Fix for bug in SitemapLoader
`aiohttp` `get` does not accept `verify` argument, and currently throws
error, so SitemapLoader is not working
This PR fixes it by removing `verify` param for `get` function call
Fixes#6107
#### Who can review?
Tag maintainers/contributors who might be interested:
@eyurtsev
---------
Co-authored-by: techcenary <127699216+techcenary@users.noreply.github.com>
Add an "eval" tag to traced evaluation runs
Most of this PR is actually
https://github.com/hwchase17/langchain/pull/7207 but I can't diff off
two separate PRs
---------
Co-authored-by: Ankush Gola <9536492+agola11@users.noreply.github.com>
### Description
This pull request introduces the "Cube Semantic Layer" document loader,
which demonstrates the retrieval of Cube's data model metadata in a
format suitable for passing to LLMs as embeddings. This enhancement aims
to provide contextual information and improve the understanding of data.
Twitter handle:
@the_cube_dev
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
This PR brings in a vectorstore interface for
[Marqo](https://www.marqo.ai/).
The Marqo vectorstore exposes some of Marqo's functionality in addition
the the VectorStore base class. The Marqo vectorstore also makes the
embedding parameter optional because inference for embeddings is an
inherent part of Marqo.
Docs, notebook examples and integration tests included.
Related PR:
https://github.com/hwchase17/langchain/pull/2807
---------
Co-authored-by: Tom Hamer <tom@marqo.ai>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Description:
Updating the docstrings for Milvus and Zilliz so that they appear
correctly on https://integrations.langchain.com/vectorstores. No changes
done to code.
Maintainer:
@baskaryan
Signed-off-by: Filip Haltmayer <filip.haltmayer@zilliz.com>
### Summary
Updates the docstrings for the unstructured base loaders so more useful
information appears on the integrations page. If these look good, will
add similar docstrings to the other loaders.
### Reviewers
- @rlancemartin
- @eyurtsev
- @hwchase17
- Description: Allow `InMemoryDocstore` to be created without passing a
dict to the constructor; the constructor can create a dict at runtime if
one isn't provided.
- Tag maintainer: @dev2049
- Description: At the moment, inserting new embeddings to pgvector is
querying all embeddings every time as the defined `embeddings`
relationship is using the default params, which sets `lazy="select"`.
This change drastically improves the performance and adds a few
additional cleanups:
* remove `collection.embeddings.append` as it was querying all
embeddings on insert, replace with `collection_id` param
* centralize storing logic in add_embeddings function to reduce
duplication
* remove boilerplate
- Issue: No issue was opened.
- Dependencies: None.
- Tag maintainer: this is a vectorstore update, so I think
@rlancemartin, @eyurtsev
- Twitter handle: @falmannaa
Hi, there
This pull request contains two commit:
**1. Implement delete interface with optional ids parameter on
AnalyticDB.**
**2. Allow customization of database connection behavior by exposing
engine_args parameter in interfaces.**
- This commit adds the `engine_args` parameter to the interfaces,
allowing users to customize the behavior of the database connection. The
`engine_args` parameter accepts a dictionary of additional arguments
that will be passed to the create_engine function. Users can now modify
various aspects of the database connection, such as connection pool size
and recycle time. This enhancement provides more flexibility and control
to users when interacting with the database through the exposed
interfaces.
This commit is related to VectorStores @rlancemartin @eyurtsev
Thank you for your attention and consideration.
- Description: This allows parameters such as `relevance_score_fn` to be
passed to the `FAISS` constructor via the `load_local()` class method.
- Tag maintainer: @rlancemartin @eyurtsev
This fixes#4833 and the critical vulnerability
https://nvd.nist.gov/vuln/detail/CVE-2023-34540
Previously, the JIRA API Wrapper had a mode that simply pipelined user
input into an `exec()` function.
[The intended use of the 'other' mode is to cover any of Atlassian's API
that don't have an existing
interface](cc33bde74f/langchain/tools/jira/prompt.py (L24))
Fortunately all of the [Atlassian JIRA API methods are subfunctions of
their `Jira`
class](https://atlassian-python-api.readthedocs.io/jira.html), so this
implementation calls these subfunctions directly.
As well as passing a string representation of the function to call, the
implementation flexibly allows for optionally passing args and/or
keyword-args. These are given as part of the dictionary input. Example:
```
{
"function": "update_issue_field", #function to execute
"args": [ #list of ordered args similar to other examples in this JiraAPIWrapper
"key",
{"summary": "New summary"}
],
"kwargs": {} #dict of key value keyword-args pairs
}
```
the above is equivalent to `self.jira.update_issue_field("key",
{"summary": "New summary"})`
Alternate query schema designs are welcome to make querying easier
without passing and evaluating arbitrary python code. I considered
parsing (without evaluating) input python code and extracting the
function, args, and kwargs from there and then pipelining them into the
callable function via `*f(args, **kwargs)` - but this seemed more
direct.
@vowelparrot @dev2049
---------
Co-authored-by: Jamal Rahman <jamal.rahman@builder.ai>
added tutorials.mdx; updated youtube.mdx
Rationale: the Tutorials section in the documentation is top-priority.
(for example, https://pytorch.org/docs/stable/index.html) Not every
project has resources to make tutorials. We have such a privilege.
Community experts created several tutorials on YouTube. But the tutorial
links are now hidden on the YouTube page and not easily discovered by
first-time visitors.
- Added new videos and tutorials that were created since the last
update.
- Made some reprioritization between videos on the base of the view
numbers.
#### Who can review?
- @hwchase17
- @dev2049
## Description
Added Office365 tool modules to `__init__.py` files
## Issue
As described in Issue
https://github.com/hwchase17/langchain/issues/6936, the Office365
toolkit can't be loaded easily because it is not included in the
`__init__.py` files.
## Reviewer
@dev2049
Description:
The OpenAI "embeddings" API intermittently falls into a failure state
where an embedding is returned as [ Nan ], rather than the expected 1536
floats. This patch checks for that state (specifically, for an embedding
of length 1) and if it occurs, throws an ApiError, which will cause the
chunk to be retried.
Issue:
I have been unable to find an official langchain issue for this problem,
but it is discussed (by another user) at
https://stackoverflow.com/questions/76469415/getting-embeddings-of-length-1-from-langchain-openaiembeddings
Maintainer: @dev2049
Testing:
Since this is an intermittent OpenAI issue, I have not provided a unit
or integration test. The provided code has, though, been run
successfully over several million tokens.
---------
Co-authored-by: William Webber <william@williamwebber.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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- [x] wire up tools
- [x] wire up retrievers
- [x] add integration test
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Description: Fix steamship import error
When running multi_modal_output_agent:
field "steamship" not yet prepared so type is still a ForwardRef, you
might need to call SteamshipImageGenerationTool.update_forward_refs().
Tag maintainer: @hinthornw
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Description: If their are missing or extra variables when validating
Jinja 2 template then a warning is issued rather than raising an
exception. This allows for better flexibility for the developer as
described in #7044. Also changed the relevant test so pytest is checking
for raised warnings rather than exceptions.
- Issue: #7044
- Tag maintainer: @hwchase17, @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fixing issue with SelfQueryRetriever due to unsupported LIKE and CONTAIN
comparators in Chroma's WHERE filter statements. This pull request
introduces a redefined set of comparators in Chroma to address the
problem and make it compatible with SelfQueryRetriever. For information
on the comparators supported by Chroma's filter, please refer to
https://docs.trychroma.com/usage-guide#using-where-filters.
<img width="495" alt="image"
src="https://github.com/hwchase17/langchain/assets/22267652/34789191-0293-4f63-9bdf-ad1e1f2567c4">
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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This PR makes the `textstat` library optional in the Flyte callback
handler.
@hinthornw, would you mind reviewing this PR since you merged the flyte
callback handler code previously?
---------
Signed-off-by: Samhita Alla <aallasamhita@gmail.com>
- Description: added some documentation to the Pinecone vector store
docs page.
- Issue: #7126
- Dependencies: None
- Tag maintainer: @baskaryan
I can add more documentation on the Pinecone integration functions as I
am going to go in great depth into this area. Just wanted to check with
the maintainers is if this is all good.
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- Memory: @hwchase17
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- Tracing / Callbacks: @agola11
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Replace this comment with:
- Description: Replace `if var is not None:` with `if var:`, a concise
and pythonic alternative
- Issue: N/A
- Dependencies: None
- Tag maintainer: Unsure
- Twitter handle: N/A
Signed-off-by: serhatgktp <efkan@ibm.com>
- Description: Modify the code for
AsyncIteratorCallbackHandler.on_llm_new_token to ensure that it does not
add an empty string to the result queue.
- Tag maintainer: @agola11
When using AsyncIteratorCallbackHandler with OpenAIFunctionsAgent, if
the LLM response function_call instead of direct answer, the
AsyncIteratorCallbackHandler.on_llm_new_token would be called with empty
string.
see also: langchain.chat_models.openai.ChatOpenAI._generate
An alternative solution is to modify the
langchain.chat_models.openai.ChatOpenAI._generate and do not call the
run_manager.on_llm_new_token when the token is empty string.
I am not sure which solution is better.
@hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# [SPARQL](https://www.w3.org/TR/rdf-sparql-query/) for
[LangChain](https://github.com/hwchase17/langchain)
## Description
LangChain support for knowledge graphs relying on W3C standards using
RDFlib: SPARQL/ RDF(S)/ OWL with special focus on RDF \
* Works with local files, files from the web, and SPARQL endpoints
* Supports both SELECT and UPDATE queries
* Includes both a Jupyter notebook with an example and integration tests
## Contribution compared to related PRs and discussions
* [Wikibase agent](https://github.com/hwchase17/langchain/pull/2690) -
uses SPARQL, but specifically for wikibase querying
* [Cypher qa](https://github.com/hwchase17/langchain/pull/5078) - graph
DB question answering for Neo4J via Cypher
* [PR 6050](https://github.com/hwchase17/langchain/pull/6050) - tries
something similar, but does not cover UPDATE queries and supports only
RDF
* Discussions on [w3c mailing list](mailto:semantic-web@w3.org) related
to the combination of LLMs (specifically ChatGPT) and knowledge graphs
## Dependencies
* [RDFlib](https://github.com/RDFLib/rdflib)
## Tag maintainer
Graph database related to memory -> @hwchase17
Update in_memory.py to fix "TypeError: keywords must be strings" on
certain dictionaries
Simple fix to prevent a "TypeError: keywords must be strings" error I
encountered in my use case.
@baskaryan
Thanks! Hope useful!
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fix for typos in MongoDB Atlas Vector Search documentation
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- Description: rename the invalid function name of GoogleSerperResults
Tool for OpenAIFunctionCall
- Tag maintainer: @hinthornw
When I use the GoogleSerperResults in OpenAIFunctionCall agent, the
following error occurs:
```shell
openai.error.InvalidRequestError: 'Google Serrper Results JSON' does not match '^[a-zA-Z0-9_-]{1,64}$' - 'functions.0.name'
```
So I rename the GoogleSerperResults's property "name" from "Google
Serrper Results JSON" to "google_serrper_results_json" just like
GoogleSerperRun's name: "google_serper", and it works.
I guess this should be reasonable.
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@hinthornw
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Hi @rlancemartin, @eyurtsev!
- Description: Adding HNSW extension support for Postgres. Similar to
pgvector vectorstore, with 3 differences
1. it uses HNSW extension for exact and ANN searches,
2. Vectors are of type array of real
3. Only supports L2
- Dependencies: [HNSW](https://github.com/knizhnik/hnsw) extension for
Postgres
- Example:
```python
db = HNSWVectoreStore.from_documents(
embedding=embeddings,
documents=docs,
collection_name=collection_name,
connection_string=connection_string
)
query = "What did the president say about Ketanji Brown Jackson"
docs_with_score: List[Tuple[Document, float]] =
db.similarity_search_with_score(query)
```
The example notebook is in the PR too.
- correct `endpoint_name` to `api_url`
- add `headers`
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