Fixes proxy error.
Since openai does not parse proxy parameters and uses openai.proxy
directly, the proxy method needs to be modified.
7610c5adfa/openai/api_requestor.py (LL90)
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Models
- @hwchase17
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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#### Add start index to metadata in TextSplitter
- Modified method `create_documents` to track start position of each
chunk
- The `start_index` is included in the metadata if the `add_start_index`
parameter in the class constructor is set to `True`
This enables referencing back to the original document, particularly
useful when a specific chunk is retrieved.
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This PR adds a Baseten integration. I've done my best to follow the
contributor's guidelines and add docs, an example notebook, and an
integration test modeled after similar integrations' test.
Please let me know if there is anything I can do to improve the PR. When
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https://twitter.com/philip_kiely as contributors (the note on the PR
template said to include Twitter accounts)
+ this private attribute is referenced as `arxiv_search` in internal
usage and is set when verifying the environment
twitter: @spazm
#### Who can review?
Any of @hwchase17, @leo-gan, or @bongsang might be interested in
reviewing.
+ Mismatch between `arxiv_client` attribute vs `arxiv_search` in
validation and usage is present in the initial commit by @hwchase17.
+ @leo-gan has made most of the edits.
+ @bongsang implemented pdf download.
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---------
Co-authored-by: rlm <pexpresss31@gmail.com>
Fix the document page to open both search and Mendable when pressing
Ctrl+K.
I have changed the shortcut for Mendable to Ctrl+J.
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`load_qa_with_sources_chain` method already support four type of chain,
including `map_rerank`. update document to prevent any misunderstandings
😀.
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Fixes # (issue)
No, just update document.
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Fixes#3983
Mimicing what we do for saving and loading VectorDBQA chain, I added the
logic for RetrievalQA chain.
Also added a unit test. I did not find how we test other chains for
their saving and loading functionality, so I just added a file with one
test case. Let me know if there are recommended ways to test it.
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Your PR Title (What it does)
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- Added `SingleStoreDB` vector store, which is a wrapper over the
SingleStore DB database, that can be used as a vector storage and has an
efficient similarity search.
- Added integration tests for the vector store
- Added jupyter notebook with the example
@dev2049
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Allow callbacks to monitor ConversationalRetrievalChain
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I ran into an issue where load_qa_chain was not passing the callbacks
down to the child LLM chains, and so made sure that callbacks are
propagated. There are probably more improvements to do here but this
seemed like a good place to stop.
Note that I saw a lot of references to callbacks_manager, which seems to
be deprecated. I left that code alone for now.
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in the `ElasticKnnSearch` class added 2 arguments that were not exposed
properly
`knn_search` added:
- `vector_query_field: Optional[str] = 'vector'`
-- vector_query_field: Field name to use in knn search if not default
'vector'
`knn_hybrid_search` added:
- `vector_query_field: Optional[str] = 'vector'`
-- vector_query_field: Field name to use in knn search if not default
'vector'
- `query_field: Optional[str] = 'text'`
-- query_field: Field name to use in search if not default 'text'
Fixes # https://github.com/hwchase17/langchain/issues/5633
cc: @dev2049 @hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Simply fixing a small typo in the memory page.
Also removed an extra code block at the end of the file.
Along the way, the current outputs seem to have changed in a few places
so left that for posterity, and updated the number of runs which seems
harmless, though I can clean that up if preferred.
Implementation of similarity_search_with_relevance_scores for quadrant
vector store.
As implemented the method is also compatible with other capacities such
as filtering.
Integration tests updated.
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This PR adds documentation for Shale Protocol's integration with
LangChain.
[Shale Protocol](https://shaleprotocol.com) provides forever-free
production-ready inference APIs to the open-source community. We have
global data centers and plan to support all major open LLMs (estimated
~1,000 by 2025).
The team consists of software and ML engineers, AI researchers,
designers, and operators across North America and Asia. Combined
together, the team has 50+ years experience in machine learning, cloud
infrastructure, software engineering and product development. Team
members have worked at places like Google and Microsoft.
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---------
Co-authored-by: Karen Sheng <46656667+karensheng@users.noreply.github.com>
## Changes
- Added the `stop` param to the `_VertexAICommon` class so it can be set
at llm initialization
## Example Usage
```python
VertexAI(
# ...
temperature=0.15,
max_output_tokens=128,
top_p=1,
top_k=40,
stop=["\n```"],
)
```
## Possible Reviewers
- @hwchase17
- @agola11
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Add some logging into the powerbi tool so that you can see the queries
being sent to PBI and attempts to correct them.
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### Summary
Adds an `UnstructuredCSVLoader` for loading CSVs. One advantage of using
`UnstructuredCSVLoader` relative to the standard `CSVLoader` is that if
you use `UnstructuredCSVLoader` in `"elements"` mode, an HTML
representation of the table will be available in the metadata.
#### Who can review?
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@eyurtsev
Hi! I just added an example of how to use a custom scraping function
with the sitemap loader. I recently used this feature and had to dig in
the source code to find it. I thought it might be useful to other devs
to have an example in the Jupyter Notebook directly.
I only added the example to the documentation page.
@eyurtsev I was not able to run the lint. Please let me know if I have
to do anything else.
I know this is a very small contribution, but I hope it will be
valuable. My Twitter handle is @web3Dav3.
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---------
Co-authored-by: Yessen Kanapin <yessen@deepinfra.com>
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LatexTextSplitter needs to use "\n\\\chapter" when separators are
escaped, such as "\n\\\chapter", otherwise it will report an error:
(re.error: bad escape \c at position 1 (line 2, column 1))
Fixes # (issue)
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re.error: bad escape \c at position 1 (line 2, column 1)
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Co-authored-by: Pang <ugfly@qq.com>
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Fixes#5822
I upgrade my langchain lib by execute `pip install -U langchain`, and
the verion is 0.0.192。But i found that openai.api_base not working. I
use azure openai service as openai backend, the openai.api_base is very
import for me. I hava compared tag/0.0.192 and tag/0.0.191, and figure
out that:
openai params is moved inside `_invocation_params` function，and used in
some openai invoke:
but still some case not covered like:
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
just change "to" to "too" so it matches the above prompt
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Fixes # 5807
Realigned tests with implementation.
Also reinforced folder unicity for the test_faiss_local_save_load test
using date-time suffix
#### Before submitting
- Integration test updated
- formatting and linting ok (locally)
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I added support for specifing different types with ResponseSchema
objects:
## before
`
extracted_info = ResponseSchema(name="extracted_info", description="List
of extracted information")
`
generate the following doc: ```json\n{\n\t\"extracted_info\": string //
List of extracted information}```
This brings GPT to create a JSON with only one string in the specified
field even if you requested a List in the description.
## now
`extracted_info = ResponseSchema(name="extracted_info",
type="List[string]", description="List of extracted information")
`
generate the following doc: ```json\n{\n\t\"extracted_info\":
List[string] // List of extracted information}```
This way the model responds better to the prompt generating an array of
strings.
Tag maintainers/contributors who might be interested:
Agents / Tools / Toolkits
@vowelparrot
Don't know who can be interested, I suppose this is a tool, so I tagged
you vowelparrot,
anyway, it's a minor change, and shouldn't impact any other part of the
framework.
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Some links were broken from the previous merge. This PR fixes them.
Tested locally.
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Signed-off-by: Kourosh Hakhamaneshi <kourosh@anyscale.com>
This introduces the `YoutubeAudioLoader`, which will load blobs from a
YouTube url and write them. Blobs are then parsed by
`OpenAIWhisperParser()`, as show in this
[PR](https://github.com/hwchase17/langchain/pull/5580), but we extend
the parser to split audio such that each chuck meets the 25MB OpenAI
size limit. As shown in the notebook, this enables a very simple UX:
```
# Transcribe the video to text
loader = GenericLoader(YoutubeAudioLoader([url],save_dir),OpenAIWhisperParser())
docs = loader.load()
```
Tested on full set of Karpathy lecture videos:
```
# Karpathy lecture videos
urls = ["https://youtu.be/VMj-3S1tku0"
"https://youtu.be/PaCmpygFfXo",
"https://youtu.be/TCH_1BHY58I",
"https://youtu.be/P6sfmUTpUmc",
"https://youtu.be/q8SA3rM6ckI",
"https://youtu.be/t3YJ5hKiMQ0",
"https://youtu.be/kCc8FmEb1nY"]
# Directory to save audio files
save_dir = "~/Downloads/YouTube"
# Transcribe the videos to text
loader = GenericLoader(YoutubeAudioLoader(urls,save_dir),OpenAIWhisperParser())
docs = loader.load()
```
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In the [Databricks
integration](https://python.langchain.com/en/latest/integrations/databricks.html)
and [Databricks
LLM](https://python.langchain.com/en/latest/modules/models/llms/integrations/databricks.html),
we suggestted users to set the ENV variable `DATABRICKS_API_TOKEN`.
However, this is inconsistent with the other Databricks library. To make
it consistent, this PR changes the variable from `DATABRICKS_API_TOKEN`
to `DATABRICKS_TOKEN`
After changes, there is no more `DATABRICKS_API_TOKEN` in the doc
```
$ git grep DATABRICKS_API_TOKEN|wc -l
0
$ git grep DATABRICKS_TOKEN|wc -l
8
```
cc @hwchase17 @dev2049 @mengxr since you have reviewed the previous PRs.
