Fixes # 5807
Realigned tests with implementation.
Also reinforced folder unicity for the test_faiss_local_save_load test
using date-time suffix
#### Before submitting
- Integration test updated
- formatting and linting ok (locally)
#### Who can review?
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@hwchase17 - project lead
VectorStores / Retrievers / Memory
-@dev2049
I added support for specifing different types with ResponseSchema
objects:
## before
`
extracted_info = ResponseSchema(name="extracted_info", description="List
of extracted information")
`
generate the following doc: ```json\n{\n\t\"extracted_info\": string //
List of extracted information}```
This brings GPT to create a JSON with only one string in the specified
field even if you requested a List in the description.
## now
`extracted_info = ResponseSchema(name="extracted_info",
type="List[string]", description="List of extracted information")
`
generate the following doc: ```json\n{\n\t\"extracted_info\":
List[string] // List of extracted information}```
This way the model responds better to the prompt generating an array of
strings.
Tag maintainers/contributors who might be interested:
Agents / Tools / Toolkits
@vowelparrot
Don't know who can be interested, I suppose this is a tool, so I tagged
you vowelparrot,
anyway, it's a minor change, and shouldn't impact any other part of the
framework.
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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.
# What does this PR do?
Change the HTML tags so that a tag with attributes can be found.
## Before submitting
- [x] Tests added
- [x] CI/CD validated
### Who can review?
Anyone in the community is free to review the PR once the tests have
passed. Feel free to tag
members/contributors who may be interested in your PR.
- Remove the client implementation (this breaks backwards compatibility
for existing testers. I could keep the stub in that file if we want, but
not many people are using it yet
- Add SDK as dependency
- Update the 'run_on_dataset' method to be a function that optionally
accepts a client as an argument
- Remove the langchain plus server implementation (you get it for free
with the SDK now)
We could make the SDK optional for now, but the plan is to use w/in the
tracer so it would likely become a hard dependency at some point.
# 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
Fixes#5614
#### Issue
The `***` combination produces an exception when used as a seperator in
`re.split`. Instead `\*\*\*` should be used for regex exprations.
#### Who can review?
@eyurtsev
Fixes#5699
#### Who can review?
Tag maintainers/contributors who might be interested:
@woodworker @LeSphax @johannhartmann
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
…719)
A minor update to retry Cohore API call in case of errors using tenacity
as it is done for OpenAI LLMs.
#### Who can review?
@hwchase17, @agola11
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Fixes # (issue)
#### Before submitting
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---------
Co-authored-by: Sagar Sapkota <22609549+sagar-spkt@users.noreply.github.com>
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>
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Adding a class attribute "return_generated_question" to class
"BaseConversationalRetrievalChain". If set to `True`, the chain's output
has a key "generated_question" with the question generated by the
sub-chain `question_generator` as the value. This way the generated
question can be logged.
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@dev2049 @vowelparrot
# 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.
Fixed python deprecation warning:
DeprecationWarning: invalid escape sequence '`'
backticks (`) do not have special meaning in python strings and should
not be escaped.
-- @spazm on twitter
### Who can review:
@nfcampos ported this change from javascript, @hwchase17 wrote the
original STRUCTURED_FORMAT_INSTRUCTIONS,
Zep now supports persisting custom metadata with messages and hybrid
search across both message embeddings and structured metadata. This PR
implements custom metadata and enhancements to the
`ZepChatMessageHistory` and `ZepRetriever` classes to implement this
support.
Tag maintainers/contributors who might be interested:
VectorStores / Retrievers / Memory
- @dev2049
---------
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
# Check if generated Cypher code is wrapped in backticks
Some LLMs like the VertexAI like to explain how they generated the
Cypher statement and wrap the actual code in three backticks:
I have observed a similar pattern with OpenAI chat models in a
conversational settings, where multiple user and assistant message are
provided to the LLM to generate Cypher statements, where then the LLM
wants to maybe apologize for previous steps or explain its thoughts.
Interestingly, both OpenAI and VertexAI wrap the code in three backticks
if they are doing any explaining or apologizing. Checking if the
generated cypher is wrapped in backticks seems like a low-hanging fruit
to expand the cypher search to other LLMs and conversational settings.
# Adding support to save multiple memories at a time. Cuts save time by
more then half
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@dev2049
@vowelparrot
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Fixes#5720.
A more in-depth discussion is in my comment here:
https://github.com/hwchase17/langchain/issues/5720#issuecomment-1577047018
In a nutshell, there has been a subtle change in the latest version of
GPT4Alls Python bindings. The change I submitted yesterday is compatible
with this version, however, this version is as of yet unreleased and
thus the code change breaks Langchain's wrapper under the currently
released version of GPT4All.
This pull request proposes a backwards-compatible solution.
fix for the sqlalchemy deprecated declarative_base import :
```
MovedIn20Warning: The ``declarative_base()`` function is now available as sqlalchemy.orm.declarative_base(). (deprecated since: 2.0) (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9)
Base = declarative_base() # type: Any
```
Import is wrapped in an try catch Block to fallback to the old import if
needed.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# 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>
Raises exception if OutputParsers receive a response with both a valid
action and a final answer
Currently, if an OutputParser receives a response which includes both an
action and a final answer, they return a FinalAnswer object. This allows
the parser to accept responses which propose an action and hallucinate
an answer without the action being parsed or taken by the agent.
This PR changes the logic to:
1. store a variable checking whether a response contains the
`FINAL_ANSWER_ACTION` (this is the easier condition to check).
2. store a variable checking whether the response contains a valid
action
3. if both are present, raise a new exception stating that both are
present
4. if an action is present, return an AgentAction
5. if an answer is present, return an AgentAnswer
6. if neither is present, raise the relevant exception based around the
action format (these have been kept consistent with the prior exception
messages)
Disclaimer:
* Existing mock data included strings which did include an action and an
answer. This might indicate that prioritising returning AgentAnswer was
always correct, and I am patching out desired behaviour? @hwchase17 to
advice. Curious if there are allowed cases where this is not
hallucinating, and we do want the LLM to output an action which isn't
taken.
* I have not passed `send_to_llm` through this new exception
Fixes#5601
## Who can review?
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@vowelparrot
All the queries to the database are done based on the SessionId
property, this will optimize how Mongo retrieves all messages from a
session
#### Who can review?
Tag maintainers/contributors who might be interested:
@dev2049
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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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@vowelparrot:
Minor change to the SQL agent:
Tells agent to introspect the schema of the most relevant tables, I
found this to dramatically decrease the chance that the agent wastes
times guessing column names.
