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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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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Minor change to the SingleStoreVectorStore:
Updated connection attributes names according to the SingleStoreDB
recommendations
@rlancemartin, @eyurtsev
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Description: doc string suggests `from langchain.llms import
LlamaCppEmbeddings` under `LlamaCpp()` class example but
`LlamaCppEmbeddings` is not in `langchain.llms`
Issue: None open
Tag maintainer: @baskaryan
Documentation update for [Jina
ecosystem](https://python.langchain.com/docs/ecosystem/integrations/jina)
and `langchain-serve` in the deployments section to latest features.
@hwchase17
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[Apache HugeGraph](https://github.com/apache/incubator-hugegraph) is a
convenient, efficient, and adaptable graph database, compatible with the
Apache TinkerPop3 framework and the Gremlin query language.
In this PR, the HugeGraph and HugeGraphQAChain provide the same
functionality as the existing integration with Neo4j and enables query
generation and question answering over HugeGraph database. The
difference is that the graph query language supported by HugeGraph is
not cypher but another very popular graph query language
[Gremlin](https://tinkerpop.apache.org/gremlin.html).
A notebook example and a simple test case have also been added.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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This PR introduces a new Mendable UI tailored to a better search
experience.
We're more closely integrating our traditional search with our AI
generation.
With this change, you won't have to tab back and forth between the
mendable bot and the keyword search. Both types of search are handled in
the same bar. This should make the docs easier to navigate. while still
letting users get code generations or AI-summarized answers if they so
wish. Also, it should reduce the cost.
Would love to hear your feedback :)
Cc: @dev2049 @hwchase17
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