Commit Graph

583 Commits

Author SHA1 Message Date
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
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
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
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
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
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
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
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
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
Tomaz Bratanic
dacf96895a
Add the option to use separate LLMs for GraphCypherQA chain (#9689)
The Graph Chains are different in the way that it uses two LLMChains
instead of one like the retrievalQA chains. Therefore, sometimes you
want to use different LLM to generate the database query and to generate
the final answer.

This feature would make it more convenient to use different LLMs in the
same chain.

I have also renamed the Graph DB QA Chain to Neo4j DB QA Chain in the
documentation only as it is used only for Neo4j. The naming was
ambigious as it was the first graphQA chain added and wasn't sure how do
you want to spin it.
2023-08-24 11:50:38 -07:00
Lance Martin
c37be7f5fb
Add Code LLaMA to code QA use case (#9713)
Use [Ollama integration](https://ollama.ai/blog/run-code-llama-locally).
2023-08-24 11:03:35 -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
了空
7cf5c582d2
Added a link to the dependencies document (#9703) 2023-08-24 07:23:48 -07:00
Harrison Chase
9963b32e59
Harrison/multi vector (#9700) 2023-08-24 06:42:42 -07:00
Leonid Ganeline
b048236c1a
📖 docs: integrations/agent_toolkits (#9333)
Note: There are no changes in the file names!

- The group name on the main navbar changed: `Agent toolkits` -> `Agents
& Toolkits`. Examples here are the mix of the Agent and Toolkit examples
because Agents and Toolkits in examples are always used together.
- Titles changed: removed "Agent" and "Toolkit" suffixes. The reason is
the same.
- Formatting: mostly cleaning the header structure, so it could be
better on the right-side navbar.

Main navbar is looking much cleaner now.
2023-08-23 23:17:47 -07:00
Patrick Loeber
5990651070
Add new document_loader: AssemblyAIAudioTranscriptLoader (#9667)
This PR adds a new document loader `AssemblyAIAudioTranscriptLoader`
that allows to transcribe audio files with the [AssemblyAI
API](https://www.assemblyai.com) and loads the transcribed text into
documents.

- Add new document_loader with class `AssemblyAIAudioTranscriptLoader`
- Add optional dependency `assemblyai`
- Add unit tests (using a Mock client)
- Add docs notebook

This is the equivalent to the JS integration already available in
LangChain.js. See the [LangChain JS docs AssemblyAI
page](https://js.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_loaders/assemblyai_audio_transcription).

At its simplest, you can use the loader to get a transcript back from an
audio file like this:

```python
from langchain.document_loaders.assemblyai import AssemblyAIAudioTranscriptLoader

loader =  AssemblyAIAudioTranscriptLoader(file_path="./testfile.mp3")
docs = loader.load()
```

To use it, it needs the `assemblyai` python package installed, and the
environment variable `ASSEMBLYAI_API_KEY` set with your API key.
Alternatively, the API key can also be passed as an argument.

Twitter handles to shout out if so kindly 🙇
[@AssemblyAI](https://twitter.com/AssemblyAI) and
[@patloeber](https://twitter.com/patloeber)

---------

Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
2023-08-23 22:51:19 -07:00
seamusp
25f2c82ae8
docs:misc fixes (#9671)
Improve internal consistency in LangChain documentation
- Change occurrences of eg and eg. to e.g.
- Fix headers containing unnecessary capital letters.
- Change instances of "few shot" to "few-shot".
- Add periods to end of sentences where missing.
- Minor spelling and grammar fixes.
2023-08-23 22:36:54 -07:00
Eugene Yurtsev
b88dfcb42a
Add indexing support (#9614)
This PR introduces a persistence layer to help with indexing workflows
into
vectostores.

The indexing code helps users to:

1. Avoid writing duplicated content into the vectostore
2. Avoid over-writing content if it's unchanged

Importantly, this keeps on working even if the content being written is
derived
via a set of transformations from some source content (e.g., indexing
children
documents that were derived from parent documents by chunking.)

The two main components are:

1. Persistence layer that keeps track of which keys were updated and
when.
Keeping track of the timestamp of updates, allows to clean up old
content
   safely, and with minimal complexity.
2. HashedDocument which is used to hash the contents (including
metadata) of
   the documents. We rely on the hashes for identifying duplicates.


The indexing code works with **ANY** document loader. To add
transformations
to the documents, users for now can add a custom document loader
that composes an existing loader together with document transformers.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-23 21:41:38 -04:00
Lakshay Kansal
a8c916955f
Updates to Nomic Atlas and GPT4All documentation (#9414)
Description: Updates for Nomic AI Atlas and GPT4All integrations
documentation.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-23 17:49:44 -07:00
Keras Conv3d
cbaea8d63b
tair fix distance_type error, and add hybrid search (#9531)
- fix: distance_type error, 
- feature: Tair add hybrid search

---------

Co-authored-by: thw <hanwen.thw@alibaba-inc.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-23 16:38:31 -07:00
Jacob Lee
278ef0bdcf
Adds ChatOllama (#9628)
@rlancemartin

---------

Co-authored-by: Adilkhan Sarsen <54854336+adolkhan@users.noreply.github.com>
Co-authored-by: Kim Minjong <make.dirty.code@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-23 13:02:26 -07:00
Bagatur
80dd162e0d
mv embedding cache docs (#9664) 2023-08-23 11:46:04 -07:00
Bagatur
a40c12bb88
Update the nlpcloud connector after some changes on the NLP Cloud API (#9586)
- Description: remove some text generation deprecated parameters and
update the embeddings doc,
- Tag maintainer: @rlancemartin
2023-08-23 11:35:08 -07:00
Bagatur
d8e2dd4c89 mv 2023-08-23 11:30:44 -07:00
Bagatur
e2e582f1f6
Fixed source key name for docugami loader (#8598)
The Docugami loader was not returning the source metadata key. This was
triggering this exception when used with retrievers, per
https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/schema/prompt_template.py#L193C1-L195C41

The fix is simple and just updates the metadata key name for the
document each chunk is sourced from, from "name" to "source" as
expected.

I tested by running the python notebook that has an end to end scenario
in it.

Tagging DataLoader maintainers @rlancemartin @eyurtsev
2023-08-23 11:24:55 -07:00
Zizhong Zhang
8a03836160
docs: fix PromptGuard docs (#9659)
Fix PromptGuard docs. Noticed several trivial issues on the docs when
integrating the new class.
cc @baskaryan
2023-08-23 10:04:53 -07:00
Yong woo Song
f0ae10a20e
Fix typo in tigris (#9637)
The link has a **typo** in [tigirs
docs](https://python.langchain.com/docs/integrations/providers/tigris),
so I couldn't access it. So, I have corrected it.
Thanks! ☺️
2023-08-23 07:15:18 -07:00
Junlin Zhou
5b9bdcac1b
docs: fix link url (#9643)
This pull request corrects the URL links in the Async API documentation
to align with the updated project layout. The links had not been updated
despite the changes in layout.
2023-08-23 07:05:02 -07:00
Joseph McElroy
2a06e7b216
ElasticsearchStore: improve error logging for adding documents (#9648)
Not obvious what the error is when you cannot index. This pr adds the
ability to log the first errors reason, to help the user diagnose the
issue.

Also added some more documentation for when you want to use the
vectorstore with an embedding model deployed in elasticsearch.

Credit: @elastic and @phoey1
2023-08-23 07:04:09 -07:00
Julien Salinas
f1072cc31f
Merge branch 'master' into master 2023-08-23 14:42:40 +02:00