id stringlengths 6 6 | text stringlengths 20 17.2k | title stringclasses 1
value |
|---|---|---|
159521 | import json
import logging
import os
import re
import tempfile
import time
from abc import ABC
from io import StringIO
from pathlib import Path
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterator,
List,
Mapping,
Optional,
Sequence,
Union,
)
from urllib.parse import urlparse
impo... | |
159522 | s PyPDFDirectoryLoader(BaseLoader):
"""Load a directory with `PDF` files using `pypdf` and chunks at character level.
Loader also stores page numbers in metadata.
"""
def __init__(
self,
path: Union[str, Path],
glob: str = "**/[!.]*.pdf",
silent_errors: bool = False,
... | |
159528 | import csv
from io import TextIOWrapper
from pathlib import Path
from typing import Any, Dict, Iterator, List, Optional, Sequence, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.document_loaders.helpers import detect_file_e... | |
159544 | import os
from typing import List
from langchain_community.document_loaders.unstructured import UnstructuredFileLoader
class UnstructuredPowerPointLoader(UnstructuredFileLoader):
"""Load `Microsoft PowerPoint` files using `Unstructured`.
Works with both .ppt and .pptx files.
You can run the loader in on... | |
159555 | import logging
from pathlib import Path
from typing import Iterator, Optional, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.document_loaders.helpers import detect_file_encodings
logger = logging.getLogger(__name__)
cla... | |
159560 | """Loads word documents."""
import os
import tempfile
from abc import ABC
from pathlib import Path
from typing import List, Union
from urllib.parse import urlparse
import requests
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.d... | |
159577 | import concurrent
import logging
import random
from pathlib import Path
from typing import Any, Callable, Iterator, List, Optional, Sequence, Tuple, Type, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.document_loaders.csv_... | |
159586 | import json
from pathlib import Path
from typing import Any, Callable, Dict, Iterator, Optional, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
class JSONLoader(BaseLoader):
"""
Load a `JSON` file using a `jq` schema.
Setup:
... | |
159671 | """Use to load blobs from the local file system."""
import contextlib
import mimetypes
import tempfile
from io import BufferedReader, BytesIO
from pathlib import Path
from typing import (
TYPE_CHECKING,
Callable,
Generator,
Iterable,
Iterator,
Optional,
Sequence,
TypeVar,
Union,
)
f... | |
159711 | def load_tools(
tool_names: List[str],
llm: Optional[BaseLanguageModel] = None,
callbacks: Callbacks = None,
allow_dangerous_tools: bool = False,
**kwargs: Any,
) -> List[BaseTool]:
"""Load tools based on their name.
Tools allow agents to interact with various resources and services like
... | |
159778 | """Toolkit for interacting with an SQL database."""
from typing import List
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import BaseTool
from langchain_core.tools.base import BaseToolkit
from pydantic import ConfigDict, Field
from langchain_community.tools.sql_database.tool ... | |
159780 | # flake8: noqa
SQL_PREFIX = """You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
Unless the user specifies a specific number of examples they wish to obtain, always limi... | |
159781 | """SQL agent."""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Literal,
Optional,
Sequence,
Union,
cast,
)
from langchain_core.messages import AIMessage, SystemMessage
from langchain_core.prompts import BasePromptTemplate, PromptTemplate
f... | |
159784 | @deprecated(
since="0.0.12",
removal="1.0",
alternative_import="langchain_google_vertexai.VertexAI",
)
class VertexAI(_VertexAICommon, BaseLLM):
"""Google Vertex AI large language models."""
model_name: str = "text-bison"
"The name of the Vertex AI large language model."
tuned_model_name: O... | |
159804 | from __future__ import annotations
from typing import Any, Dict, Iterator, List, Optional
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models import LanguageModelInput
from langchain_core.outputs import Generation, Ge... | |
159810 | from __future__ import annotations
import importlib.util
import logging
from typing import Any, Iterator, List, Mapping, Optional
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import BaseLLM
from langchain_... | |
159811 | def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
# List to hold all results
text_generations: List[str] = []
default_pipeline_kwargs = sel... | |
159825 | import importlib
from typing import Any, List, Mapping, Optional
from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from pydantic import ConfigDict
from langchain_community.llms.utils import enforce_stop_tokens
DEFAULT_MODEL_ID = "google/flan-t5-... | |
159856 | recated(
since="0.0.10", removal="1.0", alternative_import="langchain_openai.AzureOpenAI"
)
class AzureOpenAI(BaseOpenAI):
"""Azure-specific OpenAI large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API k... | |
159857 | recated(
since="0.0.1",
removal="1.0",
alternative_import="langchain_openai.ChatOpenAI",
)
class OpenAIChat(BaseLLM):
"""OpenAI Chat large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
... | |
159872 | def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
if self.streaming:
from transformers import TextStreamer
input_ids = self.tokenizer.encode(prompt,... | |
159888 | async def _acall(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call the textgen web API and return the output.
Args:
prompt: The prompt to use for gen... | |
159898 | async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
"""Stream OCI Data Science Model Deployment endpoint async on given prompt.
... | |
159908 | import importlib.util
import logging
from typing import Any, Callable, List, Mapping, Optional
from langchain_core.callbacks import CallbackManagerForLLMRun
from pydantic import ConfigDict
from langchain_community.llms.self_hosted import SelfHostedPipeline
from langchain_community.llms.utils import enforce_stop_token... | |
159924 | @deprecated(
since="0.3.1",
removal="1.0.0",
alternative_import="langchain_ollama.OllamaLLM",
)
class Ollama(BaseLLM, _OllamaCommon):
"""Ollama locally runs large language models.
To use, follow the instructions at https://ollama.ai/.
Example:
.. code-block:: python
from lang... | |
159941 | import json
from typing import Any, Dict, List, Mapping, Optional
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.utils import get_from_dict_or_env, pre_init
from pydantic import... | |
159945 | """.. title:: Graph Vector Store
Graph Vector Store
==================
Sometimes embedding models don't capture all the important relationships between
documents.
Graph Vector Stores are an extension to both vector stores and retrievers that allow
documents to be explicitly connected to each other.
Graph vector stor... | |
159949 | @abstractmethod
def mmr_traversal_search(
self,
query: str,
*,
k: int = 4,
depth: int = 2,
fetch_k: int = 100,
adjacent_k: int = 10,
lambda_mult: float = 0.5,
score_threshold: float = float("-inf"),
**kwargs: Any,
) -> Iterable[Docu... | |
159950 | @beta(message="Added in version 0.3.1 of langchain_community. API subject to change.")
class GraphVectorStoreRetriever(VectorStoreRetriever):
"""Retriever for GraphVectorStore.
A graph vector store retriever is a retriever that uses a graph vector store to
retrieve documents.
It is similar to a vector ... | |
159976 | # flake8: noqa
from langchain_core.prompts.prompt import PromptTemplate
_DEFAULT_ENTITY_EXTRACTION_TEMPLATE = """Extract all entities from the following text. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.
Return the output as a single comma-separated list,... | |
159998 | class PebbloRetrievalQA(Chain):
"""
Retrieval Chain with Identity & Semantic Enforcement for question-answering
against a vector database.
"""
combine_documents_chain: BaseCombineDocumentsChain
"""Chain to use to combine the documents."""
input_key: str = "query" #: :meta private:
outp... | |
160013 | from typing import List
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.messages import BaseMessage
class StreamlitChatMessageHistory(BaseChatMessageHistory):
"""
Chat message history that stores messages in Streamlit session state.
Args:
key: The key to use in... | |
160099 | import json
from pathlib import Path
from langchain_chroma import Chroma
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts... | |
160101 | import os
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_chroma import Chroma
from langchain_community.document_loaders import UnstructuredFileLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_core.documents import Document
def ingest_documents(... | |
160169 | from langchain.callbacks import streaming_stdout
from langchain_chroma import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceEndpoint
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
f... | |
160173 | # RAG - Unstructured - semi-structured
This template performs RAG on `semi-structured data`, such as a PDF with text and tables.
It uses the `unstructured` parser to extract the text and tables from the PDF and then uses the LLM to generate queries based on the user input.
See [this cookbook](https://github.com/lan... | |
160189 | # Load
import uuid
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryStore
from langchain_chroma import Chroma
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.documents import D... | |
160191 | import os
from pathlib import Path
import pypdfium2 as pdfium
from langchain_chroma import Chroma
from langchain_experimental.open_clip import OpenCLIPEmbeddings
def get_images_from_pdf(pdf_path, img_dump_path):
"""
Extract images from each page of a PDF document and save as JPEG files.
:param pdf_path:... | |
160229 | retrieval_prompt = """{retriever_description} Before beginning to research the user's question, first think for a moment inside <scratchpad> tags about what information is necessary for a well-informed answer. If the user's question is complex, you may need to decompose the query into multiple subqueries and execute th... | |
160231 | import os
import uuid
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langchain_text_splitters import RecursiveCharacterTextSplitter
from pymongo import MongoClient
PAREN... | |
160235 | import os
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_core.... | |
160242 | from pathlib import Path
import pandas as pd
from langchain.agents import AgentExecutor, OpenAIFunctionsAgent
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.prompts import ChatProm... | |
160247 | import os
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.pydantic_v1 import BaseModel
from langchain_core.runnables imp... | |
160255 | import os
from openai import OpenAI
from opensearchpy import OpenSearch
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
OPENSEARCH_URL = os.getenv("OPENSEARCH_URL", "https://localhost:9200")
OPENSEARCH_USERNAME = os.getenv("OPENSEARCH_USERNAME", "admin")
OPENSEARCH_PASSWORD = os.getenv("OPENSEARCH_PASSWORD", "admin")
OP... | |
160260 | 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... | |
160267 | import os
from langchain_community.embeddings import BedrockEmbeddings
from langchain_community.llms.bedrock import Bedrock
from langchain_community.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 ... | |
160269 | import os
from langchain_community.document_loaders import UnstructuredFileLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Redis
from langchain_text_splitters import RecursiveCharacterTextSplitter
from rag_redis.config import EMBED_MODEL, INDEX_NAME,... | |
160351 | import os
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import SingleStoreDB
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pyd... | |
160356 | 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 RunnableParallel, RunnablePassthrough
from langchain_milvus.vectorstores import Milvus
from langchain_openai import ChatOp... | |
160365 | from langchain_chroma import Chroma
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.pydantic_v1 import BaseModel
from lan... | |
160398 | from langchain_community.chat_models import ChatOpenAI
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables impor... | |
160414 | 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.... | |
160446 | 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.p... | |
160454 | [tool.poetry]
name = "rag-azure-search"
version = "0.0.1"
description = ""
authors = []
readme = "README.md"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
langchain-core = ">=0.1.5"
langchain-openai = ">=0.0.1"
azure-search-documents = ">=11.4.0"
[tool.poetry.group.dev.dependencies]
langchain-cli = ">=0.0.4"
fas... | |
160462 | from typing import Dict, List, Tuple
from langchain.agents import (
AgentExecutor,
)
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_community... | |
160472 | import os
from operator import itemgetter
from typing import List, Tuple
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchai... | |
160484 | import getpass
import os
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import Milvus
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.... | |
160489 | from typing import List
from langchain import hub
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.agents.output_parsers import ReActJsonSingleInputOutputParser
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchai... | |
160511 | import os
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CohereRerank
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.output_parsers import StrOutputParser
from ... | |
160519 | from langchain_community.chat_models import ChatOpenAI
from langchain_core.load import load
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 RunnablePassthrough
from prop... | |
160523 | 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_chroma import Chroma
from langchain_community.ch... | |
160534 | # Self-query - Qdrant
This template performs [self-querying](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/)
``using `Qdrant` and OpenAI. By default, it uses an artificial dataset of 10 documents, but you can replace it with your own dataset.
``
## Environment Setup
Set the `OPENAI_... | |
160535 | 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 ... | |
160537 | from langchain_core.prompts import PromptTemplate
llm_context_prompt_template = """
Answer the user query using provided passages. Each passage has metadata given as
a nested JSON object you can also use. When answering, cite source name of the passages
you are answering from below the answer in a unique bullet poin... | |
160538 | from string import Formatter
from typing import List
from langchain_core.documents import Document
document_template = """
PASSAGE: {page_content}
METADATA: {metadata}
"""
def combine_documents(documents: List[Document]) -> str:
"""
Combine a list of documents into a single string that might be passed furth... | |
160540 | from pathlib import Path
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Neo4jVector
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import TokenTextSplitter
txt_path = Path(__file__).parent / "dune.txt"
# Load the text file
loader ... | |
160552 | from neo4j_vector_memory.chain import chain
if __name__ == "__main__":
user_id = "user_id_1"
session_id = "session_id_1"
original_query = "What is the plot of the Dune?"
print(
chain.invoke(
{"question": original_query, "user_id": user_id, "session_id": session_id}
)
)
... | |
160553 | from operator import itemgetter
from langchain_community.vectorstores import Neo4jVector
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import (
ChatPromptTemplate,
MessagesPlaceholder,
PromptTemplate,
)
from langchain_core.pydantic_v1 import BaseModel
from langchain_... | |
160560 | import os
import re
import subprocess # nosec
import tempfile
from langchain.agents import AgentType, initialize_agent
from langchain.pydantic_v1 import BaseModel, Field, ValidationError, validator
from langchain_community.chat_models import ChatOpenAI
from langchain_core.language_models import BaseLLM
from langchain... | |
160582 | from langchain.agents import AgentExecutor, OpenAIFunctionsAgent
from langchain_core.messages import SystemMessage
from langchain_core.pydantic_v1 import BaseModel
from langchain_openai import ChatOpenAI
from langchain_robocorp import ActionServerToolkit
# Initialize LLM chat model
llm = ChatOpenAI(model="gpt-4", temp... | |
160586 | from langchain_community.vectorstores import LanceDB
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 RunnableParallel, RunnablePassthrough
from langchain_openai import Ch... | |
160610 | from operator import itemgetter
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 ConfigurableField, RunnableParallel
from langchain_openai import ChatOpenAI
from neo4j_a... | |
160615 | # Chatbot feedback
This template shows how to evaluate your chatbot without explicit user feedback.
It defines a simple chatbot in [chain.py](https://github.com/langchain-ai/langchain/blob/master/templates/chat-bot-feedback/chat_bot_feedback/chain.py) and custom evaluator that scores bot response effectiveness based ... | |
160634 | import os
from langchain_community.chat_models import ChatOpenAI
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import Weaviate
from langchain_core.output_parsers import StrOutputParser
from langchain_core... | |
160647 | [tool.poetry]
name = "sql-pgvector"
version = "0.0.1"
description = "Use pgvector for combining postgreSQL with semantic search / RAG"
authors = []
readme = "README.md"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
langchain = "^0.1"
openai = "<2"
psycopg2 = "^2.9.9"
tiktoken = "^0.5.1"
[tool.poetry.group.dev.de... | |
160649 | 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.pydan... | |
160654 | # RAG - Chroma, Ollama, Gpt4all - private
This template performs RAG with no reliance on external APIs.
It utilizes `Ollama` the LLM, `GPT4All` for embeddings, and `Chroma` for the vectorstore.
The vectorstore is created in `chain.py` and by default indexes a [popular blog posts on Agents](https://lilianweng.github... | |
160656 | # Load
from langchain_chroma import Chroma
from langchain_community.chat_models import ChatOllama
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.embeddings import GPT4AllEmbeddings
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatP... | |
160676 | from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain_chroma import Chroma
from langchain_community.chat_models import ChatOllama, ChatOpenAI
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.output_pa... | |
160687 | # RAG - Ollama, Chroma - multi-modal, multi-vector, local
Visual search is a familiar application to many with iPhones or Android devices. It allows user to search photos using natural language.
With the release of open source, multi-modal LLMs it's possible to build this kind of application for yourself for your o... | |
160708 | import os
import weaviate
from langchain.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetriever
from langchain_community.chat_models import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import Run... | |
160712 | from stepback_qa_prompting.chain import chain
if __name__ == "__main__":
chain.invoke({"question": "was chatgpt around while trump was president?"}) | |
160718 | from langchain_core.agents import AgentAction, AgentFinish
def parse_output(message: str):
FINAL_ANSWER_ACTION = "<final_answer>"
includes_answer = FINAL_ANSWER_ACTION in message
if includes_answer:
answer = message.split(FINAL_ANSWER_ACTION)[1].strip()
if "</final_answer>" in answer:
... | |
160723 | from neo4j_cypher_ft.chain import chain
if __name__ == "__main__":
original_query = "Did tom cruis act in top gun?"
print(chain.invoke({"question": original_query})) | |
160736 | import os
from langchain.retrievers.multi_query import MultiQueryRetriever
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_co... | |
160769 | from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import Lantern
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import B... | |
160798 | from langchain_community.chat_models import ChatAnthropic, ChatCohere, ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import ConfigurableField
_prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"Translate user input into pirate ... | |
160806 | from langchain_community.llms import Replicate
from langchain_core.prompts import ChatPromptTemplate
# LLM
replicate_id = "andreasjansson/llama-2-13b-chat-gguf:60ec5dda9ff9ee0b6f786c9d1157842e6ab3cc931139ad98fe99e08a35c5d4d4" # noqa: E501
model = Replicate(
model=replicate_id,
model_kwargs={"temperature": 0.8... | |
160829 | text:
- name: content
tag:
- name: doc_id
vector:
- name: content_vector
algorithm: FLAT
datatype: FLOAT32
dims: 1536
distance_metric: COSINE | |
160856 | from langchain_community.vectorstores import Neo4jVector
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 RunnableParallel, RunnablePassthrough
from langchain_openai impor... | |
160858 | import os
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langchain_text_splitters import RecursiveCharacterTextSplitter
from pymongo import MongoClient
MONGO_URI = os.en... | |
160863 | import os
from langchain_community.chat_models import ChatOpenAI
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langchain_core.output_parsers import StrOutputParser
from ... | |
160872 | import os
from pathlib import Path
import pypdfium2 as pdfium
from langchain_chroma import Chroma
from langchain_experimental.open_clip import OpenCLIPEmbeddings
def get_images_from_pdf(pdf_path, img_dump_path):
"""
Extract images from each page of a PDF document and save as JPEG files.
:param pdf_path:... | |
160886 | from langchain_chroma import Chroma
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.pydantic_v1 import BaseModel
from lan... | |
160924 | import os
from typing import List, Tuple
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_log_to_messages
from langchain.agents.output_parsers import (
ReActJsonSingleInputOutputParser,
)
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from lang... | |
160970 | nPgo=)](https://www.phorm.ai/query?projectId=c5863b56-6703-4a5d-87b6-7e6031bf16b6)
LlamaIndex (GPT Index) is a data framework for your LLM application. Building with LlamaIndex typically involves working with LlamaIndex core and a chosen set of integrations (or plugins). There are two ways to start building with Llama... | |
160978 | import os
import tempfile
from typing import List, Union
import streamlit as st
import tiktoken
from langchain.text_splitter import (
CharacterTextSplitter,
RecursiveCharacterTextSplitter,
)
from langchain.text_splitter import (
TextSplitter as LCSplitter,
)
from langchain.text_splitter import TokenTextSpl... | |
161093 | - 2023-08-29
### New Features
- Add embedding finetuning (#7452)
- Added support for RunGPT LLM (#7401)
- Integration guide and notebook with DeepEval (#7425)
- Added `VectorIndex` and `VectaraRetriever` as a managed index (#7440)
- Added support for `to_tool_list` to detect and use async functions (#7282)
## [0.8.1... | |
161101 | # 🗂️ LlamaIndex 🦙
[](https://pypi.org/project/llama-index/)
[](https://github.com/jerryjliu/llama_index/graphs/contributors)
[![Discord](https://img.shields.io/discor... | |
161109 | """Async utils."""
import asyncio
from itertools import zip_longest
from typing import Any, Coroutine, Iterable, List
def asyncio_module(show_progress: bool = False) -> Any:
if show_progress:
from tqdm.asyncio import tqdm_asyncio
module = tqdm_asyncio
else:
module = asyncio
retur... |
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