id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
c21d412e1ff1-0 | Source code for langchain.document_transformers.doctran_text_translate
from typing import Any, Optional, Sequence
from langchain.schema import BaseDocumentTransformer, Document
from langchain.utils import get_from_env
[docs]class DoctranTextTranslator(BaseDocumentTransformer):
"""Translate text documents using doct... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/doctran_text_translate.html |
c21d412e1ff1-1 | """Translates text documents using doctran."""
try:
from doctran import Doctran
doctran = Doctran(
openai_api_key=self.openai_api_key, openai_model=self.openai_api_model
)
except ImportError:
raise ImportError(
"Install doct... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/doctran_text_translate.html |
83aef77839a7-0 | Source code for langchain.document_transformers.openai_functions
"""Document transformers that use OpenAI Functions models"""
from typing import Any, Dict, Optional, Sequence, Type, Union
from pydantic import BaseModel
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions import create_taggin... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/openai_functions.html |
83aef77839a7-1 | original_documents = [
Document(page_content="Review of The Bee Movie\nBy Roger Ebert\n\This is the greatest movie ever made. 4 out of 5 stars."),
Document(page_content="Review of The Godfather\nBy Anonymous\n\nThis movie was super boring. 1 out of 5 stars.", metadata={"reliable"... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/openai_functions.html |
83aef77839a7-2 | """Create a DocumentTransformer that uses an OpenAI function chain to automatically
tag documents with metadata based on their content and an input schema.
Args:
metadata_schema: Either a dictionary or pydantic.BaseModel class. If a dictionary
is passed in, it's assumed to already be a v... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/openai_functions.html |
83aef77839a7-3 | original_documents = [
Document(page_content="Review of The Bee Movie\nBy Roger Ebert\n\This is the greatest movie ever made. 4 out of 5 stars."),
Document(page_content="Review of The Godfather\nBy Anonymous\n\nThis movie was super boring. 1 out of 5 stars.", metadata={"reliable"... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/openai_functions.html |
d3302cc9977e-0 | Source code for langchain.document_transformers.doctran_text_extract
from typing import Any, List, Optional, Sequence
from langchain.schema import BaseDocumentTransformer, Document
from langchain.utils import get_from_env
[docs]class DoctranPropertyExtractor(BaseDocumentTransformer):
"""Extract properties from text... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/doctran_text_extract.html |
d3302cc9977e-1 | transformed_document = await qa_transformer.atransform_documents(documents)
""" # noqa: E501
[docs] def __init__(
self,
properties: List[dict],
openai_api_key: Optional[str] = None,
openai_api_model: Optional[str] = None,
) -> None:
self.properties = properties
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/doctran_text_extract.html |
ecc6fad720a8-0 | Source code for langchain.document_transformers.html2text
from typing import Any, Sequence
from langchain.schema import BaseDocumentTransformer, Document
[docs]class Html2TextTransformer(BaseDocumentTransformer):
"""Replace occurrences of a particular search pattern with a replacement string
Example:
..... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/html2text.html |
43248b67c0b6-0 | Source code for langchain.document_transformers.embeddings_redundant_filter
"""Transform documents"""
from typing import Any, Callable, List, Sequence
import numpy as np
from pydantic import BaseModel, Field
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseDocumentTransformer, Document
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/embeddings_redundant_filter.html |
43248b67c0b6-1 | redundant_stacked = np.column_stack(redundant)
redundant_sorted = np.argsort(similarity[redundant])[::-1]
included_idxs = set(range(len(embedded_documents)))
for first_idx, second_idx in redundant_stacked[redundant_sorted]:
if first_idx in included_idxs and second_idx in included_idxs:
#... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/embeddings_redundant_filter.html |
43248b67c0b6-2 | )
closest_indices = []
# Loop through the number of clusters you have
for i in range(num_clusters):
# Get the list of distances from that particular cluster center
distances = np.linalg.norm(
embedded_documents - kmeans.cluster_centers_[i], axis=1
)
# Find the ind... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/embeddings_redundant_filter.html |
43248b67c0b6-3 | ) -> Sequence[Document]:
"""Filter down documents."""
stateful_documents = get_stateful_documents(documents)
embedded_documents = _get_embeddings_from_stateful_docs(
self.embeddings, stateful_documents
)
included_idxs = _filter_similar_embeddings(
embedded... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/embeddings_redundant_filter.html |
43248b67c0b6-4 | """ By default duplicated results are skipped and replaced by the next closest
vector in the cluster. If remove_duplicates is true no replacement will be done:
This could dramatically reduce results when there is a lot of overlap between
clusters.
"""
class Config:
"""Configuration for thi... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/embeddings_redundant_filter.html |
e3148d32a137-0 | Source code for langchain.document_transformers.long_context_reorder
"""Reorder documents"""
from typing import Any, List, Sequence
from pydantic import BaseModel
from langchain.schema import BaseDocumentTransformer, Document
def _litm_reordering(documents: List[Document]) -> List[Document]:
"""Los in the middle re... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/long_context_reorder.html |
d2815dbbd993-0 | Source code for langchain.document_transformers.doctran_text_qa
from typing import Any, Optional, Sequence
from langchain.schema import BaseDocumentTransformer, Document
from langchain.utils import get_from_env
[docs]class DoctranQATransformer(BaseDocumentTransformer):
"""Extract QA from text documents using doctra... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/doctran_text_qa.html |
d2815dbbd993-1 | from doctran import Doctran
doctran = Doctran(
openai_api_key=self.openai_api_key, openai_model=self.openai_api_model
)
except ImportError:
raise ImportError(
"Install doctran to use this parser. (pip install doctran)"
)
for... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers/doctran_text_qa.html |
5689a72b0123-0 | Source code for langchain.callbacks.arthur_callback
"""ArthurAI's Callback Handler."""
from __future__ import annotations
import os
import uuid
from collections import defaultdict
from datetime import datetime
from time import time
from typing import TYPE_CHECKING, Any, DefaultDict, Dict, List, Optional, Union
import n... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arthur_callback.html |
5689a72b0123-1 | """
[docs] def __init__(
self,
arthur_model: ArthurModel,
) -> None:
"""Initialize callback handler."""
super().__init__()
arthurai = _lazy_load_arthur()
Stage = arthurai.common.constants.Stage
ValueType = arthurai.common.constants.ValueType
self.ar... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arthur_callback.html |
5689a72b0123-2 | arthur_url: Optional[str] = "https://app.arthur.ai",
arthur_login: Optional[str] = None,
arthur_password: Optional[str] = None,
) -> ArthurCallbackHandler:
"""Initialize callback handler from Arthur credentials.
Args:
model_id (str): The ID of the arthur model to log to.
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arthur_callback.html |
5689a72b0123-3 | )
# get model from Arthur by the provided model ID
try:
arthur_model = arthur.get_model(model_id)
except ResponseClientError:
raise ValueError(
f"Was unable to retrieve model with id {model_id} from Arthur."
" Make sure the ID corresponds t... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arthur_callback.html |
5689a72b0123-4 | " Restart and try running the LLM again"
) from e
# mark the duration time between on_llm_start() and on_llm_end()
time_from_start_to_end = time() - run_map_data["start_time"]
# create inferences to log to Arthur
inferences = []
for i, generations in enumerate(respons... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arthur_callback.html |
5689a72b0123-5 | # add token usage counts to the inference if the
# ArthurModel was registered to monitor token usage
if (
isinstance(response.llm_output, dict)
and TOKEN_USAGE in response.llm_output
):
token_usage = response.llm... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arthur_callback.html |
5689a72b0123-6 | """On new token, pass."""
[docs] def on_chain_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Do nothing when LLM chain outputs an error."""
[docs] def on_tool_start(
self,
serialized: Dict[str, Any],
input_str: str,
**kwargs... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arthur_callback.html |
18dfd1e6cb39-0 | Source code for langchain.callbacks.streaming_aiter_final_only
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.callbacks.streaming_aiter import AsyncIteratorCallbackHandler
from langchain.schema import LLMResult
DEFAULT_ANSWER_PREFIX_TOKENS = ["Final", "Answer", ":"]
[docs... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streaming_aiter_final_only.html |
18dfd1e6cb39-1 | """
super().__init__()
if answer_prefix_tokens is None:
self.answer_prefix_tokens = DEFAULT_ANSWER_PREFIX_TOKENS
else:
self.answer_prefix_tokens = answer_prefix_tokens
if strip_tokens:
self.answer_prefix_tokens_stripped = [
token.strip(... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streaming_aiter_final_only.html |
18dfd1e6cb39-2 | # If yes, then put tokens from now on
if self.answer_reached:
self.queue.put_nowait(token) | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streaming_aiter_final_only.html |
a1713cd8a3bd-0 | Source code for langchain.callbacks.context_callback
"""Callback handler for Context AI"""
import os
from typing import Any, Dict, List
from uuid import UUID
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import (
BaseMessage,
LLMResult,
)
[docs]def import_context() -> Any:
"... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/context_callback.html |
a1713cd8a3bd-1 | ... )
>>> chat = ChatOpenAI(
... temperature=0,
... headers={"user_id": "123"},
... callbacks=[context_callback],
... openai_api_key="API_KEY_HERE",
... )
>>> messages = [
... SystemMessage(content="You translate English to French."),
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/context_callback.html |
a1713cd8a3bd-2 | (
self.context,
self.credential,
self.conversation_model,
self.message_model,
self.message_role_model,
self.rating_model,
) = import_context()
token = token or os.environ.get("CONTEXT_TOKEN") or ""
self.client = self.context... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/context_callback.html |
a1713cd8a3bd-3 | """Run when LLM ends."""
if len(response.generations) == 0 or len(response.generations[0]) == 0:
return
if not self.chain_run_id:
generation = response.generations[0][0]
self.messages.append(
self.message_model(
message=generation.t... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/context_callback.html |
7f2162d15652-0 | Source code for langchain.callbacks.streaming_stdout_final_only
"""Callback Handler streams to stdout on new llm token."""
import sys
from typing import Any, Dict, List, Optional
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
DEFAULT_ANSWER_PREFIX_TOKENS = ["Final", "Answer", ":"]
[docs... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streaming_stdout_final_only.html |
7f2162d15652-1 | reached)
stream_prefix: Should answer prefix itself also be streamed?
"""
super().__init__()
if answer_prefix_tokens is None:
self.answer_prefix_tokens = DEFAULT_ANSWER_PREFIX_TOKENS
else:
self.answer_prefix_tokens = answer_prefix_tokens
if str... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streaming_stdout_final_only.html |
86cf9e6e837e-0 | Source code for langchain.callbacks.sagemaker_callback
import json
import os
import shutil
import tempfile
from copy import deepcopy
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.utils import (
flatten_dict,
)
from langchain.sch... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/sagemaker_callback.html |
86cf9e6e837e-1 | # Create a temporary directory
self.temp_dir = tempfile.mkdtemp()
def _reset(self) -> None:
for k, v in self.metrics.items():
self.metrics[k] = 0
[docs] def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Run when LLM... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/sagemaker_callback.html |
86cf9e6e837e-2 | [docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when LLM ends running."""
self.metrics["step"] += 1
self.metrics["llm_ends"] += 1
self.metrics["ends"] += 1
llm_ends = self.metrics["llm_ends"]
resp: Dict[str, Any] = {}
resp.update... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/sagemaker_callback.html |
86cf9e6e837e-3 | resp.update(flatten_dict(serialized))
resp.update(self.metrics)
chain_input = ",".join([f"{k}={v}" for k, v in inputs.items()])
input_resp = deepcopy(resp)
input_resp["inputs"] = chain_input
self.jsonf(input_resp, self.temp_dir, f"chain_start_{chain_starts}")
[docs] def on_cha... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/sagemaker_callback.html |
86cf9e6e837e-4 | resp: Dict[str, Any] = {}
resp.update({"action": "on_tool_start", "input_str": input_str})
resp.update(flatten_dict(serialized))
resp.update(self.metrics)
self.jsonf(resp, self.temp_dir, f"tool_start_{tool_starts}")
[docs] def on_tool_end(self, output: str, **kwargs: Any) -> None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/sagemaker_callback.html |
86cf9e6e837e-5 | """Run when agent ends running."""
self.metrics["step"] += 1
self.metrics["agent_ends"] += 1
self.metrics["ends"] += 1
agent_ends = self.metrics["agent_ends"]
resp: Dict[str, Any] = {}
resp.update(
{
"action": "on_agent_finish",
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/sagemaker_callback.html |
86cf9e6e837e-6 | save_json(data, file_path)
self.run.log_file(file_path, name=filename, is_output=is_output)
[docs] def flush_tracker(self) -> None:
"""Reset the steps and delete the temporary local directory."""
self._reset()
shutil.rmtree(self.temp_dir) | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/sagemaker_callback.html |
1bc9f68bd948-0 | Source code for langchain.callbacks.base
"""Base callback handler that can be used to handle callbacks in langchain."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union
from uuid import UUID
if TYPE_CHECKING:
from langchain.schema.agent import AgentActi... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/base.html |
1bc9f68bd948-1 | response: LLMResult,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
"""Run when LLM ends running."""
[docs] def on_llm_error(
self,
error: Union[Exception, KeyboardInterrupt],
*,
run_id: UUID,
parent_... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/base.html |
1bc9f68bd948-2 | **kwargs: Any,
) -> Any:
"""Run on agent end."""
[docs]class ToolManagerMixin:
"""Mixin for tool callbacks."""
[docs] def on_tool_end(
self,
output: str,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
"""Run ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/base.html |
1bc9f68bd948-3 | ) -> Any:
"""Run when a chat model starts running."""
raise NotImplementedError(
f"{self.__class__.__name__} does not implement `on_chat_model_start`"
)
[docs] def on_retriever_start(
self,
serialized: Dict[str, Any],
query: str,
*,
run_id: ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/base.html |
1bc9f68bd948-4 | self,
text: str,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
"""Run on arbitrary text."""
[docs]class BaseCallbackHandler(
LLMManagerMixin,
ChainManagerMixin,
ToolManagerMixin,
RetrieverManagerMixin,
CallbackMana... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/base.html |
1bc9f68bd948-5 | tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> None:
"""Run when LLM starts running."""
[docs] async def on_chat_model_start(
self,
serialized: Dict[str, Any],
messages: List[List[BaseMessage]],
*,
r... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/base.html |
1bc9f68bd948-6 | error: Union[Exception, KeyboardInterrupt],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
tags: Optional[List[str]] = None,
**kwargs: Any,
) -> None:
"""Run when LLM errors."""
[docs] async def on_chain_start(
self,
serialized: Dict[str, An... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/base.html |
1bc9f68bd948-7 | tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> None:
"""Run when tool starts running."""
[docs] async def on_tool_end(
self,
output: str,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/base.html |
1bc9f68bd948-8 | finish: AgentFinish,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
tags: Optional[List[str]] = None,
**kwargs: Any,
) -> None:
"""Run on agent end."""
[docs] async def on_retriever_start(
self,
serialized: Dict[str, Any],
query: str... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/base.html |
1bc9f68bd948-9 | inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,
parent_run_id: Optional[UUID] = None,
*,
tags: Optional[List[str]] = None,
inheritable_tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
inheritable_metadata: Optional[Dict[str, A... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/base.html |
1bc9f68bd948-10 | """Set handlers as the only handlers on the callback manager."""
self.handlers = []
self.inheritable_handlers = []
for handler in handlers:
self.add_handler(handler, inherit=inherit)
[docs] def set_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/base.html |
efd28c4c050e-0 | Source code for langchain.callbacks.arize_callback
from datetime import datetime
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.utils import import_pandas
from langchain.schema import AgentAction, AgentFinish, LLMResult
[docs]class A... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arize_callback.html |
efd28c4c050e-1 | self.arize_client = Client(space_key=SPACE_KEY, api_key=API_KEY)
if SPACE_KEY == "SPACE_KEY" or API_KEY == "API_KEY":
raise ValueError("❌ CHANGE SPACE AND API KEYS")
else:
print("✅ Arize client setup done! Now you can start using Arize!")
[docs] def on_llm_start(
self,... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arize_callback.html |
efd28c4c050e-2 | for generations in response.generations:
for generation in generations:
prompt = self.prompt_records[self.step]
self.step = self.step + 1
prompt_embedding = pd.Series(
self.generator.generate_embeddings(
text_col=pd.... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arize_callback.html |
efd28c4c050e-3 | "completion_token",
"total_token",
],
prompt_column_names=prompt_columns,
response_column_names=response_columns,
)
response_from_arize = self.arize_client.log(
dataframe=df,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arize_callback.html |
efd28c4c050e-4 | pass
[docs] def on_tool_end(
self,
output: str,
observation_prefix: Optional[str] = None,
llm_prefix: Optional[str] = None,
**kwargs: Any,
) -> None:
pass
[docs] def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/arize_callback.html |
78ff214d00ae-0 | Source code for langchain.callbacks.mlflow_callback
import os
import random
import string
import tempfile
import traceback
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.utils import... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-1 | "flesch_reading_ease": textstat.flesch_reading_ease(text),
"flesch_kincaid_grade": textstat.flesch_kincaid_grade(text),
"smog_index": textstat.smog_index(text),
"coleman_liau_index": textstat.coleman_liau_index(text),
"automated_readability_index": textstat.automated_readability_index(te... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-2 | doc, style="ent", jupyter=False, page=True
)
text_visualizations = {
"dependency_tree": dep_out,
"entities": ent_out,
}
resp.update(text_visualizations)
return resp
[docs]def construct_html_from_prompt_and_generation(prompt: str, generation: str) -> Any:
"... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-3 | self.mlf_expid = self.mlflow.tracking.fluent._get_experiment_id()
self.mlf_exp = self.mlflow.get_experiment(self.mlf_expid)
else:
tracking_uri = get_from_dict_or_env(
kwargs, "tracking_uri", "MLFLOW_TRACKING_URI", ""
)
self.mlflow.set_tracking_uri(... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-4 | ):
self.mlflow.end_run()
[docs] def metric(self, key: str, value: float) -> None:
"""To log metric to mlflow server."""
with self.mlflow.start_run(
run_id=self.run.info.run_id, experiment_id=self.mlf_expid
):
self.mlflow.log_metric(key, value)
[docs] def... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-5 | ):
self.mlflow.log_text(html, f"{filename}.html")
[docs] def text(self, text: str, filename: str) -> None:
"""To log the input text as text file artifact."""
with self.mlflow.start_run(
run_id=self.run.info.run_id, experiment_id=self.mlf_expid
):
self.mlflo... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-6 | """
[docs] def __init__(
self,
name: Optional[str] = "langchainrun-%",
experiment: Optional[str] = "langchain",
tags: Optional[Dict] = {},
tracking_uri: Optional[str] = None,
) -> None:
"""Initialize callback handler."""
import_pandas()
import_texts... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-7 | "on_llm_end_records": [],
"on_chain_start_records": [],
"on_chain_end_records": [],
"on_tool_start_records": [],
"on_tool_end_records": [],
"on_text_records": [],
"on_agent_finish_records": [],
"on_agent_action_records": [],
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-8 | """Run when LLM generates a new token."""
self.metrics["step"] += 1
self.metrics["llm_streams"] += 1
llm_streams = self.metrics["llm_streams"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_llm_new_token", "token": token})
resp.update(self.metrics)
self.mlfl... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-9 | self.mlflg.metrics(
complexity_metrics,
step=self.metrics["step"],
)
self.records["on_llm_end_records"].append(generation_resp)
self.records["action_records"].append(generation_resp)
self.mlflg.jsonf(resp, f"llm_end_... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-10 | input_resp = deepcopy(resp)
input_resp["inputs"] = chain_input
self.records["on_chain_start_records"].append(input_resp)
self.records["action_records"].append(input_resp)
self.mlflg.jsonf(input_resp, f"chain_start_{chain_starts}")
[docs] def on_chain_end(self, outputs: Dict[str, Any],... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-11 | self.metrics["starts"] += 1
tool_starts = self.metrics["tool_starts"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_tool_start", "input_str": input_str})
resp.update(flatten_dict(serialized))
resp.update(self.metrics)
self.mlflg.metrics(self.metrics, step=self.metr... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-12 | """
self.metrics["step"] += 1
self.metrics["text_ctr"] += 1
text_ctr = self.metrics["text_ctr"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_text", "text": text})
resp.update(self.metrics)
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-13 | tool_starts = self.metrics["tool_starts"]
resp: Dict[str, Any] = {}
resp.update(
{
"action": "on_agent_action",
"tool": action.tool,
"tool_input": action.tool_input,
"log": action.log,
}
)
resp.update... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-14 | .rename({"step": "prompt_step"}, axis=1)
)
complexity_metrics_columns = []
visualizations_columns = []
complexity_metrics_columns = [
"flesch_reading_ease",
"flesch_kincaid_grade",
"smog_index",
"coleman_liau_index",
"automated_... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
78ff214d00ae-15 | ),
axis=1,
)
return session_analysis_df
[docs] def flush_tracker(self, langchain_asset: Any = None, finish: bool = False) -> None:
pd = import_pandas()
self.mlflg.table("action_records", pd.DataFrame(self.records["action_records"]))
session_analysis_df = self._crea... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
a0c6a7c808d4-0 | Source code for langchain.callbacks.argilla_callback
import os
import warnings
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction, AgentFinish, LLMResult
[docs]class ArgillaCallbackHandler(BaseCallbackHandler):
"""Cal... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
a0c6a7c808d4-1 | >>> argilla_callback = ArgillaCallbackHandler(
... dataset_name="my-dataset",
... workspace_name="my-workspace",
... api_url="http://localhost:6900",
... api_key="argilla.apikey",
... )
>>> llm = OpenAI(
... temperature=0,
... callb... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
a0c6a7c808d4-2 | `FeedbackDataset` lives in. Defaults to `None`, which means that either
`ARGILLA_API_URL` environment variable or the default
http://localhost:6900 will be used.
api_key: API Key to connect to the Argilla Server. Defaults to `None`, which
means that either `AR... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
a0c6a7c808d4-3 | " set, it will default to `argilla.apikey`."
),
)
# Connect to Argilla with the provided credentials, if applicable
try:
rg.init(
api_key=api_key,
api_url=api_url,
)
except Exception as e:
raise Conne... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
a0c6a7c808d4-4 | " If the problem persists please report it to"
" https://github.com/argilla-io/argilla/issues with the label"
" `langchain`."
) from e
supported_fields = ["prompt", "response"]
if supported_fields != [field.name for field in self.dataset.fields]:
r... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
a0c6a7c808d4-5 | [docs] def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Do nothing when a new token is generated."""
pass
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Log records to Argilla when an LLM ends."""
# Do nothing if there's a parent_run_id... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
a0c6a7c808d4-6 | we don't log the same input prompt twice, once when the LLM starts and once
when the chain starts.
"""
if "input" in inputs:
self.prompts.update(
{
str(kwargs["parent_run_id"] or kwargs["run_id"]): (
inputs["input"]
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
a0c6a7c808d4-7 | self.dataset.add_records(
records=[
{
"fields": {
"prompt": " ".join(prompts), # type: ignore
"response": chain_output_val.strip(),
},
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
a0c6a7c808d4-8 | ) -> None:
"""Do nothing when tool outputs an error."""
pass
[docs] def on_text(self, text: str, **kwargs: Any) -> None:
"""Do nothing"""
pass
[docs] def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
"""Do nothing"""
pass | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
e1a4bfce8eda-0 | Source code for langchain.callbacks.wandb_callback
import json
import tempfile
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.utils import (
BaseMetadataCallbackHandler... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
e1a4bfce8eda-1 | Parameters:
text (str): The text to analyze.
complexity_metrics (bool): Whether to compute complexity metrics.
visualize (bool): Whether to visualize the text.
nlp (spacy.lang): The spacy language model to use for visualization.
output_dir (str): The directory to save the visuali... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
e1a4bfce8eda-2 | "gutierrez_polini": textstat.gutierrez_polini(text),
"crawford": textstat.crawford(text),
"gulpease_index": textstat.gulpease_index(text),
"osman": textstat.osman(text),
}
resp.update(text_complexity_metrics)
if visualize and nlp and output_dir is not None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
e1a4bfce8eda-3 | formatted_prompt = prompt.replace("\n", "<br>")
formatted_generation = generation.replace("\n", "<br>")
return wandb.Html(
f"""
<p style="color:black;">{formatted_prompt}:</p>
<blockquote>
<p style="color:green;">
{formatted_generation}
</p>
</blockquote>
""",
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
e1a4bfce8eda-4 | group: Optional[str] = None,
name: Optional[str] = None,
notes: Optional[str] = None,
visualize: bool = False,
complexity_metrics: bool = False,
stream_logs: bool = False,
) -> None:
"""Initialize callback handler."""
wandb = import_wandb()
import_pand... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
e1a4bfce8eda-5 | def _init_resp(self) -> Dict:
return {k: None for k in self.callback_columns}
[docs] def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Run when LLM starts."""
self.step += 1
self.llm_starts += 1
self.starts += 1
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
e1a4bfce8eda-6 | self.ends += 1
resp = self._init_resp()
resp.update({"action": "on_llm_end"})
resp.update(flatten_dict(response.llm_output or {}))
resp.update(self.get_custom_callback_meta())
for generations in response.generations:
for generation in generations:
gene... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
e1a4bfce8eda-7 | self.on_chain_start_records.append(input_resp)
self.action_records.append(input_resp)
if self.stream_logs:
self.run.log(input_resp)
elif isinstance(chain_input, list):
for inp in chain_input:
input_resp = deepcopy(resp)
input_re... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
e1a4bfce8eda-8 | resp.update({"action": "on_tool_start", "input_str": input_str})
resp.update(flatten_dict(serialized))
resp.update(self.get_custom_callback_meta())
self.on_tool_start_records.append(resp)
self.action_records.append(resp)
if self.stream_logs:
self.run.log(resp)
[docs] ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
e1a4bfce8eda-9 | """Run when agent ends running."""
self.step += 1
self.agent_ends += 1
self.ends += 1
resp = self._init_resp()
resp.update(
{
"action": "on_agent_finish",
"output": finish.return_values["output"],
"log": finish.log,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
e1a4bfce8eda-10 | .dropna(axis=1)
.rename({"step": "prompt_step"}, axis=1)
)
complexity_metrics_columns = []
visualizations_columns = []
if self.complexity_metrics:
complexity_metrics_columns = [
"flesch_reading_ease",
"flesch_kincaid_grade",
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
e1a4bfce8eda-11 | row["prompts"], row["output"]
),
axis=1,
)
return session_analysis_df
[docs] def flush_tracker(
self,
langchain_asset: Any = None,
reset: bool = True,
finish: bool = False,
job_type: Optional[str] = None,
project: Optional[str] =... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
e1a4bfce8eda-12 | }
)
if langchain_asset:
langchain_asset_path = Path(self.temp_dir.name, "model.json")
model_artifact = wandb.Artifact(name="model", type="model")
model_artifact.add(action_records_table, name="action_records")
model_artifact.add(session_analysis_table, nam... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/wandb_callback.html |
e13a71a3d9d2-0 | Source code for langchain.callbacks.aim_callback
from copy import deepcopy
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction, AgentFinish, LLMResult
[docs]def import_aim() -> Any:
"""Import the aim python package and... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
e13a71a3d9d2-1 | llm_ends (int): The number of times the llm end method has been called.
llm_streams (int): The number of times the text method has been called.
tool_starts (int): The number of times the tool start method has been called.
tool_ends (int): The number of times the tool end method has been called.
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
e13a71a3d9d2-2 | """Whether to ignore agent callbacks."""
return self.ignore_agent_
@property
def ignore_retriever(self) -> bool:
"""Whether to ignore retriever callbacks."""
return self.ignore_retriever_
[docs] def get_custom_callback_meta(self) -> Dict[str, Any]:
return {
"step":... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
e13a71a3d9d2-3 | """Callback Handler that logs to Aim.
Parameters:
repo (:obj:`str`, optional): Aim repository path or Repo object to which
Run object is bound. If skipped, default Repo is used.
experiment_name (:obj:`str`, optional): Sets Run's `experiment` property.
'default' if not specifi... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
e13a71a3d9d2-4 | self._run_hash = self._run.hash
self.action_records: list = []
[docs] def setup(self, **kwargs: Any) -> None:
aim = import_aim()
if not self._run:
if self._run_hash:
self._run = aim.Run(
self._run_hash,
repo=self.repo,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
e13a71a3d9d2-5 | self.llm_ends += 1
self.ends += 1
resp = {"action": "on_llm_end"}
resp.update(self.get_custom_callback_meta())
response_res = deepcopy(response)
generated = [
aim.Text(generation.text)
for generations in response_res.generations
for generation ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
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