one in
2. SalesConversationChain - generate next message
Schema:
https://images-genai.s3.us-east-1.amazonaws.com/architecture2.png
my original repo: https://github.com/filip-michalsky/SalesGPT
This example creates a sales person named Ted Lasso who is trying to
sell you mattresses.
Happy to update based on your feedback.
Thanks, Filip
https://twitter.com/FilipMichalsky
Simplifies the [Two Agent
D&D](https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html)
example with a cleaner, simpler interface that is extensible for
multiple agents.
`DialogueAgent`:
- `send()`: applies the chatmodel to the message history and returns the
message string
- `receive(name, message)`: adds the `message` spoken by `name` to
message history
The `DialogueSimulator` class takes a list of agents. At each step, it
performs the following:
1. Select the next speaker
2. Calls the next speaker to send a message
3. Broadcasts the message to all other agents
4. Update the step counter.
The selection of the next speaker can be implemented as any function,
but in this case we simply loop through the agents.
Update Alchemy Key URL in Blockchain Document Loader. I want to say
thank you for the incredible work the LangChain library creators have
done.
I am amazed at how seamlessly the Loader integrates with Ethereum
Mainnet, Ethereum Testnet, Polygon Mainnet, and Polygon Testnet, and I
am excited to see how this technology can be extended in the future.
@hwchase17 - Please let me know if I can improve or if I have missed any
community guidelines in making the edit? Thank you again for your hard
work and dedication to the open source community.
Improved `arxiv/tool.py` by adding more specific information to the
`description`. It would help with selecting `arxiv` tool between other
tools.
Improved `arxiv.ipynb` with more useful descriptions.
In this notebook, we show how we can use concepts from
[CAMEL](https://www.camel-ai.org/) to simulate a role-playing game with
a protagonist and a dungeon master. To simulate this game, we create a
`TwoAgentSimulator` class that coordinates the dialogue between the two
agents.
My attempt at improving the `Chain`'s `Getting Started` docs and
`LLMChain` docs. Might need some proof-reading as English is not my
first language.
In LLM examples, I replaced the example use case when a simpler one
(shorter LLM output) to reduce cognitive load.
Updated `Getting Started` page of `Prompt Templates` to showcase more
features provided by the class. Might need some proof reading because
apparently English is not my first language.
Now it is hard to search for the integration points between
data_loaders, retrievers, tools, etc.
I've placed links to all groups of providers and integrations on the
`ecosystem` page.
So, it is easy to navigate between all integrations from a single
location.
Improvements
* set default num_workers for ingestion to 0
* upgraded notebooks for avoiding dataset creation ambiguity
* added `force_delete_dataset_by_path`
* bumped deeplake to 3.3.0
* creds arg passing to deeplake object that would allow custom S3
Notes
* please double check if poetry is not messed up (thanks!)
Asks
* Would be great to create a shared slack channel for quick questions
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
The detailed walkthrough of the Weaviate wrapper was pointing to the
getting-started notebook. Fixed it to point to the Weaviable notebook in
the examples folder.
This pull request adds a ChatGPT document loader to the document loaders
module in `langchain/document_loaders/chatgpt.py`. Additionally, it
includes an example Jupyter notebook in
`docs/modules/indexes/document_loaders/examples/chatgpt_loader.ipynb`
which uses fake sample data based on the original structure of the
`conversations.json` file.
The following files were added/modified:
- `langchain/document_loaders/__init__.py`
- `langchain/document_loaders/chatgpt.py`
- `docs/modules/indexes/document_loaders/examples/chatgpt_loader.ipynb`
-
`docs/modules/indexes/document_loaders/examples/example_data/fake_conversations.json`
This pull request was made in response to the recent release of ChatGPT
data exports by email:
https://help.openai.com/en/articles/7260999-how-do-i-export-my-chatgpt-history
Hi there！
I'm excited to open this PR to add support for using a fully Postgres
syntax compatible database 'AnalyticDB' as a vector.
As AnalyticDB has been proved can be used with AutoGPT,
ChatGPT-Retrieve-Plugin, and LLama-Index, I think it is also good for
you.
AnalyticDB is a distributed Alibaba Cloud-Native vector database. It
works better when data comes to large scale. The PR includes:
- [x] A new memory: AnalyticDBVector
- [x] A suite of integration tests verifies the AnalyticDB integration
I have read your [contributing
guidelines](72b7d76d79/.github/CONTRIBUTING.md).
And I have passed the tests below
- [x] make format
- [x] make lint
- [x] make coverage
- [x] make test
First cut of a supabase vectorstore loosely patterned on the langchainjs
equivalent. Doesn't support async operations which is a limitation of
the supabase python client.
---------
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
I have noticed a typo error in the `custom_mrkl_agents.ipynb` document
while trying the example from the documentation page. As a result, I
have opened a pull request (PR) to address this minor issue, even though
it may seem insignificant 😂.
The following calls were throwing an exception:
575b717d10/docs/use_cases/evaluation/agent_vectordb_sota_pg.ipynb (L192)575b717d10/docs/use_cases/evaluation/agent_vectordb_sota_pg.ipynb (L239)
Exception:
```
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[14], line 1
----> 1 chain_sota = RetrievalQA.from_chain_type(llm=OpenAI(temperature=0), chain_type="stuff", retriever=vectorstore_sota, input_key="question")
File ~/github/langchain/venv/lib/python3.9/site-packages/langchain/chains/retrieval_qa/base.py:89, in BaseRetrievalQA.from_chain_type(cls, llm, chain_type, chain_type_kwargs, **kwargs)
85 _chain_type_kwargs = chain_type_kwargs or {}
86 combine_documents_chain = load_qa_chain(
87 llm, chain_type=chain_type, **_chain_type_kwargs
88 )
---> 89 return cls(combine_documents_chain=combine_documents_chain, **kwargs)
File ~/github/langchain/venv/lib/python3.9/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for RetrievalQA
retriever
instance of BaseRetriever expected (type=type_error.arbitrary_type; expected_arbitrary_type=BaseRetriever)
```
The vectorstores had to be converted to retrievers:
`vectorstore_sota.as_retriever()` and `vectorstore_pg.as_retriever()`.
The PR also:
- adds the file `paul_graham_essay.txt` referenced by this notebook
- adds to gitignore *.pkl and *.bin files that are generated by this
notebook
Interestingly enough, the performance of the prediction greatly
increased (new version of langchain or ne version of OpenAI models since
the last run of the notebook): from 19/33 correct to 28/33 correct!
- Remove dynamic model creation in the `args()` property. _Only infer
for the decorator (and add an argument to NOT infer if someone wishes to
only pass as a string)_
- Update the validation example to make it less likely to be
misinterpreted as a "safe" way to run a repl
There is one example of "Multi-argument tools" in the custom_tools.ipynb
from yesterday, but we could add more. The output parsing for the base
MRKL agent hasn't been adapted to handle structured args at this point
in time
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
## Use `index_id` over `app_id`
We made a major update to index + retrieve based on Metal Indexes
(instead of apps). With this change, we accept an index instead of an
app in each of our respective core apis. [More details
here](https://docs.getmetal.io/api-reference/core/indexing).
## What is this PR for:
* This PR adds a commented line of code in the documentation that shows
how someone can use the Pinecone client with an already existing
Pinecone index
* The documentation currently only shows how to create a pinecone index
from langchain documents but not how to load one that already exists
- Updated `langchain/docs/modules/models/llms/integrations/` notebooks:
added links to the original sites, the install information, etc.
- Added the `nlpcloud` notebook.
- Removed "Example" from Titles of some notebooks, so all notebook
titles are consistent.
### https://github.com/hwchase17/langchain/issues/2997
Replaced `conversation.memory.store` to
`conversation.memory.entity_store.store`
As conversation.memory.store doesn't exist and re-ran the whole file.
- Most important - fixes the relevance_fn name in the notebook to align
with the docs
- Updates comments for the summary:
<img width="787" alt="image"
src="https://user-images.githubusercontent.com/130414180/232520616-2a99e8c3-a821-40c2-a0d5-3f3ea196c9bb.png">
- The new conversation is a bit better, still unfortunate they try to
schedule a followup.
- Rm the max dialogue turns argument to the conversation function
Add a time-weighted memory retriever and a notebook that approximates a
Generative Agent from https://arxiv.org/pdf/2304.03442.pdf
The "daily plan" components are removed for now since they are less
useful without a virtual world, but the memory is an interesting
component to build off.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Use numexpr evaluate instead of the python REPL to avoid malicious code
injection.
Tested against the (limited) math dataset and got the same score as
before.
For more permissive tools (like the REPL tool itself), other approaches
ought to be provided (some combination of Sanitizer + Restricted python
+ unprivileged-docker + ...), but for a calculator tool, only
mathematical expressions should be permitted.
See https://github.com/hwchase17/langchain/issues/814
Last week I added the `PDFMinerPDFasHTMLLoader`. I am adding some
example code in the notebook to serve as a tutorial for how that loader
can be used to create snippets of a pdf that are structured within
sections. All the other loaders only provide the `Document` objects
segmented by pages but that's pretty loose given the amount of other
metadata that can be extracted.
With the new loader, one can leverage font-size of the text to decide
when a new sections starts and can segment the text more semantically as
shown in the tutorial notebook. The cell shows that we are able to find
the content of entire section under **Related Work** for the example pdf
which is spread across 2 pages and hence is stored as two separate
documents by other loaders
Add SVM retriever class, based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb.
Testing still WIP, but the logic is correct (I have a local
implementation outside of Langchain working).
---------
Co-authored-by: Lance Martin <122662504+PineappleExpress808@users.noreply.github.com>
Co-authored-by: rlm <31treehaus@31s-MacBook-Pro.local>
Minor cosmetic changes
- Activeloop environment cred authentication in notebooks with
`getpass.getpass` (instead of CLI which not always works)
- much faster tests with Deep Lake pytest mode on
- Deep Lake kwargs pass
Notes
- I put pytest environment creds inside `vectorstores/conftest.py`, but
feel free to suggest a better location. For context, if I put in
`test_deeplake.py`, `ruff` doesn't let me to set them before import
deeplake
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
Note to self: Always run integration tests, even on "that last minute
change you thought would be safe" :)
---------
Co-authored-by: Mike Lambert <mike.lambert@anthropic.com>
**About**
Specify encoding to avoid UnicodeDecodeError when reading .txt for users
who are following the tutorial.
**Reference**
```
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 1205: character maps to <undefined>
```
**Environment**
OS: Win 11
Python: 3.8
Allows users to specify what files should be loaded instead of
indiscriminately loading the entire repo.
extends #2851
NOTE: for reviewers, `hide whitespace` option recommended since I
changed the indentation of an if-block to use `continue` instead so it
looks less like a Christmas tree :)
