### 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
Not obvious what the error is when you cannot index. This pr adds the
ability to log the first errors reason, to help the user diagnose the
issue.
Also added some more documentation for when you want to use the
vectorstore with an embedding model deployed in elasticsearch.
Credit: @elastic and @phoey1
- Description: a description of the change
when I set `content_format=ContentFormat.VIEW` and
`keep_markdown_format=True` on ConfluenceLoader, it shows the following
error:
```
langchain/document_loaders/confluence.py", line 459, in process_page
page["body"]["storage"]["value"], heading_style="ATX"
KeyError: 'storage'
```
The reason is because the content format was set to `view` but it was
still trying to get the content from `page["body"]["storage"]["value"]`.
Also added the other content formats which are supported by Atlassian
API
https://stackoverflow.com/questions/34353955/confluence-rest-api-expanding-page-body-when-retrieving-page-by-title/34363386#34363386
- Issue: the issue # it fixes (if applicable),
Not applicable.
- Dependencies: any dependencies required for this change,
Added optional dependency `markdownify` if anyone wants to extract in
markdown format.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Added the capability to handles structured data from
google enterprise search,
- Issue: Retriever failed when underline search engine was integrated
with structured data,
- Dependencies: google-api-core
- Tag maintainer: @jarokaz
- Twitter handle: anifort
Please make sure you're PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
---------
Co-authored-by: Christos Aniftos <aniftos@google.com>
Co-authored-by: Holt Skinner <13262395+holtskinner@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Updates the hub stubs to not fail when no api key is found. For
supporting singleton tenants and default values from sdk 0.1.6.
Also adds the ability to define is_public and description for backup
repo creation on push.
Currently, generation_info is not respected by only reflecting messages
in chunks. Change it to add generations so that generation chunks are
merged properly.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Description: current code does not work very well on jupyter notebook,
so I changed the code so that it imports `tqdm.auto` instead.
- Issue: #9582
- Dependencies: N/A
- Tag maintainer: @hwchase17, @baskaryan
- Twitter handle: N/A
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
It's possible that langchain-experimental works fine with the latest
*published* langchain, but is broken with the langchain on `master`.
Unfortunately, you can see this is currently the case — this is why this
PR also includes a minor fix for the `langchain` package itself.
We want to catch situations like that *before* releasing a new
langchain, hence this test.
# Description
This PR introduces a new toolkit for interacting with the AINetwork
blockchain. The toolkit provides a set of tools for performing various
operations on the AINetwork blockchain, such as transferring AIN,
reading and writing values to the blockchain database, managing apps,
setting rules and owners.
# Dependencies
[ain-py](https://github.com/ainblockchain/ain-py) >= 1.0.2
# Misc
The example notebook
(langchain/docs/extras/integrations/toolkits/ainetwork.ipynb) is in the
PR
---------
Co-authored-by: kriii <kriii@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Introduces a conditional in `ArangoGraph.generate_schema()` to exclude
empty ArangoDB Collections from the schema
- Add empty collection test case
Issue: N/A
Dependencies: None
### Description
Polars is a DataFrame interface on top of an OLAP Query Engine
implemented in Rust.
Polars is faster to read than pandas, so I'm looking forward to seeing
it added to the document loader.
### Dependencies
polars (https://pola-rs.github.io/polars-book/user-guide/)
---------
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.
@eyurtsev , @baskaryan
Thanks
Add PromptGuard integration
-------
There are two approaches to integrate PromptGuard with a LangChain
application.
1. PromptGuardLLMWrapper
2. functions that can be used in LangChain expression.
-----
- Dependencies
`promptguard` python package, which is a runtime requirement if you'd
try out the demo.
- @baskaryan @hwchase17 Thanks for the ideas and suggestions along the
development process.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description
When we're loading documents using `ConfluenceLoader`:`load` function
and, if both `include_comments=True` and `keep_markdown_format=True`,
we're getting an error saying `NameError: free variable 'BeautifulSoup'
referenced before assignment in enclosing scope`.
loader = ConfluenceLoader(url="URI", token="TOKEN")
documents = loader.load(
space_key="SPACE",
include_comments=True,
keep_markdown_format=True,
)
This happens because previous imports only consider the
`keep_markdown_format` parameter, however to include the comments, it's
using `BeautifulSoup`
Now it's fixed to handle all four scenarios considering both
`include_comments` and `keep_markdown_format`.
### Twitter
`@SathinduGA`
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Allows the user of `ConfluenceLoader` to pass a
`requests.Session` object in lieu of an authentication mechanism
- Issue: None
- Dependencies: None
- Tag maintainer: @hwchase17
- Improved docs
- Improved performance in multiple ways through batching, threading,
etc.
- fixed error message
- Added support for metadata filtering during similarity search.
@baskaryan PTAL
[Epsilla](https://github.com/epsilla-cloud/vectordb) vectordb is an
open-source vector database that leverages the advanced academic
parallel graph traversal techniques for vector indexing.
This PR adds basic integration with
[pyepsilla](https://github.com/epsilla-cloud/epsilla-python-client)(Epsilla
vectordb python client) as a vectorstore.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
The package is linted with mypy, so its type hints are correct and
should be exposed publicly. Without this file, the type hints remain
private and cannot be used by downstream users of the package.
- Description: Updated marqo integration to use tensor_fields instead of
non_tensor_fields. Upgraded marqo version to 1.2.4
- Dependencies: marqo 1.2.4
---------
Co-authored-by: Raynor Kirkson E. Chavez <raynor.chavez@192.168.254.171>
Co-authored-by: Bagatur <baskaryan@gmail.com>
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- Description: support [ERNIE
Embedding-V1](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/alj562vvu),
which is part of ERNIE ecology
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Changed metadata retrieval so that it combines Vectara
doc level and part level metadata
- Tag maintainer: @rlancemartin
- Twitter handle: @ofermend
**Description**:
- Uniformed the current valid suffixes (file formats) for loading agents
from hubs and files (to better handle future additions);
- Clarified exception messages (also in unit test).
@rlancemartin The current implementation within `Geopandas.GeoDataFrame`
loader uses the python builtin `str()` function on the input geometries.
While this looks very close to WKT (Well known text), Python's str
function doesn't guarantee that.
In the interest of interop., I've changed to the of use `wkt` property
on the Shapely geometries for generating the text representation of the
geometries.
Also, included here:
- validation of the input `page_content_column` as being a GeoSeries.
- geometry `crs` (Coordinate Reference System) / bounds
(xmin/ymin/xmax/ymax) added to Document metadata. Having the CRS is
critical... having the bounds is just helpful!
I think there is a larger question of "Should the geometry live in the
`page_content`, or should the record be better summarized and tuck the
geom into metadata?" ...something for another day and another PR.
This is an extension of #8104. I updated some of the signatures so all
the tests pass.
@danhnn I couldn't commit to your PR, so I created a new one. Thanks for
your contribution!
@baskaryan Could you please merge it?
---------
Co-authored-by: Danh Nguyen <dnncntt@gmail.com>
### Summary
Fixes a bug from #7850 where post processing functions in Unstructured
loaders were not apply. Adds a assertion to the test to verify the post
processing function was applied and also updates the explanation in the
example notebook.
Issue: https://github.com/langchain-ai/langchain/issues/9401
In the Async mode, SequentialChain implementation seems to run the same
callbacks over and over since it is re-using the same callbacks object.
Langchain version: 0.0.264, master
The implementation of this aysnc route differs from the sync route and
sync approach follows the right pattern of generating a new callbacks
object instead of re-using the old one and thus avoiding the cascading
run of callbacks at each step.
Async mode:
```
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
...
for i, chain in enumerate(self.chains):
_input = await chain.arun(_input, callbacks=callbacks)
...
```
Regular mode:
```
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
for i, chain in enumerate(self.chains):
_input = chain.run(_input, callbacks=_run_manager.get_child(f"step_{i+1}"))
...
```
Notice how we are reusing the callbacks object in the Async code which
will have a cascading effect as we run through the chain. It runs the
same callbacks over and over resulting in issues.
Solution:
Define the async function in the same pattern as the regular one and
added tests.
---------
Co-authored-by: vamsee_yarlagadda <vamsee.y@airbnb.com>
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📜
- updated the top-level descriptions to a consistent format;
- changed the format of several 100% internal functions from "name" to
"_name". So, these functions are not shown in the Top-level API
Reference page (with lists of classes/functions)
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Refactored code to ensure consistent handling of ImportError. Replaced
instances of raising ValueError with raising ImportError.
The choice of raising a ValueError here is somewhat unconventional and
might lead to confusion for anyone reading the code. Typically, when
dealing with import-related errors, the recommended approach is to raise
an ImportError with a descriptive message explaining the issue. This
provides a clearer indication that the problem is related to importing
the required module.
@hwchase17 , @baskaryan , @eyurtsev
Thanks
Aashish
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR fills in more missing type annotations on pydantic models.
It's OK if it missed some annotations, we just don't want it to get
annotations wrong at this stage.
I'll do a few more passes over the same files!
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gets announced and you'd like a mention, we'll gladly shout you out!
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submitting. Run `make format`, `make lint` and `make test` to check this
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tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. These live is docs/extras
directory.
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@baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
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This PR fixes the Airbyte loaders when doing incremental syncs. The
notebooks are calling out to access `loader.last_state` to get the
current state of incremental syncs, but this didn't work due to a
refactoring of how the loaders are structured internally in the original
PR.
This PR fixes the issue by adding a `last_state` property that forwards
the state correctly from the CDK adapter.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Type:
Improvement
---
## Description:
Running QAWithSourcesChain sometimes raises ValueError as mentioned in
issue #7184:
```
ValueError: too many values to unpack (expected 2)
Traceback:
response = qa({"question": pregunta}, return_only_outputs=True)
File "C:\Anaconda3\envs\iagen_3_10\lib\site-packages\langchain\chains\base.py", line 166, in __call__
raise e
File "C:\Anaconda3\envs\iagen_3_10\lib\site-packages\langchain\chains\base.py", line 160, in __call__
self._call(inputs, run_manager=run_manager)
File "C:\Anaconda3\envs\iagen_3_10\lib\site-packages\langchain\chains\qa_with_sources\base.py", line 132, in _call
answer, sources = re.split(r"SOURCES:\s", answer)
```
This is due to LLM model generating subsequent question, answer and
sources, that is complement in a similar form as below:
```
<final_answer>
SOURCES: <sources>
QUESTION: <new_or_repeated_question>
FINAL ANSWER: <new_or_repeated_final_answer>
SOURCES: <new_or_repeated_sources>
```
It leads the following line
```
re.split(r"SOURCES:\s", answer)
```
to return more than 2 elements and result in ValueError. The simple fix
is to split also with "QUESTION:\s" and take the first two elements:
```
answer, sources = re.split(r"SOURCES:\s|QUESTION:\s", answer)[:2]
```
Sometimes LLM might also generate some other texts, like alternative
answers in a form:
```
<final_answer_1>
SOURCES: <sources>
<final_answer_2>
SOURCES: <sources>
<final_answer_3>
SOURCES: <sources>
```
In such cases it is the best to split previously obtained sources with
new line:
```
sources = re.split(r"\n", sources.lstrip())[0]
```
---
## Issue:
Resolves#7184
---
## Maintainer:
@baskaryan
I quick change to allow the output key of create_openai_fn_chain to
optionally be changed.
@baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Added improvements in Nebula LLM to perform auto-retry;
more generation parameters supported. Conversation is no longer required
to be passed in the LLM object. Examples are updated.
- Issue: N/A
- Dependencies: N/A
- Tag maintainer: @baskaryan
- Twitter handle: symbldotai
---------
Co-authored-by: toshishjawale <toshish@symbl.ai>
Update documentation and URLs for the Langchain Context integration.
We've moved from getcontext.ai to context.ai \o/
Thanks in advance for the review!
* PR updates test.yml to test with both pydantic versions
* Code should be refactored to make it easier to do testing in matrix
format w/ packages
* Added steps to assert that pydantic version in the environment is as
expected
Now with ElasticsearchStore VectorStore merged, i've added support for
the self-query retriever.
I've added a notebook also to demonstrate capability. I've also added
unit tests.
**Credit**
@elastic and @phoey1 on twitter.
# Poetry updates
This PR updates LangChains poetry file to remove
any dependencies that aren't pydantic v2 compatible yet.
All packages remain usable under pydantic v1, and can be installed
separately.
## Bumping the following packages:
* langsmith
## Removing the following packages
not used in extended unit-tests:
* zep-python, anthropic, jina, spacy, steamship, betabageldb
not used at all:
* octoai-sdk
Cleaning up extras w/ for removed packages.
## Snapshots updated
Some snapshots had to be updated due to a change in the data model in
langsmith. RunType used to be Union of Enum and string and was changed
to be string only.
This PR adds serialization support for protocol bufferes in
`WandbTracer`. This allows code generation chains to be visualized.
Additionally, it also fixes a minor bug where the settings are not
honored when a run is initialized before using the `WandbTracer`
@agola11
---------
Co-authored-by: Bharat Ramanathan <ramanathan.parameshwaran@gohuddl.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Todo:
- [x] Connection options (cloud, localhost url, es_connection) support
- [x] Logging support
- [x] Customisable field support
- [x] Distance Similarity support
- [x] Metadata support
- [x] Metadata Filter support
- [x] Retrieval Strategies
- [x] Approx
- [x] Approx with Hybrid
- [x] Exact
- [x] Custom
- [x] ELSER (excluding hybrid as we are working on RRF support)
- [x] integration tests
- [x] Documentation
👋 this is a contribution to improve Elasticsearch integration with
Langchain. Its based loosely on the changes that are in master but with
some notable changes:
## Package name & design improvements
The import name is now `ElasticsearchStore`, to aid discoverability of
the VectorStore.
```py
## Before
from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch, ElasticKnnSearch
## Now
from langchain.vectorstores.elasticsearch import ElasticsearchStore
```
## Retrieval Strategy support
Before we had a number of classes, depending on the strategy you wanted.
`ElasticKnnSearch` for approx, `ElasticVectorSearch` for exact / brute
force.
With `ElasticsearchStore` we have retrieval strategies:
### Approx Example
Default strategy for the vast majority of developers who use
Elasticsearch will be inferring the embeddings from outside of
Elasticsearch. Uses KNN functionality of _search.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index"
)
output = docsearch.similarity_search("foo", k=1)
```
### Approx, with hybrid
Developers who want to search, using both the embedding and the text
bm25 match. Its simple to enable.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index",
strategy=ElasticsearchStore.ApproxRetrievalStrategy(hybrid=True)
)
output = docsearch.similarity_search("foo", k=1)
```
### Approx, with `query_model_id`
Developers who want to infer within Elasticsearch, using the model
loaded in the ml node.
This relies on the developer to setup the pipeline and index if they
wish to embed the text in Elasticsearch. Example of this in the test.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index",
strategy=ElasticsearchStore.ApproxRetrievalStrategy(
query_model_id="sentence-transformers__all-minilm-l6-v2"
),
)
output = docsearch.similarity_search("foo", k=1)
```
### I want to provide my own custom Elasticsearch Query
You might want to have more control over the query, to perform
multi-phase retrieval such as LTR, linearly boosting on document
parameters like recently updated or geo-distance. You can do this with
`custom_query_fn`
```py
def my_custom_query(query_body: dict, query: str) -> dict:
return {"query": {"match": {"text": {"query": "bar"}}}}
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts, FakeEmbeddings(), **elasticsearch_connection, index_name=index_name
)
docsearch.similarity_search("foo", k=1, custom_query=my_custom_query)
```
### Exact Example
Developers who have a small dataset in Elasticsearch, dont want the cost
of indexing the dims vs tradeoff on cost at query time. Uses
script_score.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index",
strategy=ElasticsearchStore.ExactRetrievalStrategy(),
)
output = docsearch.similarity_search("foo", k=1)
```
### ELSER Example
Elastic provides its own sparse vector model called ELSER. With these
changes, its really easy to use. The vector store creates a pipeline and
index thats setup for ELSER. All the developer needs to do is configure,
ingest and query via langchain tooling.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index",
strategy=ElasticsearchStore.SparseVectorStrategy(),
)
output = docsearch.similarity_search("foo", k=1)
```
## Architecture
In future, we can introduce new strategies and allow us to not break bwc
as we evolve the index / query strategy.
## Credit
On release, could you credit @elastic and @phoey1 please? Thank you!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Updated prompts for the MultiOn toolkit for better functionality
- Non-blocking but good to have it merged to improve the overall
performance for the toolkit
@hinthornw @hwchase17
---------
Co-authored-by: Naman Garg <ngarg3@binghamton.edu>
Add ability to track langchain usage for Rockset. Rockset's new python
client allows setting this. To prevent old clients from failing, it
ignore if setting throws exception (we can't track old versions)
Tested locally with old and new Rockset python client
cc @baskaryan
2 things:
- Implement the private method rather than the public one so callbacks
are handled properly
- Add search_kwargs (Open to not adding this if we are trying to
deprecate this UX but seems like as a user i'd assume similar args to
the vector store retriever. In fact some may assume this implements the
same interface but I'm not dealing with that here)
-
First of a few PRs to add full compatibility to both pydantic v1 and v2.
This PR creates pydantic v1 namespace and adds it to sys.modules.
Upcoming changes:
1. Handle `openapi-schema-pydantic = "^1.2"` and dependent chains/tools
2. bump dependencies to versions that are cross compatible for pydantic
or remove them (see below)
3. Add tests to github workflows to test with pydantic v1 and v2
**Dependencies**
From a quick look (could be wrong since was done manually)
**dependencies pinning pydantic below 2** (some of these can be bumped
to newer versions are provide cross-compatible code)
anthropic
bentoml
confection
fastapi
langsmith
octoai-sdk
openapi-schema-pydantic
qdrant-client
spacy
steamship
thinc
zep-python
Unpinned
marqo (*)
nomic (*)
xinference(*)
