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Builds on #14040 with community refactor merged and notebook updated.
Note that with this refactor, models will be imported from
`langchain_community.chat_models.huggingface` rather than the main
`langchain` repo.
---------
Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
Signed-off-by: ugm2 <unaigaraymaestre@gmail.com>
Signed-off-by: Yuchen Liang <yuchenl3@andrew.cmu.edu>
Co-authored-by: Andrew Reed <andrew.reed.r@gmail.com>
Co-authored-by: Andrew Reed <areed1242@gmail.com>
Co-authored-by: A-Roucher <aymeric.roucher@gmail.com>
Co-authored-by: Aymeric Roucher <69208727+A-Roucher@users.noreply.github.com>
Replace this entire comment with:
- **Description:** @kurtisvg has raised a point that it's a good idea to
have a fixed version for embeddings (since otherwise a user might run a
query with one version vs a vectorstore where another version was used).
In order to avoid breaking changes, I'd suggest to give users a warning,
and make a `model_name` a required argument in 1.5 months.
Surrealdb client changes from 0.3.1 to 0.3.2 broke the surrealdb vectore
integration.
This PR updates the code to work with the updated client. The change is
backwards compatible with previous versions of surrealdb client.
Also expanded the vector store implementation to store and retrieve
metadata that's included with the document object.
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- **Description:** Fixed jaguar.py to import JaguarHttpClient with try
and catch
- **Issue:** the issue # Unable to use the JaguarHttpClient at run time
- **Dependencies:** It requires "pip install -U jaguardb-http-client"
- **Twitter handle:** workbot
---------
Co-authored-by: JY <jyjy@jaguardb>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**
For the Momento Vector Index (MVI) vector store implementation, pass
through `filter_expression` kwarg to the MVI client, if specified. This
change will enable the MVI self query implementation in a future PR.
Also fixes some integration tests.
- **Description:** Fix typo in class Docstring to replace
AZURE_OPENAI_API_ENDPOINT by AZURE_OPENAI_ENDPOINT
- **Issue:** the issue #14901
- **Dependencies:** NA
- **Twitter handle:**
Co-authored-by: Yacine Bouakkaz <Yacine.Bouakkaz@evokegroup.com>
* This PR adds `stream` implementations to Runnable Branch.
* Runnable Branch still does not support `transform` so it'll break streaming if it happens in middle or end of sequence, but will work if happens at beginning of sequence.
* Fixes use the async callback manager for async methods
* Handle BaseException rather than Exception, so more errors could be logged as errors when they are encountered
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Description: Adding Summarization to Vectara, to reflect it provides not
only vector-store type functionality but also can return a summary.
Also added:
MMR capability (in the Vectara platform side)
Updated templates
Updated documentation and IPYNB examples
Tag maintainer: @baskaryan
Twitter handle: @ofermend
---------
Co-authored-by: Ofer Mendelevitch <ofermend@gmail.com>
**What is the reproduce code?**
```python
from langchain.chains import LLMChain, load_chain
from langchain.llms import Databricks
from langchain.prompts import PromptTemplate
def transform_output(response):
# Extract the answer from the responses.
return str(response["candidates"][0]["text"])
def transform_input(**request):
full_prompt = f"""{request["prompt"]}
Be Concise.
"""
request["prompt"] = full_prompt
return request
chat_model = Databricks(
endpoint_name="llama2-13B-chat-Brambles",
transform_input_fn=transform_input,
transform_output_fn=transform_output,
verbose=True,
)
print(f"Test chat model: {chat_model('What is Apache Spark')}") # This works
llm_chain = LLMChain(llm=chat_model, prompt=PromptTemplate.from_template("{chat_input}"))
llm_chain("colorful socks") # this works
llm_chain.save("databricks_llm_chain.yaml") # transform_input_fn and transform_output_fn are not serialized into the model yaml file
loaded_chain = load_chain("databricks_llm_chain.yaml") # The Databricks LLM is recreated with transform_input_fn=None, transform_output_fn=None.
loaded_chain("colorful socks") # Thus this errors. The transform_output_fn is needed to produce the correct output
```
Error:
```
File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-6c34afab-3473-421d-877f-1ef18930ef4d/lib/python3.10/site-packages/pydantic/v1/main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for Generation
text
str type expected (type=type_error.str)
request payload: {'query': 'What is a databricks notebook?'}'}
```
**What does the error mean?**
When the LLM generates an answer, represented by a Generation data
object. The Generation data object takes a str field called text, e.g.
Generation(text=”blah”). However, the Databricks LLM tried to put a
non-str to text, e.g. Generation(text={“candidates”:[{“text”: “blah”}]})
Thus, pydantic errors.
**Why the output format becomes incorrect after saving and loading the
Databricks LLM?**
Databrick LLM does not support serializing transform_input_fn and
transform_output_fn, so they are not serialized into the model yaml
file. When the Databricks LLM is loaded, it is recreated with
transform_input_fn=None, transform_output_fn=None. Without
transform_output_fn, the output text is not unwrapped, thus errors.
Missing transform_output_fn causes this error.
Missing transform_input_fn causes the additional prompt “Be Concise.” to
be lost after saving and loading.
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---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
This PR intends to add support for Qdrant's new [sparse vector
retrieval](https://qdrant.tech/articles/sparse-vectors/) by introducing
a new retriever class, `QdrantSparseVectorRetriever`.
Necessary usage docs and integration tests have been added for the
retriever.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:**
This PR fixes the issue faces with duplicate input id in Clarifai
vectorstore class when ingesting documents into the vectorstore more
than the batch size.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
Similar to https://github.com/langchain-ai/langchain/issues/5861, I've
experienced `KeyError`s resulting from unsafe lookups in the
`convert_dict_to_message` function in [this
file](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/adapters/openai.py).
While that issue focused on `KeyError 'content'`, I've opened another
issue (#14764) about how the problem still exists in the same function
but with `KeyError 'role'`. The fix for #5861 only added a safe lookup
to the specific line that was giving them trouble.. This PR fixes the
unsafe lookup in the rest of the function but the problem still exists
across the repo.
## Issues
* #14764
* #5861
## Dependencies
* None
## Checklist
[x] make format
[x] make lint
[ ] make test - Results in `make: *** No rule to make target 'test'.
Stop.`
## Maintainers
* @hinthornw
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR adds support for PygmalionAI's [Aphrodite
Engine](https://github.com/PygmalionAI/aphrodite-engine), based on
vLLM's attention mechanism. At the moment, this PR does not include
support for the API servers, but they will be added in a later PR.
The only dependency as of now is `aphrodite-engine==0.4.2`. We pin the
version to prevent breakage due to changes in the aphrodite-engine
library.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Introducing an ability to work with the
[YandexGPT](https://cloud.yandex.com/en/services/yandexgpt) embeddings
models.
---------
Co-authored-by: Dmitry Tyumentsev <dmitry.tyumentsev@raftds.com>
- **Description:** Modify community chat model vertexai to handle png
and other image types encoded in base64
- **Dependencies:** added `import re` but no new dependencies.
This addresses a problem where the vertexai method
_parse_chat_history_gemini() was only recognizing image uris in jpeg
format. I made a simple change to cover other extension types.
- **Description:** The Qianfan SDK offers multiple authentication
methods, but in the `QianfanEndpoint` of Langchain, it currently only
supports authentication through AK and SK. In order to accommodate users
who wish to use alternative authentication methods, this pull request
makes AK and SK optional. This change should not impact existing users,
while allowing users to configure other authentication methods as per
the Qianfan SDK documentation.
- **Issue:** /
- **Dependencies:** No
- **Tag maintainer:** No
- **Twitter handle:**
Added Entry ID as a return value inside get_summaries_as_docs
- **Description:** Added the Entry ID as a return, so it's easier to
track the IDs of the papers that are being returned.
With the addition return of the entry ID in functions like
ArxivRetriever, it will be easier to reference the ID of the paper
itself.
- Description: Just a minor add to the documentation to clarify how to
load all files from a folder. I assumed and try to do it specifying it
in the bucket (BUCKET/FOLDER), instead of using the prefix.
- **Description:** Documentation update. The custom tool notebook
documentation is updated to revome the warning caused by directly
instantiating of the LLMMathChain with an llm which is is deprecated.
The from_llm class method is used instead. LLM output results gets
updated as well.
- **Issue:** no applicable
- **Dependencies:** No dependencies
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @ybouakkaz
Co-authored-by: Yacine Bouakkaz <Yacine.Bouakkaz@evokegroup.com>
