id stringlengths 14 16 | text stringlengths 44 2.73k | source stringlengths 49 115 |
|---|---|---|
c4e97f8adab2-0 | Source code for langchain.memory.simple
from typing import Any, Dict, List
from langchain.schema import BaseMemory
[docs]class SimpleMemory(BaseMemory):
"""Simple memory for storing context or other bits of information that shouldn't
ever change between prompts.
"""
memories: Dict[str, Any] = dict()
... | https://python.langchain.com/en/latest/_modules/langchain/memory/simple.html |
1ffebbffbaa5-0 | Source code for langchain.memory.entity
import logging
from abc import ABC, abstractmethod
from itertools import islice
from typing import Any, Dict, Iterable, List, Optional
from pydantic import Field
from langchain.chains.llm import LLMChain
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memor... | https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
1ffebbffbaa5-1 | [docs] def set(self, key: str, value: Optional[str]) -> None:
self.store[key] = value
[docs] def delete(self, key: str) -> None:
del self.store[key]
[docs] def exists(self, key: str) -> bool:
return key in self.store
[docs] def clear(self) -> None:
return self.store.clear()
[... | https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
1ffebbffbaa5-2 | except redis.exceptions.ConnectionError as error:
logger.error(error)
self.session_id = session_id
self.key_prefix = key_prefix
self.ttl = ttl
self.recall_ttl = recall_ttl or ttl
@property
def full_key_prefix(self) -> str:
return f"{self.key_prefix}:{self.sess... | https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
1ffebbffbaa5-3 | yield batch
for keybatch in batched(
self.redis_client.scan_iter(f"{self.full_key_prefix}:*"), 500
):
self.redis_client.delete(*keybatch)
[docs]class ConversationEntityMemory(BaseChatMemory):
"""Entity extractor & summarizer to memory."""
human_prefix: str = "Human"
a... | https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
1ffebbffbaa5-4 | history=buffer_string,
input=inputs[prompt_input_key],
)
if output.strip() == "NONE":
entities = []
else:
entities = [w.strip() for w in output.split(",")]
entity_summaries = {}
for entity in entities:
entity_summaries[entity] = sel... | https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
1ffebbffbaa5-5 | """Clear memory contents."""
self.chat_memory.clear()
self.entity_cache.clear()
self.entity_store.clear()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
2d9aef25e9e0-0 | Source code for langchain.memory.readonly
from typing import Any, Dict, List
from langchain.schema import BaseMemory
[docs]class ReadOnlySharedMemory(BaseMemory):
"""A memory wrapper that is read-only and cannot be changed."""
memory: BaseMemory
@property
def memory_variables(self) -> List[str]:
... | https://python.langchain.com/en/latest/_modules/langchain/memory/readonly.html |
b099e9283533-0 | Source code for langchain.memory.combined
from typing import Any, Dict, List
from langchain.schema import BaseMemory
[docs]class CombinedMemory(BaseMemory):
"""Class for combining multiple memories' data together."""
memories: List[BaseMemory]
"""For tracking all the memories that should be accessed."""
... | https://python.langchain.com/en/latest/_modules/langchain/memory/combined.html |
a1172f84beca-0 | Source code for langchain.memory.chat_message_histories.in_memory
from typing import List
from pydantic import BaseModel
from langchain.schema import (
AIMessage,
BaseChatMessageHistory,
BaseMessage,
HumanMessage,
)
[docs]class ChatMessageHistory(BaseChatMessageHistory, BaseModel):
messages: List[Ba... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/in_memory.html |
7c8e25388bd1-0 | Source code for langchain.memory.chat_message_histories.cosmos_db
"""Azure CosmosDB Memory History."""
from __future__ import annotations
import logging
from types import TracebackType
from typing import TYPE_CHECKING, Any, List, Optional, Type
from langchain.schema import (
AIMessage,
BaseChatMessageHistory,
... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
7c8e25388bd1-1 | self.credential = credential
self.session_id = session_id
self.user_id = user_id
self.ttl = ttl
self._client: Optional[CosmosClient] = None
self._container: Optional[ContainerProxy] = None
self.messages: List[BaseMessage] = []
[docs] def prepare_cosmos(self) -> None:
... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
7c8e25388bd1-2 | ) -> None:
"""Context manager exit"""
self.upsert_messages()
if self._client:
self._client.__exit__(exc_type, exc_val, traceback)
[docs] def load_messages(self) -> None:
"""Retrieve the messages from Cosmos"""
if not self._container:
raise ValueError("C... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
7c8e25388bd1-3 | self.messages.append(new_message)
if not self._container:
raise ValueError("Container not initialized")
self._container.upsert_item(
body={
"id": self.session_id,
"user_id": self.user_id,
"messages": messages_to_dict(self.messages),... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
0add942c6e9b-0 | Source code for langchain.memory.chat_message_histories.postgres
import json
import logging
from typing import List
from langchain.schema import (
AIMessage,
BaseChatMessageHistory,
BaseMessage,
HumanMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
DEFAULT_CO... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/postgres.html |
0add942c6e9b-1 | messages = messages_from_dict(items)
return messages
[docs] def add_user_message(self, message: str) -> None:
self.append(HumanMessage(content=message))
[docs] def add_ai_message(self, message: str) -> None:
self.append(AIMessage(content=message))
[docs] def append(self, message: BaseMe... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/postgres.html |
403b288bc7f4-0 | Source code for langchain.memory.chat_message_histories.redis
import json
import logging
from typing import List, Optional
from langchain.schema import (
AIMessage,
BaseChatMessageHistory,
BaseMessage,
HumanMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
[do... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/redis.html |
403b288bc7f4-1 | self.append(HumanMessage(content=message))
[docs] def add_ai_message(self, message: str) -> None:
self.append(AIMessage(content=message))
[docs] def append(self, message: BaseMessage) -> None:
"""Append the message to the record in Redis"""
self.redis_client.lpush(self.key, json.dumps(_mes... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/redis.html |
0f898b7504e8-0 | Source code for langchain.memory.chat_message_histories.dynamodb
import logging
from typing import List
from langchain.schema import (
AIMessage,
BaseChatMessageHistory,
BaseMessage,
HumanMessage,
_message_to_dict,
messages_from_dict,
messages_to_dict,
)
logger = logging.getLogger(__name__)
... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/dynamodb.html |
0f898b7504e8-1 | items = []
messages = messages_from_dict(items)
return messages
[docs] def add_user_message(self, message: str) -> None:
self.append(HumanMessage(content=message))
[docs] def add_ai_message(self, message: str) -> None:
self.append(AIMessage(content=message))
[docs] def append(se... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/dynamodb.html |
17593e1d3102-0 | Source code for langchain.retrievers.time_weighted_retriever
"""Retriever that combines embedding similarity with recency in retrieving values."""
from copy import deepcopy
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from pydantic import BaseModel, Field
from langchain.schema impor... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
17593e1d3102-1 | """
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,
) -> float:
"""Return the combined score for a ... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
17593e1d3102-2 | 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://python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
17593e1d3102-3 | 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."""
current_time = kwargs.get("current_time", datetime.now(... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
c05ab6c49cd1-0 | Source code for langchain.retrievers.metal
from typing import Any, List, Optional
from langchain.schema import BaseRetriever, Document
[docs]class MetalRetriever(BaseRetriever):
def __init__(self, client: Any, params: Optional[dict] = None):
from metal_sdk.metal import Metal
if not isinstance(client... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/metal.html |
d65ece8a88cc-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://python.langchain.com/en/latest/_modules/langchain/retrievers/remote_retriever.html |
9ebf99031286-0 | 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://python.langchain.com/en/latest/_modules/langchain/retrievers/contextual_compression.html |
9ebf99031286-1 | return list(compressed_docs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/retrievers/contextual_compression.html |
2b5d1e151199-0 | 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://python.langchain.com/en/latest/_modules/langchain/retrievers/pinecone_hybrid_search.html |
2b5d1e151199-1 | vectors = []
# loop through the data and create dictionaries for upserts
for doc_id, sparse, dense, metadata in zip(
batch_ids, sparse_embeds, dense_embeds, meta
):
vectors.append(
{
"id": doc_id,
"sparse_values": sp... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/pinecone_hybrid_search.html |
2b5d1e151199-2 | [docs] def get_relevant_documents(self, query: str) -> List[Document]:
from pinecone_text.hybrid import hybrid_convex_scale
sparse_vec = self.sparse_encoder.encode_queries(query)
# convert the question into a dense vector
dense_vec = self.embeddings.embed_query(query)
# scale ... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/pinecone_hybrid_search.html |
d8ed6a7c7997-0 | 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):
datastore_url: str
top_k: Optional[int]
api_key: Optional[str]
def __init__(
self,
... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/databerry.html |
d8ed6a7c7997-1 | 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://python.langchain.com/en/latest/_modules/langchain/retrievers/databerry.html |
84ddf7a71e3b-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://python.langchain.com/en/latest/_modules/langchain/retrievers/weaviate_hybrid_search.html |
84ddf7a71e3b-1 | """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}
... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/weaviate_hybrid_search.html |
75cea1bb0d64-0 | 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://python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
75cea1bb0d64-1 | 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://python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
75cea1bb0d64-2 | 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://python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
8d74bfe05b3a-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 typing import Any, Dict, List, Optional
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/tfidf.html |
8d74bfe05b3a-1 | results = cosine_similarity(self.tfidf_array, query_vec).reshape(
(-1,)
) # Op -- (n_docs,1) -- Cosine Sim with each doc
return_docs = []
for i in results.argsort()[-self.k :][::-1]:
return_docs.append(self.docs[i])
return return_docs
[docs] async def aget_rel... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/tfidf.html |
05a85015c944-0 | 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://python.langchain.com/en/latest/_modules/langchain/retrievers/chatgpt_plugin_retriever.html |
05a85015c944-1 | docs = []
for d in results:
content = d.pop("text")
docs.append(Document(page_content=content, metadata=d))
return docs
def _create_request(self, query: str) -> tuple[str, dict, dict]:
url = f"{self.url}/query"
json = {
"queries": [
... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/chatgpt_plugin_retriever.html |
52a91d9379ad-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://python.langchain.com/en/latest/_modules/langchain/retrievers/svm.html |
52a91d9379ad-1 | y[0] = 1
clf = svm.LinearSVC(
class_weight="balanced", verbose=False, max_iter=10000, tol=1e-6, C=0.1
)
clf.fit(x, y)
similarities = clf.decision_function(x)
sorted_ix = np.argsort(-similarities)
# svm.LinearSVC in scikit-learn is non-deterministic.
# ... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/svm.html |
71031a6b20f8-0 | Source code for langchain.retrievers.document_compressors.chain_extract
"""DocumentFilter that uses an LLM chain to extract the relevant parts of documents."""
from typing import Any, Callable, Dict, Optional, Sequence
from langchain import LLMChain, PromptTemplate
from langchain.retrievers.document_compressors.base im... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
71031a6b20f8-1 | self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Compress page content of raw documents."""
compressed_docs = []
for doc in documents:
_input = self.get_input(query, doc)
output = self.llm_chain.predict_and_parse(**_input)
if len... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
0057c77c99f2-0 | Source code for langchain.retrievers.document_compressors.base
"""Interface for retrieved document compressors."""
from abc import ABC, abstractmethod
from typing import List, Sequence, Union
from pydantic import BaseModel
from langchain.schema import BaseDocumentTransformer, Document
class BaseDocumentCompressor(BaseM... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/base.html |
0057c77c99f2-1 | self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Compress retrieved documents given the query context."""
for _transformer in self.transformers:
if isinstance(_transformer, BaseDocumentCompressor):
documents = await _transformer.acompress_docume... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/base.html |
9e51316d4329-0 | Source code for langchain.retrievers.document_compressors.embeddings_filter
"""Document compressor that uses embeddings to drop documents unrelated to the query."""
from typing import Callable, Dict, Optional, Sequence
import numpy as np
from pydantic import root_validator
from langchain.document_transformers import (
... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/embeddings_filter.html |
9e51316d4329-1 | return values
[docs] def compress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Filter documents based on similarity of their embeddings to the query."""
stateful_documents = get_stateful_documents(documents)
embedded_documents = _get_embed... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/embeddings_filter.html |
156d71e386ee-0 | Source code for langchain.retrievers.document_compressors.chain_filter
"""Filter that uses an LLM to drop documents that aren't relevant to the query."""
from typing import Any, Callable, Dict, Optional, Sequence
from langchain import BasePromptTemplate, LLMChain, PromptTemplate
from langchain.output_parsers.boolean im... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_filter.html |
156d71e386ee-1 | include_doc = self.llm_chain.predict_and_parse(**_input)
if include_doc:
filtered_docs.append(doc)
return filtered_docs
[docs] async def acompress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Filter down documents."""
... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_filter.html |
d4307603436c-0 | Source code for langchain.utilities.arxiv
"""Util that calls Arxiv."""
import logging
from typing import Any, Dict, List
from pydantic import BaseModel, Extra, root_validator
from langchain.schema import Document
logger = logging.getLogger(__name__)
[docs]class ArxivAPIWrapper(BaseModel):
"""Wrapper around ArxivAPI... | https://python.langchain.com/en/latest/_modules/langchain/utilities/arxiv.html |
d4307603436c-1 | """Validate that the python package exists in environment."""
try:
import arxiv
values["arxiv_search"] = arxiv.Search
values["arxiv_exceptions"] = (
arxiv.ArxivError,
arxiv.UnexpectedEmptyPageError,
arxiv.HTTPError,
... | https://python.langchain.com/en/latest/_modules/langchain/utilities/arxiv.html |
d4307603436c-2 | """
Run Arxiv search and get the PDF documents plus the meta information.
See https://lukasschwab.me/arxiv.py/index.html#Search
Returns: a list of documents with the document.page_content in PDF format
"""
try:
import fitz
except ImportError:
raise... | https://python.langchain.com/en/latest/_modules/langchain/utilities/arxiv.html |
d4307603436c-3 | **add_meta,
}
),
)
docs.append(doc)
except FileNotFoundError as f_ex:
logger.debug(f_ex)
return docs
except self.arxiv_exceptions as ex:
logger.... | https://python.langchain.com/en/latest/_modules/langchain/utilities/arxiv.html |
16eb807beff4-0 | Source code for langchain.utilities.python
import sys
from io import StringIO
from typing import Dict, Optional
from pydantic import BaseModel, Field
[docs]class PythonREPL(BaseModel):
"""Simulates a standalone Python REPL."""
globals: Optional[Dict] = Field(default_factory=dict, alias="_globals")
locals: O... | https://python.langchain.com/en/latest/_modules/langchain/utilities/python.html |
66c1beb03497-0 | Source code for langchain.utilities.serpapi
"""Chain that calls SerpAPI.
Heavily borrowed from https://github.com/ofirpress/self-ask
"""
import os
import sys
from typing import Any, Dict, Optional, Tuple
import aiohttp
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.utils import get_from_dic... | https://python.langchain.com/en/latest/_modules/langchain/utilities/serpapi.html |
66c1beb03497-1 | aiosession: Optional[aiohttp.ClientSession] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python packag... | https://python.langchain.com/en/latest/_modules/langchain/utilities/serpapi.html |
66c1beb03497-2 | """Use aiohttp to run query through SerpAPI and return the results async."""
def construct_url_and_params() -> Tuple[str, Dict[str, str]]:
params = self.get_params(query)
params["source"] = "python"
if self.serpapi_api_key:
params["serp_api_key"] = self.serpap... | https://python.langchain.com/en/latest/_modules/langchain/utilities/serpapi.html |
66c1beb03497-3 | toret = res["answer_box"]["snippet"]
elif (
"answer_box" in res.keys()
and "snippet_highlighted_words" in res["answer_box"].keys()
):
toret = res["answer_box"]["snippet_highlighted_words"][0]
elif (
"sports_results" in res.keys()
and "g... | https://python.langchain.com/en/latest/_modules/langchain/utilities/serpapi.html |
d171baae5457-0 | Source code for langchain.utilities.wolfram_alpha
"""Util that calls WolframAlpha."""
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class WolframAlphaAPIWrapper(BaseModel):
"""Wrapper for Wolfram Alpha.
Docs fo... | https://python.langchain.com/en/latest/_modules/langchain/utilities/wolfram_alpha.html |
d171baae5457-1 | res = self.wolfram_client.query(query)
try:
assumption = next(res.pods).text
answer = next(res.results).text
except StopIteration:
return "Wolfram Alpha wasn't able to answer it"
if answer is None or answer == "":
# We don't want to return the assu... | https://python.langchain.com/en/latest/_modules/langchain/utilities/wolfram_alpha.html |
1faf6bf5a1bf-0 | Source code for langchain.utilities.searx_search
"""Utility for using SearxNG meta search API.
SearxNG is a privacy-friendly free metasearch engine that aggregates results from
`multiple search engines
<https://docs.searxng.org/admin/engines/configured_engines.html>`_ and databases and
supports the `OpenSearch
<https:... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
1faf6bf5a1bf-1 | Other methods are are available for convenience.
:class:`SearxResults` is a convenience wrapper around the raw json result.
Example usage of the ``run`` method to make a search:
.. code-block:: python
s.run(query="what is the best search engine?")
Engine Parameters
-----------------
You can pass any `accept... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
1faf6bf5a1bf-2 | .. code-block:: python
# select the github engine and pass the search suffix
s = SearchWrapper("langchain library", query_suffix="!gh")
s = SearchWrapper("langchain library")
# select github the conventional google search syntax
s.run("large language models", query_suffix="site:g... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
1faf6bf5a1bf-3 | return {"language": "en", "format": "json"}
[docs]class SearxResults(dict):
"""Dict like wrapper around search api results."""
_data = ""
def __init__(self, data: str):
"""Take a raw result from Searx and make it into a dict like object."""
json_data = json.loads(data)
super().__init... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
1faf6bf5a1bf-4 | .. code-block:: python
from langchain.utilities import SearxSearchWrapper
# note the unsecure parameter is not needed if you pass the url scheme as
# http
searx = SearxSearchWrapper(searx_host="http://localhost:8888",
un... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
1faf6bf5a1bf-5 | if categories:
values["params"]["categories"] = ",".join(categories)
searx_host = get_from_dict_or_env(values, "searx_host", "SEARX_HOST")
if not searx_host.startswith("http"):
print(
f"Warning: missing the url scheme on host \
! assuming secure ht... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
1faf6bf5a1bf-6 | ) as response:
if not response.ok:
raise ValueError("Searx API returned an error: ", response.text)
result = SearxResults(await response.text())
self._result = result
else:
async with self.aiosession.get(
... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
1faf6bf5a1bf-7 | searx.run("what is the weather in France ?", engine="qwant")
# the same result can be achieved using the `!` syntax of searx
# to select the engine using `query_suffix`
searx.run("what is the weather in France ?", query_suffix="!qwant")
"""
_params = {
... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
1faf6bf5a1bf-8 | ) -> str:
"""Asynchronously version of `run`."""
_params = {
"q": query,
}
params = {**self.params, **_params, **kwargs}
if self.query_suffix and len(self.query_suffix) > 0:
params["q"] += " " + self.query_suffix
if isinstance(query_suffix, str) an... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
1faf6bf5a1bf-9 | categories: List of categories to use for the query.
**kwargs: extra parameters to pass to the searx API.
Returns:
Dict with the following keys:
{
snippet: The description of the result.
title: The title of the result.
link: T... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
1faf6bf5a1bf-10 | self,
query: str,
num_results: int,
engines: Optional[List[str]] = None,
query_suffix: Optional[str] = "",
**kwargs: Any,
) -> List[Dict]:
"""Asynchronously query with json results.
Uses aiohttp. See `results` for more info.
"""
_params = {
... | https://python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
6d5bbb9165ee-0 | Source code for langchain.utilities.bing_search
"""Util that calls Bing Search.
In order to set this up, follow instructions at:
https://levelup.gitconnected.com/api-tutorial-how-to-use-bing-web-search-api-in-python-4165d5592a7e
"""
from typing import Dict, List
import requests
from pydantic import BaseModel, Extra, ro... | https://python.langchain.com/en/latest/_modules/langchain/utilities/bing_search.html |
6d5bbb9165ee-1 | bing_subscription_key = get_from_dict_or_env(
values, "bing_subscription_key", "BING_SUBSCRIPTION_KEY"
)
values["bing_subscription_key"] = bing_subscription_key
bing_search_url = get_from_dict_or_env(
values,
"bing_search_url",
"BING_SEARCH_URL",
... | https://python.langchain.com/en/latest/_modules/langchain/utilities/bing_search.html |
6d5bbb9165ee-2 | "snippet": result["snippet"],
"title": result["name"],
"link": result["url"],
}
metadata_results.append(metadata_result)
return metadata_results
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/utilities/bing_search.html |
6ee68da6c8e6-0 | Source code for langchain.utilities.google_places_api
"""Chain that calls Google Places API.
"""
import logging
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class GooglePlacesAPIWrapper(BaseModel):
"""Wrapper arou... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_places_api.html |
6ee68da6c8e6-1 | except ImportError:
raise ValueError(
"Could not import googlemaps python packge. "
"Please install it with `pip install googlemaps`."
)
return values
[docs] def run(self, query: str) -> str:
"""Run Places search and get k number of places that ... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_places_api.html |
6ee68da6c8e6-2 | "formatted_address", "Unknown"
)
phone_number = place_details.get("result", {}).get(
"formatted_phone_number", "Unknown"
)
website = place_details.get("result", {}).get("website", "Unknown")
formatted_details = (
f"{name}\nAddre... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_places_api.html |
09ea87c975f5-0 | Source code for langchain.utilities.apify
from typing import Any, Callable, Dict, Optional
from pydantic import BaseModel, root_validator
from langchain.document_loaders import ApifyDatasetLoader
from langchain.document_loaders.base import Document
from langchain.utils import get_from_dict_or_env
[docs]class ApifyWrapp... | https://python.langchain.com/en/latest/_modules/langchain/utilities/apify.html |
09ea87c975f5-1 | *,
build: Optional[str] = None,
memory_mbytes: Optional[int] = None,
timeout_secs: Optional[int] = None,
) -> ApifyDatasetLoader:
"""Run an Actor on the Apify platform and wait for results to be ready.
Args:
actor_id (str): The ID or name of the Actor on the Apify... | https://python.langchain.com/en/latest/_modules/langchain/utilities/apify.html |
09ea87c975f5-2 | memory_mbytes: Optional[int] = None,
timeout_secs: Optional[int] = None,
) -> ApifyDatasetLoader:
"""Run an Actor on the Apify platform and wait for results to be ready.
Args:
actor_id (str): The ID or name of the Actor on the Apify platform.
run_input (Dict): The inp... | https://python.langchain.com/en/latest/_modules/langchain/utilities/apify.html |
9585245cc724-0 | Source code for langchain.utilities.powerbi
"""Wrapper around a Power BI endpoint."""
from __future__ import annotations
import logging
import os
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Union
import aiohttp
import requests
from aiohttp import ServerTimeoutError
from pydantic import BaseMo... | https://python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
9585245cc724-1 | arbitrary_types_allowed = True
@root_validator(pre=True, allow_reuse=True)
def token_or_credential_present(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Validate that at least one of token and credentials is present."""
if "token" in values or "credential" in values:
return valu... | https://python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
9585245cc724-2 | """Get names of tables available."""
return self.table_names
[docs] def get_schemas(self) -> str:
"""Get the available schema's."""
if self.schemas:
return ", ".join([f"{key}: {value}" for key, value in self.schemas.items()])
return "No known schema's yet. Use the schema_p... | https://python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
9585245cc724-3 | ) -> str:
"""Get information about specified tables."""
tables_requested = self._get_tables_to_query(table_names)
tables_todo = self._get_tables_todo(tables_requested)
for table in tables_todo:
try:
result = self.run(
f"EVALUATE TOPN({self.... | https://python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
9585245cc724-4 | if "bad request" in str(exc).lower():
return SCHEMA_ERROR_RESPONSE
if "unauthorized" in str(exc).lower():
return UNAUTHORIZED_RESPONSE
return str(exc)
self.schemas[table] = json_to_md(result["results"][0]["tables"][0]["rows"])
r... | https://python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
9585245cc724-5 | ) as response:
response.raise_for_status()
response_json = await response.json()
return response_json
def json_to_md(
json_contents: List[Dict[str, Union[str, int, float]]],
table_name: Optional[str] = None,
) -> str:
"""Converts a JSON object to a markdown ta... | https://python.langchain.com/en/latest/_modules/langchain/utilities/powerbi.html |
55fd5ee48c3c-0 | Source code for langchain.utilities.wikipedia
"""Util that calls Wikipedia."""
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
WIKIPEDIA_MAX_QUERY_LENGTH = 300
[docs]class WikipediaAPIWrapper(BaseModel):
"""Wrapper around WikipediaAPI.
To use, you should have the ``w... | https://python.langchain.com/en/latest/_modules/langchain/utilities/wikipedia.html |
55fd5ee48c3c-1 | summary = self.fetch_formatted_page_summary(search_results[i])
if summary is not None:
summaries.append(summary)
return "\n\n".join(summaries)
[docs] def fetch_formatted_page_summary(self, page: str) -> Optional[str]:
try:
wiki_page = self.wiki_client.page(titl... | https://python.langchain.com/en/latest/_modules/langchain/utilities/wikipedia.html |
33d1a35dc91b-0 | Source code for langchain.utilities.openweathermap
"""Util that calls OpenWeatherMap using PyOWM."""
from typing import Any, Dict, Optional
from pydantic import Extra, root_validator
from langchain.tools.base import BaseModel
from langchain.utils import get_from_dict_or_env
[docs]class OpenWeatherMapAPIWrapper(BaseMode... | https://python.langchain.com/en/latest/_modules/langchain/utilities/openweathermap.html |
33d1a35dc91b-1 | temperature = w.temperature("celsius")
rain = w.rain
heat_index = w.heat_index
clouds = w.clouds
return (
f"In {location}, the current weather is as follows:\n"
f"Detailed status: {detailed_status}\n"
f"Wind speed: {wind['speed']} m/s, direction: {wind... | https://python.langchain.com/en/latest/_modules/langchain/utilities/openweathermap.html |
726849bdef50-0 | Source code for langchain.utilities.bash
"""Wrapper around subprocess to run commands."""
from __future__ import annotations
import platform
import re
import subprocess
from typing import TYPE_CHECKING, List, Union
from uuid import uuid4
if TYPE_CHECKING:
import pexpect
def _lazy_import_pexpect() -> pexpect:
""... | https://python.langchain.com/en/latest/_modules/langchain/utilities/bash.html |
726849bdef50-1 | # Set the custom prompt
process.sendline("PS1=" + prompt)
process.expect_exact(prompt, timeout=10)
return process
[docs] def run(self, commands: Union[str, List[str]]) -> str:
"""Run commands and return final output."""
if isinstance(commands, str):
commands = [com... | https://python.langchain.com/en/latest/_modules/langchain/utilities/bash.html |
726849bdef50-2 | self.process.expect(self.prompt, timeout=10)
self.process.sendline("")
try:
self.process.expect([self.prompt, pexpect.EOF], timeout=10)
except pexpect.TIMEOUT:
return f"Timeout error while executing command {command}"
if self.process.after == pexpect.EOF:
... | https://python.langchain.com/en/latest/_modules/langchain/utilities/bash.html |
1e7368da0eb9-0 | Source code for langchain.utilities.google_serper
"""Util that calls Google Search using the Serper.dev API."""
from typing import Dict, Optional
import requests
from pydantic.class_validators import root_validator
from pydantic.main import BaseModel
from langchain.utils import get_from_dict_or_env
[docs]class GoogleSe... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_serper.html |
1e7368da0eb9-1 | snippets = []
if results.get("answerBox"):
answer_box = results.get("answerBox", {})
if answer_box.get("answer"):
return answer_box.get("answer")
elif answer_box.get("snippet"):
return answer_box.get("snippet").replace("\n", " ")
el... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_serper.html |
1e7368da0eb9-2 | )
response.raise_for_status()
search_results = response.json()
return search_results
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_serper.html |
aee2531c542c-0 | Source code for langchain.utilities.google_search
"""Util that calls Google Search."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class GoogleSearchAPIWrapper(BaseModel):
"""Wrapper for Google Search API.
... | https://python.langchain.com/en/latest/_modules/langchain/utilities/google_search.html |
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