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closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | """Wrapper around Redis vector database."""
from __future__ import annotations
import logging
import os
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Mapping,
Optional,
Tuple,
Type,
Union,
cast, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | )
import numpy as np
import yaml
from langchain._api import deprecated
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
from langchain.schema.vectorstore import VectorStore, VectorStoreRetriever
from langchain.utilities.redis import (
_array_to_buffer,
_buffer_to_array,
check_redis_module_exist,
get_client,
)
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.redis.constants import (
REDIS_REQUIRED_MODULES,
REDIS_TAG_SEPARATOR,
)
from langchain.vectorstores.utils import maximal_marginal_relevance
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
from redis.client import Redis as RedisType
from redis.commands.search.query import Query
from langchain.vectorstores.redis.filters import RedisFilterExpression
from langchain.vectorstores.redis.schema import RedisModel
def _redis_key(prefix: str) -> str: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | """Redis key schema for a given prefix."""
return f"{prefix}:{uuid.uuid4().hex}"
def _redis_prefix(index_name: str) -> str:
"""Redis key prefix for a given index."""
return f"doc:{index_name}"
def _default_relevance_score(val: float) -> float:
return 1 - val
def check_index_exists(client: RedisType, index_name: str) -> bool:
"""Check if Redis index exists."""
try:
client.ft(index_name).info()
except:
logger.info("Index does not exist")
return False
logger.info("Index already exists")
return True
class Redis(VectorStore):
"""Redis vector database.
To use, you should have the ``redis`` python package installed
and have a running Redis Enterprise or Redis-Stack server
For production use cases, it is recommended to use Redis Enterprise
as the scaling, performance, stability and availability is much
better than Redis-Stack.
For testing and prototyping, however, this is not required.
Redis-Stack is available as a docker container the full vector
search API available.
.. code-block:: bash
# to run redis stack in docker locally
docker run -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
Once running, you can connect to the redis server with the following url schemas: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | - redis://<host>:<port> # simple connection
- redis://<username>:<password>@<host>:<port> # connection with authentication
- rediss://<host>:<port> # connection with SSL
- rediss://<username>:<password>@<host>:<port> # connection with SSL and auth
Examples:
The following examples show various ways to use the Redis VectorStore with
LangChain.
For all the following examples assume we have the following imports:
.. code-block:: python
from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
Initialize, create index, and load Documents
.. code-block:: python
from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
rds = Redis.from_documents(
documents, # a list of Document objects from loaders or created
embeddings, # an Embeddings object
redis_url="redis://localhost:6379",
)
Initialize, create index, and load Documents with metadata
.. code-block:: python
rds = Redis.from_texts(
texts, # a list of strings
metadata, # a list of metadata dicts
embeddings, # an Embeddings object
redis_url="redis://localhost:6379",
)
Initialize, create index, and load Documents with metadata and return keys
.. code-block:: python |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | rds, keys = Redis.from_texts_return_keys(
texts, # a list of strings
metadata, # a list of metadata dicts
embeddings, # an Embeddings object
redis_url="redis://localhost:6379",
)
For use cases where the index needs to stay alive, you can initialize
with an index name such that it's easier to reference later
.. code-block:: python
rds = Redis.from_texts(
texts, # a list of strings
metadata, # a list of metadata dicts
embeddings, # an Embeddings object
index_name="my-index",
redis_url="redis://localhost:6379",
)
Initialize and connect to an existing index (from above)
.. code-block:: python
rds = Redis.from_existing_index(
embeddings, # an Embeddings object
index_name="my-index",
redis_url="redis://localhost:6379",
)
Advanced examples:
Custom vector schema can be supplied to change the way that
Redis creates the underlying vector schema. This is useful
for production use cases where you want to optimize the
vector schema for your use case. ex. using HNSW instead of
FLAT (knn) which is the default
.. code-block:: python |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | vector_schema = {
"algorithm": "HNSW"
}
rds = Redis.from_texts(
texts, # a list of strings
metadata, # a list of metadata dicts
embeddings, # an Embeddings object
vector_schema=vector_schema,
redis_url="redis://localhost:6379",
)
Custom index schema can be supplied to change the way that the
metadata is indexed. This is useful for you would like to use the
hybrid querying (filtering) capability of Redis.
By default, this implementation will automatically generate the index
schema according to the following rules:
- All strings are indexed as text fields
- All numbers are indexed as numeric fields
- All lists of strings are indexed as tag fields (joined by
langchain.vectorstores.redis.constants.REDIS_TAG_SEPARATOR)
- All None values are not indexed but still stored in Redis these are
not retrievable through the interface here, but the raw Redis client
can be used to retrieve them.
- All other types are not indexed
To override these rules, you can pass in a custom index schema like the following
.. code-block:: yaml
tag:
- name: credit_score
text:
- name: user
- name: job |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | Typically, the ``credit_score`` field would be a text field since it's a string,
however, we can override this behavior by specifying the field type as shown with
the yaml config (can also be a dictionary) above and the code below.
.. code-block:: python
rds = Redis.from_texts(
texts, # a list of strings
metadata, # a list of metadata dicts
embeddings, # an Embeddings object
index_schema="path/to/index_schema.yaml", # can also be a dictionary
redis_url="redis://localhost:6379",
)
When connecting to an existing index where a custom schema has been applied, it's
important to pass in the same schema to the ``from_existing_index`` method.
Otherwise, the schema for newly added samples will be incorrect and metadata
will not be returned.
"""
DEFAULT_VECTOR_SCHEMA = {
"name": "content_vector",
"algorithm": "FLAT",
"dims": 1536,
"distance_metric": "COSINE",
"datatype": "FLOAT32",
}
def __init__(
self,
redis_url: str,
index_name: str,
embedding: Embeddings,
index_schema: Optional[Union[Dict[str, str], str, os.PathLike]] = None,
vector_schema: Optional[Dict[str, Union[str, int]]] = None, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | relevance_score_fn: Optional[Callable[[float], float]] = None,
**kwargs: Any,
):
"""Initialize with necessary components."""
self._check_deprecated_kwargs(kwargs)
try:
import redis
except ImportError as e:
raise ImportError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
) from e
self.index_name = index_name
self._embeddings = embedding
try:
redis_client = get_client(redis_url=redis_url, **kwargs)
check_redis_module_exist(redis_client, REDIS_REQUIRED_MODULES)
except ValueError as e:
raise ValueError(f"Redis failed to connect: {e}")
self.client = redis_client
self.relevance_score_fn = relevance_score_fn
self._schema = self._get_schema_with_defaults(index_schema, vector_schema)
@property
def embeddings(self) -> Optional[Embeddings]:
"""Access the query embedding object if available."""
return self._embeddings
@classmethod
def from_texts_return_keys( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
index_name: Optional[str] = None,
index_schema: Optional[Union[Dict[str, str], str, os.PathLike]] = None,
vector_schema: Optional[Dict[str, Union[str, int]]] = None,
**kwargs: Any,
) -> Tuple[Redis, List[str]]:
"""Create a Redis vectorstore from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new Redis index if it doesn't already exist
3. Adds the documents to the newly created Redis index.
4. Returns the keys of the newly created documents once stored.
This method will generate schema based on the metadata passed in
if the `index_schema` is not defined. If the `index_schema` is defined,
it will compare against the generated schema and warn if there are
differences. If you are purposefully defining the schema for the
metadata, then you can ignore that warning.
To examine the schema options, initialize an instance of this class
and print out the schema using the `Redis.schema`` property. This
will include the content and content_vector classes which are
always present in the langchain schema.
Example:
.. code-block:: python
from langchain.vectorstores import Redis |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
redis, keys = Redis.from_texts_return_keys(
texts,
embeddings,
redis_url="redis://localhost:6379"
)
Args:
texts (List[str]): List of texts to add to the vectorstore.
embedding (Embeddings): Embeddings to use for the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadata
dicts to add to the vectorstore. Defaults to None.
index_name (Optional[str], optional): Optional name of the index to
create or add to. Defaults to None.
index_schema (Optional[Union[Dict[str, str], str, os.PathLike]], optional):
Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
vector_schema (Optional[Dict[str, Union[str, int]]], optional): Optional
vector schema to use. Defaults to None.
**kwargs (Any): Additional keyword arguments to pass to the Redis client.
Returns:
Tuple[Redis, List[str]]: Tuple of the Redis instance and the keys of
the newly created documents.
Raises:
ValueError: If the number of metadatas does not match the number of texts.
"""
try:
import redis
from langchain.vectorstores.redis.schema import read_schema |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | except ImportError as e:
raise ImportError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
) from e
redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
if "redis_url" in kwargs:
kwargs.pop("redis_url")
if "generate" in kwargs:
kwargs.pop("generate")
keys = None
if "keys" in kwargs:
keys = kwargs.pop("keys")
if not index_name:
index_name = uuid.uuid4().hex
if metadatas:
if isinstance(metadatas, list) and len(metadatas) != len(texts):
raise ValueError("Number of metadatas must match number of texts")
if not (isinstance(metadatas, list) and isinstance(metadatas[0], dict)):
raise ValueError("Metadatas must be a list of dicts")
generated_schema = _generate_field_schema(metadatas[0])
if index_schema:
user_schema = read_schema(index_schema) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | if user_schema != generated_schema:
logger.warning(
"`index_schema` does not match generated metadata schema.\n"
+ "If you meant to manually override the schema, please "
+ "ignore this message.\n"
+ f"index_schema: {user_schema}\n"
+ f"generated_schema: {generated_schema}\n"
)
else:
index_schema = generated_schema
instance = cls(
redis_url,
index_name,
embedding,
index_schema=index_schema,
vector_schema=vector_schema,
**kwargs,
)
embeddings = embedding.embed_documents(texts)
instance._create_index(dim=len(embeddings[0]))
keys = instance.add_texts(texts, metadatas, embeddings, keys=keys)
return instance, keys
@classmethod
def from_texts( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | cls: Type[Redis],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
index_name: Optional[str] = None,
index_schema: Optional[Union[Dict[str, str], str, os.PathLike]] = None,
vector_schema: Optional[Dict[str, Union[str, int]]] = None,
**kwargs: Any,
) -> Redis:
"""Create a Redis vectorstore from a list of texts.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new Redis index if it doesn't already exist
3. Adds the documents to the newly created Redis index.
This method will generate schema based on the metadata passed in
if the `index_schema` is not defined. If the `index_schema` is defined,
it will compare against the generated schema and warn if there are
differences. If you are purposefully defining the schema for the
metadata, then you can ignore that warning.
To examine the schema options, initialize an instance of this class
and print out the schema using the `Redis.schema`` property. This
will include the content and content_vector classes which are
always present in the langchain schema.
Example:
.. code-block:: python
from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
redisearch = RediSearch.from_texts( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | texts,
embeddings,
redis_url="redis://username:password@localhost:6379"
)
Args:
texts (List[str]): List of texts to add to the vectorstore.
embedding (Embeddings): Embedding model class (i.e. OpenAIEmbeddings)
for embedding queries.
metadatas (Optional[List[dict]], optional): Optional list of metadata dicts
to add to the vectorstore. Defaults to None.
index_name (Optional[str], optional): Optional name of the index to create
or add to. Defaults to None.
index_schema (Optional[Union[Dict[str, str], str, os.PathLike]], optional):
Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
vector_schema (Optional[Dict[str, Union[str, int]]], optional): Optional
vector schema to use. Defaults to None.
**kwargs (Any): Additional keyword arguments to pass to the Redis client.
Returns:
Redis: Redis VectorStore instance.
Raises:
ValueError: If the number of metadatas does not match the number of texts.
ImportError: If the redis python package is not installed.
"""
instance, _ = cls.from_texts_return_keys(
texts,
embedding,
metadatas=metadatas,
index_name=index_name,
index_schema=index_schema, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | vector_schema=vector_schema,
**kwargs,
)
return instance
@classmethod
def from_existing_index(
cls,
embedding: Embeddings,
index_name: str,
schema: Union[Dict[str, str], str, os.PathLike],
**kwargs: Any,
) -> Redis:
"""Connect to an existing Redis index.
Example:
.. code-block:: python
from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
redisearch = Redis.from_existing_index(
embeddings,
index_name="my-index",
redis_url="redis://username:password@localhost:6379"
)
Args:
embedding (Embeddings): Embedding model class (i.e. OpenAIEmbeddings)
for embedding queries.
index_name (str): Name of the index to connect to.
schema (Union[Dict[str, str], str, os.PathLike]): Schema of the index
and the vector schema. Can be a dict, or path to yaml file
**kwargs (Any): Additional keyword arguments to pass to the Redis client. |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | Returns:
Redis: Redis VectorStore instance.
Raises:
ValueError: If the index does not exist.
ImportError: If the redis python package is not installed.
"""
redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
try:
if "redis_url" in kwargs:
kwargs.pop("redis_url")
client = get_client(redis_url=redis_url, **kwargs)
check_redis_module_exist(client, REDIS_REQUIRED_MODULES)
assert check_index_exists(
client, index_name
), f"Index {index_name} does not exist"
except Exception as e:
raise ValueError(f"Redis failed to connect: {e}")
return cls(
redis_url,
index_name,
embedding,
index_schema=schema,
**kwargs,
)
@property
def schema(self) -> Dict[str, List[Any]]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | """Return the schema of the index."""
return self._schema.as_dict()
def write_schema(self, path: Union[str, os.PathLike]) -> None:
"""Write the schema to a yaml file."""
with open(path, "w+") as f:
yaml.dump(self.schema, f)
@staticmethod
def delete(
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> bool:
"""
Delete a Redis entry.
Args:
ids: List of ids (keys in redis) to delete.
redis_url: Redis connection url. This should be passed in the kwargs
or set as an environment variable: REDIS_URL.
Returns:
bool: Whether or not the deletions were successful.
Raises:
ValueError: If the redis python package is not installed.
ValueError: If the ids (keys in redis) are not provided
"""
redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
if ids is None:
raise ValueError("'ids' (keys)() were not provided.")
try:
import redis |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
try:
if "redis_url" in kwargs:
kwargs.pop("redis_url")
client = get_client(redis_url=redis_url, **kwargs)
except ValueError as e:
raise ValueError(f"Your redis connected error: {e}")
try:
client.delete(*ids)
logger.info("Entries deleted")
return True
except:
return False
@staticmethod
def drop_index(
index_name: str,
delete_documents: bool,
**kwargs: Any,
) -> bool:
"""
Drop a Redis search index.
Args: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | index_name (str): Name of the index to drop.
delete_documents (bool): Whether to drop the associated documents.
Returns:
bool: Whether or not the drop was successful.
"""
redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
try:
import redis
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
try:
if "redis_url" in kwargs:
kwargs.pop("redis_url")
client = get_client(redis_url=redis_url, **kwargs)
except ValueError as e:
raise ValueError(f"Your redis connected error: {e}")
try:
client.ft(index_name).dropindex(delete_documents)
logger.info("Drop index")
return True
except:
return False
def add_texts( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
embeddings: Optional[List[List[float]]] = None,
batch_size: int = 1000,
clean_metadata: bool = True,
**kwargs: Any,
) -> List[str]:
"""Add more texts to the vectorstore.
Args:
texts (Iterable[str]): Iterable of strings/text to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
Defaults to None.
embeddings (Optional[List[List[float]]], optional): Optional pre-generated
embeddings. Defaults to None.
keys (List[str]) or ids (List[str]): Identifiers of entries.
Defaults to None.
batch_size (int, optional): Batch size to use for writes. Defaults to 1000.
Returns:
List[str]: List of ids added to the vectorstore
"""
ids = []
prefix = _redis_prefix(self.index_name)
keys_or_ids = kwargs.get("keys", kwargs.get("ids"))
if metadatas:
if isinstance(metadatas, list) and len(metadatas) != len(texts):
raise ValueError("Number of metadatas must match number of texts") |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | if not (isinstance(metadatas, list) and isinstance(metadatas[0], dict)):
raise ValueError("Metadatas must be a list of dicts")
pipeline = self.client.pipeline(transaction=False)
for i, text in enumerate(texts):
key = keys_or_ids[i] if keys_or_ids else _redis_key(prefix)
metadata = metadatas[i] if metadatas else {}
metadata = _prepare_metadata(metadata) if clean_metadata else metadata
embedding = (
embeddings[i] if embeddings else self._embeddings.embed_query(text)
)
pipeline.hset(
key,
mapping={
self._schema.content_key: text,
self._schema.content_vector_key: _array_to_buffer(
embedding, self._schema.vector_dtype
),
**metadata,
},
)
ids.append(key)
if i % batch_size == 0:
pipeline.execute()
pipeline.execute()
return ids
def as_retriever(self, **kwargs: Any) -> RedisVectorStoreRetriever: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | tags = kwargs.pop("tags", None) or []
tags.extend(self._get_retriever_tags())
return RedisVectorStoreRetriever(vectorstore=self, **kwargs, tags=tags)
@deprecated("0.0.272", alternative="similarity_search(distance_threshold=0.1)")
def similarity_search_limit_score(
self, query: str, k: int = 4, score_threshold: float = 0.2, **kwargs: Any
) -> List[Document]:
"""
Returns the most similar indexed documents to the query text within the
score_threshold range.
Deprecated: Use similarity_search with distance_threshold instead.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
score_threshold (float): The minimum matching *distance* required
for a document to be considered a match. Defaults to 0.2.
Returns:
List[Document]: A list of documents that are most similar to the query text
including the match score for each document.
Note:
If there are no documents that satisfy the score_threshold value,
an empty list is returned.
"""
return self.similarity_search(
query, k=k, distance_threshold=score_threshold, **kwargs
)
def similarity_search_with_score( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | self,
query: str,
k: int = 4,
filter: Optional[RedisFilterExpression] = None,
return_metadata: bool = True,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Run similarity search with **vector distance**.
The "scores" returned from this function are the raw vector
distances from the query vector. For similarity scores, use
``similarity_search_with_relevance_scores``.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
filter (RedisFilterExpression, optional): Optional metadata filter.
Defaults to None.
return_metadata (bool, optional): Whether to return metadata.
Defaults to True.
Returns:
List[Tuple[Document, float]]: A list of documents that are
most similar to the query with the distance for each document.
"""
try: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | import redis
except ImportError as e:
raise ImportError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
) from e
if "score_threshold" in kwargs:
logger.warning(
"score_threshold is deprecated. Use distance_threshold instead."
+ "score_threshold should only be used in "
+ "similarity_search_with_relevance_scores."
+ "score_threshold will be removed in a future release.",
)
query_embedding = self._embeddings.embed_query(query)
redis_query, params_dict = self._prepare_query(
query_embedding,
k=k,
filter=filter,
with_metadata=return_metadata,
with_distance=True,
**kwargs,
)
try:
results = self.client.ft(self.index_name).search(redis_query, params_dict)
except redis.exceptions.ResponseError as e:
if str(e).split(" ")[0] == "Syntax":
raise ValueError( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | "Query failed with syntax error. "
+ "This is likely due to malformation of "
+ "filter, vector, or query argument"
) from e
raise e
docs_with_scores: List[Tuple[Document, float]] = []
for result in results.docs:
metadata = {}
if return_metadata:
metadata = {"id": result.id}
metadata.update(self._collect_metadata(result))
doc = Document(page_content=result.content, metadata=metadata)
distance = self._calculate_fp_distance(result.distance)
docs_with_scores.append((doc, distance))
return docs_with_scores
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[RedisFilterExpression] = None,
return_metadata: bool = True,
distance_threshold: Optional[float] = None,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
filter (RedisFilterExpression, optional): Optional metadata filter. |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | Defaults to None.
return_metadata (bool, optional): Whether to return metadata.
Defaults to True.
distance_threshold (Optional[float], optional): Maximum vector distance
between selected documents and the query vector. Defaults to None.
Returns:
List[Document]: A list of documents that are most similar to the query
text.
"""
query_embedding = self._embeddings.embed_query(query)
return self.similarity_search_by_vector(
query_embedding,
k=k,
filter=filter,
return_metadata=return_metadata,
distance_threshold=distance_threshold,
**kwargs,
)
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[RedisFilterExpression] = None,
return_metadata: bool = True,
distance_threshold: Optional[float] = None,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search between a query vector and the indexed vectors.
Args:
embedding (List[float]): The query vector for which to find similar |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | documents.
k (int): The number of documents to return. Default is 4.
filter (RedisFilterExpression, optional): Optional metadata filter.
Defaults to None.
return_metadata (bool, optional): Whether to return metadata.
Defaults to True.
distance_threshold (Optional[float], optional): Maximum vector distance
between selected documents and the query vector. Defaults to None.
Returns:
List[Document]: A list of documents that are most similar to the query
text.
"""
try:
import redis
except ImportError as e:
raise ImportError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
) from e
if "score_threshold" in kwargs:
logger.warning(
"score_threshold is deprecated. Use distance_threshold instead."
+ "score_threshold should only be used in "
+ "similarity_search_with_relevance_scores."
+ "score_threshold will be removed in a future release.",
)
redis_query, params_dict = self._prepare_query(
embedding,
k=k,
filter=filter, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | distance_threshold=distance_threshold,
with_metadata=return_metadata,
with_distance=False,
)
try:
results = self.client.ft(self.index_name).search(redis_query, params_dict)
except redis.exceptions.ResponseError as e:
if str(e).split(" ")[0] == "Syntax":
raise ValueError(
"Query failed with syntax error. "
+ "This is likely due to malformation of "
+ "filter, vector, or query argument"
) from e
raise e
docs = []
for result in results.docs:
metadata = {}
if return_metadata:
metadata = {"id": result.id}
metadata.update(self._collect_metadata(result))
content_key = self._schema.content_key
docs.append(
Document(page_content=getattr(result, content_key), metadata=metadata)
)
return docs
def max_marginal_relevance_search( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[RedisFilterExpression] = None,
return_metadata: bool = True,
distance_threshold: Optional[float] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query (str): Text to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float): Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (RedisFilterExpression, optional): Optional metadata filter.
Defaults to None.
return_metadata (bool, optional): Whether to return metadata.
Defaults to True.
distance_threshold (Optional[float], optional): Maximum vector distance
between selected documents and the query vector. Defaults to None.
Returns:
List[Document]: A list of Documents selected by maximal marginal relevance.
""" |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | query_embedding = self._embeddings.embed_query(query)
prefetch_docs = self.similarity_search_by_vector(
query_embedding,
k=fetch_k,
filter=filter,
return_metadata=return_metadata,
distance_threshold=distance_threshold,
**kwargs,
)
prefetch_ids = [doc.metadata["id"] for doc in prefetch_docs]
prefetch_embeddings = [
_buffer_to_array(
cast(
bytes,
self.client.hget(prefetch_id, self._schema.content_vector_key),
),
dtype=self._schema.vector_dtype,
)
for prefetch_id in prefetch_ids
]
selected_indices = maximal_marginal_relevance(
np.array(query_embedding), prefetch_embeddings, lambda_mult=lambda_mult, k=k
)
selected_docs = [prefetch_docs[i] for i in selected_indices]
return selected_docs
def _collect_metadata(self, result: "Document") -> Dict[str, Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | """Collect metadata from Redis.
Method ensures that there isn't a mismatch between the metadata
and the index schema passed to this class by the user or generated
by this class.
Args:
result (Document): redis.commands.search.Document object returned
from Redis.
Returns:
Dict[str, Any]: Collected metadata.
"""
meta = {}
for key in self._schema.metadata_keys:
try:
meta[key] = getattr(result, key)
except AttributeError:
logger.warning(
f"Metadata key {key} not found in metadata. "
+ "Setting to None. \n"
+ "Metadata fields defined for this instance: "
+ f"{self._schema.metadata_keys}"
)
meta[key] = None
return meta
def _prepare_query( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | self,
query_embedding: List[float],
k: int = 4,
filter: Optional[RedisFilterExpression] = None,
distance_threshold: Optional[float] = None,
with_metadata: bool = True,
with_distance: bool = False,
) -> Tuple["Query", Dict[str, Any]]:
params_dict: Dict[str, Union[str, bytes, float]] = {
"vector": _array_to_buffer(query_embedding, self._schema.vector_dtype),
}
return_fields = [self._schema.content_key]
if with_distance:
return_fields.append("distance")
if with_metadata:
return_fields.extend(self._schema.metadata_keys)
if distance_threshold:
params_dict["distance_threshold"] = distance_threshold
return (
self._prepare_range_query(
k, filter=filter, return_fields=return_fields
),
params_dict,
)
return ( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | self._prepare_vector_query(k, filter=filter, return_fields=return_fields),
params_dict,
)
def _prepare_range_query(
self,
k: int,
filter: Optional[RedisFilterExpression] = None,
return_fields: Optional[List[str]] = None,
) -> "Query":
try:
from redis.commands.search.query import Query
except ImportError as e:
raise ImportError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
) from e
return_fields = return_fields or []
vector_key = self._schema.content_vector_key
base_query = f"@{vector_key}:[VECTOR_RANGE $distance_threshold $vector]"
if filter:
base_query = "(" + base_query + " " + str(filter) + ")"
query_string = base_query + "=>{$yield_distance_as: distance}"
return (
Query(query_string)
.return_fields(*return_fields)
.sort_by("distance")
.paging(0, k)
.dialect(2)
)
def _prepare_vector_query( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | self,
k: int,
filter: Optional[RedisFilterExpression] = None,
return_fields: Optional[List[str]] = None,
) -> "Query":
"""Prepare query for vector search.
Args:
k: Number of results to return.
filter: Optional metadata filter.
Returns:
query: Query object.
"""
try:
from redis.commands.search.query import Query |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | except ImportError as e:
raise ImportError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
) from e
return_fields = return_fields or []
query_prefix = "*"
if filter:
query_prefix = f"{str(filter)}"
vector_key = self._schema.content_vector_key
base_query = f"({query_prefix})=>[KNN {k} @{vector_key} $vector AS distance]"
query = (
Query(base_query)
.return_fields(*return_fields)
.sort_by("distance")
.paging(0, k)
.dialect(2)
)
return query
def _get_schema_with_defaults(
self,
index_schema: Optional[Union[Dict[str, str], str, os.PathLike]] = None,
vector_schema: Optional[Dict[str, Union[str, int]]] = None,
) -> "RedisModel":
from langchain.vectorstores.redis.schema import RedisModel, read_schema
schema = RedisModel()
if index_schema: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | schema_values = read_schema(index_schema)
schema = RedisModel(**schema_values)
schema.add_content_field()
try:
schema.content_vector
if vector_schema:
logger.warning(
"`vector_schema` is ignored since content_vector is "
+ "overridden in `index_schema`."
)
except ValueError:
vector_field = self.DEFAULT_VECTOR_SCHEMA.copy()
if vector_schema:
vector_field.update(vector_schema)
schema.add_vector_field(vector_field)
return schema
def _create_index(self, dim: int = 1536) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | try:
from redis.commands.search.indexDefinition import (
IndexDefinition,
IndexType,
)
except ImportError:
raise ImportError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
self._schema.content_vector.dims = dim
if not check_index_exists(self.client, self.index_name):
prefix = _redis_prefix(self.index_name)
self.client.ft(self.index_name).create_index(
fields=self._schema.get_fields(),
definition=IndexDefinition(prefix=[prefix], index_type=IndexType.HASH),
)
def _calculate_fp_distance(self, distance: str) -> float: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | """Calculate the distance based on the vector datatype
Two datatypes supported:
- FLOAT32
- FLOAT64
if it's FLOAT32, we need to round the distance to 4 decimal places
otherwise, round to 7 decimal places.
"""
if self._schema.content_vector.datatype == "FLOAT32":
return round(float(distance), 4)
return round(float(distance), 7)
def _check_deprecated_kwargs(self, kwargs: Mapping[str, Any]) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | """Check for deprecated kwargs."""
deprecated_kwargs = {
"redis_host": "redis_url",
"redis_port": "redis_url",
"redis_password": "redis_url",
"content_key": "index_schema",
"vector_key": "vector_schema",
"distance_metric": "vector_schema",
}
for key, value in kwargs.items():
if key in deprecated_kwargs:
raise ValueError(
f"Keyword argument '{key}' is deprecated. "
f"Please use '{deprecated_kwargs[key]}' instead."
)
def _select_relevance_score_fn(self) -> Callable[[float], float]:
if self.relevance_score_fn:
return self.relevance_score_fn
metric_map = {
"COSINE": self._cosine_relevance_score_fn,
"IP": self._max_inner_product_relevance_score_fn,
"L2": self._euclidean_relevance_score_fn,
}
try:
return metric_map[self._schema.content_vector.distance_metric]
except KeyError:
return _default_relevance_score
def _generate_field_schema(data: Dict[str, Any]) -> Dict[str, Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | """
Generate a schema for the search index in Redis based on the input metadata.
Given a dictionary of metadata, this function categorizes each metadata
field into one of the three categories:
- text: The field contains textual data.
- numeric: The field contains numeric data (either integer or float).
- tag: The field contains list of tags (strings).
Args
data (Dict[str, Any]): A dictionary where keys are metadata field names
and values are the metadata values.
Returns:
Dict[str, Any]: A dictionary with three keys "text", "numeric", and "tag".
Each key maps to a list of fields that belong to that category.
Raises:
ValueError: If a metadata field cannot be categorized into any of
the three known types.
"""
result: Dict[str, Any] = {
"text": [],
"numeric": [],
"tag": [],
}
for key, value in data.items():
try: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | int(value)
result["numeric"].append({"name": key})
continue
except (ValueError, TypeError):
pass
if value is None:
continue
if isinstance(value, (list, tuple)):
if not value or isinstance(value[0], str):
result["tag"].append({"name": key})
else:
name = type(value[0]).__name__
raise ValueError(
f"List/tuple values should contain strings: '{key}': {name}"
)
continue
if isinstance(value, str):
result["text"].append({"name": key})
continue
name = type(value).__name__
raise ValueError(
"Could not generate Redis index field type mapping "
+ f"for metadata: '{key}': {name}"
)
return result
def _prepare_metadata(metadata: Dict[str, Any]) -> Dict[str, Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | """
Prepare metadata for indexing in Redis by sanitizing its values.
- String, integer, and float values remain unchanged.
- None or empty values are replaced with empty strings.
- Lists/tuples of strings are joined into a single string with a comma separator.
Args:
metadata (Dict[str, Any]): A dictionary where keys are metadata
field names and values are the metadata values.
Returns:
Dict[str, Any]: A sanitized dictionary ready for indexing in Redis.
Raises:
ValueError: If any metadata value is not one of the known
types (string, int, float, or list of strings).
"""
def raise_error(key: str, value: Any) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | raise ValueError(
f"Metadata value for key '{key}' must be a string, int, "
+ f"float, or list of strings. Got {type(value).__name__}"
)
clean_meta: Dict[str, Union[str, float, int]] = {}
for key, value in metadata.items():
if not value:
clean_meta[key] = ""
continue
if isinstance(value, (str, int, float)):
clean_meta[key] = value
elif isinstance(value, (list, tuple)):
if not value or isinstance(value[0], str):
clean_meta[key] = REDIS_TAG_SEPARATOR.join(value)
else:
raise_error(key, value)
else:
raise_error(key, value)
return clean_meta
class RedisVectorStoreRetriever(VectorStoreRetriever): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | """Retriever for Redis VectorStore."""
vectorstore: Redis
"""Redis VectorStore."""
search_type: str = "similarity"
"""Type of search to perform. Can be either
'similarity',
'similarity_distance_threshold',
'similarity_score_threshold'
"""
search_kwargs: Dict[str, Any] = {
"k": 4,
"score_threshold": 0.9,
"distance_threshold": None,
}
"""Default search kwargs."""
allowed_search_types = [
"similarity",
"similarity_distance_threshold",
"similarity_score_threshold",
"mmr",
]
"""Allowed search types."""
class Config: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | """Configuration for this pydantic object."""
arbitrary_types_allowed = True
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
if self.search_type == "similarity":
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
elif self.search_type == "similarity_distance_threshold":
if self.search_kwargs["distance_threshold"] is None:
raise ValueError(
"distance_threshold must be provided for "
+ "similarity_distance_threshold retriever"
)
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
elif self.search_type == "similarity_score_threshold":
docs_and_similarities = (
self.vectorstore.similarity_search_with_relevance_scores(
query, **self.search_kwargs
)
)
docs = [doc for doc, _ in docs_and_similarities]
elif self.search_type == "mmr":
docs = self.vectorstore.max_marginal_relevance_search(
query, **self.search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def _aget_relevant_documents( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 11,197 | Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"] | ### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd" | https://github.com/langchain-ai/langchain/issues/11197 | https://github.com/langchain-ai/langchain/pull/11257 | 079d1f3b8e8cf7a4aaa60009fe4402169cd62d8a | d5c2ce7c2e1179907400f2c96fc6309a54cbce6a | "2023-09-28T19:57:36Z" | python | "2023-10-24T17:51:25Z" | libs/langchain/langchain/vectorstores/redis/base.py | self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
) -> List[Document]:
raise NotImplementedError("RedisVectorStoreRetriever does not support async")
def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
"""Add documents to vectorstore."""
return self.vectorstore.add_documents(documents, **kwargs)
async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Add documents to vectorstore."""
return await self.vectorstore.aadd_documents(documents, **kwargs) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 8,907 | RdfGraph schema retrieval queries for the relation types are not linked by the correct comment variable | ### System Info
langchain = 0.0.251
Python = 3.10.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create an OWL ontology called `dbpedia_sample.ttl` with the following:
``` turtle
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix wikidata: <http://www.wikidata.org/entity/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix : <http://dbpedia.org/ontology/> .
:Actor
a owl:Class ;
rdfs:comment "An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity."@en ;
rdfs:label "actor"@en ;
rdfs:subClassOf :Artist ;
owl:equivalentClass wikidata:Q33999 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:Actor> .
:AdministrativeRegion
a owl:Class ;
rdfs:comment "A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)"@en ;
rdfs:label "administrative region"@en ;
rdfs:subClassOf :Region ;
owl:equivalentClass <http://schema.org/AdministrativeArea>, wikidata:Q3455524 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:AdministrativeRegion> .
:birthPlace
a rdf:Property, owl:ObjectProperty ;
rdfs:comment "where the person was born"@en ;
rdfs:domain :Animal ;
rdfs:label "birth place"@en ;
rdfs:range :Place ;
rdfs:subPropertyOf dul:hasLocation ;
owl:equivalentProperty <http://schema.org/birthPlace>, wikidata:P19 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyProperty:birthPlace> .
```
2. Run
``` python
from langchain.graphs import RdfGraph
graph = RdfGraph(
source_file="dbpedia_sample.ttl",
serialization="ttl",
standard="owl"
)
print(graph.get_schema)
```
3. Output
```
In the following, each IRI is followed by the local name and optionally its description in parentheses.
The OWL graph supports the following node types:
<http://dbpedia.org/ontology/Actor> (Actor, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/AdministrativeRegion> (AdministrativeRegion, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration))
The OWL graph supports the following object properties, i.e., relationships between objects:
<http://dbpedia.org/ontology/birthPlace> (birthPlace, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/birthPlace> (birthPlace, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)), <http://dbpedia.org/ontology/birthPlace> (birthPlace, where the person was born)
The OWL graph supports the following data properties, i.e., relationships between objects and literals:
```
### Expected behavior
The issue is that in the SPARQL queries getting the properties the `rdfs:comment` triple pattern always refers to the variable `?cls` which obviously comes from copy/paste code.
For example, getting the RDFS properties via
``` python
rel_query_rdf = prefixes["rdfs"] + (
"""SELECT DISTINCT ?rel ?com\n"""
"""WHERE { \n"""
""" ?subj ?rel ?obj . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
```
you can see that the `OPTIONAL` clause refers to `?cls`, but it should be `?rel`.
The same holds for all other queries regarding properties.
The current status leads to a cartesian product of properties and all `rdfs:comment` vlaues in the dataset, which can be horribly large and of course leads to misleading and huge prompts (see the output of my sample in the "reproduction" part) | https://github.com/langchain-ai/langchain/issues/8907 | https://github.com/langchain-ai/langchain/pull/9136 | d9f1bcf366b5a66021d246d8e9c56e76fe60ead1 | cce132d1460b4f52541cb4a6f13219fb8fe4f907 | "2023-08-08T10:57:54Z" | python | "2023-10-25T20:36:57Z" | libs/langchain/langchain/graphs/rdf_graph.py | from __future__ import annotations
from typing import (
TYPE_CHECKING,
List,
Optional,
)
if TYPE_CHECKING:
import rdflib
prefixes = {
"owl": """PREFIX owl: <http://www.w3.org/2002/07/owl#>\n""",
"rdf": """PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\n""",
"rdfs": """PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n""",
"xsd": """PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>\n""",
}
cls_query_rdf = prefixes["rdfs"] + (
"""SELECT DISTINCT ?cls ?com\n"""
"""WHERE { \n""" |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 8,907 | RdfGraph schema retrieval queries for the relation types are not linked by the correct comment variable | ### System Info
langchain = 0.0.251
Python = 3.10.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create an OWL ontology called `dbpedia_sample.ttl` with the following:
``` turtle
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix wikidata: <http://www.wikidata.org/entity/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix : <http://dbpedia.org/ontology/> .
:Actor
a owl:Class ;
rdfs:comment "An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity."@en ;
rdfs:label "actor"@en ;
rdfs:subClassOf :Artist ;
owl:equivalentClass wikidata:Q33999 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:Actor> .
:AdministrativeRegion
a owl:Class ;
rdfs:comment "A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)"@en ;
rdfs:label "administrative region"@en ;
rdfs:subClassOf :Region ;
owl:equivalentClass <http://schema.org/AdministrativeArea>, wikidata:Q3455524 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:AdministrativeRegion> .
:birthPlace
a rdf:Property, owl:ObjectProperty ;
rdfs:comment "where the person was born"@en ;
rdfs:domain :Animal ;
rdfs:label "birth place"@en ;
rdfs:range :Place ;
rdfs:subPropertyOf dul:hasLocation ;
owl:equivalentProperty <http://schema.org/birthPlace>, wikidata:P19 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyProperty:birthPlace> .
```
2. Run
``` python
from langchain.graphs import RdfGraph
graph = RdfGraph(
source_file="dbpedia_sample.ttl",
serialization="ttl",
standard="owl"
)
print(graph.get_schema)
```
3. Output
```
In the following, each IRI is followed by the local name and optionally its description in parentheses.
The OWL graph supports the following node types:
<http://dbpedia.org/ontology/Actor> (Actor, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/AdministrativeRegion> (AdministrativeRegion, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration))
The OWL graph supports the following object properties, i.e., relationships between objects:
<http://dbpedia.org/ontology/birthPlace> (birthPlace, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/birthPlace> (birthPlace, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)), <http://dbpedia.org/ontology/birthPlace> (birthPlace, where the person was born)
The OWL graph supports the following data properties, i.e., relationships between objects and literals:
```
### Expected behavior
The issue is that in the SPARQL queries getting the properties the `rdfs:comment` triple pattern always refers to the variable `?cls` which obviously comes from copy/paste code.
For example, getting the RDFS properties via
``` python
rel_query_rdf = prefixes["rdfs"] + (
"""SELECT DISTINCT ?rel ?com\n"""
"""WHERE { \n"""
""" ?subj ?rel ?obj . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
```
you can see that the `OPTIONAL` clause refers to `?cls`, but it should be `?rel`.
The same holds for all other queries regarding properties.
The current status leads to a cartesian product of properties and all `rdfs:comment` vlaues in the dataset, which can be horribly large and of course leads to misleading and huge prompts (see the output of my sample in the "reproduction" part) | https://github.com/langchain-ai/langchain/issues/8907 | https://github.com/langchain-ai/langchain/pull/9136 | d9f1bcf366b5a66021d246d8e9c56e76fe60ead1 | cce132d1460b4f52541cb4a6f13219fb8fe4f907 | "2023-08-08T10:57:54Z" | python | "2023-10-25T20:36:57Z" | libs/langchain/langchain/graphs/rdf_graph.py | """ ?instance a ?cls . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
cls_query_rdfs = prefixes["rdfs"] + (
"""SELECT DISTINCT ?cls ?com\n"""
"""WHERE { \n"""
""" ?instance a/rdfs:subClassOf* ?cls . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
cls_query_owl = prefixes["rdfs"] + (
"""SELECT DISTINCT ?cls ?com\n"""
"""WHERE { \n"""
""" ?instance a/rdfs:subClassOf* ?cls . \n"""
""" FILTER (isIRI(?cls)) . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
rel_query_rdf = prefixes["rdfs"] + (
"""SELECT DISTINCT ?rel ?com\n"""
"""WHERE { \n"""
""" ?subj ?rel ?obj . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
rel_query_rdfs = (
prefixes["rdf"]
+ prefixes["rdfs"]
+ ( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 8,907 | RdfGraph schema retrieval queries for the relation types are not linked by the correct comment variable | ### System Info
langchain = 0.0.251
Python = 3.10.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create an OWL ontology called `dbpedia_sample.ttl` with the following:
``` turtle
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix wikidata: <http://www.wikidata.org/entity/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix : <http://dbpedia.org/ontology/> .
:Actor
a owl:Class ;
rdfs:comment "An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity."@en ;
rdfs:label "actor"@en ;
rdfs:subClassOf :Artist ;
owl:equivalentClass wikidata:Q33999 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:Actor> .
:AdministrativeRegion
a owl:Class ;
rdfs:comment "A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)"@en ;
rdfs:label "administrative region"@en ;
rdfs:subClassOf :Region ;
owl:equivalentClass <http://schema.org/AdministrativeArea>, wikidata:Q3455524 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:AdministrativeRegion> .
:birthPlace
a rdf:Property, owl:ObjectProperty ;
rdfs:comment "where the person was born"@en ;
rdfs:domain :Animal ;
rdfs:label "birth place"@en ;
rdfs:range :Place ;
rdfs:subPropertyOf dul:hasLocation ;
owl:equivalentProperty <http://schema.org/birthPlace>, wikidata:P19 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyProperty:birthPlace> .
```
2. Run
``` python
from langchain.graphs import RdfGraph
graph = RdfGraph(
source_file="dbpedia_sample.ttl",
serialization="ttl",
standard="owl"
)
print(graph.get_schema)
```
3. Output
```
In the following, each IRI is followed by the local name and optionally its description in parentheses.
The OWL graph supports the following node types:
<http://dbpedia.org/ontology/Actor> (Actor, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/AdministrativeRegion> (AdministrativeRegion, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration))
The OWL graph supports the following object properties, i.e., relationships between objects:
<http://dbpedia.org/ontology/birthPlace> (birthPlace, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/birthPlace> (birthPlace, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)), <http://dbpedia.org/ontology/birthPlace> (birthPlace, where the person was born)
The OWL graph supports the following data properties, i.e., relationships between objects and literals:
```
### Expected behavior
The issue is that in the SPARQL queries getting the properties the `rdfs:comment` triple pattern always refers to the variable `?cls` which obviously comes from copy/paste code.
For example, getting the RDFS properties via
``` python
rel_query_rdf = prefixes["rdfs"] + (
"""SELECT DISTINCT ?rel ?com\n"""
"""WHERE { \n"""
""" ?subj ?rel ?obj . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
```
you can see that the `OPTIONAL` clause refers to `?cls`, but it should be `?rel`.
The same holds for all other queries regarding properties.
The current status leads to a cartesian product of properties and all `rdfs:comment` vlaues in the dataset, which can be horribly large and of course leads to misleading and huge prompts (see the output of my sample in the "reproduction" part) | https://github.com/langchain-ai/langchain/issues/8907 | https://github.com/langchain-ai/langchain/pull/9136 | d9f1bcf366b5a66021d246d8e9c56e76fe60ead1 | cce132d1460b4f52541cb4a6f13219fb8fe4f907 | "2023-08-08T10:57:54Z" | python | "2023-10-25T20:36:57Z" | libs/langchain/langchain/graphs/rdf_graph.py | """SELECT DISTINCT ?rel ?com\n"""
"""WHERE { \n"""
""" ?rel a/rdfs:subPropertyOf* rdf:Property . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
)
op_query_owl = (
prefixes["rdfs"]
+ prefixes["owl"]
+ (
"""SELECT DISTINCT ?op ?com\n"""
"""WHERE { \n"""
""" ?op a/rdfs:subPropertyOf* owl:ObjectProperty . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
)
dp_query_owl = (
prefixes["rdfs"]
+ prefixes["owl"]
+ (
"""SELECT DISTINCT ?dp ?com\n"""
"""WHERE { \n"""
""" ?dp a/rdfs:subPropertyOf* owl:DatatypeProperty . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
)
class RdfGraph: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 8,907 | RdfGraph schema retrieval queries for the relation types are not linked by the correct comment variable | ### System Info
langchain = 0.0.251
Python = 3.10.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create an OWL ontology called `dbpedia_sample.ttl` with the following:
``` turtle
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix wikidata: <http://www.wikidata.org/entity/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix : <http://dbpedia.org/ontology/> .
:Actor
a owl:Class ;
rdfs:comment "An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity."@en ;
rdfs:label "actor"@en ;
rdfs:subClassOf :Artist ;
owl:equivalentClass wikidata:Q33999 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:Actor> .
:AdministrativeRegion
a owl:Class ;
rdfs:comment "A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)"@en ;
rdfs:label "administrative region"@en ;
rdfs:subClassOf :Region ;
owl:equivalentClass <http://schema.org/AdministrativeArea>, wikidata:Q3455524 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:AdministrativeRegion> .
:birthPlace
a rdf:Property, owl:ObjectProperty ;
rdfs:comment "where the person was born"@en ;
rdfs:domain :Animal ;
rdfs:label "birth place"@en ;
rdfs:range :Place ;
rdfs:subPropertyOf dul:hasLocation ;
owl:equivalentProperty <http://schema.org/birthPlace>, wikidata:P19 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyProperty:birthPlace> .
```
2. Run
``` python
from langchain.graphs import RdfGraph
graph = RdfGraph(
source_file="dbpedia_sample.ttl",
serialization="ttl",
standard="owl"
)
print(graph.get_schema)
```
3. Output
```
In the following, each IRI is followed by the local name and optionally its description in parentheses.
The OWL graph supports the following node types:
<http://dbpedia.org/ontology/Actor> (Actor, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/AdministrativeRegion> (AdministrativeRegion, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration))
The OWL graph supports the following object properties, i.e., relationships between objects:
<http://dbpedia.org/ontology/birthPlace> (birthPlace, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/birthPlace> (birthPlace, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)), <http://dbpedia.org/ontology/birthPlace> (birthPlace, where the person was born)
The OWL graph supports the following data properties, i.e., relationships between objects and literals:
```
### Expected behavior
The issue is that in the SPARQL queries getting the properties the `rdfs:comment` triple pattern always refers to the variable `?cls` which obviously comes from copy/paste code.
For example, getting the RDFS properties via
``` python
rel_query_rdf = prefixes["rdfs"] + (
"""SELECT DISTINCT ?rel ?com\n"""
"""WHERE { \n"""
""" ?subj ?rel ?obj . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
```
you can see that the `OPTIONAL` clause refers to `?cls`, but it should be `?rel`.
The same holds for all other queries regarding properties.
The current status leads to a cartesian product of properties and all `rdfs:comment` vlaues in the dataset, which can be horribly large and of course leads to misleading and huge prompts (see the output of my sample in the "reproduction" part) | https://github.com/langchain-ai/langchain/issues/8907 | https://github.com/langchain-ai/langchain/pull/9136 | d9f1bcf366b5a66021d246d8e9c56e76fe60ead1 | cce132d1460b4f52541cb4a6f13219fb8fe4f907 | "2023-08-08T10:57:54Z" | python | "2023-10-25T20:36:57Z" | libs/langchain/langchain/graphs/rdf_graph.py | """RDFlib wrapper for graph operations.
Modes:
* local: Local file - can be queried and changed
* online: Online file - can only be queried, changes can be stored locally
* store: Triple store - can be queried and changed if update_endpoint available
Together with a source file, the serialization should be specified.
*Security note*: Make sure that the database connection uses credentials
that are narrowly-scoped to only include necessary permissions.
Failure to do so may result in data corruption or loss, since the calling
code may attempt commands that would result in deletion, mutation
of data if appropriately prompted or reading sensitive data if such
data is present in the database.
The best way to guard against such negative outcomes is to (as appropriate)
limit the permissions granted to the credentials used with this tool.
See https://python.langchain.com/docs/security for more information.
"""
def __init__(
self,
source_file: Optional[str] = None,
serialization: Optional[str] = "ttl",
query_endpoint: Optional[str] = None,
update_endpoint: Optional[str] = None,
standard: Optional[str] = "rdf",
local_copy: Optional[str] = None,
) -> None:
"""
Set up the RDFlib graph
:param source_file: either a path for a local file or a URL
:param serialization: serialization of the input
:param query_endpoint: SPARQL endpoint for queries, read access |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 8,907 | RdfGraph schema retrieval queries for the relation types are not linked by the correct comment variable | ### System Info
langchain = 0.0.251
Python = 3.10.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create an OWL ontology called `dbpedia_sample.ttl` with the following:
``` turtle
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix wikidata: <http://www.wikidata.org/entity/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix : <http://dbpedia.org/ontology/> .
:Actor
a owl:Class ;
rdfs:comment "An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity."@en ;
rdfs:label "actor"@en ;
rdfs:subClassOf :Artist ;
owl:equivalentClass wikidata:Q33999 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:Actor> .
:AdministrativeRegion
a owl:Class ;
rdfs:comment "A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)"@en ;
rdfs:label "administrative region"@en ;
rdfs:subClassOf :Region ;
owl:equivalentClass <http://schema.org/AdministrativeArea>, wikidata:Q3455524 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:AdministrativeRegion> .
:birthPlace
a rdf:Property, owl:ObjectProperty ;
rdfs:comment "where the person was born"@en ;
rdfs:domain :Animal ;
rdfs:label "birth place"@en ;
rdfs:range :Place ;
rdfs:subPropertyOf dul:hasLocation ;
owl:equivalentProperty <http://schema.org/birthPlace>, wikidata:P19 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyProperty:birthPlace> .
```
2. Run
``` python
from langchain.graphs import RdfGraph
graph = RdfGraph(
source_file="dbpedia_sample.ttl",
serialization="ttl",
standard="owl"
)
print(graph.get_schema)
```
3. Output
```
In the following, each IRI is followed by the local name and optionally its description in parentheses.
The OWL graph supports the following node types:
<http://dbpedia.org/ontology/Actor> (Actor, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/AdministrativeRegion> (AdministrativeRegion, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration))
The OWL graph supports the following object properties, i.e., relationships between objects:
<http://dbpedia.org/ontology/birthPlace> (birthPlace, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/birthPlace> (birthPlace, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)), <http://dbpedia.org/ontology/birthPlace> (birthPlace, where the person was born)
The OWL graph supports the following data properties, i.e., relationships between objects and literals:
```
### Expected behavior
The issue is that in the SPARQL queries getting the properties the `rdfs:comment` triple pattern always refers to the variable `?cls` which obviously comes from copy/paste code.
For example, getting the RDFS properties via
``` python
rel_query_rdf = prefixes["rdfs"] + (
"""SELECT DISTINCT ?rel ?com\n"""
"""WHERE { \n"""
""" ?subj ?rel ?obj . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
```
you can see that the `OPTIONAL` clause refers to `?cls`, but it should be `?rel`.
The same holds for all other queries regarding properties.
The current status leads to a cartesian product of properties and all `rdfs:comment` vlaues in the dataset, which can be horribly large and of course leads to misleading and huge prompts (see the output of my sample in the "reproduction" part) | https://github.com/langchain-ai/langchain/issues/8907 | https://github.com/langchain-ai/langchain/pull/9136 | d9f1bcf366b5a66021d246d8e9c56e76fe60ead1 | cce132d1460b4f52541cb4a6f13219fb8fe4f907 | "2023-08-08T10:57:54Z" | python | "2023-10-25T20:36:57Z" | libs/langchain/langchain/graphs/rdf_graph.py | :param update_endpoint: SPARQL endpoint for UPDATE queries, write access
:param standard: RDF, RDFS, or OWL
:param local_copy: new local copy for storing changes
"""
self.source_file = source_file
self.serialization = serialization
self.query_endpoint = query_endpoint
self.update_endpoint = update_endpoint
self.standard = standard
self.local_copy = local_copy
try:
import rdflib
from rdflib.graph import DATASET_DEFAULT_GRAPH_ID as default
from rdflib.plugins.stores import sparqlstore
except ImportError:
raise ValueError(
"Could not import rdflib python package. "
"Please install it with `pip install rdflib`."
)
if self.standard not in (supported_standards := ("rdf", "rdfs", "owl")):
raise ValueError(
f"Invalid standard. Supported standards are: {supported_standards}."
)
if (
not source_file
and not query_endpoint
or source_file
and (query_endpoint or update_endpoint)
):
raise ValueError( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 8,907 | RdfGraph schema retrieval queries for the relation types are not linked by the correct comment variable | ### System Info
langchain = 0.0.251
Python = 3.10.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create an OWL ontology called `dbpedia_sample.ttl` with the following:
``` turtle
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix wikidata: <http://www.wikidata.org/entity/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix : <http://dbpedia.org/ontology/> .
:Actor
a owl:Class ;
rdfs:comment "An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity."@en ;
rdfs:label "actor"@en ;
rdfs:subClassOf :Artist ;
owl:equivalentClass wikidata:Q33999 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:Actor> .
:AdministrativeRegion
a owl:Class ;
rdfs:comment "A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)"@en ;
rdfs:label "administrative region"@en ;
rdfs:subClassOf :Region ;
owl:equivalentClass <http://schema.org/AdministrativeArea>, wikidata:Q3455524 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:AdministrativeRegion> .
:birthPlace
a rdf:Property, owl:ObjectProperty ;
rdfs:comment "where the person was born"@en ;
rdfs:domain :Animal ;
rdfs:label "birth place"@en ;
rdfs:range :Place ;
rdfs:subPropertyOf dul:hasLocation ;
owl:equivalentProperty <http://schema.org/birthPlace>, wikidata:P19 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyProperty:birthPlace> .
```
2. Run
``` python
from langchain.graphs import RdfGraph
graph = RdfGraph(
source_file="dbpedia_sample.ttl",
serialization="ttl",
standard="owl"
)
print(graph.get_schema)
```
3. Output
```
In the following, each IRI is followed by the local name and optionally its description in parentheses.
The OWL graph supports the following node types:
<http://dbpedia.org/ontology/Actor> (Actor, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/AdministrativeRegion> (AdministrativeRegion, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration))
The OWL graph supports the following object properties, i.e., relationships between objects:
<http://dbpedia.org/ontology/birthPlace> (birthPlace, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/birthPlace> (birthPlace, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)), <http://dbpedia.org/ontology/birthPlace> (birthPlace, where the person was born)
The OWL graph supports the following data properties, i.e., relationships between objects and literals:
```
### Expected behavior
The issue is that in the SPARQL queries getting the properties the `rdfs:comment` triple pattern always refers to the variable `?cls` which obviously comes from copy/paste code.
For example, getting the RDFS properties via
``` python
rel_query_rdf = prefixes["rdfs"] + (
"""SELECT DISTINCT ?rel ?com\n"""
"""WHERE { \n"""
""" ?subj ?rel ?obj . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
```
you can see that the `OPTIONAL` clause refers to `?cls`, but it should be `?rel`.
The same holds for all other queries regarding properties.
The current status leads to a cartesian product of properties and all `rdfs:comment` vlaues in the dataset, which can be horribly large and of course leads to misleading and huge prompts (see the output of my sample in the "reproduction" part) | https://github.com/langchain-ai/langchain/issues/8907 | https://github.com/langchain-ai/langchain/pull/9136 | d9f1bcf366b5a66021d246d8e9c56e76fe60ead1 | cce132d1460b4f52541cb4a6f13219fb8fe4f907 | "2023-08-08T10:57:54Z" | python | "2023-10-25T20:36:57Z" | libs/langchain/langchain/graphs/rdf_graph.py | "Could not unambiguously initialize the graph wrapper. "
"Specify either a file (local or online) via the source_file "
"or a triple store via the endpoints."
)
if source_file:
if source_file.startswith("http"):
self.mode = "online"
else:
self.mode = "local"
if self.local_copy is None:
self.local_copy = self.source_file
self.graph = rdflib.Graph()
self.graph.parse(source_file, format=self.serialization)
if query_endpoint:
self.mode = "store"
if not update_endpoint:
self._store = sparqlstore.SPARQLStore()
self._store.open(query_endpoint)
else:
self._store = sparqlstore.SPARQLUpdateStore()
self._store.open((query_endpoint, update_endpoint))
self.graph = rdflib.Graph(self._store, identifier=default)
if not len(self.graph):
raise AssertionError("The graph is empty.")
self.schema = ""
self.load_schema()
@property
def get_schema(self) -> str: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 8,907 | RdfGraph schema retrieval queries for the relation types are not linked by the correct comment variable | ### System Info
langchain = 0.0.251
Python = 3.10.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create an OWL ontology called `dbpedia_sample.ttl` with the following:
``` turtle
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix wikidata: <http://www.wikidata.org/entity/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix : <http://dbpedia.org/ontology/> .
:Actor
a owl:Class ;
rdfs:comment "An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity."@en ;
rdfs:label "actor"@en ;
rdfs:subClassOf :Artist ;
owl:equivalentClass wikidata:Q33999 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:Actor> .
:AdministrativeRegion
a owl:Class ;
rdfs:comment "A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)"@en ;
rdfs:label "administrative region"@en ;
rdfs:subClassOf :Region ;
owl:equivalentClass <http://schema.org/AdministrativeArea>, wikidata:Q3455524 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:AdministrativeRegion> .
:birthPlace
a rdf:Property, owl:ObjectProperty ;
rdfs:comment "where the person was born"@en ;
rdfs:domain :Animal ;
rdfs:label "birth place"@en ;
rdfs:range :Place ;
rdfs:subPropertyOf dul:hasLocation ;
owl:equivalentProperty <http://schema.org/birthPlace>, wikidata:P19 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyProperty:birthPlace> .
```
2. Run
``` python
from langchain.graphs import RdfGraph
graph = RdfGraph(
source_file="dbpedia_sample.ttl",
serialization="ttl",
standard="owl"
)
print(graph.get_schema)
```
3. Output
```
In the following, each IRI is followed by the local name and optionally its description in parentheses.
The OWL graph supports the following node types:
<http://dbpedia.org/ontology/Actor> (Actor, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/AdministrativeRegion> (AdministrativeRegion, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration))
The OWL graph supports the following object properties, i.e., relationships between objects:
<http://dbpedia.org/ontology/birthPlace> (birthPlace, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/birthPlace> (birthPlace, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)), <http://dbpedia.org/ontology/birthPlace> (birthPlace, where the person was born)
The OWL graph supports the following data properties, i.e., relationships between objects and literals:
```
### Expected behavior
The issue is that in the SPARQL queries getting the properties the `rdfs:comment` triple pattern always refers to the variable `?cls` which obviously comes from copy/paste code.
For example, getting the RDFS properties via
``` python
rel_query_rdf = prefixes["rdfs"] + (
"""SELECT DISTINCT ?rel ?com\n"""
"""WHERE { \n"""
""" ?subj ?rel ?obj . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
```
you can see that the `OPTIONAL` clause refers to `?cls`, but it should be `?rel`.
The same holds for all other queries regarding properties.
The current status leads to a cartesian product of properties and all `rdfs:comment` vlaues in the dataset, which can be horribly large and of course leads to misleading and huge prompts (see the output of my sample in the "reproduction" part) | https://github.com/langchain-ai/langchain/issues/8907 | https://github.com/langchain-ai/langchain/pull/9136 | d9f1bcf366b5a66021d246d8e9c56e76fe60ead1 | cce132d1460b4f52541cb4a6f13219fb8fe4f907 | "2023-08-08T10:57:54Z" | python | "2023-10-25T20:36:57Z" | libs/langchain/langchain/graphs/rdf_graph.py | """
Returns the schema of the graph database.
"""
return self.schema
def query(
self,
query: str,
) -> List[rdflib.query.ResultRow]:
"""
Query the graph.
"""
from rdflib.exceptions import ParserError
from rdflib.query import ResultRow
try:
res = self.graph.query(query)
except ParserError as e:
raise ValueError("Generated SPARQL statement is invalid\n" f"{e}")
return [r for r in res if isinstance(r, ResultRow)]
def update( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 8,907 | RdfGraph schema retrieval queries for the relation types are not linked by the correct comment variable | ### System Info
langchain = 0.0.251
Python = 3.10.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create an OWL ontology called `dbpedia_sample.ttl` with the following:
``` turtle
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix wikidata: <http://www.wikidata.org/entity/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix : <http://dbpedia.org/ontology/> .
:Actor
a owl:Class ;
rdfs:comment "An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity."@en ;
rdfs:label "actor"@en ;
rdfs:subClassOf :Artist ;
owl:equivalentClass wikidata:Q33999 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:Actor> .
:AdministrativeRegion
a owl:Class ;
rdfs:comment "A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)"@en ;
rdfs:label "administrative region"@en ;
rdfs:subClassOf :Region ;
owl:equivalentClass <http://schema.org/AdministrativeArea>, wikidata:Q3455524 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:AdministrativeRegion> .
:birthPlace
a rdf:Property, owl:ObjectProperty ;
rdfs:comment "where the person was born"@en ;
rdfs:domain :Animal ;
rdfs:label "birth place"@en ;
rdfs:range :Place ;
rdfs:subPropertyOf dul:hasLocation ;
owl:equivalentProperty <http://schema.org/birthPlace>, wikidata:P19 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyProperty:birthPlace> .
```
2. Run
``` python
from langchain.graphs import RdfGraph
graph = RdfGraph(
source_file="dbpedia_sample.ttl",
serialization="ttl",
standard="owl"
)
print(graph.get_schema)
```
3. Output
```
In the following, each IRI is followed by the local name and optionally its description in parentheses.
The OWL graph supports the following node types:
<http://dbpedia.org/ontology/Actor> (Actor, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/AdministrativeRegion> (AdministrativeRegion, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration))
The OWL graph supports the following object properties, i.e., relationships between objects:
<http://dbpedia.org/ontology/birthPlace> (birthPlace, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/birthPlace> (birthPlace, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)), <http://dbpedia.org/ontology/birthPlace> (birthPlace, where the person was born)
The OWL graph supports the following data properties, i.e., relationships between objects and literals:
```
### Expected behavior
The issue is that in the SPARQL queries getting the properties the `rdfs:comment` triple pattern always refers to the variable `?cls` which obviously comes from copy/paste code.
For example, getting the RDFS properties via
``` python
rel_query_rdf = prefixes["rdfs"] + (
"""SELECT DISTINCT ?rel ?com\n"""
"""WHERE { \n"""
""" ?subj ?rel ?obj . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
```
you can see that the `OPTIONAL` clause refers to `?cls`, but it should be `?rel`.
The same holds for all other queries regarding properties.
The current status leads to a cartesian product of properties and all `rdfs:comment` vlaues in the dataset, which can be horribly large and of course leads to misleading and huge prompts (see the output of my sample in the "reproduction" part) | https://github.com/langchain-ai/langchain/issues/8907 | https://github.com/langchain-ai/langchain/pull/9136 | d9f1bcf366b5a66021d246d8e9c56e76fe60ead1 | cce132d1460b4f52541cb4a6f13219fb8fe4f907 | "2023-08-08T10:57:54Z" | python | "2023-10-25T20:36:57Z" | libs/langchain/langchain/graphs/rdf_graph.py | self,
query: str,
) -> None:
"""
Update the graph.
"""
from rdflib.exceptions import ParserError
try:
self.graph.update(query)
except ParserError as e:
raise ValueError("Generated SPARQL statement is invalid\n" f"{e}")
if self.local_copy:
self.graph.serialize(
destination=self.local_copy, format=self.local_copy.split(".")[-1]
)
else:
raise ValueError("No target file specified for saving the updated file.")
@staticmethod
def _get_local_name(iri: str) -> str: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 8,907 | RdfGraph schema retrieval queries for the relation types are not linked by the correct comment variable | ### System Info
langchain = 0.0.251
Python = 3.10.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create an OWL ontology called `dbpedia_sample.ttl` with the following:
``` turtle
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix wikidata: <http://www.wikidata.org/entity/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix : <http://dbpedia.org/ontology/> .
:Actor
a owl:Class ;
rdfs:comment "An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity."@en ;
rdfs:label "actor"@en ;
rdfs:subClassOf :Artist ;
owl:equivalentClass wikidata:Q33999 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:Actor> .
:AdministrativeRegion
a owl:Class ;
rdfs:comment "A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)"@en ;
rdfs:label "administrative region"@en ;
rdfs:subClassOf :Region ;
owl:equivalentClass <http://schema.org/AdministrativeArea>, wikidata:Q3455524 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:AdministrativeRegion> .
:birthPlace
a rdf:Property, owl:ObjectProperty ;
rdfs:comment "where the person was born"@en ;
rdfs:domain :Animal ;
rdfs:label "birth place"@en ;
rdfs:range :Place ;
rdfs:subPropertyOf dul:hasLocation ;
owl:equivalentProperty <http://schema.org/birthPlace>, wikidata:P19 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyProperty:birthPlace> .
```
2. Run
``` python
from langchain.graphs import RdfGraph
graph = RdfGraph(
source_file="dbpedia_sample.ttl",
serialization="ttl",
standard="owl"
)
print(graph.get_schema)
```
3. Output
```
In the following, each IRI is followed by the local name and optionally its description in parentheses.
The OWL graph supports the following node types:
<http://dbpedia.org/ontology/Actor> (Actor, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/AdministrativeRegion> (AdministrativeRegion, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration))
The OWL graph supports the following object properties, i.e., relationships between objects:
<http://dbpedia.org/ontology/birthPlace> (birthPlace, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/birthPlace> (birthPlace, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)), <http://dbpedia.org/ontology/birthPlace> (birthPlace, where the person was born)
The OWL graph supports the following data properties, i.e., relationships between objects and literals:
```
### Expected behavior
The issue is that in the SPARQL queries getting the properties the `rdfs:comment` triple pattern always refers to the variable `?cls` which obviously comes from copy/paste code.
For example, getting the RDFS properties via
``` python
rel_query_rdf = prefixes["rdfs"] + (
"""SELECT DISTINCT ?rel ?com\n"""
"""WHERE { \n"""
""" ?subj ?rel ?obj . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
```
you can see that the `OPTIONAL` clause refers to `?cls`, but it should be `?rel`.
The same holds for all other queries regarding properties.
The current status leads to a cartesian product of properties and all `rdfs:comment` vlaues in the dataset, which can be horribly large and of course leads to misleading and huge prompts (see the output of my sample in the "reproduction" part) | https://github.com/langchain-ai/langchain/issues/8907 | https://github.com/langchain-ai/langchain/pull/9136 | d9f1bcf366b5a66021d246d8e9c56e76fe60ead1 | cce132d1460b4f52541cb4a6f13219fb8fe4f907 | "2023-08-08T10:57:54Z" | python | "2023-10-25T20:36:57Z" | libs/langchain/langchain/graphs/rdf_graph.py | if "#" in iri:
local_name = iri.split("#")[-1]
elif "/" in iri:
local_name = iri.split("/")[-1]
else:
raise ValueError(f"Unexpected IRI '{iri}', contains neither '#' nor '/'.")
return local_name
def _res_to_str(self, res: rdflib.query.ResultRow, var: str) -> str:
return (
"<"
+ str(res[var])
+ "> ("
+ self._get_local_name(res[var])
+ ", "
+ str(res["com"])
+ ")"
)
def load_schema(self) -> None:
"""
Load the graph schema information.
"""
def _rdf_s_schema(
classes: List[rdflib.query.ResultRow],
relationships: List[rdflib.query.ResultRow],
) -> str:
return (
f"In the following, each IRI is followed by the local name and "
f"optionally its description in parentheses. \n"
f"The RDF graph supports the following node types:\n"
f'{", ".join([self._res_to_str(r, "cls") for r in classes])}\n' |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 8,907 | RdfGraph schema retrieval queries for the relation types are not linked by the correct comment variable | ### System Info
langchain = 0.0.251
Python = 3.10.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create an OWL ontology called `dbpedia_sample.ttl` with the following:
``` turtle
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix wikidata: <http://www.wikidata.org/entity/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix : <http://dbpedia.org/ontology/> .
:Actor
a owl:Class ;
rdfs:comment "An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity."@en ;
rdfs:label "actor"@en ;
rdfs:subClassOf :Artist ;
owl:equivalentClass wikidata:Q33999 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:Actor> .
:AdministrativeRegion
a owl:Class ;
rdfs:comment "A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)"@en ;
rdfs:label "administrative region"@en ;
rdfs:subClassOf :Region ;
owl:equivalentClass <http://schema.org/AdministrativeArea>, wikidata:Q3455524 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:AdministrativeRegion> .
:birthPlace
a rdf:Property, owl:ObjectProperty ;
rdfs:comment "where the person was born"@en ;
rdfs:domain :Animal ;
rdfs:label "birth place"@en ;
rdfs:range :Place ;
rdfs:subPropertyOf dul:hasLocation ;
owl:equivalentProperty <http://schema.org/birthPlace>, wikidata:P19 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyProperty:birthPlace> .
```
2. Run
``` python
from langchain.graphs import RdfGraph
graph = RdfGraph(
source_file="dbpedia_sample.ttl",
serialization="ttl",
standard="owl"
)
print(graph.get_schema)
```
3. Output
```
In the following, each IRI is followed by the local name and optionally its description in parentheses.
The OWL graph supports the following node types:
<http://dbpedia.org/ontology/Actor> (Actor, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/AdministrativeRegion> (AdministrativeRegion, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration))
The OWL graph supports the following object properties, i.e., relationships between objects:
<http://dbpedia.org/ontology/birthPlace> (birthPlace, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/birthPlace> (birthPlace, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)), <http://dbpedia.org/ontology/birthPlace> (birthPlace, where the person was born)
The OWL graph supports the following data properties, i.e., relationships between objects and literals:
```
### Expected behavior
The issue is that in the SPARQL queries getting the properties the `rdfs:comment` triple pattern always refers to the variable `?cls` which obviously comes from copy/paste code.
For example, getting the RDFS properties via
``` python
rel_query_rdf = prefixes["rdfs"] + (
"""SELECT DISTINCT ?rel ?com\n"""
"""WHERE { \n"""
""" ?subj ?rel ?obj . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
```
you can see that the `OPTIONAL` clause refers to `?cls`, but it should be `?rel`.
The same holds for all other queries regarding properties.
The current status leads to a cartesian product of properties and all `rdfs:comment` vlaues in the dataset, which can be horribly large and of course leads to misleading and huge prompts (see the output of my sample in the "reproduction" part) | https://github.com/langchain-ai/langchain/issues/8907 | https://github.com/langchain-ai/langchain/pull/9136 | d9f1bcf366b5a66021d246d8e9c56e76fe60ead1 | cce132d1460b4f52541cb4a6f13219fb8fe4f907 | "2023-08-08T10:57:54Z" | python | "2023-10-25T20:36:57Z" | libs/langchain/langchain/graphs/rdf_graph.py | f"The RDF graph supports the following relationships:\n"
f'{", ".join([self._res_to_str(r, "rel") for r in relationships])}\n'
)
if self.standard == "rdf":
clss = self.query(cls_query_rdf)
rels = self.query(rel_query_rdf)
self.schema = _rdf_s_schema(clss, rels)
elif self.standard == "rdfs":
clss = self.query(cls_query_rdfs)
rels = self.query(rel_query_rdfs)
self.schema = _rdf_s_schema(clss, rels)
elif self.standard == "owl":
clss = self.query(cls_query_owl)
ops = self.query(op_query_owl)
dps = self.query(dp_query_owl)
self.schema = (
f"In the following, each IRI is followed by the local name and "
f"optionally its description in parentheses. \n"
f"The OWL graph supports the following node types:\n"
f'{", ".join([self._res_to_str(r, "cls") for r in clss])}\n'
f"The OWL graph supports the following object properties, "
f"i.e., relationships between objects:\n"
f'{", ".join([self._res_to_str(r, "op") for r in ops])}\n'
f"The OWL graph supports the following data properties, "
f"i.e., relationships between objects and literals:\n"
f'{", ".join([self._res_to_str(r, "dp") for r in dps])}\n'
)
else:
raise ValueError(f"Mode '{self.standard}' is currently not supported.") |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | """Module contains common parsers for PDFs."""
from __future__ import annotations
import warnings
from typing import (
TYPE_CHECKING,
Any,
Iterable,
Iterator,
Mapping,
Optional,
Sequence, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | Union,
)
from urllib.parse import urlparse
import numpy as np
from langchain.document_loaders.base import BaseBlobParser
from langchain.document_loaders.blob_loaders import Blob
from langchain.schema import Document
if TYPE_CHECKING:
import fitz.fitz
import pdfminer.layout
import pdfplumber.page
import pypdf._page
import pypdfium2._helpers.page
_PDF_FILTER_WITH_LOSS = ["DCTDecode", "DCT", "JPXDecode"]
_PDF_FILTER_WITHOUT_LOSS = [
"LZWDecode",
"LZW",
"FlateDecode",
"Fl",
"ASCII85Decode",
"A85",
"ASCIIHexDecode",
"AHx",
"RunLengthDecode",
"RL",
"CCITTFaxDecode",
"CCF",
"JBIG2Decode",
]
def extract_from_images_with_rapidocr( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | images: Sequence[Union[Iterable[np.ndarray], bytes]]
) -> str:
"""Extract text from images with RapidOCR.
Args:
images: Images to extract text from.
Returns:
Text extracted from images.
Raises:
ImportError: If `rapidocr-onnxruntime` package is not installed.
"""
try:
from rapidocr_onnxruntime import RapidOCR
except ImportError:
raise ImportError(
"`rapidocr-onnxruntime` package not found, please install it with "
"`pip install rapidocr-onnxruntime`"
)
ocr = RapidOCR()
text = ""
for img in images:
result, _ = ocr(img)
if result:
result = [text[1] for text in result]
text += "\n".join(result)
return text
class PyPDFParser(BaseBlobParser): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | """Load `PDF` using `pypdf`"""
def __init__(
self, password: Optional[Union[str, bytes]] = None, extract_images: bool = False
):
self.password = password
self.extract_images = extract_images
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""Lazily parse the blob."""
import pypdf
with blob.as_bytes_io() as pdf_file_obj:
pdf_reader = pypdf.PdfReader(pdf_file_obj, password=self.password)
yield from [
Document(
page_content=page.extract_text()
+ self._extract_images_from_page(page),
metadata={"source": blob.source, "page": page_number},
)
for page_number, page in enumerate(pdf_reader.pages)
]
def _extract_images_from_page(self, page: pypdf._page.PageObject) -> str: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | """Extract images from page and get the text with RapidOCR."""
if not self.extract_images or "/XObject" not in page["/Resources"].keys():
return ""
xObject = page["/Resources"]["/XObject"].get_object()
images = []
for obj in xObject:
if xObject[obj]["/Subtype"] == "/Image":
if xObject[obj]["/Filter"][1:] in _PDF_FILTER_WITHOUT_LOSS:
height, width = xObject[obj]["/Height"], xObject[obj]["/Width"]
images.append(
np.frombuffer(xObject[obj].get_data(), dtype=np.uint8).reshape(
height, width, -1
)
)
elif xObject[obj]["/Filter"][1:] in _PDF_FILTER_WITH_LOSS:
images.append(xObject[obj].get_data())
else:
warnings.warn("Unknown PDF Filter!")
return extract_from_images_with_rapidocr(images)
class PDFMinerParser(BaseBlobParser): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | """Parse `PDF` using `PDFMiner`."""
def __init__(self, extract_images: bool = False):
self.extract_images = extract_images
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""Lazily parse the blob."""
if not self.extract_images:
from pdfminer.high_level import extract_text
with blob.as_bytes_io() as pdf_file_obj:
text = extract_text(pdf_file_obj)
metadata = {"source": blob.source}
yield Document(page_content=text, metadata=metadata)
else:
import io
from pdfminer.converter import PDFPageAggregator, TextConverter
from pdfminer.layout import LAParams
from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager
from pdfminer.pdfpage import PDFPage
text_io = io.StringIO()
with blob.as_bytes_io() as pdf_file_obj:
pages = PDFPage.get_pages(pdf_file_obj)
rsrcmgr = PDFResourceManager()
device_for_text = TextConverter(rsrcmgr, text_io, laparams=LAParams())
device_for_image = PDFPageAggregator(rsrcmgr, laparams=LAParams())
interpreter_for_text = PDFPageInterpreter(rsrcmgr, device_for_text)
interpreter_for_image = PDFPageInterpreter(rsrcmgr, device_for_image)
for i, page in enumerate(pages):
interpreter_for_text.process_page(page)
interpreter_for_image.process_page(page)
content = text_io.getvalue() + self._extract_images_from_page(
device_for_image.get_result() |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | )
text_io.truncate(0)
text_io.seek(0)
metadata = {"source": blob.source, "page": str(i)}
yield Document(page_content=content, metadata=metadata)
def _extract_images_from_page(self, page: pdfminer.layout.LTPage) -> str:
"""Extract images from page and get the text with RapidOCR."""
import pdfminer
def get_image(layout_object: Any) -> Any:
if isinstance(layout_object, pdfminer.layout.LTImage):
return layout_object
if isinstance(layout_object, pdfminer.layout.LTContainer):
for child in layout_object:
return get_image(child)
else:
return None
images = []
for img in list(filter(bool, map(get_image, page))):
if img.stream["Filter"].name in _PDF_FILTER_WITHOUT_LOSS:
images.append(
np.frombuffer(img.stream.get_data(), dtype=np.uint8).reshape(
img.stream["Height"], img.stream["Width"], -1
)
)
elif img.stream["Filter"].name in _PDF_FILTER_WITH_LOSS:
images.append(img.stream.get_data())
else:
warnings.warn("Unknown PDF Filter!")
return extract_from_images_with_rapidocr(images)
class PyMuPDFParser(BaseBlobParser): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | """Parse `PDF` using `PyMuPDF`."""
def __init__(
self,
text_kwargs: Optional[Mapping[str, Any]] = None,
extract_images: bool = False,
) -> None:
"""Initialize the parser.
Args:
text_kwargs: Keyword arguments to pass to ``fitz.Page.get_text()``.
"""
self.text_kwargs = text_kwargs or {}
self.extract_images = extract_images
def lazy_parse(self, blob: Blob) -> Iterator[Document]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | """Lazily parse the blob."""
import fitz
with blob.as_bytes_io() as file_path:
doc = fitz.open(file_path)
yield from [
Document(
page_content=page.get_text(**self.text_kwargs)
+ self._extract_images_from_page(doc, page),
metadata=dict(
{
"source": blob.source,
"file_path": blob.source,
"page": page.number,
"total_pages": len(doc),
},
**{
k: doc.metadata[k]
for k in doc.metadata
if type(doc.metadata[k]) in [str, int]
},
),
)
for page in doc
]
def _extract_images_from_page( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | self, doc: fitz.fitz.Document, page: fitz.fitz.Page
) -> str:
"""Extract images from page and get the text with RapidOCR."""
if not self.extract_images:
return ""
import fitz
img_list = page.get_images()
imgs = []
for img in img_list:
xref = img[0]
pix = fitz.Pixmap(doc, xref)
imgs.append(
np.frombuffer(pix.samples, dtype=np.uint8).reshape(
pix.height, pix.width, -1
)
)
return extract_from_images_with_rapidocr(imgs)
class PyPDFium2Parser(BaseBlobParser):
"""Parse `PDF` with `PyPDFium2`."""
def __init__(self, extract_images: bool = False) -> None:
"""Initialize the parser."""
try:
import pypdfium2
except ImportError:
raise ImportError(
"pypdfium2 package not found, please install it with"
" `pip install pypdfium2`"
)
self.extract_images = extract_images
def lazy_parse(self, blob: Blob) -> Iterator[Document]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | """Lazily parse the blob."""
import pypdfium2
with blob.as_bytes_io() as file_path:
pdf_reader = pypdfium2.PdfDocument(file_path, autoclose=True)
try:
for page_number, page in enumerate(pdf_reader):
text_page = page.get_textpage()
content = text_page.get_text_range()
text_page.close()
content += "\n" + self._extract_images_from_page(page)
page.close()
metadata = {"source": blob.source, "page": page_number}
yield Document(page_content=content, metadata=metadata)
finally:
pdf_reader.close()
def _extract_images_from_page(self, page: pypdfium2._helpers.page.PdfPage) -> str:
"""Extract images from page and get the text with RapidOCR."""
if not self.extract_images:
return ""
import pypdfium2.raw as pdfium_c
images = list(page.get_objects(filter=(pdfium_c.FPDF_PAGEOBJ_IMAGE,)))
images = list(map(lambda x: x.get_bitmap().to_numpy(), images))
return extract_from_images_with_rapidocr(images)
class PDFPlumberParser(BaseBlobParser): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | """Parse `PDF` with `PDFPlumber`."""
def __init__(
self,
text_kwargs: Optional[Mapping[str, Any]] = None,
dedupe: bool = False,
extract_images: bool = False,
) -> None:
"""Initialize the parser.
Args:
text_kwargs: Keyword arguments to pass to ``pdfplumber.Page.extract_text()``
dedupe: Avoiding the error of duplicate characters if `dedupe=True`.
"""
self.text_kwargs = text_kwargs or {}
self.dedupe = dedupe
self.extract_images = extract_images
def lazy_parse(self, blob: Blob) -> Iterator[Document]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | """Lazily parse the blob."""
import pdfplumber
with blob.as_bytes_io() as file_path:
doc = pdfplumber.open(file_path)
yield from [
Document(
page_content=self._process_page_content(page)
+ "\n"
+ self._extract_images_from_page(page),
metadata=dict(
{
"source": blob.source,
"file_path": blob.source,
"page": page.page_number - 1,
"total_pages": len(doc.pages),
},
**{
k: doc.metadata[k]
for k in doc.metadata
if type(doc.metadata[k]) in [str, int]
},
),
)
for page in doc.pages
]
def _process_page_content(self, page: pdfplumber.page.Page) -> str: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | """Process the page content based on dedupe."""
if self.dedupe:
return page.dedupe_chars().extract_text(**self.text_kwargs)
return page.extract_text(**self.text_kwargs)
def _extract_images_from_page(self, page: pdfplumber.page.Page) -> str:
"""Extract images from page and get the text with RapidOCR."""
if not self.extract_images:
return ""
images = []
for img in page.images:
if img["stream"]["Filter"].name in _PDF_FILTER_WITHOUT_LOSS:
images.append(
np.frombuffer(img["stream"].get_data(), dtype=np.uint8).reshape(
img["stream"]["Height"], img["stream"]["Width"], -1
)
)
elif img["stream"]["Filter"].name in _PDF_FILTER_WITH_LOSS:
images.append(img["stream"].get_data())
else:
warnings.warn("Unknown PDF Filter!")
return extract_from_images_with_rapidocr(images)
class AmazonTextractPDFParser(BaseBlobParser): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | """Send `PDF` files to `Amazon Textract` and parse them.
For parsing multi-page PDFs, they have to reside on S3.
"""
def __init__(
self,
textract_features: Optional[Sequence[int]] = None,
client: Optional[Any] = None,
) -> None:
"""Initializes the parser.
Args:
textract_features: Features to be used for extraction, each feature
should be passed as an int that conforms to the enum
`Textract_Features`, see `amazon-textract-caller` pkg
client: boto3 textract client
"""
try:
import textractcaller as tc
self.tc = tc
if textract_features is not None:
self.textract_features = [
tc.Textract_Features(f) for f in textract_features |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | ]
else:
self.textract_features = []
except ImportError:
raise ImportError(
"Could not import amazon-textract-caller python package. "
"Please install it with `pip install amazon-textract-caller`."
)
if not client:
try:
import boto3
self.boto3_textract_client = boto3.client("textract")
except ImportError:
raise ImportError(
"Could not import boto3 python package. "
"Please install it with `pip install boto3`."
)
else:
self.boto3_textract_client = client
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""Iterates over the Blob pages and returns an Iterator with a Document
for each page, like the other parsers If multi-page document, blob.path
has to be set to the S3 URI and for single page docs the blob.data is taken
"""
url_parse_result = urlparse(str(blob.path)) if blob.path else None
if (
url_parse_result
and url_parse_result.scheme == "s3"
and url_parse_result.netloc |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | ):
textract_response_json = self.tc.call_textract(
input_document=str(blob.path),
features=self.textract_features,
boto3_textract_client=self.boto3_textract_client,
)
else:
textract_response_json = self.tc.call_textract(
input_document=blob.as_bytes(),
features=self.textract_features,
call_mode=self.tc.Textract_Call_Mode.FORCE_SYNC,
boto3_textract_client=self.boto3_textract_client,
)
current_text = ""
current_page = 1
for block in textract_response_json["Blocks"]:
if "Page" in block and not (int(block["Page"]) == current_page):
yield Document(
page_content=current_text,
metadata={"source": blob.source, "page": current_page},
)
current_text = ""
current_page = int(block["Page"])
if "Text" in block:
current_text += block["Text"] + " "
yield Document(
page_content=current_text,
metadata={"source": blob.source, "page": current_page},
)
class DocumentIntelligenceParser(BaseBlobParser): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/langchain/document_loaders/parsers/pdf.py | """Loads a PDF with Azure Document Intelligence
(formerly Forms Recognizer) and chunks at character level."""
def __init__(self, client: Any, model: str):
self.client = client
self.model = model
def _generate_docs(self, blob: Blob, result: Any) -> Iterator[Document]:
for p in result.pages:
content = " ".join([line.content for line in p.lines])
d = Document(
page_content=content,
metadata={
"source": blob.source,
"page": p.page_number,
},
)
yield d
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""Lazily parse the blob."""
with blob.as_bytes_io() as file_obj:
poller = self.client.begin_analyze_document(self.model, file_obj)
result = poller.result()
docs = self._generate_docs(blob, result)
yield from docs |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/tests/integration_tests/document_loaders/test_pdf.py | from pathlib import Path
from typing import Sequence, Union
import pytest
from langchain.document_loaders import (
AmazonTextractPDFLoader,
MathpixPDFLoader,
PDFMinerLoader,
PDFMinerPDFasHTMLLoader,
PyMuPDFLoader,
PyPDFium2Loader,
PyPDFLoader,
UnstructuredPDFLoader,
)
def test_unstructured_pdf_loader_elements_mode() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/tests/integration_tests/document_loaders/test_pdf.py | """Test unstructured loader with various modes."""
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
loader = UnstructuredPDFLoader(str(file_path), mode="elements")
docs = loader.load()
assert len(docs) == 2
def test_unstructured_pdf_loader_paged_mode() -> None:
"""Test unstructured loader with various modes."""
file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
loader = UnstructuredPDFLoader(str(file_path), mode="paged")
docs = loader.load()
assert len(docs) == 16
def test_unstructured_pdf_loader_default_mode() -> None:
"""Test unstructured loader."""
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
loader = UnstructuredPDFLoader(str(file_path))
docs = loader.load()
assert len(docs) == 1
def test_pdfminer_loader() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/tests/integration_tests/document_loaders/test_pdf.py | """Test PDFMiner loader."""
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
loader = PDFMinerLoader(str(file_path))
docs = loader.load()
assert len(docs) == 1
file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
loader = PDFMinerLoader(str(file_path))
docs = loader.load()
assert len(docs) == 1
def test_pdfminer_pdf_as_html_loader() -> None:
"""Test PDFMinerPDFasHTMLLoader."""
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
loader = PDFMinerPDFasHTMLLoader(str(file_path))
docs = loader.load()
assert len(docs) == 1
file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
loader = PDFMinerPDFasHTMLLoader(str(file_path))
docs = loader.load()
assert len(docs) == 1
def test_pypdf_loader() -> None:
"""Test PyPDFLoader."""
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
loader = PyPDFLoader(str(file_path))
docs = loader.load()
assert len(docs) == 1
file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
loader = PyPDFLoader(str(file_path))
docs = loader.load()
assert len(docs) == 16
def test_pypdfium2_loader() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/tests/integration_tests/document_loaders/test_pdf.py | """Test PyPDFium2Loader."""
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
loader = PyPDFium2Loader(str(file_path))
docs = loader.load()
assert len(docs) == 1
file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
loader = PyPDFium2Loader(str(file_path))
docs = loader.load()
assert len(docs) == 16
def test_pymupdf_loader() -> None:
"""Test PyMuPDF loader."""
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
loader = PyMuPDFLoader(str(file_path))
docs = loader.load()
assert len(docs) == 1
file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
loader = PyMuPDFLoader(str(file_path))
docs = loader.load()
assert len(docs) == 16
assert loader.web_path is None
web_path = "https://people.sc.fsu.edu/~jpeterson/hello_world.pdf"
loader = PyMuPDFLoader(web_path)
docs = loader.load()
assert loader.web_path == web_path
assert loader.file_path != web_path
assert len(docs) == 1
def test_mathpix_loader() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/tests/integration_tests/document_loaders/test_pdf.py | file_path = Path(__file__).parent.parent / "examples/hello.pdf"
loader = MathpixPDFLoader(str(file_path))
docs = loader.load()
assert len(docs) == 1
print(docs[0].page_content)
file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
loader = MathpixPDFLoader(str(file_path))
docs = loader.load()
assert len(docs) == 1
print(docs[0].page_content)
@pytest.mark.parametrize(
"file_path, features, docs_length, create_client",
[
(
(
"https://amazon-textract-public-content.s3.us-east-2.amazonaws.com"
"/langchain/alejandro_rosalez_sample_1.jpg"
),
["FORMS", "TABLES"],
1,
False,
),
(str(Path(__file__).parent.parent / "examples/hello.pdf"), ["FORMS"], 1, False),
(
"s3://amazon-textract-public-content/langchain/layout-parser-paper.pdf",
None,
16,
True, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,068 | feat: Add Linearized output to Textract PDFLoader | ### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature. | https://github.com/langchain-ai/langchain/issues/12068 | https://github.com/langchain-ai/langchain/pull/12446 | a7d5e0ce8a30bd81b8f7b544a4859c31d5f25445 | 0c7f1d8b219e87e3ffd14a15a452622c532c7e95 | "2023-10-20T08:28:07Z" | python | "2023-10-31T01:02:10Z" | libs/langchain/tests/integration_tests/document_loaders/test_pdf.py | ),
],
)
@pytest.mark.skip(reason="Requires AWS credentials to run")
def test_amazontextract_loader(
file_path: str,
features: Union[Sequence[str], None],
docs_length: int,
create_client: bool,
) -> None:
if create_client:
import boto3
textract_client = boto3.client("textract", region_name="us-east-2")
loader = AmazonTextractPDFLoader(
file_path, textract_features=features, client=textract_client
)
else:
loader = AmazonTextractPDFLoader(file_path, textract_features=features)
docs = loader.load()
assert len(docs) == docs_length
@pytest.mark.skip(reason="Requires AWS credentials to run")
def test_amazontextract_loader_failures() -> None:
two_page_pdf = str(
Path(__file__).parent.parent / "examples/multi-page-forms-sample-2-page.pdf"
)
loader = AmazonTextractPDFLoader(two_page_pdf)
with pytest.raises(ValueError):
loader.load() |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | from __future__ import annotations
import logging
import warnings
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Sequence,
Set,
Tuple,
Union,
)
import numpy as np
from tenacity import (
AsyncRetrying,
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env, get_pydantic_field_names
logger = logging.getLogger(__name__)
def _create_retry_decorator(embeddings: OpenAIEmbeddings) -> Callable[[Any], Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | import openai
min_seconds = 4
max_seconds = 10
return retry(
reraise=True,
stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def _async_retry_decorator(embeddings: OpenAIEmbeddings) -> Any: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | import openai
min_seconds = 4
max_seconds = 10
async_retrying = AsyncRetrying(
reraise=True,
stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def wrap(func: Callable) -> Callable: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | async def wrapped_f(*args: Any, **kwargs: Any) -> Callable:
async for _ in async_retrying:
return await func(*args, **kwargs)
raise AssertionError("this is unreachable")
return wrapped_f
return wrap
def _check_response(response: dict, skip_empty: bool = False) -> dict:
if any(len(d["embedding"]) == 1 for d in response["data"]) and not skip_empty:
import openai
raise openai.error.APIError("OpenAI API returned an empty embedding")
return response
def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
"""Use tenacity to retry the embedding call."""
retry_decorator = _create_retry_decorator(embeddings)
@retry_decorator
def _embed_with_retry(**kwargs: Any) -> Any:
response = embeddings.client.create(**kwargs)
return _check_response(response, skip_empty=embeddings.skip_empty)
return _embed_with_retry(**kwargs)
async def async_embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
"""Use tenacity to retry the embedding call."""
@_async_retry_decorator(embeddings)
async def _async_embed_with_retry(**kwargs: Any) -> Any:
response = await embeddings.client.acreate(**kwargs)
return _check_response(response, skip_empty=embeddings.skip_empty)
return await _async_embed_with_retry(**kwargs)
class OpenAIEmbeddings(BaseModel, Embeddings): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | """OpenAI embedding models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key or pass it
as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.embeddings import OpenAIEmbeddings
openai = OpenAIEmbeddings(openai_api_key="my-api-key")
In order to use the library with Microsoft Azure endpoints, you need to set
the OPENAI_API_TYPE, OPENAI_API_BASE, OPENAI_API_KEY and OPENAI_API_VERSION.
The OPENAI_API_TYPE must be set to 'azure' and the others correspond to
the properties of your endpoint. |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | In addition, the deployment name must be passed as the model parameter.
Example:
.. code-block:: python
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
os.environ["OPENAI_API_VERSION"] = "2023-05-15"
os.environ["OPENAI_PROXY"] = "http://your-corporate-proxy:8080"
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(
deployment="your-embeddings-deployment-name",
model="your-embeddings-model-name",
openai_api_base="https://your-endpoint.openai.azure.com/",
openai_api_type="azure",
)
text = "This is a test query."
query_result = embeddings.embed_query(text)
"""
client: Any = None
model: str = "text-embedding-ada-002"
deployment: str = model
openai_api_version: Optional[str] = None
openai_api_base: Optional[str] = None
openai_api_type: Optional[str] = None
openai_proxy: Optional[str] = None
embedding_ctx_length: int = 8191 |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | """The maximum number of tokens to embed at once."""
openai_api_key: Optional[str] = None
openai_organization: Optional[str] = None
allowed_special: Union[Literal["all"], Set[str]] = set()
disallowed_special: Union[Literal["all"], Set[str], Sequence[str]] = "all"
chunk_size: int = 1000
"""Maximum number of texts to embed in each batch"""
max_retries: int = 6
"""Maximum number of retries to make when generating."""
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout in seconds for the OpenAPI request."""
headers: Any = None
tiktoken_model_name: Optional[str] = None
"""The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here."""
show_progress_bar: bool = False
"""Whether to show a progress bar when embedding."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
skip_empty: bool = False
"""Whether to skip empty strings when embedding or raise an error.
Defaults to not skipping."""
class Config: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | """Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
warnings.warn(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
return values
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | """Validate that api key and python package exists in environment."""
values["openai_api_key"] = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
values["openai_api_base"] = get_from_dict_or_env(
values,
"openai_api_base",
"OPENAI_API_BASE",
default="",
)
values["openai_api_type"] = get_from_dict_or_env(
values,
"openai_api_type",
"OPENAI_API_TYPE",
default="",
)
values["openai_proxy"] = get_from_dict_or_env(
values,
"openai_proxy",
"OPENAI_PROXY",
default="",
)
if values["openai_api_type"] in ("azure", "azure_ad", "azuread"):
default_api_version = "2022-12-01" |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | default_chunk_size = 16
else:
default_api_version = ""
default_chunk_size = 1000
values["openai_api_version"] = get_from_dict_or_env(
values,
"openai_api_version",
"OPENAI_API_VERSION",
default=default_api_version,
)
values["openai_organization"] = get_from_dict_or_env(
values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
if "chunk_size" not in values:
values["chunk_size"] = default_chunk_size
try:
import openai
values["client"] = openai.Embedding
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
return values
@property
def _invocation_params(self) -> Dict: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | openai_args = {
"model": self.model,
"request_timeout": self.request_timeout,
"headers": self.headers,
"api_key": self.openai_api_key,
"organization": self.openai_organization,
"api_base": self.openai_api_base,
"api_type": self.openai_api_type,
"api_version": self.openai_api_version,
**self.model_kwargs,
}
if self.openai_api_type in ("azure", "azure_ad", "azuread"):
openai_args["engine"] = self.deployment
if self.openai_proxy:
try:
import openai
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
openai.proxy = {
"http": self.openai_proxy,
"https": self.openai_proxy,
}
return openai_args
def _get_len_safe_embeddings( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
) -> List[List[float]]:
embeddings: List[List[float]] = [[] for _ in range(len(texts))]
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to for OpenAIEmbeddings. "
"Please install it with `pip install tiktoken`."
)
tokens = [] |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | indices = []
model_name = self.tiktoken_model_name or self.model
try:
encoding = tiktoken.encoding_for_model(model_name)
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
model = "cl100k_base"
encoding = tiktoken.get_encoding(model)
for i, text in enumerate(texts):
if self.model.endswith("001"):
text = text.replace("\n", " ")
token = encoding.encode(
text,
allowed_special=self.allowed_special,
disallowed_special=self.disallowed_special,
)
for j in range(0, len(token), self.embedding_ctx_length):
tokens.append(token[j : j + self.embedding_ctx_length])
indices.append(i)
batched_embeddings: List[List[float]] = []
_chunk_size = chunk_size or self.chunk_size
if self.show_progress_bar:
try:
from tqdm.auto import tqdm
_iter = tqdm(range(0, len(tokens), _chunk_size))
except ImportError:
_iter = range(0, len(tokens), _chunk_size)
else: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | _iter = range(0, len(tokens), _chunk_size)
for i in _iter:
response = embed_with_retry(
self,
input=tokens[i : i + _chunk_size],
**self._invocation_params,
)
batched_embeddings.extend(r["embedding"] for r in response["data"])
results: List[List[List[float]]] = [[] for _ in range(len(texts))]
num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
for i in range(len(indices)):
if self.skip_empty and len(batched_embeddings[i]) == 1:
continue
results[indices[i]].append(batched_embeddings[i])
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
for i in range(len(texts)):
_result = results[i]
if len(_result) == 0:
average = embed_with_retry(
self,
input="",
**self._invocation_params,
)["data"][0]["embedding"]
else:
average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
embeddings[i] = (average / np.linalg.norm(average)).tolist()
return embeddings
async def _aget_len_safe_embeddings( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
) -> List[List[float]]:
embeddings: List[List[float]] = [[] for _ in range(len(texts))]
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to for OpenAIEmbeddings. "
"Please install it with `pip install tiktoken`."
)
tokens = []
indices = []
model_name = self.tiktoken_model_name or self.model
try:
encoding = tiktoken.encoding_for_model(model_name)
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
model = "cl100k_base"
encoding = tiktoken.get_encoding(model)
for i, text in enumerate(texts):
if self.model.endswith("001"):
text = text.replace("\n", " ")
token = encoding.encode(
text,
allowed_special=self.allowed_special,
disallowed_special=self.disallowed_special,
) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | for j in range(0, len(token), self.embedding_ctx_length):
tokens.append(token[j : j + self.embedding_ctx_length])
indices.append(i)
batched_embeddings: List[List[float]] = []
_chunk_size = chunk_size or self.chunk_size
for i in range(0, len(tokens), _chunk_size):
response = await async_embed_with_retry(
self,
input=tokens[i : i + _chunk_size],
**self._invocation_params,
)
batched_embeddings.extend(r["embedding"] for r in response["data"])
results: List[List[List[float]]] = [[] for _ in range(len(texts))]
num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
for i in range(len(indices)):
results[indices[i]].append(batched_embeddings[i])
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
for i in range(len(texts)):
_result = results[i]
if len(_result) == 0:
average = (
await async_embed_with_retry(
self,
input="",
**self._invocation_params,
)
)["data"][0]["embedding"]
else:
average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
embeddings[i] = (average / np.linalg.norm(average)).tolist() |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | return embeddings
def embed_documents(
self, texts: List[str], chunk_size: Optional[int] = 0
) -> List[List[float]]:
"""Call out to OpenAI's embedding endpoint for embedding search docs.
Args:
texts: The list of texts to embed.
chunk_size: The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns:
List of embeddings, one for each text.
"""
return self._get_len_safe_embeddings(texts, engine=self.deployment)
async def aembed_documents(
self, texts: List[str], chunk_size: Optional[int] = 0
) -> List[List[float]]:
"""Call out to OpenAI's embedding endpoint async for embedding search docs.
Args:
texts: The list of texts to embed.
chunk_size: The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns:
List of embeddings, one for each text.
"""
return await self._aget_len_safe_embeddings(texts, engine=self.deployment)
def embed_query(self, text: str) -> List[float]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/langchain/embeddings/openai.py | """Call out to OpenAI's embedding endpoint for embedding query text.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
return self.embed_documents([text])[0]
async def aembed_query(self, text: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint async for embedding query text.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
embeddings = await self.aembed_documents([text])
return embeddings[0] |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/tests/integration_tests/embeddings/test_openai.py | """Test openai embeddings."""
import os
import numpy as np
import openai
import pytest
from langchain.embeddings.openai import OpenAIEmbeddings
@pytest.mark.scheduled
def test_openai_embedding_documents() -> None:
"""Test openai embeddings."""
documents = ["foo bar"]
embedding = OpenAIEmbeddings()
output = embedding.embed_documents(documents)
assert len(output) == 1
assert len(output[0]) == 1536
@pytest.mark.scheduled
def test_openai_embedding_documents_multiple() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 12,943 | OpenAIEmbeddings() does not work because of these bugs | ### System Info
Python Version: 3.11
LangChain Version: 0.0.331
OpenAI Version: 1.0.0
### Who can help?
@hwchase17, @agola11, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following error has been caused due to the recent change in version of OpenAI to 1.0.0
**Use OpenAI==0.28.1 to fix this error**
With the code:
`embeddings = OpenAIEmbeddings()`
The error produced is:
`AttributeError: module 'openai' has no attribute 'Embedding'. Did you mean: 'embeddings'?`
I went through the `langchain/embeddings/openai.py` file and then changed `value["client"] = openai.Embedding` to `value["client"] = openai.embeddings`, but then I receive this new error:
`AttributeError: module 'openai' has no attribute 'error'` in the same file (`langchain/embeddings/openai.py`)
### Expected behavior
There should be no error when calling this function. | https://github.com/langchain-ai/langchain/issues/12943 | https://github.com/langchain-ai/langchain/pull/12969 | fdbb45d79e69485e0892dadf48b32dc8efadde9b | 0c81cd923e04bb68fdf3ad299946d7fa85a21f9f | "2023-11-06T17:56:29Z" | python | "2023-11-07T02:52:33Z" | libs/langchain/tests/integration_tests/embeddings/test_openai.py | """Test openai embeddings."""
documents = ["foo bar", "bar foo", "foo"]
embedding = OpenAIEmbeddings(chunk_size=2)
embedding.embedding_ctx_length = 8191
output = embedding.embed_documents(documents)
assert len(output) == 3
assert len(output[0]) == 1536
assert len(output[1]) == 1536
assert len(output[2]) == 1536
@pytest.mark.scheduled
@pytest.mark.asyncio
async def test_openai_embedding_documents_async_multiple() -> None:
"""Test openai embeddings."""
documents = ["foo bar", "bar foo", "foo"]
embedding = OpenAIEmbeddings(chunk_size=2)
embedding.embedding_ctx_length = 8191
output = await embedding.aembed_documents(documents)
assert len(output) == 3
assert len(output[0]) == 1536
assert len(output[1]) == 1536
assert len(output[2]) == 1536
@pytest.mark.scheduled
def test_openai_embedding_query() -> None: |
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