Commit Graph

326 Commits

Author SHA1 Message Date
ItzPAX
cbb4860fcd
fix typo in aleph_alpha.ipynb (#10478)
fixes the aleph_alpha.ipynb typo from contnt to content
2023-09-13 17:09:11 -07:00
stonekim
adabdfdfc7
Add Baidu Qianfan endpoint for LLM (#10496)
- Description:
* Baidu AI Cloud's [Qianfan
Platform](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) is an
all-in-one platform for large model development and service deployment,
catering to enterprise developers in China. Qianfan Platform offers a
wide range of resources, including the Wenxin Yiyan model (ERNIE-Bot)
and various third-party open-source models.
- Issue: none
- Dependencies: 
    * qianfan
- Tag maintainer: @baskaryan
- Twitter handle:

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-13 16:23:49 -07:00
Leonid Ganeline
f4e6eac3b6
docs: self-query consistency (#10502)
The `self-que[ring`
navbar](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/)
has repeated `self-quering` repeated in each menu item. I've simplified
it to be more readable
- removed `self-quering` from a title of each page;
- added description to the vector stores
- added description and link to the Integration Card
(`integrations/providers`) of the vector stores when they are missed.
2023-09-13 14:43:04 -07:00
Stefano Lottini
415d38ae62
Cassandra Vector Store, add metadata filtering + improvements (#9280)
This PR addresses a few minor issues with the Cassandra vector store
implementation and extends the store to support Metadata search.

Thanks to the latest cassIO library (>=0.1.0), metadata filtering is
available in the store.

Further,
- the "relevance" score is prevented from being flipped in the [0,1]
interval, thus ensuring that 1 corresponds to the closest vector (this
is related to how the underlying cassIO class returns the cosine
difference);
- bumped the cassIO package version both in the notebooks and the
pyproject.toml;
- adjusted the textfile location for the vector-store example after the
reshuffling of the Langchain repo dir structure;
- added demonstration of metadata filtering in the Cassandra vector
store notebook;
- better docstring for the Cassandra vector store class;
- fixed test flakiness and removed offending out-of-place escape chars
from a test module docstring;

To my knowledge all relevant tests pass and mypy+black+ruff don't
complain. (mypy gives unrelated errors in other modules, which clearly
don't depend on the content of this PR).

Thank you!
Stefano

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-13 14:18:39 -07:00
Joshua Sundance Bailey
85e05fa5d6
ArcGISLoader: add keyword arguments, error handling, and better tests (#10558)
* More clarity around how geometry is handled. Not returned by default;
when returned, stored in metadata. This is because it's usually a waste
of tokens, but it should be accessible if needed.
* User can supply layer description to avoid errors when layer
properties are inaccessible due to passthrough access.
* Enhanced testing
* Updated notebook

---------

Co-authored-by: Connor Sutton <connor.sutton@swca.com>
Co-authored-by: connorsutton <135151649+connorsutton@users.noreply.github.com>
2023-09-13 14:12:42 -07:00
wxd
f9636b6cd2
add vearch repository link (#10491)
- Description: add vearch repository link
2023-09-13 12:06:47 -07:00
Bagatur
97122fb577
Integration with ElevenLabs text to speech (#10181)
- Description: adds integration with ElevenLabs text-to-speech
[component](https://github.com/elevenlabs/elevenlabs-python) in the
similar way it has been already done for [azure cognitive
services](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/tools/azure_cognitive_services/text2speech.py)
  - Dependencies: elevenlabs
  - Twitter handle: @deepsense_ai, @matt_wosinski
- Future plans: refactor both implementations in order to avoid dumping
speech file, but rather to keep it in memory.
2023-09-12 22:56:53 -07:00
Riyadh Rahman
d45b042d3e
Added gitlab toolkit and notebook (#10384)
### Description

Adds Gitlab toolkit functionality for agent

### Twitter handle

@_laplaceon

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-11 16:16:50 -07:00
Bagatur
70b6897dc1
Mv vearch provider doc (#10466) 2023-09-11 15:00:40 -07:00
Rajesh Kumar
737b75d278
Latest version of HazyResearch/manifest doesn't support accessing "client" directly (#10389)
**Description:** 
The latest version of HazyResearch/manifest doesn't support accessing
the "client" directly. The latest version supports connection pools and
a client has to be requested from the client pool.
**Issue:**
No matching issue was found
**Dependencies:** 
The manifest.ipynb file in docs/extras/integrations/llms need to be
updated
**Twitter handle:** 
@hrk_cbe
2023-09-11 14:22:53 -07:00
Christopher Pereira
4c732c8894
Fixed documentation (#10451)
It's ._collection, not ._collection_
2023-09-11 11:51:58 -07:00
John Mai
e0d45e6a09
Implemented MMR search for PGVector (#10396)
Description: Implemented MMR search for PGVector.
Issue: #7466
Dependencies: None
Tag maintainer: 
Twitter handle: @JohnMai95
2023-09-09 15:26:22 -07:00
ColabDog
6ad6bb46c4
Feature/add deepeval (#10349)
Description: Adding `DeepEval` - which provides an opinionated framework
for testing and evaluating LLMs
Issue: Missing Deepeval
Dependencies: Optional DeepEval dependency
Tag maintainer: @baskaryan   (not 100% sure)
Twitter handle: https://twitter.com/ColabDog
2023-09-09 13:28:17 -07:00
eryk-dsai
675d57df50
New LLM integration: Ctranslate2 (#10400)
## Description:

I've integrated CTranslate2 with LangChain. CTranlate2 is a recently
popular library for efficient inference with Transformer models that
compares favorably to alternatives such as HF Text Generation Inference
and vLLM in
[benchmarks](https://hamel.dev/notes/llm/inference/03_inference.html).
2023-09-09 13:19:00 -07:00
zhanghexian
62fa2bc518
Add Vearch vectorstore (#9846)
---------

Co-authored-by: zhanghexian1 <zhanghexian1@jd.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-09-08 16:51:14 -07:00
Bagatur
7203c97e8f
Add redis self-query support (#10199) 2023-09-08 16:43:16 -07:00
Hugues
3e5a143625
Enhancements and bug fixes for LLMonitorCallbackHandler (#10297)
Hi @baskaryan,

I've made updates to LLMonitorCallbackHandler to address a few bugs
reported by users
These changes don't alter the fundamental behavior of the callback
handler.

Thanks you!

---------

Co-authored-by: vincelwt <vince@lyser.io>
2023-09-08 15:56:42 -07:00
Hamza Tahboub
8c0f391815
Implemented MMR search for Redis (#10140)
Description: Implemented MMR search for Redis. Pretty straightforward,
just using the already implemented MMR method on similarity
search–fetched docs.
Issue: #10059
Dependencies: None
Twitter handle: @hamza_tahboub

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-08 15:14:44 -07:00
Bagatur
9095dc69ac Konko fix dependency 2023-09-08 10:06:37 -07:00
Michael Haddad
c6b27b3692
add konko chat_model files (#10267)
_Thank you to the LangChain team for the great project and in advance
for your review. Let me know if I can provide any other additional
information or do things differently in the future to make your lives
easier 🙏 _

@hwchase17 please let me know if you're not the right person to review 😄

This PR enables LangChain to access the Konko API via the chat_models
API wrapper.

Konko API is a fully managed API designed to help application
developers:

1. Select the right LLM(s) for their application
2. Prototype with various open-source and proprietary LLMs
3. Move to production in-line with their security, privacy, throughput,
latency SLAs without infrastructure set-up or administration using Konko
AI's SOC 2 compliant infrastructure

_Note on integration tests:_ 
We added 14 integration tests. They will all fail unless you export the
right API keys. 13 will pass with a KONKO_API_KEY provided and the other
one will pass with a OPENAI_API_KEY provided. When both are provided,
all 14 integration tests pass. If you would like to test this yourself,
please let me know and I can provide some temporary keys.

### Installation and Setup

1. **First you'll need an API key**
2. **Install Konko AI's Python SDK**
    1. Enable a Python3.8+ environment
    
    `pip install konko`
    
3.  **Set API Keys**
    
          **Option 1:** Set Environment Variables
    
    You can set environment variables for
    
    1. KONKO_API_KEY (Required)
    2. OPENAI_API_KEY (Optional)
    
    In your current shell session, use the export command:
    
    `export KONKO_API_KEY={your_KONKO_API_KEY_here}`
    `export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional`
    
Alternatively, you can add the above lines directly to your shell
startup script (such as .bashrc or .bash_profile for Bash shell and
.zshrc for Zsh shell) to have them set automatically every time a new
shell session starts.
    
    **Option 2:** Set API Keys Programmatically
    
If you prefer to set your API keys directly within your Python script or
Jupyter notebook, you can use the following commands:
    
    ```python
    konko.set_api_key('your_KONKO_API_KEY_here')
    konko.set_openai_api_key('your_OPENAI_API_KEY_here') # Optional
    
    ```
    

### Calling a model

Find a model on the [[Konko Introduction
page](https://docs.konko.ai/docs#available-models)](https://docs.konko.ai/docs#available-models)

For example, for this [[LLama 2
model](https://docs.konko.ai/docs/meta-llama-2-13b-chat)](https://docs.konko.ai/docs/meta-llama-2-13b-chat).
The model id would be: `"meta-llama/Llama-2-13b-chat-hf"`

Another way to find the list of models running on the Konko instance is
through this
[[endpoint](https://docs.konko.ai/reference/listmodels)](https://docs.konko.ai/reference/listmodels).

From here, we can initialize our model:

```python
chat_instance = ChatKonko(max_tokens=10, model = 'meta-llama/Llama-2-13b-chat-hf')

```

And run it:

```python
msg = HumanMessage(content="Hi")
chat_response = chat_instance([msg])

```
2023-09-08 10:00:55 -07:00
Mateusz Wosinski
69fe0621d4
Merge branch 'master' into deepsense/text-to-speech 2023-09-08 08:09:01 +02:00
Leonid Ganeline
fdba711d28
docs integrations/embeddings consistency (#10302)
Updated `integrations/embeddings`: fixed titles; added links,
descriptions
Updated `integrations/providers`.
2023-09-07 19:53:33 -07:00
Greg Richardson
300559695b
Supabase vector self querying retriever (#10304)
## Description
Adds Supabase Vector as a self-querying retriever.

- Designed to be backwards compatible with existing `filter` logic on
`SupabaseVectorStore`.
- Adds new filter `postgrest_filter` to `SupabaseVectorStore`
`similarity_search()` methods
- Supports entire PostgREST [filter query
language](https://postgrest.org/en/stable/references/api/tables_views.html#read)
(used by self-querying retriever, but also works as an escape hatch for
more query control)
- `SupabaseVectorTranslator` converts Langchain filter into the above
PostgREST query
- Adds Jupyter Notebook for the self-querying retriever
- Adds tests

## Tag maintainer
@hwchase17

## Twitter handle
[@ggrdson](https://twitter.com/ggrdson)
2023-09-07 15:03:26 -07:00
mateusz.wosinski
868db99b17 Merge branch 'master' into deepsense/text-to-speech 2023-09-07 19:43:03 +02:00
Ofer Mendelevitch
a9eb7c6cfc
Adding Self-querying for Vectara (#10332)
- Description: Adding support for self-querying to Vectara integration
  - Issue: per customer request
  - Tag maintainer: @rlancemartin @baskaryan 
  - Twitter handle: @ofermend 

Also updated some documentation, added self-query testing, and a demo
notebook with self-query example.
2023-09-07 10:24:50 -07:00
Bagatur
f0ccce76fe
nuclia db nit (#10334) 2023-09-07 09:48:56 -07:00
Bagatur
205f406485
nuclia nb nit (#10331) 2023-09-07 08:49:33 -07:00
Eric BREHAULT
19b4ecdc39
Implement NucliaDB vector store (#10236)
# Description

This pull request allows to use the
[NucliaDB](https://docs.nuclia.dev/docs/docs/nucliadb/intro) as a vector
store in LangChain.

It works with both a [local NucliaDB
instance](https://docs.nuclia.dev/docs/docs/nucliadb/deploy/basics) or
with [Nuclia Cloud](https://nuclia.cloud).

# Dependencies

It requires an up-to-date version of the `nuclia` Python package.

@rlancemartin, @eyurtsev, @hinthornw, please review it when you have a
moment :)

Note: our Twitter handler is `@NucliaAI`
2023-09-06 16:26:14 -07:00
JaéGeR
b8669b249e
Added Hugging face inference api (#10280)
Embed documents without locally downloading the HF model


---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-06 14:55:48 -07:00
Pihplipe Oegr
bce38b7163
Add notebook example to use sqlite-vss as a vector store. (#10292)
Follow-up PR for https://github.com/langchain-ai/langchain/pull/10047,
simply adding a notebook quickstart example for the vector store with
SQLite, using the class SQLiteVSS.

Maintainer tag @baskaryan

Co-authored-by: Philippe Oger <philippe.oger@adevinta.com>
2023-09-06 13:46:59 -07:00
Nik
49341483da
Update Banana.dev docs to latest correct usage (#10183)
- Description: this PR updates all Banana.dev-related docs to match the
latest client usage. The code in the docs before this PR were out of
date and would never run.
- Issue: [#6404](https://github.com/langchain-ai/langchain/issues/6404)
- Dependencies: -
- Tag maintainer:  
- Twitter handle: [BananaDev_ ](https://twitter.com/BananaDev_ )
2023-09-06 07:46:17 -07:00
mateusz.wosinski
7b7bea5424 Fix linters, update notebook 2023-09-06 10:22:42 +02:00
Bagatur
a94dc6ee44
model garden nit (#10194) 2023-09-04 11:42:35 -07:00
Aashish Saini
8bba69ffd0
Fixed some grammatical typos in doc files (#10191)
Fixed some grammatical typos in doc files
CC: @baskaryan, @eyurtsev, @rlancemartin.

---------

Co-authored-by: Aashish Saini <141953346+AashishSainiShorthillsAI@users.noreply.github.com>
Co-authored-by: AryamanJaiswalShorthillsAI <142397527+AryamanJaiswalShorthillsAI@users.noreply.github.com>
Co-authored-by: Adarsh Shrivastav <142413097+AdarshKumarShorthillsAI@users.noreply.github.com>
Co-authored-by: Vishal <141389263+VishalYadavShorthillsAI@users.noreply.github.com>
Co-authored-by: ChetnaGuptaShorthillsAI <142381084+ChetnaGuptaShorthillsAI@users.noreply.github.com>
Co-authored-by: PankajKumarShorthillsAI <142473460+PankajKumarShorthillsAI@users.noreply.github.com>
Co-authored-by: AbhishekYadavShorthillsAI <142393903+AbhishekYadavShorthillsAI@users.noreply.github.com>
Co-authored-by: AmitSinghShorthillsAI <142410046+AmitSinghShorthillsAI@users.noreply.github.com>
Co-authored-by: Md Nazish Arman <142379599+MdNazishArmanShorthillsAI@users.noreply.github.com>
Co-authored-by: KamalSharmaShorthillsAI <142474019+KamalSharmaShorthillsAI@users.noreply.github.com>
Co-authored-by: Lakshya <lakshyagupta87@yahoo.com>
Co-authored-by: Aayush <142384656+AayushShorthillsAI@users.noreply.github.com>
Co-authored-by: AnujMauryaShorthillsAI <142393269+AnujMauryaShorthillsAI@users.noreply.github.com>
2023-09-04 10:48:08 -07:00
mateusz.wosinski
6b9529e11a Update notebook 2023-09-04 15:23:24 +02:00
mateusz.wosinski
800fe4a73f Integration with eleven labs 2023-09-04 15:23:24 +02:00
Aashish Saini
f6f0b0f975
Fixed typo in bittensor.mdx (#10160)
Fixed Typo in bittenaor.mdx

---------

Co-authored-by: Aashish Saini <141953346+AashishSainiShorthillsAI@users.noreply.github.com>
Co-authored-by: AryamanJaiswalShorthillsAI <142397527+AryamanJaiswalShorthillsAI@users.noreply.github.com>
Co-authored-by: Adarsh Shrivastav <142413097+AdarshKumarShorthillsAI@users.noreply.github.com>
Co-authored-by: Vishal <141389263+VishalYadavShorthillsAI@users.noreply.github.com>
Co-authored-by: ChetnaGuptaShorthillsAI <142381084+ChetnaGuptaShorthillsAI@users.noreply.github.com>
Co-authored-by: PankajKumarShorthillsAI <142473460+PankajKumarShorthillsAI@users.noreply.github.com>
Co-authored-by: AbhishekYadavShorthillsAI <142393903+AbhishekYadavShorthillsAI@users.noreply.github.com>
Co-authored-by: Aayush <142384656+AayushShorthillsAI@users.noreply.github.com>
2023-09-03 21:49:33 -07:00
Christophe Bornet
803d0d9656
Add the possibility to configure boto3 in the S3 loaders (#9304)
- Description: this PR adds the possibility to configure boto3 in the S3
loaders. Any named argument you add will be used to create the Boto3
session. This is useful when the AWS credentials can't be passed as env
variables or can't be read from the credentials file.
  - Issue: N/A
  - Dependencies: N/A
  - Tag maintainer: ?
  - Twitter handle: cbornet_

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-03 21:06:49 -07:00
Leonid Ganeline
03174c91d0
docs: MLflow API and examples (#9547)
Added docs and links to the API and examples provided by MLflow itself
2023-09-03 20:52:20 -07:00
Leonid Ganeline
056e59672b
docs: DeepLake example (#9663)
Updated the `Deep Lake` example. Added a link to an example provided by
Activeloop.
2023-09-03 20:42:52 -07:00
seamusp
43c4c6dfcc
docs: misc modelIO fixes (#9734)
Various improvements to the Model I/O section of the documentation

- Changed "Chat Model" to "chat model" in a few spots for internal
consistency
- Minor spelling & grammar fixes to improve readability & comprehension
2023-09-03 20:33:20 -07:00
Terry Tan
8bc452a466
Enhance Google search tool SerpApi response (#10157)
Enhance SerpApi response which potential to have more relevant output.

<img width="345" alt="Screenshot 2023-09-01 at 8 26 13 AM"
src="https://github.com/langchain-ai/langchain/assets/10222402/80ff684d-e02e-4143-b218-5c1b102cbf75">

Query: What is the weather in Pomfret?

**Before:**

> I should look up the current weather conditions.
...
Final Answer: The current weather in Pomfret is 73°F with 1% chance of
precipitation and winds at 10 mph.

**After:**

> I should look up the current weather conditions.
...
Final Answer: The current weather in Pomfret is 62°F, 1% precipitation,
61% humidity, and 4 mph wind.

---

Query: Top team in english premier league?

**Before:**

> I need to find out which team is currently at the top of the English
Premier League
...
Final Answer: Liverpool FC is currently at the top of the English
Premier League.

**After:**

> I need to find out which team is currently at the top of the English
Premier League
...
Final Answer: Man City is currently at the top of the English Premier
League.

---

Query: Top team in english premier league?

**Before:**

> I need to find out which team is currently at the top of the English
Premier League
...
Final Answer: Liverpool FC is currently at the top of the English
Premier League.


**After:**

> I need to find out which team is currently at the top of the English
Premier League
...
Final Answer: Man City is currently at the top of the English Premier
League.

---

Query: Any upcoming events in Paris?

**Before:**

> I should look for events in Paris
Action: Search
...
Final Answer: Upcoming events in Paris this month include Whit Sunday &
Whit Monday (French National Holiday), Makeup in Paris, Paris Jazz
Festival, Fete de la Musique, and Salon International de la Maison de.

**After:**

> I should look for events in Paris
Action: Search
...
Final Answer: Upcoming events in Paris include Elektric Park 2023, The
Aces, and BEING AS AN OCEAN.
2023-09-03 20:24:19 -07:00
Philippe PRADOS
f59e5d48ed
Google drive integration (lite) (#9999)
My other
[pull-request](https://github.com/langchain-ai/langchain/pull/5135) is
too big to be acceptable.
I propose another 'lite' version.

I update only notebook to propose an integration with the external
project
[`langchain-googledrive`](https://github.com/pprados/langchain-googledrive).

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-09-03 15:54:42 -07:00
Viktor Zhemchuzhnikov
507e46844e
Extend SQLChatMessageHistory (#9849)
### Description

There is a really nice class for saving chat messages into a database -
SQLChatMessageHistory.
It leverages SqlAlchemy to be compatible with any supported database (in
contrast with PostgresChatMessageHistory, which is basically the same
but is limited to Postgres).

However, the class is not really customizable in terms of what you can
store. I can imagine a lot of use cases, when one will need to save a
message date, along with some additional metadata.

To solve this, I propose to extract the converting logic from
BaseMessage to SQLAlchemy model (and vice versa) into a separate class -
message converter. So instead of rewriting the whole
SQLChatMessageHistory class, a user will only need to write a custom
model and a simple mapping class, and pass its instance as a parameter.

I also noticed that there is no documentation on this class, so I added
that too, with an example of custom message converter.

### Issue

N/A

### Dependencies

N/A

### Tag maintainer

Not yet

### Twitter handle

N/A
2023-09-03 15:49:53 -07:00
Leonid Ganeline
a52fe9528e
docs: fixed title in Bittensor example (#9893)
Fixed title in the `Bittensor` example. The old title brakes the sorted
order of items in the navbar.
Added some formatting.
2023-09-03 15:10:42 -07:00
Josh White
bc8cceebf7
Extend DynamoDBChatMessageHistory to support composite keys (#9896)
- Description: Adds two optional parameters to the
DynamoDBChatMessageHistory class to enable users to pass in a name for
their PrimaryKey, or a Key object itself to enable the use of composite
keys, a common DynamoDB paradigm.
  
[AWS DynamoDB Key
docs](https://aws.amazon.com/blogs/database/choosing-the-right-dynamodb-partition-key/)
  
  - Issue: N/A
  - Dependencies: N/A
  - Twitter handle: N/A

---------

Co-authored-by: Josh White <josh@ctrlstack.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-03 15:05:16 -07:00
Blake (Yung Cher Ho)
f4bed8a04c
Takeoff baseurl support (#10091)
## Description
This PR introduces a minor change to the TitanTakeoff integration. 
Instead of specifying a port on localhost, this PR will allow users to
specify a baseURL instead. This will allow users to use the integration
if they have TitanTakeoff deployed externally (not on localhost). This
removes the hardcoded reference to localhost "http://localhost:{port}".

### Info about Titan Takeoff
Titan Takeoff is an inference server created by
[TitanML](https://www.titanml.co/) that allows you to deploy large
language models locally on your hardware in a single command. Most
generative model architectures are included, such as Falcon, Llama 2,
GPT2, T5 and many more.

Read more about Titan Takeoff here:
-
[Blog](https://medium.com/@TitanML/introducing-titan-takeoff-6c30e55a8e1e)
- [Docs](https://docs.titanml.co/docs/titan-takeoff/getting-started)

### Dependencies
No new dependencies are introduced. However, users will need to install
the titan-iris package in their local environment and start the Titan
Takeoff inferencing server in order to use the Titan Takeoff
integration.

Thanks for your help and please let me know if you have any questions.
cc: @hwchase17 @baskaryan

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-09-03 14:45:59 -07:00
Leonid Ganeline
2221194450
Yahoo Finance News tool (#10014)
Added:
- the `Yahoo Finance News` tool
- Ut-s
- An example
2023-09-03 14:43:57 -07:00
Ismail Pelaseyed
5c3e9c9083
Add example of running Q&A over structured data using the Airbyte loaders and pandas (#10069)
- Description: Added example of running Q&A over structured data using
the `Airbyte` loaders and `pandas`
  - Dependencies: any dependencies required for this change,
  - Tag maintainer: @hwchase17 
  - Twitter handle: @pelaseyed
2023-09-03 14:32:33 -07:00
Lars von Wedel
6d82503eb1
Add parser and loader for Azure document intelligence service. (#10136)
Hi,

this PR contains loader / parser for Azure Document intelligence which
is a ML-based service to ingest arbitrary PDFs / images, even if
scanned. The loader generates Documents by pages of the original
document. This is my first contribution to LangChain.

Unfortunately I could not find the correct place for test cases. Happy
to add one if you can point me to the location, but as this is a
cloud-based service, a test would require network access and credentials
- so might be of limited help.

Dependencies: The needed dependency was already part of pyproject.toml,
no change.
Twitter: feel free to mention @LarsAC on the announcement
2023-09-03 14:25:39 -07:00
Leonid Kuligin
30239b3025
added support for inference from Model Garden (#9367)
#8850

---------

Co-authored-by: Leonid Kuligin <kuligin@google.com>
2023-09-01 15:58:21 -07:00
Leonid Ganeline
54a8df87b9
📖 docs: fixed integration/llms navbar (#9277)
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`
2023-09-01 15:30:37 -07:00
Bagatur
b485c3048b
rm base64 images from docs (#10110)
Causing problems indexing docs and notebook images don't render after markdown conversion anyways
2023-09-01 15:15:12 -07:00
Leonid Ganeline
37e435bd00
docs: youtube_search tool example update (#9958)
Added a link to source package; updated title, description.
2023-09-01 13:32:27 -07:00
Leonid Ganeline
3b8ee74e38
docs: google-drive-tool example fix (#10000)
This notebook was mistakenly placed in the `toolkits` folder and appears
within `Agents & Toolkits` menu. But it should be in `Tools`.
Moved example into `tools/`; updated title to consistent format.
2023-09-01 13:31:26 -07:00
Benjamin Matson
58d7d86e51
feat: add bedrock chat model (#8017)
Replace this comment with:
  - Description: Add Bedrock implementation of Anthropic Claude for Chat
  - Tag maintainer: @hwchase17, @baskaryan
  - Twitter handle: @bwmatson

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-01 13:16:57 -07:00
KyrianC
491089754d
EdenAI LLM update. Add models name option (#8963)
This PR follows the **Eden AI (LLM + embeddings) integration**. #8633 

We added an optional parameter to choose different AI models for
providers (like 'text-bison' for provider 'google', 'text-davinci-003'
for provider 'openai', etc.).

Usage:

```python
llm = EdenAI(
    feature="text",
    provider="google",
    params={
        "model": "text-bison",  # new
        "temperature": 0.2,
        "max_tokens": 250,
    },
)

```

You can also change the provider + model after initialization
```python
llm = EdenAI(
    feature="text",
    provider="google",
    params={
        "temperature": 0.2,
        "max_tokens": 250,
    },
)

prompt = """
hi 
"""

llm(prompt, providers='openai', model='text-davinci-003')  # change provider & model
```

The jupyter notebook as been updated with an example well.


Ping: @hwchase17, @baskaryan

---------

Co-authored-by: RedhaWassim <rwasssim@gmail.com>
Co-authored-by: sam <melaine.samy@gmail.com>
2023-09-01 12:11:33 -07:00
Bagatur
b927277809
Bagatur/eden type 2 (#10102) 2023-09-01 10:27:27 -07:00
Bagatur
d4380339c1
eden tool nb nit (#10101) 2023-09-01 10:16:39 -07:00
KyrianC
c7a5504789
Add EdenAI Tools (#9764)
This PR follows the Eden AI (LLM + embeddings) integration. #8633

We added different Tools to empower agents with new capabilities :

- text: explicit content detection

- image: explicit content detection

- image: object detection

- OCR: invoice parsing

- OCR: ID parsing

- audio: speech to text

- audio: text to speech

 
We plan to add more in the future (like translation, language detection,
+ others).


Usage:

```python
llm=EdenAI(feature="text",provider="openai", params={"temperature" : 0.2,"max_tokens" : 250})

tools = [
    EdenAiTextModerationTool(providers=["openai"],language="en"),
    EdenAiObjectDetectionTool(providers=["google","api4ai"]),
    EdenAiTextToSpeechTool(providers=["amazon"],language="en",voice="MALE"),
    EdenAiExplicitImageTool(providers=["amazon","google"]),
    EdenAiSpeechToTextTool(providers=["amazon"]),
    EdenAiParsingIDTool(providers=["amazon","klippa"],language="en"),
    EdenAiParsingInvoiceTool(providers=["amazon","google"],language="en"),
]

agent_chain = initialize_agent(
    tools,
    llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True,
    return_intermediate_steps=True,
)

result = agent_chain(""" i have this text : 'i want to slap you' 
                   first : i want to know if this text contains explicit content or not .
                   second : if it does contain explicit content i want to know what is the explicit content in this text, 
                   third : i want to make the text into speech .
                   if there is URL in the observations , you will always put it in the output (final answer) .
                   """)
```

output: 
>  Entering new AgentExecutor chain...
> I need to extract the information from the ID and then convert it to
text and then to speech
> Action: edenai_identity_parsing
> Action Input:
"https://www.citizencard.com/images/citizencard-uk-id-card-2023.jpg"
> Observation: last_name : 
>   value : ANGELA
> given_names : 
>   value : GREENE
> birth_place : 
> birth_date : 
>   value : 2000-11-09
> issuance_date : 
> expire_date : 
> document_id : 
> issuing_state : 
> address : 
> age : 
> country : 
> document_type : 
>   value : DRIVER LICENSE FRONT
> gender : 
> image_id : 
> image_signature : 
> mrz : 
> nationality : 
> Thought: I now need to convert the information to text and then to
speech
> Action: edenai_text_to_speech
> Action Input: "Welcome Angela Greene!"
> Observation:
https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5yHAJjf657u7Z1lFTBMoXGBuw1VYmyno-3TAiPeUcVlQXPueJ-ymZXmwaITmGOfH7HipZngZBziofRAFdhMYbIjYhegu5jS7TxHwRuox32A__&Key-Pair-Id=K1F55BTI9AHGIK
> Thought: I now know the final answer
> Final Answer:
https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5y
> 
>  Finished chain.

Other examples are available in the jupyter notebook.


This PR is made in parallel with  EdenAI LLM update #8963 
I apologize for the messy PR. While working in implementing Tools we
realized there was a few problems we needed to fix on LLM as well.

Ping: @hwchase17, @baskaryan

---------

Co-authored-by: RedhaWassim <rwasssim@gmail.com>
2023-09-01 08:26:56 -07:00
Stefano Lottini
c710c7303f
fix wrong import line in cassandra doc page for vector store (#10041)
This fixes the exampe import line in the general "cassandra" doc page
mdx file. (it was erroneously a copy of the chat message history import
statement found below).
2023-08-31 16:05:46 -07:00
Zizhong Zhang
641b71e2cd
refactor: rename to OpaquePrompts (#10013)
Renamed to OpaquePrompts

cc @baskaryan Thanks in advance!
2023-08-31 12:21:24 -07:00
Bagatur
3efab8d3df
implement vectorstores by tencent vectordb (#9989)
Hi there!
I'm excited to open this PR to add support for using 'Tencent Cloud
VectorDB' as a vector store.

Tencent Cloud VectorDB is a fully-managed, self-developed,
enterprise-level distributed database service designed for storing,
retrieving, and analyzing multi-dimensional vector data. The database
supports multiple index types and similarity calculation methods, with a
single index supporting vector scales up to 1 billion and capable of
handling millions of QPS with millisecond-level query latency. Tencent
Cloud VectorDB not only provides external knowledge bases for large
models to improve their accuracy, but also has wide applications in AI
fields such as recommendation systems, NLP services, computer vision,
and intelligent customer service.

The PR includes:
 Implementation of Vectorstore.

I have read your [contributing
guidelines](72b7d76d79/.github/CONTRIBUTING.md).
And I have passed the tests below

 make format
 make lint
 make coverage
 make test
2023-08-31 00:48:25 -07:00
Bagatur
b1644bc9ad cr 2023-08-31 00:43:34 -07:00
Cameron Vetter
e37d51cab6
fix scoring profile example (#10016)
- Description: A change in the documentation example for Azure Cognitive
Vector Search with Scoring Profile so the example works as written
  - Issue: #10015 
  - Dependencies: None
  - Tag maintainer: @baskaryan @ruoccofabrizio
  - Twitter handle: @poshporcupine
2023-08-31 00:35:06 -07:00
Tomaz Bratanic
f2e8399cc8
Fix link in Neo4j provider page (#10023) 2023-08-31 00:32:42 -07:00
Christophe Bornet
9870bfb9cd
Add bucket and object key to metadata in S3 loader (#9317)
- Description: this PR adds `s3_object_key` and `s3_bucket` to the doc
metadata when loading an S3 file. This is particularly useful when using
`S3DirectoryLoader` to remove the files from the dir once they have been
processed (getting the object keys from the metadata `source` field
seems brittle)
  - Dependencies: N/A
  - Tag maintainer: ?
  - Twitter handle: _cbornet

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
2023-08-30 11:03:24 -04:00
wlleiiwang
8c4e29240c implement vectorstores by tencent vectordb 2023-08-30 16:40:58 +08:00
Leonid Ganeline
d03d6f6fd9
Merge branch 'master' into docs-tools-menu 2023-08-29 15:57:25 -07:00
Bagatur
8fb0a9594c
Add LLMonitor Callback Handler Integration - open-source observability & analytics (#9870)
Adds support for [llmonitor](https://llmonitor.com) callbacks.

It enables:
- Requests tracking / logging / analytics
- Error debugging
- Cost analytics
- User tracking

Let me know if anythings neds to be changed for merge.

Thank you!
2023-08-29 15:49:01 -07:00
leo-gan
8c1678a8c7 Updated titles, descriptions. 2023-08-29 15:42:28 -07:00
Bagatur
8f199239b8
docs: llms/google vertex AI example update (#9960)
Updated title, description, added sections.
2023-08-29 15:07:18 -07:00
Bagatur
16eb935469
Fix for similarity_search_with_score (#9903)
- Description: the implementation for similarity_search_with_score did
not actually include a score or logic to filter. Now fixed.
- Tag maintainer: @rlancemartin
- Twitter handle: @ofermend
2023-08-29 15:04:48 -07:00
leo-gan
210de0c66b Updated title, description, added sections 2023-08-29 14:31:33 -07:00
Cameron Hutchison
bcc3463ff4
docs: Azure AD Authentication for Azure OpenAI (#9951)
# Description
This PR adds additional documentation on how to use Azure Active
Directory to authenticate to an OpenAI service within Azure. This method
of authentication allows organizations with more complex security
requirements to use Azure OpenAI.

# Issue
N/A

# Dependencies
N/A

# Twitter
https://twitter.com/CamAHutchison
2023-08-29 14:29:27 -07:00
Corvus Lee
0fb95ebe66
Docs: enrich SageMaker endpoint embeddings with docstrings and examples (#9924)
Description: added comments to address the relationship between
input/output transformations and the customised inference.py script.
2023-08-29 11:38:52 -07:00
Tomaz Bratanic
6092422e10
Add neo4j provider page (#9941) 2023-08-29 10:09:51 -07:00
Tomaz Bratanic
db13fba7ea
Add neo4j vector support (#9770)
Neo4j has added vector index integration just recently. To allow both
ingestion and integrating it as vector RAG applications, I wrapped it as
a vector store as the implementation is completely different from
`GraphCypherQAChain`. Here, we are not generating any Cypher statements
at query time, we are simply doing the vector similarity search using
the new vector index as if we were dealing with a vector database.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-29 07:54:20 -07:00
Tudor Golubenco
171b0b183b
Pre-release Xata version no longer required (#9915)
Tiny PR: Since we've released version 1.0.0 of the python SDK, we no
longer need to specify the pre-release version when pip installing.
2023-08-29 07:21:22 -07:00
Mike Nitsenko
c80e406e95
Cube semantic loader: allow cubes processing (#9927)
We've started to receive feedback (after launch) that using only views
is confusing.
We're considering this as a good practice, as a view serves as a
"facade" for your data - however, we decided to let users decide this on
their own.

Solves the questions from:
- https://github.com/cube-js/cube/issues/7028
- https://github.com/langchain-ai/langchain/pull/9690
2023-08-29 07:21:01 -07:00
Ofer Mendelevitch
8b8d2a6535 fixed similarity_search_with_score to really use a score
updated unit test with a test for score threshold
Updated demo notebook
2023-08-28 22:26:55 -07:00
Mazhar (Taha) Mumbaiwala
e80834d783
docs: Fix spelling mistakes in Etherscan.ipynb (#9845) 2023-08-28 19:30:00 -07:00
Philippe PRADOS
7fdb7439e0
Update google drive notebooks (#9851)
Update google drive doc loader and retriever notebooks. Show how to use with langchain-googledrive package.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-28 19:29:35 -07:00
Leonid Ganeline
b1bffea9c7
docs: fix for title of llm_caching nb (#9891)
Fixed title for the `extras/integrations/llms/llm_caching.ipynb`.
Existing title breaks the sorted order of items in the navbar.
Updated some formatting.
2023-08-28 18:34:04 -07:00
Leonid Ganeline
e01b00aa54
docs: ainetwork update (#9871)
* Added links to the AI Network
* Made title consistent to other tool kits
* Added `integrations/providers/` integration card page
* **No changes** in the example code!
2023-08-28 18:16:22 -07:00
Leonid Ganeline
cf122b6269
docs: Infino example fix (#9888)
- Fixed a broken link in the `integrations/providers/infino.mdx`
- Fixed a title in the `integration/collbacks/infino.ipynb` example
- Updated text format in this example.
2023-08-28 17:42:11 -07:00
William FH
b14d74dd4d
iMessage loader (#9832)
Add an iMessage chat loader
2023-08-28 13:43:59 -07:00
Lance Martin
8393ba9dab
Add instructions for GGUF (#9874)
llama.cpp migrated to GGUF model format, and new releases (e.g.,
[here](https://huggingface.co/TheBloke)) now use GGUF.
2023-08-28 12:56:46 -07:00
hughcrt
3a4d4c940c Change video width 2023-08-28 19:26:33 +02:00
hughcrt
97741d41c5 Add LLMonitorCallbackHandler 2023-08-28 19:24:50 +02:00
eryk-dsai
7f5713b80a
feat: grammar-based sampling in llama-cpp (#9712)
## Description 

The following PR enables the [grammar-based
sampling](https://github.com/ggerganov/llama.cpp/tree/master/grammars)
in llama-cpp LLM.

In short, loading file with formal grammar definition will constrain
model outputs. For instance, one can force the model to generate valid
JSON or generate only python lists.

In the follow-up PR we will add:
* docs with some description why it is cool and how it works
* maybe some code sample for some task such as in llama repo

---------

Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-28 09:52:55 -07:00
Harrison Chase
c1badc1fa2
add gmail loader (#9810) 2023-08-27 17:18:09 -07:00
Sam Partee
a28eea5767
Redis metadata filtering and specification, index customization (#8612)
### Description

The previous Redis implementation did not allow for the user to specify
the index configuration (i.e. changing the underlying algorithm) or add
additional metadata to use for querying (i.e. hybrid or "filtered"
search).

This PR introduces the ability to specify custom index attributes and
metadata attributes as well as use that metadata in filtered queries.
Overall, more structure was introduced to the Redis implementation that
should allow for easier maintainability moving forward.

# New Features

The following features are now available with the Redis integration into
Langchain

## Index schema generation

The schema for the index will now be automatically generated if not
specified by the user. For example, the data above has the multiple
metadata categories. The the following example

```python

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores.redis import Redis

embeddings = OpenAIEmbeddings()


rds, keys = Redis.from_texts_return_keys(
    texts,
    embeddings,
    metadatas=metadata,
    redis_url="redis://localhost:6379",
    index_name="users"
)
```

Loading the data in through this and the other ``from_documents`` and
``from_texts`` methods will now generate index schema in Redis like the
following.

view index schema with the ``redisvl`` tool. [link](redisvl.com)

```bash
$ rvl index info -i users
```


Index Information:
| Index Name | Storage Type | Prefixes | Index Options | Indexing |

|--------------|----------------|---------------|-----------------|------------|
| users | HASH | ['doc:users'] | [] | 0 |
Index Fields:
| Name | Attribute | Type | Field Option | Option Value |

|----------------|----------------|---------|----------------|----------------|
| user | user | TEXT | WEIGHT | 1 |
| job | job | TEXT | WEIGHT | 1 |
| credit_score | credit_score | TEXT | WEIGHT | 1 |
| content | content | TEXT | WEIGHT | 1 |
| age | age | NUMERIC | | |
| content_vector | content_vector | VECTOR | | |


### Custom Metadata specification

The metadata schema generation has the following rules
1. All text fields are indexed as text fields.
2. All numeric fields are index as numeric fields.

If you would like to have a text field as a tag field, users can specify
overrides like the following for the example data

```python

# this can also be a path to a yaml file
index_schema = {
    "text": [{"name": "user"}, {"name": "job"}],
    "tag": [{"name": "credit_score"}],
    "numeric": [{"name": "age"}],
}

rds, keys = Redis.from_texts_return_keys(
    texts,
    embeddings,
    metadatas=metadata,
    redis_url="redis://localhost:6379",
    index_name="users"
)
```
This will change the index specification to 

Index Information:
| Index Name | Storage Type | Prefixes | Index Options | Indexing |

|--------------|----------------|----------------|-----------------|------------|
| users2 | HASH | ['doc:users2'] | [] | 0 |
Index Fields:
| Name | Attribute | Type | Field Option | Option Value |

|----------------|----------------|---------|----------------|----------------|
| user | user | TEXT | WEIGHT | 1 |
| job | job | TEXT | WEIGHT | 1 |
| content | content | TEXT | WEIGHT | 1 |
| credit_score | credit_score | TAG | SEPARATOR | , |
| age | age | NUMERIC | | |
| content_vector | content_vector | VECTOR | | |


and throw a warning to the user (log output) that the generated schema
does not match the specified schema.

```text
index_schema does not match generated schema from metadata.
index_schema: {'text': [{'name': 'user'}, {'name': 'job'}], 'tag': [{'name': 'credit_score'}], 'numeric': [{'name': 'age'}]}
generated_schema: {'text': [{'name': 'user'}, {'name': 'job'}, {'name': 'credit_score'}], 'numeric': [{'name': 'age'}]}
```

As long as this is on purpose,  this is fine.

The schema can be defined as a yaml file or a dictionary

```yaml

text:
  - name: user
  - name: job
tag:
  - name: credit_score
numeric:
  - name: age

```

and you pass in a path like

```python
rds, keys = Redis.from_texts_return_keys(
    texts,
    embeddings,
    metadatas=metadata,
    redis_url="redis://localhost:6379",
    index_name="users3",
    index_schema=Path("sample1.yml").resolve()
)
```

Which will create the same schema as defined in the dictionary example


Index Information:
| Index Name | Storage Type | Prefixes | Index Options | Indexing |

|--------------|----------------|----------------|-----------------|------------|
| users3 | HASH | ['doc:users3'] | [] | 0 |
Index Fields:
| Name | Attribute | Type | Field Option | Option Value |

|----------------|----------------|---------|----------------|----------------|
| user | user | TEXT | WEIGHT | 1 |
| job | job | TEXT | WEIGHT | 1 |
| content | content | TEXT | WEIGHT | 1 |
| credit_score | credit_score | TAG | SEPARATOR | , |
| age | age | NUMERIC | | |
| content_vector | content_vector | VECTOR | | |



### Custom Vector Indexing Schema

Users with large use cases may want to change how they formulate the
vector index created by Langchain

To utilize all the features of Redis for vector database use cases like
this, you can now do the following to pass in index attribute modifiers
like changing the indexing algorithm to HNSW.

```python
vector_schema = {
    "algorithm": "HNSW"
}

rds, keys = Redis.from_texts_return_keys(
    texts,
    embeddings,
    metadatas=metadata,
    redis_url="redis://localhost:6379",
    index_name="users3",
    vector_schema=vector_schema
)

```

A more complex example may look like

```python
vector_schema = {
    "algorithm": "HNSW",
    "ef_construction": 200,
    "ef_runtime": 20
}

rds, keys = Redis.from_texts_return_keys(
    texts,
    embeddings,
    metadatas=metadata,
    redis_url="redis://localhost:6379",
    index_name="users3",
    vector_schema=vector_schema
)
```

All names correspond to the arguments you would set if using Redis-py or
RedisVL. (put in doc link later)


### Better Querying

Both vector queries and Range (limit) queries are now available and
metadata is returned by default. The outputs are shown.

```python
>>> query = "foo"
>>> results = rds.similarity_search(query, k=1)
>>> print(results)
[Document(page_content='foo', metadata={'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '14', 'id': 'doc:users:657a47d7db8b447e88598b83da879b9d', 'score': '7.15255737305e-07'})]

>>> results = rds.similarity_search_with_score(query, k=1, return_metadata=False)
>>> print(results) # no metadata, but with scores
[(Document(page_content='foo', metadata={}), 7.15255737305e-07)]

>>> results = rds.similarity_search_limit_score(query, k=6, score_threshold=0.0001)
>>> print(len(results)) # range query (only above threshold even if k is higher)
4
```

### Custom metadata filtering

A big advantage of Redis in this space is being able to do filtering on
data stored alongside the vector itself. With the example above, the
following is now possible in langchain. The equivalence operators are
overridden to describe a new expression language that mimic that of
[redisvl](redisvl.com). This allows for arbitrarily long sequences of
filters that resemble SQL commands that can be used directly with vector
queries and range queries.

There are two interfaces by which to do so and both are shown. 

```python

>>> from langchain.vectorstores.redis import RedisFilter, RedisNum, RedisText

>>> age_filter = RedisFilter.num("age") > 18
>>> age_filter = RedisNum("age") > 18 # equivalent
>>> results = rds.similarity_search(query, filter=age_filter)
>>> print(len(results))
3

>>> job_filter = RedisFilter.text("job") == "engineer" 
>>> job_filter = RedisText("job") == "engineer" # equivalent
>>> results = rds.similarity_search(query, filter=job_filter)
>>> print(len(results))
2

# fuzzy match text search
>>> job_filter = RedisFilter.text("job") % "eng*"
>>> results = rds.similarity_search(query, filter=job_filter)
>>> print(len(results))
2


# combined filters (AND)
>>> combined = age_filter & job_filter
>>> results = rds.similarity_search(query, filter=combined)
>>> print(len(results))
1

# combined filters (OR)
>>> combined = age_filter | job_filter
>>> results = rds.similarity_search(query, filter=combined)
>>> print(len(results))
4
```

All the above filter results can be checked against the data above.


### Other

  - Issue: #3967 
  - Dependencies: No added dependencies
  - Tag maintainer: @hwchase17 @baskaryan @rlancemartin 
  - Twitter handle: @sampartee

---------

Co-authored-by: Naresh Rangan <naresh.rangan0@walmart.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-25 17:22:50 -07:00
Fabrizio Ruocco
cacaf487c3
Azure Cognitive Search - update sdk b8, mod user agent, search with scores (#9191)
Description: Update Azure Cognitive Search SDK to version b8 (breaking
change)
Customizable User Agent.
Implemented Similarity search with scores 

@baskaryan

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-25 02:34:09 -07:00
Margaret Qian
30151c99c7
Update Mosaic endpoint input/output api (#7391)
As noted in prior PRs (https://github.com/hwchase17/langchain/pull/6060,
https://github.com/hwchase17/langchain/pull/7348), the input/output
format has changed a few times as we've stabilized our inference API.
This PR updates the API to the latest stable version as indicated in our
docs: https://docs.mosaicml.com/en/latest/inference.html

The input format looks like this:

`{"inputs": [<prompt>]}
`

The output format looks like this:
`
{"outputs": [<output_text>]}
`
---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-24 22:13:17 -07:00
Harrison Chase
ade482c17e
add twitter chat loader doc (#9737) 2023-08-24 21:55:22 -07:00
Leonid Kuligin
87da56fb1e
Added a pdf parser based on DocAI (#9579)
#9578

---------

Co-authored-by: Leonid Kuligin <kuligin@google.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
2023-08-24 21:44:49 -07:00
Tudor Golubenco
dc30edf51c
Xata as a chat message memory store (#9719)
This adds Xata as a memory store also to the python version of
LangChain, similar to the [one for
LangChain.js](https://github.com/hwchase17/langchainjs/pull/2217).

I have added a Jupyter Notebook with a simple and a more complex example
using an agent.

To run the integration test, you need to execute something like:

```
XATA_API_KEY='xau_...' XATA_DB_URL="https://demo-uni3q8.eu-west-1.xata.sh/db/langchain"  poetry run pytest tests/integration_tests/memory/test_xata.py
```

Where `langchain` is the database you create in Xata.
2023-08-24 17:37:46 -07:00
William FH
dff00ea91e
Chat Loaders (#9708)
Still working out interface/notebooks + need discord data dump to test
out things other than copy+paste

Update:
- Going to remove the 'user_id' arg in the loaders themselves and just
standardize on putting the "sender" arg in the extra kwargs. Then can
provide a utility function to map these to ai and human messages
- Going to move the discord one into just a notebook since I don't have
a good dump to test on and copy+paste maybe isn't the greatest thing to
support in v0
- Need to do more testing on slack since it seems the dump only includes
channels and NOT 1 on 1 convos
-

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-08-24 17:23:27 -07:00
Patrick Loeber
6bedfdf25a
Fix docs for AssemblyAIAudioTranscriptLoader (shorter import path) (#9687)
Uses the shorter import path

`from langchain.document_loaders import` instead of the full path
`from langchain.document_loaders.assemblyai`

Applies those changes to the docs and the unit test.

See #9667 that adds this new loader.
2023-08-24 07:24:53 -07:00