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closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/huggingface_hub.py | """Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
huggingfacehub_api_token = get_from_dict_or_env(
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
try:
from huggingface_hub.inference_api import InferenceApi
repo_id = values["repo_id"]
client = InferenceApi(
repo_id=repo_id,
token=huggingfacehub_api_token,
task=values.get("task"),
)
if client.task not in VALID_TASKS:
raise ValueError(
f"Got invalid task {client.task}, "
f"currently only {VALID_TASKS} are supported"
)
values["client"] = client
except ImportError:
raise ValueError(
"Could not import huggingface_hub python package. "
"Please install it with `pip install huggingface_hub`."
)
return values
@property
def _identifying_params(self) -> Mapping[str, Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/huggingface_hub.py | """Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"repo_id": self.repo_id, "task": self.task},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "huggingface_hub"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/huggingface_hub.py | """Call out to HuggingFace Hub's inference endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = hf("Tell me a joke.")
"""
_model_kwargs = self.model_kwargs or {}
response = self.client(inputs=prompt, params=_model_kwargs)
if "error" in response:
raise ValueError(f"Error raised by inference API: {response['error']}")
if self.client.task == "text-generation":
text = response[0]["generated_text"][len(prompt) :]
elif self.client.task == "text2text-generation":
text = response[0]["generated_text"]
else:
raise ValueError(
f"Got invalid task {self.client.task}, "
f"currently only {VALID_TASKS} are supported"
)
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/huggingface_pipeline.py | """Wrapper around HuggingFace Pipeline APIs."""
import importlib.util
import logging
from typing import Any, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
DEFAULT_MODEL_ID = "gpt2"
DEFAULT_TASK = "text-generation"
VALID_TASKS = ("text2text-generation", "text-generation")
logger = logging.getLogger(__name__)
class HuggingFacePipeline(LLM): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/huggingface_pipeline.py | """Wrapper around HuggingFace Pipeline API.
To use, you should have the ``transformers`` python package installed.
Only supports `text-generation` and `text2text-generation` for now.
Example using from_model_id:
.. code-block:: python
from langchain.llms import HuggingFacePipeline
hf = HuggingFacePipeline.from_model_id(
model_id="gpt2", task="text-generation"
)
Example passing pipeline in directly:
.. code-block:: python
from langchain.llms import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
hf = HuggingFacePipeline(pipeline=pipe)
"""
pipeline: Any
model_id: str = DEFAULT_MODEL_ID
"""Model name to use."""
model_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model."""
class Config: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/huggingface_pipeline.py | """Configuration for this pydantic object."""
extra = Extra.forbid
@classmethod
def from_model_id(
cls,
model_id: str,
task: str,
device: int = -1,
model_kwargs: Optional[dict] = None,
**kwargs: Any,
) -> LLM:
"""Construct the pipeline object from model_id and task."""
try:
from transformers import (
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoTokenizer,
)
from transformers import pipeline as hf_pipeline
except ImportError:
raise ValueError(
"Could not import transformers python package. "
"Please install it with `pip install transformers`."
)
_model_kwargs = model_kwargs or {}
tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
try: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/huggingface_pipeline.py | if task == "text-generation":
model = AutoModelForCausalLM.from_pretrained(model_id, **_model_kwargs)
elif task == "text2text-generation":
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **_model_kwargs)
else:
raise ValueError(
f"Got invalid task {task}, "
f"currently only {VALID_TASKS} are supported"
)
except ImportError as e:
raise ValueError(
f"Could not load the {task} model due to missing dependencies."
) from e
if importlib.util.find_spec("torch") is not None:
import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or (device >= cuda_device_count):
raise ValueError(
f"Got device=={device}, "
f"device is required to be within [-1, {cuda_device_count})"
)
if device < 0 and cuda_device_count > 0:
logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 (default) for CPU and "
"can be a positive integer associated with CUDA device id.",
cuda_device_count,
)
if "trust_remote_code" in _model_kwargs: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/huggingface_pipeline.py | _model_kwargs = {
k: v for k, v in _model_kwargs.items() if k != "trust_remote_code"
}
pipeline = hf_pipeline(
task=task,
model=model,
tokenizer=tokenizer,
device=device,
model_kwargs=_model_kwargs,
)
if pipeline.task not in VALID_TASKS:
raise ValueError(
f"Got invalid task {pipeline.task}, "
f"currently only {VALID_TASKS} are supported"
)
return cls(
pipeline=pipeline,
model_id=model_id,
model_kwargs=_model_kwargs,
**kwargs,
)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"model_id": self.model_id},
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/huggingface_pipeline.py | return "huggingface_pipeline"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
response = self.pipeline(prompt)
if self.pipeline.task == "text-generation":
text = response[0]["generated_text"][len(prompt) :]
elif self.pipeline.task == "text2text-generation":
text = response[0]["generated_text"]
else:
raise ValueError(
f"Got invalid task {self.pipeline.task}, "
f"currently only {VALID_TASKS} are supported"
)
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/self_hosted_hugging_face.py | """Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware."""
import importlib.util
import logging
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.self_hosted import SelfHostedPipeline
from langchain.llms.utils import enforce_stop_tokens
DEFAULT_MODEL_ID = "gpt2"
DEFAULT_TASK = "text-generation"
VALID_TASKS = ("text2text-generation", "text-generation")
logger = logging.getLogger(__name__)
def _generate_text( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/self_hosted_hugging_face.py | pipeline: Any,
prompt: str,
*args: Any,
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> str:
"""Inference function to send to the remote hardware.
Accepts a Hugging Face pipeline (or more likely,
a key pointing to such a pipeline on the cluster's object store)
and returns generated text.
"""
response = pipeline(prompt, *args, **kwargs)
if pipeline.task == "text-generation":
text = response[0]["generated_text"][len(prompt) :]
elif pipeline.task == "text2text-generation":
text = response[0]["generated_text"]
else:
raise ValueError(
f"Got invalid task {pipeline.task}, "
f"currently only {VALID_TASKS} are supported"
)
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text
def _load_transformer( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/self_hosted_hugging_face.py | model_id: str = DEFAULT_MODEL_ID,
task: str = DEFAULT_TASK,
device: int = 0,
model_kwargs: Optional[dict] = None,
) -> Any:
"""Inference function to send to the remote hardware.
Accepts a huggingface model_id and returns a pipeline for the task.
"""
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer
from transformers import pipeline as hf_pipeline
_model_kwargs = model_kwargs or {}
tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
try:
if task == "text-generation":
model = AutoModelForCausalLM.from_pretrained(model_id, **_model_kwargs)
elif task == "text2text-generation":
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **_model_kwargs)
else:
raise ValueError(
f"Got invalid task {task}, "
f"currently only {VALID_TASKS} are supported"
)
except ImportError as e:
raise ValueError(
f"Could not load the {task} model due to missing dependencies."
) from e |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/self_hosted_hugging_face.py | if importlib.util.find_spec("torch") is not None:
import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or (device >= cuda_device_count):
raise ValueError(
f"Got device=={device}, "
f"device is required to be within [-1, {cuda_device_count})"
)
if device < 0 and cuda_device_count > 0:
logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 for CPU and "
"can be a positive integer associated with CUDA device id.",
cuda_device_count,
)
pipeline = hf_pipeline(
task=task,
model=model,
tokenizer=tokenizer,
device=device,
model_kwargs=_model_kwargs,
)
if pipeline.task not in VALID_TASKS:
raise ValueError(
f"Got invalid task {pipeline.task}, "
f"currently only {VALID_TASKS} are supported"
)
return pipeline
class SelfHostedHuggingFaceLLM(SelfHostedPipeline): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/self_hosted_hugging_face.py | """Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another cloud
like Paperspace, Coreweave, etc.).
To use, you should have the ``runhouse`` python package installed.
Only supports `text-generation` and `text2text-generation` for now.
Example using from_model_id:
.. code-block:: python
from langchain.llms import SelfHostedHuggingFaceLLM
import runhouse as rh
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
hf = SelfHostedHuggingFaceLLM(
model_id="google/flan-t5-large", task="text2text-generation",
hardware=gpu
)
Example passing fn that generates a pipeline (bc the pipeline is not serializable):
.. code-block:: python
from langchain.llms import SelfHostedHuggingFaceLLM
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
def get_pipeline(): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/self_hosted_hugging_face.py | model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer
)
return pipe
hf = SelfHostedHuggingFaceLLM(
model_load_fn=get_pipeline, model_id="gpt2", hardware=gpu)
"""
model_id: str = DEFAULT_MODEL_ID
"""Hugging Face model_id to load the model."""
task: str = DEFAULT_TASK
"""Hugging Face task (either "text-generation" or "text2text-generation")."""
device: int = 0
"""Device to use for inference. -1 for CPU, 0 for GPU, 1 for second GPU, etc."""
model_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model."""
hardware: Any
"""Remote hardware to send the inference function to."""
model_reqs: List[str] = ["./", "transformers", "torch"]
"""Requirements to install on hardware to inference the model."""
model_load_fn: Callable = _load_transformer
"""Function to load the model remotely on the server."""
inference_fn: Callable = _generate_text
"""Inference function to send to the remote hardware."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def __init__(self, **kwargs: Any): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | langchain/llms/self_hosted_hugging_face.py | """Construct the pipeline remotely using an auxiliary function.
The load function needs to be importable to be imported
and run on the server, i.e. in a module and not a REPL or closure.
Then, initialize the remote inference function.
"""
load_fn_kwargs = {
"model_id": kwargs.get("model_id", DEFAULT_MODEL_ID),
"task": kwargs.get("task", DEFAULT_TASK),
"device": kwargs.get("device", 0),
"model_kwargs": kwargs.get("model_kwargs", None),
}
super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"model_id": self.model_id},
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str:
return "selfhosted_huggingface_pipeline"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
return self.client(pipeline=self.pipeline_ref, prompt=prompt, stop=stop) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | tests/integration_tests/llms/test_huggingface_endpoint.py | """Test HuggingFace API wrapper."""
import unittest
from pathlib import Path
import pytest
from langchain.llms.huggingface_endpoint import HuggingFaceEndpoint
from langchain.llms.loading import load_llm
from tests.integration_tests.llms.utils import assert_llm_equality
@unittest.skip(
"This test requires an inference endpoint. Tested with Hugging Face endpoints"
)
def test_huggingface_endpoint_text_generation() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | tests/integration_tests/llms/test_huggingface_endpoint.py | """Test valid call to HuggingFace text generation model."""
llm = HuggingFaceEndpoint(
endpoint_url="", task="text-generation", model_kwargs={"max_new_tokens": 10}
)
output = llm("Say foo:")
print(output)
assert isinstance(output, str)
@unittest.skip(
"This test requires an inference endpoint. Tested with Hugging Face endpoints"
)
def test_huggingface_endpoint_text2text_generation() -> None:
"""Test valid call to HuggingFace text2text model."""
llm = HuggingFaceEndpoint(endpoint_url="", task="text2text-generation")
output = llm("The capital of New York is")
assert output == "Albany"
def test_huggingface_endpoint_call_error() -> None:
"""Test valid call to HuggingFace that errors."""
llm = HuggingFaceEndpoint(model_kwargs={"max_new_tokens": -1})
with pytest.raises(ValueError):
llm("Say foo:")
def test_saving_loading_endpoint_llm(tmp_path: Path) -> None:
"""Test saving/loading an HuggingFaceHub LLM."""
llm = HuggingFaceEndpoint(
endpoint_url="", task="text-generation", model_kwargs={"max_new_tokens": 10}
)
llm.save(file_path=tmp_path / "hf.yaml")
loaded_llm = load_llm(tmp_path / "hf.yaml")
assert_llm_equality(llm, loaded_llm) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | tests/integration_tests/llms/test_huggingface_hub.py | """Test HuggingFace API wrapper."""
from pathlib import Path
import pytest
from langchain.llms.huggingface_hub import HuggingFaceHub
from langchain.llms.loading import load_llm
from tests.integration_tests.llms.utils import assert_llm_equality
def test_huggingface_text_generation() -> None:
"""Test valid call to HuggingFace text generation model."""
llm = HuggingFaceHub(repo_id="gpt2", model_kwargs={"max_new_tokens": 10})
output = llm("Say foo:")
assert isinstance(output, str)
def test_huggingface_text2text_generation() -> None:
"""Test valid call to HuggingFace text2text model."""
llm = HuggingFaceHub(repo_id="google/flan-t5-xl")
output = llm("The capital of New York is")
assert output == "Albany"
def test_huggingface_call_error() -> None:
"""Test valid call to HuggingFace that errors."""
llm = HuggingFaceHub(model_kwargs={"max_new_tokens": -1})
with pytest.raises(ValueError):
llm("Say foo:")
def test_saving_loading_llm(tmp_path: Path) -> None:
"""Test saving/loading an HuggingFaceHub LLM."""
llm = HuggingFaceHub(repo_id="gpt2", model_kwargs={"max_new_tokens": 10})
llm.save(file_path=tmp_path / "hf.yaml")
loaded_llm = load_llm(tmp_path / "hf.yaml")
assert_llm_equality(llm, loaded_llm) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | tests/integration_tests/llms/test_huggingface_pipeline.py | """Test HuggingFace Pipeline wrapper."""
from pathlib import Path
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
from langchain.llms.loading import load_llm
from tests.integration_tests.llms.utils import assert_llm_equality
def test_huggingface_pipeline_text_generation() -> None:
"""Test valid call to HuggingFace text generation model."""
llm = HuggingFacePipeline.from_model_id(
model_id="gpt2", task="text-generation", model_kwargs={"max_new_tokens": 10}
)
output = llm("Say foo:")
assert isinstance(output, str)
def test_huggingface_pipeline_text2text_generation() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | tests/integration_tests/llms/test_huggingface_pipeline.py | """Test valid call to HuggingFace text2text generation model."""
llm = HuggingFacePipeline.from_model_id(
model_id="google/flan-t5-small", task="text2text-generation"
)
output = llm("Say foo:")
assert isinstance(output, str)
def test_saving_loading_llm(tmp_path: Path) -> None:
"""Test saving/loading an HuggingFaceHub LLM."""
llm = HuggingFacePipeline.from_model_id(
model_id="gpt2", task="text-generation", model_kwargs={"max_new_tokens": 10}
)
llm.save(file_path=tmp_path / "hf.yaml")
loaded_llm = load_llm(tmp_path / "hf.yaml")
assert_llm_equality(llm, loaded_llm)
def test_init_with_pipeline() -> None:
"""Test initialization with a HF pipeline."""
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
llm = HuggingFacePipeline(pipeline=pipe)
output = llm("Say foo:")
assert isinstance(output, str) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | tests/integration_tests/llms/test_self_hosted_llm.py | """Test Self-hosted LLMs."""
import pickle
from typing import Any, List, Optional
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from langchain.llms import SelfHostedHuggingFaceLLM, SelfHostedPipeline
model_reqs = ["pip:./", "transformers", "torch"]
def get_remote_instance() -> Any: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | tests/integration_tests/llms/test_self_hosted_llm.py | """Get remote instance for testing."""
import runhouse as rh
return rh.cluster(name="rh-a10x", instance_type="A100:1", use_spot=False)
def test_self_hosted_huggingface_pipeline_text_generation() -> None:
"""Test valid call to self-hosted HuggingFace text generation model."""
gpu = get_remote_instance()
llm = SelfHostedHuggingFaceLLM(
model_id="gpt2",
task="text-generation",
model_kwargs={"n_positions": 1024},
hardware=gpu,
model_reqs=model_reqs,
)
output = llm("Say foo:")
assert isinstance(output, str)
def test_self_hosted_huggingface_pipeline_text2text_generation() -> None:
"""Test valid call to self-hosted HuggingFace text2text generation model."""
gpu = get_remote_instance()
llm = SelfHostedHuggingFaceLLM(
model_id="google/flan-t5-small",
task="text2text-generation",
hardware=gpu,
model_reqs=model_reqs,
)
output = llm("Say foo:")
assert isinstance(output, str)
def load_pipeline() -> Any: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | tests/integration_tests/llms/test_self_hosted_llm.py | """Load pipeline for testing."""
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
return pipe
def inference_fn(pipeline: Any, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Inference function for testing."""
return pipeline(prompt)[0]["generated_text"]
def test_init_with_local_pipeline() -> None:
"""Test initialization with a self-hosted HF pipeline."""
gpu = get_remote_instance()
pipeline = load_pipeline()
llm = SelfHostedPipeline.from_pipeline(
pipeline=pipeline,
hardware=gpu,
model_reqs=model_reqs,
inference_fn=inference_fn,
)
output = llm("Say foo:")
assert isinstance(output, str)
def test_init_with_pipeline_path() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | "2023-05-15T11:23:49Z" | python | "2023-05-15T23:26:17Z" | tests/integration_tests/llms/test_self_hosted_llm.py | """Test initialization with a self-hosted HF pipeline."""
gpu = get_remote_instance()
pipeline = load_pipeline()
import runhouse as rh
rh.blob(pickle.dumps(pipeline), path="models/pipeline.pkl").save().to(
gpu, path="models"
)
llm = SelfHostedPipeline.from_pipeline(
pipeline="models/pipeline.pkl",
hardware=gpu,
model_reqs=model_reqs,
inference_fn=inference_fn,
)
output = llm("Say foo:")
assert isinstance(output, str)
def test_init_with_pipeline_fn() -> None:
"""Test initialization with a self-hosted HF pipeline."""
gpu = get_remote_instance()
llm = SelfHostedPipeline(
model_load_fn=load_pipeline,
hardware=gpu,
model_reqs=model_reqs,
inference_fn=inference_fn,
)
output = llm("Say foo:")
assert isinstance(output, str) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | """Wrapper around Activeloop Deep Lake."""
from __future__ import annotations
import logging
import uuid
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
logger = logging.getLogger(__name__)
distance_metric_map = {
"l2": lambda a, b: np.linalg.norm(a - b, axis=1, ord=2),
"l1": lambda a, b: np.linalg.norm(a - b, axis=1, ord=1),
"max": lambda a, b: np.linalg.norm(a - b, axis=1, ord=np.inf),
"cos": lambda a, b: np.dot(a, b.T)
/ (np.linalg.norm(a) * np.linalg.norm(b, axis=1)),
"dot": lambda a, b: np.dot(a, b.T),
}
def vector_search( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | query_embedding: np.ndarray,
data_vectors: np.ndarray,
distance_metric: str = "L2",
k: Optional[int] = 4,
) -> Tuple[List, List]:
"""Naive search for nearest neighbors
args:
query_embedding: np.ndarray
data_vectors: np.ndarray
k (int): number of nearest neighbors
distance_metric: distance function 'L2' for Euclidean, 'L1' for Nuclear, 'Max'
l-infinity distnace, 'cos' for cosine similarity, 'dot' for dot product
returns:
nearest_indices: List, indices of nearest neighbors
"""
if data_vectors.shape[0] == 0:
return [], []
distances = distance_metric_map[distance_metric](query_embedding, data_vectors)
nearest_indices = np.argsort(distances)
nearest_indices = (
nearest_indices[::-1][:k] if distance_metric in ["cos"] else nearest_indices[:k]
)
return nearest_indices.tolist(), distances[nearest_indices].tolist()
def dp_filter(x: dict, filter: Dict[str, str]) -> bool: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | """Filter helper function for Deep Lake"""
metadata = x["metadata"].data()["value"]
return all(k in metadata and v == metadata[k] for k, v in filter.items())
class DeepLake(VectorStore):
"""Wrapper around Deep Lake, a data lake for deep learning applications.
We implement naive similarity search and filtering for fast prototyping,
but it can be extended with Tensor Query Language (TQL) for production use cases
over billion rows.
Why Deep Lake?
- Not only stores embeddings, but also the original data with version control.
- Serverless, doesn't require another service and can be used with major
cloud providers (S3, GCS, etc.)
- More than just a multi-modal vector store. You can use the dataset
to fine-tune your own LLM models.
To use, you should have the ``deeplake`` python package installed.
Example:
.. code-block:: python
from langchain.vectorstores import DeepLake
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = DeepLake("langchain_store", embeddings.embed_query)
"""
_LANGCHAIN_DEFAULT_DEEPLAKE_PATH = "./deeplake/"
def __init__( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | self,
dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
token: Optional[str] = None,
embedding_function: Optional[Embeddings] = None,
read_only: Optional[bool] = False,
ingestion_batch_size: int = 1024,
num_workers: int = 0,
verbose: bool = True,
**kwargs: Any,
) -> None:
"""Initialize with Deep Lake client."""
self.ingestion_batch_size = ingestion_batch_size
self.num_workers = num_workers
self.verbose = verbose
try:
import deeplake
from deeplake.constants import MB
except ImportError:
raise ValueError(
"Could not import deeplake python package. "
"Please install it with `pip install deeplake`."
)
self._deeplake = deeplake
self.dataset_path = dataset_path
creds_args = {"creds": kwargs["creds"]} if "creds" in kwargs else {}
if (
deeplake.exists(dataset_path, token=token, **creds_args)
and "overwrite" not in kwargs
): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | self.ds = deeplake.load(
dataset_path,
token=token,
read_only=read_only,
verbose=self.verbose,
**kwargs,
)
logger.info(f"Loading deeplake {dataset_path} from storage.")
if self.verbose:
print(
f"Deep Lake Dataset in {dataset_path} already exists, "
f"loading from the storage"
)
self.ds.summary()
else:
if "overwrite" in kwargs:
del kwargs["overwrite"]
self.ds = deeplake.empty(
dataset_path,
token=token,
overwrite=True,
verbose=self.verbose,
**kwargs,
)
with self.ds:
self.ds.create_tensor(
"text",
htype="text",
create_id_tensor=False,
create_sample_info_tensor=False, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | create_shape_tensor=False,
chunk_compression="lz4",
)
self.ds.create_tensor(
"metadata",
htype="json",
create_id_tensor=False,
create_sample_info_tensor=False,
create_shape_tensor=False,
chunk_compression="lz4",
)
self.ds.create_tensor(
"embedding",
htype="generic",
dtype=np.float32,
create_id_tensor=False,
create_sample_info_tensor=False,
max_chunk_size=64 * MB,
create_shape_tensor=True,
)
self.ds.create_tensor(
"ids",
htype="text",
create_id_tensor=False,
create_sample_info_tensor=False,
create_shape_tensor=False,
chunk_compression="lz4",
)
self._embedding_function = embedding_function
def add_texts( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts (Iterable[str]): Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]], optional): Optional list of IDs.
Returns:
List[str]: List of IDs of the added texts.
"""
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
text_list = list(texts)
if metadatas is None:
metadatas = [{}] * len(text_list)
elements = list(zip(text_list, metadatas, ids))
@self._deeplake.compute
def ingest(sample_in: list, sample_out: list) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | text_list = [s[0] for s in sample_in]
embeds: Sequence[Optional[np.ndarray]] = []
if self._embedding_function is not None:
embeddings = self._embedding_function.embed_documents(text_list)
embeds = [np.array(e, dtype=np.float32) for e in embeddings]
else:
embeds = [None] * len(text_list)
for s, e in zip(sample_in, embeds):
sample_out.append(
{
"text": s[0],
"metadata": s[1],
"ids": s[2],
"embedding": e,
}
)
batch_size = min(self.ingestion_batch_size, len(elements))
if batch_size == 0:
return []
batched = [
elements[i : i + batch_size] for i in range(0, len(elements), batch_size)
]
ingest().eval(
batched,
self.ds,
num_workers=min(self.num_workers, len(batched) // max(self.num_workers, 1)), |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | **kwargs,
)
self.ds.commit(allow_empty=True)
if self.verbose:
self.ds.summary()
return ids
def _search_helper(
self,
query: Any[str, None] = None,
embedding: Any[float, None] = None,
k: int = 4,
distance_metric: str = "L2",
use_maximal_marginal_relevance: Optional[bool] = False,
fetch_k: Optional[int] = 20,
filter: Optional[Any[Dict[str, str], Callable, str]] = None,
return_score: Optional[bool] = False,
**kwargs: Any,
) -> Any[List[Document], List[Tuple[Document, float]]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
embedding: Embedding function to use. Defaults to None.
k: Number of Documents to return. Defaults to 4.
distance_metric: `L2` for Euclidean, `L1` for Nuclear,
`max` L-infinity distance, `cos` for cosine similarity,
'dot' for dot product. Defaults to `L2`.
filter: Attribute filter by metadata example {'key': 'value'}. It can also
take [Deep Lake filter]
(https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)
Defaults to None. |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | maximal_marginal_relevance: Whether to use maximal marginal relevance.
Defaults to False.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
return_score: Whether to return the score. Defaults to False.
Returns:
List of Documents selected by the specified distance metric,
if return_score True, return a tuple of (Document, score)
"""
view = self.ds
if filter is not None:
if isinstance(filter, dict):
filter = partial(dp_filter, filter=filter)
view = view.filter(filter)
if len(view) == 0:
return []
if self._embedding_function is None:
view = view.filter(lambda x: query in x["text"].data()["value"])
scores = [1.0] * len(view)
if use_maximal_marginal_relevance:
raise ValueError(
"For MMR search, you must specify an embedding function on"
"creation."
)
else:
emb = embedding or self._embedding_function.embed_query(
query
)
query_emb = np.array(emb, dtype=np.float32) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | embeddings = view.embedding.numpy(fetch_chunks=True)
k_search = fetch_k if use_maximal_marginal_relevance else k
indices, scores = vector_search(
query_emb,
embeddings,
k=k_search,
distance_metric=distance_metric.lower(),
)
view = view[indices]
if use_maximal_marginal_relevance:
lambda_mult = kwargs.get("lambda_mult", 0.5)
indices = maximal_marginal_relevance(
query_emb,
embeddings[indices],
k=min(k, len(indices)),
lambda_mult=lambda_mult,
)
view = view[indices]
scores = [scores[i] for i in indices]
docs = [
Document(
page_content=el["text"].data()["value"],
metadata=el["metadata"].data()["value"],
)
for el in view
]
if return_score:
return [(doc, score) for doc, score in zip(docs, scores)]
return docs
def similarity_search( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: text to embed and run the query on.
k: Number of Documents to return.
Defaults to 4.
query: Text to look up documents similar to.
embedding: Embedding function to use.
Defaults to None.
k: Number of Documents to return.
Defaults to 4.
distance_metric: `L2` for Euclidean, `L1` for Nuclear, `max`
L-infinity distance, `cos` for cosine similarity, 'dot' for dot product
Defaults to `L2`.
filter: Attribute filter by metadata example {'key': 'value'}.
Defaults to None.
maximal_marginal_relevance: Whether to use maximal marginal relevance.
Defaults to False.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
return_score: Whether to return the score. Defaults to False.
Returns:
List of Documents most similar to the query vector.
"""
return self._search_helper(query=query, k=k, **kwargs)
def similarity_search_by_vector( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query vector.
"""
return self._search_helper(embedding=embedding, k=k, **kwargs)
def similarity_search_with_score( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | self,
query: str,
distance_metric: str = "L2",
k: int = 4,
filter: Optional[Dict[str, str]] = None,
) -> List[Tuple[Document, float]]:
"""Run similarity search with Deep Lake with distance returned.
Args:
query (str): Query text to search for.
distance_metric: `L2` for Euclidean, `L1` for Nuclear, `max` L-infinity
distance, `cos` for cosine similarity, 'dot' for dot product.
Defaults to `L2`.
k (int): Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of documents most similar to the query
text with distance in float.
"""
return self._search_helper(
query=query,
k=k,
filter=filter,
return_score=True,
distance_metric=distance_metric,
)
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | lambda_mult: float = 0.5,
**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:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: 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.
Returns:
List of Documents selected by maximal marginal relevance.
"""
return self._search_helper(
embedding=embedding,
k=k,
fetch_k=fetch_k,
use_maximal_marginal_relevance=True,
lambda_mult=lambda_mult,
**kwargs,
)
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | lambda_mult: float = 0.5,
**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: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: 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.
Returns:
List of Documents selected by maximal marginal relevance.
"""
if self._embedding_function is None:
raise ValueError(
"For MMR search, you must specify an embedding function on" "creation."
)
return self._search_helper(
query=query,
k=k,
fetch_k=fetch_k,
use_maximal_marginal_relevance=True,
lambda_mult=lambda_mult,
**kwargs,
)
@classmethod |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | def from_texts(
cls,
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
**kwargs: Any,
) -> DeepLake:
"""Create a Deep Lake dataset from a raw documents.
If a dataset_path is specified, the dataset will be persisted in that location,
otherwise by default at `./deeplake`
Args:
path (str, pathlib.Path): - The full path to the dataset. Can be:
- Deep Lake cloud path of the form ``hub://username/dataset_name``.
To write to Deep Lake cloud datasets,
ensure that you are logged in to Deep Lake
(use 'activeloop login' from command line)
- AWS S3 path of the form ``s3://bucketname/path/to/dataset``.
Credentials are required in either the environment
- Google Cloud Storage path of the form
``gcs://bucketname/path/to/dataset``Credentials are required
in either the environment
- Local file system path of the form ``./path/to/dataset`` or
``~/path/to/dataset`` or ``path/to/dataset``.
- In-memory path of the form ``mem://path/to/dataset`` which doesn't
save the dataset, but keeps it in memory instead.
Should be used only for testing as it does not persist.
documents (List[Document]): List of documents to add.
embedding (Optional[Embeddings]): Embedding function. Defaults to None. |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
ids (Optional[List[str]]): List of document IDs. Defaults to None.
Returns:
DeepLake: Deep Lake dataset.
"""
deeplake_dataset = cls(
dataset_path=dataset_path, embedding_function=embedding, **kwargs
)
deeplake_dataset.add_texts(texts=texts, metadatas=metadatas, ids=ids)
return deeplake_dataset
def delete(
self,
ids: Any[List[str], None] = None,
filter: Any[Dict[str, str], None] = None,
delete_all: Any[bool, None] = None,
) -> bool:
"""Delete the entities in the dataset
Args:
ids (Optional[List[str]], optional): The document_ids to delete.
Defaults to None.
filter (Optional[Dict[str, str]], optional): The filter to delete by.
Defaults to None.
delete_all (Optional[bool], optional): Whether to drop the dataset.
Defaults to None.
"""
if delete_all:
self.ds.delete(large_ok=True)
return True
view = None
if ids: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | langchain/vectorstores/deeplake.py | view = self.ds.filter(lambda x: x["ids"].data()["value"] in ids)
ids = list(view.sample_indices)
if filter:
if view is None:
view = self.ds
view = view.filter(partial(dp_filter, filter=filter))
ids = list(view.sample_indices)
with self.ds:
for id in sorted(ids)[::-1]:
self.ds.pop(id)
self.ds.commit(f"deleted {len(ids)} samples", allow_empty=True)
return True
@classmethod
def force_delete_by_path(cls, path: str) -> None:
"""Force delete dataset by path"""
try:
import deeplake
except ImportError:
raise ValueError(
"Could not import deeplake python package. "
"Please install it with `pip install deeplake`."
)
deeplake.delete(path, large_ok=True, force=True)
def delete_dataset(self) -> None:
"""Delete the collection."""
self.delete(delete_all=True)
def persist(self) -> None:
"""Persist the collection."""
self.ds.flush() |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | tests/integration_tests/vectorstores/test_deeplake.py | """Test Deep Lake functionality."""
import deeplake
import pytest
from pytest import FixtureRequest
from langchain.docstore.document import Document
from langchain.vectorstores import DeepLake
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
@pytest.fixture
def deeplake_datastore() -> DeepLake:
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = DeepLake.from_texts(
dataset_path="mem://test_path",
texts=texts,
metadatas=metadatas,
embedding=FakeEmbeddings(),
)
return docsearch
@pytest.fixture(params=["L1", "L2", "max", "cos"])
def distance_metric(request: FixtureRequest) -> str: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | tests/integration_tests/vectorstores/test_deeplake.py | return request.param
def test_deeplake() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = DeepLake.from_texts(
dataset_path="mem://test_path", texts=texts, embedding=FakeEmbeddings()
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
def test_deeplake_with_metadatas() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = DeepLake.from_texts(
dataset_path="mem://test_path",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
def test_deeplakewith_persistence() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | tests/integration_tests/vectorstores/test_deeplake.py | """Test end to end construction and search, with persistence."""
dataset_path = "./tests/persist_dir"
if deeplake.exists(dataset_path):
deeplake.delete(dataset_path)
texts = ["foo", "bar", "baz"]
docsearch = DeepLake.from_texts(
dataset_path=dataset_path,
texts=texts,
embedding=FakeEmbeddings(),
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
docsearch.persist()
docsearch = DeepLake(
dataset_path=dataset_path,
embedding_function=FakeEmbeddings(),
)
output = docsearch.similarity_search("foo", k=1)
docsearch.delete_dataset()
def test_similarity_search(deeplake_datastore: DeepLake, distance_metric: str) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | tests/integration_tests/vectorstores/test_deeplake.py | """Test similarity search."""
output = deeplake_datastore.similarity_search(
"foo", k=1, distance_metric=distance_metric
)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
deeplake_datastore.delete_dataset()
def test_similarity_search_by_vector(
deeplake_datastore: DeepLake, distance_metric: str
) -> None:
"""Test similarity search by vector."""
embeddings = FakeEmbeddings().embed_documents(["foo", "bar", "baz"])
output = deeplake_datastore.similarity_search_by_vector(
embeddings[1], k=1, distance_metric=distance_metric
)
assert output == [Document(page_content="bar", metadata={"page": "1"})]
deeplake_datastore.delete_dataset()
def test_similarity_search_with_score( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | tests/integration_tests/vectorstores/test_deeplake.py | deeplake_datastore: DeepLake, distance_metric: str
) -> None:
"""Test similarity search with score."""
output, score = deeplake_datastore.similarity_search_with_score(
"foo", k=1, distance_metric=distance_metric
)[0]
assert output == Document(page_content="foo", metadata={"page": "0"})
if distance_metric == "cos":
assert score == 1.0
else:
assert score == 0.0
deeplake_datastore.delete_dataset()
def test_similarity_search_with_filter(
deeplake_datastore: DeepLake, distance_metric: str
) -> None:
"""Test similarity search."""
output = deeplake_datastore.similarity_search(
"foo", k=1, distance_metric=distance_metric, filter={"page": "1"}
)
assert output == [Document(page_content="bar", metadata={"page": "1"})]
deeplake_datastore.delete_dataset()
def test_max_marginal_relevance_search(deeplake_datastore: DeepLake) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | "2023-05-14T19:15:22Z" | python | "2023-05-16T00:39:16Z" | tests/integration_tests/vectorstores/test_deeplake.py | """Test max marginal relevance search by vector."""
output = deeplake_datastore.max_marginal_relevance_search("foo", k=1, fetch_k=2)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
embeddings = FakeEmbeddings().embed_documents(["foo", "bar", "baz"])
output = deeplake_datastore.max_marginal_relevance_search_by_vector(
embeddings[0], k=1, fetch_k=2
)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
deeplake_datastore.delete_dataset()
def test_delete_dataset_by_ids(deeplake_datastore: DeepLake) -> None:
"""Test delete dataset."""
id = deeplake_datastore.ds.ids.data()["value"][0]
deeplake_datastore.delete(ids=[id])
assert deeplake_datastore.similarity_search("foo", k=1, filter={"page": "0"}) == []
assert len(deeplake_datastore.ds) == 2
deeplake_datastore.delete_dataset()
def test_delete_dataset_by_filter(deeplake_datastore: DeepLake) -> None:
"""Test delete dataset."""
deeplake_datastore.delete(filter={"page": "1"})
assert deeplake_datastore.similarity_search("bar", k=1, filter={"page": "1"}) == []
assert len(deeplake_datastore.ds) == 2
deeplake_datastore.delete_dataset()
def test_delete_by_path(deeplake_datastore: DeepLake) -> None:
"""Test delete dataset."""
path = deeplake_datastore.dataset_path
DeepLake.force_delete_by_path(path)
assert not deeplake.exists(path) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | from __future__ import annotations
import asyncio
import functools
import logging
import os
import warnings
from contextlib import contextmanager
from contextvars import ContextVar
from typing import Any, Dict, Generator, List, Optional, Type, TypeVar, Union, cast
from uuid import UUID, uuid4
from langchain.callbacks.base import (
BaseCallbackHandler,
BaseCallbackManager,
ChainManagerMixin,
LLMManagerMixin,
RunManagerMixin,
ToolManagerMixin, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | )
from langchain.callbacks.openai_info import OpenAICallbackHandler
from langchain.callbacks.stdout import StdOutCallbackHandler
from langchain.callbacks.tracers.langchain import LangChainTracer
from langchain.callbacks.tracers.langchain_v1 import LangChainTracerV1, TracerSessionV1
from langchain.callbacks.tracers.schemas import TracerSession
from langchain.schema import (
AgentAction,
AgentFinish,
BaseMessage,
LLMResult,
get_buffer_string,
)
logger = logging.getLogger(__name__)
Callbacks = Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]
openai_callback_var: ContextVar[Optional[OpenAICallbackHandler]] = ContextVar(
"openai_callback", default=None
)
tracing_callback_var: ContextVar[
Optional[LangChainTracerV1]
] = ContextVar(
"tracing_callback", default=None
)
tracing_v2_callback_var: ContextVar[
Optional[LangChainTracer]
] = ContextVar(
"tracing_callback_v2", default=None
)
@contextmanager
def get_openai_callback() -> Generator[OpenAICallbackHandler, None, None]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Get OpenAI callback handler in a context manager."""
cb = OpenAICallbackHandler()
openai_callback_var.set(cb)
yield cb
openai_callback_var.set(None)
@contextmanager
def tracing_enabled(
session_name: str = "default",
) -> Generator[TracerSessionV1, None, None]:
"""Get Tracer in a context manager."""
cb = LangChainTracerV1()
session = cast(TracerSessionV1, cb.load_session(session_name))
tracing_callback_var.set(cb)
yield session
tracing_callback_var.set(None)
@contextmanager
def tracing_v2_enabled( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | session_name: Optional[str] = None,
*,
example_id: Optional[Union[str, UUID]] = None,
tenant_id: Optional[str] = None,
session_extra: Optional[Dict[str, Any]] = None,
) -> Generator[TracerSession, None, None]:
"""Get the experimental tracer handler in a context manager."""
warnings.warn(
"The experimental tracing v2 is in development. "
"This is not yet stable and may change in the future."
)
if isinstance(example_id, str):
example_id = UUID(example_id)
cb = LangChainTracer(
tenant_id=tenant_id,
session_name=session_name,
example_id=example_id,
session_extra=session_extra,
)
session = cb.ensure_session()
tracing_v2_callback_var.set(cb)
yield session
tracing_v2_callback_var.set(None)
def _handle_event( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | handlers: List[BaseCallbackHandler],
event_name: str,
ignore_condition_name: Optional[str],
*args: Any,
**kwargs: Any,
) -> None:
"""Generic event handler for CallbackManager."""
message_strings: Optional[List[str]] = None
for handler in handlers:
try:
if ignore_condition_name is None or not getattr(
handler, ignore_condition_name
):
getattr(handler, event_name)(*args, **kwargs)
except NotImplementedError as e:
if event_name == "on_chat_model_start":
if message_strings is None:
message_strings = [get_buffer_string(m) for m in args[1]]
_handle_event(
[handler],
"on_llm_start", |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | "ignore_llm",
args[0],
message_strings,
*args[2:],
**kwargs,
)
else:
logger.warning(f"Error in {event_name} callback: {e}")
except Exception as e:
logging.warning(f"Error in {event_name} callback: {e}")
async def _ahandle_event_for_handler(
handler: BaseCallbackHandler,
event_name: str,
ignore_condition_name: Optional[str],
*args: Any,
**kwargs: Any,
) -> None:
try:
if ignore_condition_name is None or not getattr(handler, ignore_condition_name):
event = getattr(handler, event_name)
if asyncio.iscoroutinefunction(event):
await event(*args, **kwargs)
else:
await asyncio.get_event_loop().run_in_executor(
None, functools.partial(event, *args, **kwargs)
)
except NotImplementedError as e:
if event_name == "on_chat_model_start":
message_strings = [get_buffer_string(m) for m in args[1]]
await _ahandle_event_for_handler( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | handler,
"on_llm",
"ignore_llm",
args[0],
message_strings,
*args[2:],
**kwargs,
)
else:
logger.warning(f"Error in {event_name} callback: {e}")
except Exception as e:
logger.warning(f"Error in {event_name} callback: {e}")
async def _ahandle_event(
handlers: List[BaseCallbackHandler],
event_name: str,
ignore_condition_name: Optional[str],
*args: Any,
**kwargs: Any,
) -> None:
"""Generic event handler for AsyncCallbackManager."""
await asyncio.gather(
*(
_ahandle_event_for_handler(
handler, event_name, ignore_condition_name, *args, **kwargs
)
for handler in handlers
)
)
BRM = TypeVar("BRM", bound="BaseRunManager")
class BaseRunManager(RunManagerMixin): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Base class for run manager (a bound callback manager)."""
def __init__(
self,
run_id: UUID,
handlers: List[BaseCallbackHandler],
inheritable_handlers: List[BaseCallbackHandler],
parent_run_id: Optional[UUID] = None,
) -> None:
"""Initialize run manager."""
self.run_id = run_id
self.handlers = handlers
self.inheritable_handlers = inheritable_handlers
self.parent_run_id = parent_run_id
@classmethod
def get_noop_manager(cls: Type[BRM]) -> BRM:
"""Return a manager that doesn't perform any operations."""
return cls(uuid4(), [], [])
class RunManager(BaseRunManager): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Sync Run Manager."""
def on_text(
self,
text: str,
**kwargs: Any,
) -> Any:
"""Run when text is received."""
_handle_event(
self.handlers,
"on_text",
None,
text,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
class AsyncRunManager(BaseRunManager): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Async Run Manager."""
async def on_text(
self,
text: str,
**kwargs: Any,
) -> Any:
"""Run when text is received."""
await _ahandle_event(
self.handlers,
"on_text",
None,
text,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
class CallbackManagerForLLMRun(RunManager, LLMManagerMixin): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Callback manager for LLM run."""
def on_llm_new_token(
self,
token: str,
**kwargs: Any,
) -> None:
"""Run when LLM generates a new token."""
_handle_event(
self.handlers,
"on_llm_new_token",
"ignore_llm",
token=token,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Run when LLM ends running."""
_handle_event(
self.handlers,
"on_llm_end",
"ignore_llm",
response,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
def on_llm_error(
self,
error: Union[Exception, KeyboardInterrupt],
**kwargs: Any,
) -> None:
"""Run when LLM errors."""
_handle_event(
self.handlers,
"on_llm_error",
"ignore_llm",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
class AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Async callback manager for LLM run."""
async def on_llm_new_token(
self,
token: str,
**kwargs: Any,
) -> None:
"""Run when LLM generates a new token."""
await _ahandle_event(
self.handlers,
"on_llm_new_token",
"ignore_llm",
token,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when LLM ends running."""
await _ahandle_event(
self.handlers,
"on_llm_end",
"ignore_llm",
response,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
async def on_llm_error( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | self,
error: Union[Exception, KeyboardInterrupt],
**kwargs: Any,
) -> None:
"""Run when LLM errors."""
await _ahandle_event(
self.handlers,
"on_llm_error",
"ignore_llm",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
class CallbackManagerForChainRun(RunManager, ChainManagerMixin):
"""Callback manager for chain run."""
def get_child(self) -> CallbackManager:
"""Get a child callback manager."""
manager = CallbackManager([], parent_run_id=self.run_id)
manager.set_handlers(self.inheritable_handlers)
return manager
def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Run when chain ends running."""
_handle_event(
self.handlers,
"on_chain_end",
"ignore_chain",
outputs,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
def on_chain_error(
self,
error: Union[Exception, KeyboardInterrupt],
**kwargs: Any,
) -> None:
"""Run when chain errors."""
_handle_event(
self.handlers,
"on_chain_error",
"ignore_chain",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Run when agent action is received."""
_handle_event(
self.handlers,
"on_agent_action",
"ignore_agent",
action,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
"""Run when agent finish is received."""
_handle_event(
self.handlers,
"on_agent_finish",
"ignore_agent",
finish,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
class AsyncCallbackManagerForChainRun(AsyncRunManager, ChainManagerMixin):
"""Async callback manager for chain run."""
def get_child(self) -> AsyncCallbackManager:
"""Get a child callback manager."""
manager = AsyncCallbackManager([], parent_run_id=self.run_id)
manager.set_handlers(self.inheritable_handlers)
return manager
async def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Run when chain ends running."""
await _ahandle_event(
self.handlers,
"on_chain_end",
"ignore_chain",
outputs,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
async def on_chain_error(
self,
error: Union[Exception, KeyboardInterrupt],
**kwargs: Any,
) -> None:
"""Run when chain errors."""
await _ahandle_event(
self.handlers,
"on_chain_error",
"ignore_chain",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
async def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Run when agent action is received."""
await _ahandle_event(
self.handlers,
"on_agent_action",
"ignore_agent",
action,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
"""Run when agent finish is received."""
await _ahandle_event(
self.handlers,
"on_agent_finish",
"ignore_agent",
finish,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
class CallbackManagerForToolRun(RunManager, ToolManagerMixin): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Callback manager for tool run."""
def get_child(self) -> CallbackManager:
"""Get a child callback manager."""
manager = CallbackManager([], parent_run_id=self.run_id)
manager.set_handlers(self.inheritable_handlers)
return manager
def on_tool_end(
self,
output: str,
**kwargs: Any,
) -> None:
"""Run when tool ends running."""
_handle_event(
self.handlers,
"on_tool_end",
"ignore_agent",
output,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
def on_tool_error( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | self,
error: Union[Exception, KeyboardInterrupt],
**kwargs: Any,
) -> None:
"""Run when tool errors."""
_handle_event(
self.handlers,
"on_tool_error",
"ignore_agent",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
class AsyncCallbackManagerForToolRun(AsyncRunManager, ToolManagerMixin):
"""Async callback manager for tool run."""
def get_child(self) -> AsyncCallbackManager:
"""Get a child callback manager."""
manager = AsyncCallbackManager([], parent_run_id=self.run_id)
manager.set_handlers(self.inheritable_handlers)
return manager
async def on_tool_end(self, output: str, **kwargs: Any) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Run when tool ends running."""
await _ahandle_event(
self.handlers,
"on_tool_end",
"ignore_agent",
output,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
async def on_tool_error(
self,
error: Union[Exception, KeyboardInterrupt],
**kwargs: Any,
) -> None:
"""Run when tool errors."""
await _ahandle_event(
self.handlers,
"on_tool_error",
"ignore_agent",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
class CallbackManager(BaseCallbackManager): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Callback manager that can be used to handle callbacks from langchain."""
def on_llm_start(
self,
serialized: Dict[str, Any],
prompts: List[str],
run_id: Optional[UUID] = None,
**kwargs: Any,
) -> CallbackManagerForLLMRun:
"""Run when LLM starts running."""
if run_id is None:
run_id = uuid4()
_handle_event(
self.handlers,
"on_llm_start",
"ignore_llm",
serialized,
prompts,
run_id=run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
return CallbackManagerForLLMRun(
run_id, self.handlers, self.inheritable_handlers, self.parent_run_id
)
def on_chat_model_start( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | self,
serialized: Dict[str, Any],
messages: List[List[BaseMessage]],
run_id: Optional[UUID] = None,
**kwargs: Any,
) -> CallbackManagerForLLMRun:
"""Run when LLM starts running."""
if run_id is None:
run_id = uuid4()
_handle_event(
self.handlers,
"on_chat_model_start",
"ignore_chat_model",
serialized,
messages,
run_id=run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
return CallbackManagerForLLMRun(
run_id, self.handlers, self.inheritable_handlers, self.parent_run_id
)
def on_chain_start( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | self,
serialized: Dict[str, Any],
inputs: Dict[str, Any],
run_id: Optional[UUID] = None,
**kwargs: Any,
) -> CallbackManagerForChainRun:
"""Run when chain starts running."""
if run_id is None:
run_id = uuid4()
_handle_event(
self.handlers,
"on_chain_start",
"ignore_chain",
serialized,
inputs,
run_id=run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
return CallbackManagerForChainRun(
run_id, self.handlers, self.inheritable_handlers, self.parent_run_id
)
def on_tool_start( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | self,
serialized: Dict[str, Any],
input_str: str,
run_id: Optional[UUID] = None,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> CallbackManagerForToolRun:
"""Run when tool starts running."""
if run_id is None:
run_id = uuid4()
_handle_event(
self.handlers,
"on_tool_start",
"ignore_agent",
serialized,
input_str,
run_id=run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
return CallbackManagerForToolRun(
run_id, self.handlers, self.inheritable_handlers, self.parent_run_id
)
@classmethod
def configure( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | cls,
inheritable_callbacks: Callbacks = None,
local_callbacks: Callbacks = None,
verbose: bool = False,
) -> CallbackManager:
"""Configure the callback manager."""
return _configure(cls, inheritable_callbacks, local_callbacks, verbose)
class AsyncCallbackManager(BaseCallbackManager): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | """Async callback manager that can be used to handle callbacks from LangChain."""
@property
def is_async(self) -> bool:
"""Return whether the handler is async."""
return True
async def on_llm_start(
self,
serialized: Dict[str, Any],
prompts: List[str],
run_id: Optional[UUID] = None,
**kwargs: Any,
) -> AsyncCallbackManagerForLLMRun:
"""Run when LLM starts running."""
if run_id is None:
run_id = uuid4()
await _ahandle_event(
self.handlers,
"on_llm_start",
"ignore_llm",
serialized,
prompts,
run_id=run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
return AsyncCallbackManagerForLLMRun(
run_id, self.handlers, self.inheritable_handlers, self.parent_run_id
)
async def on_chat_model_start( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | self,
serialized: Dict[str, Any],
messages: List[List[BaseMessage]],
run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
if run_id is None:
run_id = uuid4()
await _ahandle_event(
self.handlers,
"on_chat_model_start",
"ignore_chat_model",
serialized,
messages,
run_id=run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
return AsyncCallbackManagerForLLMRun(
run_id, self.handlers, self.inheritable_handlers, self.parent_run_id
)
async def on_chain_start( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | self,
serialized: Dict[str, Any],
inputs: Dict[str, Any],
run_id: Optional[UUID] = None,
**kwargs: Any,
) -> AsyncCallbackManagerForChainRun:
"""Run when chain starts running."""
if run_id is None:
run_id = uuid4()
await _ahandle_event(
self.handlers,
"on_chain_start",
"ignore_chain",
serialized,
inputs,
run_id=run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
return AsyncCallbackManagerForChainRun(
run_id, self.handlers, self.inheritable_handlers, self.parent_run_id
)
async def on_tool_start( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | self,
serialized: Dict[str, Any],
input_str: str,
run_id: Optional[UUID] = None,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> AsyncCallbackManagerForToolRun:
"""Run when tool starts running."""
if run_id is None:
run_id = uuid4()
await _ahandle_event(
self.handlers,
"on_tool_start",
"ignore_agent",
serialized,
input_str,
run_id=run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
return AsyncCallbackManagerForToolRun(
run_id, self.handlers, self.inheritable_handlers, self.parent_run_id
)
@classmethod
def configure( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | cls,
inheritable_callbacks: Callbacks = None,
local_callbacks: Callbacks = None,
verbose: bool = False,
) -> AsyncCallbackManager:
"""Configure the callback manager."""
return _configure(cls, inheritable_callbacks, local_callbacks, verbose)
T = TypeVar("T", CallbackManager, AsyncCallbackManager)
def _configure(
callback_manager_cls: Type[T],
inheritable_callbacks: Callbacks = None,
local_callbacks: Callbacks = None,
verbose: bool = False,
) -> T:
"""Configure the callback manager."""
callback_manager = callback_manager_cls([])
if inheritable_callbacks or local_callbacks:
if isinstance(inheritable_callbacks, list) or inheritable_callbacks is None:
inheritable_callbacks_ = inheritable_callbacks or []
callback_manager = callback_manager_cls(
handlers=inheritable_callbacks_.copy(),
inheritable_handlers=inheritable_callbacks_.copy(),
)
else:
callback_manager = callback_manager_cls(
handlers=inheritable_callbacks.handlers, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | inheritable_handlers=inheritable_callbacks.inheritable_handlers,
parent_run_id=inheritable_callbacks.parent_run_id,
)
local_handlers_ = (
local_callbacks
if isinstance(local_callbacks, list)
else (local_callbacks.handlers if local_callbacks else [])
)
for handler in local_handlers_:
callback_manager.add_handler(handler, False)
tracer = tracing_callback_var.get()
open_ai = openai_callback_var.get()
tracing_enabled_ = (
os.environ.get("LANGCHAIN_TRACING") is not None
or tracer is not None
or os.environ.get("LANGCHAIN_HANDLER") is not None
)
tracer_v2 = tracing_v2_callback_var.get()
tracing_v2_enabled_ = (
os.environ.get("LANGCHAIN_TRACING_V2") is not None or tracer_v2 is not None
)
tracer_session = os.environ.get("LANGCHAIN_SESSION")
if tracer_session is None:
tracer_session = "default"
if verbose or tracing_enabled_ or tracing_v2_enabled_ or open_ai is not None:
if verbose and not any(
isinstance(handler, StdOutCallbackHandler)
for handler in callback_manager.handlers
):
callback_manager.add_handler(StdOutCallbackHandler(), False) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | "2023-05-15T06:30:00Z" | python | "2023-05-16T01:36:21Z" | langchain/callbacks/manager.py | if tracing_enabled_ and not any(
isinstance(handler, LangChainTracerV1)
for handler in callback_manager.handlers
):
if tracer:
callback_manager.add_handler(tracer, True)
else:
handler = LangChainTracerV1()
handler.load_session(tracer_session)
callback_manager.add_handler(handler, True)
if tracing_v2_enabled_ and not any(
isinstance(handler, LangChainTracer)
for handler in callback_manager.handlers
):
if tracer_v2:
callback_manager.add_handler(tracer_v2, True)
else:
try:
handler = LangChainTracer(session_name=tracer_session)
handler.ensure_session()
callback_manager.add_handler(handler, True)
except Exception as e:
logger.debug("Unable to load requested LangChainTracer", e)
if open_ai is not None and not any(
isinstance(handler, OpenAICallbackHandler)
for handler in callback_manager.handlers
):
callback_manager.add_handler(open_ai, True)
return callback_manager |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/retrievers/weaviate_hybrid_search.py | """Wrapper around weaviate vector database."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from uuid import uuid4
from pydantic import Extra
from langchain.docstore.document import Document
from langchain.schema import BaseRetriever
class WeaviateHybridSearchRetriever(BaseRetriever): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/retrievers/weaviate_hybrid_search.py | def __init__(
self,
client: Any,
index_name: str,
text_key: str,
alpha: float = 0.5,
k: int = 4,
attributes: Optional[List[str]] = None,
):
try:
import weaviate
except ImportError:
raise ValueError(
"Could not import weaviate python package. "
"Please install it with `pip install weaviate-client`."
)
if not isinstance(client, weaviate.Client):
raise ValueError(
f"client should be an instance of weaviate.Client, got {type(client)}"
)
self._client = client
self.k = k
self.alpha = alpha
self._index_name = index_name
self._text_key = text_key
self._query_attrs = [self._text_key]
if attributes is not None:
self._query_attrs.extend(attributes)
class Config: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/retrievers/weaviate_hybrid_search.py | """Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
def add_documents(self, docs: List[Document]) -> List[str]:
"""Upload documents to Weaviate."""
from weaviate.util import get_valid_uuid
with self._client.batch as batch:
ids = []
for i, doc in enumerate(docs):
metadata = doc.metadata or {}
data_properties = {self._text_key: doc.page_content, **metadata}
_id = get_valid_uuid(uuid4())
batch.add_data_object(data_properties, self._index_name, _id)
ids.append(_id)
return ids
def get_relevant_documents( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/retrievers/weaviate_hybrid_search.py | self, query: str, where_filter: Optional[Dict[str, object]] = None
) -> List[Document]:
"""Look up similar documents in Weaviate."""
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if where_filter:
query_obj = query_obj.with_where(where_filter)
result = query_obj.with_hybrid(query, alpha=self.alpha).with_limit(self.k).do()
if "errors" in result:
raise ValueError(f"Error during query: {result['errors']}")
docs = []
for res in result["data"]["Get"][self._index_name]:
text = res.pop(self._text_key)
docs.append(Document(page_content=text, metadata=res))
return docs
async def aget_relevant_documents(
self, query: str, where_filter: Optional[Dict[str, object]] = None
) -> List[Document]:
raise NotImplementedError |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/vectorstores/redis.py | """Wrapper around Redis vector database."""
from __future__ import annotations
import json
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Literal,
Mapping,
Optional,
Tuple,
Type,
)
import numpy as np
from pydantic import BaseModel, root_validator
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/vectorstores/redis.py | from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore, VectorStoreRetriever
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
from redis.client import Redis as RedisType
from redis.commands.search.query import Query
REDIS_REQUIRED_MODULES = [
{"name": "search", "ver": 20400},
{"name": "searchlight", "ver": 20400},
]
REDIS_DISTANCE_METRICS = Literal["COSINE", "IP", "L2"]
def _check_redis_module_exist(client: RedisType, required_modules: List[dict]) -> None:
"""Check if the correct Redis modules are installed."""
installed_modules = client.module_list()
installed_modules = {
module[b"name"].decode("utf-8"): module for module in installed_modules
}
for module in required_modules:
if module["name"] in installed_modules and int(
installed_modules[module["name"]][b"ver"]
) >= int(module["ver"]):
return
error_message = (
"You must add the RediSearch (>= 2.4) module from Redis Stack. "
"Please refer to Redis Stack docs: https://redis.io/docs/stack/"
)
logging.error(error_message)
raise ValueError(error_message)
def _check_index_exists(client: RedisType, index_name: str) -> bool: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/vectorstores/redis.py | """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
def _redis_key(prefix: str) -> str:
"""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
class Redis(VectorStore): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/vectorstores/redis.py | """Wrapper around Redis vector database.
To use, you should have the ``redis`` python package installed.
Example:
.. code-block:: python
from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Redis(
redis_url="redis://username:password@localhost:6379"
index_name="my-index",
embedding_function=embeddings.embed_query,
)
"""
def __init__(
self,
redis_url: str,
index_name: str,
embedding_function: Callable,
content_key: str = "content",
metadata_key: str = "metadata",
vector_key: str = "content_vector",
relevance_score_fn: Optional[ |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/vectorstores/redis.py | Callable[[float], float]
] = _default_relevance_score,
**kwargs: Any,
):
"""Initialize with necessary components."""
try:
import redis
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
self.embedding_function = embedding_function
self.index_name = index_name
try:
redis_client = redis.from_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.content_key = content_key
self.metadata_key = metadata_key
self.vector_key = vector_key
self.relevance_score_fn = relevance_score_fn
def _create_index(
self, dim: int = 1536, distance_metric: REDIS_DISTANCE_METRICS = "COSINE"
) -> None:
try: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/vectorstores/redis.py | from redis.commands.search.field import TextField, VectorField
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
if not _check_index_exists(self.client, self.index_name):
schema = (
TextField(name=self.content_key),
TextField(name=self.metadata_key),
VectorField(
self.vector_key,
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": dim,
"DISTANCE_METRIC": distance_metric,
},
),
)
prefix = _redis_prefix(self.index_name)
self.client.ft(self.index_name).create_index(
fields=schema,
definition=IndexDefinition(prefix=[prefix], index_type=IndexType.HASH),
)
def add_texts( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/vectorstores/redis.py | self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
embeddings: Optional[List[List[float]]] = None,
keys: Optional[List[str]] = None,
batch_size: int = 1000,
**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 (Optional[List[str]], optional): Optional key values to use as ids.
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)
pipeline = self.client.pipeline(transaction=False)
for i, text in enumerate(texts):
key = keys[i] if keys else _redis_key(prefix)
metadata = metadatas[i] if metadatas else {} |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/vectorstores/redis.py | embedding = embeddings[i] if embeddings else self.embedding_function(text)
pipeline.hset(
key,
mapping={
self.content_key: text,
self.vector_key: np.array(embedding, dtype=np.float32).tobytes(),
self.metadata_key: json.dumps(metadata),
},
)
ids.append(key)
if i % batch_size == 0:
pipeline.execute()
pipeline.execute()
return ids
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
docs_and_scores = self.similarity_search_with_score(query, k=k)
return [doc for doc, _ in docs_and_scores]
def similarity_search_limit_score( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/vectorstores/redis.py | 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.
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 score required for a document
to be considered a match. Defaults to 0.2.
Because the similarity calculation algorithm is based on cosine similarity,
the smaller the angle, the higher the similarity.
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.
"""
docs_and_scores = self.similarity_search_with_score(query, k=k)
return [doc for doc, score in docs_and_scores if score < score_threshold]
def _prepare_query(self, k: int) -> Query: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/vectorstores/redis.py | try:
from redis.commands.search.query import Query
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
hybrid_fields = "*"
base_query = (
f"{hybrid_fields}=>[KNN {k} @{self.vector_key} $vector AS vector_score]"
)
return_fields = [self.metadata_key, self.content_key, "vector_score"]
return (
Query(base_query)
.return_fields(*return_fields)
.sort_by("vector_score")
.paging(0, k)
.dialect(2)
)
def similarity_search_with_score( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/vectorstores/redis.py | self, query: str, k: int = 4
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for each
"""
embedding = self.embedding_function(query)
redis_query = self._prepare_query(k)
params_dict: Mapping[str, str] = {
"vector": np.array(embedding)
.astype(dtype=np.float32)
.tobytes()
} |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/vectorstores/redis.py | results = self.client.ft(self.index_name).search(redis_query, params_dict)
docs = [
(
Document(
page_content=result.content, metadata=json.loads(result.metadata)
),
float(result.vector_score),
)
for result in results.docs
]
return docs
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores, normalized on a scale from 0 to 1.
0 is dissimilar, 1 is most similar.
"""
if self.relevance_score_fn is None:
raise ValueError(
"relevance_score_fn must be provided to"
" Weaviate constructor to normalize scores"
)
docs_and_scores = self.similarity_search_with_score(query, k=k)
return [(doc, self.relevance_score_fn(score)) for doc, score in docs_and_scores]
@classmethod
def from_texts_return_keys( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | "2023-05-16T15:31:48Z" | python | "2023-05-16T22:26:46Z" | langchain/vectorstores/redis.py | cls: Type[Redis],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
index_name: Optional[str] = None,
content_key: str = "content",
metadata_key: str = "metadata",
vector_key: str = "content_vector",
distance_metric: REDIS_DISTANCE_METRICS = "COSINE",
**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 index for the embeddings in Redis.
3. Adds the documents to the newly created Redis index.
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
redisearch = RediSearch.from_texts( |