id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 54 121 |
|---|---|---|
6cc2d514820a-10 | """Run when chain ends running."""
self.metrics["step"] += 1
self.metrics["chain_ends"] += 1
self.metrics["ends"] += 1
chain_ends = self.metrics["chain_ends"]
resp: Dict[str, Any] = {}
chain_output = ",".join([f"{k}={v}" for k, v in outputs.items()])
resp.update({... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
6cc2d514820a-11 | self.records["on_tool_start_records"].append(resp)
self.records["action_records"].append(resp)
self.mlflg.jsonf(resp, f"tool_start_{tool_starts}")
[docs] def on_tool_end(self, output: str, **kwargs: Any) -> None:
"""Run when tool ends running."""
self.metrics["step"] += 1
self... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
6cc2d514820a-12 | self.records["on_text_records"].append(resp)
self.records["action_records"].append(resp)
self.mlflg.jsonf(resp, f"on_text_{text_ctr}")
[docs] def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
"""Run when agent ends running."""
self.metrics["step"] += 1
sel... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
6cc2d514820a-13 | self.mlflg.metrics(self.metrics, step=self.metrics["step"])
self.records["on_agent_action_records"].append(resp)
self.records["action_records"].append(resp)
self.mlflg.jsonf(resp, f"agent_action_{tool_starts}")
def _create_session_analysis_df(self) -> Any:
"""Create a dataframe with ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
6cc2d514820a-14 | [
"step",
"text",
"token_usage_total_tokens",
"token_usage_prompt_tokens",
"token_usage_completion_tokens",
]
+ complexity_metrics_columns
+ visualizations_columns
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
6cc2d514820a-15 | try:
langchain_asset.save(langchain_asset_path)
self.mlflg.artifact(langchain_asset_path)
except ValueError:
try:
langchain_asset.save_agent(langchain_asset_path)
self.mlflg.artifact(langchain_ass... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/mlflow_callback.html |
44b25e5f9279-0 | 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 |
44b25e5f9279-1 | >>> 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 |
44b25e5f9279-2 | `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 |
44b25e5f9279-3 | " 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 |
44b25e5f9279-4 | " 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 |
44b25e5f9279-5 | [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 |
44b25e5f9279-6 | 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 |
44b25e5f9279-7 | 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 |
44b25e5f9279-8 | ) -> 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 |
0e3d28e4f822-0 | Source code for langchain.callbacks.comet_ml_callback
import tempfile
from copy import deepcopy
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Sequence, Union
import langchain
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.utils import (
BaseMetad... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
0e3d28e4f822-1 | "automated_readability_index": textstat.automated_readability_index(text),
"dale_chall_readability_score": textstat.dale_chall_readability_score(text),
"difficult_words": textstat.difficult_words(text),
"linsear_write_formula": textstat.linsear_write_formula(text),
"gunning_fog": textsta... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
0e3d28e4f822-2 | stream_logs (bool): Whether to stream callback actions to Comet
This handler will utilize the associated callback method and formats
the input of each callback function with metadata regarding the state of LLM run,
and adds the response to the list of records for both the {method}_records and
action. It... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
0e3d28e4f822-3 | "based on updates to `langchain`. Please report any issues to "
"https://github.com/comet-ml/issue-tracking/issues with the tag "
"`langchain`."
)
self.comet_ml.LOGGER.warning(warning)
self.callback_columns: list = []
self.action_records: list = []
self.co... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
0e3d28e4f822-4 | self.llm_streams += 1
resp = self._init_resp()
resp.update({"action": "on_llm_new_token", "token": token})
resp.update(self.get_custom_callback_meta())
self.action_records.append(resp)
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when LLM end... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
0e3d28e4f822-5 | [docs] def on_llm_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Run when LLM errors."""
self.step += 1
self.errors += 1
[docs] def on_chain_start(
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
) -> Non... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
0e3d28e4f822-6 | if isinstance(chain_output_val, str):
output_resp = deepcopy(resp)
if self.stream_logs:
self._log_stream(chain_output_val, resp, self.step)
output_resp.update({chain_output_key: chain_output_val})
self.action_records.append(output_resp)... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
0e3d28e4f822-7 | resp.update(self.get_custom_callback_meta())
if self.stream_logs:
self._log_stream(output, resp, self.step)
resp.update({"output": output})
self.action_records.append(resp)
[docs] def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> N... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
0e3d28e4f822-8 | """Run on agent action."""
self.step += 1
self.tool_starts += 1
self.starts += 1
tool = action.tool
tool_input = str(action.tool_input)
log = action.log
resp = self._init_resp()
resp.update({"action": "on_agent_action", "log": log, "tool": tool})
r... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
0e3d28e4f822-9 | return resp
[docs] def flush_tracker(
self,
langchain_asset: Any = None,
task_type: Optional[str] = "inference",
workspace: Optional[str] = None,
project_name: Optional[str] = "comet-langchain-demo",
tags: Optional[Sequence] = None,
name: Optional[str] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
0e3d28e4f822-10 | self.experiment.log_text(prompt, metadata=metadata, step=step)
def _log_model(self, langchain_asset: Any) -> None:
model_parameters = self._get_llm_parameters(langchain_asset)
self.experiment.log_parameters(model_parameters, prefix="model")
langchain_asset_path = Path(self.temp_dir.name, "mo... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
0e3d28e4f822-11 | # Log the langchain low-level records as a JSON file directly
self.experiment.log_asset_data(
self.action_records, "langchain-action_records.json", metadata=metadata
)
except Exception:
self.comet_ml.LOGGER.warning(
"Failed to log session data ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
0e3d28e4f822-12 | )
self.experiment.log_asset_data(
html,
name=f"langchain-viz-{visualization}-{idx}.html",
metadata={"prompt": prompt},
step=idx,
)
except Exception as e:
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
0e3d28e4f822-13 | self.reset_callback_meta()
self.temp_dir = tempfile.TemporaryDirectory()
def _create_session_analysis_dataframe(self, langchain_asset: Any = None) -> dict:
pd = import_pandas()
llm_parameters = self._get_llm_parameters(langchain_asset)
num_generations_per_prompt = llm_parameters.get(... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/comet_ml_callback.html |
49dfeee28dca-0 | Source code for langchain.callbacks.streamlit
from __future__ import annotations
from typing import TYPE_CHECKING, Optional
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.streamlit.streamlit_callback_handler import (
LLMThoughtLabeler as LLMThoughtLabeler,
)
from langchain.callbac... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit.html |
49dfeee28dca-1 | If True, LLM thought expanders will be collapsed when completed.
Defaults to True.
thought_labeler
An optional custom LLMThoughtLabeler instance. If unspecified, the handler
will use the default thought labeling logic. Defaults to None.
Returns
-------
A new StreamlitCallbackHand... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit.html |
081c917dc5f2-0 | Source code for langchain.callbacks.streamlit.streamlit_callback_handler
"""Callback Handler that prints to streamlit."""
from __future__ import annotations
from enum import Enum
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
081c917dc5f2-1 | """Return the markdown label for a new LLMThought that doesn't have
an associated tool yet.
"""
return f"{THINKING_EMOJI} **Thinking...**"
[docs] def get_tool_label(self, tool: ToolRecord, is_complete: bool) -> str:
"""Return the label for an LLMThought that has an associated
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
081c917dc5f2-2 | """
return f"{CHECKMARK_EMOJI} **Complete!**"
class LLMThought:
def __init__(
self,
parent_container: DeltaGenerator,
labeler: LLMThoughtLabeler,
expanded: bool,
collapse_on_complete: bool,
):
self._container = MutableExpander(
parent_container... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
081c917dc5f2-3 | self._llm_token_writer_idx = self._container.markdown(
self._llm_token_stream, index=self._llm_token_writer_idx
)
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
# `response` is the concatenation of all the tokens received by the LLM.
# If we're receiving stream... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
081c917dc5f2-4 | def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
self._container.markdown("**Tool encountered an error...**")
self._container.exception(error)
def on_agent_action(
self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
081c917dc5f2-5 | *,
max_thought_containers: int = 4,
expand_new_thoughts: bool = True,
collapse_completed_thoughts: bool = True,
thought_labeler: Optional[LLMThoughtLabeler] = None,
):
"""Create a StreamlitCallbackHandler instance.
Parameters
----------
parent_containe... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
081c917dc5f2-6 | self._collapse_completed_thoughts = collapse_completed_thoughts
self._thought_labeler = thought_labeler or LLMThoughtLabeler()
def _require_current_thought(self) -> LLMThought:
"""Return our current LLMThought. Raise an error if we have no current
thought.
"""
if self._curren... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
081c917dc5f2-7 | self._current_thought = None
def _prune_old_thought_containers(self) -> None:
"""If we have too many thoughts onscreen, move older thoughts to the
'history container.'
"""
while (
self._num_thought_containers > self._max_thought_containers
and len(self._comple... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
081c917dc5f2-8 | )
self._current_thought.on_llm_start(serialized, prompts)
# We don't prune_old_thought_containers here, because our container won't
# be visible until it has a child.
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
self._require_current_thought().on_llm_new_token(token... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
081c917dc5f2-9 | )
self._complete_current_thought()
def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
self._require_current_thought().on_tool_error(error, **kwargs)
self._prune_old_thought_containers()
def on_text(
self,
text: str,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
50a8cc43f1f5-0 | Source code for langchain.retrievers.zep
from __future__ import annotations
from typing import TYPE_CHECKING, Dict, List, Optional
from langchain.schema import BaseRetriever, Document
if TYPE_CHECKING:
from zep_python import MemorySearchResult
[docs]class ZepRetriever(BaseRetriever):
"""A Retriever implementati... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/zep.html |
50a8cc43f1f5-1 | )
for r in results
if r.message
]
[docs] def get_relevant_documents(
self, query: str, metadata: Optional[Dict] = None
) -> List[Document]:
from zep_python import MemorySearchPayload
payload: MemorySearchPayload = MemorySearchPayload(
text=query... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/zep.html |
d37c2f8845b4-0 | Source code for langchain.retrievers.chatgpt_plugin_retriever
from __future__ import annotations
from typing import List, Optional
import aiohttp
import requests
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs]class ChatGPTPluginRetriever(BaseRetriever, BaseModel):
url: str... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/chatgpt_plugin_retriever.html |
d37c2f8845b4-1 | ) as response:
res = await response.json()
results = res["results"][0]["results"]
docs = []
for d in results:
content = d.pop("text")
metadata = d.pop("metadata", d)
if metadata.get("source_id"):
metadata["source"] = metadata.po... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/chatgpt_plugin_retriever.html |
798a81eca712-0 | Source code for langchain.retrievers.databerry
from typing import List, Optional
import aiohttp
import requests
from langchain.schema import BaseRetriever, Document
[docs]class DataberryRetriever(BaseRetriever):
"""Retriever that uses the Databerry API."""
datastore_url: str
top_k: Optional[int]
api_key... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/databerry.html |
798a81eca712-1 | self.datastore_url,
json={
"query": query,
**({"topK": self.top_k} if self.top_k is not None else {}),
},
headers={
"Content-Type": "application/json",
**(
{"Authorizat... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/databerry.html |
69576b9724ba-0 | Source code for langchain.retrievers.time_weighted_retriever
"""Retriever that combines embedding similarity with recency in retrieving values."""
import datetime
from copy import deepcopy
from typing import Any, Dict, List, Optional, Tuple
from pydantic import BaseModel, Field
from langchain.schema import BaseRetrieve... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
69576b9724ba-1 | """
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
def _get_combined_score(
self,
document: Document,
vector_relevance: Optional[float],
current_time: datetime.datetime,
) -> float:
"""Return the combined sco... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
69576b9724ba-2 | for doc in self.memory_stream[-self.k :]
}
# If a doc is considered salient, update the salience score
docs_and_scores.update(self.get_salient_docs(query))
rescored_docs = [
(doc, self._get_combined_score(doc, relevance, current_time))
for doc, relevance in docs_a... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
69576b9724ba-3 | doc.metadata["buffer_idx"] = len(self.memory_stream) + i
self.memory_stream.extend(dup_docs)
return self.vectorstore.add_documents(dup_docs, **kwargs)
[docs] async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Add documents to vectorstore.""... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
488c4e327251-0 | Source code for langchain.retrievers.tfidf
"""TF-IDF Retriever.
Largely based on
https://github.com/asvskartheek/Text-Retrieval/blob/master/TF-IDF%20Search%20Engine%20(SKLEARN).ipynb"""
from __future__ import annotations
from typing import Any, Dict, Iterable, List, Optional
from pydantic import BaseModel
from langchai... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/tfidf.html |
488c4e327251-1 | return cls(vectorizer=vectorizer, docs=docs, tfidf_array=tfidf_array, **kwargs)
[docs] @classmethod
def from_documents(
cls,
documents: Iterable[Document],
*,
tfidf_params: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> TFIDFRetriever:
texts, metadatas = ... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/tfidf.html |
129763b8fd6d-0 | Source code for langchain.retrievers.milvus
"""Milvus Retriever"""
import warnings
from typing import Any, Dict, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever, Document
from langchain.vectorstores.milvus import Milvus
# TODO: Update to MilvusClient + Hybrid S... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/milvus.html |
129763b8fd6d-1 | raise NotImplementedError
def MilvusRetreiver(*args: Any, **kwargs: Any) -> MilvusRetriever:
"""Deprecated MilvusRetreiver. Please use MilvusRetriever ('i' before 'e') instead.
Args:
*args:
**kwargs:
Returns:
MilvusRetriever
"""
warnings.warn(
"MilvusRetreiver will be... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/milvus.html |
bd13a981659d-0 | Source code for langchain.retrievers.arxiv
from typing import List
from langchain.schema import BaseRetriever, Document
from langchain.utilities.arxiv import ArxivAPIWrapper
[docs]class ArxivRetriever(BaseRetriever, ArxivAPIWrapper):
"""
It is effectively a wrapper for ArxivAPIWrapper.
It wraps load() to ge... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/arxiv.html |
cb932f461fbf-0 | Source code for langchain.retrievers.docarray
from enum import Enum
from typing import Any, Dict, List, Optional, Union
import numpy as np
from pydantic import BaseModel
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever, Document
from langchain.vectorstores.utils import maximal... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/docarray.html |
cb932f461fbf-1 | """Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] def get_relevant_documents(self, query: str) -> List[Document]:
"""Get documents relevant for a query.
Args:
query: string to find relevant documents for
Returns:
List of releva... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/docarray.html |
cb932f461fbf-2 | if self.filters:
query = (
self.index.build_query() # get empty query object
.find(
query=query_emb, search_field=search_field
) # add vector similarity search
.filter(**filter_args) # add filter search
.b... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/docarray.html |
cb932f461fbf-3 | else getattr(doc, self.search_field)
for doc in docs
],
k=self.top_k,
)
results = [self._docarray_to_langchain_doc(docs[idx]) for idx in mmr_selected]
return results
def _docarray_to_langchain_doc(self, doc: Union[Dict[str, Any], Any]) -> Document:
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/docarray.html |
8b809e712593-0 | Source code for langchain.retrievers.weaviate_hybrid_search
"""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 BaseR... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/weaviate_hybrid_search.html |
8b809e712593-1 | "properties": [{"name": self._text_key, "dataType": ["text"]}],
"vectorizer": "text2vec-openai",
}
if not self._client.schema.exists(self._index_name):
self._client.schema.create_class(class_obj)
[docs] class Config:
"""Configuration for this pydantic object."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/weaviate_hybrid_search.html |
8b809e712593-2 | 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"]["... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/weaviate_hybrid_search.html |
f97ea17bf25a-0 | Source code for langchain.retrievers.kendra
import re
from typing import Any, Dict, List, Literal, Optional
from pydantic import BaseModel, Extra
from langchain.docstore.document import Document
from langchain.schema import BaseRetriever
def clean_excerpt(excerpt: str) -> str:
if not excerpt:
return excerpt... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
f97ea17bf25a-1 | def get_attribute_value(self) -> str:
if not self.AdditionalAttributes:
return ""
if not self.AdditionalAttributes[0]:
return ""
else:
return self.AdditionalAttributes[0].get_value_text()
def get_excerpt(self) -> str:
if (
self.Addition... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
f97ea17bf25a-2 | Key: str
Value: DocumentAttributeValue
class RetrieveResultItem(BaseModel, extra=Extra.allow):
Content: Optional[str]
DocumentAttributes: Optional[List[DocumentAttribute]] = []
DocumentId: Optional[str]
DocumentTitle: Optional[str]
DocumentURI: Optional[str]
Id: Optional[str]
def get_exc... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
f97ea17bf25a-3 | or ~/.aws/config files, which has either access keys or role information
specified. If not specified, the default credential profile or, if on an
EC2 instance, credentials from IMDS will be used.
top_k: No of results to return
attribute_filter: Additional filtering of results bas... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
f97ea17bf25a-4 | "Please install it with `pip install boto3`."
)
except Exception as e:
raise ValueError(
"Could not load credentials to authenticate with AWS client. "
"Please check that credentials in the specified "
"profile name are valid."
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
f97ea17bf25a-5 | """Run search on Kendra index and get top k documents
Example:
.. code-block:: python
docs = retriever.get_relevant_documents('This is my query')
"""
docs = self._kendra_query(query, self.top_k, self.attribute_filter)
return docs
[docs] async def aget_relevant_docu... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
e06e5440ee3c-0 | Source code for langchain.retrievers.vespa_retriever
"""Wrapper for retrieving documents from Vespa."""
from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Sequence, Union
from langchain.schema import BaseRetriever, Document
if TYPE_CHECKING:
from ves... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/vespa_retriever.html |
e06e5440ee3c-1 | docs.append(Document(page_content=page_content, metadata=metadata))
return docs
[docs] def get_relevant_documents(self, query: str) -> List[Document]:
body = self._query_body.copy()
body["query"] = query
return self._query(body)
[docs] async def aget_relevant_documents(self, query:... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/vespa_retriever.html |
e06e5440ee3c-2 | document metadata. Defaults to empty tuple ().
sources (Sequence[str] or "*" or None): Sources to retrieve
from. Defaults to None.
_filter (Optional[str]): Document filter condition expressed in YQL.
Defaults to None.
yql (Optional[str]): Full YQL quer... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/vespa_retriever.html |
6b265ab5c2bf-0 | Source code for langchain.retrievers.knn
"""KNN Retriever.
Largely based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""
from __future__ import annotations
import concurrent.futures
from typing import Any, List, Optional
import numpy as np
from pydantic import BaseModel
from langchain.embedding... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/knn.html |
6b265ab5c2bf-1 | query_embeds = np.array(self.embeddings.embed_query(query))
# calc L2 norm
index_embeds = self.index / np.sqrt((self.index**2).sum(1, keepdims=True))
query_embeds = query_embeds / np.sqrt((query_embeds**2).sum())
similarities = index_embeds.dot(query_embeds)
sorted_ix = np.argsor... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/knn.html |
a1924ce4a9bf-0 | Source code for langchain.retrievers.llama_index
from typing import Any, Dict, List, cast
from pydantic import BaseModel, Field
from langchain.schema import BaseRetriever, Document
[docs]class LlamaIndexRetriever(BaseRetriever, BaseModel):
"""Question-answering with sources over an LlamaIndex data structure."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/llama_index.html |
a1924ce4a9bf-1 | graph: Any
query_configs: List[Dict] = Field(default_factory=list)
[docs] def get_relevant_documents(self, query: str) -> List[Document]:
"""Get documents relevant for a query."""
try:
from llama_index.composability.graph import (
QUERY_CONFIG_TYPE,
Com... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/llama_index.html |
7e7ae4388417-0 | Source code for langchain.retrievers.azure_cognitive_search
"""Retriever wrapper for Azure Cognitive Search."""
from __future__ import annotations
import json
from typing import Dict, List, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.schema import BaseRet... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/azure_cognitive_search.html |
7e7ae4388417-1 | )
values["api_key"] = get_from_dict_or_env(
values, "api_key", "AZURE_COGNITIVE_SEARCH_API_KEY"
)
return values
def _build_search_url(self, query: str) -> str:
base_url = f"https://{self.service_name}.search.windows.net/"
endpoint_path = f"indexes/{self.index_name... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/azure_cognitive_search.html |
7e7ae4388417-2 | search_results = self._search(query)
return [
Document(page_content=result.pop(self.content_key), metadata=result)
for result in search_results
]
[docs] async def aget_relevant_documents(self, query: str) -> List[Document]:
search_results = await self._asearch(query)
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/azure_cognitive_search.html |
53acd95d0c09-0 | Source code for langchain.retrievers.contextual_compression
"""Retriever that wraps a base retriever and filters the results."""
from typing import List
from pydantic import BaseModel, Extra
from langchain.retrievers.document_compressors.base import (
BaseDocumentCompressor,
)
from langchain.schema import BaseRetri... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/contextual_compression.html |
53acd95d0c09-1 | compressed_docs = await self.base_compressor.acompress_documents(
docs, query
)
return list(compressed_docs)
else:
return [] | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/contextual_compression.html |
557bcb2e522a-0 | Source code for langchain.retrievers.elastic_search_bm25
"""Wrapper around Elasticsearch vector database."""
from __future__ import annotations
import uuid
from typing import Any, Iterable, List
from langchain.docstore.document import Document
from langchain.schema import BaseRetriever
[docs]class ElasticSearchBM25Retr... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
557bcb2e522a-1 | self.index_name = index_name
[docs] @classmethod
def create(
cls, elasticsearch_url: str, index_name: str, k1: float = 2.0, b: float = 0.75
) -> ElasticSearchBM25Retriever:
from elasticsearch import Elasticsearch
# Create an Elasticsearch client instance
es = Elasticsearch(ela... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
557bcb2e522a-2 | raise ValueError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
requests = []
ids = []
for i, text in enumerate(texts):
_id = str(uuid.uuid4())
request = {
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
0cb27424848f-0 | Source code for langchain.retrievers.svm
"""SMV Retriever.
Largely based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""
from __future__ import annotations
import concurrent.futures
from typing import Any, List, Optional
import numpy as np
from pydantic import BaseModel
from langchain.embedding... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/svm.html |
0cb27424848f-1 | query_embeds = np.array(self.embeddings.embed_query(query))
x = np.concatenate([query_embeds[None, ...], self.index])
y = np.zeros(x.shape[0])
y[0] = 1
clf = svm.LinearSVC(
class_weight="balanced", verbose=False, max_iter=10000, tol=1e-6, C=0.1
)
clf.fit(x, y)... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/svm.html |
7c5c7d771277-0 | Source code for langchain.retrievers.pinecone_hybrid_search
"""Taken from: https://docs.pinecone.io/docs/hybrid-search"""
import hashlib
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRe... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/pinecone_hybrid_search.html |
7c5c7d771277-1 | for i in _iterator:
# find end of batch
i_end = min(i + batch_size, len(contexts))
# extract batch
context_batch = contexts[i:i_end]
batch_ids = ids[i:i_end]
metadata_batch = (
metadatas[i:i_end] if metadatas else [{} for _ in context_batch]
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/pinecone_hybrid_search.html |
7c5c7d771277-2 | arbitrary_types_allowed = True
[docs] def add_texts(
self,
texts: List[str],
ids: Optional[List[str]] = None,
metadatas: Optional[List[dict]] = None,
) -> None:
create_index(
texts,
self.index,
self.embeddings,
self.sparse_en... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/pinecone_hybrid_search.html |
7c5c7d771277-3 | top_k=self.top_k,
include_metadata=True,
)
final_result = []
for res in result["matches"]:
context = res["metadata"].pop("context")
final_result.append(
Document(page_content=context, metadata=res["metadata"])
)
# return sea... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/pinecone_hybrid_search.html |
0db63f20f3f0-0 | Source code for langchain.retrievers.merger_retriever
from typing import List
from langchain.schema import BaseRetriever, Document
[docs]class MergerRetriever(BaseRetriever):
"""
This class merges the results of multiple retrievers.
Args:
retrievers: A list of retrievers to merge.
"""
def __... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/merger_retriever.html |
0db63f20f3f0-1 | Returns:
A list of merged documents.
"""
# Get the results of all retrievers.
retriever_docs = [
retriever.get_relevant_documents(query) for retriever in self.retrievers
]
# Merge the results of the retrievers.
merged_documents = []
max_doc... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/merger_retriever.html |
592182c4a717-0 | Source code for langchain.retrievers.wikipedia
from typing import List
from langchain.schema import BaseRetriever, Document
from langchain.utilities.wikipedia import WikipediaAPIWrapper
[docs]class WikipediaRetriever(BaseRetriever, WikipediaAPIWrapper):
"""
It is effectively a wrapper for WikipediaAPIWrapper.
... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/wikipedia.html |
5b5249fc3718-0 | Source code for langchain.retrievers.metal
from typing import Any, List, Optional
from langchain.schema import BaseRetriever, Document
[docs]class MetalRetriever(BaseRetriever):
"""Retriever that uses the Metal API."""
def __init__(self, client: Any, params: Optional[dict] = None):
from metal_sdk.metal ... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/metal.html |
e9064c86de22-0 | Source code for langchain.retrievers.pupmed
from typing import List
from langchain.schema import BaseRetriever, Document
from langchain.utilities.pupmed import PubMedAPIWrapper
[docs]class PubMedRetriever(BaseRetriever, PubMedAPIWrapper):
"""
It is effectively a wrapper for PubMedAPIWrapper.
It wraps load()... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/pupmed.html |
3b6cef46ca82-0 | Source code for langchain.retrievers.remote_retriever
from typing import List, Optional
import aiohttp
import requests
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs]class RemoteLangChainRetriever(BaseRetriever, BaseModel):
url: str
headers: Optional[dict] = None
i... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/remote_retriever.html |
afce1c8babc6-0 | Source code for langchain.retrievers.zilliz
"""Zilliz Retriever"""
import warnings
from typing import Any, Dict, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever, Document
from langchain.vectorstores.zilliz import Zilliz
# TODO: Update to ZillizClient + Hybrid S... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/zilliz.html |
afce1c8babc6-1 | raise NotImplementedError
def ZillizRetreiver(*args: Any, **kwargs: Any) -> ZillizRetriever:
"""
Deprecated ZillizRetreiver. Please use ZillizRetriever ('i' before 'e') instead.
Args:
*args:
**kwargs:
Returns:
ZillizRetriever
"""
warnings.warn(
"ZillizRetreiver wi... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/zilliz.html |
b0635f6496b6-0 | Source code for langchain.retrievers.self_query.base
"""Retriever that generates and executes structured queries over its own data source."""
from typing import Any, Dict, List, Optional, Type, cast
from pydantic import BaseModel, Field, root_validator
from langchain import LLMChain
from langchain.base_language import ... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/base.html |
b0635f6496b6-1 | if vectorstore_cls not in BUILTIN_TRANSLATORS:
raise ValueError(
f"Self query retriever with Vector Store type {vectorstore_cls}"
f" not supported."
)
if isinstance(vectorstore, Qdrant):
return QdrantTranslator(metadata_key=vectorstore.metadata_payload_key)
elif i... | https://api.python.langchain.com/en/latest/_modules/langchain/retrievers/self_query/base.html |
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