The intermediate steps example in docs has an example on how to retrieve
and display the intermediate steps.
But the intermediate steps object is of type AgentAction which cannot be
passed to json.dumps (it raises an error).
I replaced it with Langchain's dumps function (from langchain.load.dump
import dumps) which is the preferred way to do so.
**Description:**
As long as `enforce_stop_tokens` returns a first occurrence, we can
speed up the execution by setting the optional `maxsplit` parameter to
1.
Tag maintainer:
@agola11
@hwchase17
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---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** New metadata fields were added to
`unstructured==0.10.15`, and our hosted api has been updated to reflect
this. When users call `partition_via_api` with an older version of the
library, they'll hit a parsing error related to the new fields.
Description
* Refactor Fireworks within Langchain LLMs.
* Remove FireworksChat within Langchain LLMs.
* Add ChatFireworks (which uses chat completion api) to Langchain chat
models.
* Users have to install `fireworks-ai` and register an api key to use
the api.
Issue - Not applicable
Dependencies - None
Tag maintainer - @rlancemartin @baskaryan
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- **Tag maintainer:** @hwchase17
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---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
This enables bulk args like `chunk_size` to be passed down from the
ingest methods (from_text, from_documents) to be passed down to the bulk
API.
This helps alleviate issues where bulk importing a large amount of
documents into Elasticsearch was resulting in a timeout.
Contribution Shoutout
- @elastic
- [x] Updated Integration tests
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Fixed navbar:
- renamed several files, so ToC is sorted correctly
- made ToC items consistent: formatted several Titles
- added several links
- reformatted several docs to a consistent format
- renamed several files (removed `_example` suffix)
- added renamed files to the `docs/docs_skeleton/vercel.json`
Sometimes you don't want the LLM to be aware of the whole graph schema,
and want it to ignore parts of the graph when it is constructing Cypher
statements.
- **Description**: Adding retrievers for [kay.ai](https://kay.ai) and
SEC filings powered by Kay and Cybersyn. Kay provides context as a
service: it's an API built for RAG.
- **Issue**: N/A
- **Dependencies**: Just added a dep to the
[kay](https://pypi.org/project/kay/) package
- **Tag maintainer**: @baskaryan @hwchase17 Discussed in slack
- **Twtter handle:** [@vishalrohra_](https://twitter.com/vishalrohra_)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
The huggingface pipeline in langchain (used for locally hosted models)
does not support batching. If you send in a batch of prompts, it just
processes them serially using the base implementation of _generate:
https://github.com/docugami/langchain/blob/master/libs/langchain/langchain/llms/base.py#L1004C2-L1004C29
This PR adds support for batching in this pipeline, so that GPUs can be
fully saturated. I updated the accompanying notebook to show GPU batch
inference.
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
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Closes#8842
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- Description: fix `ChatMessageChunk` concat error
- Issue: #10173
- Dependencies: None
- Tag maintainer: @baskaryan, @eyurtsev, @rlancemartin
- Twitter handle: None
---------
Co-authored-by: wangshuai.scotty <wangshuai.scotty@bytedance.com>
Co-authored-by: Nuno Campos <nuno@boringbits.io>
This PR aims at showcasing how to use vLLM's OpenAI-compatible chat API.
### Context
Lanchain already supports vLLM and its OpenAI-compatible `Completion`
API. However, the `ChatCompletion` API was not aligned with OpenAI and
for this reason I've waited for this
[PR](https://github.com/vllm-project/vllm/pull/852) to be merged before
adding this notebook to langchain.
### Description
This PR makes the following changes to OpenSearch:
1. Pass optional ids with `from_texts`
2. Pass an optional index name with `add_texts` and `search` instead of
using the same index name that was used during `from_texts`
### Issue
https://github.com/langchain-ai/langchain/issues/10967
### Maintainers
@rlancemartin, @eyurtsev, @navneet1v
Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
LLMRails Embedding Integration
This PR provides integration with LLMRails. Implemented here are:
langchain/embeddings/llm_rails.py
docs/extras/integrations/text_embedding/llm_rails.ipynb
Hi @hwchase17 after adding our vectorstore integration to langchain with
confirmation of you and @baskaryan, now we want to add our embedding
integration
---------
Co-authored-by: Anar Aliyev <aaliyev@mgmt.cloudnet.services>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Adds support for gradient.ai's embedding model.
This will remain a Draft, as the code will likely be refactored with the
`pip install gradientai` python sdk.
