id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
28,732 | import base64
import io
import uuid
from io import BytesIO
from pathlib import Path
import pypdfium2 as pdfium
from langchain_core.documents import Document
from langchain_core.messages import HumanMessage
from langchain_openai.chat_models import ChatOpenAI
from PIL import Image
from rag_redis_multi_modal_multi_vector.... | Generate summaries for images :param img_base64_list: Base64 encoded images :return: List of image summaries and processed images |
28,733 | import base64
import io
import uuid
from io import BytesIO
from pathlib import Path
import pypdfium2 as pdfium
from langchain_core.documents import Document
from langchain_core.messages import HumanMessage
from langchain_openai.chat_models import ChatOpenAI
from PIL import Image
from rag_redis_multi_modal_multi_vector.... | Extract images from each page of a PDF document and save as JPEG files. :param pdf_path: A string representing the path to the PDF file. |
28,734 | import base64
import io
import uuid
from io import BytesIO
from pathlib import Path
import pypdfium2 as pdfium
from langchain_core.documents import Document
from langchain_core.messages import HumanMessage
from langchain_openai.chat_models import ChatOpenAI
from PIL import Image
from rag_redis_multi_modal_multi_vector.... | Convert PIL images to Base64 encoded strings :param pil_image: PIL image :return: Re-sized Base64 string |
28,735 | import base64
import io
import uuid
from io import BytesIO
from pathlib import Path
import pypdfium2 as pdfium
from langchain_core.documents import Document
from langchain_core.messages import HumanMessage
from langchain_openai.chat_models import ChatOpenAI
from PIL import Image
from rag_redis_multi_modal_multi_vector.... | Index image summaries in the db. :param image_summaries: Image summaries :param images: Base64 encoded images :return: Retriever |
28,736 | import os
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain_community.storage import RedisStore
from langchain_community.vectorstores import Redis as RedisVectorDB
from langchain_openai.embeddings import OpenAIEmbeddings
def get_boolean_env_var(var_name, default_value=False):
"""Retr... | null |
28,737 | import base64
import io
from langchain.pydantic_v1 import BaseModel
from langchain_core.documents import Document
from langchain_core.messages import HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_openai impo... | Multi-modal RAG chain, :return: A chain of functions representing the multi-modal RAG process. |
28,738 | import os
from typing import List, Optional
from langchain.chains.query_constructor.schema import AttributeInfo
from langchain.retrievers import SelfQueryRetriever
from langchain_community.llms import BaseLLM
from langchain_community.vectorstores.qdrant import Qdrant
from langchain_core.documents import Document
from l... | Create a chain that can be used to query a Qdrant vector store with a self-querying capability. By default, this chain will use the OpenAI LLM and OpenAIEmbeddings, and work with the default document contents and metadata field info. You can override these defaults by passing in your own values. :param llm: an LLM to u... |
28,739 | import os
from typing import List, Optional
from langchain.chains.query_constructor.schema import AttributeInfo
from langchain.retrievers import SelfQueryRetriever
from langchain_community.llms import BaseLLM
from langchain_community.vectorstores.qdrant import Qdrant
from langchain_core.documents import Document
from l... | Initialize a vector store with a set of documents. By default, the documents will be compatible with the default metadata field info. You can override these defaults by passing in your own values. :param embeddings: an Embeddings to use for generating queries :param collection_name: name of the Qdrant collection to use... |
28,740 | from typing import List, Tuple
from langchain_community.chat_models import ChatOpenAI
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.pydantic_v1 import Ba... | null |
28,741 | from typing import List, Tuple
from langchain_community.chat_models import ChatOpenAI
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.pydantic_v1 import Ba... | null |
28,742 | from typing import List, Tuple
from langchain_community.chat_models import ChatOpenAI
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.pydantic_v1 import Ba... | null |
28,743 | from typing import Dict, List, Tuple
from langchain.agents import (
AgentExecutor,
Tool,
)
from langchain.agents.format_scratchpad import format_to_openai_functions
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain_community.chat_models import ChatOpenAI
from langchain_... | null |
28,744 | from typing import Dict, List, Tuple
from langchain.agents import (
AgentExecutor,
Tool,
)
from langchain.agents.format_scratchpad import format_to_openai_functions
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain_community.chat_models import ChatOpenAI
from langchain_... | null |
28,745 | from typing import Dict, List, Tuple
from langchain.agents import (
AgentExecutor,
Tool,
)
from langchain.agents.format_scratchpad import format_to_openai_functions
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain_community.chat_models import ChatOpenAI
from langchain_... | null |
28,746 | from typing import Dict, List, Tuple
from langchain.agents import (
AgentExecutor,
Tool,
)
from langchain.agents.format_scratchpad import format_to_openai_functions
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain_community.chat_models import ChatOpenAI
from langchain_... | null |
28,747 | import os
from langchain_community.document_loaders import JSONLoader
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_elasticsearch import ElasticsearchStore
from langchain_text_splitters import RecursiveCharacterTextSplitter
def _metadata_func(record: dict, metadata: dict) -> dict:
meta... | null |
28,748 | import os
from operator import itemgetter
from typing import List, Tuple
from langchain.retrievers import SelfQueryRetriever
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.output_parsers import StrOutputParser
from langchain_core.pr... | null |
28,749 | import os
from operator import itemgetter
from typing import List, Tuple
from langchain.retrievers import SelfQueryRetriever
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.output_parsers import StrOutputParser
from langchain_core.pr... | null |
28,750 | import os
from operator import itemgetter
from typing import List, Tuple
from langchain.retrievers import SelfQueryRetriever
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.output_parsers import StrOutputParser
from langchain_core.pr... | null |
28,751 | import os
import re
from langchain.sql_database import SQLDatabase
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydant... | null |
28,752 | import os
import re
from langchain.sql_database import SQLDatabase
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydant... | null |
28,753 | import os
import re
from langchain.sql_database import SQLDatabase
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydant... | null |
28,754 | from typing import Any, Dict, List
from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
from langchain.memory import ChatMessageHistory
from langchain_community.chat_models import ChatOpenAI
from langchain_community.graphs import Neo4jGraph
from langchain_core.output_parsers import StrOutputP... | null |
28,755 | from typing import Any, Dict, List
from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
from langchain.memory import ChatMessageHistory
from langchain_community.chat_models import ChatOpenAI
from langchain_community.graphs import Neo4jGraph
from langchain_core.output_parsers import StrOutputP... | null |
28,756 | from operator import itemgetter
from typing import List, Optional, Tuple
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_core.messages import BaseMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.p... | null |
28,757 | from operator import itemgetter
from typing import List, Optional, Tuple
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_core.messages import BaseMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.p... | null |
28,758 | import os
from langchain_community.document_loaders import JSONLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_elasticsearch import ElasticsearchStore
from langchain_text_splitters import RecursiveCharacterTextSplitter
def metadata_func(record: dict, metadata: dict) -> dict:
... | null |
28,759 | from langchain import hub
from langchain.load import dumps, loads
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.output_parsers import StrOutputParser
from langchain_core.pydantic_v1 import BaseModel
from langchain_pinecone import P... | null |
28,760 | from operator import itemgetter
from typing import Literal
from langchain.retrievers import (
ArxivRetriever,
KayAiRetriever,
PubMedRetriever,
WikipediaRetriever,
)
from langchain.utils.openai_functions import convert_pydantic_to_openai_function
from langchain_community.chat_models import ChatOpenAI
fro... | null |
28,761 | import base64
import io
import os
import uuid
from io import BytesIO
from pathlib import Path
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import LocalFileStore
from langchain_community.chat_models import ChatOllama
from langchain_community.embeddings import OllamaEmbeddings... | Generate summaries for images :param img_base64_list: Base64 encoded images :return: List of image summaries and processed images |
28,762 | import base64
import io
import os
import uuid
from io import BytesIO
from pathlib import Path
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import LocalFileStore
from langchain_community.chat_models import ChatOllama
from langchain_community.embeddings import OllamaEmbeddings... | Extract images. :param img_path: A string representing the path to the images. |
28,763 | import base64
import io
import os
import uuid
from io import BytesIO
from pathlib import Path
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import LocalFileStore
from langchain_community.chat_models import ChatOllama
from langchain_community.embeddings import OllamaEmbeddings... | Resize an image encoded as a Base64 string :param base64_string: Base64 string :param size: Image size :return: Re-sized Base64 string |
28,764 | import base64
import io
import os
import uuid
from io import BytesIO
from pathlib import Path
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import LocalFileStore
from langchain_community.chat_models import ChatOllama
from langchain_community.embeddings import OllamaEmbeddings... | Convert PIL images to Base64 encoded strings :param pil_image: PIL image :return: Re-sized Base64 string |
28,765 | import base64
import io
import os
import uuid
from io import BytesIO
from pathlib import Path
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import LocalFileStore
from langchain_community.chat_models import ChatOllama
from langchain_community.embeddings import OllamaEmbeddings... | Create retriever that indexes summaries, but returns raw images or texts :param vectorstore: Vectorstore to store embedded image sumamries :param image_summaries: Image summaries :param images: Base64 encoded images :return: Retriever |
28,766 | import base64
import io
from pathlib import Path
from langchain.pydantic_v1 import BaseModel
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import LocalFileStore
from langchain_community.chat_models import ChatOllama
from langchain_community.embeddings import OllamaEmbeddings
... | Resize an image encoded as a Base64 string. :param base64_string: A Base64 encoded string of the image to be resized. :param size: A tuple representing the new size (width, height) for the image. :return: A Base64 encoded string of the resized image. |
28,767 | import base64
import io
from pathlib import Path
from langchain.pydantic_v1 import BaseModel
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import LocalFileStore
from langchain_community.chat_models import ChatOllama
from langchain_community.embeddings import OllamaEmbeddings
... | Multi-modal RAG chain, :param retriever: A function that retrieves the necessary context for the model. :return: A chain of functions representing the multi-modal RAG process. |
28,768 | from typing import List
def _list_indices(database, include_indices=None, ignore_indices=None) -> List[str]:
all_indices = [index["index"] for index in database.cat.indices(format="json")]
if include_indices:
all_indices = [i for i in all_indices if i in include_indices]
if ignore_indices:
a... | null |
28,769 | import os
import tempfile
from datetime import datetime, timedelta
import requests
from langchain_community.document_loaders import JSONLoader
from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores.timescalevector import TimescaleVector
from langchain_text_splitters.cha... | null |
28,770 | import json
from datetime import datetime
from enum import Enum
from operator import itemgetter
from typing import Any, Dict, Sequence
from langchain.chains.openai_functions import convert_to_openai_function
from langchain_community.chat_models import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
fro... | null |
28,771 | import json
from datetime import datetime
from enum import Enum
from operator import itemgetter
from typing import Any, Dict, Sequence
from langchain.chains.openai_functions import convert_to_openai_function
from langchain_community.chat_models import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
fro... | null |
28,772 | import json
from typing import Any
import requests
from bs4 import BeautifulSoup
from langchain.retrievers.tavily_search_api import TavilySearchAPIRetriever
from langchain_community.chat_models import ChatOpenAI
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_core.messages import Sys... | null |
28,773 | import json
from typing import Any
import requests
from bs4 import BeautifulSoup
from langchain.retrievers.tavily_search_api import TavilySearchAPIRetriever
from langchain_community.chat_models import ChatOpenAI
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_core.messages import Sys... | null |
28,774 | import json
from typing import Any
import requests
from bs4 import BeautifulSoup
from langchain.retrievers.tavily_search_api import TavilySearchAPIRetriever
from langchain_community.chat_models import ChatOpenAI
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_core.messages import Sys... | null |
28,775 | from langchain.utilities import DuckDuckGoSearchAPIWrapper
from langchain_community.chat_models import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate
from langchain_core.runnables import RunnableLambda
search =... | null |
28,776 | import base64
import io
import os
import uuid
from io import BytesIO
from pathlib import Path
import pypdfium2 as pdfium
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import LocalFileStore, UpstashRedisByteStore
from langchain_community.chat_models import ChatOpenAI
from lang... | Generate summaries for images :param img_base64_list: Base64 encoded images :return: List of image summaries and processed images |
28,777 | import base64
import io
import os
import uuid
from io import BytesIO
from pathlib import Path
import pypdfium2 as pdfium
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import LocalFileStore, UpstashRedisByteStore
from langchain_community.chat_models import ChatOpenAI
from lang... | Extract images from each page of a PDF document and save as JPEG files. :param pdf_path: A string representing the path to the PDF file. |
28,778 | import base64
import io
import os
import uuid
from io import BytesIO
from pathlib import Path
import pypdfium2 as pdfium
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import LocalFileStore, UpstashRedisByteStore
from langchain_community.chat_models import ChatOpenAI
from lang... | Convert PIL images to Base64 encoded strings :param pil_image: PIL image :return: Re-sized Base64 string |
28,779 | import base64
import io
import os
import uuid
from io import BytesIO
from pathlib import Path
import pypdfium2 as pdfium
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import LocalFileStore, UpstashRedisByteStore
from langchain_community.chat_models import ChatOpenAI
from lang... | Create retriever that indexes summaries, but returns raw images or texts :param vectorstore: Vectorstore to store embedded image sumamries :param image_summaries: Image summaries :param images: Base64 encoded images :param local_file_store: Use local file storage :return: Retriever |
28,780 | import base64
import io
import os
from pathlib import Path
from langchain.pydantic_v1 import BaseModel
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import LocalFileStore, UpstashRedisByteStore
from langchain_community.chat_models import ChatOpenAI
from langchain_community.em... | Multi-modal RAG chain, :param retriever: A function that retrieves the necessary context for the model. :return: A chain of functions representing the multi-modal RAG process. |
28,781 | from typing import List, Optional
from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
from langchain.chains.openai_functions import create_structured_output_chain
from langchain_community.chat_models import ChatOpenAI
from langchain_community.graphs import Neo4jGraph
from langchain_core.outp... | null |
28,782 | from __future__ import annotations
from typing import List, Optional
from langchain import hub
from langchain.callbacks.tracers.evaluation import EvaluatorCallbackHandler
from langchain.callbacks.tracers.schemas import Run
from langchain.schema import (
AIMessage,
BaseMessage,
HumanMessage,
StrOutputPar... | Format messages and convert to a single string. |
28,783 | from __future__ import annotations
from typing import List, Optional
from langchain import hub
from langchain.callbacks.tracers.evaluation import EvaluatorCallbackHandler
from langchain.callbacks.tracers.schemas import Run
from langchain.schema import (
AIMessage,
BaseMessage,
HumanMessage,
StrOutputPar... | Normalize the score to be between 0 and 1. |
28,784 | from __future__ import annotations
from typing import List, Optional
from langchain import hub
from langchain.callbacks.tracers.evaluation import EvaluatorCallbackHandler
from langchain.callbacks.tracers.schemas import Run
from langchain.schema import (
AIMessage,
BaseMessage,
HumanMessage,
StrOutputPar... | null |
28,785 | from pathlib import Path
from langchain.utilities import SQLDatabase
from langchain_community.llms import Replicate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import Runnab... | null |
28,786 | from pathlib import Path
from langchain.utilities import SQLDatabase
from langchain_community.llms import Replicate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import Runnab... | null |
28,787 | from pathlib import Path
from langchain.memory import ConversationBufferMemory
from langchain.utilities import SQLDatabase
from langchain_community.chat_models import ChatOllama
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from lang... | null |
28,788 | from pathlib import Path
from langchain.memory import ConversationBufferMemory
from langchain.utilities import SQLDatabase
from langchain_community.chat_models import ChatOllama
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from lang... | null |
28,789 | from pathlib import Path
from langchain.memory import ConversationBufferMemory
from langchain.utilities import SQLDatabase
from langchain_community.chat_models import ChatOllama
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from lang... | null |
28,790 | import os
from typing import List, Tuple
from google.cloud import dlp_v2
from langchain_community.chat_models import ChatVertexAI
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlacehold... | null |
28,791 | import os
from typing import List, Tuple
from google.cloud import dlp_v2
from langchain_community.chat_models import ChatVertexAI
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlacehold... | Uses the Data Loss Prevention API to deidentify sensitive data in a string by replacing matched input values with the info type. Args: project: The Google Cloud project id to use as a parent resource. input_str: The string to deidentify (will be treated as text). info_types: A list of strings representing info types to... |
28,792 | import base64
import io
from pathlib import Path
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
from langchain_core.messages import HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.pydantic_v1 import BaseModel
from langchain_co... | Multi-modal RAG chain, :param retriever: A function that retrieves the necessary context for the model. :return: A chain of functions representing the multi-modal RAG process. |
28,794 | import os
from pathlib import Path
import requests
from langchain.memory import ConversationBufferMemory
from langchain.utilities import SQLDatabase
from langchain_community.llms import LlamaCpp
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlac... | null |
28,795 | import os
from pathlib import Path
import requests
from langchain.memory import ConversationBufferMemory
from langchain.utilities import SQLDatabase
from langchain_community.llms import LlamaCpp
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlac... | null |
28,796 | import os
from pathlib import Path
import requests
from langchain.memory import ConversationBufferMemory
from langchain.utilities import SQLDatabase
from langchain_community.llms import LlamaCpp
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlac... | null |
28,797 | import os
import tempfile
from datetime import datetime, timedelta
import requests
from langchain_community.document_loaders import JSONLoader
from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores.timescalevector import TimescaleVector
from langchain_text_splitters.cha... | null |
28,798 | import os
from datetime import datetime, timedelta
from operator import itemgetter
from typing import List, Optional, Tuple
from dotenv import find_dotenv, load_dotenv
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores... | null |
28,799 | import os
from datetime import datetime, timedelta
from operator import itemgetter
from typing import List, Optional, Tuple
from dotenv import find_dotenv, load_dotenv
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores... | null |
28,800 | import os
from datetime import datetime, timedelta
from operator import itemgetter
from typing import List, Optional, Tuple
from dotenv import find_dotenv, load_dotenv
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores... | null |
28,801 | import os
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores.opensearch_vector_search import (
OpenSearchVectorSearch,
)
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ... | null |
28,802 | from pathlib import Path
from langchain.memory import ConversationBufferMemory
from langchain.pydantic_v1 import BaseModel
from langchain_community.chat_models import ChatOllama, ChatOpenAI
from langchain_community.utilities import SQLDatabase
from langchain_core.output_parsers import StrOutputParser
from langchain_cor... | null |
28,803 | from pathlib import Path
from langchain.memory import ConversationBufferMemory
from langchain.pydantic_v1 import BaseModel
from langchain_community.chat_models import ChatOllama, ChatOpenAI
from langchain_community.utilities import SQLDatabase
from langchain_core.output_parsers import StrOutputParser
from langchain_cor... | null |
28,804 | import json
from typing import Any
import requests
from bs4 import BeautifulSoup
from langchain.utilities import DuckDuckGoSearchAPIWrapper
from langchain_community.chat_models import ChatOpenAI
from langchain_core.messages import SystemMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_co... | null |
28,805 | import json
from typing import Any
import requests
from bs4 import BeautifulSoup
from langchain.utilities import DuckDuckGoSearchAPIWrapper
from langchain_community.chat_models import ChatOpenAI
from langchain_core.messages import SystemMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_co... | null |
28,806 | import json
from typing import Any
import requests
from bs4 import BeautifulSoup
from langchain.utilities import DuckDuckGoSearchAPIWrapper
from langchain_community.chat_models import ChatOpenAI
from langchain_core.messages import SystemMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_co... | null |
28,807 | import os
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import MomentoVectorIndex
from langchain_text_splitters import RecursiveCharacterTextSplitter
from momento import (
CredentialProvider,
Previ... | null |
28,808 | from typing import Any, Dict, List, Union
from langchain.memory import ChatMessageHistory
from langchain_community.graphs import Neo4jGraph
from langchain_core.messages import AIMessage, HumanMessage
graph = Neo4jGraph()
def convert_messages(input: List[Dict[str, Any]]) -> ChatMessageHistory:
history = ChatMessageH... | null |
28,809 | from typing import Any, Dict, List, Union
from langchain.memory import ChatMessageHistory
from langchain_community.graphs import Neo4jGraph
from langchain_core.messages import AIMessage, HumanMessage
graph = Neo4jGraph()
def save_history(input: Dict[str, Any]) -> str:
input["context"] = [el.metadata["id"] for el i... | null |
28,810 | from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableBranch
from .blurb_matcher import book_rec_chain
from .chat import chat
from .library_info import library_info
from .rag import librarian_rag
def extract_op_field... | null |
28,811 | from langchain.retrievers import CohereRagRetriever
from langchain_community.chat_models import ChatCohere
def get_docs_message(message):
docs = rag.get_relevant_documents(message)
message_doc = next(
(x for x in docs if x.metadata.get("type") == "model_response"), None
)
return message_doc.page... | null |
28,812 | from typing import Optional, Type
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool
from neo4j_semantic_layer.utils import get_candidates, get_user_id, graph
recommendatio... | Recommends movies based on user's history and preference for a specific movie and/or genre. Returns: str: A string containing a list of recommended movies, or an error message. |
28,813 | from typing import Optional, Type
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool
from neo4j_semantic_layer.utils import get_candidates, graph
description_query = """
MA... | null |
28,814 | from typing import List, Tuple
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_to_openai_function_messages
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.py... | null |
28,815 | from typing import Optional, Type
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool
from neo4j_semantic_layer.utils import get_candidates, get_user_id, graph
store_rating_... | null |
28,816 | import os
from typing import List, Tuple
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_to_openai_function_messages
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from l... | null |
28,817 | from typing import List, Tuple
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_to_openai_function_messages
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.utilities.tavily_search import TavilySearchAPIWrapper
from langchain_com... | null |
28,818 | import re
from langchain_core.agents import AgentAction, AgentFinish
from .agent_scratchpad import _format_docs
def extract_between_tags(tag: str, string: str, strip: bool = True) -> str:
ext_list = re.findall(f"<{tag}\s?>(.+?)</{tag}\s?>", string, re.DOTALL)
if strip:
ext_list = [e.strip() for e in ext... | null |
28,819 | def _format_docs(docs):
result = "\n".join(
[
f'<item index="{i+1}">\n<page_content>\n{r}\n</page_content>\n</item>'
for i, r in enumerate(docs)
]
)
return result
def format_agent_scratchpad(intermediate_steps):
thoughts = ""
for action, observation in interm... | null |
28,820 | from langchain.retrievers import WikipediaRetriever
from langchain.tools import tool
retriever = WikipediaRetriever()
The provided code snippet includes necessary dependencies for implementing the `search` function. Write a Python function `def search(query)` to solve the following problem:
Search with the retriever.
... | Search with the retriever. |
28,821 | from collections.abc import Sequence
import numpy as np
import scipy.stats
import torch
The provided code snippet includes necessary dependencies for implementing the `NCD` function. Write a Python function `def NCD(c1: float, c2: float, c12: float) -> float` to solve the following problem:
Calculates Normalized Compr... | Calculates Normalized Compression Distance (NCD). Arguments: c1 (float): The compressed length of the first object. c2 (float): The compressed length of the second object. c12 (float): The compressed length of the concatenation of the first and second objects. Returns: float: The Normalized Compression Distance c1 and ... |
28,822 | from collections.abc import Sequence
import numpy as np
import scipy.stats
import torch
The provided code snippet includes necessary dependencies for implementing the `CLM` function. Write a Python function `def CLM(c1, c2, c12)` to solve the following problem:
Calculates Compression-based Length Measure (CLM). Argume... | Calculates Compression-based Length Measure (CLM). Arguments: c1: The compressed length of the first object. c2: The compressed length of the second object. c12: The compressed length of the concatenation of the first and second objects. Returns: float: The Compression-based Length Measure value between c1 and c2. Form... |
28,823 | from collections.abc import Sequence
import numpy as np
import scipy.stats
import torch
The provided code snippet includes necessary dependencies for implementing the `CDM` function. Write a Python function `def CDM(c1: float, c2: float, c12: float) -> float` to solve the following problem:
Calculates Compound Dissimi... | Calculates Compound Dissimilarity Measure (CDM). Arguments: c1 (float): The compressed length of the first object. c2 (float): The compressed length of the second object. c12 (float): The compressed length of the concatenation of the first and second objects. Returns: float: The Compound Dissimilarity Measure value bet... |
28,824 | from collections.abc import Sequence
import numpy as np
import scipy.stats
import torch
The provided code snippet includes necessary dependencies for implementing the `MSE` function. Write a Python function `def MSE(v1: np.ndarray, v2: np.ndarray) -> float` to solve the following problem:
Calculates Mean Squared Error... | Calculates Mean Squared Error (MSE). Arguments: v1 (np.ndarray): The first array. v2 (np.ndarray): The second array. Returns: float: The Mean Squared Error value, representing the average squared difference between v1 and v2. Formula: MSE(v1, v2) = Σ((v1 - v2) ** 2) / len(v1) |
28,825 | from collections.abc import Sequence
import numpy as np
import scipy.stats
import torch
The provided code snippet includes necessary dependencies for implementing the `agg_by_concat_space` function. Write a Python function `def agg_by_concat_space(t1: str, t2: str) -> str` to solve the following problem:
Combines `t1`... | Combines `t1` and `t2` with a space. Arguments: t1 (str): First item. t2 (str): Second item. Returns: str: `{t1} {t2}` |
28,826 | from collections.abc import Sequence
import numpy as np
import scipy.stats
import torch
The provided code snippet includes necessary dependencies for implementing the `agg_by_jag_word` function. Write a Python function `def agg_by_jag_word(t1: str, t2: str) -> str` to solve the following problem:
# TODO: Better descri... | # TODO: Better description Arguments: t1 (str): First item. t2 (str): Second item. Returns: str: |
28,827 | from collections.abc import Sequence
import numpy as np
import scipy.stats
import torch
The provided code snippet includes necessary dependencies for implementing the `agg_by_jag_char` function. Write a Python function `def agg_by_jag_char(t1: str, t2: str)` to solve the following problem:
# TODO: Better description A... | # TODO: Better description Arguments: t1 (str): First item. t2 (str): Second item. Returns: str: |
28,828 | from collections.abc import Sequence
import numpy as np
import scipy.stats
import torch
The provided code snippet includes necessary dependencies for implementing the `aggregate_strings` function. Write a Python function `def aggregate_strings(stringa: str, stringb: str, by_character: bool = False) -> str` to solve th... | Aggregates strings. Arguments: stringa (str): First item. stringb (str): Second item. by_character (bool): True if you want to join the combined string by character, Else combines by word Returns: str: combination of stringa and stringb |
28,829 | from collections.abc import Sequence
import numpy as np
import scipy.stats
import torch
The provided code snippet includes necessary dependencies for implementing the `agg_by_avg` function. Write a Python function `def agg_by_avg(i1: torch.Tensor, i2: torch.Tensor) -> torch.Tensor` to solve the following problem:
Calc... | Calculates the average of i1 and i2, rounding to the shortest. Arguments: i1 (torch.Tensor): First series of numbers. i2 (torch.Tensor): Second series of numbers. Returns: torch.Tensor: Average of the two series of numbers. |
28,830 | from collections.abc import Sequence
import numpy as np
import scipy.stats
import torch
The provided code snippet includes necessary dependencies for implementing the `agg_by_min_or_max` function. Write a Python function `def agg_by_min_or_max( i1: torch.Tensor, i2: torch.Tensor, aggregate_by_minimum: bool = False... | Calculates the average of i1 and i2, rounding to the shortest. Arguments: i1 (torch.Tensor): First series of numbers. i2 (torch.Tensor): Second series of numbers. aggregate_by_minimum (bool): True to take the minimum of the two series. False to take the maximum instead. Returns: torch.Tensor: Average of the two series. |
28,831 | from collections.abc import Sequence
import numpy as np
import scipy.stats
import torch
The provided code snippet includes necessary dependencies for implementing the `agg_by_stack` function. Write a Python function `def agg_by_stack(i1: torch.Tensor, i2: torch.Tensor) -> torch.Tensor` to solve the following problem:
... | Combines `i1` and `i2` via `torch.stack`. Arguments: i1 (torch.Tensor): First series of numbers. i2 (torch.Tensor): Second series of numbers. Returns: torch.Tensor: Stack of the two series. |
28,832 | from collections.abc import Sequence
import numpy as np
import scipy.stats
import torch
The provided code snippet includes necessary dependencies for implementing the `mean_confidence_interval` function. Write a Python function `def mean_confidence_interval(data: Sequence, confidence: float = 0.95) -> tuple` to solve ... | Computes the mean confidence interval of `data` with `confidence` Arguments: data (Sequence): Data to compute a confidence interval over. confidence (float): Level to compute confidence. Returns: tuple: (Mean, quantile-error-size) |
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