Makes it easier to do recursion using regular python compositional
patterns
```py
def lambda_decorator(func):
"""Decorate function as a RunnableLambda"""
return runnable.RunnableLambda(func)
@lambda_decorator
def fibonacci(a, config: runnable.RunnableConfig) -> int:
if a <= 1:
return a
else:
return fibonacci.invoke(
a - 1, config
) + fibonacci.invoke(a - 2, config)
fibonacci.invoke(10)
```
https://smith.langchain.com/public/cb98edb4-3a09-4798-9c22-a930037faf88/r
Also makes it more natural to do things like error handle and call other
langchain objects in ways we probably don't want to support in
`with_fallbacks()`
```py
@lambda_decorator
def handle_errors(a, config: runnable.RunnableConfig) -> int:
try:
return my_chain.invoke(a, config)
except MyExceptionType as exc:
return my_other_chain.invoke({"original": a, "error": exc}, config)
```
In this case, the next chain takes in the exception object. Maybe this
could be something we toggle in `with_fallbacks` but I fear we'll get
into uglier APIs + heavier cognitive load if we try to do too much there
---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
- Description: Fix bug in SPARQL intent selection
- Issue: After the change in #7758 the intent is always set to "UPDATE".
Indeed, if the answer to the prompt contains only "SELECT" the
`find("SELECT")` operation returns a higher value w.r.t. `-1` returned
by `find("UPDATE")`.
- Dependencies: None,
- Tag maintainer: @baskaryan @aditya-29
- Twitter handle: @mario_scrock
Text Generation Inference's client permits the use of a None temperature
as seen
[here](033230ae66/clients/python/text_generation/client.py (L71C9-L71C20)).
While I haved dived into TGI's server code and don't know about the
implications of using None as a temperature setting, I think we should
grant users the option to pass None as a temperature parameter to TGI.
#9304 introduced a critical bug. The S3DirectoryLoader fails completely
because boto3 checks the naming of kw arguments and one of the args is
badly named (very sorry for that)
cc @baskaryan
Changes in:
- `create_sql_agent` function so that user can easily add custom tools
as complement for the toolkit.
- updating **sql use case** notebook to showcase 2 examples of extra
tools.
Motivation for these changes is having the possibility of including
domain expert knowledge to the agent, which improves accuracy and
reduces time/tokens.
---------
Co-authored-by: Manuel Soria <manuel.soria@greyscaleai.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
### Issue
This pull request addresses a lingering issue identified in PR #7070. In
that previous pull request, an attempt was made to address the problem
of empty embeddings when using the `OpenAIEmbeddings` class. While PR
#7070 introduced a mechanism to retry requests for embeddings, it didn't
fully resolve the issue as empty embeddings still occasionally
persisted.
### Problem
In certain specific use cases, empty embeddings can be encountered when
requesting data from the OpenAI API. In some cases, these empty
embeddings can be skipped or removed without affecting the functionality
of the application. However, they might not always be resolved through
retries, and their presence can adversely affect the functionality of
applications relying on the `OpenAIEmbeddings` class.
### Solution
To provide a more robust solution for handling empty embeddings, we
propose the introduction of an optional parameter, `skip_empty`, in the
`OpenAIEmbeddings` class. When set to `True`, this parameter will enable
the behavior of automatically skipping empty embeddings, ensuring that
problematic empty embeddings do not disrupt the processing flow. The
developer will be able to optionally toggle this behavior if needed
without disrupting the application flow.
## Changes Made
- Added an optional parameter, `skip_empty`, to the `OpenAIEmbeddings`
class.
- When `skip_empty` is set to `True`, empty embeddings are automatically
skipped without causing errors or disruptions.
### Example Usage
```python
from openai.embeddings import OpenAIEmbeddings
# Initialize the OpenAIEmbeddings class with skip_empty=True
embeddings = OpenAIEmbeddings(api_key="your_api_key", skip_empty=True)
# Request embeddings, empty embeddings are automatically skipped. docs is a variable containing the already splitted text.
results = embeddings.embed_documents(docs)
# Process results without interruption from empty embeddings
```
- Description:
Add a 'download_dir' argument to VLLM model (to change the cache
download directotu when retrieving a model from HF hub)
- Issue:
On some remote machine, I want the cache dir to be in a volume where I
have space (models are heavy nowadays). Sometimes the default HF cache
dir might not be what we want.
- Dependencies:
None
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
I have restructured the code to ensure uniform handling of ImportError.
In place of previously used ValueError, I've adopted the standard
practice of raising ImportError with explanatory messages. This
modification enhances code readability and clarifies that any problems
stem from module importation.
---------
Co-authored-by: Aashish Saini <141953346+AashishSainiShorthillsAI@users.noreply.github.com>
Co-authored-by: AryamanJaiswalShorthillsAI <142397527+AryamanJaiswalShorthillsAI@users.noreply.github.com>
Co-authored-by: Adarsh Shrivastav <142413097+AdarshKumarShorthillsAI@users.noreply.github.com>
Co-authored-by: Vishal <141389263+VishalYadavShorthillsAI@users.noreply.github.com>
Co-authored-by: ChetnaGuptaShorthillsAI <142381084+ChetnaGuptaShorthillsAI@users.noreply.github.com>
Co-authored-by: PankajKumarShorthillsAI <142473460+PankajKumarShorthillsAI@users.noreply.github.com>
Co-authored-by: AbhishekYadavShorthillsAI <142393903+AbhishekYadavShorthillsAI@users.noreply.github.com>
Co-authored-by: AmitSinghShorthillsAI <142410046+AmitSinghShorthillsAI@users.noreply.github.com>
Co-authored-by: Aayush <142384656+AayushShorthillsAI@users.noreply.github.com>
Co-authored-by: AnujMauryaShorthillsAI <142393269+AnujMauryaShorthillsAI@users.noreply.github.com>
Previous PR #9353 has incomplete type checks and deprecation warnings.
This PR will fix those type check and add deprecation warning to myscale
vectorstore
(Reopen PR #7706, hope this problem can fix.)
When using `pdfplumber`, some documents may be parsed incorrectly,
resulting in **duplicated characters**.
Taking the
[linked](https://bruusgaard.no/wp-content/uploads/2021/05/Datasheet1000-series.pdf)
document as an example:
## Before
```python
from langchain.document_loaders import PDFPlumberLoader
pdf_file = 'file.pdf'
loader = PDFPlumberLoader(pdf_file)
docs = loader.load()
print(docs[0].page_content)
```
Results:
```
11000000 SSeerriieess
PPoorrttaabbllee ssiinnggllee ggaass ddeetteeccttoorrss ffoorr HHyyddrrooggeenn aanndd CCoommbbuussttiibbllee ggaasseess
TThhee RRiikkeenn KKeeiikkii GGPP--11000000 iiss aa ccoommppaacctt aanndd
lliigghhttwweeiigghhtt ggaass ddeetteeccttoorr wwiitthh hhiigghh sseennssiittiivviittyy ffoorr
tthhee ddeetteeccttiioonn ooff hhyyddrrooccaarrbboonnss.. TThhee mmeeaassuurreemmeenntt
iiss ppeerrffoorrmmeedd ffoorr tthhiiss ppuurrppoossee bbyy mmeeaannss ooff ccaattaallyyttiicc
sseennssoorr.. TThhee GGPP--11000000 hhaass aa bbuuiilltt--iinn ppuummpp wwiitthh
ppuummpp bboooosstteerr ffuunnccttiioonn aanndd aa ddiirreecctt sseelleeccttiioonn ffrroomm
aa lliisstt ooff 2255 hhyyddrrooccaarrbboonnss ffoorr eexxaacctt aalliiggnnmmeenntt ooff tthhee
ttaarrggeett ggaass -- OOnnllyy ccaalliibbrraattiioonn oonn CCHH iiss nneecceessssaarryy..
44
FFeeaattuurreess
TThhee RRiikkeenn KKeeiikkii 110000vvvvttaabbllee ssiinnggllee HHyyddrrooggeenn aanndd
CCoommbbuussttiibbllee ggaass ddeetteeccttoorrss..
TThheerree aarree 33 ssttaannddaarrdd mmooddeellss::
GGPP--11000000:: 00--1100%%LLEELL // 00--110000%%LLEELL ›› LLEELL ddeetteeccttoorr
NNCC--11000000:: 00--11000000ppppmm // 00--1100000000ppppmm ›› PPPPMM
ddeetteeccttoorr
DDiirreecctt rreeaaddiinngg ooff tthhee ccoonncceennttrraattiioonn vvaalluueess ooff
ccoommbbuussttiibbllee ggaasseess ooff 2255 ggaasseess ((55 NNPP--11000000))..
EEaassyy ooppeerraattiioonn ffeeaattuurree ooff cchhaannggiinngg tthhee ggaass nnaammee
ddiissppllaayy wwiitthh 11 sswwiittcchh bbuuttttoonn..
LLoonngg ddiissttaannccee ddrraawwiinngg ppoossssiibbllee wwiitthh tthhee ppuummpp
bboooosstteerr ffuunnccttiioonn..
VVaarriioouuss ccoommbbuussttiibbllee ggaasseess ccaann bbee mmeeaassuurreedd bbyy tthhee
ppppmm oorrddeerr wwiitthh NNCC--11000000..
www.bruusgaard.no postmaster@bruusgaard.no +47 67 54 93 30 Rev: 446-2
```
We can see that there are a large number of duplicated characters in the
text, which can cause issues in subsequent applications.
## After
Therefore, based on the
[solution](https://github.com/jsvine/pdfplumber/issues/71) provided by
the `pdfplumber` source project. I added the `"dedupe_chars()"` method
to address this problem. (Just pass the parameter `dedupe` to `True`)
```python
from langchain.document_loaders import PDFPlumberLoader
pdf_file = 'file.pdf'
loader = PDFPlumberLoader(pdf_file, dedupe=True)
docs = loader.load()
print(docs[0].page_content)
```
Results:
```
1000 Series
Portable single gas detectors for Hydrogen and Combustible gases
The Riken Keiki GP-1000 is a compact and
lightweight gas detector with high sensitivity for
the detection of hydrocarbons. The measurement
is performed for this purpose by means of catalytic
sensor. The GP-1000 has a built-in pump with
pump booster function and a direct selection from
a list of 25 hydrocarbons for exact alignment of the
target gas - Only calibration on CH is necessary.
4
Features
The Riken Keiki 100vvtable single Hydrogen and
Combustible gas detectors.
There are 3 standard models:
GP-1000: 0-10%LEL / 0-100%LEL › LEL detector
NC-1000: 0-1000ppm / 0-10000ppm › PPM
detector
Direct reading of the concentration values of
combustible gases of 25 gases (5 NP-1000).
Easy operation feature of changing the gas name
display with 1 switch button.
Long distance drawing possible with the pump
booster function.
Various combustible gases can be measured by the
ppm order with NC-1000.
www.bruusgaard.no postmaster@bruusgaard.no +47 67 54 93 30 Rev: 446-2
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
I have restructured the code to ensure uniform handling of ImportError.
In place of previously used ValueError, I've adopted the standard
practice of raising ImportError with explanatory messages. This
modification enhances code readability and clarifies that any problems
stem from module importation.
---------
Co-authored-by: Aashish Saini <141953346+AashishSainiShorthillsAI@users.noreply.github.com>
Co-authored-by: AryamanJaiswalShorthillsAI <142397527+AryamanJaiswalShorthillsAI@users.noreply.github.com>
Co-authored-by: Adarsh Shrivastav <142413097+AdarshKumarShorthillsAI@users.noreply.github.com>
Co-authored-by: Vishal <141389263+VishalYadavShorthillsAI@users.noreply.github.com>
Co-authored-by: ChetnaGuptaShorthillsAI <142381084+ChetnaGuptaShorthillsAI@users.noreply.github.com>
Co-authored-by: PankajKumarShorthillsAI <142473460+PankajKumarShorthillsAI@users.noreply.github.com>
Co-authored-by: AbhishekYadavShorthillsAI <142393903+AbhishekYadavShorthillsAI@users.noreply.github.com>
Co-authored-by: AmitSinghShorthillsAI <142410046+AmitSinghShorthillsAI@users.noreply.github.com>
Co-authored-by: Aayush <142384656+AayushShorthillsAI@users.noreply.github.com>
Co-authored-by: AnujMauryaShorthillsAI <142393269+AnujMauryaShorthillsAI@users.noreply.github.com>
- Implemented the MilvusTranslator for self-querying using Milvus vector
store
- Made unit tests to test its functionality
- Documented the Milvus self-querying
- Description: this PR adds the possibility to configure boto3 in the S3
loaders. Any named argument you add will be used to create the Boto3
session. This is useful when the AWS credentials can't be passed as env
variables or can't be read from the credentials file.
- Issue: N/A
- Dependencies: N/A
- Tag maintainer: ?
- Twitter handle: cbornet_
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR implements two new classes in the cache module: `CassandraCache`
and `CassandraSemanticCache`, similar in structure and functionality to
their Redis counterpart: providing a cache for the response to a
(prompt, llm) pair.
Integration tests are included. Moreover, linting and type checks are
all passing on my machine.
Dependencies: the `pyproject.toml` and `poetry.lock` have the newest
version of cassIO (the very same as in the Cassandra vector store
metadata PR, submitted as #9280).
If I may suggest, this issue and #9280 might be reviewed together (as
they bring the same poetry changes along), so I'm tagging @baskaryan who
already helped out a little with poetry-related conflicts there. (Thank
you!)
I'd be happy to add a short notebook if this is deemed necessary (but it
seems to me that, contrary e.g. to vector stores, caches are not covered
in specific notebooks).
Thank you!
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Enhance SerpApi response which potential to have more relevant output.
<img width="345" alt="Screenshot 2023-09-01 at 8 26 13 AM"
src="https://github.com/langchain-ai/langchain/assets/10222402/80ff684d-e02e-4143-b218-5c1b102cbf75">
Query: What is the weather in Pomfret?
**Before:**
> I should look up the current weather conditions.
...
Final Answer: The current weather in Pomfret is 73°F with 1% chance of
precipitation and winds at 10 mph.
**After:**
> I should look up the current weather conditions.
...
Final Answer: The current weather in Pomfret is 62°F, 1% precipitation,
61% humidity, and 4 mph wind.
---
Query: Top team in english premier league?
**Before:**
> I need to find out which team is currently at the top of the English
Premier League
...
Final Answer: Liverpool FC is currently at the top of the English
Premier League.
**After:**
> I need to find out which team is currently at the top of the English
Premier League
...
Final Answer: Man City is currently at the top of the English Premier
League.
---
Query: Top team in english premier league?
**Before:**
> I need to find out which team is currently at the top of the English
Premier League
...
Final Answer: Liverpool FC is currently at the top of the English
Premier League.
**After:**
> I need to find out which team is currently at the top of the English
Premier League
...
Final Answer: Man City is currently at the top of the English Premier
League.
---
Query: Any upcoming events in Paris?
**Before:**
> I should look for events in Paris
Action: Search
...
Final Answer: Upcoming events in Paris this month include Whit Sunday &
Whit Monday (French National Holiday), Makeup in Paris, Paris Jazz
Festival, Fete de la Musique, and Salon International de la Maison de.
**After:**
> I should look for events in Paris
Action: Search
...
Final Answer: Upcoming events in Paris include Elektric Park 2023, The
Aces, and BEING AS AN OCEAN.
JSONLoader.load does not specify `encoding` in
`self.file_path.read_text()` as `self.file_path.open()`
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Description:
Gmail message retrieval in GmailGetMessage and GmailSearch returned an
empty string when encountering multipart emails. This change correctly
extracts the email body for multipart emails.
Dependencies: None
@hwchase17 @vowelparrot
# Description
This change allows you to customize the prompt used in
`create_extraction_chain` as well as `create_extraction_chain_pydantic`.
It also adds the `verbose` argument to
`create_extraction_chain_pydantic` - because `create_extraction_chain`
had it already and `create_extraction_chain_pydantic` did not.
# Issue
N/A
# Dependencies
N/A
# Twitter
https://twitter.com/CamAHutchison
Hi,
- Description:
- Solves the issue #6478.
- Includes some additional rework on the `JSONLoader` class:
- Getting metadata is decoupled from `_get_text`
- Validating metadata_func is perform now by `_validate_metadata_func`,
instead of `_validate_content_key`
- Issue: #6478
- Dependencies: NA
- Tag maintainer: @hwchase17
Description: Adds tags and dataview fields to ObsidianLoader doc
metadata.
- Issue: #9800, #4991
- Dependencies: none
- Tag maintainer: My best guess is @hwchase17 looking through the git
logs
- Twitter handle: I don't use twitter, sorry!
### Description
There is a really nice class for saving chat messages into a database -
SQLChatMessageHistory.
It leverages SqlAlchemy to be compatible with any supported database (in
contrast with PostgresChatMessageHistory, which is basically the same
but is limited to Postgres).
However, the class is not really customizable in terms of what you can
store. I can imagine a lot of use cases, when one will need to save a
message date, along with some additional metadata.
To solve this, I propose to extract the converting logic from
BaseMessage to SQLAlchemy model (and vice versa) into a separate class -
message converter. So instead of rewriting the whole
SQLChatMessageHistory class, a user will only need to write a custom
model and a simple mapping class, and pass its instance as a parameter.
I also noticed that there is no documentation on this class, so I added
that too, with an example of custom message converter.
### Issue
N/A
### Dependencies
N/A
### Tag maintainer
Not yet
### Twitter handle
N/A
Description: new chain for logical fallacy removal from model output in
chain and docs
Issue: n/a see above
Dependencies: none
Tag maintainer: @hinthornw in past from my end but not sure who that
would be for maintenance of chains
Twitter handle: no twitter feel free to call out my git user if shout
out j-space-b
Note: created documentation in docs/extras
---------
Co-authored-by: Jon Bennion <jb@Jons-MacBook-Pro.local>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Issue: closes#9855
* consolidates `from_texts` and `add_texts` functions for pinecone
upsert
* adds two types of batching (one for embeddings and one for index
upsert)
* adds thread pool size when instantiating pinecone index
## Description
When the `MultiQueryRetriever` is used to get the list of documents
relevant according to a query, inside a vector store, and at least one
of these contain metadata with nested dictionaries, a `TypeError:
unhashable type: 'dict'` exception is thrown.
This is caused by the `unique_union` function which, to guarantee the
uniqueness of the returned documents, tries, unsuccessfully, to hash the
nested dictionaries and use them as a part of key.
```python
unique_documents_dict = {
(doc.page_content, tuple(sorted(doc.metadata.items()))): doc
for doc in documents
}
```
## Issue
#9872 (MultiQueryRetriever (get_relevant_documents) raises TypeError:
unhashable type: 'dict' with dic metadata)
## Solution
A possible solution is to dump the metadata dict to a string and use it
as a part of hashed key.
```python
unique_documents_dict = {
(doc.page_content, json.dumps(doc.metadata, sort_keys=True)): doc
for doc in documents
}
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Hi, this PR enables configuring the html2text package, instead of being
bound to use the hardcoded values. While simply passing `ignore_links`
and `ignore_images` to the `transform_documents` method was possible, I
preferred passing them to the `__init__` method for 2 reasons:
1. It is more efficient in case of subsequent calls to
`transform_documents`.
2. It allows to move the "complexity" to the instantiation, keeping the
actual execution simple and general enough. IMO the transformers should
all follow this pattern, allowing something like this:
```python
# Instantiate transformers
transformers = [
TransformerA(foo='bar'),
TransformerB(bar='foo'),
# others
]
# During execution, call them sequentially
documents = ...
for tr in transformers:
documents = tr.transform_documents(documents)
```
Thanks for the reviews!
---------
Co-authored-by: taamedag <Davide.Menini@swisscom.com>
If last_accessed_at metadata is a float use it as a timestamp. This
allows to support vector stores that do not store datetime objects like
ChromaDb.
Fixes: https://github.com/langchain-ai/langchain/issues/3685
<!-- Thank you for contributing to LangChain!
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- Description: Adds two optional parameters to the
DynamoDBChatMessageHistory class to enable users to pass in a name for
their PrimaryKey, or a Key object itself to enable the use of composite
keys, a common DynamoDB paradigm.
[AWS DynamoDB Key
docs](https://aws.amazon.com/blogs/database/choosing-the-right-dynamodb-partition-key/)
- Issue: N/A
- Dependencies: N/A
- Twitter handle: N/A
---------
Co-authored-by: Josh White <josh@ctrlstack.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Add SQLDatabaseSequentialChain Class to __init__.py so it can be
accessed and used
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: SQLDatabaseSequentialChain is not found when importing
Langchain_experimental package, when I open __init__.py
Langchain_expermental.sql, I found that SQLDatabaseSequentialChain is
imported and add to __all__ list
- Issue: SQLDatabaseSequentialChain is not found in
Langchain_experimental package
- Dependencies: None,
- Tag maintainer: None,
- Twitter handle: None,
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
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network access,
2. an example notebook showing its use. These live is docs/extras
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The output at times lacks the closing markdown code block. The prompt is
changed to explicitly request the closing backticks.
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submitting. Run `make format`, `make lint` and `make test` to check this
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## Description
This PR introduces a minor change to the TitanTakeoff integration.
Instead of specifying a port on localhost, this PR will allow users to
specify a baseURL instead. This will allow users to use the integration
if they have TitanTakeoff deployed externally (not on localhost). This
removes the hardcoded reference to localhost "http://localhost:{port}".
### Info about Titan Takeoff
Titan Takeoff is an inference server created by
[TitanML](https://www.titanml.co/) that allows you to deploy large
language models locally on your hardware in a single command. Most
generative model architectures are included, such as Falcon, Llama 2,
GPT2, T5 and many more.
Read more about Titan Takeoff here:
-
[Blog](https://medium.com/@TitanML/introducing-titan-takeoff-6c30e55a8e1e)
- [Docs](https://docs.titanml.co/docs/titan-takeoff/getting-started)
### Dependencies
No new dependencies are introduced. However, users will need to install
the titan-iris package in their local environment and start the Titan
Takeoff inferencing server in order to use the Titan Takeoff
integration.
Thanks for your help and please let me know if you have any questions.
cc: @hwchase17 @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Hi,
this PR contains loader / parser for Azure Document intelligence which
is a ML-based service to ingest arbitrary PDFs / images, even if
scanned. The loader generates Documents by pages of the original
document. This is my first contribution to LangChain.
Unfortunately I could not find the correct place for test cases. Happy
to add one if you can point me to the location, but as this is a
cloud-based service, a test would require network access and credentials
- so might be of limited help.
Dependencies: The needed dependency was already part of pyproject.toml,
no change.
Twitter: feel free to mention @LarsAC on the announcement
This small PR aims at supporting the following missing parameters in the
`HuggingfaceTextGen` LLM:
- `return_full_text` - sometimes useful for completion tasks
- `do_sample` - quite handy to control the randomness of the model.
- `watermark`
@hwchase17 @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR follows the **Eden AI (LLM + embeddings) integration**. #8633
We added an optional parameter to choose different AI models for
providers (like 'text-bison' for provider 'google', 'text-davinci-003'
for provider 'openai', etc.).
Usage:
```python
llm = EdenAI(
feature="text",
provider="google",
params={
"model": "text-bison", # new
"temperature": 0.2,
"max_tokens": 250,
},
)
```
You can also change the provider + model after initialization
```python
llm = EdenAI(
feature="text",
provider="google",
params={
"temperature": 0.2,
"max_tokens": 250,
},
)
prompt = """
hi
"""
llm(prompt, providers='openai', model='text-davinci-003') # change provider & model
```
The jupyter notebook as been updated with an example well.
Ping: @hwchase17, @baskaryan
---------
Co-authored-by: RedhaWassim <rwasssim@gmail.com>
Co-authored-by: sam <melaine.samy@gmail.com>
Adapting Microsoft Presidio to other languages requires a bit more work,
so for now it will be good idea to remove the language option to choose,
so as not to cause errors and confusion.
https://microsoft.github.io/presidio/analyzer/languages/
I will handle different languages after the weekend 😄
This adds sqlite-vss as an option for a vector database. Contains the
code and a few tests. Tests are passing and the library sqlite-vss is
added as optional as explained in the contributing guidelines. I
adjusted the code for lint/black/ and mypy. It looks that everything is
currently passing.
Adding sqlite-vss was mentioned in this issue:
https://github.com/langchain-ai/langchain/issues/1019.
Also mentioned here in the sqlite-vss repo for the curious:
https://github.com/asg017/sqlite-vss/issues/66
Maintainer tag: @baskaryan
---------
Co-authored-by: Philippe Oger <philippe.oger@adevinta.com>
This PR fixes an issues I found when upgrading to a more recent version
of Langchain. I was using 0.0.142 before, and this issue popped up
already when the `_custom_parser` was added to `output_parsers/json`.
Anyway, the issue is that the parser tries to escape quotes when they
are double-escaped (e.g. `\\"`), leading to OutputParserException.
This is particularly undesired in my app, because I have an Agent that
uses a single input Tool, which expects as input a JSON string with the
structure:
```python
{
"foo": string,
"bar": string
}
```
The LLM (GPT3.5) response is (almost) always something like
`"action_input": "{\\"foo\\": \\"bar\\", \\"bar\\": \\"foo\\"}"` and
since the upgrade this is not correctly parsed.
---------
Co-authored-by: taamedag <Davide.Menini@swisscom.com>
Adds a call to Pydantic's `update_forward_refs` for the `Run` class (in
addition to the `ChainRun` and `ToolRun` classes, for which that method
is already called). Without it, the self-reference of child classes
(type `List[Run]`) is problematic. For example:
```python
from langchain.callbacks import StdOutCallbackHandler
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from wandb.integration.langchain import WandbTracer
llm = OpenAI()
prompt = PromptTemplate.from_template("1 + {number} = ")
chain = LLMChain(llm=llm, prompt=prompt, callbacks=[StdOutCallbackHandler(), WandbTracer()])
print(chain.run(number=2))
```
results in the following output before the change
```
WARNING:root:Error in on_chain_start callback: field "child_runs" not yet prepared so type is still a ForwardRef, you might need to call Run.update_forward_refs().
> Entering new LLMChain chain...
Prompt after formatting:
1 + 2 =
WARNING:root:Error in on_chain_end callback: No chain Run found to be traced
> Finished chain.
3
```
but afterwards the callback error messages are gone.
Hi there！
I'm excited to open this PR to add support for using 'Tencent Cloud
VectorDB' as a vector store.
Tencent Cloud VectorDB is a fully-managed, self-developed,
enterprise-level distributed database service designed for storing,
retrieving, and analyzing multi-dimensional vector data. The database
supports multiple index types and similarity calculation methods, with a
single index supporting vector scales up to 1 billion and capable of
handling millions of QPS with millisecond-level query latency. Tencent
Cloud VectorDB not only provides external knowledge bases for large
models to improve their accuracy, but also has wide applications in AI
fields such as recommendation systems, NLP services, computer vision,
and intelligent customer service.
The PR includes:
Implementation of Vectorstore.
I have read your [contributing
guidelines](72b7d76d79/.github/CONTRIBUTING.md).
And I have passed the tests below
make format
make lint
make coverage
make test
This PR brings structural updates to `PlaywrightURLLoader`, aiming at
making the code more readable and extensible through the abstraction of
page evaluation logic. These changes also align this implementation with
a similar structure used in LangChain.js.
The key enhancements include:
1. Introduction of 'PlaywrightEvaluator', an abstract base class for all
evaluators.
2. Creation of 'UnstructuredHtmlEvaluator', a concrete class
implementing 'PlaywrightEvaluator', which uses `unstructured` library
for processing page's HTML content.
3. Extension of 'PlaywrightURLLoader' constructor to optionally accept
an evaluator of the type 'PlaywrightEvaluator'. It defaults to
'UnstructuredHtmlEvaluator' if no evaluator is provided.
4. Refactoring of 'load' and 'aload' methods to use the 'evaluate' and
'evaluate_async' methods of the provided 'PageEvaluator' for page
content handling.
This update brings flexibility to 'PlaywrightURLLoader' as it can now
utilize different evaluators for page processing depending on the
requirement. The abstraction also improves code maintainability and
readability.
Twitter: @ywkim
- Description: Add bloomz_7b, llama-2-7b, llama-2-13b, llama-2-70b to
ErnieBotChat, which only supported ERNIE-Bot-turbo and ERNIE-Bot.
- Issue: #10022,
- Dependencies: no extra dependencies
---------
Co-authored-by: hetianfeng <hetianfeng@meituan.com>
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: this PR adds `s3_object_key` and `s3_bucket` to the doc
metadata when loading an S3 file. This is particularly useful when using
`S3DirectoryLoader` to remove the files from the dir once they have been
processed (getting the object keys from the metadata `source` field
seems brittle)
- Dependencies: N/A
- Tag maintainer: ?
- Twitter handle: _cbornet
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR makes the following changes:
1. Documents become serializable using langhchain serialization
2. Make a utility to create a docstore kw store
Will help to address issue here:
https://github.com/langchain-ai/langchain/issues/9345
In the function _load_run_evaluators the function _get_keys was not
called if only custom_evaluators parameter is used
- Description: In the function _load_run_evaluators the function
_get_keys was not called if only custom_evaluators parameter is used,
- Issue: no issue created for this yet,
- Dependencies: None,
- Tag maintainer: @vowelparrot,
- Twitter handle: Buckler89
---------
Co-authored-by: ddroghini <d.droghini@mflgroup.com>
Description: This commit uses the new Service object in Selenium
webdriver as executable_path has been [deprecated and removed in
selenium version
4.11.2](9f5801c82f)
Issue: https://github.com/langchain-ai/langchain/issues/9808
Tag Maintainer: @eyurtsev
- Description: In my previous PR, I had modified the code to catch all
kinds of [SOURCES, sources, Source, Sources]. However, this change
included checking for a colon or a white space which should actually
have been only checking for a colon.
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
Adds support for [llmonitor](https://llmonitor.com) callbacks.
It enables:
- Requests tracking / logging / analytics
- Error debugging
- Cost analytics
- User tracking
Let me know if anythings neds to be changed for merge.
Thank you!
Co-authored-by: Daniel Brenot <dbrenot@pelmorex.com>
Co-authored-by: Daniel <daniel.alexander.brenot@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: the implementation for similarity_search_with_score did
not actually include a score or logic to filter. Now fixed.
- Tag maintainer: @rlancemartin
- Twitter handle: @ofermend
Recently we made the decision that PromptGuard takes a list of strings
instead of a string.
@ggroode implemented the integration change.
---------
Co-authored-by: ggroode <ggroode@berkeley.edu>
Co-authored-by: ggroode <46691276+ggroode@users.noreply.github.com>
Clearly document that the PAL and CPAL techniques involve generating
code, and that such code must be properly sandboxed and given
appropriate narrowly-scoped credentials in order to ensure security.
While our implementations include some mitigations, Python and SQL
sandboxing is well-known to be a very hard problem and our mitigations
are no replacement for proper sandboxing and permissions management. The
implementation of such techniques must be performed outside the scope of
the Python process where this package's code runs, so its correct setup
and administration must therefore be the responsibility of the user of
this code.
- Description: added the _cosine_relevance_score_fn to
_select_relevance_score_fn of faiss.py to enable the use of cosine
distance for similarity for this vector store and to comply with the
Error Message, that implies, that cosine should be a valid distance
strategy
- Issue: no relevant Issue found, but needed this function myself and
tested it in a private repo
- Dependencies: none
Neo4j has added vector index integration just recently. To allow both
ingestion and integrating it as vector RAG applications, I wrapped it as
a vector store as the implementation is completely different from
`GraphCypherQAChain`. Here, we are not generating any Cypher statements
at query time, we are simply doing the vector similarity search using
the new vector index as if we were dealing with a vector database.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Mypy was not able to determine a good type for `type_to_loader_dict`,
since the values in the dict are functions whose return types are
related to each other in a complex way. One can see this by adding a
line like `reveal_type(type_to_loader_dict)` and running mypy, which
will get mypy to show what type it has inferred for that value.
Adding an explicit type hint to help out mypy avoids the need for a mypy
suppression and allows the code to type-check cleanly.
In order to use `requires` marker in langchain-experimental, there's a
need for *conftest.py* file inside. Everything is identical to the main
langchain module.
Co-authored-by: maks-operlejn-ds <maks.operlejn@gmail.com>
We always overwrote the required args but we infer them by default.
Doing it only the old way makes it so the llm guesses even if an arg is
optional (e.g., for uuids)
The most reliable way to not have a chain run an undesirable SQL command
is to not give it database permissions to run that command. That way the
database itself performs the rule enforcement, so it's much easier to
configure and use properly than anything we could add in ourselves.
## Description
The following PR enables the [grammar-based
sampling](https://github.com/ggerganov/llama.cpp/tree/master/grammars)
in llama-cpp LLM.
In short, loading file with formal grammar definition will constrain
model outputs. For instance, one can force the model to generate valid
JSON or generate only python lists.
In the follow-up PR we will add:
* docs with some description why it is cool and how it works
* maybe some code sample for some task such as in llama repo
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Expose classmethods to convenient initialize the vectostore.
The purpose of this PR is to make it easy for users to initialize an
empty vectorstore that's properly pre-configured without having to index
documents into it via `from_documents`.
This will make it easier for users to rely on the following indexing
code: https://github.com/langchain-ai/langchain/pull/9614
to help manage data in the qdrant vectorstore.
### Description
The previous Redis implementation did not allow for the user to specify
the index configuration (i.e. changing the underlying algorithm) or add
additional metadata to use for querying (i.e. hybrid or "filtered"
search).
This PR introduces the ability to specify custom index attributes and
metadata attributes as well as use that metadata in filtered queries.
Overall, more structure was introduced to the Redis implementation that
should allow for easier maintainability moving forward.
# New Features
The following features are now available with the Redis integration into
Langchain
## Index schema generation
The schema for the index will now be automatically generated if not
specified by the user. For example, the data above has the multiple
metadata categories. The the following example
```python
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores.redis import Redis
embeddings = OpenAIEmbeddings()
rds, keys = Redis.from_texts_return_keys(
texts,
embeddings,
metadatas=metadata,
redis_url="redis://localhost:6379",
index_name="users"
)
```
Loading the data in through this and the other ``from_documents`` and
``from_texts`` methods will now generate index schema in Redis like the
following.
view index schema with the ``redisvl`` tool. [link](redisvl.com)
```bash
$ rvl index info -i users
```
Index Information:
| Index Name | Storage Type | Prefixes | Index Options | Indexing |
|--------------|----------------|---------------|-----------------|------------|
| users | HASH | ['doc:users'] | [] | 0 |
Index Fields:
| Name | Attribute | Type | Field Option | Option Value |
|----------------|----------------|---------|----------------|----------------|
| user | user | TEXT | WEIGHT | 1 |
| job | job | TEXT | WEIGHT | 1 |
| credit_score | credit_score | TEXT | WEIGHT | 1 |
| content | content | TEXT | WEIGHT | 1 |
| age | age | NUMERIC | | |
| content_vector | content_vector | VECTOR | | |
### Custom Metadata specification
The metadata schema generation has the following rules
1. All text fields are indexed as text fields.
2. All numeric fields are index as numeric fields.
If you would like to have a text field as a tag field, users can specify
overrides like the following for the example data
```python
# this can also be a path to a yaml file
index_schema = {
"text": [{"name": "user"}, {"name": "job"}],
"tag": [{"name": "credit_score"}],
"numeric": [{"name": "age"}],
}
rds, keys = Redis.from_texts_return_keys(
texts,
embeddings,
metadatas=metadata,
redis_url="redis://localhost:6379",
index_name="users"
)
```
This will change the index specification to
Index Information:
| Index Name | Storage Type | Prefixes | Index Options | Indexing |
|--------------|----------------|----------------|-----------------|------------|
| users2 | HASH | ['doc:users2'] | [] | 0 |
Index Fields:
| Name | Attribute | Type | Field Option | Option Value |
|----------------|----------------|---------|----------------|----------------|
| user | user | TEXT | WEIGHT | 1 |
| job | job | TEXT | WEIGHT | 1 |
| content | content | TEXT | WEIGHT | 1 |
| credit_score | credit_score | TAG | SEPARATOR | , |
| age | age | NUMERIC | | |
| content_vector | content_vector | VECTOR | | |
and throw a warning to the user (log output) that the generated schema
does not match the specified schema.
```text
index_schema does not match generated schema from metadata.
index_schema: {'text': [{'name': 'user'}, {'name': 'job'}], 'tag': [{'name': 'credit_score'}], 'numeric': [{'name': 'age'}]}
generated_schema: {'text': [{'name': 'user'}, {'name': 'job'}, {'name': 'credit_score'}], 'numeric': [{'name': 'age'}]}
```
As long as this is on purpose, this is fine.
The schema can be defined as a yaml file or a dictionary
```yaml
text:
- name: user
- name: job
tag:
- name: credit_score
numeric:
- name: age
```
and you pass in a path like
```python
rds, keys = Redis.from_texts_return_keys(
texts,
embeddings,
metadatas=metadata,
redis_url="redis://localhost:6379",
index_name="users3",
index_schema=Path("sample1.yml").resolve()
)
```
Which will create the same schema as defined in the dictionary example
Index Information:
| Index Name | Storage Type | Prefixes | Index Options | Indexing |
|--------------|----------------|----------------|-----------------|------------|
| users3 | HASH | ['doc:users3'] | [] | 0 |
Index Fields:
| Name | Attribute | Type | Field Option | Option Value |
|----------------|----------------|---------|----------------|----------------|
| user | user | TEXT | WEIGHT | 1 |
| job | job | TEXT | WEIGHT | 1 |
| content | content | TEXT | WEIGHT | 1 |
| credit_score | credit_score | TAG | SEPARATOR | , |
| age | age | NUMERIC | | |
| content_vector | content_vector | VECTOR | | |
### Custom Vector Indexing Schema
Users with large use cases may want to change how they formulate the
vector index created by Langchain
To utilize all the features of Redis for vector database use cases like
this, you can now do the following to pass in index attribute modifiers
like changing the indexing algorithm to HNSW.
```python
vector_schema = {
"algorithm": "HNSW"
}
rds, keys = Redis.from_texts_return_keys(
texts,
embeddings,
metadatas=metadata,
redis_url="redis://localhost:6379",
index_name="users3",
vector_schema=vector_schema
)
```
A more complex example may look like
```python
vector_schema = {
"algorithm": "HNSW",
"ef_construction": 200,
"ef_runtime": 20
}
rds, keys = Redis.from_texts_return_keys(
texts,
embeddings,
metadatas=metadata,
redis_url="redis://localhost:6379",
index_name="users3",
vector_schema=vector_schema
)
```
All names correspond to the arguments you would set if using Redis-py or
RedisVL. (put in doc link later)
### Better Querying
Both vector queries and Range (limit) queries are now available and
metadata is returned by default. The outputs are shown.
```python
>>> query = "foo"
>>> results = rds.similarity_search(query, k=1)
>>> print(results)
[Document(page_content='foo', metadata={'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '14', 'id': 'doc:users:657a47d7db8b447e88598b83da879b9d', 'score': '7.15255737305e-07'})]
>>> results = rds.similarity_search_with_score(query, k=1, return_metadata=False)
>>> print(results) # no metadata, but with scores
[(Document(page_content='foo', metadata={}), 7.15255737305e-07)]
>>> results = rds.similarity_search_limit_score(query, k=6, score_threshold=0.0001)
>>> print(len(results)) # range query (only above threshold even if k is higher)
4
```
### Custom metadata filtering
A big advantage of Redis in this space is being able to do filtering on
data stored alongside the vector itself. With the example above, the
following is now possible in langchain. The equivalence operators are
overridden to describe a new expression language that mimic that of
[redisvl](redisvl.com). This allows for arbitrarily long sequences of
filters that resemble SQL commands that can be used directly with vector
queries and range queries.
There are two interfaces by which to do so and both are shown.
```python
>>> from langchain.vectorstores.redis import RedisFilter, RedisNum, RedisText
>>> age_filter = RedisFilter.num("age") > 18
>>> age_filter = RedisNum("age") > 18 # equivalent
>>> results = rds.similarity_search(query, filter=age_filter)
>>> print(len(results))
3
>>> job_filter = RedisFilter.text("job") == "engineer"
>>> job_filter = RedisText("job") == "engineer" # equivalent
>>> results = rds.similarity_search(query, filter=job_filter)
>>> print(len(results))
2
# fuzzy match text search
>>> job_filter = RedisFilter.text("job") % "eng*"
>>> results = rds.similarity_search(query, filter=job_filter)
>>> print(len(results))
2
# combined filters (AND)
>>> combined = age_filter & job_filter
>>> results = rds.similarity_search(query, filter=combined)
>>> print(len(results))
1
# combined filters (OR)
>>> combined = age_filter | job_filter
>>> results = rds.similarity_search(query, filter=combined)
>>> print(len(results))
4
```
All the above filter results can be checked against the data above.
### Other
- Issue: #3967
- Dependencies: No added dependencies
- Tag maintainer: @hwchase17 @baskaryan @rlancemartin
- Twitter handle: @sampartee
---------
Co-authored-by: Naresh Rangan <naresh.rangan0@walmart.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR implements a custom chain that wraps Amazon Comprehend API
calls. The custom chain is aimed to be used with LLM chains to provide
moderation capability that let’s you detect and redact PII, Toxic and
Intent content in the LLM prompt, or the LLM response. The
implementation accepts a configuration object to control what checks
will be performed on a LLM prompt and can be used in a variety of setups
using the LangChain expression language to not only detect the
configured info in chains, but also other constructs such as a
retriever.
The included sample notebook goes over the different configuration
options and how to use it with other chains.
### Usage sample
```python
from langchain_experimental.comprehend_moderation import BaseModerationActions, BaseModerationFilters
moderation_config = {
"filters":[
BaseModerationFilters.PII,
BaseModerationFilters.TOXICITY,
BaseModerationFilters.INTENT
],
"pii":{
"action": BaseModerationActions.ALLOW,
"threshold":0.5,
"labels":["SSN"],
"mask_character": "X"
},
"toxicity":{
"action": BaseModerationActions.STOP,
"threshold":0.5
},
"intent":{
"action": BaseModerationActions.STOP,
"threshold":0.5
}
}
comp_moderation_with_config = AmazonComprehendModerationChain(
moderation_config=moderation_config, #specify the configuration
client=comprehend_client, #optionally pass the Boto3 Client
verbose=True
)
template = """Question: {question}
Answer:"""
prompt = PromptTemplate(template=template, input_variables=["question"])
responses = [
"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.",
"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here."
]
llm = FakeListLLM(responses=responses)
llm_chain = LLMChain(prompt=prompt, llm=llm)
chain = (
prompt
| comp_moderation_with_config
| {llm_chain.input_keys[0]: lambda x: x['output'] }
| llm_chain
| { "input": lambda x: x['text'] }
| comp_moderation_with_config
)
response = chain.invoke({"question": "A sample SSN number looks like this 123-456-7890. Can you give me some more samples?"})
print(response['output'])
```
### Output
```
> Entering new AmazonComprehendModerationChain chain...
Running AmazonComprehendModerationChain...
Running pii validation...
Found PII content..stopping..
The prompt contains PII entities and cannot be processed
```
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
Co-authored-by: Anjan Biswas <anjanavb@amazon.com>
Co-authored-by: Jha <nikjha@amazon.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR fixes `QuestionListOutputParser` text splitting.
`QuestionListOutputParser` incorrectly splits numbered list text into
lines. If text doesn't end with `\n` , the regex doesn't capture the
last item. So it always returns `n - 1` items, and
`WebResearchRetriever.llm_chain` generates less queries than requested
in the search prompt.
How to reproduce:
```python
from langchain.retrievers.web_research import QuestionListOutputParser
parser = QuestionListOutputParser()
good = parser.parse(
"""1. This is line one.
2. This is line two.
""" # <-- !
)
bad = parser.parse(
"""1. This is line one.
2. This is line two.""" # <-- No new line.
)
assert good.lines == ['1. This is line one.\n', '2. This is line two.\n'], good.lines
assert bad.lines == ['1. This is line one.\n', '2. This is line two.'], bad.lines
```
NOTE: Last item will not contain a line break but this seems ok because
the items are stripped in the
`WebResearchRetriever.clean_search_query()`.
Description: You cannot execute spark_sql with versions prior to 3.4 due
to the introduction of pyspark.errors in version 3.4.
And if you are below you get 3.4 "pyspark is not installed. Please
install it with pip nstall pyspark" which is not helpful. Also if you
not have pyspark installed you get already the error in init. I would
return all errors. But if you have a different idea feel free to
comment.
Issue: None
Dependencies: None
Maintainer:
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description:
- adding implementation of delete for pgvector
- adding modification time in docs metadata for confluence and google
drive.
Issue:
https://github.com/langchain-ai/langchain/issues/9312
Tag maintainer: @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This adds Xata as a memory store also to the python version of
LangChain, similar to the [one for
LangChain.js](https://github.com/hwchase17/langchainjs/pull/2217).
I have added a Jupyter Notebook with a simple and a more complex example
using an agent.
To run the integration test, you need to execute something like:
```
XATA_API_KEY='xau_...' XATA_DB_URL="https://demo-uni3q8.eu-west-1.xata.sh/db/langchain" poetry run pytest tests/integration_tests/memory/test_xata.py
```
Where `langchain` is the database you create in Xata.
Still working out interface/notebooks + need discord data dump to test
out things other than copy+paste
Update:
- Going to remove the 'user_id' arg in the loaders themselves and just
standardize on putting the "sender" arg in the extra kwargs. Then can
provide a utility function to map these to ai and human messages
- Going to move the discord one into just a notebook since I don't have
a good dump to test on and copy+paste maybe isn't the greatest thing to
support in v0
- Need to do more testing on slack since it seems the dump only includes
channels and NOT 1 on 1 convos
-
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Adds the qdrant search filter/params to the
`max_marginal_relevance_search` method, which is present on others. I
did not add `offset` for pagination, because it's behavior would be
ambiguous in this setting (since we fetch extra and down-select).
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Kacper Łukawski <lukawski.kacper@gmail.com>
The Graph Chains are different in the way that it uses two LLMChains
instead of one like the retrievalQA chains. Therefore, sometimes you
want to use different LLM to generate the database query and to generate
the final answer.
This feature would make it more convenient to use different LLMs in the
same chain.
I have also renamed the Graph DB QA Chain to Neo4j DB QA Chain in the
documentation only as it is used only for Neo4j. The naming was
ambigious as it was the first graphQA chain added and wasn't sure how do
you want to spin it.
Uses the shorter import path
`from langchain.document_loaders import` instead of the full path
`from langchain.document_loaders.assemblyai`
Applies those changes to the docs and the unit test.
See #9667 that adds this new loader.
⏳
- updated the top-level descriptions to a consistent format;
- changed several `ValueError` to `ImportError` in the import cases;
- changed the format of several internal functions from "name" to
"_name". So, these functions are not shown in the Top-level API
Reference page (with lists of classes/functions)
Currently, ChatOpenAI._stream does not reflect finish_reason to
generation_info. Change it to reflect that.
Same patch as https://github.com/langchain-ai/langchain/pull/9431 , but
also applies to _stream.
This PR adds a new document loader `AssemblyAIAudioTranscriptLoader`
that allows to transcribe audio files with the [AssemblyAI
API](https://www.assemblyai.com) and loads the transcribed text into
documents.
- Add new document_loader with class `AssemblyAIAudioTranscriptLoader`
- Add optional dependency `assemblyai`
- Add unit tests (using a Mock client)
- Add docs notebook
This is the equivalent to the JS integration already available in
LangChain.js. See the [LangChain JS docs AssemblyAI
page](https://js.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_loaders/assemblyai_audio_transcription).
At its simplest, you can use the loader to get a transcript back from an
audio file like this:
```python
from langchain.document_loaders.assemblyai import AssemblyAIAudioTranscriptLoader
loader = AssemblyAIAudioTranscriptLoader(file_path="./testfile.mp3")
docs = loader.load()
```
To use it, it needs the `assemblyai` python package installed, and the
environment variable `ASSEMBLYAI_API_KEY` set with your API key.
Alternatively, the API key can also be passed as an argument.
Twitter handles to shout out if so kindly 🙇
[@AssemblyAI](https://twitter.com/AssemblyAI) and
[@patloeber](https://twitter.com/patloeber)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR introduces a persistence layer to help with indexing workflows
into
vectostores.
The indexing code helps users to:
1. Avoid writing duplicated content into the vectostore
2. Avoid over-writing content if it's unchanged
Importantly, this keeps on working even if the content being written is
derived
via a set of transformations from some source content (e.g., indexing
children
documents that were derived from parent documents by chunking.)
The two main components are:
1. Persistence layer that keeps track of which keys were updated and
when.
Keeping track of the timestamp of updates, allows to clean up old
content
safely, and with minimal complexity.
2. HashedDocument which is used to hash the contents (including
metadata) of
the documents. We rely on the hashes for identifying duplicates.
The indexing code works with **ANY** document loader. To add
transformations
to the documents, users for now can add a custom document loader
that composes an existing loader together with document transformers.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: ~~Creates a new root_validator in `_AnthropicCommon` that
allows the use of `model_name` and `max_tokens` keyword arguments.~~
Adds pydantic field aliases to support `model_name` and `max_tokens` as
keyword arguments. Ultimately, this makes `ChatAnthropic` more
consistent with `ChatOpenAI`, making the two classes more
interchangeable for the developer.
- Issue: https://github.com/langchain-ai/langchain/issues/9510
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
The Docugami loader was not returning the source metadata key. This was
triggering this exception when used with retrievers, per
https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/schema/prompt_template.py#L193C1-L195C41
The fix is simple and just updates the metadata key name for the
document each chunk is sourced from, from "name" to "source" as
expected.
I tested by running the python notebook that has an end to end scenario
in it.
Tagging DataLoader maintainers @rlancemartin @eyurtsev
