Commit Graph

4117 Commits

Author SHA1 Message Date
Predrag Gruevski
8dbf4cbe80
Add notice about security-sensitive experimental code to experimental README. (#9936)
It renders like this:
https://github.com/langchain-ai/langchain/tree/pg/experimental-readme/libs/experimental


![image](https://github.com/langchain-ai/langchain/assets/2348618/a5f9569d-96f6-44c6-8559-921adb3e337d)
2023-08-29 14:21:30 -04:00
Predrag Gruevski
b5cd1e0fed
Add security notices on PAL and CPAL experimental chains. (#9938)
Clearly document that the PAL and CPAL techniques involve generating
code, and that such code must be properly sandboxed and given
appropriate narrowly-scoped credentials in order to ensure security.

While our implementations include some mitigations, Python and SQL
sandboxing is well-known to be a very hard problem and our mitigations
are no replacement for proper sandboxing and permissions management. The
implementation of such techniques must be performed outside the scope of
the Python process where this package's code runs, so its correct setup
and administration must therefore be the responsibility of the user of
this code.
2023-08-29 13:51:56 -04:00
Jan-Luca Barthel
f5faac8859
addition of cosine distance function for faiss (#9939)
- Description: added the _cosine_relevance_score_fn to
_select_relevance_score_fn of faiss.py to enable the use of cosine
distance for similarity for this vector store and to comply with the
Error Message, that implies, that cosine should be a valid distance
strategy
- Issue: no relevant Issue found, but needed this function myself and
tested it in a private repo
  - Dependencies: none
2023-08-29 10:29:51 -07:00
Tomaz Bratanic
6092422e10
Add neo4j provider page (#9941) 2023-08-29 10:09:51 -07:00
Bagatur
d6957921f0
bump 276 (#9931) 2023-08-29 08:00:38 -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
Bagatur
49ebbe4bcd
fix pydantic import (#9930) 2023-08-29 07:53:01 -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
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
Xiaobing Mi
5d47833ae1
Fix typo in web_scraping.ipynb (#9835) 2023-08-28 19:26:23 -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
Predrag Gruevski
47499c6db4
Avoid type: ignore suppression by adding mypy type hint. (#9881)
Mypy was not able to determine a good type for `type_to_loader_dict`,
since the values in the dict are functions whose return types are
related to each other in a complex way. One can see this by adding a
line like `reveal_type(type_to_loader_dict)` and running mypy, which
will get mypy to show what type it has inferred for that value.

Adding an explicit type hint to help out mypy avoids the need for a mypy
suppression and allows the code to type-check cleanly.
2023-08-28 17:53:33 -07:00
maks-operlejn-ds
f327535eda
Add conftest file to langchain experimental (#9886)
In order to use `requires` marker in langchain-experimental, there's a
need for *conftest.py* file inside. Everything is identical to the main
langchain module.

Co-authored-by: maks-operlejn-ds <maks.operlejn@gmail.com>
2023-08-28 17:52:16 -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
Piyush Jain
fe1b9ee6b8
Updated notebook for comprehend moderation (#9875)
### Description
Updated the notebook for comprehend moderation.

cc @baskaryan
2023-08-28 16:01:43 -07:00
William FH
907c57e324
Add collect_runs callback (#9885) 2023-08-28 15:30:41 -07:00
William FH
3103f07e03
Use existing required args obj if specified (#9883)
We always overwrote the required args but we infer them by default.
Doing it only the old way makes it so the llm guesses even if an arg is
optional (e.g., for uuids)
2023-08-28 14:40:22 -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
Predrag Gruevski
eb3d1fa93c
Add security warning to experimental SQLDatabaseChain class. (#9867)
The most reliable way to not have a chain run an undesirable SQL command
is to not give it database permissions to run that command. That way the
database itself performs the rule enforcement, so it's much easier to
configure and use properly than anything we could add in ourselves.
2023-08-28 13:53:27 -04:00
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
William FH
cb642ef658
Return feedback (#9629)
Return the feedback values in an eval run result

Also made a helper method to display as a dataframe but it may be
overkill
2023-08-28 09:15:05 -07:00
Bagatur
5e2d0cf54e
bump 275 (#9860) 2023-08-28 07:27:07 -07:00
XUEYANZ
f97d3a76e7
Update CONTRIBUTING.md (#9817)
<!-- Thank you for contributing to LangChain!

Replace this entire comment with:
  - Description: a description of the change, 
  - Issue: the issue # it fixes (if applicable),
  - Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
gets announced and you'd like a mention, we'll gladly shout you out!

Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.

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

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

If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. These live is docs/extras
directory.

If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
 -->

Hi LangChain :) Thank you for such a great project! 
I was going through the CONTRIBUTING.md and found a few minor issues.
2023-08-28 09:38:34 -04:00
Eugene Yurtsev
5edf819524
Qdrant Client: Expose instance for creating client (#9706)
Expose classmethods to convenient initialize the vectostore.

The purpose of this PR is to make it easy for users to initialize an
empty vectorstore that's properly pre-configured without having to index
documents into it via `from_documents`.

This will make it easier for users to rely on the following indexing
code: https://github.com/langchain-ai/langchain/pull/9614
to help manage data in the qdrant vectorstore.
2023-08-28 09:30:59 -04:00
Harrison Chase
610f46d83a
accept openai terms (#9826) 2023-08-27 17:18:24 -07:00
Harrison Chase
c1badc1fa2
add gmail loader (#9810) 2023-08-27 17:18:09 -07:00
Bagatur
0d01cede03
bump 274 (#9805) 2023-08-26 12:16:26 -07:00
Vikas Sheoran
63921e327d
docs: Fix a spelling mistake in adding_memory.ipynb (#9794)
# Description 
This pull request fixes a small spelling mistake found while reading
docs.
2023-08-26 12:04:43 -07:00
Rosário P. Fernandes
aab01b55db
typo: funtions --> functions (#9784)
Minor typo in the extractions use-case
2023-08-26 11:47:47 -07:00
Nikhil Suresh
0da5803f5a
fixed regex to match sources for all cases, also includes source (#9775)
- Description: Updated the regex to handle all the different cases for
string matching (SOURCES, sources, Sources),
  - Issue: https://github.com/langchain-ai/langchain/issues/9774
  - Dependencies: N/A
2023-08-25 18:10:33 -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
Anish Shah
fa0b8f3368
fix broken wandb link in debugging page (#9771)
- Description: Fix broken hyperlink in debugging page
2023-08-25 15:34:08 -07:00
Monami Sharma
12a373810c
Fixing broken links to Moderation and Constitutional chain (#9768)
- Description: Fixing broken links for Moderation and Constitutional
chain
  - Issue: N/A
  - Twitter handle: MonamiSharma
2023-08-25 15:19:32 -07:00
nikhilkjha
d57d08fd01
Initial commit for comprehend moderator (#9665)
This PR implements a custom chain that wraps Amazon Comprehend API
calls. The custom chain is aimed to be used with LLM chains to provide
moderation capability that let’s you detect and redact PII, Toxic and
Intent content in the LLM prompt, or the LLM response. The
implementation accepts a configuration object to control what checks
will be performed on a LLM prompt and can be used in a variety of setups
using the LangChain expression language to not only detect the
configured info in chains, but also other constructs such as a
retriever.
The included sample notebook goes over the different configuration
options and how to use it with other chains.

###  Usage sample
```python
from langchain_experimental.comprehend_moderation import BaseModerationActions, BaseModerationFilters

moderation_config = { 
        "filters":[ 
                BaseModerationFilters.PII, 
                BaseModerationFilters.TOXICITY,
                BaseModerationFilters.INTENT
        ],
        "pii":{ 
                "action": BaseModerationActions.ALLOW, 
                "threshold":0.5, 
                "labels":["SSN"],
                "mask_character": "X"
        },
        "toxicity":{ 
                "action": BaseModerationActions.STOP, 
                "threshold":0.5
        },
        "intent":{ 
                "action": BaseModerationActions.STOP, 
                "threshold":0.5
        }
}

comp_moderation_with_config = AmazonComprehendModerationChain(
    moderation_config=moderation_config, #specify the configuration
    client=comprehend_client,            #optionally pass the Boto3 Client
    verbose=True
)

template = """Question: {question}

Answer:"""

prompt = PromptTemplate(template=template, input_variables=["question"])

responses = [
    "Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.", 
    "Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here."
]
llm = FakeListLLM(responses=responses)

llm_chain = LLMChain(prompt=prompt, llm=llm)

chain = ( 
    prompt 
    | comp_moderation_with_config 
    | {llm_chain.input_keys[0]: lambda x: x['output'] }  
    | llm_chain 
    | { "input": lambda x: x['text'] } 
    | comp_moderation_with_config 
)

response = chain.invoke({"question": "A sample SSN number looks like this 123-456-7890. Can you give me some more samples?"})

print(response['output'])


```
### Output
```
> Entering new AmazonComprehendModerationChain chain...
Running AmazonComprehendModerationChain...
Running pii validation...
Found PII content..stopping..
The prompt contains PII entities and cannot be processed
```

---------

Co-authored-by: Piyush Jain <piyushjain@duck.com>
Co-authored-by: Anjan Biswas <anjanavb@amazon.com>
Co-authored-by: Jha <nikjha@amazon.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-25 15:11:27 -07:00
Lance Martin
4339d21cf1
Code LLaMA in code understanding use case (#9779)
Update Code Understanding use case doc w/ Code-llama.
2023-08-25 14:24:38 -07:00
William FH
1960ac8d25
token chunks (#9739)
Co-authored-by: Andrew <abatutin@gmail.com>
2023-08-25 12:52:07 -07:00
Lance Martin
2ab04a4e32
Update agent docs, move to use-case sub-directory (#9344)
Re-structure and add new agent page
2023-08-25 11:28:55 -07:00
Lance Martin
985873c497
Update RAG use case (move to ntbk) (#9340) 2023-08-25 11:27:27 -07:00
Harrison Chase
709a67d9bf
multivector notebook (#9740) 2023-08-25 07:07:27 -07:00
Bagatur
9731ce5a40
bump 273 (#9751) 2023-08-25 03:05:04 -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
Sergey Kozlov
135cb86215
Fix QuestionListOutputParser (#9738)
This PR fixes `QuestionListOutputParser` text splitting.

`QuestionListOutputParser` incorrectly splits numbered list text into
lines. If text doesn't end with `\n` , the regex doesn't capture the
last item. So it always returns `n - 1` items, and
`WebResearchRetriever.llm_chain` generates less queries than requested
in the search prompt.

How to reproduce:

```python
from langchain.retrievers.web_research import QuestionListOutputParser

parser = QuestionListOutputParser()

good = parser.parse(
    """1. This is line one.
    2. This is line two.
    """  # <-- !
)

bad = parser.parse(
    """1. This is line one.
    2. This is line two."""    # <-- No new line.
)

assert good.lines == ['1. This is line one.\n', '2. This is line two.\n'], good.lines
assert bad.lines == ['1. This is line one.\n', '2. This is line two.'], bad.lines
```

NOTE: Last item will not contain a line break but this seems ok because
the items are stripped in the
`WebResearchRetriever.clean_search_query()`.
2023-08-25 01:47:17 -07:00
Jurik-001
d04fe0d3ea
remove Value error "pyspark is not installed. Please install it with `pip i… (#9723)
Description: You cannot execute spark_sql with versions prior to 3.4 due
to the introduction of pyspark.errors in version 3.4.
And if you are below you get 3.4 "pyspark is not installed. Please
install it with pip nstall pyspark" which is not helpful. Also if you
not have pyspark installed you get already the error in init. I would
return all errors. But if you have a different idea feel free to
comment.

Issue: None
Dependencies: None
Maintainer:

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Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-24 22:18:55 -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