Commit Graph

62 Commits

Author SHA1 Message Date
Dhruvil Shah
92f05a67a4
Add markdown to specify important arguments (#6246)
To bypass SSL verification errors during web scraping, you can include
the ssl_verify=False parameter along with the headers parameter. This
combination of arguments proves useful, especially for beginners in the
field of web scraping.

<!--
Thank you for contributing to LangChain! Your PR will appear in our
release under the title you set. Please make sure it highlights your
valuable contribution.

Replace this with a description of the change, the issue it fixes (if
applicable), and relevant context. List any dependencies required for
this change.

After you're done, someone will review your PR. They may suggest
improvements. If no one reviews your PR within a few days, feel free to
@-mention the same people again, as notifications can get lost.

Finally, we'd love to show appreciation for your contribution - if you'd
like us to shout you out on Twitter, please also include your handle!
-->

Fixes #1829 

#### Before submitting

<!-- If you're adding a new integration, please include:

1. a test for the integration - favor unit tests that does not rely on
network access.
2. an example notebook showing its use


See contribution guidelines for more information on how to write tests,
lint
etc:


https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->

#### Who can review?

Tag maintainers/contributors who might be interested:
@hwchase17 @eyurtsev 
 -->

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-06-18 17:47:00 -07:00
Harrison Chase
c0c2fd0782
Harrison/zep mem (#6388)
Co-authored-by: Daniel Chalef <131175+danielchalef@users.noreply.github.com>
2023-06-18 16:53:35 -07:00
Harrison Chase
9bf5b0defa
Harrison/myscale self query (#6376)
Co-authored-by: Fangrui Liu <fangruil@moqi.ai>
Co-authored-by: 刘 方瑞 <fangrui.liu@outlook.com>
Co-authored-by: Fangrui.Liu <fangrui.liu@ubc.ca>
2023-06-18 16:53:10 -07:00
Harrison Chase
a8cb9ee013
Harrison/gdrive enhancements (#6375)
Co-authored-by: Matt Robinson <mrobinson@unstructuredai.io>
2023-06-18 11:07:23 -07:00
Lance Martin
370becdfc2
Add self query retriever example with MD header splitting (#6359)
Flesh out the notebook example for `MarkdownHeaderTextSplitter`
2023-06-17 21:40:20 -07:00
Lance Martin
2c97fbabbd
Update MD header text splitter notebook (#6339)
Highlight use case for maintaining header groups when splitting.
2023-06-17 13:19:27 -07:00
Harrison Chase
a2bbe3dda4
Harrison/mmr support for opensearch (#6349)
Co-authored-by: Mehmet Öner Yalçın <oneryalcin@gmail.com>
2023-06-17 12:22:37 -07:00
Harrison Chase
680d6bbbf8 fix titles in documentation 2023-06-17 11:09:11 -07:00
Saba Sturua
427551eabf
DocArray as a Retriever (#6031)
## DocArray as a Retriever

[DocArray](https://github.com/docarray/docarray) is an open-source tool
for managing your multi-modal data. It offers flexibility to store and
search through your data using various document index backends. This PR
introduces `DocArrayRetriever` - which works with any available backend
and serves as a retriever for Langchain apps.

Also, I added 2 notebooks:
DocArray Backends - intro to all 5 currently supported backends, how to
initialize, index, and use them as a retriever
DocArray Usage - showcasing what additional search parameters you can
pass to create versatile retrievers

Example:
```python
from docarray.index import InMemoryExactNNIndex
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.retrievers import DocArrayRetriever


# define document schema
class MyDoc(BaseDoc):
    description: str
    description_embedding: NdArray[1536]


embeddings = OpenAIEmbeddings()
# create documents
descriptions = ["description 1", "description 2"]
desc_embeddings = embeddings.embed_documents(texts=descriptions)
docs = DocList[MyDoc](
    [
        MyDoc(description=desc, description_embedding=embedding)
        for desc, embedding in zip(descriptions, desc_embeddings)
    ]
)

# initialize document index with data
db = InMemoryExactNNIndex[MyDoc](docs)

# create a retriever
retriever = DocArrayRetriever(
    index=db,
    embeddings=embeddings,
    search_field="description_embedding",
    content_field="description",
)

# find the relevant document
doc = retriever.get_relevant_documents("action movies")
print(doc)
```

#### Who can review?

@dev2049

---------

Signed-off-by: jupyterjazz <saba.sturua@jina.ai>
2023-06-17 09:09:33 -07:00
Harrison Chase
af18413d97
Harrison/deeplake new features (#6263)
Co-authored-by: adilkhan <adilkhan.sarsen@nu.edu.kz>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-06-16 17:53:55 -07:00
ljeagle
ad324a39ae
Improve the performance of add_texts interface and upgrade the AwaDB from 0.3.2 to 0.3.3 (#6316)
1. Changed the implementation of add_texts interface for the AwaDB
vector store in order to improve the performance
2. Upgrade the AwaDB from 0.3.2 to 0.3.3

---------

Co-authored-by: vincent <awadb.vincent@gmail.com>
2023-06-16 16:50:01 -07:00
Davis Chase
87e502c6bc
Doc refactor (#6300)
Co-authored-by: jacoblee93 <jacoblee93@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-06-16 11:52:56 -07:00