Redis metadata filtering and specification, index customization
Labels: name: area: vector store, name: auto:improvement

### 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.


### Example data

Suppose we have the following sample data

```python

metadata = [
    {
        "user": "john",
        "age": 18,
        "job": "engineer",
        "credit_score": "high",
    },
    {
        "user": "derrick",
        "age": 14,
        "job": "doctor",
        "credit_score": "low",
    },
    {
        "user": "nancy",
        "age": 94,
        "job": "doctor",
        "credit_score": "high",
    },
    {
        "user": "tyler",
        "age": 100,
        "job": "engineer",
        "credit_score": "high",
    },
    {
        "user": "tim",
        "age": 12,
        "job": "dermatologist",
        "credit_score": "high",
    },
    {
        "user": "taimur",
        "age": 15,
        "job": "CEO",
        "credit_score": "low",
    },
    {
        "user": "joe",
        "age": 35,
        "job": "dentist",
        "credit_score": "medium",
    },
]

texts = ["foo", "foo", "foo", "foo", "bar", "bar", "bar"]

```

# 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.



### TODO

- [x] more tests
- [x] docstrings
- [x] docs

### Other

  - Issue: #3967 
  - Dependencies: No added dependencies
  - Tag maintainer: @hwchase17 @baskaryan @rlancemartin 
  - Twitter handle: @sampartee